diff --git a/Analysis_code/0.air_data_merge.ipynb b/Analysis_code/0.air_data_merge.ipynb
deleted file mode 100644
index f59788778fe039381495221931c3e876343f85cf..0000000000000000000000000000000000000000
--- a/Analysis_code/0.air_data_merge.ipynb
+++ /dev/null
@@ -1,1029 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import natsort\n",
-    "from datetime import datetime\n",
-    "from tqdm.auto import tqdm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_data(year):\n",
-    "    files = natsort.natsorted(os.listdir(f'../data/대기질/{year}/'))\n",
-    "    data = []\n",
-    "    for file in tqdm(files, desc=f\"Reading files...({len(files)})\"):\n",
-    "        data.append(pd.read_excel(f'../data/대기질/{year}/{file}', usecols=[\"지역\", '망', \"측정소코드\", \"측정소명\", \"측정일시\", \"O3\", \"NO2\", \"PM10\", \"PM25\", \"주소\"]))\n",
-    "\n",
-    "    return pd.concat(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 합친 데이터에 날짜 정보를 추가한다.\n",
-    "def add_date(df):\n",
-    "\n",
-    "    df[\"측정일시\"] = df[\"측정일시\"].astype(str).str[:10]\n",
-    "    df[\"측정일시\"] = pd.to_datetime(df[\"측정일시\"], format='%Y%m%d%H', errors=\"coerce\")\n",
-    "\n",
-    "    df[\"year\"] = df[\"측정일시\"].dt.year\n",
-    "    df[\"month\"] = df[\"측정일시\"].dt.month\n",
-    "    df[\"day\"] = df[\"측정일시\"].dt.day\n",
-    "    df[\"hour\"] = df[\"측정일시\"].dt.hour\n",
-    "\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import pandas as pd\n",
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "# 대기질 데이터를 불러와서 하나의 파일로 합친다.\n",
-    "def get_data(year):\n",
-    "    directory = f'../data/대기질/{year}/'\n",
-    "    files = os.listdir(directory)\n",
-    "    data = []\n",
-    "    \n",
-    "    # 파일 목록에서 디렉토리를 제외하고 오직 Excel 파일만 처리\n",
-    "    for file in tqdm(files, desc=f\"Reading files...({len(files)})\"):\n",
-    "        file_path = os.path.join(directory, file)\n",
-    "        if os.path.isfile(file_path) and file_path.endswith(('.xls', '.xlsx')):  # Excel 파일 확장자만 허용\n",
-    "            data.append(pd.read_excel(file_path, usecols=[\"지역\", '망', \"측정소코드\", \"측정소명\", \"측정일시\", \"O3\", \"NO2\", \"PM10\", \"PM25\", \"주소\"]))\n",
-    "    \n",
-    "    return pd.concat(data)\n",
-    "\n",
-    "years = [2018, 2019, 2020,2021,2022,2023] # 2018년부터 2023년까지의 데이터를 합친다.\n",
-    "for year in tqdm(years):\n",
-    "    data = get_data(year)\n",
-    "    data = add_date(data)\n",
-    "    data.reset_index(drop=True, inplace=True)\n",
-    "    data.to_feather(f\"../data/대기질/{year}.feather\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
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-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>망</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
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-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
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-       "      <td>18.0</td>\n",
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-       "      <td>서울 중구 덕수궁길 15</td>\n",
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-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
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-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 03:00:00</td>\n",
-       "      <td>0.0218</td>\n",
-       "      <td>0.0192</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.0</td>\n",
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-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 04:00:00</td>\n",
-       "      <td>0.0131</td>\n",
-       "      <td>0.0214</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4.0</td>\n",
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-       "      <th>4</th>\n",
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-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 05:00:00</td>\n",
-       "      <td>0.0131</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>21.0</td>\n",
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-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 06:00:00</td>\n",
-       "      <td>0.0115</td>\n",
-       "      <td>0.0196</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 07:00:00</td>\n",
-       "      <td>0.0094</td>\n",
-       "      <td>0.0230</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>7.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 08:00:00</td>\n",
-       "      <td>0.0222</td>\n",
-       "      <td>0.0175</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>8.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 09:00:00</td>\n",
-       "      <td>0.0396</td>\n",
-       "      <td>0.0153</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>9.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 10:00:00</td>\n",
-       "      <td>0.0530</td>\n",
-       "      <td>0.0105</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 11:00:00</td>\n",
-       "      <td>0.0607</td>\n",
-       "      <td>0.0090</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>11.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 12:00:00</td>\n",
-       "      <td>0.0688</td>\n",
-       "      <td>0.0114</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 13:00:00</td>\n",
-       "      <td>0.0758</td>\n",
-       "      <td>0.0101</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>13.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 14:00:00</td>\n",
-       "      <td>0.0743</td>\n",
-       "      <td>0.0093</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>14.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 15:00:00</td>\n",
-       "      <td>0.0749</td>\n",
-       "      <td>0.0100</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>15.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 16:00:00</td>\n",
-       "      <td>0.0716</td>\n",
-       "      <td>0.0092</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>16.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 17:00:00</td>\n",
-       "      <td>0.0613</td>\n",
-       "      <td>0.0099</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>17.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 18:00:00</td>\n",
-       "      <td>0.0496</td>\n",
-       "      <td>0.0098</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>18.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 19:00:00</td>\n",
-       "      <td>0.0473</td>\n",
-       "      <td>0.0124</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>19.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 20:00:00</td>\n",
-       "      <td>0.0498</td>\n",
-       "      <td>0.0170</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>20.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 21:00:00</td>\n",
-       "      <td>0.0616</td>\n",
-       "      <td>0.0134</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>21.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 22:00:00</td>\n",
-       "      <td>0.0543</td>\n",
-       "      <td>0.0109</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>22.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-01 23:00:00</td>\n",
-       "      <td>0.0507</td>\n",
-       "      <td>0.0113</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>23.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>NaT</td>\n",
-       "      <td>0.0427</td>\n",
-       "      <td>0.0125</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 01:00:00</td>\n",
-       "      <td>0.0334</td>\n",
-       "      <td>0.0148</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 02:00:00</td>\n",
-       "      <td>0.0337</td>\n",
-       "      <td>0.0133</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 03:00:00</td>\n",
-       "      <td>0.0260</td>\n",
-       "      <td>0.0162</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 04:00:00</td>\n",
-       "      <td>0.0195</td>\n",
-       "      <td>0.0179</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 05:00:00</td>\n",
-       "      <td>0.0171</td>\n",
-       "      <td>0.0170</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 06:00:00</td>\n",
-       "      <td>0.0181</td>\n",
-       "      <td>0.0145</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 07:00:00</td>\n",
-       "      <td>0.0174</td>\n",
-       "      <td>0.0156</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>7.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 08:00:00</td>\n",
-       "      <td>0.0213</td>\n",
-       "      <td>0.0147</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>8.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 09:00:00</td>\n",
-       "      <td>0.0267</td>\n",
-       "      <td>0.0143</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>9.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 10:00:00</td>\n",
-       "      <td>0.0289</td>\n",
-       "      <td>0.0155</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>10.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 11:00:00</td>\n",
-       "      <td>0.0381</td>\n",
-       "      <td>0.0108</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>11.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 12:00:00</td>\n",
-       "      <td>0.0441</td>\n",
-       "      <td>0.0079</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 13:00:00</td>\n",
-       "      <td>0.0489</td>\n",
-       "      <td>0.0067</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>13.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 14:00:00</td>\n",
-       "      <td>0.0498</td>\n",
-       "      <td>0.0072</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>14.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 15:00:00</td>\n",
-       "      <td>0.0459</td>\n",
-       "      <td>0.0073</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>15.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2023-07-02 16:00:00</td>\n",
-       "      <td>0.0474</td>\n",
-       "      <td>0.0079</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>16.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       지역     망   측정소코드 측정소명                측정일시      O3     NO2  PM10  PM25  \\\n",
-       "0   서울 중구  도시대기  111121   중구 2023-07-01 01:00:00  0.0249  0.0188  21.0  19.0   \n",
-       "1   서울 중구  도시대기  111121   중구 2023-07-01 02:00:00  0.0263  0.0163  18.0  15.0   \n",
-       "2   서울 중구  도시대기  111121   중구 2023-07-01 03:00:00  0.0218  0.0192  24.0  21.0   \n",
-       "3   서울 중구  도시대기  111121   중구 2023-07-01 04:00:00  0.0131  0.0214  25.0  19.0   \n",
-       "4   서울 중구  도시대기  111121   중구 2023-07-01 05:00:00  0.0131  0.0160  25.0  21.0   \n",
-       "5   서울 중구  도시대기  111121   중구 2023-07-01 06:00:00  0.0115  0.0196  23.0  18.0   \n",
-       "6   서울 중구  도시대기  111121   중구 2023-07-01 07:00:00  0.0094  0.0230  26.0  21.0   \n",
-       "7   서울 중구  도시대기  111121   중구 2023-07-01 08:00:00  0.0222  0.0175  26.0  20.0   \n",
-       "8   서울 중구  도시대기  111121   중구 2023-07-01 09:00:00  0.0396  0.0153  27.0  20.0   \n",
-       "9   서울 중구  도시대기  111121   중구 2023-07-01 10:00:00  0.0530  0.0105  19.0  16.0   \n",
-       "10  서울 중구  도시대기  111121   중구 2023-07-01 11:00:00  0.0607  0.0090  20.0  20.0   \n",
-       "11  서울 중구  도시대기  111121   중구 2023-07-01 12:00:00  0.0688  0.0114  20.0  17.0   \n",
-       "12  서울 중구  도시대기  111121   중구 2023-07-01 13:00:00  0.0758  0.0101  23.0  17.0   \n",
-       "13  서울 중구  도시대기  111121   중구 2023-07-01 14:00:00  0.0743  0.0093  20.0  17.0   \n",
-       "14  서울 중구  도시대기  111121   중구 2023-07-01 15:00:00  0.0749  0.0100  19.0  11.0   \n",
-       "15  서울 중구  도시대기  111121   중구 2023-07-01 16:00:00  0.0716  0.0092  19.0  15.0   \n",
-       "16  서울 중구  도시대기  111121   중구 2023-07-01 17:00:00  0.0613  0.0099  18.0  15.0   \n",
-       "17  서울 중구  도시대기  111121   중구 2023-07-01 18:00:00  0.0496  0.0098  18.0  14.0   \n",
-       "18  서울 중구  도시대기  111121   중구 2023-07-01 19:00:00  0.0473  0.0124  17.0  17.0   \n",
-       "19  서울 중구  도시대기  111121   중구 2023-07-01 20:00:00  0.0498  0.0170  17.0  15.0   \n",
-       "20  서울 중구  도시대기  111121   중구 2023-07-01 21:00:00  0.0616  0.0134  23.0  20.0   \n",
-       "21  서울 중구  도시대기  111121   중구 2023-07-01 22:00:00  0.0543  0.0109  18.0  16.0   \n",
-       "22  서울 중구  도시대기  111121   중구 2023-07-01 23:00:00  0.0507  0.0113  17.0  16.0   \n",
-       "23  서울 중구  도시대기  111121   중구                 NaT  0.0427  0.0125  17.0  16.0   \n",
-       "24  서울 중구  도시대기  111121   중구 2023-07-02 01:00:00  0.0334  0.0148  21.0  20.0   \n",
-       "25  서울 중구  도시대기  111121   중구 2023-07-02 02:00:00  0.0337  0.0133  22.0  18.0   \n",
-       "26  서울 중구  도시대기  111121   중구 2023-07-02 03:00:00  0.0260  0.0162  25.0  20.0   \n",
-       "27  서울 중구  도시대기  111121   중구 2023-07-02 04:00:00  0.0195  0.0179  22.0  18.0   \n",
-       "28  서울 중구  도시대기  111121   중구 2023-07-02 05:00:00  0.0171  0.0170  19.0  17.0   \n",
-       "29  서울 중구  도시대기  111121   중구 2023-07-02 06:00:00  0.0181  0.0145  14.0  10.0   \n",
-       "30  서울 중구  도시대기  111121   중구 2023-07-02 07:00:00  0.0174  0.0156  11.0  10.0   \n",
-       "31  서울 중구  도시대기  111121   중구 2023-07-02 08:00:00  0.0213  0.0147  12.0   9.0   \n",
-       "32  서울 중구  도시대기  111121   중구 2023-07-02 09:00:00  0.0267  0.0143  11.0  10.0   \n",
-       "33  서울 중구  도시대기  111121   중구 2023-07-02 10:00:00  0.0289  0.0155  12.0   9.0   \n",
-       "34  서울 중구  도시대기  111121   중구 2023-07-02 11:00:00  0.0381  0.0108  13.0  13.0   \n",
-       "35  서울 중구  도시대기  111121   중구 2023-07-02 12:00:00  0.0441  0.0079  13.0  12.0   \n",
-       "36  서울 중구  도시대기  111121   중구 2023-07-02 13:00:00  0.0489  0.0067   8.0  10.0   \n",
-       "37  서울 중구  도시대기  111121   중구 2023-07-02 14:00:00  0.0498  0.0072  11.0  10.0   \n",
-       "38  서울 중구  도시대기  111121   중구 2023-07-02 15:00:00  0.0459  0.0073  14.0  12.0   \n",
-       "39  서울 중구  도시대기  111121   중구 2023-07-02 16:00:00  0.0474  0.0079  12.0  11.0   \n",
-       "\n",
-       "               주소    year  month  day  hour  \n",
-       "0   서울 중구 덕수궁길 15  2023.0    7.0  1.0   1.0  \n",
-       "1   서울 중구 덕수궁길 15  2023.0    7.0  1.0   2.0  \n",
-       "2   서울 중구 덕수궁길 15  2023.0    7.0  1.0   3.0  \n",
-       "3   서울 중구 덕수궁길 15  2023.0    7.0  1.0   4.0  \n",
-       "4   서울 중구 덕수궁길 15  2023.0    7.0  1.0   5.0  \n",
-       "5   서울 중구 덕수궁길 15  2023.0    7.0  1.0   6.0  \n",
-       "6   서울 중구 덕수궁길 15  2023.0    7.0  1.0   7.0  \n",
-       "7   서울 중구 덕수궁길 15  2023.0    7.0  1.0   8.0  \n",
-       "8   서울 중구 덕수궁길 15  2023.0    7.0  1.0   9.0  \n",
-       "9   서울 중구 덕수궁길 15  2023.0    7.0  1.0  10.0  \n",
-       "10  서울 중구 덕수궁길 15  2023.0    7.0  1.0  11.0  \n",
-       "11  서울 중구 덕수궁길 15  2023.0    7.0  1.0  12.0  \n",
-       "12  서울 중구 덕수궁길 15  2023.0    7.0  1.0  13.0  \n",
-       "13  서울 중구 덕수궁길 15  2023.0    7.0  1.0  14.0  \n",
-       "14  서울 중구 덕수궁길 15  2023.0    7.0  1.0  15.0  \n",
-       "15  서울 중구 덕수궁길 15  2023.0    7.0  1.0  16.0  \n",
-       "16  서울 중구 덕수궁길 15  2023.0    7.0  1.0  17.0  \n",
-       "17  서울 중구 덕수궁길 15  2023.0    7.0  1.0  18.0  \n",
-       "18  서울 중구 덕수궁길 15  2023.0    7.0  1.0  19.0  \n",
-       "19  서울 중구 덕수궁길 15  2023.0    7.0  1.0  20.0  \n",
-       "20  서울 중구 덕수궁길 15  2023.0    7.0  1.0  21.0  \n",
-       "21  서울 중구 덕수궁길 15  2023.0    7.0  1.0  22.0  \n",
-       "22  서울 중구 덕수궁길 15  2023.0    7.0  1.0  23.0  \n",
-       "23  서울 중구 덕수궁길 15     NaN    NaN  NaN   NaN  \n",
-       "24  서울 중구 덕수궁길 15  2023.0    7.0  2.0   1.0  \n",
-       "25  서울 중구 덕수궁길 15  2023.0    7.0  2.0   2.0  \n",
-       "26  서울 중구 덕수궁길 15  2023.0    7.0  2.0   3.0  \n",
-       "27  서울 중구 덕수궁길 15  2023.0    7.0  2.0   4.0  \n",
-       "28  서울 중구 덕수궁길 15  2023.0    7.0  2.0   5.0  \n",
-       "29  서울 중구 덕수궁길 15  2023.0    7.0  2.0   6.0  \n",
-       "30  서울 중구 덕수궁길 15  2023.0    7.0  2.0   7.0  \n",
-       "31  서울 중구 덕수궁길 15  2023.0    7.0  2.0   8.0  \n",
-       "32  서울 중구 덕수궁길 15  2023.0    7.0  2.0   9.0  \n",
-       "33  서울 중구 덕수궁길 15  2023.0    7.0  2.0  10.0  \n",
-       "34  서울 중구 덕수궁길 15  2023.0    7.0  2.0  11.0  \n",
-       "35  서울 중구 덕수궁길 15  2023.0    7.0  2.0  12.0  \n",
-       "36  서울 중구 덕수궁길 15  2023.0    7.0  2.0  13.0  \n",
-       "37  서울 중구 덕수궁길 15  2023.0    7.0  2.0  14.0  \n",
-       "38  서울 중구 덕수궁길 15  2023.0    7.0  2.0  15.0  \n",
-       "39  서울 중구 덕수궁길 15  2023.0    7.0  2.0  16.0  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head(40)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Analysis_code/1.data_merge.ipynb b/Analysis_code/1.data_merge.ipynb
deleted file mode 100644
index 01b16a80d56c8724ff823c2b3a4995cddd1ba651..0000000000000000000000000000000000000000
--- a/Analysis_code/1.data_merge.ipynb
+++ /dev/null
@@ -1,18714 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from tqdm import trange, notebook, tqdm\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air_2017= pd.read_excel(\"../data/대기질/2017_12.xlsx\") "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>망</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>SO2</th>\n",
-       "      <th>CO</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120101</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.028</td>\n",
-       "      <td>0.016</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120102</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.027</td>\n",
-       "      <td>0.015</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120103</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.030</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120104</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.033</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120105</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.033</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120106</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.027</td>\n",
-       "      <td>0.014</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120107</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.020</td>\n",
-       "      <td>0.021</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120108</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.039</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120109</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.043</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120110</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.047</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120111</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.049</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120112</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>0.046</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120113</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.014</td>\n",
-       "      <td>0.036</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120114</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.015</td>\n",
-       "      <td>0.037</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120115</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.014</td>\n",
-       "      <td>0.038</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120116</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.016</td>\n",
-       "      <td>0.033</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120117</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.014</td>\n",
-       "      <td>0.033</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120118</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.044</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120119</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.048</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120120</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.049</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120121</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.049</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120122</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>0.052</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120123</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.053</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120124</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.056</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120201</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.055</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120202</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.051</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120203</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.045</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120204</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.046</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120205</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.043</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120206</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.044</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       지역     망   측정소코드 측정소명        측정일시    SO2   CO     O3    NO2  PM10  \\\n",
-       "0   서울 중구  도시대기  111121   중구  2017120101  0.004  0.4  0.028  0.016  22.0   \n",
-       "1   서울 중구  도시대기  111121   중구  2017120102  0.004  0.4  0.027  0.015  18.0   \n",
-       "2   서울 중구  도시대기  111121   중구  2017120103  0.004  0.4  0.030  0.011  18.0   \n",
-       "3   서울 중구  도시대기  111121   중구  2017120104  0.004  0.4  0.033  0.008  18.0   \n",
-       "4   서울 중구  도시대기  111121   중구  2017120105  0.004  0.4  0.033  0.008  18.0   \n",
-       "5   서울 중구  도시대기  111121   중구  2017120106  0.004  0.4  0.027  0.014  17.0   \n",
-       "6   서울 중구  도시대기  111121   중구  2017120107  0.004  0.4  0.020  0.021  16.0   \n",
-       "7   서울 중구  도시대기  111121   중구  2017120108  0.004  0.5  0.006  0.039  18.0   \n",
-       "8   서울 중구  도시대기  111121   중구  2017120109  0.004  0.6  0.006  0.043  19.0   \n",
-       "9   서울 중구  도시대기  111121   중구  2017120110  0.004  0.8  0.005  0.047  20.0   \n",
-       "10  서울 중구  도시대기  111121   중구  2017120111  0.005  0.7  0.006  0.049  27.0   \n",
-       "11  서울 중구  도시대기  111121   중구  2017120112  0.004  0.6  0.008  0.046  25.0   \n",
-       "12  서울 중구  도시대기  111121   중구  2017120113  0.004  0.5  0.014  0.036  22.0   \n",
-       "13  서울 중구  도시대기  111121   중구  2017120114  0.004  0.5  0.015  0.037  23.0   \n",
-       "14  서울 중구  도시대기  111121   중구  2017120115  0.005  0.5  0.014  0.038  29.0   \n",
-       "15  서울 중구  도시대기  111121   중구  2017120116  0.005  0.4  0.016  0.033  22.0   \n",
-       "16  서울 중구  도시대기  111121   중구  2017120117  0.004  0.4  0.014  0.033  20.0   \n",
-       "17  서울 중구  도시대기  111121   중구  2017120118  0.004  0.4  0.005  0.044  20.0   \n",
-       "18  서울 중구  도시대기  111121   중구  2017120119  0.004  0.6  0.002  0.048  22.0   \n",
-       "19  서울 중구  도시대기  111121   중구  2017120120  0.004  0.6  0.002  0.049  24.0   \n",
-       "20  서울 중구  도시대기  111121   중구  2017120121  0.004  0.6  0.002  0.049  22.0   \n",
-       "21  서울 중구  도시대기  111121   중구  2017120122  0.004  0.7  0.001  0.052  23.0   \n",
-       "22  서울 중구  도시대기  111121   중구  2017120123  0.004  0.7  0.002  0.053  25.0   \n",
-       "23  서울 중구  도시대기  111121   중구  2017120124  0.004  0.8  0.002  0.056  26.0   \n",
-       "24  서울 중구  도시대기  111121   중구  2017120201  0.004  0.9  0.002  0.055  29.0   \n",
-       "25  서울 중구  도시대기  111121   중구  2017120202  0.004  0.9  0.002  0.051  29.0   \n",
-       "26  서울 중구  도시대기  111121   중구  2017120203  0.004  0.8  0.002  0.045  27.0   \n",
-       "27  서울 중구  도시대기  111121   중구  2017120204  0.004  0.8  0.002  0.046  32.0   \n",
-       "28  서울 중구  도시대기  111121   중구  2017120205  0.004  0.7  0.002  0.043  36.0   \n",
-       "29  서울 중구  도시대기  111121   중구  2017120206  0.004  0.8  0.002  0.044  33.0   \n",
-       "\n",
-       "    PM25             주소  \n",
-       "0   14.0  서울 중구 덕수궁길 15  \n",
-       "1   11.0  서울 중구 덕수궁길 15  \n",
-       "2   13.0  서울 중구 덕수궁길 15  \n",
-       "3   11.0  서울 중구 덕수궁길 15  \n",
-       "4    9.0  서울 중구 덕수궁길 15  \n",
-       "5   10.0  서울 중구 덕수궁길 15  \n",
-       "6   10.0  서울 중구 덕수궁길 15  \n",
-       "7   10.0  서울 중구 덕수궁길 15  \n",
-       "8   11.0  서울 중구 덕수궁길 15  \n",
-       "9   11.0  서울 중구 덕수궁길 15  \n",
-       "10  14.0  서울 중구 덕수궁길 15  \n",
-       "11  14.0  서울 중구 덕수궁길 15  \n",
-       "12  10.0  서울 중구 덕수궁길 15  \n",
-       "13  14.0  서울 중구 덕수궁길 15  \n",
-       "14  18.0  서울 중구 덕수궁길 15  \n",
-       "15  12.0  서울 중구 덕수궁길 15  \n",
-       "16  10.0  서울 중구 덕수궁길 15  \n",
-       "17  11.0  서울 중구 덕수궁길 15  \n",
-       "18  13.0  서울 중구 덕수궁길 15  \n",
-       "19  16.0  서울 중구 덕수궁길 15  \n",
-       "20  17.0  서울 중구 덕수궁길 15  \n",
-       "21  16.0  서울 중구 덕수궁길 15  \n",
-       "22  17.0  서울 중구 덕수궁길 15  \n",
-       "23  18.0  서울 중구 덕수궁길 15  \n",
-       "24  20.0  서울 중구 덕수궁길 15  \n",
-       "25  20.0  서울 중구 덕수궁길 15  \n",
-       "26  19.0  서울 중구 덕수궁길 15  \n",
-       "27  22.0  서울 중구 덕수궁길 15  \n",
-       "28  27.0  서울 중구 덕수궁길 15  \n",
-       "29  22.0  서울 중구 덕수궁길 15  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air_2017.head(30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air_2017['year'] = air_2017['측정일시'].apply(lambda x: int(str(x)[:4])) # 년도\n",
-    "air_2017['month'] = air_2017['측정일시'].apply(lambda x: int(str(x)[4:6])) # 월\n",
-    "air_2017['day'] = air_2017['측정일시'].apply(lambda x: int(str(x)[6:8])) # 일\n",
-    "air_2017['hour'] = air_2017['측정일시'].apply(lambda x: int(str(x)[8:10])) # 시"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>망</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>SO2</th>\n",
-       "      <th>CO</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120101</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.028</td>\n",
-       "      <td>0.016</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120102</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.027</td>\n",
-       "      <td>0.015</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120103</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.030</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120104</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.033</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120105</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.033</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120106</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.027</td>\n",
-       "      <td>0.014</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120107</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.020</td>\n",
-       "      <td>0.021</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120108</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.039</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120109</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.043</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120110</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.047</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120111</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.049</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120112</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>0.046</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120113</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.014</td>\n",
-       "      <td>0.036</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120114</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.015</td>\n",
-       "      <td>0.037</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120115</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.014</td>\n",
-       "      <td>0.038</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120116</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.016</td>\n",
-       "      <td>0.033</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120117</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.014</td>\n",
-       "      <td>0.033</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120118</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.044</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120119</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.048</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120120</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.049</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120121</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.049</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120122</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>0.052</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120123</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.053</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120124</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.056</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120201</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.055</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120202</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.051</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120203</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.045</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120204</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.046</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120205</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.043</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017120206</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.044</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2017</td>\n",
-       "      <td>12</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       지역     망   측정소코드 측정소명        측정일시    SO2   CO     O3    NO2  PM10  \\\n",
-       "0   서울 중구  도시대기  111121   중구  2017120101  0.004  0.4  0.028  0.016  22.0   \n",
-       "1   서울 중구  도시대기  111121   중구  2017120102  0.004  0.4  0.027  0.015  18.0   \n",
-       "2   서울 중구  도시대기  111121   중구  2017120103  0.004  0.4  0.030  0.011  18.0   \n",
-       "3   서울 중구  도시대기  111121   중구  2017120104  0.004  0.4  0.033  0.008  18.0   \n",
-       "4   서울 중구  도시대기  111121   중구  2017120105  0.004  0.4  0.033  0.008  18.0   \n",
-       "5   서울 중구  도시대기  111121   중구  2017120106  0.004  0.4  0.027  0.014  17.0   \n",
-       "6   서울 중구  도시대기  111121   중구  2017120107  0.004  0.4  0.020  0.021  16.0   \n",
-       "7   서울 중구  도시대기  111121   중구  2017120108  0.004  0.5  0.006  0.039  18.0   \n",
-       "8   서울 중구  도시대기  111121   중구  2017120109  0.004  0.6  0.006  0.043  19.0   \n",
-       "9   서울 중구  도시대기  111121   중구  2017120110  0.004  0.8  0.005  0.047  20.0   \n",
-       "10  서울 중구  도시대기  111121   중구  2017120111  0.005  0.7  0.006  0.049  27.0   \n",
-       "11  서울 중구  도시대기  111121   중구  2017120112  0.004  0.6  0.008  0.046  25.0   \n",
-       "12  서울 중구  도시대기  111121   중구  2017120113  0.004  0.5  0.014  0.036  22.0   \n",
-       "13  서울 중구  도시대기  111121   중구  2017120114  0.004  0.5  0.015  0.037  23.0   \n",
-       "14  서울 중구  도시대기  111121   중구  2017120115  0.005  0.5  0.014  0.038  29.0   \n",
-       "15  서울 중구  도시대기  111121   중구  2017120116  0.005  0.4  0.016  0.033  22.0   \n",
-       "16  서울 중구  도시대기  111121   중구  2017120117  0.004  0.4  0.014  0.033  20.0   \n",
-       "17  서울 중구  도시대기  111121   중구  2017120118  0.004  0.4  0.005  0.044  20.0   \n",
-       "18  서울 중구  도시대기  111121   중구  2017120119  0.004  0.6  0.002  0.048  22.0   \n",
-       "19  서울 중구  도시대기  111121   중구  2017120120  0.004  0.6  0.002  0.049  24.0   \n",
-       "20  서울 중구  도시대기  111121   중구  2017120121  0.004  0.6  0.002  0.049  22.0   \n",
-       "21  서울 중구  도시대기  111121   중구  2017120122  0.004  0.7  0.001  0.052  23.0   \n",
-       "22  서울 중구  도시대기  111121   중구  2017120123  0.004  0.7  0.002  0.053  25.0   \n",
-       "23  서울 중구  도시대기  111121   중구  2017120124  0.004  0.8  0.002  0.056  26.0   \n",
-       "24  서울 중구  도시대기  111121   중구  2017120201  0.004  0.9  0.002  0.055  29.0   \n",
-       "25  서울 중구  도시대기  111121   중구  2017120202  0.004  0.9  0.002  0.051  29.0   \n",
-       "26  서울 중구  도시대기  111121   중구  2017120203  0.004  0.8  0.002  0.045  27.0   \n",
-       "27  서울 중구  도시대기  111121   중구  2017120204  0.004  0.8  0.002  0.046  32.0   \n",
-       "28  서울 중구  도시대기  111121   중구  2017120205  0.004  0.7  0.002  0.043  36.0   \n",
-       "29  서울 중구  도시대기  111121   중구  2017120206  0.004  0.8  0.002  0.044  33.0   \n",
-       "\n",
-       "    PM25             주소  year  month  day  hour  \n",
-       "0   14.0  서울 중구 덕수궁길 15  2017     12    1     1  \n",
-       "1   11.0  서울 중구 덕수궁길 15  2017     12    1     2  \n",
-       "2   13.0  서울 중구 덕수궁길 15  2017     12    1     3  \n",
-       "3   11.0  서울 중구 덕수궁길 15  2017     12    1     4  \n",
-       "4    9.0  서울 중구 덕수궁길 15  2017     12    1     5  \n",
-       "5   10.0  서울 중구 덕수궁길 15  2017     12    1     6  \n",
-       "6   10.0  서울 중구 덕수궁길 15  2017     12    1     7  \n",
-       "7   10.0  서울 중구 덕수궁길 15  2017     12    1     8  \n",
-       "8   11.0  서울 중구 덕수궁길 15  2017     12    1     9  \n",
-       "9   11.0  서울 중구 덕수궁길 15  2017     12    1    10  \n",
-       "10  14.0  서울 중구 덕수궁길 15  2017     12    1    11  \n",
-       "11  14.0  서울 중구 덕수궁길 15  2017     12    1    12  \n",
-       "12  10.0  서울 중구 덕수궁길 15  2017     12    1    13  \n",
-       "13  14.0  서울 중구 덕수궁길 15  2017     12    1    14  \n",
-       "14  18.0  서울 중구 덕수궁길 15  2017     12    1    15  \n",
-       "15  12.0  서울 중구 덕수궁길 15  2017     12    1    16  \n",
-       "16  10.0  서울 중구 덕수궁길 15  2017     12    1    17  \n",
-       "17  11.0  서울 중구 덕수궁길 15  2017     12    1    18  \n",
-       "18  13.0  서울 중구 덕수궁길 15  2017     12    1    19  \n",
-       "19  16.0  서울 중구 덕수궁길 15  2017     12    1    20  \n",
-       "20  17.0  서울 중구 덕수궁길 15  2017     12    1    21  \n",
-       "21  16.0  서울 중구 덕수궁길 15  2017     12    1    22  \n",
-       "22  17.0  서울 중구 덕수궁길 15  2017     12    1    23  \n",
-       "23  18.0  서울 중구 덕수궁길 15  2017     12    1    24  \n",
-       "24  20.0  서울 중구 덕수궁길 15  2017     12    2     1  \n",
-       "25  20.0  서울 중구 덕수궁길 15  2017     12    2     2  \n",
-       "26  19.0  서울 중구 덕수궁길 15  2017     12    2     3  \n",
-       "27  22.0  서울 중구 덕수궁길 15  2017     12    2     4  \n",
-       "28  27.0  서울 중구 덕수궁길 15  2017     12    2     5  \n",
-       "29  22.0  서울 중구 덕수궁길 15  2017     12    2     6  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air_2017.head(30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air_2017.loc[(air_2017['year'] == 2017) & (air_2017['month'] == 12) & (air_2017['day'] == 31) & (air_2017['hour'] == 24),['year','month','day','hour']] = [2018,1,1,0] "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 2017년 12월 31일 24시 -> 2018년 1월 1일 0시\n",
-    "air_2017_1231 = air_2017.loc[(air_2017['year'] == 2018) & (air_2017['month'] == 1) & (air_2017['day'] == 1) & (air_2017['hour'] == 0),].copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>망</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>SO2</th>\n",
-       "      <th>CO</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>743</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2017123124</td>\n",
-       "      <td>0.0040</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.013</td>\n",
-       "      <td>0.0280</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1487</th>\n",
-       "      <td>서울 용산구</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>111122</td>\n",
-       "      <td>한강대로</td>\n",
-       "      <td>2017123124</td>\n",
-       "      <td>0.0040</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>0.0390</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>서울 용산구 한강대로 405</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2231</th>\n",
-       "      <td>서울 종로구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111123</td>\n",
-       "      <td>종로구</td>\n",
-       "      <td>2017123124</td>\n",
-       "      <td>0.0060</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.018</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 종로구 종로35가길 19</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2975</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>111124</td>\n",
-       "      <td>청계천로</td>\n",
-       "      <td>2017123124</td>\n",
-       "      <td>0.0060</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>0.016</td>\n",
-       "      <td>0.0250</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>서울 중구 청계천로 184</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3719</th>\n",
-       "      <td>서울 종로구</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>111125</td>\n",
-       "      <td>종로</td>\n",
-       "      <td>2017123124</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>서울 종로구 종로 169</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>250200</th>\n",
-       "      <td>경기 부천시</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>831154</td>\n",
-       "      <td>오정동</td>\n",
-       "      <td>2017123124</td>\n",
-       "      <td>0.0050</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>0.017</td>\n",
-       "      <td>0.0190</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>경기 부천시 성오로 172</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>250944</th>\n",
-       "      <td>경기 부천시</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>831155</td>\n",
-       "      <td>송내대로(중동)</td>\n",
-       "      <td>2017123124</td>\n",
-       "      <td>0.0030</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.018</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>경기 부천시 송내대로 262</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>251688</th>\n",
-       "      <td>인천 강화군</td>\n",
-       "      <td>교외대기</td>\n",
-       "      <td>831481</td>\n",
-       "      <td>석모리</td>\n",
-       "      <td>2017123124</td>\n",
-       "      <td>0.0028</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>0.037</td>\n",
-       "      <td>0.0031</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>인천 강화군 삼산면 석모리</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>252432</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>교외대기</td>\n",
-       "      <td>831491</td>\n",
-       "      <td>덕적도</td>\n",
-       "      <td>2017123124</td>\n",
-       "      <td>0.0021</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.040</td>\n",
-       "      <td>0.0022</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>인천 옹진군 덕적면 진리</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>253176</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831492</td>\n",
-       "      <td>백령도</td>\n",
-       "      <td>2017123124</td>\n",
-       "      <td>0.0028</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.029</td>\n",
-       "      <td>0.0034</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>인천 옹진군 백령면 연화리</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>343 rows × 16 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            지역       망   측정소코드      측정소명        측정일시     SO2   CO     O3  \\\n",
-       "743      서울 중구    도시대기  111121        중구  2017123124  0.0040  0.5  0.013   \n",
-       "1487    서울 용산구   도로변대기  111122      한강대로  2017123124  0.0040  0.5  0.009   \n",
-       "2231    서울 종로구    도시대기  111123       종로구  2017123124  0.0060  0.4  0.018   \n",
-       "2975     서울 중구   도로변대기  111124      청계천로  2017123124  0.0060  0.3  0.016   \n",
-       "3719    서울 종로구   도로변대기  111125        종로  2017123124     NaN  NaN    NaN   \n",
-       "...        ...     ...     ...       ...         ...     ...  ...    ...   \n",
-       "250200  경기 부천시    도시대기  831154       오정동  2017123124  0.0050  0.3  0.017   \n",
-       "250944  경기 부천시   도로변대기  831155  송내대로(중동)  2017123124  0.0030  0.6  0.018   \n",
-       "251688  인천 강화군    교외대기  831481       석모리  2017123124  0.0028  0.3  0.037   \n",
-       "252432  인천 옹진군    교외대기  831491       덕적도  2017123124  0.0021  0.6  0.040   \n",
-       "253176  인천 옹진군  국가배경농도  831492       백령도  2017123124  0.0028  0.6  0.029   \n",
-       "\n",
-       "           NO2  PM10  PM25                주소  year  month  day  hour  \n",
-       "743     0.0280  31.0  16.0     서울 중구 덕수궁길 15  2018      1    1     0  \n",
-       "1487    0.0390  32.0   NaN   서울 용산구 한강대로 405  2018      1    1     0  \n",
-       "2231    0.0240  30.0  15.0  서울 종로구 종로35가길 19  2018      1    1     0  \n",
-       "2975    0.0250  32.0   NaN    서울 중구 청계천로 184  2018      1    1     0  \n",
-       "3719       NaN   NaN   NaN     서울 종로구 종로 169  2018      1    1     0  \n",
-       "...        ...   ...   ...               ...   ...    ...  ...   ...  \n",
-       "250200  0.0190  35.0  28.0    경기 부천시 성오로 172  2018      1    1     0  \n",
-       "250944  0.0160  42.0   NaN   경기 부천시 송내대로 262  2018      1    1     0  \n",
-       "251688  0.0031  32.0   NaN    인천 강화군 삼산면 석모리  2018      1    1     0  \n",
-       "252432  0.0022  36.0   NaN     인천 옹진군 덕적면 진리  2018      1    1     0  \n",
-       "253176  0.0034  37.0  16.0    인천 옹진군 백령면 연화리  2018      1    1     0  \n",
-       "\n",
-       "[343 rows x 16 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air_2017_1231"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air_2017_1231['year'] = air_2017_1231['year'].astype(str)\n",
-    "air_2017_1231['month'] = air_2017_1231['month'].astype(str)\n",
-    "air_2017_1231['day'] = air_2017_1231['day'].astype(str)\n",
-    "air_2017_1231['hour'] = air_2017_1231['hour'].astype(str)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air_2017_1231['측정일시'] = pd.to_datetime(\n",
-    "    air_2017_1231['year'] + \"-\" + air_2017_1231['month'] + \"-\" + air_2017_1231['day'] + \" \" +\n",
-    "    air_2017_1231['hour'] + \":\" + \"00\" + \":\" + \"00\",\n",
-    "    format='%Y-%m-%d %H:%M:%S'\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>망</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>SO2</th>\n",
-       "      <th>CO</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>743</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.0040</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.013</td>\n",
-       "      <td>0.0280</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1487</th>\n",
-       "      <td>서울 용산구</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>111122</td>\n",
-       "      <td>한강대로</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.0040</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>0.0390</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>서울 용산구 한강대로 405</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2231</th>\n",
-       "      <td>서울 종로구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111123</td>\n",
-       "      <td>종로구</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.0060</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.018</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 종로구 종로35가길 19</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2975</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>111124</td>\n",
-       "      <td>청계천로</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.0060</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>0.016</td>\n",
-       "      <td>0.0250</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>서울 중구 청계천로 184</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3719</th>\n",
-       "      <td>서울 종로구</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>111125</td>\n",
-       "      <td>종로</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>서울 종로구 종로 169</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>250200</th>\n",
-       "      <td>경기 부천시</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>831154</td>\n",
-       "      <td>오정동</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.0050</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>0.017</td>\n",
-       "      <td>0.0190</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>경기 부천시 성오로 172</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>250944</th>\n",
-       "      <td>경기 부천시</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>831155</td>\n",
-       "      <td>송내대로(중동)</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.0030</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.018</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>경기 부천시 송내대로 262</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>251688</th>\n",
-       "      <td>인천 강화군</td>\n",
-       "      <td>교외대기</td>\n",
-       "      <td>831481</td>\n",
-       "      <td>석모리</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.0028</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>0.037</td>\n",
-       "      <td>0.0031</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>인천 강화군 삼산면 석모리</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>252432</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>교외대기</td>\n",
-       "      <td>831491</td>\n",
-       "      <td>덕적도</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.0021</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.040</td>\n",
-       "      <td>0.0022</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>인천 옹진군 덕적면 진리</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>253176</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831492</td>\n",
-       "      <td>백령도</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.0028</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.029</td>\n",
-       "      <td>0.0034</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>인천 옹진군 백령면 연화리</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>343 rows × 16 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            지역       망   측정소코드      측정소명       측정일시     SO2   CO     O3  \\\n",
-       "743      서울 중구    도시대기  111121        중구 2018-01-01  0.0040  0.5  0.013   \n",
-       "1487    서울 용산구   도로변대기  111122      한강대로 2018-01-01  0.0040  0.5  0.009   \n",
-       "2231    서울 종로구    도시대기  111123       종로구 2018-01-01  0.0060  0.4  0.018   \n",
-       "2975     서울 중구   도로변대기  111124      청계천로 2018-01-01  0.0060  0.3  0.016   \n",
-       "3719    서울 종로구   도로변대기  111125        종로 2018-01-01     NaN  NaN    NaN   \n",
-       "...        ...     ...     ...       ...        ...     ...  ...    ...   \n",
-       "250200  경기 부천시    도시대기  831154       오정동 2018-01-01  0.0050  0.3  0.017   \n",
-       "250944  경기 부천시   도로변대기  831155  송내대로(중동) 2018-01-01  0.0030  0.6  0.018   \n",
-       "251688  인천 강화군    교외대기  831481       석모리 2018-01-01  0.0028  0.3  0.037   \n",
-       "252432  인천 옹진군    교외대기  831491       덕적도 2018-01-01  0.0021  0.6  0.040   \n",
-       "253176  인천 옹진군  국가배경농도  831492       백령도 2018-01-01  0.0028  0.6  0.029   \n",
-       "\n",
-       "           NO2  PM10  PM25                주소  year month day hour  \n",
-       "743     0.0280  31.0  16.0     서울 중구 덕수궁길 15  2018     1   1    0  \n",
-       "1487    0.0390  32.0   NaN   서울 용산구 한강대로 405  2018     1   1    0  \n",
-       "2231    0.0240  30.0  15.0  서울 종로구 종로35가길 19  2018     1   1    0  \n",
-       "2975    0.0250  32.0   NaN    서울 중구 청계천로 184  2018     1   1    0  \n",
-       "3719       NaN   NaN   NaN     서울 종로구 종로 169  2018     1   1    0  \n",
-       "...        ...   ...   ...               ...   ...   ...  ..  ...  \n",
-       "250200  0.0190  35.0  28.0    경기 부천시 성오로 172  2018     1   1    0  \n",
-       "250944  0.0160  42.0   NaN   경기 부천시 송내대로 262  2018     1   1    0  \n",
-       "251688  0.0031  32.0   NaN    인천 강화군 삼산면 석모리  2018     1   1    0  \n",
-       "252432  0.0022  36.0   NaN     인천 옹진군 덕적면 진리  2018     1   1    0  \n",
-       "253176  0.0034  37.0  16.0    인천 옹진군 백령면 연화리  2018     1   1    0  \n",
-       "\n",
-       "[343 rows x 16 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air_2017_1231"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# SO2, CO feature는 시정관련 feature가 아니라고 판단해서 제거\n",
-    "air_2017_1231.drop(columns=['SO2', 'CO'], inplace=True, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>망</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
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-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>743</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.013</td>\n",
-       "      <td>0.0280</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1487</th>\n",
-       "      <td>서울 용산구</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>111122</td>\n",
-       "      <td>한강대로</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>0.0390</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>서울 용산구 한강대로 405</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2231</th>\n",
-       "      <td>서울 종로구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111123</td>\n",
-       "      <td>종로구</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.018</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 종로구 종로35가길 19</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2975</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>111124</td>\n",
-       "      <td>청계천로</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.016</td>\n",
-       "      <td>0.0250</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>서울 중구 청계천로 184</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3719</th>\n",
-       "      <td>서울 종로구</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>111125</td>\n",
-       "      <td>종로</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>서울 종로구 종로 169</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>250200</th>\n",
-       "      <td>경기 부천시</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>831154</td>\n",
-       "      <td>오정동</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.017</td>\n",
-       "      <td>0.0190</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>경기 부천시 성오로 172</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>250944</th>\n",
-       "      <td>경기 부천시</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>831155</td>\n",
-       "      <td>송내대로(중동)</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.018</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>경기 부천시 송내대로 262</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>251688</th>\n",
-       "      <td>인천 강화군</td>\n",
-       "      <td>교외대기</td>\n",
-       "      <td>831481</td>\n",
-       "      <td>석모리</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.037</td>\n",
-       "      <td>0.0031</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>인천 강화군 삼산면 석모리</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>252432</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>교외대기</td>\n",
-       "      <td>831491</td>\n",
-       "      <td>덕적도</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.040</td>\n",
-       "      <td>0.0022</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>인천 옹진군 덕적면 진리</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>253176</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831492</td>\n",
-       "      <td>백령도</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.029</td>\n",
-       "      <td>0.0034</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>인천 옹진군 백령면 연화리</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>343 rows × 14 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            지역       망   측정소코드      측정소명       측정일시     O3     NO2  PM10  \\\n",
-       "743      서울 중구    도시대기  111121        중구 2018-01-01  0.013  0.0280  31.0   \n",
-       "1487    서울 용산구   도로변대기  111122      한강대로 2018-01-01  0.009  0.0390  32.0   \n",
-       "2231    서울 종로구    도시대기  111123       종로구 2018-01-01  0.018  0.0240  30.0   \n",
-       "2975     서울 중구   도로변대기  111124      청계천로 2018-01-01  0.016  0.0250  32.0   \n",
-       "3719    서울 종로구   도로변대기  111125        종로 2018-01-01    NaN     NaN   NaN   \n",
-       "...        ...     ...     ...       ...        ...    ...     ...   ...   \n",
-       "250200  경기 부천시    도시대기  831154       오정동 2018-01-01  0.017  0.0190  35.0   \n",
-       "250944  경기 부천시   도로변대기  831155  송내대로(중동) 2018-01-01  0.018  0.0160  42.0   \n",
-       "251688  인천 강화군    교외대기  831481       석모리 2018-01-01  0.037  0.0031  32.0   \n",
-       "252432  인천 옹진군    교외대기  831491       덕적도 2018-01-01  0.040  0.0022  36.0   \n",
-       "253176  인천 옹진군  국가배경농도  831492       백령도 2018-01-01  0.029  0.0034  37.0   \n",
-       "\n",
-       "        PM25                주소  year month day hour  \n",
-       "743     16.0     서울 중구 덕수궁길 15  2018     1   1    0  \n",
-       "1487     NaN   서울 용산구 한강대로 405  2018     1   1    0  \n",
-       "2231    15.0  서울 종로구 종로35가길 19  2018     1   1    0  \n",
-       "2975     NaN    서울 중구 청계천로 184  2018     1   1    0  \n",
-       "3719     NaN     서울 종로구 종로 169  2018     1   1    0  \n",
-       "...      ...               ...   ...   ...  ..  ...  \n",
-       "250200  28.0    경기 부천시 성오로 172  2018     1   1    0  \n",
-       "250944   NaN   경기 부천시 송내대로 262  2018     1   1    0  \n",
-       "251688   NaN    인천 강화군 삼산면 석모리  2018     1   1    0  \n",
-       "252432   NaN     인천 옹진군 덕적면 진리  2018     1   1    0  \n",
-       "253176  16.0    인천 옹진군 백령면 연화리  2018     1   1    0  \n",
-       "\n",
-       "[343 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air_2017_1231"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 13.18 GB\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air_2018= pd.read_feather(\"../data/대기질/2018.feather\") # 2018년 데이터\n",
-    "air_2019= pd.read_feather(\"../data/대기질/2019.feather\") # 2019년 데이터\n",
-    "air_2020= pd.read_feather(\"../data/대기질/2020.feather\") # 2020년 데이터\n",
-    "air_2021= pd.read_feather(\"../data/대기질/2021.feather\") # 2021년 데이터\n",
-    "air_2022= pd.read_feather(\"../data/대기질/2022.feather\") # 2022년 데이터\n",
-    "air_2023= pd.read_feather(\"../data/대기질/2023.feather\") # 2023년 데이터\n",
-    "\n",
-    "\n",
-    "# 2017년 12월 31일 24시 데이터 + 2018년 ~ 2023년 데이터 합치기\n",
-    "air= pd.concat([air_2017_1231, air_2018, air_2019, air_2020, air_2021, air_2022, air_2023], axis=0).copy() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 23.13 GB\n",
-      "현재 메모리 사용량: 20.2%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n",
-    "print(f\"현재 메모리 사용량: {psutil.virtual_memory().percent}%\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "del([air_2017_1231, air_2018, air_2019, air_2020, air_2021, air_2022, air_2023]) # 사용하지 않는 데이터 삭제"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "196"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import gc\n",
-    "gc.collect() # 메모리 정리"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 22.08 GB\n",
-      "현재 메모리 사용량: 19.4%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n",
-    "print(f\"현재 메모리 사용량: {psutil.virtual_memory().percent}%\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>망</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>743</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.0130</td>\n",
-       "      <td>0.0280</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1487</th>\n",
-       "      <td>서울 용산구</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>111122</td>\n",
-       "      <td>한강대로</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.0090</td>\n",
-       "      <td>0.0390</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>서울 용산구 한강대로 405</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2231</th>\n",
-       "      <td>서울 종로구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111123</td>\n",
-       "      <td>종로구</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.0180</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 종로구 종로35가길 19</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2975</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>111124</td>\n",
-       "      <td>청계천로</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>0.0250</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>서울 중구 청계천로 184</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3719</th>\n",
-       "      <td>서울 종로구</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>111125</td>\n",
-       "      <td>종로</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>서울 종로구 종로 169</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5667284</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 20:00:00</td>\n",
-       "      <td>0.0415</td>\n",
-       "      <td>0.0086</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>20.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5667285</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 21:00:00</td>\n",
-       "      <td>0.0409</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>21.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5667286</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 22:00:00</td>\n",
-       "      <td>0.0414</td>\n",
-       "      <td>0.0111</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>22.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5667287</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 23:00:00</td>\n",
-       "      <td>0.0411</td>\n",
-       "      <td>0.0112</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>23.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5667288</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>NaT</td>\n",
-       "      <td>0.0402</td>\n",
-       "      <td>0.0098</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28178690 rows × 14 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             지역       망   측정소코드  측정소명                측정일시      O3     NO2  \\\n",
-       "743       서울 중구    도시대기  111121    중구 2018-01-01 00:00:00  0.0130  0.0280   \n",
-       "1487     서울 용산구   도로변대기  111122  한강대로 2018-01-01 00:00:00  0.0090  0.0390   \n",
-       "2231     서울 종로구    도시대기  111123   종로구 2018-01-01 00:00:00  0.0180  0.0240   \n",
-       "2975      서울 중구   도로변대기  111124  청계천로 2018-01-01 00:00:00  0.0160  0.0250   \n",
-       "3719     서울 종로구   도로변대기  111125    종로 2018-01-01 00:00:00     NaN     NaN   \n",
-       "...         ...     ...     ...   ...                 ...     ...     ...   \n",
-       "5667284  인천 옹진군  국가배경농도  831495    울도 2023-12-31 20:00:00  0.0415  0.0086   \n",
-       "5667285  인천 옹진군  국가배경농도  831495    울도 2023-12-31 21:00:00  0.0409  0.0095   \n",
-       "5667286  인천 옹진군  국가배경농도  831495    울도 2023-12-31 22:00:00  0.0414  0.0111   \n",
-       "5667287  인천 옹진군  국가배경농도  831495    울도 2023-12-31 23:00:00  0.0411  0.0112   \n",
-       "5667288  인천 옹진군  국가배경농도  831495    울도                 NaT  0.0402  0.0098   \n",
-       "\n",
-       "         PM10  PM25                   주소    year month   day  hour  \n",
-       "743      31.0  16.0        서울 중구 덕수궁길 15    2018     1     1     0  \n",
-       "1487     32.0   NaN      서울 용산구 한강대로 405    2018     1     1     0  \n",
-       "2231     30.0  15.0     서울 종로구 종로35가길 19    2018     1     1     0  \n",
-       "2975     32.0   NaN       서울 중구 청계천로 184    2018     1     1     0  \n",
-       "3719      NaN   NaN        서울 종로구 종로 169    2018     1     1     0  \n",
-       "...       ...   ...                  ...     ...   ...   ...   ...  \n",
-       "5667284  64.0  57.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  20.0  \n",
-       "5667285  62.0  50.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  21.0  \n",
-       "5667286  65.0  59.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  22.0  \n",
-       "5667287  63.0  54.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  23.0  \n",
-       "5667288  67.0  59.0  인천 옹진군 덕적면 울도리 85번지     NaN   NaN   NaN   NaN  \n",
-       "\n",
-       "[28178690 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "지역             0\n",
-       "망              0\n",
-       "측정소코드          0\n",
-       "측정소명           0\n",
-       "측정일시     1174098\n",
-       "O3       1152586\n",
-       "NO2      1172765\n",
-       "PM10     1441909\n",
-       "PM25     1970712\n",
-       "주소             0\n",
-       "year     1174098\n",
-       "month    1174098\n",
-       "day      1174098\n",
-       "hour     1174098\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(air).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(28178690, 14)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **기상청 ASOS 기상관측 데이터는 00시~ 23시로 구성되어있고 에어코리아 대기질 데이터는 01시~24시로 구성되어있기 때문에 통일을 시켜줘야 한다. 그렇게 때문에 대기질 데이터의 24시를 00시로 바꿔주는 작업을 진행한다**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 자정 데이터가 na라고 표시되어 있음 그걸 해결하기 위해 na를 자정 데이터로 채워줌\n",
-    "air['측정일시'] = air['측정일시'].fillna(air['측정일시'].shift() + pd.Timedelta(hours=1)).copy() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air.sort_values(by= ['측정소코드', '측정일시'], inplace=True)\n",
-    "air.reset_index(drop=True, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>망</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.013</td>\n",
-       "      <td>0.028</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 01:00:00</td>\n",
-       "      <td>0.020</td>\n",
-       "      <td>0.020</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00:00</td>\n",
-       "      <td>0.024</td>\n",
-       "      <td>0.016</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00:00</td>\n",
-       "      <td>0.018</td>\n",
-       "      <td>0.022</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00:00</td>\n",
-       "      <td>0.010</td>\n",
-       "      <td>0.030</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 05:00:00</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>0.029</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 06:00:00</td>\n",
-       "      <td>0.012</td>\n",
-       "      <td>0.027</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 07:00:00</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>0.030</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>7.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 08:00:00</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>0.032</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>8.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 09:00:00</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.037</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>9.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 10:00:00</td>\n",
-       "      <td>0.018</td>\n",
-       "      <td>0.024</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 11:00:00</td>\n",
-       "      <td>0.020</td>\n",
-       "      <td>0.023</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>11.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 12:00:00</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>0.018</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 13:00:00</td>\n",
-       "      <td>0.030</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>13.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 14:00:00</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>0.018</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>14.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 15:00:00</td>\n",
-       "      <td>0.028</td>\n",
-       "      <td>0.017</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>15.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 16:00:00</td>\n",
-       "      <td>0.026</td>\n",
-       "      <td>0.019</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>16.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 17:00:00</td>\n",
-       "      <td>0.026</td>\n",
-       "      <td>0.019</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>17.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 18:00:00</td>\n",
-       "      <td>0.016</td>\n",
-       "      <td>0.028</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>18.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 19:00:00</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>0.037</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>19.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 20:00:00</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.047</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>20.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 21:00:00</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.047</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>21.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 22:00:00</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.049</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>22.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 23:00:00</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>0.053</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>23.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-02 00:00:00</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.050</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-02 01:00:00</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.050</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-02 02:00:00</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.048</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-02 03:00:00</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.047</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-02 04:00:00</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.051</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-02 05:00:00</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.054</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       지역     망   측정소코드 측정소명                측정일시     O3    NO2  PM10  PM25  \\\n",
-       "0   서울 중구  도시대기  111121   중구 2018-01-01 00:00:00  0.013  0.028  31.0  16.0   \n",
-       "1   서울 중구  도시대기  111121   중구 2018-01-01 01:00:00  0.020  0.020  34.0  19.0   \n",
-       "2   서울 중구  도시대기  111121   중구 2018-01-01 02:00:00  0.024  0.016  27.0  14.0   \n",
-       "3   서울 중구  도시대기  111121   중구 2018-01-01 03:00:00  0.018  0.022  26.0  14.0   \n",
-       "4   서울 중구  도시대기  111121   중구 2018-01-01 04:00:00  0.010  0.030  26.0  15.0   \n",
-       "5   서울 중구  도시대기  111121   중구 2018-01-01 05:00:00  0.011  0.029  28.0  16.0   \n",
-       "6   서울 중구  도시대기  111121   중구 2018-01-01 06:00:00  0.012  0.027  29.0  17.0   \n",
-       "7   서울 중구  도시대기  111121   중구 2018-01-01 07:00:00  0.009  0.030  28.0  16.0   \n",
-       "8   서울 중구  도시대기  111121   중구 2018-01-01 08:00:00  0.009  0.032  27.0  14.0   \n",
-       "9   서울 중구  도시대기  111121   중구 2018-01-01 09:00:00  0.005  0.037  27.0  14.0   \n",
-       "10  서울 중구  도시대기  111121   중구 2018-01-01 10:00:00  0.018  0.024  25.0  12.0   \n",
-       "11  서울 중구  도시대기  111121   중구 2018-01-01 11:00:00  0.020  0.023  31.0  16.0   \n",
-       "12  서울 중구  도시대기  111121   중구 2018-01-01 12:00:00  0.025  0.018  30.0  17.0   \n",
-       "13  서울 중구  도시대기  111121   중구 2018-01-01 13:00:00  0.030  0.011  24.0  13.0   \n",
-       "14  서울 중구  도시대기  111121   중구 2018-01-01 14:00:00  0.025  0.018  31.0  17.0   \n",
-       "15  서울 중구  도시대기  111121   중구 2018-01-01 15:00:00  0.028  0.017  41.0  23.0   \n",
-       "16  서울 중구  도시대기  111121   중구 2018-01-01 16:00:00  0.026  0.019  42.0  21.0   \n",
-       "17  서울 중구  도시대기  111121   중구 2018-01-01 17:00:00  0.026  0.019  40.0  21.0   \n",
-       "18  서울 중구  도시대기  111121   중구 2018-01-01 18:00:00  0.016  0.028  37.0  19.0   \n",
-       "19  서울 중구  도시대기  111121   중구 2018-01-01 19:00:00  0.009  0.037  39.0  22.0   \n",
-       "20  서울 중구  도시대기  111121   중구 2018-01-01 20:00:00  0.002  0.047  38.0  21.0   \n",
-       "21  서울 중구  도시대기  111121   중구 2018-01-01 21:00:00  0.002  0.047  38.0  21.0   \n",
-       "22  서울 중구  도시대기  111121   중구 2018-01-01 22:00:00  0.002  0.049  38.0  20.0   \n",
-       "23  서울 중구  도시대기  111121   중구 2018-01-01 23:00:00  0.001  0.053  41.0  23.0   \n",
-       "24  서울 중구  도시대기  111121   중구 2018-01-02 00:00:00  0.002  0.050  37.0  22.0   \n",
-       "25  서울 중구  도시대기  111121   중구 2018-01-02 01:00:00  0.002  0.050  40.0  24.0   \n",
-       "26  서울 중구  도시대기  111121   중구 2018-01-02 02:00:00  0.002  0.048  36.0  22.0   \n",
-       "27  서울 중구  도시대기  111121   중구 2018-01-02 03:00:00  0.002  0.047  37.0  24.0   \n",
-       "28  서울 중구  도시대기  111121   중구 2018-01-02 04:00:00  0.002  0.051  36.0  22.0   \n",
-       "29  서울 중구  도시대기  111121   중구 2018-01-02 05:00:00  0.002  0.054  40.0  26.0   \n",
-       "\n",
-       "               주소    year month  day  hour  \n",
-       "0   서울 중구 덕수궁길 15    2018     1    1     0  \n",
-       "1   서울 중구 덕수궁길 15  2018.0   1.0  1.0   1.0  \n",
-       "2   서울 중구 덕수궁길 15  2018.0   1.0  1.0   2.0  \n",
-       "3   서울 중구 덕수궁길 15  2018.0   1.0  1.0   3.0  \n",
-       "4   서울 중구 덕수궁길 15  2018.0   1.0  1.0   4.0  \n",
-       "5   서울 중구 덕수궁길 15  2018.0   1.0  1.0   5.0  \n",
-       "6   서울 중구 덕수궁길 15  2018.0   1.0  1.0   6.0  \n",
-       "7   서울 중구 덕수궁길 15  2018.0   1.0  1.0   7.0  \n",
-       "8   서울 중구 덕수궁길 15  2018.0   1.0  1.0   8.0  \n",
-       "9   서울 중구 덕수궁길 15  2018.0   1.0  1.0   9.0  \n",
-       "10  서울 중구 덕수궁길 15  2018.0   1.0  1.0  10.0  \n",
-       "11  서울 중구 덕수궁길 15  2018.0   1.0  1.0  11.0  \n",
-       "12  서울 중구 덕수궁길 15  2018.0   1.0  1.0  12.0  \n",
-       "13  서울 중구 덕수궁길 15  2018.0   1.0  1.0  13.0  \n",
-       "14  서울 중구 덕수궁길 15  2018.0   1.0  1.0  14.0  \n",
-       "15  서울 중구 덕수궁길 15  2018.0   1.0  1.0  15.0  \n",
-       "16  서울 중구 덕수궁길 15  2018.0   1.0  1.0  16.0  \n",
-       "17  서울 중구 덕수궁길 15  2018.0   1.0  1.0  17.0  \n",
-       "18  서울 중구 덕수궁길 15  2018.0   1.0  1.0  18.0  \n",
-       "19  서울 중구 덕수궁길 15  2018.0   1.0  1.0  19.0  \n",
-       "20  서울 중구 덕수궁길 15  2018.0   1.0  1.0  20.0  \n",
-       "21  서울 중구 덕수궁길 15  2018.0   1.0  1.0  21.0  \n",
-       "22  서울 중구 덕수궁길 15  2018.0   1.0  1.0  22.0  \n",
-       "23  서울 중구 덕수궁길 15  2018.0   1.0  1.0  23.0  \n",
-       "24  서울 중구 덕수궁길 15     NaN   NaN  NaN   NaN  \n",
-       "25  서울 중구 덕수궁길 15  2018.0   1.0  2.0   1.0  \n",
-       "26  서울 중구 덕수궁길 15  2018.0   1.0  2.0   2.0  \n",
-       "27  서울 중구 덕수궁길 15  2018.0   1.0  2.0   3.0  \n",
-       "28  서울 중구 덕수궁길 15  2018.0   1.0  2.0   4.0  \n",
-       "29  서울 중구 덕수궁길 15  2018.0   1.0  2.0   5.0  "
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air.head(30) # 자정데이터가 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>망</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
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-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
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-       "      <th>year</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>28178660</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-30 19:00:00</td>\n",
-       "      <td>0.0262</td>\n",
-       "      <td>0.0170</td>\n",
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-       "      <td>58.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
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-       "      <td>19.0</td>\n",
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-       "    <tr>\n",
-       "      <th>28178661</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-30 20:00:00</td>\n",
-       "      <td>0.0281</td>\n",
-       "      <td>0.0193</td>\n",
-       "      <td>58.0</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>20.0</td>\n",
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-       "    <tr>\n",
-       "      <th>28178662</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-30 21:00:00</td>\n",
-       "      <td>0.0342</td>\n",
-       "      <td>0.0171</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>21.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178663</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-30 22:00:00</td>\n",
-       "      <td>0.0421</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>22.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178664</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-30 23:00:00</td>\n",
-       "      <td>0.0403</td>\n",
-       "      <td>0.0108</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>23.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178665</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 00:00:00</td>\n",
-       "      <td>0.0377</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178666</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 01:00:00</td>\n",
-       "      <td>0.0365</td>\n",
-       "      <td>0.0097</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178667</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 02:00:00</td>\n",
-       "      <td>0.0382</td>\n",
-       "      <td>0.0093</td>\n",
-       "      <td>73.0</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178668</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 03:00:00</td>\n",
-       "      <td>0.0368</td>\n",
-       "      <td>0.0165</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178669</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 04:00:00</td>\n",
-       "      <td>0.0400</td>\n",
-       "      <td>0.0121</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178670</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 05:00:00</td>\n",
-       "      <td>0.0411</td>\n",
-       "      <td>0.0089</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178671</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 06:00:00</td>\n",
-       "      <td>0.0279</td>\n",
-       "      <td>0.0089</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178672</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 07:00:00</td>\n",
-       "      <td>0.0322</td>\n",
-       "      <td>0.0076</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>7.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178673</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 08:00:00</td>\n",
-       "      <td>0.0393</td>\n",
-       "      <td>0.0055</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>8.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178674</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 09:00:00</td>\n",
-       "      <td>0.0399</td>\n",
-       "      <td>0.0053</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>9.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178675</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 10:00:00</td>\n",
-       "      <td>0.0445</td>\n",
-       "      <td>0.0053</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>10.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178676</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 11:00:00</td>\n",
-       "      <td>0.0452</td>\n",
-       "      <td>0.0055</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>11.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178677</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 12:00:00</td>\n",
-       "      <td>0.0439</td>\n",
-       "      <td>0.0063</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178678</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 13:00:00</td>\n",
-       "      <td>0.0418</td>\n",
-       "      <td>0.0066</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>13.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178679</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 14:00:00</td>\n",
-       "      <td>0.0425</td>\n",
-       "      <td>0.0067</td>\n",
-       "      <td>53.0</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>14.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178680</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 15:00:00</td>\n",
-       "      <td>0.0434</td>\n",
-       "      <td>0.0068</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>15.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178681</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 16:00:00</td>\n",
-       "      <td>0.0437</td>\n",
-       "      <td>0.0075</td>\n",
-       "      <td>61.0</td>\n",
-       "      <td>46.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178682</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 17:00:00</td>\n",
-       "      <td>0.0445</td>\n",
-       "      <td>0.0080</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>17.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178683</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 18:00:00</td>\n",
-       "      <td>0.0445</td>\n",
-       "      <td>0.0086</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>18.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178684</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 19:00:00</td>\n",
-       "      <td>0.0440</td>\n",
-       "      <td>0.0075</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>19.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178685</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 20:00:00</td>\n",
-       "      <td>0.0415</td>\n",
-       "      <td>0.0086</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>20.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178686</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 21:00:00</td>\n",
-       "      <td>0.0409</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>21.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178687</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 22:00:00</td>\n",
-       "      <td>0.0414</td>\n",
-       "      <td>0.0111</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>22.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178688</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 23:00:00</td>\n",
-       "      <td>0.0411</td>\n",
-       "      <td>0.0112</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>23.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178689</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>0.0402</td>\n",
-       "      <td>0.0098</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역       망   측정소코드 측정소명                측정일시      O3     NO2  \\\n",
-       "28178660  인천 옹진군  국가배경농도  831495   울도 2023-12-30 19:00:00  0.0262  0.0170   \n",
-       "28178661  인천 옹진군  국가배경농도  831495   울도 2023-12-30 20:00:00  0.0281  0.0193   \n",
-       "28178662  인천 옹진군  국가배경농도  831495   울도 2023-12-30 21:00:00  0.0342  0.0171   \n",
-       "28178663  인천 옹진군  국가배경농도  831495   울도 2023-12-30 22:00:00  0.0421  0.0095   \n",
-       "28178664  인천 옹진군  국가배경농도  831495   울도 2023-12-30 23:00:00  0.0403  0.0108   \n",
-       "28178665  인천 옹진군  국가배경농도  831495   울도 2023-12-31 00:00:00  0.0377  0.0095   \n",
-       "28178666  인천 옹진군  국가배경농도  831495   울도 2023-12-31 01:00:00  0.0365  0.0097   \n",
-       "28178667  인천 옹진군  국가배경농도  831495   울도 2023-12-31 02:00:00  0.0382  0.0093   \n",
-       "28178668  인천 옹진군  국가배경농도  831495   울도 2023-12-31 03:00:00  0.0368  0.0165   \n",
-       "28178669  인천 옹진군  국가배경농도  831495   울도 2023-12-31 04:00:00  0.0400  0.0121   \n",
-       "28178670  인천 옹진군  국가배경농도  831495   울도 2023-12-31 05:00:00  0.0411  0.0089   \n",
-       "28178671  인천 옹진군  국가배경농도  831495   울도 2023-12-31 06:00:00  0.0279  0.0089   \n",
-       "28178672  인천 옹진군  국가배경농도  831495   울도 2023-12-31 07:00:00  0.0322  0.0076   \n",
-       "28178673  인천 옹진군  국가배경농도  831495   울도 2023-12-31 08:00:00  0.0393  0.0055   \n",
-       "28178674  인천 옹진군  국가배경농도  831495   울도 2023-12-31 09:00:00  0.0399  0.0053   \n",
-       "28178675  인천 옹진군  국가배경농도  831495   울도 2023-12-31 10:00:00  0.0445  0.0053   \n",
-       "28178676  인천 옹진군  국가배경농도  831495   울도 2023-12-31 11:00:00  0.0452  0.0055   \n",
-       "28178677  인천 옹진군  국가배경농도  831495   울도 2023-12-31 12:00:00  0.0439  0.0063   \n",
-       "28178678  인천 옹진군  국가배경농도  831495   울도 2023-12-31 13:00:00  0.0418  0.0066   \n",
-       "28178679  인천 옹진군  국가배경농도  831495   울도 2023-12-31 14:00:00  0.0425  0.0067   \n",
-       "28178680  인천 옹진군  국가배경농도  831495   울도 2023-12-31 15:00:00  0.0434  0.0068   \n",
-       "28178681  인천 옹진군  국가배경농도  831495   울도 2023-12-31 16:00:00  0.0437  0.0075   \n",
-       "28178682  인천 옹진군  국가배경농도  831495   울도 2023-12-31 17:00:00  0.0445  0.0080   \n",
-       "28178683  인천 옹진군  국가배경농도  831495   울도 2023-12-31 18:00:00  0.0445  0.0086   \n",
-       "28178684  인천 옹진군  국가배경농도  831495   울도 2023-12-31 19:00:00  0.0440  0.0075   \n",
-       "28178685  인천 옹진군  국가배경농도  831495   울도 2023-12-31 20:00:00  0.0415  0.0086   \n",
-       "28178686  인천 옹진군  국가배경농도  831495   울도 2023-12-31 21:00:00  0.0409  0.0095   \n",
-       "28178687  인천 옹진군  국가배경농도  831495   울도 2023-12-31 22:00:00  0.0414  0.0111   \n",
-       "28178688  인천 옹진군  국가배경농도  831495   울도 2023-12-31 23:00:00  0.0411  0.0112   \n",
-       "28178689  인천 옹진군  국가배경농도  831495   울도 2024-01-01 00:00:00  0.0402  0.0098   \n",
-       "\n",
-       "          PM10  PM25                   주소    year month   day  hour  \n",
-       "28178660  67.0  58.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  30.0  19.0  \n",
-       "28178661  58.0  47.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  30.0  20.0  \n",
-       "28178662  56.0  41.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  30.0  21.0  \n",
-       "28178663  54.0  38.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  30.0  22.0  \n",
-       "28178664  51.0  38.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  30.0  23.0  \n",
-       "28178665  62.0  38.0  인천 옹진군 덕적면 울도리 85번지     NaN   NaN   NaN   NaN  \n",
-       "28178666  56.0  48.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0   1.0  \n",
-       "28178667  73.0  55.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0   2.0  \n",
-       "28178668  85.0  70.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0   3.0  \n",
-       "28178669  69.0  52.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0   4.0  \n",
-       "28178670  33.0  18.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0   5.0  \n",
-       "28178671  31.0  17.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0   6.0  \n",
-       "28178672  23.0  13.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0   7.0  \n",
-       "28178673  23.0  10.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0   8.0  \n",
-       "28178674  20.0  11.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0   9.0  \n",
-       "28178675  27.0  15.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  10.0  \n",
-       "28178676  33.0  21.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  11.0  \n",
-       "28178677  39.0  28.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  12.0  \n",
-       "28178678  51.0  39.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  13.0  \n",
-       "28178679  53.0  40.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  14.0  \n",
-       "28178680  56.0  36.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  15.0  \n",
-       "28178681  61.0  46.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  16.0  \n",
-       "28178682  59.0  50.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  17.0  \n",
-       "28178683  67.0  51.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  18.0  \n",
-       "28178684  67.0  57.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  19.0  \n",
-       "28178685  64.0  57.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  20.0  \n",
-       "28178686  62.0  50.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  21.0  \n",
-       "28178687  65.0  59.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  22.0  \n",
-       "28178688  63.0  54.0  인천 옹진군 덕적면 울도리 85번지  2023.0  12.0  31.0  23.0  \n",
-       "28178689  67.0  59.0  인천 옹진군 덕적면 울도리 85번지     NaN   NaN   NaN   NaN  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air.tail(30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(air['측정일시']).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air['year'] = air['측정일시'].dt.year # 연도, 월, 일, 시간 변수 생성\n",
-    "air['month'] = air['측정일시'].dt.month\n",
-    "air['day'] = air['측정일시'].dt.day\n",
-    "air['hour'] = air['측정일시'].dt.hour"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 21.42 GB\n",
-      "현재 메모리 사용량: 18.9%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n",
-    "print(f\"현재 메모리 사용량: {psutil.virtual_memory().percent}%\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>망</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.0130</td>\n",
-       "      <td>0.0280</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 01:00:00</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00:00</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00:00</td>\n",
-       "      <td>0.0180</td>\n",
-       "      <td>0.0220</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00:00</td>\n",
-       "      <td>0.0100</td>\n",
-       "      <td>0.0300</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178685</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 20:00:00</td>\n",
-       "      <td>0.0415</td>\n",
-       "      <td>0.0086</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178686</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 21:00:00</td>\n",
-       "      <td>0.0409</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178687</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 22:00:00</td>\n",
-       "      <td>0.0414</td>\n",
-       "      <td>0.0111</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178688</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 23:00:00</td>\n",
-       "      <td>0.0411</td>\n",
-       "      <td>0.0112</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178689</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2024-01-01 00:00:00</td>\n",
-       "      <td>0.0402</td>\n",
-       "      <td>0.0098</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2024</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28178690 rows × 14 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역       망   측정소코드 측정소명                측정일시      O3     NO2  \\\n",
-       "0          서울 중구    도시대기  111121   중구 2018-01-01 00:00:00  0.0130  0.0280   \n",
-       "1          서울 중구    도시대기  111121   중구 2018-01-01 01:00:00  0.0200  0.0200   \n",
-       "2          서울 중구    도시대기  111121   중구 2018-01-01 02:00:00  0.0240  0.0160   \n",
-       "3          서울 중구    도시대기  111121   중구 2018-01-01 03:00:00  0.0180  0.0220   \n",
-       "4          서울 중구    도시대기  111121   중구 2018-01-01 04:00:00  0.0100  0.0300   \n",
-       "...          ...     ...     ...  ...                 ...     ...     ...   \n",
-       "28178685  인천 옹진군  국가배경농도  831495   울도 2023-12-31 20:00:00  0.0415  0.0086   \n",
-       "28178686  인천 옹진군  국가배경농도  831495   울도 2023-12-31 21:00:00  0.0409  0.0095   \n",
-       "28178687  인천 옹진군  국가배경농도  831495   울도 2023-12-31 22:00:00  0.0414  0.0111   \n",
-       "28178688  인천 옹진군  국가배경농도  831495   울도 2023-12-31 23:00:00  0.0411  0.0112   \n",
-       "28178689  인천 옹진군  국가배경농도  831495   울도 2024-01-01 00:00:00  0.0402  0.0098   \n",
-       "\n",
-       "          PM10  PM25                   주소  year  month  day  hour  \n",
-       "0         31.0  16.0        서울 중구 덕수궁길 15  2018      1    1     0  \n",
-       "1         34.0  19.0        서울 중구 덕수궁길 15  2018      1    1     1  \n",
-       "2         27.0  14.0        서울 중구 덕수궁길 15  2018      1    1     2  \n",
-       "3         26.0  14.0        서울 중구 덕수궁길 15  2018      1    1     3  \n",
-       "4         26.0  15.0        서울 중구 덕수궁길 15  2018      1    1     4  \n",
-       "...        ...   ...                  ...   ...    ...  ...   ...  \n",
-       "28178685  64.0  57.0  인천 옹진군 덕적면 울도리 85번지  2023     12   31    20  \n",
-       "28178686  62.0  50.0  인천 옹진군 덕적면 울도리 85번지  2023     12   31    21  \n",
-       "28178687  65.0  59.0  인천 옹진군 덕적면 울도리 85번지  2023     12   31    22  \n",
-       "28178688  63.0  54.0  인천 옹진군 덕적면 울도리 85번지  2023     12   31    23  \n",
-       "28178689  67.0  59.0  인천 옹진군 덕적면 울도리 85번지  2024      1    1     0  \n",
-       "\n",
-       "[28178690 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "660"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(air['측정소코드'].unique()) # 에어 코리아 지상대기질 관측 지점의 개수"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "652"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 23년에 있던 측정소코드가 24년에 없는 경우가 존재함\n",
-    "len(air.loc[(air['year'] == 2024) & (air['month'] == 1) & (air['day'] == 1) & (air['hour'] == 0),'측정소코드'].unique())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 660/660 [02:15<00:00,  4.86it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "not_in = [] \n",
-    "for i in tqdm(air['측정소코드'].unique()): \n",
-    "    if i not in air.loc[(air['year'] == 2024) & (air['month'] == 1) & (air['day'] == 1) & (air['hour'] == 0),'측정소코드'].unique(): # 2024년 1월 1일 0시에 측정소코드가 없는 것들\n",
-    "        not_in.append(i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>망</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>16224095</th>\n",
-       "      <td>대구 북구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>422153</td>\n",
-       "      <td>노원동(폐쇄)</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.010</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>대구 북구 노원동 3가 262번지(삼영초등학교)(3공단로 14길 31)</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14353039</th>\n",
-       "      <td>전남 광양시</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>336354</td>\n",
-       "      <td>진상면(폐쇄)</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.028</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>58.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>전남 광양시 진상면 신시길 227(섬거리)</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13079677</th>\n",
-       "      <td>광주 광산구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>324141</td>\n",
-       "      <td>송정1동(폐쇄)</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.019</td>\n",
-       "      <td>0.019</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>광주 광산구 광산로 70</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24853957</th>\n",
-       "      <td>전북 익산시</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>735132</td>\n",
-       "      <td>남중동</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.021</td>\n",
-       "      <td>0.018</td>\n",
-       "      <td>53.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>전북 익산시 인북로 32길 1 (익산시의회)</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13079678</th>\n",
-       "      <td>광주 광산구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>324141</td>\n",
-       "      <td>송정1동(폐쇄)</td>\n",
-       "      <td>2018-01-01 01:00:00</td>\n",
-       "      <td>0.020</td>\n",
-       "      <td>0.016</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>광주 광산구 광산로 70</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18836468</th>\n",
-       "      <td>경북 군위군</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>437561</td>\n",
-       "      <td>군위읍</td>\n",
-       "      <td>2023-06-29 21:00:00</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>대구광역시 군위군 군위읍 군청로 158</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>6</td>\n",
-       "      <td>29</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18836469</th>\n",
-       "      <td>경북 군위군</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>437561</td>\n",
-       "      <td>군위읍</td>\n",
-       "      <td>2023-06-29 22:00:00</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>대구광역시 군위군 군위읍 군청로 158</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>6</td>\n",
-       "      <td>29</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18836470</th>\n",
-       "      <td>경북 군위군</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>437561</td>\n",
-       "      <td>군위읍</td>\n",
-       "      <td>2023-06-29 23:00:00</td>\n",
-       "      <td>0.027</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>대구광역시 군위군 군위읍 군청로 158</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>6</td>\n",
-       "      <td>29</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18836471</th>\n",
-       "      <td>경북 군위군</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>437561</td>\n",
-       "      <td>군위읍</td>\n",
-       "      <td>2023-06-30 00:00:00</td>\n",
-       "      <td>0.030</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>대구광역시 군위군 군위읍 군청로 158</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>6</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18836472</th>\n",
-       "      <td>경북 군위군</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>437561</td>\n",
-       "      <td>군위읍</td>\n",
-       "      <td>2023-06-30 01:00:00</td>\n",
-       "      <td>0.040</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>대구광역시 군위군 군위읍 군청로 158</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>6</td>\n",
-       "      <td>30</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>223932 rows × 14 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역     망   측정소코드      측정소명                측정일시     O3    NO2  \\\n",
-       "16224095   대구 북구  도시대기  422153   노원동(폐쇄) 2018-01-01 00:00:00  0.010  0.025   \n",
-       "14353039  전남 광양시  도시대기  336354   진상면(폐쇄) 2018-01-01 00:00:00  0.028  0.005   \n",
-       "13079677  광주 광산구  도시대기  324141  송정1동(폐쇄) 2018-01-01 00:00:00  0.019  0.019   \n",
-       "24853957  전북 익산시  도시대기  735132       남중동 2018-01-01 00:00:00  0.021  0.018   \n",
-       "13079678  광주 광산구  도시대기  324141  송정1동(폐쇄) 2018-01-01 01:00:00  0.020  0.016   \n",
-       "...          ...   ...     ...       ...                 ...    ...    ...   \n",
-       "18836468  경북 군위군  도시대기  437561       군위읍 2023-06-29 21:00:00  0.025  0.005   \n",
-       "18836469  경북 군위군  도시대기  437561       군위읍 2023-06-29 22:00:00  0.025  0.005   \n",
-       "18836470  경북 군위군  도시대기  437561       군위읍 2023-06-29 23:00:00  0.027  0.005   \n",
-       "18836471  경북 군위군  도시대기  437561       군위읍 2023-06-30 00:00:00  0.030  0.005   \n",
-       "18836472  경북 군위군  도시대기  437561       군위읍 2023-06-30 01:00:00  0.040  0.004   \n",
-       "\n",
-       "          PM10  PM25                                       주소  year  month  \\\n",
-       "16224095  50.0  11.0  대구 북구 노원동 3가 262번지(삼영초등학교)(3공단로 14길 31)  2018      1   \n",
-       "14353039  58.0  19.0                  전남 광양시 진상면 신시길 227(섬거리)  2018      1   \n",
-       "13079677  67.0  18.0                            광주 광산구 광산로 70  2018      1   \n",
-       "24853957  53.0  19.0                 전북 익산시 인북로 32길 1 (익산시의회)  2018      1   \n",
-       "13079678  62.0  17.0                            광주 광산구 광산로 70  2018      1   \n",
-       "...        ...   ...                                      ...   ...    ...   \n",
-       "18836468   3.0   1.0                    대구광역시 군위군 군위읍 군청로 158  2023      6   \n",
-       "18836469   4.0   2.0                    대구광역시 군위군 군위읍 군청로 158  2023      6   \n",
-       "18836470   7.0   2.0                    대구광역시 군위군 군위읍 군청로 158  2023      6   \n",
-       "18836471  11.0   1.0                    대구광역시 군위군 군위읍 군청로 158  2023      6   \n",
-       "18836472  14.0   6.0                    대구광역시 군위군 군위읍 군청로 158  2023      6   \n",
-       "\n",
-       "          day  hour  \n",
-       "16224095    1     0  \n",
-       "14353039    1     0  \n",
-       "13079677    1     0  \n",
-       "24853957    1     0  \n",
-       "13079678    1     1  \n",
-       "...       ...   ...  \n",
-       "18836468   29    21  \n",
-       "18836469   29    22  \n",
-       "18836470   29    23  \n",
-       "18836471   30     0  \n",
-       "18836472   30     1  \n",
-       "\n",
-       "[223932 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air.loc[air['측정소코드'].isin(not_in),:].sort_values(by=['측정일시']) # 2024년 1월 1일 0시에 측정이 없는 측정소코드\n",
-    "# 맨 밑에 있는 데이터가 2023년 6월 30일 01시 인것으로 보아 6월 30일 01시 이후로는 지점의 종류가 감소"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>망</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>52584</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2024-01-01</td>\n",
-       "      <td>0.0013</td>\n",
-       "      <td>0.0491</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2024</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>105169</th>\n",
-       "      <td>서울 용산구</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>111122</td>\n",
-       "      <td>한강대로</td>\n",
-       "      <td>2024-01-01</td>\n",
-       "      <td>0.0017</td>\n",
-       "      <td>0.0474</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>서울 용산구 한강대로 405</td>\n",
-       "      <td>2024</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>157754</th>\n",
-       "      <td>서울 종로구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111123</td>\n",
-       "      <td>종로구</td>\n",
-       "      <td>2024-01-01</td>\n",
-       "      <td>0.0021</td>\n",
-       "      <td>0.0437</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>서울 종로구 종로35가길 19</td>\n",
-       "      <td>2024</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>210339</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>111124</td>\n",
-       "      <td>청계천로</td>\n",
-       "      <td>2024-01-01</td>\n",
-       "      <td>0.0025</td>\n",
-       "      <td>0.0326</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>서울 중구 청계천로 184</td>\n",
-       "      <td>2024</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>262924</th>\n",
-       "      <td>서울 종로구</td>\n",
-       "      <td>도로변대기</td>\n",
-       "      <td>111125</td>\n",
-       "      <td>종로</td>\n",
-       "      <td>2024-01-01</td>\n",
-       "      <td>0.0022</td>\n",
-       "      <td>0.0311</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>서울 종로구 종로 169</td>\n",
-       "      <td>2024</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28032552</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>교외대기</td>\n",
-       "      <td>831491</td>\n",
-       "      <td>덕적도</td>\n",
-       "      <td>2024-01-01</td>\n",
-       "      <td>0.0498</td>\n",
-       "      <td>0.0060</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>인천 옹진군 덕적면 진리</td>\n",
-       "      <td>2024</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28085137</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831492</td>\n",
-       "      <td>백령도</td>\n",
-       "      <td>2024-01-01</td>\n",
-       "      <td>0.0394</td>\n",
-       "      <td>0.0092</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>58.0</td>\n",
-       "      <td>인천 옹진군 백령면 연화리</td>\n",
-       "      <td>2024</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28120249</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>831493</td>\n",
-       "      <td>영흥</td>\n",
-       "      <td>2024-01-01</td>\n",
-       "      <td>0.0318</td>\n",
-       "      <td>0.0134</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>인천광역시 옹진군 영흥면 영흥로251번길 90</td>\n",
-       "      <td>2024</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28148737</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831494</td>\n",
-       "      <td>연평도</td>\n",
-       "      <td>2024-01-01</td>\n",
-       "      <td>0.0353</td>\n",
-       "      <td>0.0077</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>인천 옹진군 연평면 연평리 631</td>\n",
-       "      <td>2024</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178689</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2024-01-01</td>\n",
-       "      <td>0.0402</td>\n",
-       "      <td>0.0098</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2024</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>652 rows × 14 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역       망   측정소코드  측정소명       측정일시      O3     NO2  PM10  PM25  \\\n",
-       "52584      서울 중구    도시대기  111121    중구 2024-01-01  0.0013  0.0491  26.0  26.0   \n",
-       "105169    서울 용산구   도로변대기  111122  한강대로 2024-01-01  0.0017  0.0474  36.0  28.0   \n",
-       "157754    서울 종로구    도시대기  111123   종로구 2024-01-01  0.0021  0.0437  28.0  26.0   \n",
-       "210339     서울 중구   도로변대기  111124  청계천로 2024-01-01  0.0025  0.0326  29.0  23.0   \n",
-       "262924    서울 종로구   도로변대기  111125    종로 2024-01-01  0.0022  0.0311  26.0  17.0   \n",
-       "...          ...     ...     ...   ...        ...     ...     ...   ...   ...   \n",
-       "28032552  인천 옹진군    교외대기  831491   덕적도 2024-01-01  0.0498  0.0060  64.0  56.0   \n",
-       "28085137  인천 옹진군  국가배경농도  831492   백령도 2024-01-01  0.0394  0.0092  78.0  58.0   \n",
-       "28120249  인천 옹진군    도시대기  831493    영흥 2024-01-01  0.0318  0.0134  30.0   NaN   \n",
-       "28148737  인천 옹진군  국가배경농도  831494   연평도 2024-01-01  0.0353  0.0077  70.0   NaN   \n",
-       "28178689  인천 옹진군  국가배경농도  831495    울도 2024-01-01  0.0402  0.0098  67.0  59.0   \n",
-       "\n",
-       "                                 주소  year  month  day  hour  \n",
-       "52584                 서울 중구 덕수궁길 15  2024      1    1     0  \n",
-       "105169              서울 용산구 한강대로 405  2024      1    1     0  \n",
-       "157754             서울 종로구 종로35가길 19  2024      1    1     0  \n",
-       "210339               서울 중구 청계천로 184  2024      1    1     0  \n",
-       "262924                서울 종로구 종로 169  2024      1    1     0  \n",
-       "...                             ...   ...    ...  ...   ...  \n",
-       "28032552              인천 옹진군 덕적면 진리  2024      1    1     0  \n",
-       "28085137             인천 옹진군 백령면 연화리  2024      1    1     0  \n",
-       "28120249  인천광역시 옹진군 영흥면 영흥로251번길 90  2024      1    1     0  \n",
-       "28148737         인천 옹진군 연평면 연평리 631  2024      1    1     0  \n",
-       "28178689        인천 옹진군 덕적면 울도리 85번지  2024      1    1     0  \n",
-       "\n",
-       "[652 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 23년까지의 데이터만 사용하기로 결정했기 때문에 24년 데이터가 몇개 있는지 확인\n",
-    "air.loc[(air['year'] == 2024) & (air['month'] == 1) & (air['day'] == 1) & (air['hour'] == 0),] "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(28178690, 14)"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air.shape "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "del(not_in)     # 사용하지 않는 데이터 삭제"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "83"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import gc\n",
-    "gc.collect() # 메모리 정리"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air.to_feather(\"../data/대기질/air.feather\") # 데이터 저장"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 21.99 GB\n",
-      "현재 메모리 사용량: 19.3%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n",
-    "print(f\"현재 메모리 사용량: {psutil.virtual_memory().percent}%\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "del(air)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "92"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import gc\n",
-    "gc.collect()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 21.99 GB\n",
-      "현재 메모리 사용량: 19.3%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n",
-    "print(f\"현재 메모리 사용량: {psutil.virtual_memory().percent}%\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 커널 종료후 재시작 (메모리 부족)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 13.28 GB\n",
-      "현재 메모리 사용량: 12.4%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n",
-    "print(f\"현재 메모리 사용량: {psutil.virtual_memory().percent}%\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "air= pd.read_feather(\"../data/대기질/air.feather\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 16.40 GB\n",
-      "현재 메모리 사용량: 14.9%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n",
-    "print(f\"현재 메모리 사용량: {psutil.virtual_memory().percent}%\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 23년까지의 데이터만 사용하기로 결정했기 때문에 24년 데이터는 제거하고 업데이트\n",
-    "air= air.loc[~((air['year'] == 2024) & (air['month'] == 1) & (air['day'] == 1) & (air['hour'] == 0)),:] # 2024년 1월 1일 0시 데이터 제외 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "지역             0\n",
-       "망              0\n",
-       "측정소코드          0\n",
-       "측정소명           0\n",
-       "측정일시           0\n",
-       "O3       1152577\n",
-       "NO2      1172756\n",
-       "PM10     1441888\n",
-       "PM25     1970684\n",
-       "주소             0\n",
-       "year           0\n",
-       "month          0\n",
-       "day            0\n",
-       "hour           0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(air).sum() # 결측치 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(28178038, 14)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air.reset_index(drop=True, inplace=True) # 인덱스 초기화"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>망</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.0130</td>\n",
-       "      <td>0.0280</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 01:00:00</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00:00</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00:00</td>\n",
-       "      <td>0.0180</td>\n",
-       "      <td>0.0220</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>도시대기</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00:00</td>\n",
-       "      <td>0.0100</td>\n",
-       "      <td>0.0300</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178033</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 19:00:00</td>\n",
-       "      <td>0.0440</td>\n",
-       "      <td>0.0075</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178034</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 20:00:00</td>\n",
-       "      <td>0.0415</td>\n",
-       "      <td>0.0086</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178035</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 21:00:00</td>\n",
-       "      <td>0.0409</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178036</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 22:00:00</td>\n",
-       "      <td>0.0414</td>\n",
-       "      <td>0.0111</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178037</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 23:00:00</td>\n",
-       "      <td>0.0411</td>\n",
-       "      <td>0.0112</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28178038 rows × 14 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역       망   측정소코드 측정소명                측정일시      O3     NO2  \\\n",
-       "0          서울 중구    도시대기  111121   중구 2018-01-01 00:00:00  0.0130  0.0280   \n",
-       "1          서울 중구    도시대기  111121   중구 2018-01-01 01:00:00  0.0200  0.0200   \n",
-       "2          서울 중구    도시대기  111121   중구 2018-01-01 02:00:00  0.0240  0.0160   \n",
-       "3          서울 중구    도시대기  111121   중구 2018-01-01 03:00:00  0.0180  0.0220   \n",
-       "4          서울 중구    도시대기  111121   중구 2018-01-01 04:00:00  0.0100  0.0300   \n",
-       "...          ...     ...     ...  ...                 ...     ...     ...   \n",
-       "28178033  인천 옹진군  국가배경농도  831495   울도 2023-12-31 19:00:00  0.0440  0.0075   \n",
-       "28178034  인천 옹진군  국가배경농도  831495   울도 2023-12-31 20:00:00  0.0415  0.0086   \n",
-       "28178035  인천 옹진군  국가배경농도  831495   울도 2023-12-31 21:00:00  0.0409  0.0095   \n",
-       "28178036  인천 옹진군  국가배경농도  831495   울도 2023-12-31 22:00:00  0.0414  0.0111   \n",
-       "28178037  인천 옹진군  국가배경농도  831495   울도 2023-12-31 23:00:00  0.0411  0.0112   \n",
-       "\n",
-       "          PM10  PM25                   주소  year  month  day  hour  \n",
-       "0         31.0  16.0        서울 중구 덕수궁길 15  2018      1    1     0  \n",
-       "1         34.0  19.0        서울 중구 덕수궁길 15  2018      1    1     1  \n",
-       "2         27.0  14.0        서울 중구 덕수궁길 15  2018      1    1     2  \n",
-       "3         26.0  14.0        서울 중구 덕수궁길 15  2018      1    1     3  \n",
-       "4         26.0  15.0        서울 중구 덕수궁길 15  2018      1    1     4  \n",
-       "...        ...   ...                  ...   ...    ...  ...   ...  \n",
-       "28178033  67.0  57.0  인천 옹진군 덕적면 울도리 85번지  2023     12   31    19  \n",
-       "28178034  64.0  57.0  인천 옹진군 덕적면 울도리 85번지  2023     12   31    20  \n",
-       "28178035  62.0  50.0  인천 옹진군 덕적면 울도리 85번지  2023     12   31    21  \n",
-       "28178036  65.0  59.0  인천 옹진군 덕적면 울도리 85번지  2023     12   31    22  \n",
-       "28178037  63.0  54.0  인천 옹진군 덕적면 울도리 85번지  2023     12   31    23  \n",
-       "\n",
-       "[28178038 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air.drop(['망'], axis=1, inplace=True) # 망 feature 제거"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.0130</td>\n",
-       "      <td>0.0280</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 01:00:00</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00:00</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00:00</td>\n",
-       "      <td>0.0180</td>\n",
-       "      <td>0.0220</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00:00</td>\n",
-       "      <td>0.0100</td>\n",
-       "      <td>0.0300</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178033</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 19:00:00</td>\n",
-       "      <td>0.0440</td>\n",
-       "      <td>0.0075</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178034</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 20:00:00</td>\n",
-       "      <td>0.0415</td>\n",
-       "      <td>0.0086</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178035</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 21:00:00</td>\n",
-       "      <td>0.0409</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178036</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 22:00:00</td>\n",
-       "      <td>0.0414</td>\n",
-       "      <td>0.0111</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178037</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 23:00:00</td>\n",
-       "      <td>0.0411</td>\n",
-       "      <td>0.0112</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28178038 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역   측정소코드 측정소명                측정일시      O3     NO2  PM10  PM25  \\\n",
-       "0          서울 중구  111121   중구 2018-01-01 00:00:00  0.0130  0.0280  31.0  16.0   \n",
-       "1          서울 중구  111121   중구 2018-01-01 01:00:00  0.0200  0.0200  34.0  19.0   \n",
-       "2          서울 중구  111121   중구 2018-01-01 02:00:00  0.0240  0.0160  27.0  14.0   \n",
-       "3          서울 중구  111121   중구 2018-01-01 03:00:00  0.0180  0.0220  26.0  14.0   \n",
-       "4          서울 중구  111121   중구 2018-01-01 04:00:00  0.0100  0.0300  26.0  15.0   \n",
-       "...          ...     ...  ...                 ...     ...     ...   ...   ...   \n",
-       "28178033  인천 옹진군  831495   울도 2023-12-31 19:00:00  0.0440  0.0075  67.0  57.0   \n",
-       "28178034  인천 옹진군  831495   울도 2023-12-31 20:00:00  0.0415  0.0086  64.0  57.0   \n",
-       "28178035  인천 옹진군  831495   울도 2023-12-31 21:00:00  0.0409  0.0095  62.0  50.0   \n",
-       "28178036  인천 옹진군  831495   울도 2023-12-31 22:00:00  0.0414  0.0111  65.0  59.0   \n",
-       "28178037  인천 옹진군  831495   울도 2023-12-31 23:00:00  0.0411  0.0112  63.0  54.0   \n",
-       "\n",
-       "                           주소  year  month  day  hour  \n",
-       "0               서울 중구 덕수궁길 15  2018      1    1     0  \n",
-       "1               서울 중구 덕수궁길 15  2018      1    1     1  \n",
-       "2               서울 중구 덕수궁길 15  2018      1    1     2  \n",
-       "3               서울 중구 덕수궁길 15  2018      1    1     3  \n",
-       "4               서울 중구 덕수궁길 15  2018      1    1     4  \n",
-       "...                       ...   ...    ...  ...   ...  \n",
-       "28178033  인천 옹진군 덕적면 울도리 85번지  2023     12   31    19  \n",
-       "28178034  인천 옹진군 덕적면 울도리 85번지  2023     12   31    20  \n",
-       "28178035  인천 옹진군 덕적면 울도리 85번지  2023     12   31    21  \n",
-       "28178036  인천 옹진군 덕적면 울도리 85번지  2023     12   31    22  \n",
-       "28178037  인천 옹진군 덕적면 울도리 85번지  2023     12   31    23  \n",
-       "\n",
-       "[28178038 rows x 13 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air['year'] = air['측정일시'].dt.year # 측정일시에서 년, 월, 일, 시간 추출\n",
-    "air['month'] = air['측정일시'].dt.month \n",
-    "air['day'] = air['측정일시'].dt.day\n",
-    "air['hour'] = air['측정일시'].dt.hour"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 28178038 entries, 0 to 28178037\n",
-      "Data columns (total 13 columns):\n",
-      " #   Column  Dtype         \n",
-      "---  ------  -----         \n",
-      " 0   지역      object        \n",
-      " 1   측정소코드   int64         \n",
-      " 2   측정소명    object        \n",
-      " 3   측정일시    datetime64[ns]\n",
-      " 4   O3      float64       \n",
-      " 5   NO2     float64       \n",
-      " 6   PM10    float64       \n",
-      " 7   PM25    float64       \n",
-      " 8   주소      object        \n",
-      " 9   year    int64         \n",
-      " 10  month   int64         \n",
-      " 11  day     int64         \n",
-      " 12  hour    int64         \n",
-      "dtypes: datetime64[ns](1), float64(4), int64(5), object(3)\n",
-      "memory usage: 2.7+ GB\n"
-     ]
-    }
-   ],
-   "source": [
-    "air.info() # 데이터 타입 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air.to_feather(\"../data/대기질/air.feather\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 메모리 부족으로 인한 커널 재시작"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from tqdm import tqdm, notebook, trange"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 2018~2023 ASOS 데이터 불러오기\n",
-    "asos_2018= pd.read_csv(\"../data/ASOS/2018.csv\", encoding='cp949') # 2018년 ASOS 데이터\n",
-    "asos_2019= pd.read_csv(\"../data/ASOS/2019.csv\", encoding='cp949') # 2019년 ASOS 데이터\n",
-    "asos_2020= pd.read_csv(\"../data/ASOS/2020.csv\", encoding='cp949') # 2020년 ASOS 데이터\n",
-    "asos_2021= pd.read_csv(\"../data/ASOS/2021.csv\", encoding='cp949') # 2021년 ASOS 데이터\n",
-    "asos_2022= pd.read_csv(\"../data/ASOS/2022.csv\", encoding='cp949') # 2022년 ASOS 데이터\n",
-    "asos_2023= pd.read_csv(\"../data/ASOS/2023.csv\", encoding='cp949') # 2023년 ASOS 데이터\n",
-    "\n",
-    "# 2018~2023 ASOS 데이터 합치기\n",
-    "asos= pd.concat([asos_2018, asos_2019, asos_2020, asos_2021, asos_2022, asos_2023], axis=0).copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "del([asos_2018, asos_2019, asos_2020, asos_2021, asos_2022, asos_2023]) # 사용하지 않는 데이터 삭제"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "596"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import gc\n",
-    "gc.collect() # 메모리 정리"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 14.81 GB\n",
-      "현재 메모리 사용량: 13.6%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n",
-    "print(f\"현재 메모리 사용량: {psutil.virtual_memory().percent}%\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5011265, 30)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos.shape # 데이터 shape 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "asos.reset_index(drop=True, inplace=True)    # 인덱스 초기화"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "지점                  0\n",
-       "지점명                 0\n",
-       "일시                  0\n",
-       "기온(°C)           4197\n",
-       "기온 QC플래그      4282900\n",
-       "강수량(mm)       4533611\n",
-       "강수량 QC플래그     4093970\n",
-       "풍속(m/s)          9726\n",
-       "풍속 QC플래그      4319125\n",
-       "풍향(16방위)        10294\n",
-       "풍향 QC플래그      4318626\n",
-       "습도(%)            6266\n",
-       "습도 QC플래그      4284220\n",
-       "증기압(hPa)         6486\n",
-       "이슬점온도(°C)        6534\n",
-       "현지기압(hPa)        4613\n",
-       "현지기압 QC플래그    4285584\n",
-       "해면기압(hPa)        5024\n",
-       "해면기압 QC플래그    4285200\n",
-       "일조(hr)        2275882\n",
-       "일조 QC플래그      2375218\n",
-       "일사(MJ/m2)     3691960\n",
-       "일사 QC플래그      1161673\n",
-       "적설(cm)        4905762\n",
-       "전운량(10분위)      479380\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(asos).sum()[:25] # 결측치 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "중하층운량(10분위)     517822\n",
-       "최저운고(100m )    2791937\n",
-       "시정(10m)          71059\n",
-       "지면온도(°C)          9742\n",
-       "지면온도 QC플래그     4260093\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(asos).sum()[25:] # 결측치 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 년, 월, 일, 시간 컬럼 생성\n",
-    "asos['year'], asos['month'], asos['day'], asos['hour']= pd.to_datetime(asos['일시']).dt.year, pd.to_datetime(asos['일시']).dt.month, pd.to_datetime(asos['일시']).dt.day, pd.to_datetime(asos['일시']).dt.hour"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "asos.sort_values(by=['지점', '일시'], inplace=True) # 지점, 일시 기준으로 정렬"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['지점', '지점명', '일시', '기온(°C)', '기온 QC플래그', '강수량(mm)', '강수량 QC플래그',\n",
-       "       '풍속(m/s)', '풍속 QC플래그', '풍향(16방위)', '풍향 QC플래그', '습도(%)', '습도 QC플래그',\n",
-       "       '증기압(hPa)', '이슬점온도(°C)', '현지기압(hPa)', '현지기압 QC플래그', '해면기압(hPa)',\n",
-       "       '해면기압 QC플래그', '일조(hr)', '일조 QC플래그', '일사(MJ/m2)', '일사 QC플래그', '적설(cm)',\n",
-       "       '전운량(10분위)', '중하층운량(10분위)', '최저운고(100m )', '시정(10m)', '지면온도(°C)',\n",
-       "       '지면온도 QC플래그', 'year', 'month', 'day', 'hour'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos.columns # 컬럼 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "asos.reset_index(drop=True, inplace=True) # 인덱스 초기화"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['속초', '북춘천', '철원', '동두천', '파주', '대관령', '춘천', '백령도', '북강릉', '강릉',\n",
-       "       '동해', '서울', '인천', '원주', '울릉도', '수원', '영월', '충주', '서산', '울진', '청주',\n",
-       "       '대전', '추풍령', '안동', '상주', '포항', '군산', '대구', '전주', '울산', '창원', '광주',\n",
-       "       '부산', '통영', '목포', '여수', '흑산도', '완도', '고창', '순천', '진도(첨찰산)', '홍성',\n",
-       "       '서청주', '제주', '고산', '성산', '서귀포', '진주', '강화', '양평', '이천', '인제', '홍천',\n",
-       "       '태백', '정선군', '제천', '보은', '천안', '보령', '부여', '금산', '세종', '부안', '임실',\n",
-       "       '정읍', '남원', '장수', '고창군', '영광군', '김해시', '순창군', '북창원', '양산시', '보성군',\n",
-       "       '강진군', '장흥', '해남', '고흥', '의령군', '함양군', '광양시', '진도군', '봉화', '영주',\n",
-       "       '문경', '청송군', '영덕', '의성', '구미', '영천', '경주시', '거창', '합천', '밀양', '산청',\n",
-       "       '거제', '남해', '북부산'], dtype=object)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos['지점명'].unique() # 지점명 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "asos.to_feather(\"../data/ASOS/asos.feather\") # asos 데이터 feather 형태의 데이터로 저장"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# asos 지점명과 air 측정소명의 일치하지 않는 데이터가 꽤 많아서 지점명과 측정소명만으로 두 데이터를 merge 시키는 것에 한계를 느꼈음."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 그렇기 때문에 asos 지점에 맞는 좌표 값을 기입해주고 air 데이터는 DataON에서 제공해주는 대기질 데이터에 측정소의 좌표 값이 있기 때문에 그것을 활용하여 좌표 값을 얻은 다음 asos의 지점 A가 있다 치고 air 데이터의 지점 B,C,D,F가 있다고 하면 A-B,A-C, A-D, A-F 간의 거리를 구한 다음 거리가 가장 짧을 것을 묶어줄 생각이다."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### **메모리 부족으로 인해 커널 재시작**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 13.38 GB\n",
-      "현재 메모리 사용량: 12.4%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n",
-    "print(f\"현재 메모리 사용량: {psutil.virtual_memory().percent}%\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "asos = pd.read_feather(\"../data/ASOS/asos.feather\") # asos 데이터 불러오기"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지점</th>\n",
-       "      <th>지점명</th>\n",
-       "      <th>일시</th>\n",
-       "      <th>기온(°C)</th>\n",
-       "      <th>기온 QC플래그</th>\n",
-       "      <th>강수량(mm)</th>\n",
-       "      <th>강수량 QC플래그</th>\n",
-       "      <th>풍속(m/s)</th>\n",
-       "      <th>풍속 QC플래그</th>\n",
-       "      <th>풍향(16방위)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>전운량(10분위)</th>\n",
-       "      <th>중하층운량(10분위)</th>\n",
-       "      <th>최저운고(100m )</th>\n",
-       "      <th>시정(10m)</th>\n",
-       "      <th>지면온도(°C)</th>\n",
-       "      <th>지면온도 QC플래그</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 00:00</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-2.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 01:00</td>\n",
-       "      <td>-2.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-2.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 02:00</td>\n",
-       "      <td>-2.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 03:00</td>\n",
-       "      <td>-2.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-3.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 04:00</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-3.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011260</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 19:00</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>320.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4501.0</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011261</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 20:00</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>320.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4240.0</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011262</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 21:00</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3708.0</td>\n",
-       "      <td>1.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011263</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 22:00</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>290.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2751.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011264</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 23:00</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>360.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2222.0</td>\n",
-       "      <td>-0.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5011265 rows × 34 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          지점  지점명                일시  기온(°C)  기온 QC플래그  강수량(mm)  강수량 QC플래그  \\\n",
-       "0         90   속초  2018-01-01 00:00    -1.0       0.0      NaN        NaN   \n",
-       "1         90   속초  2018-01-01 01:00    -2.1       0.0      NaN        NaN   \n",
-       "2         90   속초  2018-01-01 02:00    -2.1       0.0      NaN        NaN   \n",
-       "3         90   속초  2018-01-01 03:00    -2.2       0.0      NaN        NaN   \n",
-       "4         90   속초  2018-01-01 04:00    -2.0       0.0      NaN        NaN   \n",
-       "...      ...  ...               ...     ...       ...      ...        ...   \n",
-       "5011260  296  북부산  2023-12-31 19:00     6.6       NaN      NaN        9.0   \n",
-       "5011261  296  북부산  2023-12-31 20:00     5.9       NaN      NaN        9.0   \n",
-       "5011262  296  북부산  2023-12-31 21:00     5.1       NaN      NaN        9.0   \n",
-       "5011263  296  북부산  2023-12-31 22:00     3.5       NaN      NaN        9.0   \n",
-       "5011264  296  북부산  2023-12-31 23:00     1.8       NaN      NaN        9.0   \n",
-       "\n",
-       "         풍속(m/s)  풍속 QC플래그  풍향(16방위)  ...  전운량(10분위)  중하층운량(10분위)  \\\n",
-       "0            1.1       NaN     250.0  ...        NaN          NaN   \n",
-       "1            1.7       0.0     230.0  ...        NaN          NaN   \n",
-       "2            1.4       0.0     160.0  ...        NaN          NaN   \n",
-       "3            0.9       0.0     230.0  ...        NaN          NaN   \n",
-       "4            1.2       0.0     250.0  ...        NaN          NaN   \n",
-       "...          ...       ...       ...  ...        ...          ...   \n",
-       "5011260      1.5       NaN     320.0  ...        0.0          0.0   \n",
-       "5011261      1.8       NaN     320.0  ...        0.0          0.0   \n",
-       "5011262      1.2       NaN     250.0  ...        0.0          0.0   \n",
-       "5011263      0.7       NaN     290.0  ...        0.0          0.0   \n",
-       "5011264      0.5       NaN     360.0  ...        0.0          0.0   \n",
-       "\n",
-       "         최저운고(100m )  시정(10m)  지면온도(°C)  지면온도 QC플래그  year  month  day  hour  \n",
-       "0                NaN   2000.0      -2.3         0.0  2018      1    1     0  \n",
-       "1                NaN   2000.0      -2.7         0.0  2018      1    1     1  \n",
-       "2                NaN   2000.0      -3.0         0.0  2018      1    1     2  \n",
-       "3                NaN   2000.0      -3.2         0.0  2018      1    1     3  \n",
-       "4                NaN   2000.0      -3.3         0.0  2018      1    1     4  \n",
-       "...              ...      ...       ...         ...   ...    ...  ...   ...  \n",
-       "5011260          NaN   4501.0       2.8         NaN  2023     12   31    19  \n",
-       "5011261          NaN   4240.0       2.1         NaN  2023     12   31    20  \n",
-       "5011262          NaN   3708.0       1.1         NaN  2023     12   31    21  \n",
-       "5011263          NaN   2751.0       0.2         NaN  2023     12   31    22  \n",
-       "5011264          NaN   2222.0      -0.3         NaN  2023     12   31    23  \n",
-       "\n",
-       "[5011265 rows x 34 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ASOS 지점명에 해당하는 좌표 값을 기상청 홈페이지에서 검색하여 하나씩 노가다로 추출 필요\n",
-    "pd.DataFrame(pd.Series(asos['지점명'].unique()).sort_values().reset_index(drop=True), columns=['지점명']).to_excel(\"../data/ASOS/지점명.xlsx\", index=False) # 이 지점명을 토대로 위도 경도 하나씩 98개 데이터 기입 필요"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stn_info= pd.read_excel(\"../data/ASOS/asos_지점명_위도경도.xlsx\") # 위도 경도 데이터 불러오기"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지점명</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>강릉</td>\n",
-       "      <td>37.75147</td>\n",
-       "      <td>128.89099</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>강진군</td>\n",
-       "      <td>34.64464</td>\n",
-       "      <td>126.78405</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>강화</td>\n",
-       "      <td>37.70739</td>\n",
-       "      <td>126.44634</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>거제</td>\n",
-       "      <td>34.88818</td>\n",
-       "      <td>128.60459</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>거창</td>\n",
-       "      <td>35.66739</td>\n",
-       "      <td>127.90990</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>합천</td>\n",
-       "      <td>35.56505</td>\n",
-       "      <td>128.16994</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94</th>\n",
-       "      <td>해남</td>\n",
-       "      <td>34.55375</td>\n",
-       "      <td>126.56907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>홍성</td>\n",
-       "      <td>36.65759</td>\n",
-       "      <td>126.68772</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>홍천</td>\n",
-       "      <td>37.6836</td>\n",
-       "      <td>127.88043</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>흑산도</td>\n",
-       "      <td>34.68719</td>\n",
-       "      <td>125.45105</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>98 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    지점명        위도         경도\n",
-       "0    강릉  37.75147  128.89099\n",
-       "1   강진군  34.64464  126.78405\n",
-       "2    강화  37.70739  126.44634\n",
-       "3    거제  34.88818  128.60459\n",
-       "4    거창  35.66739  127.90990\n",
-       "..  ...       ...        ...\n",
-       "93   합천  35.56505  128.16994\n",
-       "94   해남  34.55375  126.56907\n",
-       "95   홍성  36.65759  126.68772\n",
-       "96   홍천   37.6836  127.88043\n",
-       "97  흑산도  34.68719  125.45105\n",
-       "\n",
-       "[98 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stn_info"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "36.57293    1\n",
-       "38.05986    1\n",
-       "35.17294    1\n",
-       "37.88589    1\n",
-       "37.97396    1\n",
-       "           ..\n",
-       "36.10565    1\n",
-       "34.81732    1\n",
-       "38.25085    1\n",
-       "37.90188    1\n",
-       "34.76335    1\n",
-       "Name: 위도, Length: 98, dtype: int64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 위도 값 중복이 있는지 확인(value_counts는 기본적으로 갯수가 많은 순서대로 정렬이기 떄문에 가장 위의 값이 1이면 중복이 없는 것)\n",
-    "stn_info['위도'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "128.51687    1\n",
-       "129.20123    1\n",
-       "125.45105    1\n",
-       "126.59900    1\n",
-       "127.74063    1\n",
-       "            ..\n",
-       "127.49446    1\n",
-       "127.73570    1\n",
-       "126.55744    1\n",
-       "127.73415    1\n",
-       "128.14879    1\n",
-       "Name: 경도, Length: 98, dtype: int64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 경도 값 중복이 있는지 확인(value_counts는 기본적으로 갯수가 많은 순서대로 정렬이기 떄문에 가장 위의 값이 1이면 중복이 없는 것)\n",
-    "stn_info['경도'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 98 entries, 0 to 97\n",
-      "Data columns (total 3 columns):\n",
-      " #   Column  Non-Null Count  Dtype  \n",
-      "---  ------  --------------  -----  \n",
-      " 0   지점명     98 non-null     object \n",
-      " 1   위도      98 non-null     object \n",
-      " 2   경도      98 non-null     float64\n",
-      "dtypes: float64(1), object(2)\n",
-      "memory usage: 2.4+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "stn_info.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 위도 데이터 타입 변경\n",
-    "stn_info['위도']= stn_info['위도'].astype(\"float\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 98 entries, 0 to 97\n",
-      "Data columns (total 3 columns):\n",
-      " #   Column  Non-Null Count  Dtype  \n",
-      "---  ------  --------------  -----  \n",
-      " 0   지점명     98 non-null     object \n",
-      " 1   위도      98 non-null     float64\n",
-      " 2   경도      98 non-null     float64\n",
-      "dtypes: float64(2), object(1)\n",
-      "memory usage: 2.4+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "stn_info.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 98/98 [00:46<00:00,  2.09it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tqdm import tqdm, trange, notebook\n",
-    "\n",
-    "# asos 데이터에 각 지점에 맞는 위도와 경도를 기상청에서 얻은 정보를 기반으로 추가한다.\n",
-    "for i in tqdm(stn_info['지점명']): \n",
-    "    asos.loc[asos['지점명']==i, '위도']= stn_info.loc[stn_info['지점명']==i, '위도'].values[0]\n",
-    "    asos.loc[asos['지점명']==i, '경도']= stn_info.loc[stn_info['지점명']==i, '경도'].values[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "98"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(asos['지점명'].unique()) # 98개"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(98,)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stn_info['위도'].unique().shape # 98개"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(98,)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stn_info['경도'].unique().shape # 98개"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 98/98 [00:00<00:00, 2219.76it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in tqdm(range(stn_info.shape[0])):  # 위도 중복 확인\n",
-    "    for j in range(i+1,stn_info.shape[0]):\n",
-    "        if stn_info['위도'][i] == stn_info['위도'][j]:\n",
-    "            print(stn_info['지점명'][i], stn_info['지점명'][j])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "asos.to_feather(\"../data/ASOS/asos_fill_위경.feather\") # 위도 경도 추가한 asos 데이터 feather 형태로 저장"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 메모리 부족으로 인한 커널 재시작"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air= pd.read_feather(\"../data/대기질/air.feather\") # 대기질 데이터 불러오기\n",
-    "asos= pd.read_feather(\"../data/ASOS/asos_fill_위경.feather\") # 위도와 경도가 추가된 asos 데이터 불러오기"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지점</th>\n",
-       "      <th>지점명</th>\n",
-       "      <th>일시</th>\n",
-       "      <th>기온(°C)</th>\n",
-       "      <th>기온 QC플래그</th>\n",
-       "      <th>강수량(mm)</th>\n",
-       "      <th>강수량 QC플래그</th>\n",
-       "      <th>풍속(m/s)</th>\n",
-       "      <th>풍속 QC플래그</th>\n",
-       "      <th>풍향(16방위)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>최저운고(100m )</th>\n",
-       "      <th>시정(10m)</th>\n",
-       "      <th>지면온도(°C)</th>\n",
-       "      <th>지면온도 QC플래그</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 00:00</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-2.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 01:00</td>\n",
-       "      <td>-2.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-2.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 02:00</td>\n",
-       "      <td>-2.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 03:00</td>\n",
-       "      <td>-2.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-3.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 04:00</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-3.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011260</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 19:00</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>320.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4501.0</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011261</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 20:00</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>320.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4240.0</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011262</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 21:00</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3708.0</td>\n",
-       "      <td>1.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011263</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 22:00</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>290.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2751.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011264</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 23:00</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>360.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2222.0</td>\n",
-       "      <td>-0.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5011265 rows × 36 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          지점  지점명                일시  기온(°C)  기온 QC플래그  강수량(mm)  강수량 QC플래그  \\\n",
-       "0         90   속초  2018-01-01 00:00    -1.0       0.0      NaN        NaN   \n",
-       "1         90   속초  2018-01-01 01:00    -2.1       0.0      NaN        NaN   \n",
-       "2         90   속초  2018-01-01 02:00    -2.1       0.0      NaN        NaN   \n",
-       "3         90   속초  2018-01-01 03:00    -2.2       0.0      NaN        NaN   \n",
-       "4         90   속초  2018-01-01 04:00    -2.0       0.0      NaN        NaN   \n",
-       "...      ...  ...               ...     ...       ...      ...        ...   \n",
-       "5011260  296  북부산  2023-12-31 19:00     6.6       NaN      NaN        9.0   \n",
-       "5011261  296  북부산  2023-12-31 20:00     5.9       NaN      NaN        9.0   \n",
-       "5011262  296  북부산  2023-12-31 21:00     5.1       NaN      NaN        9.0   \n",
-       "5011263  296  북부산  2023-12-31 22:00     3.5       NaN      NaN        9.0   \n",
-       "5011264  296  북부산  2023-12-31 23:00     1.8       NaN      NaN        9.0   \n",
-       "\n",
-       "         풍속(m/s)  풍속 QC플래그  풍향(16방위)  ...  최저운고(100m )  시정(10m)  지면온도(°C)  \\\n",
-       "0            1.1       NaN     250.0  ...          NaN   2000.0      -2.3   \n",
-       "1            1.7       0.0     230.0  ...          NaN   2000.0      -2.7   \n",
-       "2            1.4       0.0     160.0  ...          NaN   2000.0      -3.0   \n",
-       "3            0.9       0.0     230.0  ...          NaN   2000.0      -3.2   \n",
-       "4            1.2       0.0     250.0  ...          NaN   2000.0      -3.3   \n",
-       "...          ...       ...       ...  ...          ...      ...       ...   \n",
-       "5011260      1.5       NaN     320.0  ...          NaN   4501.0       2.8   \n",
-       "5011261      1.8       NaN     320.0  ...          NaN   4240.0       2.1   \n",
-       "5011262      1.2       NaN     250.0  ...          NaN   3708.0       1.1   \n",
-       "5011263      0.7       NaN     290.0  ...          NaN   2751.0       0.2   \n",
-       "5011264      0.5       NaN     360.0  ...          NaN   2222.0      -0.3   \n",
-       "\n",
-       "         지면온도 QC플래그  year  month  day  hour         위도          경도  \n",
-       "0               0.0  2018      1    1     0  38.250850  128.564730  \n",
-       "1               0.0  2018      1    1     1  38.250850  128.564730  \n",
-       "2               0.0  2018      1    1     2  38.250850  128.564730  \n",
-       "3               0.0  2018      1    1     3  38.250850  128.564730  \n",
-       "4               0.0  2018      1    1     4  38.250850  128.564730  \n",
-       "...             ...   ...    ...  ...   ...        ...         ...  \n",
-       "5011260         NaN  2023     12   31    19  35.217781  128.960242  \n",
-       "5011261         NaN  2023     12   31    20  35.217781  128.960242  \n",
-       "5011262         NaN  2023     12   31    21  35.217781  128.960242  \n",
-       "5011263         NaN  2023     12   31    22  35.217781  128.960242  \n",
-       "5011264         NaN  2023     12   31    23  35.217781  128.960242  \n",
-       "\n",
-       "[5011265 rows x 36 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
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-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
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-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.0130</td>\n",
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-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
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-       "      <td>서울 중구</td>\n",
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-       "      <td>0.0200</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00:00</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00:00</td>\n",
-       "      <td>0.0180</td>\n",
-       "      <td>0.0220</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00:00</td>\n",
-       "      <td>0.0100</td>\n",
-       "      <td>0.0300</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
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-       "    <tr>\n",
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-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178033</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 19:00:00</td>\n",
-       "      <td>0.0440</td>\n",
-       "      <td>0.0075</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
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-       "    <tr>\n",
-       "      <th>28178034</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 20:00:00</td>\n",
-       "      <td>0.0415</td>\n",
-       "      <td>0.0086</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178035</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 21:00:00</td>\n",
-       "      <td>0.0409</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178036</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 22:00:00</td>\n",
-       "      <td>0.0414</td>\n",
-       "      <td>0.0111</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178037</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 23:00:00</td>\n",
-       "      <td>0.0411</td>\n",
-       "      <td>0.0112</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28178038 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역   측정소코드 측정소명                측정일시      O3     NO2  PM10  PM25  \\\n",
-       "0          서울 중구  111121   중구 2018-01-01 00:00:00  0.0130  0.0280  31.0  16.0   \n",
-       "1          서울 중구  111121   중구 2018-01-01 01:00:00  0.0200  0.0200  34.0  19.0   \n",
-       "2          서울 중구  111121   중구 2018-01-01 02:00:00  0.0240  0.0160  27.0  14.0   \n",
-       "3          서울 중구  111121   중구 2018-01-01 03:00:00  0.0180  0.0220  26.0  14.0   \n",
-       "4          서울 중구  111121   중구 2018-01-01 04:00:00  0.0100  0.0300  26.0  15.0   \n",
-       "...          ...     ...  ...                 ...     ...     ...   ...   ...   \n",
-       "28178033  인천 옹진군  831495   울도 2023-12-31 19:00:00  0.0440  0.0075  67.0  57.0   \n",
-       "28178034  인천 옹진군  831495   울도 2023-12-31 20:00:00  0.0415  0.0086  64.0  57.0   \n",
-       "28178035  인천 옹진군  831495   울도 2023-12-31 21:00:00  0.0409  0.0095  62.0  50.0   \n",
-       "28178036  인천 옹진군  831495   울도 2023-12-31 22:00:00  0.0414  0.0111  65.0  59.0   \n",
-       "28178037  인천 옹진군  831495   울도 2023-12-31 23:00:00  0.0411  0.0112  63.0  54.0   \n",
-       "\n",
-       "                           주소  year  month  day  hour  \n",
-       "0               서울 중구 덕수궁길 15  2018      1    1     0  \n",
-       "1               서울 중구 덕수궁길 15  2018      1    1     1  \n",
-       "2               서울 중구 덕수궁길 15  2018      1    1     2  \n",
-       "3               서울 중구 덕수궁길 15  2018      1    1     3  \n",
-       "4               서울 중구 덕수궁길 15  2018      1    1     4  \n",
-       "...                       ...   ...    ...  ...   ...  \n",
-       "28178033  인천 옹진군 덕적면 울도리 85번지  2023     12   31    19  \n",
-       "28178034  인천 옹진군 덕적면 울도리 85번지  2023     12   31    20  \n",
-       "28178035  인천 옹진군 덕적면 울도리 85번지  2023     12   31    21  \n",
-       "28178036  인천 옹진군 덕적면 울도리 85번지  2023     12   31    22  \n",
-       "28178037  인천 옹진군 덕적면 울도리 85번지  2023     12   31    23  \n",
-       "\n",
-       "[28178038 rows x 13 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ASOS 데이터에 위도 경도 기입은 완료\n",
-    "\n",
-    "\n",
-    "## AIR 데이터에 위도와 경도 기입할 차례"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "447"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import gc\n",
-    "gc.collect()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "asos['year'] = asos['year'].astype(\"int\") # year 데이터 타입 변경\n",
-    "asos['month'] = asos['month'].astype(\"int\") # month 데이터 타입 변경\n",
-    "asos['day'] = asos['day'].astype(\"int\") # day 데이터 타입 변경\n",
-    "asos['hour'] = asos['hour'].astype(\"int\") # hour 데이터 타입 변경"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "30"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos.columns.tolist().index(\"year\") # year 컬럼 인덱스 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 5011265 entries, 0 to 5011264\n",
-      "Data columns (total 16 columns):\n",
-      " #   Column       Dtype  \n",
-      "---  ------       -----  \n",
-      " 0   일조 QC플래그     float64\n",
-      " 1   일사(MJ/m2)    float64\n",
-      " 2   일사 QC플래그     float64\n",
-      " 3   적설(cm)       float64\n",
-      " 4   전운량(10분위)    float64\n",
-      " 5   중하층운량(10분위)  float64\n",
-      " 6   최저운고(100m )  float64\n",
-      " 7   시정(10m)      float64\n",
-      " 8   지면온도(°C)     float64\n",
-      " 9   지면온도 QC플래그   float64\n",
-      " 10  year         int64  \n",
-      " 11  month        int64  \n",
-      " 12  day          int64  \n",
-      " 13  hour         int64  \n",
-      " 14  위도           float64\n",
-      " 15  경도           float64\n",
-      "dtypes: float64(12), int64(4)\n",
-      "memory usage: 611.7 MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "asos.iloc[:,20:].info() # year, month, day, hour 변경된 데이터 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지점</th>\n",
-       "      <th>지점명</th>\n",
-       "      <th>일시</th>\n",
-       "      <th>기온(°C)</th>\n",
-       "      <th>기온 QC플래그</th>\n",
-       "      <th>강수량(mm)</th>\n",
-       "      <th>강수량 QC플래그</th>\n",
-       "      <th>풍속(m/s)</th>\n",
-       "      <th>풍속 QC플래그</th>\n",
-       "      <th>풍향(16방위)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>최저운고(100m )</th>\n",
-       "      <th>시정(10m)</th>\n",
-       "      <th>지면온도(°C)</th>\n",
-       "      <th>지면온도 QC플래그</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 00:00</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>2000.0</td>\n",
-       "      <td>-2.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>38.25085</td>\n",
-       "      <td>128.56473</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 01:00</td>\n",
-       "      <td>-2.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-2.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>38.25085</td>\n",
-       "      <td>128.56473</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 02:00</td>\n",
-       "      <td>-2.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>38.25085</td>\n",
-       "      <td>128.56473</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 03:00</td>\n",
-       "      <td>-2.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-3.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>38.25085</td>\n",
-       "      <td>128.56473</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 04:00</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-3.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>38.25085</td>\n",
-       "      <td>128.56473</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 36 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   지점 지점명                일시  기온(°C)  기온 QC플래그  강수량(mm)  강수량 QC플래그  풍속(m/s)  \\\n",
-       "0  90  속초  2018-01-01 00:00    -1.0       0.0      NaN        NaN      1.1   \n",
-       "1  90  속초  2018-01-01 01:00    -2.1       0.0      NaN        NaN      1.7   \n",
-       "2  90  속초  2018-01-01 02:00    -2.1       0.0      NaN        NaN      1.4   \n",
-       "3  90  속초  2018-01-01 03:00    -2.2       0.0      NaN        NaN      0.9   \n",
-       "4  90  속초  2018-01-01 04:00    -2.0       0.0      NaN        NaN      1.2   \n",
-       "\n",
-       "   풍속 QC플래그  풍향(16방위)  ...  최저운고(100m )  시정(10m)  지면온도(°C)  지면온도 QC플래그  year  \\\n",
-       "0       NaN     250.0  ...          NaN   2000.0      -2.3         0.0  2018   \n",
-       "1       0.0     230.0  ...          NaN   2000.0      -2.7         0.0  2018   \n",
-       "2       0.0     160.0  ...          NaN   2000.0      -3.0         0.0  2018   \n",
-       "3       0.0     230.0  ...          NaN   2000.0      -3.2         0.0  2018   \n",
-       "4       0.0     250.0  ...          NaN   2000.0      -3.3         0.0  2018   \n",
-       "\n",
-       "   month  day  hour        위도         경도  \n",
-       "0      1    1     0  38.25085  128.56473  \n",
-       "1      1    1     1  38.25085  128.56473  \n",
-       "2      1    1     2  38.25085  128.56473  \n",
-       "3      1    1     3  38.25085  128.56473  \n",
-       "4      1    1     4  38.25085  128.56473  \n",
-       "\n",
-       "[5 rows x 36 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.0130</td>\n",
-       "      <td>0.0280</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 01:00:00</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00:00</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00:00</td>\n",
-       "      <td>0.0180</td>\n",
-       "      <td>0.0220</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00:00</td>\n",
-       "      <td>0.0100</td>\n",
-       "      <td>0.0300</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178033</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 19:00:00</td>\n",
-       "      <td>0.0440</td>\n",
-       "      <td>0.0075</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178034</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 20:00:00</td>\n",
-       "      <td>0.0415</td>\n",
-       "      <td>0.0086</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178035</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 21:00:00</td>\n",
-       "      <td>0.0409</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178036</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 22:00:00</td>\n",
-       "      <td>0.0414</td>\n",
-       "      <td>0.0111</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178037</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 23:00:00</td>\n",
-       "      <td>0.0411</td>\n",
-       "      <td>0.0112</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28178038 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역   측정소코드 측정소명                측정일시      O3     NO2  PM10  PM25  \\\n",
-       "0          서울 중구  111121   중구 2018-01-01 00:00:00  0.0130  0.0280  31.0  16.0   \n",
-       "1          서울 중구  111121   중구 2018-01-01 01:00:00  0.0200  0.0200  34.0  19.0   \n",
-       "2          서울 중구  111121   중구 2018-01-01 02:00:00  0.0240  0.0160  27.0  14.0   \n",
-       "3          서울 중구  111121   중구 2018-01-01 03:00:00  0.0180  0.0220  26.0  14.0   \n",
-       "4          서울 중구  111121   중구 2018-01-01 04:00:00  0.0100  0.0300  26.0  15.0   \n",
-       "...          ...     ...  ...                 ...     ...     ...   ...   ...   \n",
-       "28178033  인천 옹진군  831495   울도 2023-12-31 19:00:00  0.0440  0.0075  67.0  57.0   \n",
-       "28178034  인천 옹진군  831495   울도 2023-12-31 20:00:00  0.0415  0.0086  64.0  57.0   \n",
-       "28178035  인천 옹진군  831495   울도 2023-12-31 21:00:00  0.0409  0.0095  62.0  50.0   \n",
-       "28178036  인천 옹진군  831495   울도 2023-12-31 22:00:00  0.0414  0.0111  65.0  59.0   \n",
-       "28178037  인천 옹진군  831495   울도 2023-12-31 23:00:00  0.0411  0.0112  63.0  54.0   \n",
-       "\n",
-       "                           주소  year  month  day  hour  \n",
-       "0               서울 중구 덕수궁길 15  2018      1    1     0  \n",
-       "1               서울 중구 덕수궁길 15  2018      1    1     1  \n",
-       "2               서울 중구 덕수궁길 15  2018      1    1     2  \n",
-       "3               서울 중구 덕수궁길 15  2018      1    1     3  \n",
-       "4               서울 중구 덕수궁길 15  2018      1    1     4  \n",
-       "...                       ...   ...    ...  ...   ...  \n",
-       "28178033  인천 옹진군 덕적면 울도리 85번지  2023     12   31    19  \n",
-       "28178034  인천 옹진군 덕적면 울도리 85번지  2023     12   31    20  \n",
-       "28178035  인천 옹진군 덕적면 울도리 85번지  2023     12   31    21  \n",
-       "28178036  인천 옹진군 덕적면 울도리 85번지  2023     12   31    22  \n",
-       "28178037  인천 옹진군 덕적면 울도리 85번지  2023     12   31    23  \n",
-       "\n",
-       "[28178038 rows x 13 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "660"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(air['측정소코드'].unique()) # 측정소명 개수 확인"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 커널 재시작"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os\n",
-    "\n",
-    "# 데이터온에서 다운받은 데이터들이 저장된 디렉토리 경로에서 파일들을 불러와서 리스트로 저장\n",
-    "\n",
-    "# 파일들이 저장된 디렉토리 경로\n",
-    "directory = '../data/dataon'\n",
-    "\n",
-    "# 디렉토리 내의 모든 파일을 리스트로 불러오기\n",
-    "all_files = [os.path.join(directory, f) for f in os.listdir(directory) if f.endswith('.csv')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_files.sort() # 파일 이름 순서대로 정렬"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['../data/dataon/Korea_PM_20180101.csv',\n",
-       " '../data/dataon/Korea_PM_20180102.csv',\n",
-       " '../data/dataon/Korea_PM_20180104.csv',\n",
-       " '../data/dataon/Korea_PM_20180105.csv',\n",
-       " '../data/dataon/Korea_PM_20180106.csv',\n",
-       " '../data/dataon/Korea_PM_20180107.csv',\n",
-       " '../data/dataon/Korea_PM_20180108.csv',\n",
-       " '../data/dataon/Korea_PM_20180109.csv',\n",
-       " '../data/dataon/Korea_PM_20180110.csv',\n",
-       " '../data/dataon/Korea_PM_20180111.csv',\n",
-       " '../data/dataon/Korea_PM_20180112.csv',\n",
-       " '../data/dataon/Korea_PM_20180113.csv',\n",
-       " '../data/dataon/Korea_PM_20180114.csv',\n",
-       " '../data/dataon/Korea_PM_20180115.csv',\n",
-       " '../data/dataon/Korea_PM_20180116.csv',\n",
-       " '../data/dataon/Korea_PM_20180117.csv',\n",
-       " '../data/dataon/Korea_PM_20180118.csv',\n",
-       " '../data/dataon/Korea_PM_20180119.csv',\n",
-       " '../data/dataon/Korea_PM_20180120.csv',\n",
-       " '../data/dataon/Korea_PM_20180121.csv',\n",
-       " '../data/dataon/Korea_PM_20180122.csv',\n",
-       " '../data/dataon/Korea_PM_20180123.csv',\n",
-       " '../data/dataon/Korea_PM_20180124.csv',\n",
-       " '../data/dataon/Korea_PM_20180125.csv',\n",
-       " '../data/dataon/Korea_PM_20180126.csv',\n",
-       " '../data/dataon/Korea_PM_20180127.csv',\n",
-       " '../data/dataon/Korea_PM_20180128.csv',\n",
-       " '../data/dataon/Korea_PM_20180129.csv',\n",
-       " '../data/dataon/Korea_PM_20180130.csv',\n",
-       " '../data/dataon/Korea_PM_20180131.csv',\n",
-       " '../data/dataon/Korea_PM_20180201.csv',\n",
-       " '../data/dataon/Korea_PM_20180202.csv',\n",
-       " '../data/dataon/Korea_PM_20180203.csv',\n",
-       " '../data/dataon/Korea_PM_20180204.csv',\n",
-       " '../data/dataon/Korea_PM_20180205.csv',\n",
-       " '../data/dataon/Korea_PM_20180206.csv',\n",
-       " '../data/dataon/Korea_PM_20180207.csv',\n",
-       " '../data/dataon/Korea_PM_20180208.csv',\n",
-       " '../data/dataon/Korea_PM_20180209.csv',\n",
-       " '../data/dataon/Korea_PM_20180210.csv',\n",
-       " '../data/dataon/Korea_PM_20180211.csv',\n",
-       " '../data/dataon/Korea_PM_20180212.csv',\n",
-       " '../data/dataon/Korea_PM_20180213.csv',\n",
-       " '../data/dataon/Korea_PM_20180214.csv',\n",
-       " '../data/dataon/Korea_PM_20180215.csv',\n",
-       " '../data/dataon/Korea_PM_20180216.csv',\n",
-       " '../data/dataon/Korea_PM_20180217.csv',\n",
-       " '../data/dataon/Korea_PM_20180218.csv',\n",
-       " '../data/dataon/Korea_PM_20180219.csv',\n",
-       " '../data/dataon/Korea_PM_20180220.csv',\n",
-       " '../data/dataon/Korea_PM_20180221.csv',\n",
-       " '../data/dataon/Korea_PM_20180222.csv',\n",
-       " '../data/dataon/Korea_PM_20180223.csv',\n",
-       " '../data/dataon/Korea_PM_20180224.csv',\n",
-       " '../data/dataon/Korea_PM_20180225.csv',\n",
-       " '../data/dataon/Korea_PM_20180226.csv',\n",
-       " '../data/dataon/Korea_PM_20180227.csv',\n",
-       " '../data/dataon/Korea_PM_20180228.csv',\n",
-       " '../data/dataon/Korea_PM_20180301.csv',\n",
-       " '../data/dataon/Korea_PM_20180302.csv',\n",
-       " '../data/dataon/Korea_PM_20180303.csv',\n",
-       " '../data/dataon/Korea_PM_20180304.csv',\n",
-       " '../data/dataon/Korea_PM_20180305.csv',\n",
-       " '../data/dataon/Korea_PM_20180306.csv',\n",
-       " '../data/dataon/Korea_PM_20180307.csv',\n",
-       " '../data/dataon/Korea_PM_20180308.csv',\n",
-       " '../data/dataon/Korea_PM_20180309.csv',\n",
-       " '../data/dataon/Korea_PM_20180310.csv',\n",
-       " '../data/dataon/Korea_PM_20180311.csv',\n",
-       " '../data/dataon/Korea_PM_20180312.csv',\n",
-       " '../data/dataon/Korea_PM_20180313.csv',\n",
-       " '../data/dataon/Korea_PM_20180314.csv',\n",
-       " '../data/dataon/Korea_PM_20180315.csv',\n",
-       " '../data/dataon/Korea_PM_20180316.csv',\n",
-       " '../data/dataon/Korea_PM_20180317.csv',\n",
-       " '../data/dataon/Korea_PM_20180318.csv',\n",
-       " '../data/dataon/Korea_PM_20180319.csv',\n",
-       " '../data/dataon/Korea_PM_20180320.csv',\n",
-       " '../data/dataon/Korea_PM_20180321.csv',\n",
-       " '../data/dataon/Korea_PM_20180322.csv',\n",
-       " '../data/dataon/Korea_PM_20180323.csv',\n",
-       " '../data/dataon/Korea_PM_20180324.csv',\n",
-       " '../data/dataon/Korea_PM_20180325.csv',\n",
-       " '../data/dataon/Korea_PM_20180326.csv',\n",
-       " '../data/dataon/Korea_PM_20180327.csv',\n",
-       " '../data/dataon/Korea_PM_20180328.csv',\n",
-       " '../data/dataon/Korea_PM_20180329.csv',\n",
-       " '../data/dataon/Korea_PM_20180330.csv',\n",
-       " '../data/dataon/Korea_PM_20180331.csv',\n",
-       " '../data/dataon/Korea_PM_20180401.csv',\n",
-       " '../data/dataon/Korea_PM_20180402.csv',\n",
-       " '../data/dataon/Korea_PM_20180403.csv',\n",
-       " '../data/dataon/Korea_PM_20180404.csv',\n",
-       " '../data/dataon/Korea_PM_20180405.csv',\n",
-       " '../data/dataon/Korea_PM_20180406.csv',\n",
-       " '../data/dataon/Korea_PM_20180407.csv',\n",
-       " '../data/dataon/Korea_PM_20180408.csv',\n",
-       " '../data/dataon/Korea_PM_20180409.csv',\n",
-       " '../data/dataon/Korea_PM_20180410.csv',\n",
-       " '../data/dataon/Korea_PM_20180411.csv',\n",
-       " '../data/dataon/Korea_PM_20180412.csv',\n",
-       " '../data/dataon/Korea_PM_20180413.csv',\n",
-       " '../data/dataon/Korea_PM_20180414.csv',\n",
-       " '../data/dataon/Korea_PM_20180415.csv',\n",
-       " '../data/dataon/Korea_PM_20180416.csv',\n",
-       " '../data/dataon/Korea_PM_20180417.csv',\n",
-       " '../data/dataon/Korea_PM_20180418.csv',\n",
-       " '../data/dataon/Korea_PM_20180419.csv',\n",
-       " '../data/dataon/Korea_PM_20180420.csv',\n",
-       " '../data/dataon/Korea_PM_20180421.csv',\n",
-       " '../data/dataon/Korea_PM_20180422.csv',\n",
-       " '../data/dataon/Korea_PM_20180423.csv',\n",
-       " '../data/dataon/Korea_PM_20180424.csv',\n",
-       " '../data/dataon/Korea_PM_20180425.csv',\n",
-       " '../data/dataon/Korea_PM_20180426.csv',\n",
-       " '../data/dataon/Korea_PM_20180427.csv',\n",
-       " '../data/dataon/Korea_PM_20180428.csv',\n",
-       " '../data/dataon/Korea_PM_20180429.csv',\n",
-       " '../data/dataon/Korea_PM_20180430.csv',\n",
-       " '../data/dataon/Korea_PM_20180501.csv',\n",
-       " '../data/dataon/Korea_PM_20180502.csv',\n",
-       " '../data/dataon/Korea_PM_20180503.csv',\n",
-       " '../data/dataon/Korea_PM_20180504.csv',\n",
-       " '../data/dataon/Korea_PM_20180505.csv',\n",
-       " '../data/dataon/Korea_PM_20180506.csv',\n",
-       " '../data/dataon/Korea_PM_20180507.csv',\n",
-       " '../data/dataon/Korea_PM_20180508.csv',\n",
-       " '../data/dataon/Korea_PM_20180509.csv',\n",
-       " '../data/dataon/Korea_PM_20180510.csv',\n",
-       " '../data/dataon/Korea_PM_20180511.csv',\n",
-       " '../data/dataon/Korea_PM_20180512.csv',\n",
-       " '../data/dataon/Korea_PM_20180513.csv',\n",
-       " '../data/dataon/Korea_PM_20180514.csv',\n",
-       " '../data/dataon/Korea_PM_20180515.csv',\n",
-       " '../data/dataon/Korea_PM_20180516.csv',\n",
-       " '../data/dataon/Korea_PM_20180517.csv',\n",
-       " '../data/dataon/Korea_PM_20180518.csv',\n",
-       " '../data/dataon/Korea_PM_20180519.csv',\n",
-       " '../data/dataon/Korea_PM_20180520.csv',\n",
-       " '../data/dataon/Korea_PM_20180521.csv',\n",
-       " '../data/dataon/Korea_PM_20180522.csv',\n",
-       " '../data/dataon/Korea_PM_20180523.csv',\n",
-       " '../data/dataon/Korea_PM_20180524.csv',\n",
-       " '../data/dataon/Korea_PM_20180525.csv',\n",
-       " '../data/dataon/Korea_PM_20180526.csv',\n",
-       " '../data/dataon/Korea_PM_20180527.csv',\n",
-       " '../data/dataon/Korea_PM_20180528.csv',\n",
-       " '../data/dataon/Korea_PM_20180529.csv',\n",
-       " '../data/dataon/Korea_PM_20180530.csv',\n",
-       " '../data/dataon/Korea_PM_20180531.csv',\n",
-       " '../data/dataon/Korea_PM_20180601.csv',\n",
-       " '../data/dataon/Korea_PM_20180602.csv',\n",
-       " '../data/dataon/Korea_PM_20180603.csv',\n",
-       " '../data/dataon/Korea_PM_20180604.csv',\n",
-       " '../data/dataon/Korea_PM_20180605.csv',\n",
-       " '../data/dataon/Korea_PM_20180606.csv',\n",
-       " '../data/dataon/Korea_PM_20180607.csv',\n",
-       " '../data/dataon/Korea_PM_20180608.csv',\n",
-       " '../data/dataon/Korea_PM_20180609.csv',\n",
-       " '../data/dataon/Korea_PM_20180610.csv',\n",
-       " '../data/dataon/Korea_PM_20180611.csv',\n",
-       " '../data/dataon/Korea_PM_20180612.csv',\n",
-       " '../data/dataon/Korea_PM_20180613.csv',\n",
-       " '../data/dataon/Korea_PM_20180614.csv',\n",
-       " '../data/dataon/Korea_PM_20180615.csv',\n",
-       " '../data/dataon/Korea_PM_20180616.csv',\n",
-       " '../data/dataon/Korea_PM_20180617.csv',\n",
-       " '../data/dataon/Korea_PM_20180618.csv',\n",
-       " '../data/dataon/Korea_PM_20180619.csv',\n",
-       " '../data/dataon/Korea_PM_20180620.csv',\n",
-       " '../data/dataon/Korea_PM_20180621.csv',\n",
-       " '../data/dataon/Korea_PM_20180622.csv',\n",
-       " '../data/dataon/Korea_PM_20180623.csv',\n",
-       " '../data/dataon/Korea_PM_20180624.csv',\n",
-       " '../data/dataon/Korea_PM_20180625.csv',\n",
-       " '../data/dataon/Korea_PM_20180626.csv',\n",
-       " '../data/dataon/Korea_PM_20180627.csv',\n",
-       " '../data/dataon/Korea_PM_20180628.csv',\n",
-       " '../data/dataon/Korea_PM_20180629.csv',\n",
-       " '../data/dataon/Korea_PM_20180630.csv',\n",
-       " '../data/dataon/Korea_PM_20180701.csv',\n",
-       " '../data/dataon/Korea_PM_20180702.csv',\n",
-       " '../data/dataon/Korea_PM_20180703.csv',\n",
-       " '../data/dataon/Korea_PM_20180704.csv',\n",
-       " '../data/dataon/Korea_PM_20180705.csv',\n",
-       " '../data/dataon/Korea_PM_20180706.csv',\n",
-       " '../data/dataon/Korea_PM_20180707.csv',\n",
-       " '../data/dataon/Korea_PM_20180708.csv',\n",
-       " '../data/dataon/Korea_PM_20180709.csv',\n",
-       " '../data/dataon/Korea_PM_20180710.csv',\n",
-       " '../data/dataon/Korea_PM_20180711.csv',\n",
-       " '../data/dataon/Korea_PM_20180712.csv',\n",
-       " '../data/dataon/Korea_PM_20180713.csv',\n",
-       " '../data/dataon/Korea_PM_20180714.csv',\n",
-       " '../data/dataon/Korea_PM_20180715.csv',\n",
-       " '../data/dataon/Korea_PM_20180716.csv',\n",
-       " '../data/dataon/Korea_PM_20180717.csv',\n",
-       " '../data/dataon/Korea_PM_20180718.csv',\n",
-       " '../data/dataon/Korea_PM_20180719.csv',\n",
-       " '../data/dataon/Korea_PM_20180720.csv',\n",
-       " '../data/dataon/Korea_PM_20180721.csv',\n",
-       " '../data/dataon/Korea_PM_20180722.csv',\n",
-       " '../data/dataon/Korea_PM_20180723.csv',\n",
-       " '../data/dataon/Korea_PM_20180724.csv',\n",
-       " '../data/dataon/Korea_PM_20180725.csv',\n",
-       " '../data/dataon/Korea_PM_20180726.csv',\n",
-       " '../data/dataon/Korea_PM_20180727.csv',\n",
-       " '../data/dataon/Korea_PM_20180728.csv',\n",
-       " '../data/dataon/Korea_PM_20180729.csv',\n",
-       " '../data/dataon/Korea_PM_20180730.csv',\n",
-       " '../data/dataon/Korea_PM_20180731.csv',\n",
-       " '../data/dataon/Korea_PM_20180801.csv',\n",
-       " '../data/dataon/Korea_PM_20180802.csv',\n",
-       " '../data/dataon/Korea_PM_20180803.csv',\n",
-       " '../data/dataon/Korea_PM_20180804.csv',\n",
-       " '../data/dataon/Korea_PM_20180805.csv',\n",
-       " '../data/dataon/Korea_PM_20180806.csv',\n",
-       " '../data/dataon/Korea_PM_20180807.csv',\n",
-       " '../data/dataon/Korea_PM_20180808.csv',\n",
-       " '../data/dataon/Korea_PM_20180809.csv',\n",
-       " '../data/dataon/Korea_PM_20180810.csv',\n",
-       " '../data/dataon/Korea_PM_20180811.csv',\n",
-       " '../data/dataon/Korea_PM_20180812.csv',\n",
-       " '../data/dataon/Korea_PM_20180813.csv',\n",
-       " '../data/dataon/Korea_PM_20180814.csv',\n",
-       " '../data/dataon/Korea_PM_20180815.csv',\n",
-       " '../data/dataon/Korea_PM_20180816.csv',\n",
-       " '../data/dataon/Korea_PM_20180817.csv',\n",
-       " '../data/dataon/Korea_PM_20180818.csv',\n",
-       " '../data/dataon/Korea_PM_20180819.csv',\n",
-       " '../data/dataon/Korea_PM_20180820.csv',\n",
-       " '../data/dataon/Korea_PM_20180821.csv',\n",
-       " '../data/dataon/Korea_PM_20180822.csv',\n",
-       " '../data/dataon/Korea_PM_20180823.csv',\n",
-       " '../data/dataon/Korea_PM_20180824.csv',\n",
-       " '../data/dataon/Korea_PM_20180825.csv',\n",
-       " '../data/dataon/Korea_PM_20180826.csv',\n",
-       " '../data/dataon/Korea_PM_20180827.csv',\n",
-       " '../data/dataon/Korea_PM_20180828.csv',\n",
-       " '../data/dataon/Korea_PM_20180829.csv',\n",
-       " '../data/dataon/Korea_PM_20180830.csv',\n",
-       " '../data/dataon/Korea_PM_20180831.csv',\n",
-       " '../data/dataon/Korea_PM_20180901.csv',\n",
-       " '../data/dataon/Korea_PM_20180902.csv',\n",
-       " '../data/dataon/Korea_PM_20180903.csv',\n",
-       " '../data/dataon/Korea_PM_20180904.csv',\n",
-       " '../data/dataon/Korea_PM_20180905.csv',\n",
-       " '../data/dataon/Korea_PM_20180906.csv',\n",
-       " '../data/dataon/Korea_PM_20180907.csv',\n",
-       " '../data/dataon/Korea_PM_20180908.csv',\n",
-       " '../data/dataon/Korea_PM_20180909.csv',\n",
-       " '../data/dataon/Korea_PM_20180910.csv',\n",
-       " '../data/dataon/Korea_PM_20180911.csv',\n",
-       " '../data/dataon/Korea_PM_20180912.csv',\n",
-       " '../data/dataon/Korea_PM_20180913.csv',\n",
-       " '../data/dataon/Korea_PM_20180914.csv',\n",
-       " '../data/dataon/Korea_PM_20180915.csv',\n",
-       " '../data/dataon/Korea_PM_20180916.csv',\n",
-       " '../data/dataon/Korea_PM_20180917.csv',\n",
-       " '../data/dataon/Korea_PM_20180918.csv',\n",
-       " '../data/dataon/Korea_PM_20180919.csv',\n",
-       " '../data/dataon/Korea_PM_20180920.csv',\n",
-       " '../data/dataon/Korea_PM_20180921.csv',\n",
-       " '../data/dataon/Korea_PM_20180922.csv',\n",
-       " '../data/dataon/Korea_PM_20180923.csv',\n",
-       " '../data/dataon/Korea_PM_20180924.csv',\n",
-       " '../data/dataon/Korea_PM_20180925.csv',\n",
-       " '../data/dataon/Korea_PM_20180926.csv',\n",
-       " '../data/dataon/Korea_PM_20180927.csv',\n",
-       " '../data/dataon/Korea_PM_20180928.csv',\n",
-       " '../data/dataon/Korea_PM_20180929.csv',\n",
-       " '../data/dataon/Korea_PM_20180930.csv',\n",
-       " '../data/dataon/Korea_PM_20181001.csv',\n",
-       " '../data/dataon/Korea_PM_20181002.csv',\n",
-       " '../data/dataon/Korea_PM_20181003.csv',\n",
-       " '../data/dataon/Korea_PM_20181004.csv',\n",
-       " '../data/dataon/Korea_PM_20181005.csv',\n",
-       " '../data/dataon/Korea_PM_20181006.csv',\n",
-       " '../data/dataon/Korea_PM_20181007.csv',\n",
-       " '../data/dataon/Korea_PM_20181008.csv',\n",
-       " '../data/dataon/Korea_PM_20181009.csv',\n",
-       " '../data/dataon/Korea_PM_20181010.csv',\n",
-       " '../data/dataon/Korea_PM_20181011.csv',\n",
-       " '../data/dataon/Korea_PM_20181012.csv',\n",
-       " '../data/dataon/Korea_PM_20181013.csv',\n",
-       " '../data/dataon/Korea_PM_20181014.csv',\n",
-       " '../data/dataon/Korea_PM_20181015.csv',\n",
-       " '../data/dataon/Korea_PM_20181016.csv',\n",
-       " '../data/dataon/Korea_PM_20181017.csv',\n",
-       " '../data/dataon/Korea_PM_20181018.csv',\n",
-       " '../data/dataon/Korea_PM_20181019.csv',\n",
-       " '../data/dataon/Korea_PM_20181020.csv',\n",
-       " '../data/dataon/Korea_PM_20181021.csv',\n",
-       " '../data/dataon/Korea_PM_20181022.csv',\n",
-       " '../data/dataon/Korea_PM_20181023.csv',\n",
-       " '../data/dataon/Korea_PM_20181024.csv',\n",
-       " '../data/dataon/Korea_PM_20181025.csv',\n",
-       " '../data/dataon/Korea_PM_20181026.csv',\n",
-       " '../data/dataon/Korea_PM_20181027.csv',\n",
-       " '../data/dataon/Korea_PM_20181028.csv',\n",
-       " '../data/dataon/Korea_PM_20181029.csv',\n",
-       " '../data/dataon/Korea_PM_20181030.csv',\n",
-       " '../data/dataon/Korea_PM_20181031.csv',\n",
-       " '../data/dataon/Korea_PM_20181101.csv',\n",
-       " '../data/dataon/Korea_PM_20181102.csv',\n",
-       " '../data/dataon/Korea_PM_20181103.csv',\n",
-       " '../data/dataon/Korea_PM_20181104.csv',\n",
-       " '../data/dataon/Korea_PM_20181105.csv',\n",
-       " '../data/dataon/Korea_PM_20181106.csv',\n",
-       " '../data/dataon/Korea_PM_20181107.csv',\n",
-       " '../data/dataon/Korea_PM_20181108.csv',\n",
-       " '../data/dataon/Korea_PM_20181109.csv',\n",
-       " '../data/dataon/Korea_PM_20181110.csv',\n",
-       " '../data/dataon/Korea_PM_20181111.csv',\n",
-       " '../data/dataon/Korea_PM_20181112.csv',\n",
-       " '../data/dataon/Korea_PM_20181113.csv',\n",
-       " '../data/dataon/Korea_PM_20181114.csv',\n",
-       " '../data/dataon/Korea_PM_20181115.csv',\n",
-       " '../data/dataon/Korea_PM_20181116.csv',\n",
-       " '../data/dataon/Korea_PM_20181117.csv',\n",
-       " '../data/dataon/Korea_PM_20181118.csv',\n",
-       " '../data/dataon/Korea_PM_20181119.csv',\n",
-       " '../data/dataon/Korea_PM_20181120.csv',\n",
-       " '../data/dataon/Korea_PM_20181121.csv',\n",
-       " '../data/dataon/Korea_PM_20181122.csv',\n",
-       " '../data/dataon/Korea_PM_20181123.csv',\n",
-       " '../data/dataon/Korea_PM_20181124.csv',\n",
-       " '../data/dataon/Korea_PM_20181125.csv',\n",
-       " '../data/dataon/Korea_PM_20181126.csv',\n",
-       " '../data/dataon/Korea_PM_20181127.csv',\n",
-       " '../data/dataon/Korea_PM_20181128.csv',\n",
-       " '../data/dataon/Korea_PM_20181129.csv',\n",
-       " '../data/dataon/Korea_PM_20181130.csv',\n",
-       " '../data/dataon/Korea_PM_20190101.csv',\n",
-       " '../data/dataon/Korea_PM_20190102.csv',\n",
-       " '../data/dataon/Korea_PM_20190103.csv',\n",
-       " '../data/dataon/Korea_PM_20190104.csv',\n",
-       " '../data/dataon/Korea_PM_20190105.csv',\n",
-       " '../data/dataon/Korea_PM_20190106.csv',\n",
-       " '../data/dataon/Korea_PM_20190107.csv',\n",
-       " '../data/dataon/Korea_PM_20190108.csv',\n",
-       " '../data/dataon/Korea_PM_20190109.csv',\n",
-       " '../data/dataon/Korea_PM_20190110.csv',\n",
-       " '../data/dataon/Korea_PM_20190111.csv',\n",
-       " '../data/dataon/Korea_PM_20190112.csv',\n",
-       " '../data/dataon/Korea_PM_20190113.csv',\n",
-       " '../data/dataon/Korea_PM_20190114.csv',\n",
-       " '../data/dataon/Korea_PM_20190115.csv',\n",
-       " '../data/dataon/Korea_PM_20190116.csv',\n",
-       " '../data/dataon/Korea_PM_20190117.csv',\n",
-       " '../data/dataon/Korea_PM_20190118.csv',\n",
-       " '../data/dataon/Korea_PM_20190119.csv',\n",
-       " '../data/dataon/Korea_PM_20190120.csv',\n",
-       " '../data/dataon/Korea_PM_20190121.csv',\n",
-       " '../data/dataon/Korea_PM_20190122.csv',\n",
-       " '../data/dataon/Korea_PM_20190123.csv',\n",
-       " '../data/dataon/Korea_PM_20190124.csv',\n",
-       " '../data/dataon/Korea_PM_20190125.csv',\n",
-       " '../data/dataon/Korea_PM_20190126.csv',\n",
-       " '../data/dataon/Korea_PM_20190127.csv',\n",
-       " '../data/dataon/Korea_PM_20190128.csv',\n",
-       " '../data/dataon/Korea_PM_20190129.csv',\n",
-       " '../data/dataon/Korea_PM_20190130.csv',\n",
-       " '../data/dataon/Korea_PM_20190131.csv',\n",
-       " '../data/dataon/Korea_PM_20190201.csv',\n",
-       " '../data/dataon/Korea_PM_20190202.csv',\n",
-       " '../data/dataon/Korea_PM_20190203.csv',\n",
-       " '../data/dataon/Korea_PM_20190204.csv',\n",
-       " '../data/dataon/Korea_PM_20190205.csv',\n",
-       " '../data/dataon/Korea_PM_20190206.csv',\n",
-       " '../data/dataon/Korea_PM_20190207.csv',\n",
-       " '../data/dataon/Korea_PM_20190208.csv',\n",
-       " '../data/dataon/Korea_PM_20190209.csv',\n",
-       " '../data/dataon/Korea_PM_20190210.csv',\n",
-       " '../data/dataon/Korea_PM_20190211.csv',\n",
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-       " '../data/dataon/Korea_PM_20190213.csv',\n",
-       " '../data/dataon/Korea_PM_20190214.csv',\n",
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-       " '../data/dataon/Korea_PM_20190218.csv',\n",
-       " '../data/dataon/Korea_PM_20190219.csv',\n",
-       " '../data/dataon/Korea_PM_20190220.csv',\n",
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-       " '../data/dataon/Korea_PM_20190222.csv',\n",
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-       " '../data/dataon/Korea_PM_20190226.csv',\n",
-       " '../data/dataon/Korea_PM_20190227.csv',\n",
-       " '../data/dataon/Korea_PM_20190228.csv',\n",
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-       " '../data/dataon/Korea_PM_20190302.csv',\n",
-       " '../data/dataon/Korea_PM_20190303.csv',\n",
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-       " '../data/dataon/Korea_PM_20190308.csv',\n",
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-       " '../data/dataon/Korea_PM_20190310.csv',\n",
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-       " '../data/dataon/Korea_PM_20190312.csv',\n",
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-       " '../data/dataon/Korea_PM_20190316.csv',\n",
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-       " '../data/dataon/Korea_PM_20190320.csv',\n",
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-       " '../data/dataon/Korea_PM_20190401.csv',\n",
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-       " '../data/dataon/Korea_PM_20190520.csv',\n",
-       " '../data/dataon/Korea_PM_20190521.csv',\n",
-       " '../data/dataon/Korea_PM_20190522.csv',\n",
-       " '../data/dataon/Korea_PM_20190523.csv',\n",
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-       " '../data/dataon/Korea_PM_20190617.csv',\n",
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-       " '../data/dataon/Korea_PM_20190619.csv',\n",
-       " '../data/dataon/Korea_PM_20190620.csv',\n",
-       " '../data/dataon/Korea_PM_20190621.csv',\n",
-       " '../data/dataon/Korea_PM_20190622.csv',\n",
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-       " '../data/dataon/Korea_PM_20190625.csv',\n",
-       " '../data/dataon/Korea_PM_20190626.csv',\n",
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-       " '../data/dataon/Korea_PM_20190628.csv',\n",
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-       " '../data/dataon/Korea_PM_20190630.csv',\n",
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-       " '../data/dataon/Korea_PM_20190702.csv',\n",
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-       " '../data/dataon/Korea_PM_20190706.csv',\n",
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-       " '../data/dataon/Korea_PM_20190709.csv',\n",
-       " '../data/dataon/Korea_PM_20190710.csv',\n",
-       " '../data/dataon/Korea_PM_20190711.csv',\n",
-       " '../data/dataon/Korea_PM_20190712.csv',\n",
-       " '../data/dataon/Korea_PM_20190713.csv',\n",
-       " '../data/dataon/Korea_PM_20190714.csv',\n",
-       " '../data/dataon/Korea_PM_20190715.csv',\n",
-       " '../data/dataon/Korea_PM_20190716.csv',\n",
-       " '../data/dataon/Korea_PM_20190717.csv',\n",
-       " '../data/dataon/Korea_PM_20190718.csv',\n",
-       " '../data/dataon/Korea_PM_20190719.csv',\n",
-       " '../data/dataon/Korea_PM_20190720.csv',\n",
-       " '../data/dataon/Korea_PM_20190721.csv',\n",
-       " '../data/dataon/Korea_PM_20190722.csv',\n",
-       " '../data/dataon/Korea_PM_20190723.csv',\n",
-       " '../data/dataon/Korea_PM_20190724.csv',\n",
-       " '../data/dataon/Korea_PM_20190725.csv',\n",
-       " '../data/dataon/Korea_PM_20190726.csv',\n",
-       " '../data/dataon/Korea_PM_20190727.csv',\n",
-       " '../data/dataon/Korea_PM_20190728.csv',\n",
-       " '../data/dataon/Korea_PM_20190729.csv',\n",
-       " '../data/dataon/Korea_PM_20190730.csv',\n",
-       " '../data/dataon/Korea_PM_20190731.csv',\n",
-       " '../data/dataon/Korea_PM_20190801.csv',\n",
-       " '../data/dataon/Korea_PM_20190802.csv',\n",
-       " '../data/dataon/Korea_PM_20190803.csv',\n",
-       " '../data/dataon/Korea_PM_20190804.csv',\n",
-       " '../data/dataon/Korea_PM_20190805.csv',\n",
-       " '../data/dataon/Korea_PM_20190806.csv',\n",
-       " '../data/dataon/Korea_PM_20190807.csv',\n",
-       " '../data/dataon/Korea_PM_20190808.csv',\n",
-       " '../data/dataon/Korea_PM_20190809.csv',\n",
-       " '../data/dataon/Korea_PM_20190810.csv',\n",
-       " '../data/dataon/Korea_PM_20190811.csv',\n",
-       " '../data/dataon/Korea_PM_20190812.csv',\n",
-       " '../data/dataon/Korea_PM_20190813.csv',\n",
-       " '../data/dataon/Korea_PM_20190814.csv',\n",
-       " '../data/dataon/Korea_PM_20190815.csv',\n",
-       " '../data/dataon/Korea_PM_20190816.csv',\n",
-       " '../data/dataon/Korea_PM_20190817.csv',\n",
-       " '../data/dataon/Korea_PM_20190818.csv',\n",
-       " '../data/dataon/Korea_PM_20190819.csv',\n",
-       " '../data/dataon/Korea_PM_20190820.csv',\n",
-       " '../data/dataon/Korea_PM_20190821.csv',\n",
-       " '../data/dataon/Korea_PM_20190822.csv',\n",
-       " '../data/dataon/Korea_PM_20190823.csv',\n",
-       " '../data/dataon/Korea_PM_20190824.csv',\n",
-       " '../data/dataon/Korea_PM_20190825.csv',\n",
-       " '../data/dataon/Korea_PM_20190826.csv',\n",
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-       " '../data/dataon/Korea_PM_20190828.csv',\n",
-       " '../data/dataon/Korea_PM_20190829.csv',\n",
-       " '../data/dataon/Korea_PM_20190830.csv',\n",
-       " '../data/dataon/Korea_PM_20190831.csv',\n",
-       " '../data/dataon/Korea_PM_20191001.csv',\n",
-       " '../data/dataon/Korea_PM_20191002.csv',\n",
-       " '../data/dataon/Korea_PM_20191003.csv',\n",
-       " '../data/dataon/Korea_PM_20191004.csv',\n",
-       " '../data/dataon/Korea_PM_20191005.csv',\n",
-       " '../data/dataon/Korea_PM_20191006.csv',\n",
-       " '../data/dataon/Korea_PM_20191007.csv',\n",
-       " '../data/dataon/Korea_PM_20191008.csv',\n",
-       " '../data/dataon/Korea_PM_20191009.csv',\n",
-       " '../data/dataon/Korea_PM_20191010.csv',\n",
-       " '../data/dataon/Korea_PM_20191011.csv',\n",
-       " '../data/dataon/Korea_PM_20191012.csv',\n",
-       " '../data/dataon/Korea_PM_20191013.csv',\n",
-       " '../data/dataon/Korea_PM_20191014.csv',\n",
-       " '../data/dataon/Korea_PM_20191015.csv',\n",
-       " '../data/dataon/Korea_PM_20191016.csv',\n",
-       " '../data/dataon/Korea_PM_20191017.csv',\n",
-       " '../data/dataon/Korea_PM_20191018.csv',\n",
-       " '../data/dataon/Korea_PM_20191019.csv',\n",
-       " '../data/dataon/Korea_PM_20191020.csv',\n",
-       " '../data/dataon/Korea_PM_20191021.csv',\n",
-       " '../data/dataon/Korea_PM_20191022.csv',\n",
-       " '../data/dataon/Korea_PM_20191023.csv',\n",
-       " '../data/dataon/Korea_PM_20191024.csv',\n",
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-       " '../data/dataon/Korea_PM_20191029.csv',\n",
-       " '../data/dataon/Korea_PM_20191030.csv',\n",
-       " '../data/dataon/Korea_PM_20191031.csv',\n",
-       " '../data/dataon/Korea_PM_20191101.csv',\n",
-       " '../data/dataon/Korea_PM_20191102.csv',\n",
-       " '../data/dataon/Korea_PM_20191103.csv',\n",
-       " '../data/dataon/Korea_PM_20191104.csv',\n",
-       " '../data/dataon/Korea_PM_20191105.csv',\n",
-       " '../data/dataon/Korea_PM_20191106.csv',\n",
-       " '../data/dataon/Korea_PM_20191107.csv',\n",
-       " '../data/dataon/Korea_PM_20191108.csv',\n",
-       " '../data/dataon/Korea_PM_20191109.csv',\n",
-       " '../data/dataon/Korea_PM_20191110.csv',\n",
-       " '../data/dataon/Korea_PM_20191111.csv',\n",
-       " '../data/dataon/Korea_PM_20191112.csv',\n",
-       " '../data/dataon/Korea_PM_20191113.csv',\n",
-       " '../data/dataon/Korea_PM_20191114.csv',\n",
-       " '../data/dataon/Korea_PM_20191115.csv',\n",
-       " '../data/dataon/Korea_PM_20191116.csv',\n",
-       " '../data/dataon/Korea_PM_20191117.csv',\n",
-       " '../data/dataon/Korea_PM_20191118.csv',\n",
-       " '../data/dataon/Korea_PM_20191119.csv',\n",
-       " '../data/dataon/Korea_PM_20191120.csv',\n",
-       " '../data/dataon/Korea_PM_20191121.csv',\n",
-       " '../data/dataon/Korea_PM_20191122.csv',\n",
-       " '../data/dataon/Korea_PM_20191123.csv',\n",
-       " '../data/dataon/Korea_PM_20191124.csv',\n",
-       " '../data/dataon/Korea_PM_20191125.csv',\n",
-       " '../data/dataon/Korea_PM_20191126.csv',\n",
-       " '../data/dataon/Korea_PM_20191127.csv',\n",
-       " '../data/dataon/Korea_PM_20191128.csv',\n",
-       " '../data/dataon/Korea_PM_20191129.csv',\n",
-       " '../data/dataon/Korea_PM_20191130.csv',\n",
-       " '../data/dataon/Korea_PM_20191201.csv',\n",
-       " '../data/dataon/Korea_PM_20191202.csv',\n",
-       " '../data/dataon/Korea_PM_20191203.csv',\n",
-       " '../data/dataon/Korea_PM_20191204.csv',\n",
-       " '../data/dataon/Korea_PM_20191205.csv',\n",
-       " '../data/dataon/Korea_PM_20191206.csv',\n",
-       " '../data/dataon/Korea_PM_20191207.csv',\n",
-       " '../data/dataon/Korea_PM_20191208.csv',\n",
-       " '../data/dataon/Korea_PM_20191209.csv',\n",
-       " '../data/dataon/Korea_PM_20191210.csv',\n",
-       " '../data/dataon/Korea_PM_20191211.csv',\n",
-       " '../data/dataon/Korea_PM_20191212.csv',\n",
-       " '../data/dataon/Korea_PM_20191213.csv',\n",
-       " '../data/dataon/Korea_PM_20191214.csv',\n",
-       " '../data/dataon/Korea_PM_20191215.csv',\n",
-       " '../data/dataon/Korea_PM_20191216.csv',\n",
-       " '../data/dataon/Korea_PM_20191217.csv',\n",
-       " '../data/dataon/Korea_PM_20191218.csv',\n",
-       " '../data/dataon/Korea_PM_20191219.csv',\n",
-       " '../data/dataon/Korea_PM_20191220.csv',\n",
-       " '../data/dataon/Korea_PM_20191221.csv',\n",
-       " '../data/dataon/Korea_PM_20191222.csv',\n",
-       " '../data/dataon/Korea_PM_20191223.csv',\n",
-       " '../data/dataon/Korea_PM_20191224.csv',\n",
-       " '../data/dataon/Korea_PM_20191225.csv',\n",
-       " '../data/dataon/Korea_PM_20191226.csv',\n",
-       " '../data/dataon/Korea_PM_20191227.csv',\n",
-       " '../data/dataon/Korea_PM_20191228.csv',\n",
-       " '../data/dataon/Korea_PM_20191229.csv',\n",
-       " '../data/dataon/Korea_PM_20191230.csv',\n",
-       " '../data/dataon/Korea_PM_20191231.csv',\n",
-       " '../data/dataon/Korea_PM_20200201.csv',\n",
-       " '../data/dataon/Korea_PM_20200202.csv',\n",
-       " '../data/dataon/Korea_PM_20200203.csv',\n",
-       " '../data/dataon/Korea_PM_20200204.csv',\n",
-       " '../data/dataon/Korea_PM_20200205.csv',\n",
-       " '../data/dataon/Korea_PM_20200206.csv',\n",
-       " '../data/dataon/Korea_PM_20200207.csv',\n",
-       " '../data/dataon/Korea_PM_20200208.csv',\n",
-       " '../data/dataon/Korea_PM_20200209.csv',\n",
-       " '../data/dataon/Korea_PM_20200210.csv',\n",
-       " '../data/dataon/Korea_PM_20200211.csv',\n",
-       " '../data/dataon/Korea_PM_20200212.csv',\n",
-       " '../data/dataon/Korea_PM_20200213.csv',\n",
-       " '../data/dataon/Korea_PM_20200214.csv',\n",
-       " '../data/dataon/Korea_PM_20200215.csv',\n",
-       " '../data/dataon/Korea_PM_20200216.csv',\n",
-       " '../data/dataon/Korea_PM_20200217.csv',\n",
-       " '../data/dataon/Korea_PM_20200218.csv',\n",
-       " '../data/dataon/Korea_PM_20200219.csv',\n",
-       " '../data/dataon/Korea_PM_20200220.csv',\n",
-       " '../data/dataon/Korea_PM_20200221.csv',\n",
-       " '../data/dataon/Korea_PM_20200222.csv',\n",
-       " '../data/dataon/Korea_PM_20200223.csv',\n",
-       " '../data/dataon/Korea_PM_20200224.csv',\n",
-       " '../data/dataon/Korea_PM_20200225.csv',\n",
-       " '../data/dataon/Korea_PM_20200226.csv',\n",
-       " '../data/dataon/Korea_PM_20200227.csv',\n",
-       " '../data/dataon/Korea_PM_20200228.csv',\n",
-       " '../data/dataon/Korea_PM_20200229.csv',\n",
-       " '../data/dataon/Korea_PM_20200301.csv',\n",
-       " '../data/dataon/Korea_PM_20200302.csv',\n",
-       " '../data/dataon/Korea_PM_20200303.csv',\n",
-       " '../data/dataon/Korea_PM_20200304.csv',\n",
-       " '../data/dataon/Korea_PM_20200305.csv',\n",
-       " '../data/dataon/Korea_PM_20200306.csv',\n",
-       " '../data/dataon/Korea_PM_20200307.csv',\n",
-       " '../data/dataon/Korea_PM_20200308.csv',\n",
-       " '../data/dataon/Korea_PM_20200309.csv',\n",
-       " '../data/dataon/Korea_PM_20200310.csv',\n",
-       " '../data/dataon/Korea_PM_20200311.csv',\n",
-       " '../data/dataon/Korea_PM_20200312.csv',\n",
-       " '../data/dataon/Korea_PM_20200313.csv',\n",
-       " '../data/dataon/Korea_PM_20200314.csv',\n",
-       " '../data/dataon/Korea_PM_20200315.csv',\n",
-       " '../data/dataon/Korea_PM_20200316.csv',\n",
-       " '../data/dataon/Korea_PM_20200317.csv',\n",
-       " '../data/dataon/Korea_PM_20200318.csv',\n",
-       " '../data/dataon/Korea_PM_20200319.csv',\n",
-       " '../data/dataon/Korea_PM_20200320.csv',\n",
-       " '../data/dataon/Korea_PM_20200321.csv',\n",
-       " '../data/dataon/Korea_PM_20200322.csv',\n",
-       " '../data/dataon/Korea_PM_20200323.csv',\n",
-       " '../data/dataon/Korea_PM_20200324.csv',\n",
-       " '../data/dataon/Korea_PM_20200325.csv',\n",
-       " '../data/dataon/Korea_PM_20200326.csv',\n",
-       " '../data/dataon/Korea_PM_20200327.csv',\n",
-       " '../data/dataon/Korea_PM_20200328.csv',\n",
-       " '../data/dataon/Korea_PM_20200329.csv',\n",
-       " '../data/dataon/Korea_PM_20200330.csv',\n",
-       " '../data/dataon/Korea_PM_20200331.csv',\n",
-       " '../data/dataon/Korea_PM_20200401.csv',\n",
-       " '../data/dataon/Korea_PM_20200402.csv',\n",
-       " '../data/dataon/Korea_PM_20200403.csv',\n",
-       " '../data/dataon/Korea_PM_20200404.csv',\n",
-       " '../data/dataon/Korea_PM_20200405.csv',\n",
-       " '../data/dataon/Korea_PM_20200406.csv',\n",
-       " '../data/dataon/Korea_PM_20200407.csv',\n",
-       " '../data/dataon/Korea_PM_20200408.csv',\n",
-       " '../data/dataon/Korea_PM_20200409.csv',\n",
-       " '../data/dataon/Korea_PM_20200410.csv',\n",
-       " '../data/dataon/Korea_PM_20200411.csv',\n",
-       " '../data/dataon/Korea_PM_20200412.csv',\n",
-       " '../data/dataon/Korea_PM_20200413.csv',\n",
-       " '../data/dataon/Korea_PM_20200414.csv',\n",
-       " '../data/dataon/Korea_PM_20200415.csv',\n",
-       " '../data/dataon/Korea_PM_20200416.csv',\n",
-       " '../data/dataon/Korea_PM_20200417.csv',\n",
-       " '../data/dataon/Korea_PM_20200418.csv',\n",
-       " '../data/dataon/Korea_PM_20200419.csv',\n",
-       " '../data/dataon/Korea_PM_20200420.csv',\n",
-       " '../data/dataon/Korea_PM_20200421.csv',\n",
-       " '../data/dataon/Korea_PM_20200422.csv',\n",
-       " '../data/dataon/Korea_PM_20200423.csv',\n",
-       " '../data/dataon/Korea_PM_20200424.csv',\n",
-       " '../data/dataon/Korea_PM_20200425.csv',\n",
-       " '../data/dataon/Korea_PM_20200426.csv',\n",
-       " '../data/dataon/Korea_PM_20200427.csv',\n",
-       " '../data/dataon/Korea_PM_20200428.csv',\n",
-       " '../data/dataon/Korea_PM_20200429.csv',\n",
-       " '../data/dataon/Korea_PM_20200430.csv',\n",
-       " '../data/dataon/Korea_PM_20200501.csv',\n",
-       " '../data/dataon/Korea_PM_20200502.csv',\n",
-       " '../data/dataon/Korea_PM_20200503.csv',\n",
-       " '../data/dataon/Korea_PM_20200504.csv',\n",
-       " '../data/dataon/Korea_PM_20200505.csv',\n",
-       " '../data/dataon/Korea_PM_20200506.csv',\n",
-       " '../data/dataon/Korea_PM_20200507.csv',\n",
-       " '../data/dataon/Korea_PM_20200508.csv',\n",
-       " '../data/dataon/Korea_PM_20200509.csv',\n",
-       " '../data/dataon/Korea_PM_20200510.csv',\n",
-       " '../data/dataon/Korea_PM_20200511.csv',\n",
-       " '../data/dataon/Korea_PM_20200512.csv',\n",
-       " '../data/dataon/Korea_PM_20200513.csv',\n",
-       " '../data/dataon/Korea_PM_20200514.csv',\n",
-       " '../data/dataon/Korea_PM_20200515.csv',\n",
-       " '../data/dataon/Korea_PM_20200516.csv',\n",
-       " '../data/dataon/Korea_PM_20200517.csv',\n",
-       " '../data/dataon/Korea_PM_20200518.csv',\n",
-       " '../data/dataon/Korea_PM_20200519.csv',\n",
-       " '../data/dataon/Korea_PM_20200520.csv',\n",
-       " '../data/dataon/Korea_PM_20200521.csv',\n",
-       " '../data/dataon/Korea_PM_20200522.csv',\n",
-       " '../data/dataon/Korea_PM_20200523.csv',\n",
-       " '../data/dataon/Korea_PM_20200524.csv',\n",
-       " '../data/dataon/Korea_PM_20200525.csv',\n",
-       " '../data/dataon/Korea_PM_20200526.csv',\n",
-       " '../data/dataon/Korea_PM_20200527.csv',\n",
-       " '../data/dataon/Korea_PM_20200528.csv',\n",
-       " '../data/dataon/Korea_PM_20200529.csv',\n",
-       " '../data/dataon/Korea_PM_20200530.csv',\n",
-       " '../data/dataon/Korea_PM_20200531.csv',\n",
-       " '../data/dataon/Korea_PM_20200601.csv',\n",
-       " '../data/dataon/Korea_PM_20200602.csv',\n",
-       " '../data/dataon/Korea_PM_20200603.csv',\n",
-       " '../data/dataon/Korea_PM_20200604.csv',\n",
-       " '../data/dataon/Korea_PM_20200605.csv',\n",
-       " '../data/dataon/Korea_PM_20200606.csv',\n",
-       " '../data/dataon/Korea_PM_20200607.csv',\n",
-       " '../data/dataon/Korea_PM_20200608.csv',\n",
-       " '../data/dataon/Korea_PM_20200609.csv',\n",
-       " '../data/dataon/Korea_PM_20200610.csv',\n",
-       " '../data/dataon/Korea_PM_20200611.csv',\n",
-       " '../data/dataon/Korea_PM_20200612.csv',\n",
-       " '../data/dataon/Korea_PM_20200613.csv',\n",
-       " '../data/dataon/Korea_PM_20200614.csv',\n",
-       " '../data/dataon/Korea_PM_20200615.csv',\n",
-       " '../data/dataon/Korea_PM_20200616.csv',\n",
-       " '../data/dataon/Korea_PM_20200617.csv',\n",
-       " '../data/dataon/Korea_PM_20200618.csv',\n",
-       " '../data/dataon/Korea_PM_20200619.csv',\n",
-       " '../data/dataon/Korea_PM_20200620.csv',\n",
-       " '../data/dataon/Korea_PM_20200621.csv',\n",
-       " '../data/dataon/Korea_PM_20200622.csv',\n",
-       " '../data/dataon/Korea_PM_20200623.csv',\n",
-       " '../data/dataon/Korea_PM_20200624.csv',\n",
-       " '../data/dataon/Korea_PM_20200625.csv',\n",
-       " '../data/dataon/Korea_PM_20200626.csv',\n",
-       " '../data/dataon/Korea_PM_20200627.csv',\n",
-       " '../data/dataon/Korea_PM_20200628.csv',\n",
-       " '../data/dataon/Korea_PM_20200629.csv',\n",
-       " '../data/dataon/Korea_PM_20200630.csv',\n",
-       " '../data/dataon/Korea_PM_20200701.csv',\n",
-       " '../data/dataon/Korea_PM_20200702.csv',\n",
-       " '../data/dataon/Korea_PM_20200703.csv',\n",
-       " '../data/dataon/Korea_PM_20200704.csv',\n",
-       " '../data/dataon/Korea_PM_20200705.csv',\n",
-       " '../data/dataon/Korea_PM_20200706.csv',\n",
-       " '../data/dataon/Korea_PM_20200707.csv',\n",
-       " '../data/dataon/Korea_PM_20200708.csv',\n",
-       " '../data/dataon/Korea_PM_20200709.csv',\n",
-       " '../data/dataon/Korea_PM_20200710.csv',\n",
-       " '../data/dataon/Korea_PM_20200711.csv',\n",
-       " '../data/dataon/Korea_PM_20200712.csv',\n",
-       " '../data/dataon/Korea_PM_20200713.csv',\n",
-       " '../data/dataon/Korea_PM_20200714.csv',\n",
-       " '../data/dataon/Korea_PM_20200715.csv',\n",
-       " '../data/dataon/Korea_PM_20200716.csv',\n",
-       " '../data/dataon/Korea_PM_20200717.csv',\n",
-       " '../data/dataon/Korea_PM_20200718.csv',\n",
-       " '../data/dataon/Korea_PM_20200719.csv',\n",
-       " '../data/dataon/Korea_PM_20200720.csv',\n",
-       " '../data/dataon/Korea_PM_20200721.csv',\n",
-       " '../data/dataon/Korea_PM_20200722.csv',\n",
-       " '../data/dataon/Korea_PM_20200723.csv',\n",
-       " '../data/dataon/Korea_PM_20200724.csv',\n",
-       " '../data/dataon/Korea_PM_20200725.csv',\n",
-       " '../data/dataon/Korea_PM_20200726.csv',\n",
-       " '../data/dataon/Korea_PM_20200727.csv',\n",
-       " '../data/dataon/Korea_PM_20200728.csv',\n",
-       " '../data/dataon/Korea_PM_20200729.csv',\n",
-       " '../data/dataon/Korea_PM_20200730.csv',\n",
-       " '../data/dataon/Korea_PM_20200731.csv',\n",
-       " '../data/dataon/Korea_PM_20200801.csv',\n",
-       " '../data/dataon/Korea_PM_20200802.csv',\n",
-       " '../data/dataon/Korea_PM_20200803.csv',\n",
-       " '../data/dataon/Korea_PM_20200804.csv',\n",
-       " '../data/dataon/Korea_PM_20200805.csv',\n",
-       " '../data/dataon/Korea_PM_20200806.csv',\n",
-       " '../data/dataon/Korea_PM_20200807.csv',\n",
-       " '../data/dataon/Korea_PM_20200808.csv',\n",
-       " '../data/dataon/Korea_PM_20200809.csv',\n",
-       " '../data/dataon/Korea_PM_20200810.csv',\n",
-       " '../data/dataon/Korea_PM_20200811.csv',\n",
-       " '../data/dataon/Korea_PM_20200812.csv',\n",
-       " '../data/dataon/Korea_PM_20200813.csv',\n",
-       " '../data/dataon/Korea_PM_20200814.csv',\n",
-       " '../data/dataon/Korea_PM_20200815.csv',\n",
-       " '../data/dataon/Korea_PM_20200816.csv',\n",
-       " '../data/dataon/Korea_PM_20200817.csv',\n",
-       " '../data/dataon/Korea_PM_20200818.csv',\n",
-       " '../data/dataon/Korea_PM_20200819.csv',\n",
-       " '../data/dataon/Korea_PM_20200820.csv',\n",
-       " '../data/dataon/Korea_PM_20200821.csv',\n",
-       " '../data/dataon/Korea_PM_20200822.csv',\n",
-       " '../data/dataon/Korea_PM_20200823.csv',\n",
-       " '../data/dataon/Korea_PM_20200824.csv',\n",
-       " '../data/dataon/Korea_PM_20200825.csv',\n",
-       " '../data/dataon/Korea_PM_20200826.csv',\n",
-       " '../data/dataon/Korea_PM_20200827.csv',\n",
-       " '../data/dataon/Korea_PM_20200828.csv',\n",
-       " '../data/dataon/Korea_PM_20200829.csv',\n",
-       " '../data/dataon/Korea_PM_20200830.csv',\n",
-       " '../data/dataon/Korea_PM_20200831.csv',\n",
-       " '../data/dataon/Korea_PM_20200901.csv',\n",
-       " '../data/dataon/Korea_PM_20200902.csv',\n",
-       " '../data/dataon/Korea_PM_20200903.csv',\n",
-       " '../data/dataon/Korea_PM_20200904.csv',\n",
-       " '../data/dataon/Korea_PM_20200905.csv',\n",
-       " '../data/dataon/Korea_PM_20200906.csv',\n",
-       " '../data/dataon/Korea_PM_20200907.csv',\n",
-       " '../data/dataon/Korea_PM_20200908.csv',\n",
-       " '../data/dataon/Korea_PM_20200909.csv',\n",
-       " '../data/dataon/Korea_PM_20200910.csv',\n",
-       " '../data/dataon/Korea_PM_20200911.csv',\n",
-       " '../data/dataon/Korea_PM_20200912.csv',\n",
-       " '../data/dataon/Korea_PM_20200913.csv',\n",
-       " '../data/dataon/Korea_PM_20200914.csv',\n",
-       " '../data/dataon/Korea_PM_20200915.csv',\n",
-       " '../data/dataon/Korea_PM_20200916.csv',\n",
-       " '../data/dataon/Korea_PM_20200917.csv',\n",
-       " '../data/dataon/Korea_PM_20200918.csv',\n",
-       " '../data/dataon/Korea_PM_20200919.csv',\n",
-       " '../data/dataon/Korea_PM_20200920.csv',\n",
-       " '../data/dataon/Korea_PM_20200921.csv',\n",
-       " '../data/dataon/Korea_PM_20200922.csv',\n",
-       " '../data/dataon/Korea_PM_20200923.csv',\n",
-       " '../data/dataon/Korea_PM_20200924.csv',\n",
-       " '../data/dataon/Korea_PM_20200925.csv',\n",
-       " '../data/dataon/Korea_PM_20200926.csv',\n",
-       " '../data/dataon/Korea_PM_20200927.csv',\n",
-       " '../data/dataon/Korea_PM_20200928.csv',\n",
-       " '../data/dataon/Korea_PM_20200929.csv',\n",
-       " '../data/dataon/Korea_PM_20200930.csv',\n",
-       " '../data/dataon/Korea_PM_20201101.csv',\n",
-       " '../data/dataon/Korea_PM_20201102.csv',\n",
-       " '../data/dataon/Korea_PM_20201103.csv',\n",
-       " '../data/dataon/Korea_PM_20201104.csv',\n",
-       " '../data/dataon/Korea_PM_20201105.csv',\n",
-       " '../data/dataon/Korea_PM_20201106.csv',\n",
-       " '../data/dataon/Korea_PM_20201107.csv',\n",
-       " '../data/dataon/Korea_PM_20201108.csv',\n",
-       " '../data/dataon/Korea_PM_20201109.csv',\n",
-       " '../data/dataon/Korea_PM_20201110.csv',\n",
-       " '../data/dataon/Korea_PM_20201111.csv',\n",
-       " '../data/dataon/Korea_PM_20201112.csv',\n",
-       " '../data/dataon/Korea_PM_20201113.csv',\n",
-       " '../data/dataon/Korea_PM_20201114.csv',\n",
-       " '../data/dataon/Korea_PM_20201115.csv',\n",
-       " '../data/dataon/Korea_PM_20201116.csv',\n",
-       " '../data/dataon/Korea_PM_20201117.csv',\n",
-       " '../data/dataon/Korea_PM_20201118.csv',\n",
-       " '../data/dataon/Korea_PM_20201119.csv',\n",
-       " '../data/dataon/Korea_PM_20201120.csv',\n",
-       " '../data/dataon/Korea_PM_20201121.csv',\n",
-       " '../data/dataon/Korea_PM_20201122.csv',\n",
-       " '../data/dataon/Korea_PM_20201123.csv',\n",
-       " '../data/dataon/Korea_PM_20201124.csv',\n",
-       " '../data/dataon/Korea_PM_20201125.csv',\n",
-       " '../data/dataon/Korea_PM_20201126.csv',\n",
-       " '../data/dataon/Korea_PM_20201127.csv',\n",
-       " '../data/dataon/Korea_PM_20201128.csv',\n",
-       " '../data/dataon/Korea_PM_20201129.csv',\n",
-       " '../data/dataon/Korea_PM_20201130.csv',\n",
-       " '../data/dataon/Korea_PM_20201201.csv',\n",
-       " '../data/dataon/Korea_PM_20201202.csv',\n",
-       " '../data/dataon/Korea_PM_20201203.csv',\n",
-       " '../data/dataon/Korea_PM_20201204.csv',\n",
-       " '../data/dataon/Korea_PM_20201205.csv',\n",
-       " '../data/dataon/Korea_PM_20201206.csv',\n",
-       " '../data/dataon/Korea_PM_20201207.csv',\n",
-       " '../data/dataon/Korea_PM_20201208.csv',\n",
-       " '../data/dataon/Korea_PM_20201209.csv',\n",
-       " '../data/dataon/Korea_PM_20201210.csv',\n",
-       " '../data/dataon/Korea_PM_20201211.csv',\n",
-       " '../data/dataon/Korea_PM_20201212.csv',\n",
-       " '../data/dataon/Korea_PM_20201213.csv',\n",
-       " '../data/dataon/Korea_PM_20201214.csv',\n",
-       " '../data/dataon/Korea_PM_20201215.csv',\n",
-       " '../data/dataon/Korea_PM_20201216.csv',\n",
-       " '../data/dataon/Korea_PM_20201217.csv',\n",
-       " '../data/dataon/Korea_PM_20201218.csv',\n",
-       " '../data/dataon/Korea_PM_20201219.csv',\n",
-       " '../data/dataon/Korea_PM_20201220.csv',\n",
-       " '../data/dataon/Korea_PM_20201221.csv',\n",
-       " '../data/dataon/Korea_PM_20201222.csv',\n",
-       " '../data/dataon/Korea_PM_20201223.csv',\n",
-       " '../data/dataon/Korea_PM_20201224.csv',\n",
-       " '../data/dataon/Korea_PM_20201225.csv',\n",
-       " '../data/dataon/Korea_PM_20201226.csv',\n",
-       " '../data/dataon/Korea_PM_20201227.csv',\n",
-       " '../data/dataon/Korea_PM_20201228.csv',\n",
-       " '../data/dataon/Korea_PM_20201229.csv',\n",
-       " '../data/dataon/Korea_PM_20201230.csv',\n",
-       " '../data/dataon/Korea_PM_20201231.csv',\n",
-       " '../data/dataon/Korea_PM_20210101.csv',\n",
-       " '../data/dataon/Korea_PM_20210102.csv',\n",
-       " '../data/dataon/Korea_PM_20210103.csv',\n",
-       " '../data/dataon/Korea_PM_20210104.csv',\n",
-       " '../data/dataon/Korea_PM_20210105.csv',\n",
-       " '../data/dataon/Korea_PM_20210106.csv',\n",
-       " '../data/dataon/Korea_PM_20210107.csv',\n",
-       " '../data/dataon/Korea_PM_20210108.csv',\n",
-       " '../data/dataon/Korea_PM_20210109.csv',\n",
-       " '../data/dataon/Korea_PM_20210110.csv',\n",
-       " '../data/dataon/Korea_PM_20210111.csv',\n",
-       " '../data/dataon/Korea_PM_20210112.csv',\n",
-       " '../data/dataon/Korea_PM_20210113.csv',\n",
-       " '../data/dataon/Korea_PM_20210114.csv',\n",
-       " '../data/dataon/Korea_PM_20210115.csv',\n",
-       " '../data/dataon/Korea_PM_20210116.csv',\n",
-       " '../data/dataon/Korea_PM_20210117.csv',\n",
-       " '../data/dataon/Korea_PM_20210118.csv',\n",
-       " '../data/dataon/Korea_PM_20210119.csv',\n",
-       " '../data/dataon/Korea_PM_20210120.csv',\n",
-       " '../data/dataon/Korea_PM_20210121.csv',\n",
-       " '../data/dataon/Korea_PM_20210122.csv',\n",
-       " '../data/dataon/Korea_PM_20210123.csv',\n",
-       " '../data/dataon/Korea_PM_20210124.csv',\n",
-       " '../data/dataon/Korea_PM_20210125.csv',\n",
-       " '../data/dataon/Korea_PM_20210126.csv',\n",
-       " '../data/dataon/Korea_PM_20210127.csv',\n",
-       " '../data/dataon/Korea_PM_20210128.csv',\n",
-       " ...]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_files # 데이터가 오름차순으로 정렬되어 있음"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm import tqdm, trange, notebook"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1427/1427 [00:30<00:00, 46.43it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 여러 파일을 하나의 데이터프레임으로 합치기\n",
-    "df_list = []\n",
-    "for file in tqdm(all_files):\n",
-    "    df = pd.read_csv(file)\n",
-    "    df_list.append(df)\n",
-    "\n",
-    "# 합친 데이터프레임\n",
-    "merged_df = pd.concat(df_list, axis=0)\n",
-    "merged_df.sort_values(by=['측정소코드', '측정일시'], inplace=True) # 측정소코드, 측정일시 기준으로 정렬\n",
-    "merged_df = merged_df.reset_index(drop=True).copy()\n",
-    "# 합친 데이터를 새로운 파일로 저장\n",
-    "merged_df.to_feather(\"../data/대기질/air_dataon_merged.feather\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 에어코리아에서 제공해주는 데이터와 DataON에서 제공해주는 데이터의 차이는 위도,경도 데이터의 유무 말고는 모두 같음\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>망</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>111121.0</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 01:00</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>111121.0</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>111121.0</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>111121.0</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>111121.0</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 05:00</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110172</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495.0</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 20:00</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110173</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495.0</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 21:00</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110174</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495.0</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 22:00</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110175</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495.0</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 23:00</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110176</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495.0</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-06-01 00:00</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>17110177 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역       망     측정소코드 측정소명              측정일시  PM10  PM25  \\\n",
-       "0          서울 중구     NaN  111121.0   중구  2018-01-01 01:00  34.0  19.0   \n",
-       "1          서울 중구     NaN  111121.0   중구  2018-01-01 02:00  27.0  14.0   \n",
-       "2          서울 중구     NaN  111121.0   중구  2018-01-01 03:00  26.0  14.0   \n",
-       "3          서울 중구     NaN  111121.0   중구  2018-01-01 04:00  26.0  15.0   \n",
-       "4          서울 중구     NaN  111121.0   중구  2018-01-01 05:00  28.0  16.0   \n",
-       "...          ...     ...       ...  ...               ...   ...   ...   \n",
-       "17110172  인천 옹진군  국가배경농도  831495.0   울도  2022-05-31 20:00  24.0   6.0   \n",
-       "17110173  인천 옹진군  국가배경농도  831495.0   울도  2022-05-31 21:00  22.0   7.0   \n",
-       "17110174  인천 옹진군  국가배경농도  831495.0   울도  2022-05-31 22:00  24.0   6.0   \n",
-       "17110175  인천 옹진군  국가배경농도  831495.0   울도  2022-05-31 23:00  21.0   6.0   \n",
-       "17110176  인천 옹진군  국가배경농도  831495.0   울도  2022-06-01 00:00  24.0   8.0   \n",
-       "\n",
-       "                           주소       위도        경도  \n",
-       "0               서울 중구 덕수궁길 15  37.5643  126.9747  \n",
-       "1               서울 중구 덕수궁길 15  37.5643  126.9747  \n",
-       "2               서울 중구 덕수궁길 15  37.5643  126.9747  \n",
-       "3               서울 중구 덕수궁길 15  37.5643  126.9747  \n",
-       "4               서울 중구 덕수궁길 15  37.5643  126.9747  \n",
-       "...                       ...      ...       ...  \n",
-       "17110172  인천 옹진군 덕적면 울도리 85번지   0.0000    0.0000  \n",
-       "17110173  인천 옹진군 덕적면 울도리 85번지   0.0000    0.0000  \n",
-       "17110174  인천 옹진군 덕적면 울도리 85번지   0.0000    0.0000  \n",
-       "17110175  인천 옹진군 덕적면 울도리 85번지   0.0000    0.0000  \n",
-       "17110176  인천 옹진군 덕적면 울도리 85번지   0.0000    0.0000  \n",
-       "\n",
-       "[17110177 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "merged_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 커널 재시작"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import gc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "23"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gc.collect()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air_dataon_merged = pd.read_feather(\"../data/대기질/air_dataon_merged.feather\") # 2018~2023 대기질 데이터 불러오기"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air= pd.read_feather(\"../data/대기질/air.feather\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>망</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>None</td>\n",
-       "      <td>111121.0</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 01:00</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>None</td>\n",
-       "      <td>111121.0</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>None</td>\n",
-       "      <td>111121.0</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>None</td>\n",
-       "      <td>111121.0</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>None</td>\n",
-       "      <td>111121.0</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 05:00</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110172</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495.0</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 20:00</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110173</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495.0</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 21:00</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110174</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495.0</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 22:00</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110175</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495.0</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 23:00</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110176</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>국가배경농도</td>\n",
-       "      <td>831495.0</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-06-01 00:00</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>17110177 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역       망     측정소코드 측정소명              측정일시  PM10  PM25  \\\n",
-       "0          서울 중구    None  111121.0   중구  2018-01-01 01:00  34.0  19.0   \n",
-       "1          서울 중구    None  111121.0   중구  2018-01-01 02:00  27.0  14.0   \n",
-       "2          서울 중구    None  111121.0   중구  2018-01-01 03:00  26.0  14.0   \n",
-       "3          서울 중구    None  111121.0   중구  2018-01-01 04:00  26.0  15.0   \n",
-       "4          서울 중구    None  111121.0   중구  2018-01-01 05:00  28.0  16.0   \n",
-       "...          ...     ...       ...  ...               ...   ...   ...   \n",
-       "17110172  인천 옹진군  국가배경농도  831495.0   울도  2022-05-31 20:00  24.0   6.0   \n",
-       "17110173  인천 옹진군  국가배경농도  831495.0   울도  2022-05-31 21:00  22.0   7.0   \n",
-       "17110174  인천 옹진군  국가배경농도  831495.0   울도  2022-05-31 22:00  24.0   6.0   \n",
-       "17110175  인천 옹진군  국가배경농도  831495.0   울도  2022-05-31 23:00  21.0   6.0   \n",
-       "17110176  인천 옹진군  국가배경농도  831495.0   울도  2022-06-01 00:00  24.0   8.0   \n",
-       "\n",
-       "                           주소       위도        경도  \n",
-       "0               서울 중구 덕수궁길 15  37.5643  126.9747  \n",
-       "1               서울 중구 덕수궁길 15  37.5643  126.9747  \n",
-       "2               서울 중구 덕수궁길 15  37.5643  126.9747  \n",
-       "3               서울 중구 덕수궁길 15  37.5643  126.9747  \n",
-       "4               서울 중구 덕수궁길 15  37.5643  126.9747  \n",
-       "...                       ...      ...       ...  \n",
-       "17110172  인천 옹진군 덕적면 울도리 85번지   0.0000    0.0000  \n",
-       "17110173  인천 옹진군 덕적면 울도리 85번지   0.0000    0.0000  \n",
-       "17110174  인천 옹진군 덕적면 울도리 85번지   0.0000    0.0000  \n",
-       "17110175  인천 옹진군 덕적면 울도리 85번지   0.0000    0.0000  \n",
-       "17110176  인천 옹진군 덕적면 울도리 85번지   0.0000    0.0000  \n",
-       "\n",
-       "[17110177 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air_dataon_merged"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "지역         24528\n",
-       "망        1566691\n",
-       "측정소코드          0\n",
-       "측정소명           0\n",
-       "측정일시           0\n",
-       "PM10     1034549\n",
-       "PM25     1466484\n",
-       "주소             0\n",
-       "위도        487634\n",
-       "경도        487634\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(air_dataon_merged).sum() # na값 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air_dataon_merged.drop(['망','지역'], axis=1, inplace=True) # 망 컬럼 삭제"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "측정소코드          0\n",
-       "측정소명           0\n",
-       "측정일시           0\n",
-       "PM10     1034549\n",
-       "PM25     1466484\n",
-       "주소             0\n",
-       "위도        487634\n",
-       "경도        487634\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(air_dataon_merged).sum() # na값 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "101"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gc.collect()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.0130</td>\n",
-       "      <td>0.0280</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 01:00:00</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00:00</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00:00</td>\n",
-       "      <td>0.0180</td>\n",
-       "      <td>0.0220</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00:00</td>\n",
-       "      <td>0.0100</td>\n",
-       "      <td>0.0300</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178033</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 19:00:00</td>\n",
-       "      <td>0.0440</td>\n",
-       "      <td>0.0075</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178034</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 20:00:00</td>\n",
-       "      <td>0.0415</td>\n",
-       "      <td>0.0086</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178035</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 21:00:00</td>\n",
-       "      <td>0.0409</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178036</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 22:00:00</td>\n",
-       "      <td>0.0414</td>\n",
-       "      <td>0.0111</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178037</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 23:00:00</td>\n",
-       "      <td>0.0411</td>\n",
-       "      <td>0.0112</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28178038 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역   측정소코드 측정소명                측정일시      O3     NO2  PM10  PM25  \\\n",
-       "0          서울 중구  111121   중구 2018-01-01 00:00:00  0.0130  0.0280  31.0  16.0   \n",
-       "1          서울 중구  111121   중구 2018-01-01 01:00:00  0.0200  0.0200  34.0  19.0   \n",
-       "2          서울 중구  111121   중구 2018-01-01 02:00:00  0.0240  0.0160  27.0  14.0   \n",
-       "3          서울 중구  111121   중구 2018-01-01 03:00:00  0.0180  0.0220  26.0  14.0   \n",
-       "4          서울 중구  111121   중구 2018-01-01 04:00:00  0.0100  0.0300  26.0  15.0   \n",
-       "...          ...     ...  ...                 ...     ...     ...   ...   ...   \n",
-       "28178033  인천 옹진군  831495   울도 2023-12-31 19:00:00  0.0440  0.0075  67.0  57.0   \n",
-       "28178034  인천 옹진군  831495   울도 2023-12-31 20:00:00  0.0415  0.0086  64.0  57.0   \n",
-       "28178035  인천 옹진군  831495   울도 2023-12-31 21:00:00  0.0409  0.0095  62.0  50.0   \n",
-       "28178036  인천 옹진군  831495   울도 2023-12-31 22:00:00  0.0414  0.0111  65.0  59.0   \n",
-       "28178037  인천 옹진군  831495   울도 2023-12-31 23:00:00  0.0411  0.0112  63.0  54.0   \n",
-       "\n",
-       "                           주소  year  month  day  hour  \n",
-       "0               서울 중구 덕수궁길 15  2018      1    1     0  \n",
-       "1               서울 중구 덕수궁길 15  2018      1    1     1  \n",
-       "2               서울 중구 덕수궁길 15  2018      1    1     2  \n",
-       "3               서울 중구 덕수궁길 15  2018      1    1     3  \n",
-       "4               서울 중구 덕수궁길 15  2018      1    1     4  \n",
-       "...                       ...   ...    ...  ...   ...  \n",
-       "28178033  인천 옹진군 덕적면 울도리 85번지  2023     12   31    19  \n",
-       "28178034  인천 옹진군 덕적면 울도리 85번지  2023     12   31    20  \n",
-       "28178035  인천 옹진군 덕적면 울도리 85번지  2023     12   31    21  \n",
-       "28178036  인천 옹진군 덕적면 울도리 85번지  2023     12   31    22  \n",
-       "28178037  인천 옹진군 덕적면 울도리 85번지  2023     12   31    23  \n",
-       "\n",
-       "[28178038 rows x 13 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air_dataon_merged['측정소코드'] = air_dataon_merged['측정소코드'].astype(\"int\") # 측정소코드 데이터 타입 변경"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 01:00</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 05:00</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110172</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 20:00</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110173</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 21:00</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110174</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 22:00</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110175</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 23:00</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110176</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-06-01 00:00</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>17110177 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           측정소코드 측정소명              측정일시  PM10  PM25                   주소  \\\n",
-       "0         111121   중구  2018-01-01 01:00  34.0  19.0        서울 중구 덕수궁길 15   \n",
-       "1         111121   중구  2018-01-01 02:00  27.0  14.0        서울 중구 덕수궁길 15   \n",
-       "2         111121   중구  2018-01-01 03:00  26.0  14.0        서울 중구 덕수궁길 15   \n",
-       "3         111121   중구  2018-01-01 04:00  26.0  15.0        서울 중구 덕수궁길 15   \n",
-       "4         111121   중구  2018-01-01 05:00  28.0  16.0        서울 중구 덕수궁길 15   \n",
-       "...          ...  ...               ...   ...   ...                  ...   \n",
-       "17110172  831495   울도  2022-05-31 20:00  24.0   6.0  인천 옹진군 덕적면 울도리 85번지   \n",
-       "17110173  831495   울도  2022-05-31 21:00  22.0   7.0  인천 옹진군 덕적면 울도리 85번지   \n",
-       "17110174  831495   울도  2022-05-31 22:00  24.0   6.0  인천 옹진군 덕적면 울도리 85번지   \n",
-       "17110175  831495   울도  2022-05-31 23:00  21.0   6.0  인천 옹진군 덕적면 울도리 85번지   \n",
-       "17110176  831495   울도  2022-06-01 00:00  24.0   8.0  인천 옹진군 덕적면 울도리 85번지   \n",
-       "\n",
-       "               위도        경도  \n",
-       "0         37.5643  126.9747  \n",
-       "1         37.5643  126.9747  \n",
-       "2         37.5643  126.9747  \n",
-       "3         37.5643  126.9747  \n",
-       "4         37.5643  126.9747  \n",
-       "...           ...       ...  \n",
-       "17110172   0.0000    0.0000  \n",
-       "17110173   0.0000    0.0000  \n",
-       "17110174   0.0000    0.0000  \n",
-       "17110175   0.0000    0.0000  \n",
-       "17110176   0.0000    0.0000  \n",
-       "\n",
-       "[17110177 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air_dataon_merged"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "637"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(air_dataon_merged['측정소코드'].unique())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "660"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(air['측정소코드'].unique())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 660/660 [00:46<00:00, 14.14it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tqdm import tqdm, notebook, trange\n",
-    "count= 0\n",
-    "match= []\n",
-    "for i in tqdm(air['측정소코드'].unique()):\n",
-    "    for j in air_dataon_merged['측정소코드'].unique():\n",
-    "        if i == j:\n",
-    "            count+=1 # air 측정소코드와 air_dataon_merged 측정소코드가 일치하면 count 증가\n",
-    "            match.append(i)\n",
-    "            break # 매칭되면 다음 측정소명으로 넘어감"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "637"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "count # 매칭된 측정소명 개수 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "637"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(match)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "69"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "del([count, match])\n",
-    "gc.collect()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "637"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(air_dataon_merged['측정소코드'].unique())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "660"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(air['측정소코드'].unique()) # air 측정소코드 종류의 개수와 air_dataon_merged 측정소코드 종류의 개수가 다름 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([131133, 131235, 131236, 131245, 131246, 131247, 131345, 131346,\n",
-       "       131374, 131394, 131395, 131444, 131475, 131542, 131556, 131557,\n",
-       "       131558, 131583, 131584, 131592, 131593, 131612, 131622, 131991,\n",
-       "       132118, 132119, 132991, 132992, 132993, 132994, 221142, 221183,\n",
-       "       221193, 221213, 221253, 238113, 238134, 238203, 238212, 238363,\n",
-       "       238377, 238378, 238379, 324125, 324136, 324143, 336442, 336462,\n",
-       "       336522, 336523, 336572, 339113, 339114, 422158, 422206, 422207,\n",
-       "       422208, 422209, 437133, 437156, 437172, 437202, 437203, 437211,\n",
-       "       437221, 437412, 437561, 437571, 437581, 437591, 525144, 534343,\n",
-       "       534446, 534452, 534453, 534464, 534484, 534503, 633124, 633125,\n",
-       "       633132, 633133, 633215, 633312, 633313, 633363, 633412, 633463,\n",
-       "       735117, 735124, 735125, 735126, 735131, 735135, 735136, 735143,\n",
-       "       735173, 735192, 735193, 735362, 735601, 823612, 823613, 823636,\n",
-       "       823637, 823653, 823672, 823707, 831494, 831495])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air_dataon_merged.loc[air_dataon_merged['위도']==0, '측정소코드'].unique() # 위도가 0인 데이터 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([131133, 131235, 131236, 131245, 131246, 131247, 131345, 131346,\n",
-       "       131374, 131394, 131395, 131444, 131475, 131542, 131556, 131557,\n",
-       "       131558, 131583, 131584, 131592, 131593, 131612, 131622, 131991,\n",
-       "       132118, 132119, 132991, 132992, 132993, 132994, 221142, 221183,\n",
-       "       221193, 221213, 221253, 238113, 238134, 238203, 238212, 238363,\n",
-       "       238377, 238378, 238379, 324125, 324136, 324143, 336442, 336462,\n",
-       "       336522, 336523, 336572, 339113, 339114, 422158, 422206, 422207,\n",
-       "       422208, 422209, 437133, 437156, 437172, 437202, 437203, 437211,\n",
-       "       437221, 437412, 437561, 437571, 437581, 437591, 525144, 534343,\n",
-       "       534446, 534452, 534453, 534464, 534484, 534503, 633124, 633125,\n",
-       "       633132, 633133, 633215, 633312, 633313, 633363, 633412, 633463,\n",
-       "       735117, 735124, 735125, 735126, 735131, 735135, 735136, 735143,\n",
-       "       735173, 735192, 735193, 735362, 735601, 823612, 823613, 823636,\n",
-       "       823637, 823653, 823672, 823707, 831494, 831495])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air_dataon_merged.loc[air_dataon_merged['경도']==0, '측정소코드'].unique() # 경도가 0인 데이터 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(110,)"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 위도와 경도가 0인 측정소명 확인\n",
-    "air_dataon_merged.loc[(air_dataon_merged['위도']==0) & (air_dataon_merged['경도'] == 0), '측정소코드'].unique().shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "측정소코드          0\n",
-       "측정소명           0\n",
-       "측정일시           0\n",
-       "PM10     1034549\n",
-       "PM25     1466484\n",
-       "주소             0\n",
-       "위도        487634\n",
-       "경도        487634\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(air_dataon_merged).sum() # na값 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "air_dataon_merged.loc[air_dataon_merged['위도']==0, '위도']= np.nan # 위도가 0인 값 na로 변경\n",
-    "air_dataon_merged.loc[air_dataon_merged['경도']==0, '경도']= np.nan # 경도가 0인 값 na로 변경"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 01:00</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 05:00</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110172</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 20:00</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110173</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 21:00</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110174</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 22:00</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110175</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-05-31 23:00</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17110176</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2022-06-01 00:00</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>17110177 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           측정소코드 측정소명              측정일시  PM10  PM25                   주소  \\\n",
-       "0         111121   중구  2018-01-01 01:00  34.0  19.0        서울 중구 덕수궁길 15   \n",
-       "1         111121   중구  2018-01-01 02:00  27.0  14.0        서울 중구 덕수궁길 15   \n",
-       "2         111121   중구  2018-01-01 03:00  26.0  14.0        서울 중구 덕수궁길 15   \n",
-       "3         111121   중구  2018-01-01 04:00  26.0  15.0        서울 중구 덕수궁길 15   \n",
-       "4         111121   중구  2018-01-01 05:00  28.0  16.0        서울 중구 덕수궁길 15   \n",
-       "...          ...  ...               ...   ...   ...                  ...   \n",
-       "17110172  831495   울도  2022-05-31 20:00  24.0   6.0  인천 옹진군 덕적면 울도리 85번지   \n",
-       "17110173  831495   울도  2022-05-31 21:00  22.0   7.0  인천 옹진군 덕적면 울도리 85번지   \n",
-       "17110174  831495   울도  2022-05-31 22:00  24.0   6.0  인천 옹진군 덕적면 울도리 85번지   \n",
-       "17110175  831495   울도  2022-05-31 23:00  21.0   6.0  인천 옹진군 덕적면 울도리 85번지   \n",
-       "17110176  831495   울도  2022-06-01 00:00  24.0   8.0  인천 옹진군 덕적면 울도리 85번지   \n",
-       "\n",
-       "               위도        경도  \n",
-       "0         37.5643  126.9747  \n",
-       "1         37.5643  126.9747  \n",
-       "2         37.5643  126.9747  \n",
-       "3         37.5643  126.9747  \n",
-       "4         37.5643  126.9747  \n",
-       "...           ...       ...  \n",
-       "17110172      NaN       NaN  \n",
-       "17110173      NaN       NaN  \n",
-       "17110174      NaN       NaN  \n",
-       "17110175      NaN       NaN  \n",
-       "17110176      NaN       NaN  \n",
-       "\n",
-       "[17110177 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air_dataon_merged"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "측정소코드          0\n",
-       "측정소명           0\n",
-       "측정일시           0\n",
-       "PM10     1034549\n",
-       "PM25     1466484\n",
-       "주소             0\n",
-       "위도       2104122\n",
-       "경도       2104122\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(air_dataon_merged).sum() # na값 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air_dataon_merged.dropna(subset=['위도', '경도'], inplace=True) # 위도, 경도 na값 제거"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
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-       "      <th>17083800</th>\n",
-       "      <td>831493</td>\n",
-       "      <td>영흥</td>\n",
-       "      <td>2022-05-31 20:00</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>인천광역시 옹진군 영흥면 영흥로251번길 90</td>\n",
-       "      <td>37.2560</td>\n",
-       "      <td>126.4835</td>\n",
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-       "      <th>17083801</th>\n",
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-       "      <td>2022-05-31 21:00</td>\n",
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-       "      <td>8.0</td>\n",
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-       "      <td>831493</td>\n",
-       "      <td>영흥</td>\n",
-       "      <td>2022-05-31 22:00</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>인천광역시 옹진군 영흥면 영흥로251번길 90</td>\n",
-       "      <td>37.2560</td>\n",
-       "      <td>126.4835</td>\n",
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-       "      <th>17083803</th>\n",
-       "      <td>831493</td>\n",
-       "      <td>영흥</td>\n",
-       "      <td>2022-05-31 23:00</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>인천광역시 옹진군 영흥면 영흥로251번길 90</td>\n",
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-       "      <th>17083804</th>\n",
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-       "      <td>7.0</td>\n",
-       "      <td>인천광역시 옹진군 영흥면 영흥로251번길 90</td>\n",
-       "      <td>37.2560</td>\n",
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-      "text/plain": [
-       "           측정소코드 측정소명              측정일시  PM10  PM25  \\\n",
-       "0         111121   중구  2018-01-01 01:00  34.0  19.0   \n",
-       "1         111121   중구  2018-01-01 02:00  27.0  14.0   \n",
-       "2         111121   중구  2018-01-01 03:00  26.0  14.0   \n",
-       "3         111121   중구  2018-01-01 04:00  26.0  15.0   \n",
-       "4         111121   중구  2018-01-01 05:00  28.0  16.0   \n",
-       "...          ...  ...               ...   ...   ...   \n",
-       "17083800  831493   영흥  2022-05-31 20:00  25.0   5.0   \n",
-       "17083801  831493   영흥  2022-05-31 21:00  22.0   8.0   \n",
-       "17083802  831493   영흥  2022-05-31 22:00  25.0  11.0   \n",
-       "17083803  831493   영흥  2022-05-31 23:00  22.0   6.0   \n",
-       "17083804  831493   영흥  2022-06-01 00:00  20.0   7.0   \n",
-       "\n",
-       "                                 주소       위도        경도  \n",
-       "0                     서울 중구 덕수궁길 15  37.5643  126.9747  \n",
-       "1                     서울 중구 덕수궁길 15  37.5643  126.9747  \n",
-       "2                     서울 중구 덕수궁길 15  37.5643  126.9747  \n",
-       "3                     서울 중구 덕수궁길 15  37.5643  126.9747  \n",
-       "4                     서울 중구 덕수궁길 15  37.5643  126.9747  \n",
-       "...                             ...      ...       ...  \n",
-       "17083800  인천광역시 옹진군 영흥면 영흥로251번길 90  37.2560  126.4835  \n",
-       "17083801  인천광역시 옹진군 영흥면 영흥로251번길 90  37.2560  126.4835  \n",
-       "17083802  인천광역시 옹진군 영흥면 영흥로251번길 90  37.2560  126.4835  \n",
-       "17083803  인천광역시 옹진군 영흥면 영흥로251번길 90  37.2560  126.4835  \n",
-       "17083804  인천광역시 옹진군 영흥면 영흥로251번길 90  37.2560  126.4835  \n",
-       "\n",
-       "[15006055 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air_dataon_merged"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
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-       "      <th>지역</th>\n",
-       "      <th>측정소코드</th>\n",
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-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.0130</td>\n",
-       "      <td>0.0280</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 01:00:00</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00:00</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00:00</td>\n",
-       "      <td>0.0180</td>\n",
-       "      <td>0.0220</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00:00</td>\n",
-       "      <td>0.0100</td>\n",
-       "      <td>0.0300</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178033</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 19:00:00</td>\n",
-       "      <td>0.0440</td>\n",
-       "      <td>0.0075</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178034</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 20:00:00</td>\n",
-       "      <td>0.0415</td>\n",
-       "      <td>0.0086</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178035</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 21:00:00</td>\n",
-       "      <td>0.0409</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178036</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 22:00:00</td>\n",
-       "      <td>0.0414</td>\n",
-       "      <td>0.0111</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178037</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 23:00:00</td>\n",
-       "      <td>0.0411</td>\n",
-       "      <td>0.0112</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28178038 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역   측정소코드 측정소명                측정일시      O3     NO2  PM10  PM25  \\\n",
-       "0          서울 중구  111121   중구 2018-01-01 00:00:00  0.0130  0.0280  31.0  16.0   \n",
-       "1          서울 중구  111121   중구 2018-01-01 01:00:00  0.0200  0.0200  34.0  19.0   \n",
-       "2          서울 중구  111121   중구 2018-01-01 02:00:00  0.0240  0.0160  27.0  14.0   \n",
-       "3          서울 중구  111121   중구 2018-01-01 03:00:00  0.0180  0.0220  26.0  14.0   \n",
-       "4          서울 중구  111121   중구 2018-01-01 04:00:00  0.0100  0.0300  26.0  15.0   \n",
-       "...          ...     ...  ...                 ...     ...     ...   ...   ...   \n",
-       "28178033  인천 옹진군  831495   울도 2023-12-31 19:00:00  0.0440  0.0075  67.0  57.0   \n",
-       "28178034  인천 옹진군  831495   울도 2023-12-31 20:00:00  0.0415  0.0086  64.0  57.0   \n",
-       "28178035  인천 옹진군  831495   울도 2023-12-31 21:00:00  0.0409  0.0095  62.0  50.0   \n",
-       "28178036  인천 옹진군  831495   울도 2023-12-31 22:00:00  0.0414  0.0111  65.0  59.0   \n",
-       "28178037  인천 옹진군  831495   울도 2023-12-31 23:00:00  0.0411  0.0112  63.0  54.0   \n",
-       "\n",
-       "                           주소  year  month  day  hour  \n",
-       "0               서울 중구 덕수궁길 15  2018      1    1     0  \n",
-       "1               서울 중구 덕수궁길 15  2018      1    1     1  \n",
-       "2               서울 중구 덕수궁길 15  2018      1    1     2  \n",
-       "3               서울 중구 덕수궁길 15  2018      1    1     3  \n",
-       "4               서울 중구 덕수궁길 15  2018      1    1     4  \n",
-       "...                       ...   ...    ...  ...   ...  \n",
-       "28178033  인천 옹진군 덕적면 울도리 85번지  2023     12   31    19  \n",
-       "28178034  인천 옹진군 덕적면 울도리 85번지  2023     12   31    20  \n",
-       "28178035  인천 옹진군 덕적면 울도리 85번지  2023     12   31    21  \n",
-       "28178036  인천 옹진군 덕적면 울도리 85번지  2023     12   31    22  \n",
-       "28178037  인천 옹진군 덕적면 울도리 85번지  2023     12   31    23  \n",
-       "\n",
-       "[28178038 rows x 13 columns]"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "660"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(air['측정소코드'].unique()) # 측정소코드 종류의 개수"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "472"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(air_dataon_merged['측정소코드'].unique()) # air 측정소코드와 air_dataon_merged 측정소코드 개수가 다름"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.0130</td>\n",
-       "      <td>0.0280</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 01:00:00</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00:00</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00:00</td>\n",
-       "      <td>0.0180</td>\n",
-       "      <td>0.0220</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00:00</td>\n",
-       "      <td>0.0100</td>\n",
-       "      <td>0.0300</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178033</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 19:00:00</td>\n",
-       "      <td>0.0440</td>\n",
-       "      <td>0.0075</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178034</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 20:00:00</td>\n",
-       "      <td>0.0415</td>\n",
-       "      <td>0.0086</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178035</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 21:00:00</td>\n",
-       "      <td>0.0409</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178036</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 22:00:00</td>\n",
-       "      <td>0.0414</td>\n",
-       "      <td>0.0111</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178037</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 23:00:00</td>\n",
-       "      <td>0.0411</td>\n",
-       "      <td>0.0112</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28178038 rows × 15 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역   측정소코드 측정소명                측정일시      O3     NO2  PM10  PM25  \\\n",
-       "0          서울 중구  111121   중구 2018-01-01 00:00:00  0.0130  0.0280  31.0  16.0   \n",
-       "1          서울 중구  111121   중구 2018-01-01 01:00:00  0.0200  0.0200  34.0  19.0   \n",
-       "2          서울 중구  111121   중구 2018-01-01 02:00:00  0.0240  0.0160  27.0  14.0   \n",
-       "3          서울 중구  111121   중구 2018-01-01 03:00:00  0.0180  0.0220  26.0  14.0   \n",
-       "4          서울 중구  111121   중구 2018-01-01 04:00:00  0.0100  0.0300  26.0  15.0   \n",
-       "...          ...     ...  ...                 ...     ...     ...   ...   ...   \n",
-       "28178033  인천 옹진군  831495   울도 2023-12-31 19:00:00  0.0440  0.0075  67.0  57.0   \n",
-       "28178034  인천 옹진군  831495   울도 2023-12-31 20:00:00  0.0415  0.0086  64.0  57.0   \n",
-       "28178035  인천 옹진군  831495   울도 2023-12-31 21:00:00  0.0409  0.0095  62.0  50.0   \n",
-       "28178036  인천 옹진군  831495   울도 2023-12-31 22:00:00  0.0414  0.0111  65.0  59.0   \n",
-       "28178037  인천 옹진군  831495   울도 2023-12-31 23:00:00  0.0411  0.0112  63.0  54.0   \n",
-       "\n",
-       "                           주소  year  month  day  hour  위도  경도  \n",
-       "0               서울 중구 덕수궁길 15  2018      1    1     0 NaN NaN  \n",
-       "1               서울 중구 덕수궁길 15  2018      1    1     1 NaN NaN  \n",
-       "2               서울 중구 덕수궁길 15  2018      1    1     2 NaN NaN  \n",
-       "3               서울 중구 덕수궁길 15  2018      1    1     3 NaN NaN  \n",
-       "4               서울 중구 덕수궁길 15  2018      1    1     4 NaN NaN  \n",
-       "...                       ...   ...    ...  ...   ...  ..  ..  \n",
-       "28178033  인천 옹진군 덕적면 울도리 85번지  2023     12   31    19 NaN NaN  \n",
-       "28178034  인천 옹진군 덕적면 울도리 85번지  2023     12   31    20 NaN NaN  \n",
-       "28178035  인천 옹진군 덕적면 울도리 85번지  2023     12   31    21 NaN NaN  \n",
-       "28178036  인천 옹진군 덕적면 울도리 85번지  2023     12   31    22 NaN NaN  \n",
-       "28178037  인천 옹진군 덕적면 울도리 85번지  2023     12   31    23 NaN NaN  \n",
-       "\n",
-       "[28178038 rows x 15 columns]"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air['위도'] = np.nan # 위도 컬럼 생성\n",
-    "air['경도'] = np.nan # 경도 컬럼 생성\n",
-    "air"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "115"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gc.collect()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "23"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gc.collect()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "지역              0\n",
-       "측정소코드           0\n",
-       "측정소명            0\n",
-       "측정일시            0\n",
-       "O3        1152577\n",
-       "NO2       1172756\n",
-       "PM10      1441888\n",
-       "PM25      1970684\n",
-       "주소              0\n",
-       "year            0\n",
-       "month           0\n",
-       "day             0\n",
-       "hour            0\n",
-       "위도       28178038\n",
-       "경도       28178038\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(air).sum() # na값 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 472/472 [06:36<00:00,  1.19it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# dataon에서 얻은 데이터를 기반으로 에어코리아에서 다운받은 air 데이터에 위도와 경도를 추가\n",
-    "\n",
-    "for i in tqdm(air_dataon_merged['측정소코드'].unique()):\n",
-    "    for j in air['측정소코드'].unique():\n",
-    "        if i == j:\n",
-    "            air.loc[air['측정소코드']==j, '위도']= air_dataon_merged.loc[air_dataon_merged['측정소코드']==i, '위도'].values[0]\n",
-    "            air.loc[air['측정소코드']==j, '경도']= air_dataon_merged.loc[air_dataon_merged['측정소코드']==i, '경도'].values[0]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.0130</td>\n",
-       "      <td>0.0280</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 01:00:00</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00:00</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00:00</td>\n",
-       "      <td>0.0180</td>\n",
-       "      <td>0.0220</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00:00</td>\n",
-       "      <td>0.0100</td>\n",
-       "      <td>0.0300</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178033</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 19:00:00</td>\n",
-       "      <td>0.0440</td>\n",
-       "      <td>0.0075</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178034</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 20:00:00</td>\n",
-       "      <td>0.0415</td>\n",
-       "      <td>0.0086</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178035</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 21:00:00</td>\n",
-       "      <td>0.0409</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178036</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 22:00:00</td>\n",
-       "      <td>0.0414</td>\n",
-       "      <td>0.0111</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178037</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 23:00:00</td>\n",
-       "      <td>0.0411</td>\n",
-       "      <td>0.0112</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28178038 rows × 15 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역   측정소코드 측정소명                측정일시      O3     NO2  PM10  PM25  \\\n",
-       "0          서울 중구  111121   중구 2018-01-01 00:00:00  0.0130  0.0280  31.0  16.0   \n",
-       "1          서울 중구  111121   중구 2018-01-01 01:00:00  0.0200  0.0200  34.0  19.0   \n",
-       "2          서울 중구  111121   중구 2018-01-01 02:00:00  0.0240  0.0160  27.0  14.0   \n",
-       "3          서울 중구  111121   중구 2018-01-01 03:00:00  0.0180  0.0220  26.0  14.0   \n",
-       "4          서울 중구  111121   중구 2018-01-01 04:00:00  0.0100  0.0300  26.0  15.0   \n",
-       "...          ...     ...  ...                 ...     ...     ...   ...   ...   \n",
-       "28178033  인천 옹진군  831495   울도 2023-12-31 19:00:00  0.0440  0.0075  67.0  57.0   \n",
-       "28178034  인천 옹진군  831495   울도 2023-12-31 20:00:00  0.0415  0.0086  64.0  57.0   \n",
-       "28178035  인천 옹진군  831495   울도 2023-12-31 21:00:00  0.0409  0.0095  62.0  50.0   \n",
-       "28178036  인천 옹진군  831495   울도 2023-12-31 22:00:00  0.0414  0.0111  65.0  59.0   \n",
-       "28178037  인천 옹진군  831495   울도 2023-12-31 23:00:00  0.0411  0.0112  63.0  54.0   \n",
-       "\n",
-       "                           주소  year  month  day  hour       위도        경도  \n",
-       "0               서울 중구 덕수궁길 15  2018      1    1     0  37.5643  126.9747  \n",
-       "1               서울 중구 덕수궁길 15  2018      1    1     1  37.5643  126.9747  \n",
-       "2               서울 중구 덕수궁길 15  2018      1    1     2  37.5643  126.9747  \n",
-       "3               서울 중구 덕수궁길 15  2018      1    1     3  37.5643  126.9747  \n",
-       "4               서울 중구 덕수궁길 15  2018      1    1     4  37.5643  126.9747  \n",
-       "...                       ...   ...    ...  ...   ...      ...       ...  \n",
-       "28178033  인천 옹진군 덕적면 울도리 85번지  2023     12   31    19      NaN       NaN  \n",
-       "28178034  인천 옹진군 덕적면 울도리 85번지  2023     12   31    20      NaN       NaN  \n",
-       "28178035  인천 옹진군 덕적면 울도리 85번지  2023     12   31    21      NaN       NaN  \n",
-       "28178036  인천 옹진군 덕적면 울도리 85번지  2023     12   31    22      NaN       NaN  \n",
-       "28178037  인천 옹진군 덕적면 울도리 85번지  2023     12   31    23      NaN       NaN  \n",
-       "\n",
-       "[28178038 rows x 15 columns]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "37.6067    3\n",
-       "37.5945    2\n",
-       "35.6880    2\n",
-       "37.5027    2\n",
-       "37.5250    2\n",
-       "          ..\n",
-       "35.2115    1\n",
-       "37.7563    1\n",
-       "37.5849    1\n",
-       "37.7463    1\n",
-       "37.6520    1\n",
-       "Name: 위도, Length: 464, dtype: int64"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air.drop_duplicates(subset=['측정소코드'],inplace=False)['위도'].value_counts() # 위도 중복 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>631008</th>\n",
-       "      <td>서울 성북구</td>\n",
-       "      <td>111161</td>\n",
-       "      <td>성북구</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.023</td>\n",
-       "      <td>0.017</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>서울 성북구 삼양로2길 70</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>37.6067</td>\n",
-       "      <td>127.0273</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>683592</th>\n",
-       "      <td>서울 성북구</td>\n",
-       "      <td>111162</td>\n",
-       "      <td>정릉로</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.013</td>\n",
-       "      <td>0.036</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>서울 성북구 돈암동 8-164번지</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>37.6067</td>\n",
-       "      <td>127.0049</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6219350</th>\n",
-       "      <td>경기 김포시</td>\n",
-       "      <td>131472</td>\n",
-       "      <td>고촌읍</td>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>0.019</td>\n",
-       "      <td>0.015</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>경기 김포시 고촌읍 신곡로 152</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>37.6067</td>\n",
-       "      <td>126.7628</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             지역   측정소코드 측정소명       측정일시     O3    NO2  PM10  PM25  \\\n",
-       "631008   서울 성북구  111161  성북구 2018-01-01  0.023  0.017  30.0   9.0   \n",
-       "683592   서울 성북구  111162  정릉로 2018-01-01  0.013  0.036  44.0   NaN   \n",
-       "6219350  경기 김포시  131472  고촌읍 2018-01-01  0.019  0.015  42.0  17.0   \n",
-       "\n",
-       "                         주소  year  month  day  hour       위도        경도  \n",
-       "631008      서울 성북구 삼양로2길 70  2018      1    1     0  37.6067  127.0273  \n",
-       "683592   서울 성북구 돈암동 8-164번지  2018      1    1     0  37.6067  127.0049  \n",
-       "6219350  경기 김포시 고촌읍 신곡로 152  2018      1    1     0  37.6067  126.7628  "
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 위도가 겹치는 데이터 확인을 통해 혹시나 중복된 데이터가 있는지 확인\n",
-    "air.loc[air['위도']==37.6067,].drop_duplicates(subset=['측정소코드'],inplace=False) # 위도가 37.6067인 데이터 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([131119, 131133, 131235, 131236, 131237, 131245, 131246, 131247,\n",
-       "       131345, 131346, 131374, 131394, 131395, 131444, 131475, 131542,\n",
-       "       131556, 131557, 131558, 131583, 131584, 131592, 131593, 131602,\n",
-       "       131612, 131622, 131991, 132118, 132119, 132901, 132902, 132903,\n",
-       "       132991, 132992, 132993, 132994, 221142, 221174, 221183, 221184,\n",
-       "       221185, 221193, 221213, 221253, 221903, 221904, 221905, 221906,\n",
-       "       238113, 238134, 238147, 238162, 238185, 238203, 238212, 238363,\n",
-       "       238377, 238378, 238379, 238380, 238901, 238902, 238903, 238904,\n",
-       "       324125, 324136, 324143, 336357, 336358, 336442, 336462, 336522,\n",
-       "       336523, 336572, 336901, 336902, 336903, 336904, 336905, 339113,\n",
-       "       339114, 339116, 339117, 339124, 422112, 422158, 422206, 422207,\n",
-       "       422208, 422209, 422211, 422212, 437111, 437120, 437121, 437126,\n",
-       "       437133, 437156, 437162, 437172, 437173, 437202, 437203, 437211,\n",
-       "       437221, 437412, 437413, 437432, 437561, 437571, 437581, 437591,\n",
-       "       437901, 525144, 534343, 534435, 534436, 534446, 534448, 534452,\n",
-       "       534453, 534464, 534465, 534472, 534473, 534484, 534503, 534901,\n",
-       "       534902, 534903, 534904, 534905, 534906, 541115, 541116, 632126,\n",
-       "       632131, 632532, 633124, 633125, 633132, 633133, 633215, 633312,\n",
-       "       633313, 633363, 633412, 633463, 633472, 633482, 735117, 735118,\n",
-       "       735119, 735124, 735125, 735126, 735127, 735131, 735135, 735136,\n",
-       "       735137, 735138, 735139, 735143, 735152, 735173, 735182, 735192,\n",
-       "       735193, 735362, 735601, 735901, 823612, 823613, 823636, 823637,\n",
-       "       823653, 823663, 823664, 823672, 823707, 823901, 823902, 823903,\n",
-       "       823904, 823905, 831494, 831495])"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air.loc[pd.isna(air['위도']), '측정소코드'].unique() # 위도가 na인 측정소명 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "위도가 na인 측정소명 개수:  4627988\n"
-     ]
-    }
-   ],
-   "source": [
-    "# DataON 데이터에 존재하는 위도, 경도 데이터를 측정소 코드를 기준으로 에어코리아에서 다운로드 받은 데이터와 매칭하여 위도, 경도 데이터를 추가하였다.\n",
-    "# 그럼에도 불구하고 air 데이터에는 위도, 경도 데이터가 na로 표시된 데이터가 많다.\n",
-    "print(\"위도가 na인 측정소명 개수: \",air.loc[pd.isna(air['위도']), '측정소코드'].shape[0]) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "69"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gc.collect()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: [지역, 측정소코드, 측정소명, 측정일시, O3, NO2, PM10, PM25, 주소, year, month, day, hour, 위도, 경도]\n",
-       "Index: []"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 주소를 기반으로 위도와 경도를 추가할 예정이기 때문에 주소가 na인 데이터를 확인\n",
-    "na_address = air[air['주소'].isna() | (air['주소'] == '') | (air['주소']== \" \") | (air['주소']== \"  \")| (air['주소']== \"None\") | (air['주소']== \"NA\") | (air['주소']== \"0\") | (air['주소']== 0) | (air['주소']== \"na\") | (air['주소']== \"NONE\")] # 주소가 na인 데이터 확인\n",
-    "na_address "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "지역             0\n",
-       "측정소코드          0\n",
-       "측정소명           0\n",
-       "측정일시           0\n",
-       "O3       1152577\n",
-       "NO2      1172756\n",
-       "PM10     1441888\n",
-       "PM25     1970684\n",
-       "주소             0\n",
-       "year           0\n",
-       "month          0\n",
-       "day            0\n",
-       "hour           0\n",
-       "위도       4627988\n",
-       "경도       4627988\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(air).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "188"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(air.loc[pd.isna(air['위도']), '측정소명'].unique()) # 위도가 na인 측정소명 개수 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['호매실', '송산3동', '장현동', '서해안로', '배곧동', '경춘로', '와부읍', '진접읍', '청북읍',\n",
-       "       '고덕동', '파주읍', '오포1동', '곤지암', '부발읍', '한강로', '미사', '새솔동', '봉담읍',\n",
-       "       '서신면', '공도읍', '죽산면', '대신면', '가남읍', '전곡', '설악면', '양평읍', '연천(DMZ)',\n",
-       "       '신사우동', '온의동', '동해항', '묵호항', '호산항', '철원(DMZ)', '화천(DMZ)',\n",
-       "       '인제(DMZ)', '고성(DMZ)', '청학동', '용호동', '화명동', '삼락동', '우동', '재송동',\n",
-       "       '명지동', '회동동', '부산항', '부산신항', '부산북항', '부산감만', '내서읍', '정촌면', '삼진로',\n",
-       "       '금성면', '김해대로', '고현동', '향촌동', '물금읍', '웅촌면', '범서읍', '북부순환도로',\n",
-       "       '송정동 대기환경측정소', '송정동', '마산항', '울산항', '하동항', '삼천포항', '유촌동', '일곡동',\n",
-       "       '우산동(광주)', '봉강면', '진월면', '영암읍', '안마도', '홍도', '가거도', '보성읍', '목포항',\n",
-       "       '광양항', '여수항', '광양 중마', '광양 율촌', '조천읍', '한림읍', '화북동', '애월읍', '강정동',\n",
-       "       '남산1동', '산격동', '본동', '내당동', '침산동', '화원읍', '충혼탑', '군위읍', '우현동',\n",
-       "       '연일읍', '제철동', '양덕동', '율곡동', '진미동', '영주동', '하양읍', '진량읍', '강구면',\n",
-       "       '영해면', '문경시', '성주군', '의성읍', '안계면', '봉화군청', '영양군', '예천군', '화양읍',\n",
-       "       '포항항', '관평동', '탄천면', '합덕읍', '복운리', '장재리', '송악면', '연무읍', '성동면',\n",
-       "       '격렬비열도', '원북면', '삽교읍', '고덕면(충남)', '외연도', '정산면', '평택당진항', '대산항',\n",
-       "       '장항항', '평택당진항(당진항)', '태안항', '보령항', '보람동', '전의면', '지정면', '주문진읍',\n",
-       "       '우천면', '중앙탑면', '살미면', '영천동', '청풍면', '가덕면', '단성면', '단양읍', '감물면',\n",
-       "       '덕산읍', '소이면', '황간면', '증평읍', '혁신동', '여의동', '효자동', '옥산면', '비응도동',\n",
-       "       '말도', '소룡동2', '삼기면', '용동면', '함열읍', '춘포면', '여산면', '금마면', '영파동',\n",
-       "       '운봉읍', '계화면', '광활면', '봉동읍', '구이면', '관촌면', '청송읍', '군산항', '서해', '영종',\n",
-       "       '남동', '주안', '중봉', '효성', '임학', '서창', '아암', '경인항', '인천항', '인천 신항',\n",
-       "       '인천 북항', '인천 남항', '연평도', '울도'], dtype=object)"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air.loc[pd.isna(air['위도']), '측정소명'].unique() # 위도가 na인 측정소명 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['경기도 수원시 권선구 칠보로1번길 62', '경기도 의정부시 민락로243번길 94', '경기도 시흥시 시청로 20',\n",
-       "       '경기도 시흥시 서해안로 277', '경기도 시흥시 배곧4로 102', '경기도 남양주시 경춘로 433',\n",
-       "       '경기도 남양주시 와부읍 도곡길 7', '경기도 남양주시 진접읍 금강로 1509-26',\n",
-       "       '경기도 평택시 청북읍 안청로2길 60', '경기도 평택시 고덕면 고덕국제2로 111',\n",
-       "       '경기도 파주시 파주읍 교육길 13', '경기도 광주시 오포읍 오포로859번길 29',\n",
-       "       '경기도 광주시 곤지암읍 광여로 59', '경기도 이천시 부발읍 무촌로 117',\n",
-       "       '경기도 김포시 김포한강2로24번길 93', '경기도 하남시 아리수로 531', '경기도 화성시 수노을중앙로 178',\n",
-       "       '경기도 화성시 봉담읍 샘마을1길 8-4', '경기도 화성시 서신면 궁평항로 1702',\n",
-       "       '경기도 안성시 공도읍 공도4로 8', '경기도 안성시 죽산면 남부길 60',\n",
-       "       '경기도 여주시 대신면 율촌1길 12-10', '경기도 여주시 가남읍 태평중앙1길 20',\n",
-       "       '경기도 연천군 전곡읍 은전로 45', '경기도 가평군 설악면 한서로 8', '경기도 양평군 양평읍 마유산로 17',\n",
-       "       '경기 연천군 중면 횡산리 산108-1', '강원특별자치도 춘천시 사우4길 26',\n",
-       "       '강원특별자치도 춘천시 경춘로 2370', '강원특별자치도 동해시 대동로 210',\n",
-       "       '강원특별자치도 동해시 임항로 121', '강원특별자치도 삼척시 원덕읍 호산리 338-14',\n",
-       "       '강원특별자치도 철원군 철원읍 산명리 1345', '강원특별자치도 화천군 화천읍 풍산리 산269',\n",
-       "       '강원특별자치도 고성군 수동면 신탄리 산6번지', '강원특별자치도 고성군 수동면 외면리 산14번지',\n",
-       "       '부산광역시 영도구 청학남로13번길 18', '부산광역시 남구 이기대공원로 11',\n",
-       "       '부산광역시 북구 용당로16번길 22', '부산광역시 사상구 모덕로 2',\n",
-       "       '부산광역시 해운대구 해운대해변로 84 (우동)', '부산광역시 해운대구 센텀동로 191',\n",
-       "       '부산광역시 강서구 명지동 3513-3', '부산광역시 금정구 금사로 217', '부산광역시 강서구 신항남로 354',\n",
-       "       '부산광역시 강서구 신항남로 416', '부산광역시 동구 충장대로 314', '부산광역시 남구 북항로 105',\n",
-       "       '경상남도 창원시 마산회원구 내서읍 광려로 8', '경상남도 진주시 정촌면 예하리 1340',\n",
-       "       '경상남도 창원시 마산합포구 진동면 삼진의거대로 621', '경상남도 하동군 금성면 금성중앙길 14',\n",
-       "       '경상남도 김해시 김해대로 2515', '경상남도 거제시 계룡로 125', '경상남도 사천시 향촌5길 28',\n",
-       "       '경상남도 양산시 물금읍 황산로 384', '울산광역시 울주군 웅촌면 새초천길 12',\n",
-       "       '울산광역시 울주군 범서읍 당앞로 14-50', '울산광역시 중구 태화동 310', '울산광역시 북구 송내14길 41',\n",
-       "       '경상남도 창원시 성산구 적현로 424', '울산 남구 매암로 96', '경상남도 하동군 금성면 경제산업로 509',\n",
-       "       '경상남도 사천시 향촌동 1292', '광주광역시 서구 천변우하로 203', '광주광역시 북구 모룡대길 68',\n",
-       "       '광주광역시 광산구 우산동 1026-2', '전라남도 광양시 봉강면 조양길 46',\n",
-       "       '전라남도 광양시 진월면 선소중앙길 31', '전라남도 영암군 영암읍 낭주로 202-2',\n",
-       "       '전남 영광군 낙월면 영외리 산 118', '전라남도 신안군 흑산면 홍도1길 51-3',\n",
-       "       '전남 신안군 흑산면 가거도리 산 4', '전라남도 보성군 보성읍 현충로 42-36',\n",
-       "       '전남 영암군 삼호읍 용당리 2169-1', '전남 광양시 컨부두로 316', '전남 여수시 여객선터미널길 17',\n",
-       "       '전라남도 광양시 항만9로 70', '전라남도 광양시 광양읍 율촌산단7로 111',\n",
-       "       '제주특별자치도 제주시 조천읍 조천18길 11-1', '제주특별자치도 제주시 한림읍 한림중앙로 71-9',\n",
-       "       '제주특별자치도 제주시 화북일동 1098', '제주특별자치도 제주시 애월읍 고내리 1319',\n",
-       "       '제주특별자치도 서귀포시 일주서로 166', '대구광역시 중구 남산로2길 125', '대구광역시 북구 연암로 40',\n",
-       "       '대구광역시 달서구 구마로26길 62', '대구광역시 서구 서대구로3길 46', '대구광역시 북구 옥산로17길 21',\n",
-       "       '대구광역시 달성군 화원읍 인흥1길 12', '대구광역시 남구 앞산순환로 540',\n",
-       "       '대구광역시 군위군 군위읍 군청로 158', '경상북도 포항시 북구 새천년대로 906',\n",
-       "       '경상북도 포항시 남구 연일읍 동문로 67', '경상북도 포항시 남구 인덕로 52',\n",
-       "       '경상북도 포항시 북구 천마로 161 (양덕동)', '경북 김천시 혁신4로 21', '경상북도 구미시 이계북로 149',\n",
-       "       '경상북도 영주시 광복로 65', '경상북도 경산시 하양읍 하양로 119-1', '경상북도 경산시 진량읍 낙산길 7',\n",
-       "       '경상북도 영덕군 강구면 강구리 167', '경상북도 영덕군 영해면 예주3길 7', '경북 문경시 시청2길 45',\n",
-       "       '경상북도 성주군 성주읍 성주로 3258', '경북 의성군 의성읍 군청길 31',\n",
-       "       '경상북도 의성군 안계면 안계길 114', '경상북도 봉화군 봉화읍 봉화로 1111',\n",
-       "       '경상북도 영양군 영양읍 군청길 37', '경상북도 예천군 호명면 행복7길 25-4',\n",
-       "       '경북 청도군 화양읍 도주관로 159', '경북 포항시 남구 신항로 99-98',\n",
-       "       '대전광역시 유성구 테크노중앙로 88', '충청남도 공주시 탄천면 안터새말길 34',\n",
-       "       '충청남도 당진시 합덕읍 합덕리 344', '충청남도 당진시 송악읍 신복운로 5',\n",
-       "       '충청남도 아산시 배방읍 장재리 2116', '충청남도 아산시 송악면 송악로 790',\n",
-       "       '충청남도 논산시 연무읍 안심로 50', '충청남도 논산시 성동면 산업단지로5길 73-28',\n",
-       "       '충남 태안군 근흥면 가의도길 44-71번지', '충청남도 태안군 원북면 상리길 17-4',\n",
-       "       '충청남도 예산군 삽교읍 두리3길 33', '충청남도 예산군 고덕면 예당산단4길 147',\n",
-       "       '충남 보령시 오천면 외연도리 산64', '충청남도 청양군 정산면 칠갑산로 1861',\n",
-       "       '충청남도 당진시 신평면 매산리 976', '충남 서산시 대산읍 대죽리 1114',\n",
-       "       '충남 서천군 장항읍 장산로 270-3', '충청남도 당진시 송악읍 고대공단2길 79-30',\n",
-       "       '충청남도 태안군 원북면 발전로 457', '충청남도 보령시 오천면 오천해안로 89-37',\n",
-       "       '세종특별자치시 한누리대로 2107', '세종특별자치시 전의면 운주산로 1270',\n",
-       "       '강원특별자치도 원주시 지정면 기업도시로 200', '강원특별자치도 강릉시 주문진읍 항구로 19',\n",
-       "       '강원특별자치도 횡성군 우천면 우항1길 5-34', '충청북도 충주시 중앙탑면 기업도시로 237',\n",
-       "       '충청북도 충주시 살미면 세성양지말길 41', '충청북도 제천시 청풍호로8길 7',\n",
-       "       '충청북도 제천시 청풍면 청풍호로 2115', '충청북도 청주시 상당구 가덕면 보청대로 4650',\n",
-       "       '충북 단양군 단성면 충혼로 52-1', '충청북도 단양군 단양읍 별곡6길 26',\n",
-       "       '충청북도 괴산군 감물면 충민로신대길 13', '충청북도 진천군 덕산읍 대월로 90',\n",
-       "       '충청북도 음성군 소이면 소이로 409', '충청북도 영동군 황간면 남성리 185',\n",
-       "       '충청북도 증평군 증평읍 남하용강로 16', '전라북도 전주시 덕진구 중동로 150',\n",
-       "       '전북특별자치도 전주시 덕진구 중동로 150', '전라북도 전주시 덕진구 여암2길 9',\n",
-       "       '전북특별자치도 전주시 덕진구 여암2길 9', '전라북도 전주시 완산구 쑥고개로 259 (효자동2가)',\n",
-       "       '전북특별자치도 전주시 완산구 쑥고개로 259 (효자동2가)', '전북 군산시 옥산면 산성로 200',\n",
-       "       '전북특별자치도 군산시 옥산면 산성로 200', '전북 군산시 새만금북로 43',\n",
-       "       '전북특별자치도 군산시 새만금북로 43', '전라북도 군산시 옥도면 말도2길 29', '전라북도 군산시 동장산2길 6',\n",
-       "       '전북특별자치도 군산시 동장산2길 6', '전북 익산시 삼기면 황금로 513',\n",
-       "       '전북특별자치도 익산시 삼기면 황금로 513', '전북 익산시 용동면 용동1길 80-4',\n",
-       "       '전북특별자치도 익산시 용동면 용동1길 80-4', '전라북도 익산시 함열읍 함열중앙로 83',\n",
-       "       '전북특별자치도 익산시 함열읍 함열중앙로 83', '전라북도 익산시 춘포면 춘포2길 11',\n",
-       "       '전북특별자치도 익산시 춘포면 춘포2길 11', '전라북도 익산시 여산면 가람로 393',\n",
-       "       '전북특별자치도 익산시 여산면 가람로 393', '전라북도 익산시 금마면 무왕로 1824',\n",
-       "       '전북특별자치도 익산시 금마면 무왕로 1824', '전라북도 정읍시 영파동 232-1',\n",
-       "       '전북특별자치도 정읍시 영파동 232-1', '전라북도 남원시 운봉읍 황산로 1083',\n",
-       "       '전북특별자치도 남원시 운봉읍 황산로 1083', '전라북도 부안군 계화면 간재로 405',\n",
-       "       '전북특별자치도 부안군 계화면 간재로 405', '전라북도 김제시 광활면 지평선로 638',\n",
-       "       '전북특별자치도 김제시 광활면 지평선로 638', '전라북도 완주군 봉동읍 삼봉로 933',\n",
-       "       '전북특별자치도 완주군 봉동읍 삼봉로 933', '전라북도 완주군 구이면 덕천전원길 232-58',\n",
-       "       '전북특별자치도 완주군 구이면 덕천전원길 232-58', '전라북도 임실군 관촌면 사선1길 13',\n",
-       "       '전북특별자치도 임실군 관촌면 사선1길 13', '경상북도 청송군 청송읍 금월로 230',\n",
-       "       '전북 군산시 서해로 194', '인천광역시 중구 서해대로 365-1', '인천광역시 중구 하늘중앙로 132',\n",
-       "       '인천광역시 남동구 남동대로 668', '인천광역시 미추홀구 구월남로 27', '인천광역시 서구 중봉대로 274',\n",
-       "       '인천광역시 계양구 봉오대로600번길 14', '인천광역시 계양구 경명대로 1179 (병방동)',\n",
-       "       '인천광역시 남동구 서창남로 101', '인천광역시 연수구 센트럴로 350', '인천 서구 오류동 경인항',\n",
-       "       '인천 중구 항동7가 1-48', '인천광역시 연수구 인천신항대로 866-9',\n",
-       "       '인천광역시 서구 원석로 41 (석남동)', '인천광역시 중구 축항대로118번길 147',\n",
-       "       '인천 옹진군 연평면 연평리 631', '인천 옹진군 덕적면 울도리 85번지'], dtype=object)"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air.loc[pd.isna(air['위도']), '주소'].unique() # 위도가 na인 주소 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "206"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(air.loc[pd.isna(air['위도']), '주소'].unique()) # 위도가 na인 주소 개수 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.0130</td>\n",
-       "      <td>0.0280</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 01:00:00</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00:00</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00:00</td>\n",
-       "      <td>0.0180</td>\n",
-       "      <td>0.0220</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00:00</td>\n",
-       "      <td>0.0100</td>\n",
-       "      <td>0.0300</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>37.5643</td>\n",
-       "      <td>126.9747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178033</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 19:00:00</td>\n",
-       "      <td>0.0440</td>\n",
-       "      <td>0.0075</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178034</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 20:00:00</td>\n",
-       "      <td>0.0415</td>\n",
-       "      <td>0.0086</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178035</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 21:00:00</td>\n",
-       "      <td>0.0409</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178036</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 22:00:00</td>\n",
-       "      <td>0.0414</td>\n",
-       "      <td>0.0111</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178037</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 23:00:00</td>\n",
-       "      <td>0.0411</td>\n",
-       "      <td>0.0112</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28178038 rows × 15 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역   측정소코드 측정소명                측정일시      O3     NO2  PM10  PM25  \\\n",
-       "0          서울 중구  111121   중구 2018-01-01 00:00:00  0.0130  0.0280  31.0  16.0   \n",
-       "1          서울 중구  111121   중구 2018-01-01 01:00:00  0.0200  0.0200  34.0  19.0   \n",
-       "2          서울 중구  111121   중구 2018-01-01 02:00:00  0.0240  0.0160  27.0  14.0   \n",
-       "3          서울 중구  111121   중구 2018-01-01 03:00:00  0.0180  0.0220  26.0  14.0   \n",
-       "4          서울 중구  111121   중구 2018-01-01 04:00:00  0.0100  0.0300  26.0  15.0   \n",
-       "...          ...     ...  ...                 ...     ...     ...   ...   ...   \n",
-       "28178033  인천 옹진군  831495   울도 2023-12-31 19:00:00  0.0440  0.0075  67.0  57.0   \n",
-       "28178034  인천 옹진군  831495   울도 2023-12-31 20:00:00  0.0415  0.0086  64.0  57.0   \n",
-       "28178035  인천 옹진군  831495   울도 2023-12-31 21:00:00  0.0409  0.0095  62.0  50.0   \n",
-       "28178036  인천 옹진군  831495   울도 2023-12-31 22:00:00  0.0414  0.0111  65.0  59.0   \n",
-       "28178037  인천 옹진군  831495   울도 2023-12-31 23:00:00  0.0411  0.0112  63.0  54.0   \n",
-       "\n",
-       "                           주소  year  month  day  hour       위도        경도  \n",
-       "0               서울 중구 덕수궁길 15  2018      1    1     0  37.5643  126.9747  \n",
-       "1               서울 중구 덕수궁길 15  2018      1    1     1  37.5643  126.9747  \n",
-       "2               서울 중구 덕수궁길 15  2018      1    1     2  37.5643  126.9747  \n",
-       "3               서울 중구 덕수궁길 15  2018      1    1     3  37.5643  126.9747  \n",
-       "4               서울 중구 덕수궁길 15  2018      1    1     4  37.5643  126.9747  \n",
-       "...                       ...   ...    ...  ...   ...      ...       ...  \n",
-       "28178033  인천 옹진군 덕적면 울도리 85번지  2023     12   31    19      NaN       NaN  \n",
-       "28178034  인천 옹진군 덕적면 울도리 85번지  2023     12   31    20      NaN       NaN  \n",
-       "28178035  인천 옹진군 덕적면 울도리 85번지  2023     12   31    21      NaN       NaN  \n",
-       "28178036  인천 옹진군 덕적면 울도리 85번지  2023     12   31    22      NaN       NaN  \n",
-       "28178037  인천 옹진군 덕적면 울도리 85번지  2023     12   31    23      NaN       NaN  \n",
-       "\n",
-       "[28178038 rows x 15 columns]"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 206/206 [00:59<00:00,  3.47it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Address: 경기도 수원시 권선구 칠보로1번길 62\n",
-      "Latitude: 37.2687013\n",
-      "Longitude: 126.9354884\n",
-      "\n",
-      "Address: 경기도 의정부시 민락로243번길 94\n",
-      "Latitude: 37.7477055\n",
-      "Longitude: 127.1066326\n",
-      "\n",
-      "Address: 경기도 시흥시 시청로 20\n",
-      "Latitude: 37.3799469\n",
-      "Longitude: 126.8031765\n",
-      "\n",
-      "Address: 경기도 시흥시 서해안로 277\n",
-      "Latitude: 37.3944639\n",
-      "Longitude: 126.743953\n",
-      "\n",
-      "Address: 경기도 시흥시 배곧4로 102\n",
-      "Latitude: 37.3735088\n",
-      "Longitude: 126.7337514\n",
-      "\n",
-      "Address: 경기도 남양주시 경춘로 433\n",
-      "Latitude: 37.6079488\n",
-      "Longitude: 127.1593216\n",
-      "\n",
-      "Address: 경기도 남양주시 와부읍 도곡길 7\n",
-      "Latitude: 37.5804204\n",
-      "Longitude: 127.2260649\n",
-      "\n",
-      "Address: 경기도 남양주시 진접읍 금강로 1509-26\n",
-      "Latitude: 37.7263588\n",
-      "Longitude: 127.1898925\n",
-      "\n",
-      "Address: 경기도 평택시 청북읍 안청로2길 60\n",
-      "Latitude: 37.0215834\n",
-      "Longitude: 126.9163318\n",
-      "\n",
-      "Address: 경기도 평택시 고덕면 고덕국제2로 111\n",
-      "Latitude: 37.05311950000001\n",
-      "Longitude: 127.0462758\n",
-      "\n",
-      "Address: 경기도 파주시 파주읍 교육길 13\n",
-      "Latitude: 37.8308505\n",
-      "Longitude: 126.8330233\n",
-      "\n",
-      "Address: 경기도 광주시 오포읍 오포로859번길 29\n",
-      "Latitude: 37.366347\n",
-      "Longitude: 127.2285647\n",
-      "\n",
-      "Address: 경기도 광주시 곤지암읍 광여로 59\n",
-      "Latitude: 37.3509517\n",
-      "Longitude: 127.3521893\n",
-      "\n",
-      "Address: 경기도 이천시 부발읍 무촌로 117\n",
-      "Latitude: 37.283268\n",
-      "Longitude: 127.4866972\n",
-      "\n",
-      "Address: 경기도 김포시 김포한강2로24번길 93\n",
-      "Latitude: 37.6468307\n",
-      "Longitude: 126.6743716\n",
-      "\n",
-      "Address: 경기도 하남시 아리수로 531\n",
-      "Latitude: 37.5671161\n",
-      "Longitude: 127.1860594\n",
-      "\n",
-      "Address: 경기도 화성시 수노을중앙로 178\n",
-      "Latitude: 37.2812059\n",
-      "Longitude: 126.8187549\n",
-      "\n",
-      "Address: 경기도 화성시 봉담읍 샘마을1길 8-4\n",
-      "Latitude: 37.2194855\n",
-      "Longitude: 126.9490498\n",
-      "\n",
-      "Address: 경기도 화성시 서신면 궁평항로 1702\n",
-      "Latitude: 37.1662107\n",
-      "Longitude: 126.7088664\n",
-      "\n",
-      "Address: 경기도 안성시 공도읍 공도4로 8\n",
-      "Latitude: 37.0010908\n",
-      "Longitude: 127.1726414\n",
-      "\n",
-      "Address: 경기도 안성시 죽산면 남부길 60\n",
-      "Latitude: 37.0746422\n",
-      "Longitude: 127.4244675\n",
-      "\n",
-      "Address: 경기도 여주시 대신면 율촌1길 12-10\n",
-      "Latitude: 37.3733992\n",
-      "Longitude: 127.5863028\n",
-      "\n",
-      "Address: 경기도 여주시 가남읍 태평중앙1길 20\n",
-      "Latitude: 37.20167199999999\n",
-      "Longitude: 127.5452706\n",
-      "\n",
-      "Address: 경기도 연천군 전곡읍 은전로 45\n",
-      "Latitude: 38.0275947\n",
-      "Longitude: 127.0632063\n",
-      "\n",
-      "Address: 경기도 가평군 설악면 한서로 8\n",
-      "Latitude: 37.675968\n",
-      "Longitude: 127.494629\n",
-      "\n",
-      "Address: 경기도 양평군 양평읍 마유산로 17\n",
-      "Latitude: 37.4970925\n",
-      "Longitude: 127.4865458\n",
-      "\n",
-      "Address: 경기 연천군 중면 횡산리 산108-1\n",
-      "Latitude: 38.126438\n",
-      "Longitude: 126.9732781\n",
-      "\n",
-      "Address: 강원특별자치도 춘천시 사우4길 26\n",
-      "Latitude: 37.9056482\n",
-      "Longitude: 127.7280602\n",
-      "\n",
-      "Address: 강원특별자치도 춘천시 경춘로 2370\n",
-      "Latitude: 37.8648724\n",
-      "Longitude: 127.720974\n",
-      "\n",
-      "Address: 강원특별자치도 동해시 대동로 210\n",
-      "Latitude: 37.488497\n",
-      "Longitude: 129.1300356\n",
-      "\n",
-      "Address: 강원특별자치도 동해시 임항로 121\n",
-      "Latitude: 37.5484489\n",
-      "Longitude: 129.110772\n",
-      "\n",
-      "Address: 강원특별자치도 삼척시 원덕읍 호산리 338-14\n",
-      "Latitude: 37.1743425\n",
-      "Longitude: 129.343893\n",
-      "\n",
-      "Address: 강원특별자치도 철원군 철원읍 산명리 1345\n",
-      "Latitude: 38.2920178\n",
-      "Longitude: 127.1781293\n",
-      "\n",
-      "Address: 강원특별자치도 화천군 화천읍 풍산리 산269\n",
-      "Latitude: 38.2198155\n",
-      "Longitude: 127.7724077\n",
-      "\n",
-      "Address: 강원특별자치도 고성군 수동면 신탄리 산6번지\n",
-      "Latitude: 38.3811816\n",
-      "Longitude: 128.2577979\n",
-      "\n",
-      "Address: 강원특별자치도 고성군 수동면 외면리 산14번지\n",
-      "Latitude: 38.5632087\n",
-      "Longitude: 128.3414492\n",
-      "\n",
-      "Address: 부산광역시 영도구 청학남로13번길 18\n",
-      "Latitude: 35.0907485\n",
-      "Longitude: 129.0594904\n",
-      "\n",
-      "Address: 부산광역시 남구 이기대공원로 11\n",
-      "Latitude: 35.1258003\n",
-      "Longitude: 129.1164163\n",
-      "\n",
-      "Address: 부산광역시 북구 용당로16번길 22\n",
-      "Latitude: 35.2328515\n",
-      "Longitude: 129.0089178\n",
-      "\n",
-      "Address: 부산광역시 사상구 모덕로 2\n",
-      "Latitude: 35.1824537\n",
-      "Longitude: 128.976956\n",
-      "\n",
-      "Address: 부산광역시 해운대구 해운대해변로 84 (우동)\n",
-      "Latitude: 35.160586\n",
-      "Longitude: 129.1399465\n",
-      "\n",
-      "Address: 부산광역시 해운대구 센텀동로 191\n",
-      "Latitude: 35.1827807\n",
-      "Longitude: 129.1202801\n",
-      "\n",
-      "Address: 부산광역시 강서구 명지동 3513-3\n",
-      "Latitude: 35.1055728\n",
-      "Longitude: 128.9258618\n",
-      "\n",
-      "Address: 부산광역시 금정구 금사로 217\n",
-      "Latitude: 35.2301064\n",
-      "Longitude: 129.1206659\n",
-      "\n",
-      "Address: 부산광역시 강서구 신항남로 354\n",
-      "Latitude: 35.0802557\n",
-      "Longitude: 128.8268542\n",
-      "\n",
-      "Address: 부산광역시 강서구 신항남로 416\n",
-      "Latitude: 35.073104\n",
-      "Longitude: 128.833501\n",
-      "\n",
-      "Address: 부산광역시 동구 충장대로 314\n",
-      "Latitude: 35.1227863\n",
-      "Longitude: 129.0568997\n",
-      "\n",
-      "Address: 부산광역시 남구 북항로 105\n",
-      "Latitude: 35.1059456\n",
-      "Longitude: 129.0825838\n",
-      "\n",
-      "Address: 경상남도 창원시 마산회원구 내서읍 광려로 8\n",
-      "Latitude: 35.226291\n",
-      "Longitude: 128.5025384\n",
-      "\n",
-      "Address: 경상남도 진주시 정촌면 예하리 1340\n",
-      "Latitude: 35.124889\n",
-      "Longitude: 128.1004766\n",
-      "\n",
-      "Address: 경상남도 창원시 마산합포구 진동면 삼진의거대로 621\n",
-      "Latitude: 35.1161188\n",
-      "Longitude: 128.485312\n",
-      "\n",
-      "Address: 경상남도 하동군 금성면 금성중앙길 14\n",
-      "Latitude: 34.965974\n",
-      "Longitude: 127.7941908\n",
-      "\n",
-      "Address: 경상남도 김해시 김해대로 2515\n",
-      "Latitude: 35.228156\n",
-      "Longitude: 128.9006477\n",
-      "\n",
-      "Address: 경상남도 거제시 계룡로 125\n",
-      "Latitude: 34.8805937\n",
-      "Longitude: 128.6212211\n",
-      "\n",
-      "Address: 경상남도 사천시 향촌5길 28\n",
-      "Latitude: 34.9337743\n",
-      "Longitude: 128.0934556\n",
-      "\n",
-      "Address: 경상남도 양산시 물금읍 황산로 384\n",
-      "Latitude: 35.3103051\n",
-      "Longitude: 128.9872259\n",
-      "\n",
-      "Address: 울산광역시 울주군 웅촌면 새초천길 12\n",
-      "Latitude: 35.4612703\n",
-      "Longitude: 129.2084963\n",
-      "\n",
-      "Address: 울산광역시 울주군 범서읍 당앞로 14-50\n",
-      "Latitude: 35.57033029999999\n",
-      "Longitude: 129.223284\n",
-      "\n",
-      "Address: 울산광역시 중구 태화동 310\n",
-      "Latitude: 35.5579648\n",
-      "Longitude: 129.2814575\n",
-      "\n",
-      "Address: 울산광역시 북구 송내14길 41\n",
-      "Latitude: 35.5923096\n",
-      "Longitude: 129.3651871\n",
-      "\n",
-      "Address: 경상남도 창원시 성산구 적현로 424\n",
-      "Latitude: 35.1895435\n",
-      "Longitude: 128.5938474\n",
-      "\n",
-      "Address: 울산 남구 매암로 96\n",
-      "Latitude: 35.5167331\n",
-      "Longitude: 129.3763258\n",
-      "\n",
-      "Address: 경상남도 하동군 금성면 경제산업로 509\n",
-      "Latitude: 34.951636\n",
-      "Longitude: 127.8204985\n",
-      "\n",
-      "Address: 경상남도 사천시 향촌동 1292\n",
-      "Latitude: 34.9182613\n",
-      "Longitude: 128.0843048\n",
-      "\n",
-      "Address: 광주광역시 서구 천변우하로 203\n",
-      "Latitude: 35.1633863\n",
-      "Longitude: 126.8493017\n",
-      "\n",
-      "Address: 광주광역시 북구 모룡대길 68\n",
-      "Latitude: 35.2179317\n",
-      "Longitude: 126.8926976\n",
-      "\n",
-      "Address: 광주광역시 광산구 우산동 1026-2\n",
-      "Latitude: 35.1577625\n",
-      "Longitude: 126.8100651\n",
-      "\n",
-      "Address: 전라남도 광양시 봉강면 조양길 46\n",
-      "Latitude: 35.013973\n",
-      "Longitude: 127.5802854\n",
-      "\n",
-      "Address: 전라남도 광양시 진월면 선소중앙길 31\n",
-      "Latitude: 34.9784446\n",
-      "Longitude: 127.7580563\n",
-      "\n",
-      "Address: 전라남도 영암군 영암읍 낭주로 202-2\n",
-      "Latitude: 34.807237\n",
-      "Longitude: 126.701469\n",
-      "\n",
-      "Address: 전남 영광군 낙월면 영외리 산 118\n",
-      "Latitude: 35.3363281\n",
-      "Longitude: 126.0206231\n",
-      "\n",
-      "Address: 전라남도 신안군 흑산면 홍도1길 51-3\n",
-      "Latitude: 34.6853982\n",
-      "Longitude: 125.1918734\n",
-      "\n",
-      "Address: 전남 신안군 흑산면 가거도리 산 4\n",
-      "Latitude: 34.0908578\n",
-      "Longitude: 125.1013039\n",
-      "\n",
-      "Address: 전라남도 보성군 보성읍 현충로 42-36\n",
-      "Latitude: 34.7649425\n",
-      "Longitude: 127.0782547\n",
-      "\n",
-      "Address: 전남 영암군 삼호읍 용당리 2169-1\n",
-      "Latitude: 34.7717083\n",
-      "Longitude: 126.3922166\n",
-      "\n",
-      "Address: 전남 광양시 컨부두로 316\n",
-      "Latitude: 34.9068431\n",
-      "Longitude: 127.6680385\n",
-      "\n",
-      "Address: 전남 여수시 여객선터미널길 17\n",
-      "Latitude: 34.7385468\n",
-      "Longitude: 127.7326155\n",
-      "\n",
-      "Address: 전라남도 광양시 항만9로 70\n",
-      "Latitude: 34.921022\n",
-      "Longitude: 127.6959196\n",
-      "\n",
-      "Address: 전라남도 광양시 광양읍 율촌산단7로 111\n",
-      "Latitude: 34.89446119999999\n",
-      "Longitude: 127.6111959\n",
-      "\n",
-      "Address: 제주특별자치도 제주시 조천읍 조천18길 11-1\n",
-      "Latitude: 33.5390118\n",
-      "Longitude: 126.6427149\n",
-      "\n",
-      "Address: 제주특별자치도 제주시 한림읍 한림중앙로 71-9\n",
-      "Latitude: 33.4094321\n",
-      "Longitude: 126.2685964\n",
-      "\n",
-      "Address: 제주특별자치도 제주시 화북일동 1098\n",
-      "Latitude: 33.5170009\n",
-      "Longitude: 126.5702961\n",
-      "\n",
-      "Address: 제주특별자치도 제주시 애월읍 고내리 1319\n",
-      "Latitude: 33.4646987\n",
-      "Longitude: 126.3314337\n",
-      "\n",
-      "Address: 제주특별자치도 서귀포시 일주서로 166\n",
-      "Latitude: 33.2518481\n",
-      "Longitude: 126.4906021\n",
-      "\n",
-      "Address: 대구광역시 중구 남산로2길 125\n",
-      "Latitude: 35.857907\n",
-      "Longitude: 128.5894969\n",
-      "\n",
-      "Address: 대구광역시 북구 연암로 40\n",
-      "Latitude: 35.8926449\n",
-      "Longitude: 128.6007471\n",
-      "\n",
-      "Address: 대구광역시 달서구 구마로26길 62\n",
-      "Latitude: 35.8344259\n",
-      "Longitude: 128.5411162\n",
-      "\n",
-      "Address: 대구광역시 서구 서대구로3길 46\n",
-      "Latitude: 35.8589429\n",
-      "Longitude: 128.5519085\n",
-      "\n",
-      "Address: 대구광역시 북구 옥산로17길 21\n",
-      "Latitude: 35.8861362\n",
-      "Longitude: 128.5849779\n",
-      "\n",
-      "Address: 대구광역시 달성군 화원읍 인흥1길 12\n",
-      "Latitude: 35.7974305\n",
-      "Longitude: 128.5037874\n",
-      "\n",
-      "Address: 대구광역시 남구 앞산순환로 540\n",
-      "Latitude: 35.8317684\n",
-      "Longitude: 128.5827485\n",
-      "\n",
-      "Address: 대구광역시 군위군 군위읍 군청로 158\n",
-      "Latitude: 36.2372849\n",
-      "Longitude: 128.5744108\n",
-      "\n",
-      "Address: 경상북도 포항시 북구 새천년대로 906\n",
-      "Latitude: 36.0528499\n",
-      "Longitude: 129.3611936\n",
-      "\n",
-      "Address: 경상북도 포항시 남구 연일읍 동문로 67\n",
-      "Latitude: 35.9962626\n",
-      "Longitude: 129.3521476\n",
-      "\n",
-      "Address: 경상북도 포항시 남구 인덕로 52\n",
-      "Latitude: 35.9899753\n",
-      "Longitude: 129.3976328\n",
-      "\n",
-      "Address: 경상북도 포항시 북구 천마로 161 (양덕동)\n",
-      "Latitude: 36.0901685\n",
-      "Longitude: 129.3961353\n",
-      "\n",
-      "Address: 경북 김천시 혁신4로 21\n",
-      "Latitude: 36.121462\n",
-      "Longitude: 128.1833644\n",
-      "\n",
-      "Address: 경상북도 구미시 이계북로 149\n",
-      "Latitude: 36.1059908\n",
-      "Longitude: 128.418356\n",
-      "\n",
-      "Address: 경상북도 영주시 광복로 65\n",
-      "Latitude: 36.8286792\n",
-      "Longitude: 128.625912\n",
-      "\n",
-      "Address: 경상북도 경산시 하양읍 하양로 119-1\n",
-      "Latitude: 35.9146655\n",
-      "Longitude: 128.8200554\n",
-      "\n",
-      "Address: 경상북도 경산시 진량읍 낙산길 7\n",
-      "Latitude: 35.8756691\n",
-      "Longitude: 128.8124495\n",
-      "\n",
-      "Address: 경상북도 영덕군 강구면 강구리 167\n",
-      "Latitude: 36.3635446\n",
-      "Longitude: 129.3888018\n",
-      "\n",
-      "Address: 경상북도 영덕군 영해면 예주3길 7\n",
-      "Latitude: 36.53746539999999\n",
-      "Longitude: 129.4070884\n",
-      "\n",
-      "Address: 경북 문경시 시청2길 45\n",
-      "Latitude: 36.5867479\n",
-      "Longitude: 128.1861093\n",
-      "\n",
-      "Address: 경상북도 성주군 성주읍 성주로 3258\n",
-      "Latitude: 35.9174968\n",
-      "Longitude: 128.2901803\n",
-      "\n",
-      "Address: 경북 의성군 의성읍 군청길 31\n",
-      "Latitude: 36.3525152\n",
-      "Longitude: 128.6970106\n",
-      "\n",
-      "Address: 경상북도 의성군 안계면 안계길 114\n",
-      "Latitude: 36.3837643\n",
-      "Longitude: 128.4401647\n",
-      "\n",
-      "Address: 경상북도 봉화군 봉화읍 봉화로 1111\n",
-      "Latitude: 36.8931267\n",
-      "Longitude: 128.7325885\n",
-      "\n",
-      "Address: 경상북도 영양군 영양읍 군청길 37\n",
-      "Latitude: 36.6666895\n",
-      "Longitude: 129.1125208\n",
-      "\n",
-      "Address: 경상북도 예천군 호명면 행복7길 25-4\n",
-      "Latitude: 36.5767932\n",
-      "Longitude: 128.47329\n",
-      "\n",
-      "Address: 경북 청도군 화양읍 도주관로 159\n",
-      "Latitude: 35.6497741\n",
-      "Longitude: 128.706151\n",
-      "\n",
-      "Address: 경북 포항시 남구 신항로 99-98\n",
-      "Latitude: 36.0057836\n",
-      "Longitude: 129.4150523\n",
-      "\n",
-      "Address: 대전광역시 유성구 테크노중앙로 88\n",
-      "Latitude: 36.424847\n",
-      "Longitude: 127.3948613\n",
-      "\n",
-      "Address: 충청남도 공주시 탄천면 안터새말길 34\n",
-      "Latitude: 36.3084894\n",
-      "Longitude: 127.0677527\n",
-      "\n",
-      "Address: 충청남도 당진시 합덕읍 합덕리 344\n",
-      "Latitude: 36.7904994\n",
-      "Longitude: 126.785855\n",
-      "\n",
-      "Address: 충청남도 당진시 송악읍 신복운로 5\n",
-      "Latitude: 36.94439980000001\n",
-      "Longitude: 126.7836024\n",
-      "\n",
-      "Address: 충청남도 아산시 배방읍 장재리 2116\n",
-      "Latitude: 36.7844748\n",
-      "Longitude: 127.1013253\n",
-      "\n",
-      "Address: 충청남도 아산시 송악면 송악로 790\n",
-      "Latitude: 36.7316409\n",
-      "Longitude: 127.008961\n",
-      "\n",
-      "Address: 충청남도 논산시 연무읍 안심로 50\n",
-      "Latitude: 36.1253129\n",
-      "Longitude: 127.0989462\n",
-      "\n",
-      "Address: 충청남도 논산시 성동면 산업단지로5길 73-28\n",
-      "Latitude: 36.2151708\n",
-      "Longitude: 127.0439255\n",
-      "\n",
-      "Address: 충남 태안군 근흥면 가의도길 44-71번지\n",
-      "Latitude: 36.672586\n",
-      "Longitude: 126.0644856\n",
-      "\n",
-      "Address: 충청남도 태안군 원북면 상리길 17-4\n",
-      "Latitude: 36.8242316\n",
-      "Longitude: 126.2572303\n",
-      "\n",
-      "Address: 충청남도 예산군 삽교읍 두리3길 33\n",
-      "Latitude: 36.6880204\n",
-      "Longitude: 126.7393317\n",
-      "\n",
-      "Address: 충청남도 예산군 고덕면 예당산단4길 147\n",
-      "Latitude: 36.7626597\n",
-      "Longitude: 126.7302905\n",
-      "\n",
-      "Address: 충남 보령시 오천면 외연도리 산64\n",
-      "Latitude: 36.2294015\n",
-      "Longitude: 126.0830436\n",
-      "\n",
-      "Address: 충청남도 청양군 정산면 칠갑산로 1861\n",
-      "Latitude: 36.4149209\n",
-      "Longitude: 126.9455889\n",
-      "\n",
-      "Address: 충청남도 당진시 신평면 매산리 976\n",
-      "Latitude: 36.9554166\n",
-      "Longitude: 126.8277843\n",
-      "\n",
-      "Address: 충남 서산시 대산읍 대죽리 1114\n",
-      "Latitude: 37.0126847\n",
-      "Longitude: 126.4266258\n",
-      "\n",
-      "Address: 충남 서천군 장항읍 장산로 270-3\n",
-      "Latitude: 36.0080173\n",
-      "Longitude: 126.6902323\n",
-      "\n",
-      "Address: 충청남도 당진시 송악읍 고대공단2길 79-30\n",
-      "Latitude: 36.9858631\n",
-      "Longitude: 126.7458981\n",
-      "\n",
-      "Address: 충청남도 태안군 원북면 발전로 457\n",
-      "Latitude: 36.9033942\n",
-      "Longitude: 126.2313689\n",
-      "\n",
-      "Address: 충청남도 보령시 오천면 오천해안로 89-37\n",
-      "Latitude: 36.4021554\n",
-      "Longitude: 126.4931129\n",
-      "\n",
-      "Address: 세종특별자치시 한누리대로 2107\n",
-      "Latitude: 36.5016784\n",
-      "Longitude: 127.2872124\n",
-      "\n",
-      "Address: 세종특별자치시 전의면 운주산로 1270\n",
-      "Latitude: 36.6811539\n",
-      "Longitude: 127.1959765\n",
-      "\n",
-      "Address: 강원특별자치도 원주시 지정면 기업도시로 200\n",
-      "Latitude: 37.3716142\n",
-      "Longitude: 127.8740384\n",
-      "\n",
-      "Address: 강원특별자치도 강릉시 주문진읍 항구로 19\n",
-      "Latitude: 37.8938502\n",
-      "Longitude: 128.8233496\n",
-      "\n",
-      "Address: 강원특별자치도 횡성군 우천면 우항1길 5-34\n",
-      "Latitude: 37.4597138\n",
-      "Longitude: 128.062736\n",
-      "\n",
-      "Address: 충청북도 충주시 중앙탑면 기업도시로 237\n",
-      "Latitude: 37.0166313\n",
-      "Longitude: 127.8222303\n",
-      "\n",
-      "Address: 충청북도 충주시 살미면 세성양지말길 41\n",
-      "Latitude: 36.9053656\n",
-      "Longitude: 127.9645363\n",
-      "\n",
-      "Address: 충청북도 제천시 청풍호로8길 7\n",
-      "Latitude: 37.1246409\n",
-      "Longitude: 128.1984929\n",
-      "\n",
-      "Address: 충청북도 제천시 청풍면 청풍호로 2115\n",
-      "Latitude: 37.000632\n",
-      "Longitude: 128.1682847\n",
-      "\n",
-      "Address: 충청북도 청주시 상당구 가덕면 보청대로 4650\n",
-      "Latitude: 36.55342\n",
-      "Longitude: 127.5487467\n",
-      "\n",
-      "Address: 충북 단양군 단성면 충혼로 52-1\n",
-      "Latitude: 36.9390095\n",
-      "Longitude: 128.3207831\n",
-      "\n",
-      "Address: 충청북도 단양군 단양읍 별곡6길 26\n",
-      "Latitude: 36.9874489\n",
-      "Longitude: 128.3669855\n",
-      "\n",
-      "Address: 충청북도 괴산군 감물면 충민로신대길 13\n",
-      "Latitude: 36.8367039\n",
-      "Longitude: 127.8749123\n",
-      "\n",
-      "Address: 충청북도 진천군 덕산읍 대월로 90\n",
-      "Latitude: 36.9013547\n",
-      "Longitude: 127.5401008\n",
-      "\n",
-      "Address: 충청북도 음성군 소이면 소이로 409\n",
-      "Latitude: 36.9234047\n",
-      "Longitude: 127.7574516\n",
-      "\n",
-      "Address: 충청북도 영동군 황간면 남성리 185\n",
-      "Latitude: 36.2294858\n",
-      "Longitude: 127.9132126\n",
-      "\n",
-      "Address: 충청북도 증평군 증평읍 남하용강로 16\n",
-      "Latitude: 36.7670884\n",
-      "Longitude: 127.6044359\n",
-      "\n",
-      "Address: 전라북도 전주시 덕진구 중동로 150\n",
-      "Latitude: 35.8408284\n",
-      "Longitude: 127.0645461\n",
-      "\n",
-      "Address: 전북특별자치도 전주시 덕진구 중동로 150\n",
-      "Latitude: 35.8408284\n",
-      "Longitude: 127.0645461\n",
-      "\n",
-      "Address: 전라북도 전주시 덕진구 여암2길 9\n",
-      "Latitude: 35.8713543\n",
-      "Longitude: 127.0756245\n",
-      "\n",
-      "Address: 전북특별자치도 전주시 덕진구 여암2길 9\n",
-      "Latitude: 35.8713543\n",
-      "Longitude: 127.0756245\n",
-      "\n",
-      "Address: 전라북도 전주시 완산구 쑥고개로 259 (효자동2가)\n",
-      "Latitude: 35.8009096\n",
-      "Longitude: 127.0934069\n",
-      "\n",
-      "Address: 전북특별자치도 전주시 완산구 쑥고개로 259 (효자동2가)\n",
-      "Latitude: 35.8009096\n",
-      "Longitude: 127.0934069\n",
-      "\n",
-      "Address: 전북 군산시 옥산면 산성로 200\n",
-      "Latitude: 35.9395359\n",
-      "Longitude: 126.7482412\n",
-      "\n",
-      "Address: 전북특별자치도 군산시 옥산면 산성로 200\n",
-      "Latitude: 35.9395359\n",
-      "Longitude: 126.7482412\n",
-      "\n",
-      "Address: 전북 군산시 새만금북로 43\n",
-      "Latitude: 35.9438629\n",
-      "Longitude: 126.5410806\n",
-      "\n",
-      "Address: 전북특별자치도 군산시 새만금북로 43\n",
-      "Latitude: 35.9438629\n",
-      "Longitude: 126.5410806\n",
-      "\n",
-      "Address: 전라북도 군산시 옥도면 말도2길 29\n",
-      "Latitude: 35.8581473\n",
-      "Longitude: 126.3153337\n",
-      "\n",
-      "Address: 전라북도 군산시 동장산2길 6\n",
-      "Latitude: 35.9589156\n",
-      "Longitude: 126.5968199\n",
-      "\n",
-      "Address: 전북특별자치도 군산시 동장산2길 6\n",
-      "Latitude: 35.9589156\n",
-      "Longitude: 126.5968199\n",
-      "\n",
-      "Address: 전북 익산시 삼기면 황금로 513\n",
-      "Latitude: 36.0203572\n",
-      "Longitude: 126.984052\n",
-      "\n",
-      "Address: 전북특별자치도 익산시 삼기면 황금로 513\n",
-      "Latitude: 36.0203572\n",
-      "Longitude: 126.984052\n",
-      "\n",
-      "Address: 전북 익산시 용동면 용동1길 80-4\n",
-      "Latitude: 36.1096478\n",
-      "Longitude: 126.9916212\n",
-      "\n",
-      "Address: 전북특별자치도 익산시 용동면 용동1길 80-4\n",
-      "Latitude: 36.1096478\n",
-      "Longitude: 126.9916212\n",
-      "\n",
-      "Address: 전라북도 익산시 함열읍 함열중앙로 83\n",
-      "Latitude: 36.0783411\n",
-      "Longitude: 126.9664873\n",
-      "\n",
-      "Address: 전북특별자치도 익산시 함열읍 함열중앙로 83\n",
-      "Latitude: 36.0783411\n",
-      "Longitude: 126.9664873\n",
-      "\n",
-      "Address: 전라북도 익산시 춘포면 춘포2길 11\n",
-      "Latitude: 35.9009756\n",
-      "Longitude: 127.0051958\n",
-      "\n",
-      "Address: 전북특별자치도 익산시 춘포면 춘포2길 11\n",
-      "Latitude: 35.9009756\n",
-      "Longitude: 127.0051958\n",
-      "\n",
-      "Address: 전라북도 익산시 여산면 가람로 393\n",
-      "Latitude: 36.05981999999999\n",
-      "Longitude: 127.0834869\n",
-      "\n",
-      "Address: 전북특별자치도 익산시 여산면 가람로 393\n",
-      "Latitude: 36.05981999999999\n",
-      "Longitude: 127.0834869\n",
-      "\n",
-      "Address: 전라북도 익산시 금마면 무왕로 1824\n",
-      "Latitude: 35.9854726\n",
-      "Longitude: 127.0481811\n",
-      "\n",
-      "Address: 전북특별자치도 익산시 금마면 무왕로 1824\n",
-      "Latitude: 35.9854726\n",
-      "Longitude: 127.0481811\n",
-      "\n",
-      "Address: 전라북도 정읍시 영파동 232-1\n",
-      "Latitude: 35.6102898\n",
-      "Longitude: 126.8542513\n",
-      "\n",
-      "Address: 전북특별자치도 정읍시 영파동 232-1\n",
-      "Latitude: 35.6102898\n",
-      "Longitude: 126.8542513\n",
-      "\n",
-      "Address: 전라북도 남원시 운봉읍 황산로 1083\n",
-      "Latitude: 35.4393297\n",
-      "Longitude: 127.5292719\n",
-      "\n",
-      "Address: 전북특별자치도 남원시 운봉읍 황산로 1083\n",
-      "Latitude: 35.4393297\n",
-      "Longitude: 127.5292719\n",
-      "\n",
-      "Address: 전라북도 부안군 계화면 간재로 405\n",
-      "Latitude: 35.7614421\n",
-      "Longitude: 126.6977884\n",
-      "\n",
-      "Address: 전북특별자치도 부안군 계화면 간재로 405\n",
-      "Latitude: 35.7614421\n",
-      "Longitude: 126.6977884\n",
-      "\n",
-      "Address: 전라북도 김제시 광활면 지평선로 638\n",
-      "Latitude: 35.8357883\n",
-      "Longitude: 126.7405328\n",
-      "\n",
-      "Address: 전북특별자치도 김제시 광활면 지평선로 638\n",
-      "Latitude: 35.8357883\n",
-      "Longitude: 126.7405328\n",
-      "\n",
-      "Address: 전라북도 완주군 봉동읍 삼봉로 933\n",
-      "Latitude: 35.9413287\n",
-      "Longitude: 127.1655528\n",
-      "\n",
-      "Address: 전북특별자치도 완주군 봉동읍 삼봉로 933\n",
-      "Latitude: 35.9413287\n",
-      "Longitude: 127.1655528\n",
-      "\n",
-      "Address: 전라북도 완주군 구이면 덕천전원길 232-58\n",
-      "Latitude: 35.73745359999999\n",
-      "Longitude: 127.1338061\n",
-      "\n",
-      "Address: 전북특별자치도 완주군 구이면 덕천전원길 232-58\n",
-      "Latitude: 35.73745359999999\n",
-      "Longitude: 127.1338061\n",
-      "\n",
-      "Address: 전라북도 임실군 관촌면 사선1길 13\n",
-      "Latitude: 35.6737057\n",
-      "Longitude: 127.2707761\n",
-      "\n",
-      "Address: 전북특별자치도 임실군 관촌면 사선1길 13\n",
-      "Latitude: 35.6737057\n",
-      "Longitude: 127.2707761\n",
-      "\n",
-      "Address: 경상북도 청송군 청송읍 금월로 230\n",
-      "Latitude: 36.4293306\n",
-      "Longitude: 129.0560864\n",
-      "\n",
-      "Address: 전북 군산시 서해로 194\n",
-      "Latitude: 35.9752999\n",
-      "Longitude: 126.590464\n",
-      "\n",
-      "Address: 인천광역시 중구 서해대로 365-1\n",
-      "Latitude: 37.4599908\n",
-      "Longitude: 126.6297202\n",
-      "\n",
-      "Address: 인천광역시 중구 하늘중앙로 132\n",
-      "Latitude: 37.49500520000001\n",
-      "Longitude: 126.5607939\n",
-      "\n",
-      "Address: 인천광역시 남동구 남동대로 668\n",
-      "Latitude: 37.4419936\n",
-      "Longitude: 126.7073869\n",
-      "\n",
-      "Address: 인천광역시 미추홀구 구월남로 27\n",
-      "Latitude: 37.4552723\n",
-      "Longitude: 126.6937559\n",
-      "\n",
-      "Address: 인천광역시 서구 중봉대로 274\n",
-      "Latitude: 37.5035894\n",
-      "Longitude: 126.6489955\n",
-      "\n",
-      "Address: 인천광역시 계양구 봉오대로600번길 14\n",
-      "Latitude: 37.5290575\n",
-      "Longitude: 126.7151646\n",
-      "\n",
-      "Address: 인천광역시 계양구 경명대로 1179 (병방동)\n",
-      "Latitude: 37.5432967\n",
-      "Longitude: 126.7377312\n",
-      "\n",
-      "Address: 인천광역시 남동구 서창남로 101\n",
-      "Latitude: 37.4274542\n",
-      "Longitude: 126.7459827\n",
-      "\n",
-      "Address: 인천광역시 연수구 센트럴로 350\n",
-      "Latitude: 37.4063111\n",
-      "Longitude: 126.6352338\n",
-      "\n",
-      "Address: 인천 서구 오류동 경인항\n",
-      "Latitude: 37.5584727\n",
-      "Longitude: 126.6060332\n",
-      "\n",
-      "Address: 인천 중구 항동7가 1-48\n",
-      "Latitude: 37.462381\n",
-      "Longitude: 126.6232851\n",
-      "\n",
-      "Address: 인천광역시 연수구 인천신항대로 866-9\n",
-      "Latitude: 37.3540019\n",
-      "Longitude: 126.627735\n",
-      "\n",
-      "Address: 인천광역시 서구 원석로 41 (석남동)\n",
-      "Latitude: 37.5001301\n",
-      "Longitude: 126.6401558\n",
-      "\n",
-      "Address: 인천광역시 중구 축항대로118번길 147\n",
-      "Latitude: 37.4434015\n",
-      "Longitude: 126.5983328\n",
-      "\n",
-      "Address: 인천 옹진군 연평면 연평리 631\n",
-      "Latitude: 37.6661767\n",
-      "Longitude: 125.7023834\n",
-      "\n",
-      "Address: 인천 옹진군 덕적면 울도리 85번지\n",
-      "Latitude: 37.025088\n",
-      "Longitude: 125.9958066\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 아직 위도와 경도가 na인 데이터가 존재하기 때문에 주소를 기반으로 위도와 경도를 추가하기 위해서 \n",
-    "# 구글에서 제공하는 API를 사용하여 주소를 기반으로 위도와 경도를 추가한다.\n",
-    "\n",
-    "import requests\n",
-    "\n",
-    "api_key = \"AIzaSyDRI065JSqKKXqcmVbZQ7GiNnoKp0dA0ys\" # 구글 API 키\n",
-    "\n",
-    "\n",
-    "address_coordinates = {} # 주소를 기반으로 위도, 경도 데이터를 저장할 딕셔너리\n",
-    "for address in tqdm(air.loc[pd.isna(air['위도']), '주소'].unique()):\n",
-    "    url = f\"https://maps.googleapis.com/maps/api/geocode/json?address={address}&key={api_key}\" # 구글 API를 이용하여 위도, 경도 데이터를 가져옴\n",
-    "    \n",
-    "    response = requests.get(url) # get 요청\n",
-    "    \n",
-    "    data = response.json() # json 데이터로 변환\n",
-    "    \n",
-    "    if data['status'] == 'OK': # 정상적으로 데이터를 가져왔을 때\n",
-    "        lat = data['results'][0]['geometry']['location']['lat']\n",
-    "        lng = data['results'][0]['geometry']['location']['lng']\n",
-    "        address_coordinates[address] = (lat, lng) # 위도, 경도 데이터 추가\n",
-    "\n",
-    "for address, coordinates in address_coordinates.items():\n",
-    "    print(f\"Address: {address}\")\n",
-    "    print(f\"Latitude: {coordinates[0]}\")\n",
-    "    print(f\"Longitude: {coordinates[1]}\")\n",
-    "    print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 17.67 GB\n",
-      "현재 메모리 사용량: 15.9%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n",
-    "print(f\"현재 메모리 사용량: {psutil.virtual_memory().percent}%\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'경기도 수원시 권선구 칠보로1번길 62': (37.2687013, 126.9354884),\n",
-       " '경기도 의정부시 민락로243번길 94': (37.7477055, 127.1066326),\n",
-       " '경기도 시흥시 시청로 20': (37.3799469, 126.8031765),\n",
-       " '경기도 시흥시 서해안로 277': (37.3944639, 126.743953),\n",
-       " '경기도 시흥시 배곧4로 102': (37.3735088, 126.7337514),\n",
-       " '경기도 남양주시 경춘로 433': (37.6079488, 127.1593216),\n",
-       " '경기도 남양주시 와부읍 도곡길 7': (37.5804204, 127.2260649),\n",
-       " '경기도 남양주시 진접읍 금강로 1509-26': (37.7263588, 127.1898925),\n",
-       " '경기도 평택시 청북읍 안청로2길 60': (37.0215834, 126.9163318),\n",
-       " '경기도 평택시 고덕면 고덕국제2로 111': (37.05311950000001, 127.0462758),\n",
-       " '경기도 파주시 파주읍 교육길 13': (37.8308505, 126.8330233),\n",
-       " '경기도 광주시 오포읍 오포로859번길 29': (37.366347, 127.2285647),\n",
-       " '경기도 광주시 곤지암읍 광여로 59': (37.3509517, 127.3521893),\n",
-       " '경기도 이천시 부발읍 무촌로 117': (37.283268, 127.4866972),\n",
-       " '경기도 김포시 김포한강2로24번길 93': (37.6468307, 126.6743716),\n",
-       " '경기도 하남시 아리수로 531': (37.5671161, 127.1860594),\n",
-       " '경기도 화성시 수노을중앙로 178': (37.2812059, 126.8187549),\n",
-       " '경기도 화성시 봉담읍 샘마을1길 8-4': (37.2194855, 126.9490498),\n",
-       " '경기도 화성시 서신면 궁평항로 1702': (37.1662107, 126.7088664),\n",
-       " '경기도 안성시 공도읍 공도4로 8': (37.0010908, 127.1726414),\n",
-       " '경기도 안성시 죽산면 남부길 60': (37.0746422, 127.4244675),\n",
-       " '경기도 여주시 대신면 율촌1길 12-10': (37.3733992, 127.5863028),\n",
-       " '경기도 여주시 가남읍 태평중앙1길 20': (37.20167199999999, 127.5452706),\n",
-       " '경기도 연천군 전곡읍 은전로 45': (38.0275947, 127.0632063),\n",
-       " '경기도 가평군 설악면 한서로 8': (37.675968, 127.494629),\n",
-       " '경기도 양평군 양평읍 마유산로 17': (37.4970925, 127.4865458),\n",
-       " '경기 연천군 중면 횡산리 산108-1': (38.126438, 126.9732781),\n",
-       " '강원특별자치도 춘천시 사우4길 26': (37.9056482, 127.7280602),\n",
-       " '강원특별자치도 춘천시 경춘로 2370': (37.8648724, 127.720974),\n",
-       " '강원특별자치도 동해시 대동로 210': (37.488497, 129.1300356),\n",
-       " '강원특별자치도 동해시 임항로 121': (37.5484489, 129.110772),\n",
-       " '강원특별자치도 삼척시 원덕읍 호산리 338-14': (37.1743425, 129.343893),\n",
-       " '강원특별자치도 철원군 철원읍 산명리 1345': (38.2920178, 127.1781293),\n",
-       " '강원특별자치도 화천군 화천읍 풍산리 산269': (38.2198155, 127.7724077),\n",
-       " '강원특별자치도 고성군 수동면 신탄리 산6번지': (38.3811816, 128.2577979),\n",
-       " '강원특별자치도 고성군 수동면 외면리 산14번지': (38.5632087, 128.3414492),\n",
-       " '부산광역시 영도구 청학남로13번길 18': (35.0907485, 129.0594904),\n",
-       " '부산광역시 남구 이기대공원로 11': (35.1258003, 129.1164163),\n",
-       " '부산광역시 북구 용당로16번길 22': (35.2328515, 129.0089178),\n",
-       " '부산광역시 사상구 모덕로 2': (35.1824537, 128.976956),\n",
-       " '부산광역시 해운대구 해운대해변로 84 (우동)': (35.160586, 129.1399465),\n",
-       " '부산광역시 해운대구 센텀동로 191': (35.1827807, 129.1202801),\n",
-       " '부산광역시 강서구 명지동 3513-3': (35.1055728, 128.9258618),\n",
-       " '부산광역시 금정구 금사로 217': (35.2301064, 129.1206659),\n",
-       " '부산광역시 강서구 신항남로 354': (35.0802557, 128.8268542),\n",
-       " '부산광역시 강서구 신항남로 416': (35.073104, 128.833501),\n",
-       " '부산광역시 동구 충장대로 314': (35.1227863, 129.0568997),\n",
-       " '부산광역시 남구 북항로 105': (35.1059456, 129.0825838),\n",
-       " '경상남도 창원시 마산회원구 내서읍 광려로 8': (35.226291, 128.5025384),\n",
-       " '경상남도 진주시 정촌면 예하리 1340': (35.124889, 128.1004766),\n",
-       " '경상남도 창원시 마산합포구 진동면 삼진의거대로 621': (35.1161188, 128.485312),\n",
-       " '경상남도 하동군 금성면 금성중앙길 14': (34.965974, 127.7941908),\n",
-       " '경상남도 김해시 김해대로 2515': (35.228156, 128.9006477),\n",
-       " '경상남도 거제시 계룡로 125': (34.8805937, 128.6212211),\n",
-       " '경상남도 사천시 향촌5길 28': (34.9337743, 128.0934556),\n",
-       " '경상남도 양산시 물금읍 황산로 384': (35.3103051, 128.9872259),\n",
-       " '울산광역시 울주군 웅촌면 새초천길 12': (35.4612703, 129.2084963),\n",
-       " '울산광역시 울주군 범서읍 당앞로 14-50': (35.57033029999999, 129.223284),\n",
-       " '울산광역시 중구 태화동 310': (35.5579648, 129.2814575),\n",
-       " '울산광역시 북구 송내14길 41': (35.5923096, 129.3651871),\n",
-       " '경상남도 창원시 성산구 적현로 424': (35.1895435, 128.5938474),\n",
-       " '울산 남구 매암로 96': (35.5167331, 129.3763258),\n",
-       " '경상남도 하동군 금성면 경제산업로 509': (34.951636, 127.8204985),\n",
-       " '경상남도 사천시 향촌동 1292': (34.9182613, 128.0843048),\n",
-       " '광주광역시 서구 천변우하로 203': (35.1633863, 126.8493017),\n",
-       " '광주광역시 북구 모룡대길 68': (35.2179317, 126.8926976),\n",
-       " '광주광역시 광산구 우산동 1026-2': (35.1577625, 126.8100651),\n",
-       " '전라남도 광양시 봉강면 조양길 46': (35.013973, 127.5802854),\n",
-       " '전라남도 광양시 진월면 선소중앙길 31': (34.9784446, 127.7580563),\n",
-       " '전라남도 영암군 영암읍 낭주로 202-2': (34.807237, 126.701469),\n",
-       " '전남 영광군 낙월면 영외리 산 118': (35.3363281, 126.0206231),\n",
-       " '전라남도 신안군 흑산면 홍도1길 51-3': (34.6853982, 125.1918734),\n",
-       " '전남 신안군 흑산면 가거도리 산 4': (34.0908578, 125.1013039),\n",
-       " '전라남도 보성군 보성읍 현충로 42-36': (34.7649425, 127.0782547),\n",
-       " '전남 영암군 삼호읍 용당리 2169-1': (34.7717083, 126.3922166),\n",
-       " '전남 광양시 컨부두로 316': (34.9068431, 127.6680385),\n",
-       " '전남 여수시 여객선터미널길 17': (34.7385468, 127.7326155),\n",
-       " '전라남도 광양시 항만9로 70': (34.921022, 127.6959196),\n",
-       " '전라남도 광양시 광양읍 율촌산단7로 111': (34.89446119999999, 127.6111959),\n",
-       " '제주특별자치도 제주시 조천읍 조천18길 11-1': (33.5390118, 126.6427149),\n",
-       " '제주특별자치도 제주시 한림읍 한림중앙로 71-9': (33.4094321, 126.2685964),\n",
-       " '제주특별자치도 제주시 화북일동 1098': (33.5170009, 126.5702961),\n",
-       " '제주특별자치도 제주시 애월읍 고내리 1319': (33.4646987, 126.3314337),\n",
-       " '제주특별자치도 서귀포시 일주서로 166': (33.2518481, 126.4906021),\n",
-       " '대구광역시 중구 남산로2길 125': (35.857907, 128.5894969),\n",
-       " '대구광역시 북구 연암로 40': (35.8926449, 128.6007471),\n",
-       " '대구광역시 달서구 구마로26길 62': (35.8344259, 128.5411162),\n",
-       " '대구광역시 서구 서대구로3길 46': (35.8589429, 128.5519085),\n",
-       " '대구광역시 북구 옥산로17길 21': (35.8861362, 128.5849779),\n",
-       " '대구광역시 달성군 화원읍 인흥1길 12': (35.7974305, 128.5037874),\n",
-       " '대구광역시 남구 앞산순환로 540': (35.8317684, 128.5827485),\n",
-       " '대구광역시 군위군 군위읍 군청로 158': (36.2372849, 128.5744108),\n",
-       " '경상북도 포항시 북구 새천년대로 906': (36.0528499, 129.3611936),\n",
-       " '경상북도 포항시 남구 연일읍 동문로 67': (35.9962626, 129.3521476),\n",
-       " '경상북도 포항시 남구 인덕로 52': (35.9899753, 129.3976328),\n",
-       " '경상북도 포항시 북구 천마로 161 (양덕동)': (36.0901685, 129.3961353),\n",
-       " '경북 김천시 혁신4로 21': (36.121462, 128.1833644),\n",
-       " '경상북도 구미시 이계북로 149': (36.1059908, 128.418356),\n",
-       " '경상북도 영주시 광복로 65': (36.8286792, 128.625912),\n",
-       " '경상북도 경산시 하양읍 하양로 119-1': (35.9146655, 128.8200554),\n",
-       " '경상북도 경산시 진량읍 낙산길 7': (35.8756691, 128.8124495),\n",
-       " '경상북도 영덕군 강구면 강구리 167': (36.3635446, 129.3888018),\n",
-       " '경상북도 영덕군 영해면 예주3길 7': (36.53746539999999, 129.4070884),\n",
-       " '경북 문경시 시청2길 45': (36.5867479, 128.1861093),\n",
-       " '경상북도 성주군 성주읍 성주로 3258': (35.9174968, 128.2901803),\n",
-       " '경북 의성군 의성읍 군청길 31': (36.3525152, 128.6970106),\n",
-       " '경상북도 의성군 안계면 안계길 114': (36.3837643, 128.4401647),\n",
-       " '경상북도 봉화군 봉화읍 봉화로 1111': (36.8931267, 128.7325885),\n",
-       " '경상북도 영양군 영양읍 군청길 37': (36.6666895, 129.1125208),\n",
-       " '경상북도 예천군 호명면 행복7길 25-4': (36.5767932, 128.47329),\n",
-       " '경북 청도군 화양읍 도주관로 159': (35.6497741, 128.706151),\n",
-       " '경북 포항시 남구 신항로 99-98': (36.0057836, 129.4150523),\n",
-       " '대전광역시 유성구 테크노중앙로 88': (36.424847, 127.3948613),\n",
-       " '충청남도 공주시 탄천면 안터새말길 34': (36.3084894, 127.0677527),\n",
-       " '충청남도 당진시 합덕읍 합덕리 344': (36.7904994, 126.785855),\n",
-       " '충청남도 당진시 송악읍 신복운로 5': (36.94439980000001, 126.7836024),\n",
-       " '충청남도 아산시 배방읍 장재리 2116': (36.7844748, 127.1013253),\n",
-       " '충청남도 아산시 송악면 송악로 790': (36.7316409, 127.008961),\n",
-       " '충청남도 논산시 연무읍 안심로 50': (36.1253129, 127.0989462),\n",
-       " '충청남도 논산시 성동면 산업단지로5길 73-28': (36.2151708, 127.0439255),\n",
-       " '충남 태안군 근흥면 가의도길 44-71번지': (36.672586, 126.0644856),\n",
-       " '충청남도 태안군 원북면 상리길 17-4': (36.8242316, 126.2572303),\n",
-       " '충청남도 예산군 삽교읍 두리3길 33': (36.6880204, 126.7393317),\n",
-       " '충청남도 예산군 고덕면 예당산단4길 147': (36.7626597, 126.7302905),\n",
-       " '충남 보령시 오천면 외연도리 산64': (36.2294015, 126.0830436),\n",
-       " '충청남도 청양군 정산면 칠갑산로 1861': (36.4149209, 126.9455889),\n",
-       " '충청남도 당진시 신평면 매산리 976': (36.9554166, 126.8277843),\n",
-       " '충남 서산시 대산읍 대죽리 1114': (37.0126847, 126.4266258),\n",
-       " '충남 서천군 장항읍 장산로 270-3': (36.0080173, 126.6902323),\n",
-       " '충청남도 당진시 송악읍 고대공단2길 79-30': (36.9858631, 126.7458981),\n",
-       " '충청남도 태안군 원북면 발전로 457': (36.9033942, 126.2313689),\n",
-       " '충청남도 보령시 오천면 오천해안로 89-37': (36.4021554, 126.4931129),\n",
-       " '세종특별자치시 한누리대로 2107': (36.5016784, 127.2872124),\n",
-       " '세종특별자치시 전의면 운주산로 1270': (36.6811539, 127.1959765),\n",
-       " '강원특별자치도 원주시 지정면 기업도시로 200': (37.3716142, 127.8740384),\n",
-       " '강원특별자치도 강릉시 주문진읍 항구로 19': (37.8938502, 128.8233496),\n",
-       " '강원특별자치도 횡성군 우천면 우항1길 5-34': (37.4597138, 128.062736),\n",
-       " '충청북도 충주시 중앙탑면 기업도시로 237': (37.0166313, 127.8222303),\n",
-       " '충청북도 충주시 살미면 세성양지말길 41': (36.9053656, 127.9645363),\n",
-       " '충청북도 제천시 청풍호로8길 7': (37.1246409, 128.1984929),\n",
-       " '충청북도 제천시 청풍면 청풍호로 2115': (37.000632, 128.1682847),\n",
-       " '충청북도 청주시 상당구 가덕면 보청대로 4650': (36.55342, 127.5487467),\n",
-       " '충북 단양군 단성면 충혼로 52-1': (36.9390095, 128.3207831),\n",
-       " '충청북도 단양군 단양읍 별곡6길 26': (36.9874489, 128.3669855),\n",
-       " '충청북도 괴산군 감물면 충민로신대길 13': (36.8367039, 127.8749123),\n",
-       " '충청북도 진천군 덕산읍 대월로 90': (36.9013547, 127.5401008),\n",
-       " '충청북도 음성군 소이면 소이로 409': (36.9234047, 127.7574516),\n",
-       " '충청북도 영동군 황간면 남성리 185': (36.2294858, 127.9132126),\n",
-       " '충청북도 증평군 증평읍 남하용강로 16': (36.7670884, 127.6044359),\n",
-       " '전라북도 전주시 덕진구 중동로 150': (35.8408284, 127.0645461),\n",
-       " '전북특별자치도 전주시 덕진구 중동로 150': (35.8408284, 127.0645461),\n",
-       " '전라북도 전주시 덕진구 여암2길 9': (35.8713543, 127.0756245),\n",
-       " '전북특별자치도 전주시 덕진구 여암2길 9': (35.8713543, 127.0756245),\n",
-       " '전라북도 전주시 완산구 쑥고개로 259 (효자동2가)': (35.8009096, 127.0934069),\n",
-       " '전북특별자치도 전주시 완산구 쑥고개로 259 (효자동2가)': (35.8009096, 127.0934069),\n",
-       " '전북 군산시 옥산면 산성로 200': (35.9395359, 126.7482412),\n",
-       " '전북특별자치도 군산시 옥산면 산성로 200': (35.9395359, 126.7482412),\n",
-       " '전북 군산시 새만금북로 43': (35.9438629, 126.5410806),\n",
-       " '전북특별자치도 군산시 새만금북로 43': (35.9438629, 126.5410806),\n",
-       " '전라북도 군산시 옥도면 말도2길 29': (35.8581473, 126.3153337),\n",
-       " '전라북도 군산시 동장산2길 6': (35.9589156, 126.5968199),\n",
-       " '전북특별자치도 군산시 동장산2길 6': (35.9589156, 126.5968199),\n",
-       " '전북 익산시 삼기면 황금로 513': (36.0203572, 126.984052),\n",
-       " '전북특별자치도 익산시 삼기면 황금로 513': (36.0203572, 126.984052),\n",
-       " '전북 익산시 용동면 용동1길 80-4': (36.1096478, 126.9916212),\n",
-       " '전북특별자치도 익산시 용동면 용동1길 80-4': (36.1096478, 126.9916212),\n",
-       " '전라북도 익산시 함열읍 함열중앙로 83': (36.0783411, 126.9664873),\n",
-       " '전북특별자치도 익산시 함열읍 함열중앙로 83': (36.0783411, 126.9664873),\n",
-       " '전라북도 익산시 춘포면 춘포2길 11': (35.9009756, 127.0051958),\n",
-       " '전북특별자치도 익산시 춘포면 춘포2길 11': (35.9009756, 127.0051958),\n",
-       " '전라북도 익산시 여산면 가람로 393': (36.05981999999999, 127.0834869),\n",
-       " '전북특별자치도 익산시 여산면 가람로 393': (36.05981999999999, 127.0834869),\n",
-       " '전라북도 익산시 금마면 무왕로 1824': (35.9854726, 127.0481811),\n",
-       " '전북특별자치도 익산시 금마면 무왕로 1824': (35.9854726, 127.0481811),\n",
-       " '전라북도 정읍시 영파동 232-1': (35.6102898, 126.8542513),\n",
-       " '전북특별자치도 정읍시 영파동 232-1': (35.6102898, 126.8542513),\n",
-       " '전라북도 남원시 운봉읍 황산로 1083': (35.4393297, 127.5292719),\n",
-       " '전북특별자치도 남원시 운봉읍 황산로 1083': (35.4393297, 127.5292719),\n",
-       " '전라북도 부안군 계화면 간재로 405': (35.7614421, 126.6977884),\n",
-       " '전북특별자치도 부안군 계화면 간재로 405': (35.7614421, 126.6977884),\n",
-       " '전라북도 김제시 광활면 지평선로 638': (35.8357883, 126.7405328),\n",
-       " '전북특별자치도 김제시 광활면 지평선로 638': (35.8357883, 126.7405328),\n",
-       " '전라북도 완주군 봉동읍 삼봉로 933': (35.9413287, 127.1655528),\n",
-       " '전북특별자치도 완주군 봉동읍 삼봉로 933': (35.9413287, 127.1655528),\n",
-       " '전라북도 완주군 구이면 덕천전원길 232-58': (35.73745359999999, 127.1338061),\n",
-       " '전북특별자치도 완주군 구이면 덕천전원길 232-58': (35.73745359999999, 127.1338061),\n",
-       " '전라북도 임실군 관촌면 사선1길 13': (35.6737057, 127.2707761),\n",
-       " '전북특별자치도 임실군 관촌면 사선1길 13': (35.6737057, 127.2707761),\n",
-       " '경상북도 청송군 청송읍 금월로 230': (36.4293306, 129.0560864),\n",
-       " '전북 군산시 서해로 194': (35.9752999, 126.590464),\n",
-       " '인천광역시 중구 서해대로 365-1': (37.4599908, 126.6297202),\n",
-       " '인천광역시 중구 하늘중앙로 132': (37.49500520000001, 126.5607939),\n",
-       " '인천광역시 남동구 남동대로 668': (37.4419936, 126.7073869),\n",
-       " '인천광역시 미추홀구 구월남로 27': (37.4552723, 126.6937559),\n",
-       " '인천광역시 서구 중봉대로 274': (37.5035894, 126.6489955),\n",
-       " '인천광역시 계양구 봉오대로600번길 14': (37.5290575, 126.7151646),\n",
-       " '인천광역시 계양구 경명대로 1179 (병방동)': (37.5432967, 126.7377312),\n",
-       " '인천광역시 남동구 서창남로 101': (37.4274542, 126.7459827),\n",
-       " '인천광역시 연수구 센트럴로 350': (37.4063111, 126.6352338),\n",
-       " '인천 서구 오류동 경인항': (37.5584727, 126.6060332),\n",
-       " '인천 중구 항동7가 1-48': (37.462381, 126.6232851),\n",
-       " '인천광역시 연수구 인천신항대로 866-9': (37.3540019, 126.627735),\n",
-       " '인천광역시 서구 원석로 41 (석남동)': (37.5001301, 126.6401558),\n",
-       " '인천광역시 중구 축항대로118번길 147': (37.4434015, 126.5983328),\n",
-       " '인천 옹진군 연평면 연평리 631': (37.6661767, 125.7023834),\n",
-       " '인천 옹진군 덕적면 울도리 85번지': (37.025088, 125.9958066)}"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "address_coordinates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "지역             0\n",
-       "측정소코드          0\n",
-       "측정소명           0\n",
-       "측정일시           0\n",
-       "O3       1152577\n",
-       "NO2      1172756\n",
-       "PM10     1441888\n",
-       "PM25     1970684\n",
-       "주소             0\n",
-       "year           0\n",
-       "month          0\n",
-       "day            0\n",
-       "hour           0\n",
-       "위도       4627988\n",
-       "경도       4627988\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(air).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 206/206 [08:42<00:00,  2.54s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 주소를 기반으로 위도와 경도를 추가\n",
-    "for address, coordinates in tqdm(address_coordinates.items()):\n",
-    "    air.loc[air['주소']==address, '위도']= coordinates[0]\n",
-    "    air.loc[air['주소']==address, '경도']= coordinates[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "지역             0\n",
-       "측정소코드          0\n",
-       "측정소명           0\n",
-       "측정일시           0\n",
-       "O3       1152577\n",
-       "NO2      1172756\n",
-       "PM10     1441888\n",
-       "PM25     1970684\n",
-       "주소             0\n",
-       "year           0\n",
-       "month          0\n",
-       "day            0\n",
-       "hour           0\n",
-       "위도             0\n",
-       "경도             0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(air).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air.loc[air['위도']==0, '위도']= np.nan # 위도가 0인 값 na로 변경\n",
-    "air.loc[air['경도']==0, '경도']= np.nan # 경도가 0인 값 na로 변경"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "지역             0\n",
-       "측정소코드          0\n",
-       "측정소명           0\n",
-       "측정일시           0\n",
-       "O3       1152577\n",
-       "NO2      1172756\n",
-       "PM10     1441888\n",
-       "PM25     1970684\n",
-       "주소             0\n",
-       "year           0\n",
-       "month          0\n",
-       "day            0\n",
-       "hour           0\n",
-       "위도             0\n",
-       "경도             0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(air).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 17.67 GB\n",
-      "현재 메모리 사용량: 15.9%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n",
-    "print(f\"현재 메모리 사용량: {psutil.virtual_memory().percent}%\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air.to_feather(\"../data/대기질/air_add_위도경도.feather\") # 위도, 경도 추가한 대기질 데이터 feather 형태로 저장"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 위도와 경도를 추가한 air_add_위도경도 데이터와 위도와 경도를 추가한 asos_fill_위경 데이터를 불러 온다"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from scipy.spatial import cKDTree\n",
-    "import numpy as np\n",
-    "from tqdm import tqdm, notebook, trange"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air = pd.read_feather('../data/대기질/air_add_위도경도.feather') # 대기질 데이터 불러오기\n",
-    "asos = pd.read_feather('../data/ASOS/asos_fill_위경.feather')   # ASOS 데이터 불러오기"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지점</th>\n",
-       "      <th>지점명</th>\n",
-       "      <th>일시</th>\n",
-       "      <th>기온(°C)</th>\n",
-       "      <th>기온 QC플래그</th>\n",
-       "      <th>강수량(mm)</th>\n",
-       "      <th>강수량 QC플래그</th>\n",
-       "      <th>풍속(m/s)</th>\n",
-       "      <th>풍속 QC플래그</th>\n",
-       "      <th>풍향(16방위)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>최저운고(100m )</th>\n",
-       "      <th>시정(10m)</th>\n",
-       "      <th>지면온도(°C)</th>\n",
-       "      <th>지면온도 QC플래그</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 00:00</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-2.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 01:00</td>\n",
-       "      <td>-2.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-2.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 02:00</td>\n",
-       "      <td>-2.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 03:00</td>\n",
-       "      <td>-2.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-3.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 04:00</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-3.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011260</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 19:00</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>320.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4501.0</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011261</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 20:00</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>320.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4240.0</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011262</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 21:00</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3708.0</td>\n",
-       "      <td>1.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011263</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 22:00</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>290.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2751.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011264</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 23:00</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>360.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2222.0</td>\n",
-       "      <td>-0.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5011265 rows × 36 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          지점  지점명                일시  기온(°C)  기온 QC플래그  강수량(mm)  강수량 QC플래그  \\\n",
-       "0         90   속초  2018-01-01 00:00    -1.0       0.0      NaN        NaN   \n",
-       "1         90   속초  2018-01-01 01:00    -2.1       0.0      NaN        NaN   \n",
-       "2         90   속초  2018-01-01 02:00    -2.1       0.0      NaN        NaN   \n",
-       "3         90   속초  2018-01-01 03:00    -2.2       0.0      NaN        NaN   \n",
-       "4         90   속초  2018-01-01 04:00    -2.0       0.0      NaN        NaN   \n",
-       "...      ...  ...               ...     ...       ...      ...        ...   \n",
-       "5011260  296  북부산  2023-12-31 19:00     6.6       NaN      NaN        9.0   \n",
-       "5011261  296  북부산  2023-12-31 20:00     5.9       NaN      NaN        9.0   \n",
-       "5011262  296  북부산  2023-12-31 21:00     5.1       NaN      NaN        9.0   \n",
-       "5011263  296  북부산  2023-12-31 22:00     3.5       NaN      NaN        9.0   \n",
-       "5011264  296  북부산  2023-12-31 23:00     1.8       NaN      NaN        9.0   \n",
-       "\n",
-       "         풍속(m/s)  풍속 QC플래그  풍향(16방위)  ...  최저운고(100m )  시정(10m)  지면온도(°C)  \\\n",
-       "0            1.1       NaN     250.0  ...          NaN   2000.0      -2.3   \n",
-       "1            1.7       0.0     230.0  ...          NaN   2000.0      -2.7   \n",
-       "2            1.4       0.0     160.0  ...          NaN   2000.0      -3.0   \n",
-       "3            0.9       0.0     230.0  ...          NaN   2000.0      -3.2   \n",
-       "4            1.2       0.0     250.0  ...          NaN   2000.0      -3.3   \n",
-       "...          ...       ...       ...  ...          ...      ...       ...   \n",
-       "5011260      1.5       NaN     320.0  ...          NaN   4501.0       2.8   \n",
-       "5011261      1.8       NaN     320.0  ...          NaN   4240.0       2.1   \n",
-       "5011262      1.2       NaN     250.0  ...          NaN   3708.0       1.1   \n",
-       "5011263      0.7       NaN     290.0  ...          NaN   2751.0       0.2   \n",
-       "5011264      0.5       NaN     360.0  ...          NaN   2222.0      -0.3   \n",
-       "\n",
-       "         지면온도 QC플래그  year  month  day  hour         위도          경도  \n",
-       "0               0.0  2018      1    1     0  38.250850  128.564730  \n",
-       "1               0.0  2018      1    1     1  38.250850  128.564730  \n",
-       "2               0.0  2018      1    1     2  38.250850  128.564730  \n",
-       "3               0.0  2018      1    1     3  38.250850  128.564730  \n",
-       "4               0.0  2018      1    1     4  38.250850  128.564730  \n",
-       "...             ...   ...    ...  ...   ...        ...         ...  \n",
-       "5011260         NaN  2023     12   31    19  35.217781  128.960242  \n",
-       "5011261         NaN  2023     12   31    20  35.217781  128.960242  \n",
-       "5011262         NaN  2023     12   31    21  35.217781  128.960242  \n",
-       "5011263         NaN  2023     12   31    22  35.217781  128.960242  \n",
-       "5011264         NaN  2023     12   31    23  35.217781  128.960242  \n",
-       "\n",
-       "[5011265 rows x 36 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>측정소명</th>\n",
-       "      <th>측정일시</th>\n",
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-       "      <th>PM25</th>\n",
-       "      <th>주소</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 00:00:00</td>\n",
-       "      <td>0.0130</td>\n",
-       "      <td>0.0280</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>37.564300</td>\n",
-       "      <td>126.974700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 01:00:00</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>37.564300</td>\n",
-       "      <td>126.974700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 02:00:00</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>37.564300</td>\n",
-       "      <td>126.974700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 03:00:00</td>\n",
-       "      <td>0.0180</td>\n",
-       "      <td>0.0220</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>37.564300</td>\n",
-       "      <td>126.974700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>중구</td>\n",
-       "      <td>2018-01-01 04:00:00</td>\n",
-       "      <td>0.0100</td>\n",
-       "      <td>0.0300</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>서울 중구 덕수궁길 15</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>37.564300</td>\n",
-       "      <td>126.974700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178033</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 19:00:00</td>\n",
-       "      <td>0.0440</td>\n",
-       "      <td>0.0075</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "      <td>37.025088</td>\n",
-       "      <td>125.995807</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178034</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 20:00:00</td>\n",
-       "      <td>0.0415</td>\n",
-       "      <td>0.0086</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "      <td>37.025088</td>\n",
-       "      <td>125.995807</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178035</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 21:00:00</td>\n",
-       "      <td>0.0409</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "      <td>37.025088</td>\n",
-       "      <td>125.995807</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178036</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 22:00:00</td>\n",
-       "      <td>0.0414</td>\n",
-       "      <td>0.0111</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "      <td>37.025088</td>\n",
-       "      <td>125.995807</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178037</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>울도</td>\n",
-       "      <td>2023-12-31 23:00:00</td>\n",
-       "      <td>0.0411</td>\n",
-       "      <td>0.0112</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>인천 옹진군 덕적면 울도리 85번지</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "      <td>37.025088</td>\n",
-       "      <td>125.995807</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28178038 rows × 15 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역   측정소코드 측정소명                측정일시      O3     NO2  PM10  PM25  \\\n",
-       "0          서울 중구  111121   중구 2018-01-01 00:00:00  0.0130  0.0280  31.0  16.0   \n",
-       "1          서울 중구  111121   중구 2018-01-01 01:00:00  0.0200  0.0200  34.0  19.0   \n",
-       "2          서울 중구  111121   중구 2018-01-01 02:00:00  0.0240  0.0160  27.0  14.0   \n",
-       "3          서울 중구  111121   중구 2018-01-01 03:00:00  0.0180  0.0220  26.0  14.0   \n",
-       "4          서울 중구  111121   중구 2018-01-01 04:00:00  0.0100  0.0300  26.0  15.0   \n",
-       "...          ...     ...  ...                 ...     ...     ...   ...   ...   \n",
-       "28178033  인천 옹진군  831495   울도 2023-12-31 19:00:00  0.0440  0.0075  67.0  57.0   \n",
-       "28178034  인천 옹진군  831495   울도 2023-12-31 20:00:00  0.0415  0.0086  64.0  57.0   \n",
-       "28178035  인천 옹진군  831495   울도 2023-12-31 21:00:00  0.0409  0.0095  62.0  50.0   \n",
-       "28178036  인천 옹진군  831495   울도 2023-12-31 22:00:00  0.0414  0.0111  65.0  59.0   \n",
-       "28178037  인천 옹진군  831495   울도 2023-12-31 23:00:00  0.0411  0.0112  63.0  54.0   \n",
-       "\n",
-       "                           주소  year  month  day  hour         위도          경도  \n",
-       "0               서울 중구 덕수궁길 15  2018      1    1     0  37.564300  126.974700  \n",
-       "1               서울 중구 덕수궁길 15  2018      1    1     1  37.564300  126.974700  \n",
-       "2               서울 중구 덕수궁길 15  2018      1    1     2  37.564300  126.974700  \n",
-       "3               서울 중구 덕수궁길 15  2018      1    1     3  37.564300  126.974700  \n",
-       "4               서울 중구 덕수궁길 15  2018      1    1     4  37.564300  126.974700  \n",
-       "...                       ...   ...    ...  ...   ...        ...         ...  \n",
-       "28178033  인천 옹진군 덕적면 울도리 85번지  2023     12   31    19  37.025088  125.995807  \n",
-       "28178034  인천 옹진군 덕적면 울도리 85번지  2023     12   31    20  37.025088  125.995807  \n",
-       "28178035  인천 옹진군 덕적면 울도리 85번지  2023     12   31    21  37.025088  125.995807  \n",
-       "28178036  인천 옹진군 덕적면 울도리 85번지  2023     12   31    22  37.025088  125.995807  \n",
-       "28178037  인천 옹진군 덕적면 울도리 85번지  2023     12   31    23  37.025088  125.995807  \n",
-       "\n",
-       "[28178038 rows x 15 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['지점', '지점명', '일시', '기온(°C)', '기온 QC플래그', '강수량(mm)', '강수량 QC플래그',\n",
-       "       '풍속(m/s)', '풍속 QC플래그', '풍향(16방위)', '풍향 QC플래그', '습도(%)', '습도 QC플래그',\n",
-       "       '증기압(hPa)', '이슬점온도(°C)', '현지기압(hPa)', '현지기압 QC플래그', '해면기압(hPa)',\n",
-       "       '해면기압 QC플래그', '일조(hr)', '일조 QC플래그', '일사(MJ/m2)', '일사 QC플래그', '적설(cm)',\n",
-       "       '전운량(10분위)', '중하층운량(10분위)', '최저운고(100m )', '시정(10m)', '지면온도(°C)',\n",
-       "       '지면온도 QC플래그', 'year', 'month', 'day', 'hour', '위도', '경도'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos.columns # ASOS 데이터 컬럼 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['지역', '측정소코드', '측정소명', '측정일시', 'O3', 'NO2', 'PM10', 'PM25', '주소',\n",
-       "       'year', 'month', 'day', 'hour', '위도', '경도'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air.columns # 대기질 데이터 컬럼 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 처음에는 측정소코드, year, month을 기준으로 데이터를 merge 하려고 했지만 측정소가 중간에 없어지거나 새로 생기는 경우가 있기때문에\n",
-    "# 측정소코드, year, month, day, hour을 기준으로 merge를 진행한다.\n",
-    "\n",
-    "# 중복된 값 제거\n",
-    "air_dup = air[['측정소코드', '위도', '경도', 'year','month','day','hour']].drop_duplicates(subset=['측정소코드', 'year','month','day','hour']).copy() \n",
-    "asos_dup = asos[['지점', '위도', '경도', 'year','month','day','hour']].drop_duplicates(subset=['지점', 'year','month','day','hour']).copy()\n",
-    "# 인덱스 초기화\n",
-    "air_dup = air_dup.reset_index(drop=True).copy() \n",
-    "asos_dup = asos_dup.reset_index(drop=True).copy() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 19.87 GB\n",
-      "현재 메모리 사용량: 17.6%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n",
-    "print(f\"현재 메모리 사용량: {psutil.virtual_memory().percent}%\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 측정소코드, 년, 월, 일 기준으로 정렬\n",
-    "air_dup.sort_values(by=['측정소코드', 'year','month','day','hour'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지점</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011260</th>\n",
-       "      <td>296</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011261</th>\n",
-       "      <td>296</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011262</th>\n",
-       "      <td>296</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011263</th>\n",
-       "      <td>296</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011264</th>\n",
-       "      <td>296</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5011265 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          지점         위도          경도  year  month  day  hour\n",
-       "0         90  38.250850  128.564730  2018      1    1     0\n",
-       "1         90  38.250850  128.564730  2018      1    1     1\n",
-       "2         90  38.250850  128.564730  2018      1    1     2\n",
-       "3         90  38.250850  128.564730  2018      1    1     3\n",
-       "4         90  38.250850  128.564730  2018      1    1     4\n",
-       "...      ...        ...         ...   ...    ...  ...   ...\n",
-       "5011260  296  35.217781  128.960242  2023     12   31    19\n",
-       "5011261  296  35.217781  128.960242  2023     12   31    20\n",
-       "5011262  296  35.217781  128.960242  2023     12   31    21\n",
-       "5011263  296  35.217781  128.960242  2023     12   31    22\n",
-       "5011264  296  35.217781  128.960242  2023     12   31    23\n",
-       "\n",
-       "[5011265 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos_dup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "28178038 5011265\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(len(air_dup),len(asos_dup)) # 중복 제거한 데이터 개수 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>37.564300</td>\n",
-       "      <td>126.974700</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>37.564300</td>\n",
-       "      <td>126.974700</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>37.564300</td>\n",
-       "      <td>126.974700</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>37.564300</td>\n",
-       "      <td>126.974700</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>37.564300</td>\n",
-       "      <td>126.974700</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178033</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>37.025088</td>\n",
-       "      <td>125.995807</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178034</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>37.025088</td>\n",
-       "      <td>125.995807</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178035</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>37.025088</td>\n",
-       "      <td>125.995807</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178036</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>37.025088</td>\n",
-       "      <td>125.995807</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178037</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>37.025088</td>\n",
-       "      <td>125.995807</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28178038 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           측정소코드         위도          경도  year  month  day  hour\n",
-       "0         111121  37.564300  126.974700  2018      1    1     0\n",
-       "1         111121  37.564300  126.974700  2018      1    1     1\n",
-       "2         111121  37.564300  126.974700  2018      1    1     2\n",
-       "3         111121  37.564300  126.974700  2018      1    1     3\n",
-       "4         111121  37.564300  126.974700  2018      1    1     4\n",
-       "...          ...        ...         ...   ...    ...  ...   ...\n",
-       "28178033  831495  37.025088  125.995807  2023     12   31    19\n",
-       "28178034  831495  37.025088  125.995807  2023     12   31    20\n",
-       "28178035  831495  37.025088  125.995807  2023     12   31    21\n",
-       "28178036  831495  37.025088  125.995807  2023     12   31    22\n",
-       "28178037  831495  37.025088  125.995807  2023     12   31    23\n",
-       "\n",
-       "[28178038 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air_dup # 중복 제거한 대기질 데이터 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th>지점</th>\n",
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-       "      <th>경도</th>\n",
-       "      <th>year</th>\n",
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-       "      <td>90</td>\n",
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-       "      <td>2018</td>\n",
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-       "      <td>90</td>\n",
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-       "      <td>2018</td>\n",
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-       "      <td>1</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
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-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
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-       "      <th>5011260</th>\n",
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-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
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-       "    <tr>\n",
-       "      <th>5011262</th>\n",
-       "      <td>296</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
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-       "    <tr>\n",
-       "      <th>5011263</th>\n",
-       "      <td>296</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
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-       "    <tr>\n",
-       "      <th>5011264</th>\n",
-       "      <td>296</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5011265 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          지점         위도          경도  year  month  day  hour\n",
-       "0         90  38.250850  128.564730  2018      1    1     0\n",
-       "1         90  38.250850  128.564730  2018      1    1     1\n",
-       "2         90  38.250850  128.564730  2018      1    1     2\n",
-       "3         90  38.250850  128.564730  2018      1    1     3\n",
-       "4         90  38.250850  128.564730  2018      1    1     4\n",
-       "...      ...        ...         ...   ...    ...  ...   ...\n",
-       "5011260  296  35.217781  128.960242  2023     12   31    19\n",
-       "5011261  296  35.217781  128.960242  2023     12   31    20\n",
-       "5011262  296  35.217781  128.960242  2023     12   31    21\n",
-       "5011263  296  35.217781  128.960242  2023     12   31    22\n",
-       "5011264  296  35.217781  128.960242  2023     12   31    23\n",
-       "\n",
-       "[5011265 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos_dup # 중복 제거한 ASOS 데이터 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>측정소코드</th>\n",
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-       "      <th>year</th>\n",
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-       "  </thead>\n",
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-       "      <th>22806078</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>1</td>\n",
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-       "      <td>632461</td>\n",
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-       "      <td>632461</td>\n",
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-       "    <tr>\n",
-       "      <th>22806081</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2019</td>\n",
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-       "      <th>22806082</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
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-       "      <td>2019</td>\n",
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-       "      <td>5</td>\n",
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-       "      <th>22849560</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
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-       "      <td>2023</td>\n",
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-       "      <th>22849561</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22849562</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22849563</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22849564</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>43487 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           측정소코드       위도        경도  year  month  day  hour\n",
-       "22806078  632461  38.2069  128.5882  2019      1   15     1\n",
-       "22806079  632461  38.2069  128.5882  2019      1   15     2\n",
-       "22806080  632461  38.2069  128.5882  2019      1   15     3\n",
-       "22806081  632461  38.2069  128.5882  2019      1   15     4\n",
-       "22806082  632461  38.2069  128.5882  2019      1   15     5\n",
-       "...          ...      ...       ...   ...    ...  ...   ...\n",
-       "22849560  632461  38.2069  128.5882  2023     12   31    19\n",
-       "22849561  632461  38.2069  128.5882  2023     12   31    20\n",
-       "22849562  632461  38.2069  128.5882  2023     12   31    21\n",
-       "22849563  632461  38.2069  128.5882  2023     12   31    22\n",
-       "22849564  632461  38.2069  128.5882  2023     12   31    23\n",
-       "\n",
-       "[43487 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air_dup.loc[air_dup['측정소코드']==632461,:] # 측정소코드 632461 데이터 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air_dup.to_feather(\"../data/대기질/air_dup.feather\") # 중복 제거한 대기질 데이터 feather 형태로 저장\n",
-    "asos_dup.to_feather(\"../data/ASOS/asos_dup.feather\") # 중복 제거한 ASOS 데이터 feather 형태로 저장"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/6 [00:00<?, ?it/s]"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from scipy.spatial import cKDTree\n",
-    "from tqdm import notebook, trange, tqdm\n",
-    "\n",
-    "def haversine(lat1, lon1, lat2, lon2): # 위도, 경도를 이용하여 거리 계산 함수\n",
-    "    # 지구 반지름 (km)\n",
-    "    R = 6371.0\n",
-    "    \n",
-    "    # 위도와 경도를 라디안 단위로 변환\n",
-    "    phi1 = np.radians(lat1)\n",
-    "    phi2 = np.radians(lat2)\n",
-    "    delta_phi = np.radians(lat2 - lat1)\n",
-    "    delta_lambda = np.radians(lon2 - lon1)\n",
-    "    \n",
-    "    # 하버사인 공식 계산\n",
-    "    a = np.sin(delta_phi / 2.0)**2 + np.cos(phi1) * np.cos(phi2) * np.sin(delta_lambda / 2.0)**2\n",
-    "    c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))\n",
-    "    \n",
-    "    # 거리 계산\n",
-    "    distance = R * c\n",
-    "    return distance\n",
-    "\n",
-    "# 근접 측정소코드, 거리차이 컬럼 추가\n",
-    "asos_dup['근접 측정소코드'] = None\n",
-    "asos_dup['거리차이(km)'] = None\n",
-    "\n",
-    "# asos 데이터와 대기질 데이터의 거리 계산\n",
-    "for year in tqdm(asos_dup['year'].unique()): # year별로 \n",
-    "    for month in (asos_dup['month'].unique()): # month별로\n",
-    "        for day in (asos_dup['day'].unique()): # day별로\n",
-    "            for hour in asos_dup['hour'].unique(): # hour별로\n",
-    "                asos_date = asos_dup[(asos_dup['year'] == year) & (asos_dup['month'] == month) & (asos_dup['day'] == day)& (asos_dup['hour'] == hour)].copy()  # ASOS 데이터 중 같은 날짜, 시간 데이터 추출\n",
-    "                air_date = air_dup[(air_dup['year'] == year) & (air_dup['month'] == month) & (air_dup['day'] == day) & (air_dup['hour'] == hour) ].copy()  # 대기질 데이터 중 같은 날짜, 시간 데이터 추출\n",
-    "\n",
-    "                om_tree= cKDTree(air_date[['위도', '경도']].values) # 대기질 데이터의 위도, 경도를 이용하여 cKDTree 생성\n",
-    "                distance, index = om_tree.query(asos_date[['위도', '경도']].values, k=1)\n",
-    "                actual_distance = np.array([haversine(asos_date.iloc[i]['위도'], asos_date.iloc[i]['경도'], air_date.iloc[index[i]]['위도'], air_date.iloc[index[i]]['경도']) for i in range(len(asos_date))]) # 거리 계산 함수를 이용하여 거리 계산 \n",
-    "\n",
-    "                if len(asos_date) == len(actual_distance):\n",
-    "                    asos_date['근접 측정소코드'] = air_date.iloc[index]['측정소코드'].values\n",
-    "                    asos_date['거리차이(km)'] = actual_distance\n",
-    "                    asos_dup.loc[(asos_dup['year'] == year) & (asos_dup['month'] == month) & (asos_dup['day'] == day) & (asos_dup['hour'] == hour), '근접 측정소코드'] = asos_date['근접 측정소코드'].values\n",
-    "                    asos_dup.loc[(asos_dup['year'] == year) & (asos_dup['month'] == month) & (asos_dup['day'] == day) & (asos_dup['hour'] == hour), '거리차이(km)'] = asos_date['거리차이(km)'].values\n",
-    "\n",
-    "\n",
-    "    \n",
-    "\n",
-    "# 이 코드의 전체적인 설명은 asos의 A 지점이 있고 air에 B,C,D,E 지점이 있다고 가정하면 A지점과 B,C,D,E 지점의 거리를 계산하여 가장 가까운 지점을 찾아내는 코드이다.\n",
-    "# A 지점과 C 지점이 가장 가깝고 그다음으로 B지점이 가깝다고 가정했을 때 A지점이 18년~23년의 데이터를 가지고 있고 C지점이 20년~23년의 데이터를 가지고 있다고 B지점은 18년~23년의 데이터를 가지고 있다면\n",
-    "#  A의 에서의 가장 가까운 지점은 18년~19년에는 C지점이 되지만 20년~23년에는 B지점이 된다. 이 코드는 이러한 상황을 고려하여 거리를 계산하여 가장 가까운 지점을 찾아내는 코드이다.\n",
-    "# 또한 이코드는 년도만 고려하는 것이 아닌 년, 월, 일, 시간까지 고려하여 가장 가까운 지점을 찾아내는 코드이다.\n",
-    "# (실제로 air의 어느 지점은 00시 데이터는 존재하지 않고 01부터 존재하는 경우가 발생하여 이러한 상황을 고려하여 코드를 작성하였다.) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>22806078</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22806079</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22806080</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22806081</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22806082</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22849560</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22849561</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22849562</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22849563</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22849564</th>\n",
-       "      <td>632461</td>\n",
-       "      <td>38.2069</td>\n",
-       "      <td>128.5882</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>43487 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           측정소코드       위도        경도  year  month  day  hour\n",
-       "22806078  632461  38.2069  128.5882  2019      1   15     1\n",
-       "22806079  632461  38.2069  128.5882  2019      1   15     2\n",
-       "22806080  632461  38.2069  128.5882  2019      1   15     3\n",
-       "22806081  632461  38.2069  128.5882  2019      1   15     4\n",
-       "22806082  632461  38.2069  128.5882  2019      1   15     5\n",
-       "...          ...      ...       ...   ...    ...  ...   ...\n",
-       "22849560  632461  38.2069  128.5882  2023     12   31    19\n",
-       "22849561  632461  38.2069  128.5882  2023     12   31    20\n",
-       "22849562  632461  38.2069  128.5882  2023     12   31    21\n",
-       "22849563  632461  38.2069  128.5882  2023     12   31    22\n",
-       "22849564  632461  38.2069  128.5882  2023     12   31    23\n",
-       "\n",
-       "[43487 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 측정소코드 632461 데이터 확인 (이 측정소는 19년 1월 15일 1시부터 존재)\n",
-    "air_dup.loc[air_dup['측정소코드']==632461,:].sort_values(by=['year','month','day','hour'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지점</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "      <th>근접 측정소코드</th>\n",
-       "      <th>거리차이(km)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>9097</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.25085</td>\n",
-       "      <td>128.56473</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>1</td>\n",
-       "      <td>632461</td>\n",
-       "      <td>5.299597</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9098</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.25085</td>\n",
-       "      <td>128.56473</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>2</td>\n",
-       "      <td>632461</td>\n",
-       "      <td>5.299597</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9099</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.25085</td>\n",
-       "      <td>128.56473</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>3</td>\n",
-       "      <td>632461</td>\n",
-       "      <td>5.299597</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9100</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.25085</td>\n",
-       "      <td>128.56473</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>4</td>\n",
-       "      <td>632461</td>\n",
-       "      <td>5.299597</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9101</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.25085</td>\n",
-       "      <td>128.56473</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5</td>\n",
-       "      <td>632461</td>\n",
-       "      <td>5.299597</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52579</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.25085</td>\n",
-       "      <td>128.56473</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "      <td>632461</td>\n",
-       "      <td>5.299597</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52580</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.25085</td>\n",
-       "      <td>128.56473</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "      <td>632461</td>\n",
-       "      <td>5.299597</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52581</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.25085</td>\n",
-       "      <td>128.56473</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "      <td>632461</td>\n",
-       "      <td>5.299597</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52582</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.25085</td>\n",
-       "      <td>128.56473</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "      <td>632461</td>\n",
-       "      <td>5.299597</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52583</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.25085</td>\n",
-       "      <td>128.56473</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "      <td>632461</td>\n",
-       "      <td>5.299597</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>43487 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       지점        위도         경도  year  month  day  hour 근접 측정소코드  거리차이(km)\n",
-       "9097   90  38.25085  128.56473  2019      1   15     1   632461  5.299597\n",
-       "9098   90  38.25085  128.56473  2019      1   15     2   632461  5.299597\n",
-       "9099   90  38.25085  128.56473  2019      1   15     3   632461  5.299597\n",
-       "9100   90  38.25085  128.56473  2019      1   15     4   632461  5.299597\n",
-       "9101   90  38.25085  128.56473  2019      1   15     5   632461  5.299597\n",
-       "...    ..       ...        ...   ...    ...  ...   ...      ...       ...\n",
-       "52579  90  38.25085  128.56473  2023     12   31    19   632461  5.299597\n",
-       "52580  90  38.25085  128.56473  2023     12   31    20   632461  5.299597\n",
-       "52581  90  38.25085  128.56473  2023     12   31    21   632461  5.299597\n",
-       "52582  90  38.25085  128.56473  2023     12   31    22   632461  5.299597\n",
-       "52583  90  38.25085  128.56473  2023     12   31    23   632461  5.299597\n",
-       "\n",
-       "[43487 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 근접 측정소코드가 632461인 데이터가 air_dup과 asos 데이터가 합쳐지고 19년 1월 15일 1시부터 잘 있는지 확인\n",
-    "asos_dup.loc[asos_dup['근접 측정소코드']==632461,:].sort_values(by=['year','month','day','hour'])  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>37.564300</td>\n",
-       "      <td>126.974700</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>37.564300</td>\n",
-       "      <td>126.974700</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>37.564300</td>\n",
-       "      <td>126.974700</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>37.564300</td>\n",
-       "      <td>126.974700</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>111121</td>\n",
-       "      <td>37.564300</td>\n",
-       "      <td>126.974700</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178033</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>37.025088</td>\n",
-       "      <td>125.995807</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178034</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>37.025088</td>\n",
-       "      <td>125.995807</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178035</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>37.025088</td>\n",
-       "      <td>125.995807</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178036</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>37.025088</td>\n",
-       "      <td>125.995807</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178037</th>\n",
-       "      <td>831495</td>\n",
-       "      <td>37.025088</td>\n",
-       "      <td>125.995807</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28178038 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           측정소코드         위도          경도  year  month  day  hour\n",
-       "0         111121  37.564300  126.974700  2018      1    1     0\n",
-       "1         111121  37.564300  126.974700  2018      1    1     1\n",
-       "2         111121  37.564300  126.974700  2018      1    1     2\n",
-       "3         111121  37.564300  126.974700  2018      1    1     3\n",
-       "4         111121  37.564300  126.974700  2018      1    1     4\n",
-       "...          ...        ...         ...   ...    ...  ...   ...\n",
-       "28178033  831495  37.025088  125.995807  2023     12   31    19\n",
-       "28178034  831495  37.025088  125.995807  2023     12   31    20\n",
-       "28178035  831495  37.025088  125.995807  2023     12   31    21\n",
-       "28178036  831495  37.025088  125.995807  2023     12   31    22\n",
-       "28178037  831495  37.025088  125.995807  2023     12   31    23\n",
-       "\n",
-       "[28178038 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "air_dup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지점</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "      <th>근접 측정소코드</th>\n",
-       "      <th>거리차이(km)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>16.191384</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52584</th>\n",
-       "      <td>93</td>\n",
-       "      <td>37.947380</td>\n",
-       "      <td>127.754430</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>132112</td>\n",
-       "      <td>8.518556</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>105168</th>\n",
-       "      <td>95</td>\n",
-       "      <td>38.147870</td>\n",
-       "      <td>127.304200</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>131451</td>\n",
-       "      <td>9.479396</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>157752</th>\n",
-       "      <td>98</td>\n",
-       "      <td>37.901880</td>\n",
-       "      <td>127.060700</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>131571</td>\n",
-       "      <td>1.748777</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>210336</th>\n",
-       "      <td>99</td>\n",
-       "      <td>37.885890</td>\n",
-       "      <td>126.766480</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>131373</td>\n",
-       "      <td>3.839017</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4844993</th>\n",
-       "      <td>288</td>\n",
-       "      <td>35.491470</td>\n",
-       "      <td>128.744120</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "      <td>238401</td>\n",
-       "      <td>0.94567</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4897577</th>\n",
-       "      <td>289</td>\n",
-       "      <td>35.413000</td>\n",
-       "      <td>127.879100</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "      <td>238471</td>\n",
-       "      <td>0.504745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4950161</th>\n",
-       "      <td>294</td>\n",
-       "      <td>34.888180</td>\n",
-       "      <td>128.604590</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "      <td>238203</td>\n",
-       "      <td>1.735756</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5002745</th>\n",
-       "      <td>295</td>\n",
-       "      <td>34.816620</td>\n",
-       "      <td>127.926410</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "      <td>238461</td>\n",
-       "      <td>3.024373</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011264</th>\n",
-       "      <td>296</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5011265 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          지점         위도          경도  year  month  day  hour 근접 측정소코드  \\\n",
-       "0         90  38.250850  128.564730  2018      1    1     0   632421   \n",
-       "52584     93  37.947380  127.754430  2018      1    1     0   132112   \n",
-       "105168    95  38.147870  127.304200  2018      1    1     0   131451   \n",
-       "157752    98  37.901880  127.060700  2018      1    1     0   131571   \n",
-       "210336    99  37.885890  126.766480  2018      1    1     0   131373   \n",
-       "...      ...        ...         ...   ...    ...  ...   ...      ...   \n",
-       "4844993  288  35.491470  128.744120  2023     12   31    23   238401   \n",
-       "4897577  289  35.413000  127.879100  2023     12   31    23   238471   \n",
-       "4950161  294  34.888180  128.604590  2023     12   31    23   238203   \n",
-       "5002745  295  34.816620  127.926410  2023     12   31    23   238461   \n",
-       "5011264  296  35.217781  128.960242  2023     12   31    23   221211   \n",
-       "\n",
-       "          거리차이(km)  \n",
-       "0        16.191384  \n",
-       "52584     8.518556  \n",
-       "105168    9.479396  \n",
-       "157752    1.748777  \n",
-       "210336    3.839017  \n",
-       "...            ...  \n",
-       "4844993    0.94567  \n",
-       "4897577   0.504745  \n",
-       "4950161   1.735756  \n",
-       "5002745   3.024373  \n",
-       "5011264   1.544059  \n",
-       "\n",
-       "[5011265 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos_dup.sort_values(by=['year','month','day','hour']) # asos 데이터 정렬"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "asos_dup.to_feather(\"../data/ASOS/asos_dup.feather\") # asos 데이터 저장"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 20.75 GB\n",
-      "현재 메모리 사용량: 18.3%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n",
-    "print(f\"현재 메모리 사용량: {psutil.virtual_memory().percent}%\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "300"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import gc\n",
-    "gc.collect()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 커널 재시작"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import gc\n",
-    "gc.collect()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 13.13 GB\n",
-      "현재 메모리 사용량: 12.2%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n",
-    "print(f\"현재 메모리 사용량: {psutil.virtual_memory().percent}%\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "asos= pd.read_feather(\"../data/ASOS/asos_fill_위경.feather\") # ASOS 데이터 불러오기\n",
-    "asos_dup= pd.read_feather(\"../data/ASOS/asos_dup.feather\") # 중복 제거한 ASOS 데이터 불러오기"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지점</th>\n",
-       "      <th>지점명</th>\n",
-       "      <th>일시</th>\n",
-       "      <th>기온(°C)</th>\n",
-       "      <th>기온 QC플래그</th>\n",
-       "      <th>강수량(mm)</th>\n",
-       "      <th>강수량 QC플래그</th>\n",
-       "      <th>풍속(m/s)</th>\n",
-       "      <th>풍속 QC플래그</th>\n",
-       "      <th>풍향(16방위)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>최저운고(100m )</th>\n",
-       "      <th>시정(10m)</th>\n",
-       "      <th>지면온도(°C)</th>\n",
-       "      <th>지면온도 QC플래그</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 00:00</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-2.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 01:00</td>\n",
-       "      <td>-2.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-2.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 02:00</td>\n",
-       "      <td>-2.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 03:00</td>\n",
-       "      <td>-2.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-3.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 04:00</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>-3.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011260</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 19:00</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>320.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4501.0</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011261</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 20:00</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>320.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4240.0</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011262</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 21:00</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3708.0</td>\n",
-       "      <td>1.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011263</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 22:00</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>290.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2751.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011264</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 23:00</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>360.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2222.0</td>\n",
-       "      <td>-0.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5011265 rows × 36 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          지점  지점명                일시  기온(°C)  기온 QC플래그  강수량(mm)  강수량 QC플래그  \\\n",
-       "0         90   속초  2018-01-01 00:00    -1.0       0.0      NaN        NaN   \n",
-       "1         90   속초  2018-01-01 01:00    -2.1       0.0      NaN        NaN   \n",
-       "2         90   속초  2018-01-01 02:00    -2.1       0.0      NaN        NaN   \n",
-       "3         90   속초  2018-01-01 03:00    -2.2       0.0      NaN        NaN   \n",
-       "4         90   속초  2018-01-01 04:00    -2.0       0.0      NaN        NaN   \n",
-       "...      ...  ...               ...     ...       ...      ...        ...   \n",
-       "5011260  296  북부산  2023-12-31 19:00     6.6       NaN      NaN        9.0   \n",
-       "5011261  296  북부산  2023-12-31 20:00     5.9       NaN      NaN        9.0   \n",
-       "5011262  296  북부산  2023-12-31 21:00     5.1       NaN      NaN        9.0   \n",
-       "5011263  296  북부산  2023-12-31 22:00     3.5       NaN      NaN        9.0   \n",
-       "5011264  296  북부산  2023-12-31 23:00     1.8       NaN      NaN        9.0   \n",
-       "\n",
-       "         풍속(m/s)  풍속 QC플래그  풍향(16방위)  ...  최저운고(100m )  시정(10m)  지면온도(°C)  \\\n",
-       "0            1.1       NaN     250.0  ...          NaN   2000.0      -2.3   \n",
-       "1            1.7       0.0     230.0  ...          NaN   2000.0      -2.7   \n",
-       "2            1.4       0.0     160.0  ...          NaN   2000.0      -3.0   \n",
-       "3            0.9       0.0     230.0  ...          NaN   2000.0      -3.2   \n",
-       "4            1.2       0.0     250.0  ...          NaN   2000.0      -3.3   \n",
-       "...          ...       ...       ...  ...          ...      ...       ...   \n",
-       "5011260      1.5       NaN     320.0  ...          NaN   4501.0       2.8   \n",
-       "5011261      1.8       NaN     320.0  ...          NaN   4240.0       2.1   \n",
-       "5011262      1.2       NaN     250.0  ...          NaN   3708.0       1.1   \n",
-       "5011263      0.7       NaN     290.0  ...          NaN   2751.0       0.2   \n",
-       "5011264      0.5       NaN     360.0  ...          NaN   2222.0      -0.3   \n",
-       "\n",
-       "         지면온도 QC플래그  year  month  day  hour         위도          경도  \n",
-       "0               0.0  2018      1    1     0  38.250850  128.564730  \n",
-       "1               0.0  2018      1    1     1  38.250850  128.564730  \n",
-       "2               0.0  2018      1    1     2  38.250850  128.564730  \n",
-       "3               0.0  2018      1    1     3  38.250850  128.564730  \n",
-       "4               0.0  2018      1    1     4  38.250850  128.564730  \n",
-       "...             ...   ...    ...  ...   ...        ...         ...  \n",
-       "5011260         NaN  2023     12   31    19  35.217781  128.960242  \n",
-       "5011261         NaN  2023     12   31    20  35.217781  128.960242  \n",
-       "5011262         NaN  2023     12   31    21  35.217781  128.960242  \n",
-       "5011263         NaN  2023     12   31    22  35.217781  128.960242  \n",
-       "5011264         NaN  2023     12   31    23  35.217781  128.960242  \n",
-       "\n",
-       "[5011265 rows x 36 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지점</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
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-       "      <th>근접 측정소코드</th>\n",
-       "      <th>거리차이(km)</th>\n",
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-       "  </thead>\n",
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-       "      <td>128.564730</td>\n",
-       "      <td>2018</td>\n",
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-       "      <td>16.191384</td>\n",
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-       "      <td>90</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>2018</td>\n",
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-       "      <td>632421</td>\n",
-       "      <td>16.191384</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>16.191384</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>90</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>16.191384</td>\n",
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-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011260</th>\n",
-       "      <td>296</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011261</th>\n",
-       "      <td>296</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011262</th>\n",
-       "      <td>296</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011263</th>\n",
-       "      <td>296</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011264</th>\n",
-       "      <td>296</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5011265 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          지점         위도          경도  year  month  day  hour  근접 측정소코드  \\\n",
-       "0         90  38.250850  128.564730  2018      1    1     0    632421   \n",
-       "1         90  38.250850  128.564730  2018      1    1     1    632421   \n",
-       "2         90  38.250850  128.564730  2018      1    1     2    632421   \n",
-       "3         90  38.250850  128.564730  2018      1    1     3    632421   \n",
-       "4         90  38.250850  128.564730  2018      1    1     4    632421   \n",
-       "...      ...        ...         ...   ...    ...  ...   ...       ...   \n",
-       "5011260  296  35.217781  128.960242  2023     12   31    19    221211   \n",
-       "5011261  296  35.217781  128.960242  2023     12   31    20    221211   \n",
-       "5011262  296  35.217781  128.960242  2023     12   31    21    221211   \n",
-       "5011263  296  35.217781  128.960242  2023     12   31    22    221211   \n",
-       "5011264  296  35.217781  128.960242  2023     12   31    23    221211   \n",
-       "\n",
-       "          거리차이(km)  \n",
-       "0        16.191384  \n",
-       "1        16.191384  \n",
-       "2        16.191384  \n",
-       "3        16.191384  \n",
-       "4        16.191384  \n",
-       "...            ...  \n",
-       "5011260   1.544059  \n",
-       "5011261   1.544059  \n",
-       "5011262   1.544059  \n",
-       "5011263   1.544059  \n",
-       "5011264   1.544059  \n",
-       "\n",
-       "[5011265 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos_dup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# asos 데이터와 asos_dup 데이터를 자연조인을 통해 합친다.\n",
-    "asos_merge= pd.merge(asos, asos_dup, how='inner', left_on=['지점', 'year', 'month', 'day','hour','위도','경도'], right_on=['지점', 'year', 'month', 'day','hour','위도','경도'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "사용 중인 메모리: 16.47 GB\n",
-      "현재 메모리 사용량: 14.9%\n"
-     ]
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# 전체 메모리 중 사용 중인 메모리 (바이트 단위)\n",
-    "used_memory = psutil.virtual_memory().used\n",
-    "\n",
-    "# 바이트 단위를 GB 단위로 변환\n",
-    "used_memory_in_gb = used_memory / (1024 ** 3)\n",
-    "\n",
-    "print(f\"사용 중인 메모리: {used_memory_in_gb:.2f} GB\")\n",
-    "print(f\"현재 메모리 사용량: {psutil.virtual_memory().percent}%\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "지점                   0\n",
-       "지점명                  0\n",
-       "일시                   0\n",
-       "기온(°C)            4197\n",
-       "기온 QC플래그       4282900\n",
-       "강수량(mm)        4533611\n",
-       "강수량 QC플래그      4093970\n",
-       "풍속(m/s)           9726\n",
-       "풍속 QC플래그       4319125\n",
-       "풍향(16방위)         10294\n",
-       "풍향 QC플래그       4318626\n",
-       "습도(%)             6266\n",
-       "습도 QC플래그       4284220\n",
-       "증기압(hPa)          6486\n",
-       "이슬점온도(°C)         6534\n",
-       "현지기압(hPa)         4613\n",
-       "현지기압 QC플래그     4285584\n",
-       "해면기압(hPa)         5024\n",
-       "해면기압 QC플래그     4285200\n",
-       "일조(hr)         2275882\n",
-       "일조 QC플래그       2375218\n",
-       "일사(MJ/m2)      3691960\n",
-       "일사 QC플래그       1161673\n",
-       "적설(cm)         4905762\n",
-       "전운량(10분위)       479380\n",
-       "중하층운량(10분위)     517822\n",
-       "최저운고(100m )    2791937\n",
-       "시정(10m)          71059\n",
-       "지면온도(°C)          9742\n",
-       "지면온도 QC플래그     4260093\n",
-       "year                 0\n",
-       "month                0\n",
-       "day                  0\n",
-       "hour                 0\n",
-       "위도                   0\n",
-       "경도                   0\n",
-       "근접 측정소코드             0\n",
-       "거리차이(km)             0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(asos_merge).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "asos_merge.to_feather(\"../data/ASOS/asos_merge.feather\") # asos_merge 데이터 저장"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 커널 재시작"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import gc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "23"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gc.collect()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "asos_merge= pd.read_feather(\"../data/ASOS/asos_merge.feather\") # asos_merge 데이터 불러오기\n",
-    "air= pd.read_feather(\"../data/대기질/air_add_위도경도.feather\") # 대기질 데이터 불러오기"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "지역             0\n",
-       "측정소코드          0\n",
-       "측정소명           0\n",
-       "측정일시           0\n",
-       "O3       1152577\n",
-       "NO2      1172756\n",
-       "PM10     1441888\n",
-       "PM25     1970684\n",
-       "주소             0\n",
-       "year           0\n",
-       "month          0\n",
-       "day            0\n",
-       "hour           0\n",
-       "위도             0\n",
-       "경도             0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(air).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "지점                   0\n",
-       "지점명                  0\n",
-       "일시                   0\n",
-       "기온(°C)            4197\n",
-       "기온 QC플래그       4282900\n",
-       "강수량(mm)        4533611\n",
-       "강수량 QC플래그      4093970\n",
-       "풍속(m/s)           9726\n",
-       "풍속 QC플래그       4319125\n",
-       "풍향(16방위)         10294\n",
-       "풍향 QC플래그       4318626\n",
-       "습도(%)             6266\n",
-       "습도 QC플래그       4284220\n",
-       "증기압(hPa)          6486\n",
-       "이슬점온도(°C)         6534\n",
-       "현지기압(hPa)         4613\n",
-       "현지기압 QC플래그     4285584\n",
-       "해면기압(hPa)         5024\n",
-       "해면기압 QC플래그     4285200\n",
-       "일조(hr)         2275882\n",
-       "일조 QC플래그       2375218\n",
-       "일사(MJ/m2)      3691960\n",
-       "일사 QC플래그       1161673\n",
-       "적설(cm)         4905762\n",
-       "전운량(10분위)       479380\n",
-       "중하층운량(10분위)     517822\n",
-       "최저운고(100m )    2791937\n",
-       "시정(10m)          71059\n",
-       "지면온도(°C)          9742\n",
-       "지면온도 QC플래그     4260093\n",
-       "year                 0\n",
-       "month                0\n",
-       "day                  0\n",
-       "hour                 0\n",
-       "위도                   0\n",
-       "경도                   0\n",
-       "근접 측정소코드             0\n",
-       "거리차이(km)             0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(asos_merge).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지역</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>0.0130</td>\n",
-       "      <td>0.0280</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>0.0200</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>0.0240</td>\n",
-       "      <td>0.0160</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>0.0180</td>\n",
-       "      <td>0.0220</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>서울 중구</td>\n",
-       "      <td>111121</td>\n",
-       "      <td>0.0100</td>\n",
-       "      <td>0.0300</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178033</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>0.0440</td>\n",
-       "      <td>0.0075</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178034</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>0.0415</td>\n",
-       "      <td>0.0086</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178035</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>0.0409</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178036</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>0.0414</td>\n",
-       "      <td>0.0111</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28178037</th>\n",
-       "      <td>인천 옹진군</td>\n",
-       "      <td>831495</td>\n",
-       "      <td>0.0411</td>\n",
-       "      <td>0.0112</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>12</td>\n",
-       "      <td>31</td>\n",
-       "      <td>23</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28178038 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              지역   측정소코드      O3     NO2  PM10  PM25  year  month  day  hour\n",
-       "0          서울 중구  111121  0.0130  0.0280  31.0  16.0  2018      1    1     0\n",
-       "1          서울 중구  111121  0.0200  0.0200  34.0  19.0  2018      1    1     1\n",
-       "2          서울 중구  111121  0.0240  0.0160  27.0  14.0  2018      1    1     2\n",
-       "3          서울 중구  111121  0.0180  0.0220  26.0  14.0  2018      1    1     3\n",
-       "4          서울 중구  111121  0.0100  0.0300  26.0  15.0  2018      1    1     4\n",
-       "...          ...     ...     ...     ...   ...   ...   ...    ...  ...   ...\n",
-       "28178033  인천 옹진군  831495  0.0440  0.0075  67.0  57.0  2023     12   31    19\n",
-       "28178034  인천 옹진군  831495  0.0415  0.0086  64.0  57.0  2023     12   31    20\n",
-       "28178035  인천 옹진군  831495  0.0409  0.0095  62.0  50.0  2023     12   31    21\n",
-       "28178036  인천 옹진군  831495  0.0414  0.0111  65.0  59.0  2023     12   31    22\n",
-       "28178037  인천 옹진군  831495  0.0411  0.0112  63.0  54.0  2023     12   31    23\n",
-       "\n",
-       "[28178038 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# air 데이터의 위도, 경도, 측정소명, 측정일시, 주소 컬럼 삭제 (asos가 기준이 되기 때문에 air 데이터에서는 필요 없음)\n",
-    "air.drop(columns=['위도', '경도','측정소명','측정일시','주소'], axis=1, inplace=True)\n",
-    "air"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 지점, year, month, day, hour 순으로 정렬\n",
-    "asos_merge.sort_values(by=['지점', 'year', 'month', 'day', 'hour'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "asos_air = pd.merge( # asos 데이터와 대기질 데이터 병합\n",
-    "    asos_merge, \n",
-    "    air,  \n",
-    "    left_on=['근접 측정소코드', 'year','month','day','hour'], # 근접 측정소코드, year, month, day, hour를 기준으로 병합 \n",
-    "    right_on=['측정소코드', 'year','month','day','hour'],    # 측정소코드, year, month, day, hour를 기준으로 병합\n",
-    "    how='inner' # inner join\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지점</th>\n",
-       "      <th>지점명</th>\n",
-       "      <th>일시</th>\n",
-       "      <th>기온(°C)</th>\n",
-       "      <th>기온 QC플래그</th>\n",
-       "      <th>강수량(mm)</th>\n",
-       "      <th>강수량 QC플래그</th>\n",
-       "      <th>풍속(m/s)</th>\n",
-       "      <th>풍속 QC플래그</th>\n",
-       "      <th>풍향(16방위)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "      <th>근접 측정소코드</th>\n",
-       "      <th>거리차이(km)</th>\n",
-       "      <th>지역</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 00:00</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>16.191384</td>\n",
-       "      <td>강원 고성군</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>0.0350</td>\n",
-       "      <td>0.0042</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 01:00</td>\n",
-       "      <td>-2.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>16.191384</td>\n",
-       "      <td>강원 고성군</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>0.0350</td>\n",
-       "      <td>0.0041</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 02:00</td>\n",
-       "      <td>-2.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>16.191384</td>\n",
-       "      <td>강원 고성군</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>0.0340</td>\n",
-       "      <td>0.0042</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 03:00</td>\n",
-       "      <td>-2.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>16.191384</td>\n",
-       "      <td>강원 고성군</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>0.0340</td>\n",
-       "      <td>0.0043</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 04:00</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>16.191384</td>\n",
-       "      <td>강원 고성군</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>0.0330</td>\n",
-       "      <td>0.0044</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011260</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 19:00</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>320.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "      <td>부산 강서구</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>0.0395</td>\n",
-       "      <td>0.0072</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011261</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 20:00</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>320.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "      <td>부산 강서구</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>0.0340</td>\n",
-       "      <td>0.0102</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>7.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011262</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 21:00</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "      <td>부산 강서구</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>0.0283</td>\n",
-       "      <td>0.0134</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>9.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011263</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 22:00</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>290.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "      <td>부산 강서구</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>0.0298</td>\n",
-       "      <td>0.0100</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>11.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011264</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 23:00</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>360.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "      <td>부산 강서구</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>0.0141</td>\n",
-       "      <td>0.0226</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>15.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5011265 rows × 44 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          지점  지점명                일시  기온(°C)  기온 QC플래그  강수량(mm)  강수량 QC플래그  \\\n",
-       "0         90   속초  2018-01-01 00:00    -1.0       0.0      NaN        NaN   \n",
-       "1         90   속초  2018-01-01 01:00    -2.1       0.0      NaN        NaN   \n",
-       "2         90   속초  2018-01-01 02:00    -2.1       0.0      NaN        NaN   \n",
-       "3         90   속초  2018-01-01 03:00    -2.2       0.0      NaN        NaN   \n",
-       "4         90   속초  2018-01-01 04:00    -2.0       0.0      NaN        NaN   \n",
-       "...      ...  ...               ...     ...       ...      ...        ...   \n",
-       "5011260  296  북부산  2023-12-31 19:00     6.6       NaN      NaN        9.0   \n",
-       "5011261  296  북부산  2023-12-31 20:00     5.9       NaN      NaN        9.0   \n",
-       "5011262  296  북부산  2023-12-31 21:00     5.1       NaN      NaN        9.0   \n",
-       "5011263  296  북부산  2023-12-31 22:00     3.5       NaN      NaN        9.0   \n",
-       "5011264  296  북부산  2023-12-31 23:00     1.8       NaN      NaN        9.0   \n",
-       "\n",
-       "         풍속(m/s)  풍속 QC플래그  풍향(16방위)  ...         위도          경도  근접 측정소코드  \\\n",
-       "0            1.1       NaN     250.0  ...  38.250850  128.564730    632421   \n",
-       "1            1.7       0.0     230.0  ...  38.250850  128.564730    632421   \n",
-       "2            1.4       0.0     160.0  ...  38.250850  128.564730    632421   \n",
-       "3            0.9       0.0     230.0  ...  38.250850  128.564730    632421   \n",
-       "4            1.2       0.0     250.0  ...  38.250850  128.564730    632421   \n",
-       "...          ...       ...       ...  ...        ...         ...       ...   \n",
-       "5011260      1.5       NaN     320.0  ...  35.217781  128.960242    221211   \n",
-       "5011261      1.8       NaN     320.0  ...  35.217781  128.960242    221211   \n",
-       "5011262      1.2       NaN     250.0  ...  35.217781  128.960242    221211   \n",
-       "5011263      0.7       NaN     290.0  ...  35.217781  128.960242    221211   \n",
-       "5011264      0.5       NaN     360.0  ...  35.217781  128.960242    221211   \n",
-       "\n",
-       "          거리차이(km)      지역   측정소코드      O3     NO2  PM10  PM25  \n",
-       "0        16.191384  강원 고성군  632421  0.0350  0.0042  21.0   NaN  \n",
-       "1        16.191384  강원 고성군  632421  0.0350  0.0041  23.0   NaN  \n",
-       "2        16.191384  강원 고성군  632421  0.0340  0.0042  19.0   NaN  \n",
-       "3        16.191384  강원 고성군  632421  0.0340  0.0043  23.0   NaN  \n",
-       "4        16.191384  강원 고성군  632421  0.0330  0.0044  23.0   NaN  \n",
-       "...            ...     ...     ...     ...     ...   ...   ...  \n",
-       "5011260   1.544059  부산 강서구  221211  0.0395  0.0072  10.0   5.0  \n",
-       "5011261   1.544059  부산 강서구  221211  0.0340  0.0102   9.0   7.0  \n",
-       "5011262   1.544059  부산 강서구  221211  0.0283  0.0134  11.0   9.0  \n",
-       "5011263   1.544059  부산 강서구  221211  0.0298  0.0100  11.0  11.0  \n",
-       "5011264   1.544059  부산 강서구  221211  0.0141  0.0226  13.0  15.0  \n",
-       "\n",
-       "[5011265 rows x 44 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos_air"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "지점                   0\n",
-       "지점명                  0\n",
-       "일시                   0\n",
-       "기온(°C)            4197\n",
-       "기온 QC플래그       4282900\n",
-       "강수량(mm)        4533611\n",
-       "강수량 QC플래그      4093970\n",
-       "풍속(m/s)           9726\n",
-       "풍속 QC플래그       4319125\n",
-       "풍향(16방위)         10294\n",
-       "풍향 QC플래그       4318626\n",
-       "습도(%)             6266\n",
-       "습도 QC플래그       4284220\n",
-       "증기압(hPa)          6486\n",
-       "이슬점온도(°C)         6534\n",
-       "현지기압(hPa)         4613\n",
-       "현지기압 QC플래그     4285584\n",
-       "해면기압(hPa)         5024\n",
-       "해면기압 QC플래그     4285200\n",
-       "일조(hr)         2275882\n",
-       "일조 QC플래그       2375218\n",
-       "일사(MJ/m2)      3691960\n",
-       "일사 QC플래그       1161673\n",
-       "적설(cm)         4905762\n",
-       "전운량(10분위)       479380\n",
-       "중하층운량(10분위)     517822\n",
-       "최저운고(100m )    2791937\n",
-       "시정(10m)          71059\n",
-       "지면온도(°C)          9742\n",
-       "지면온도 QC플래그     4260093\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(asos_air).sum()[:30] # na값 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "year             0\n",
-       "month            0\n",
-       "day              0\n",
-       "hour             0\n",
-       "위도               0\n",
-       "경도               0\n",
-       "근접 측정소코드         0\n",
-       "거리차이(km)         0\n",
-       "지역               0\n",
-       "측정소코드            0\n",
-       "O3          203654\n",
-       "NO2         191150\n",
-       "PM10        266474\n",
-       "PM25        466378\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(asos_air).sum()[30:] # na값 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "asos_air.to_feather(\"../data/asos_air_merge.feather\") # asos 데이터 feather 형태로 저장"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>지점</th>\n",
-       "      <th>지점명</th>\n",
-       "      <th>일시</th>\n",
-       "      <th>기온(°C)</th>\n",
-       "      <th>기온 QC플래그</th>\n",
-       "      <th>강수량(mm)</th>\n",
-       "      <th>강수량 QC플래그</th>\n",
-       "      <th>풍속(m/s)</th>\n",
-       "      <th>풍속 QC플래그</th>\n",
-       "      <th>풍향(16방위)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>위도</th>\n",
-       "      <th>경도</th>\n",
-       "      <th>근접 측정소코드</th>\n",
-       "      <th>거리차이(km)</th>\n",
-       "      <th>지역</th>\n",
-       "      <th>측정소코드</th>\n",
-       "      <th>O3</th>\n",
-       "      <th>NO2</th>\n",
-       "      <th>PM10</th>\n",
-       "      <th>PM25</th>\n",
-       "    </tr>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 00:00</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>16.191384</td>\n",
-       "      <td>강원 고성군</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>0.0350</td>\n",
-       "      <td>0.0042</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 01:00</td>\n",
-       "      <td>-2.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>16.191384</td>\n",
-       "      <td>강원 고성군</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>0.0350</td>\n",
-       "      <td>0.0041</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 02:00</td>\n",
-       "      <td>-2.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>16.191384</td>\n",
-       "      <td>강원 고성군</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>0.0340</td>\n",
-       "      <td>0.0042</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 03:00</td>\n",
-       "      <td>-2.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>16.191384</td>\n",
-       "      <td>강원 고성군</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>0.0340</td>\n",
-       "      <td>0.0043</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>90</td>\n",
-       "      <td>속초</td>\n",
-       "      <td>2018-01-01 04:00</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38.250850</td>\n",
-       "      <td>128.564730</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>16.191384</td>\n",
-       "      <td>강원 고성군</td>\n",
-       "      <td>632421</td>\n",
-       "      <td>0.0330</td>\n",
-       "      <td>0.0044</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011260</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 19:00</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>320.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "      <td>부산 강서구</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>0.0395</td>\n",
-       "      <td>0.0072</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011261</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 20:00</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>320.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "      <td>부산 강서구</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>0.0340</td>\n",
-       "      <td>0.0102</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>7.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011262</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 21:00</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>250.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "      <td>부산 강서구</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>0.0283</td>\n",
-       "      <td>0.0134</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>9.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011263</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 22:00</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>290.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "      <td>부산 강서구</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>0.0298</td>\n",
-       "      <td>0.0100</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>11.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5011264</th>\n",
-       "      <td>296</td>\n",
-       "      <td>북부산</td>\n",
-       "      <td>2023-12-31 23:00</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>360.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>35.217781</td>\n",
-       "      <td>128.960242</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>1.544059</td>\n",
-       "      <td>부산 강서구</td>\n",
-       "      <td>221211</td>\n",
-       "      <td>0.0141</td>\n",
-       "      <td>0.0226</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>15.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5011265 rows × 44 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          지점  지점명                일시  기온(°C)  기온 QC플래그  강수량(mm)  강수량 QC플래그  \\\n",
-       "0         90   속초  2018-01-01 00:00    -1.0       0.0      NaN        NaN   \n",
-       "1         90   속초  2018-01-01 01:00    -2.1       0.0      NaN        NaN   \n",
-       "2         90   속초  2018-01-01 02:00    -2.1       0.0      NaN        NaN   \n",
-       "3         90   속초  2018-01-01 03:00    -2.2       0.0      NaN        NaN   \n",
-       "4         90   속초  2018-01-01 04:00    -2.0       0.0      NaN        NaN   \n",
-       "...      ...  ...               ...     ...       ...      ...        ...   \n",
-       "5011260  296  북부산  2023-12-31 19:00     6.6       NaN      NaN        9.0   \n",
-       "5011261  296  북부산  2023-12-31 20:00     5.9       NaN      NaN        9.0   \n",
-       "5011262  296  북부산  2023-12-31 21:00     5.1       NaN      NaN        9.0   \n",
-       "5011263  296  북부산  2023-12-31 22:00     3.5       NaN      NaN        9.0   \n",
-       "5011264  296  북부산  2023-12-31 23:00     1.8       NaN      NaN        9.0   \n",
-       "\n",
-       "         풍속(m/s)  풍속 QC플래그  풍향(16방위)  ...         위도          경도  근접 측정소코드  \\\n",
-       "0            1.1       NaN     250.0  ...  38.250850  128.564730    632421   \n",
-       "1            1.7       0.0     230.0  ...  38.250850  128.564730    632421   \n",
-       "2            1.4       0.0     160.0  ...  38.250850  128.564730    632421   \n",
-       "3            0.9       0.0     230.0  ...  38.250850  128.564730    632421   \n",
-       "4            1.2       0.0     250.0  ...  38.250850  128.564730    632421   \n",
-       "...          ...       ...       ...  ...        ...         ...       ...   \n",
-       "5011260      1.5       NaN     320.0  ...  35.217781  128.960242    221211   \n",
-       "5011261      1.8       NaN     320.0  ...  35.217781  128.960242    221211   \n",
-       "5011262      1.2       NaN     250.0  ...  35.217781  128.960242    221211   \n",
-       "5011263      0.7       NaN     290.0  ...  35.217781  128.960242    221211   \n",
-       "5011264      0.5       NaN     360.0  ...  35.217781  128.960242    221211   \n",
-       "\n",
-       "          거리차이(km)      지역   측정소코드      O3     NO2  PM10  PM25  \n",
-       "0        16.191384  강원 고성군  632421  0.0350  0.0042  21.0   NaN  \n",
-       "1        16.191384  강원 고성군  632421  0.0350  0.0041  23.0   NaN  \n",
-       "2        16.191384  강원 고성군  632421  0.0340  0.0042  19.0   NaN  \n",
-       "3        16.191384  강원 고성군  632421  0.0340  0.0043  23.0   NaN  \n",
-       "4        16.191384  강원 고성군  632421  0.0330  0.0044  23.0   NaN  \n",
-       "...            ...     ...     ...     ...     ...   ...   ...  \n",
-       "5011260   1.544059  부산 강서구  221211  0.0395  0.0072  10.0   5.0  \n",
-       "5011261   1.544059  부산 강서구  221211  0.0340  0.0102   9.0   7.0  \n",
-       "5011262   1.544059  부산 강서구  221211  0.0283  0.0134  11.0   9.0  \n",
-       "5011263   1.544059  부산 강서구  221211  0.0298  0.0100  11.0  11.0  \n",
-       "5011264   1.544059  부산 강서구  221211  0.0141  0.0226  13.0  15.0  \n",
-       "\n",
-       "[5011265 rows x 44 columns]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos_air"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['지점', '지점명', '일시', '기온(°C)', '기온 QC플래그', '강수량(mm)', '강수량 QC플래그',\n",
-       "       '풍속(m/s)', '풍속 QC플래그', '풍향(16방위)', '풍향 QC플래그', '습도(%)', '습도 QC플래그',\n",
-       "       '증기압(hPa)', '이슬점온도(°C)', '현지기압(hPa)', '현지기압 QC플래그', '해면기압(hPa)',\n",
-       "       '해면기압 QC플래그', '일조(hr)', '일조 QC플래그', '일사(MJ/m2)', '일사 QC플래그', '적설(cm)',\n",
-       "       '전운량(10분위)', '중하층운량(10분위)', '최저운고(100m )', '시정(10m)', '지면온도(°C)',\n",
-       "       '지면온도 QC플래그', 'year', 'month', 'day', 'hour', '위도', '경도', '근접 측정소코드',\n",
-       "       '거리차이(km)', '지역', '측정소코드', 'O3', 'NO2', 'PM10', 'PM25'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asos_air.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "44"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(asos_air.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "지점                  0\n",
-       "지점명                 0\n",
-       "일시                  0\n",
-       "기온(°C)           4197\n",
-       "기온 QC플래그      4282900\n",
-       "강수량(mm)       4533611\n",
-       "강수량 QC플래그     4093970\n",
-       "풍속(m/s)          9726\n",
-       "풍속 QC플래그      4319125\n",
-       "풍향(16방위)        10294\n",
-       "풍향 QC플래그      4318626\n",
-       "습도(%)            6266\n",
-       "습도 QC플래그      4284220\n",
-       "증기압(hPa)         6486\n",
-       "이슬점온도(°C)        6534\n",
-       "현지기압(hPa)        4613\n",
-       "현지기압 QC플래그    4285584\n",
-       "해면기압(hPa)        5024\n",
-       "해면기압 QC플래그    4285200\n",
-       "일조(hr)        2275882\n",
-       "일조 QC플래그      2375218\n",
-       "일사(MJ/m2)     3691960\n",
-       "일사 QC플래그      1161673\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(asos_air).sum()[:23] # na값 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "적설(cm)         4905762\n",
-       "전운량(10분위)       479380\n",
-       "중하층운량(10분위)     517822\n",
-       "최저운고(100m )    2791937\n",
-       "시정(10m)          71059\n",
-       "지면온도(°C)          9742\n",
-       "지면온도 QC플래그     4260093\n",
-       "year                 0\n",
-       "month                0\n",
-       "day                  0\n",
-       "hour                 0\n",
-       "위도                   0\n",
-       "경도                   0\n",
-       "근접 측정소코드             0\n",
-       "거리차이(km)             0\n",
-       "지역                   0\n",
-       "측정소코드                0\n",
-       "O3              203654\n",
-       "NO2             191150\n",
-       "PM10            266474\n",
-       "PM25            466378\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isna(asos_air).sum()[23:] # na값 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "PyTorch [Python/conda:env]",
-   "language": "python",
-   "name": "conda-env-PyTorch-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Analysis_code/2.eda_preproccesing.ipynb b/Analysis_code/2.eda_preproccesing.ipynb
deleted file mode 100644
index ea75a9860e70608d68e4ce7c7192b21079af8dab..0000000000000000000000000000000000000000
--- a/Analysis_code/2.eda_preproccesing.ipynb
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f8fe49fd26e48bcada89076a0b3c8ffe45e3d2e8407fe953b1558df9bfcfcddb
-size 41518580
diff --git a/Analysis_code/3.oversampling.ipynb b/Analysis_code/3.oversampling.ipynb
deleted file mode 100644
index 5e297a5d83ed62c27f99ae39eebf5d1ed8093116..0000000000000000000000000000000000000000
--- a/Analysis_code/3.oversampling.ipynb
+++ /dev/null
@@ -1,974 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SMOTENC(단순선형보간증강)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "11000 | 11000 | 21893\n",
-      "12000 | 12000 | 23686\n",
-      "12500 | 12500 | 24694\n",
-      "13000 | 13000 | 25149\n",
-      "12000 | 12000 | 23471\n",
-      "12000 | 12000 | 23798\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "from imblearn.over_sampling import SMOTENC\n",
-    "\n",
-    "# 지역별 데이터 파일 경로\n",
-    "regions = ['incheon', 'seoul','busan', 'daegu', 'daejeon', 'gwangju']\n",
-    "file_paths = [f'../data/data_for_modeling/{region}_train.csv' for region in regions]\n",
-    "output_paths = [f'../data/data_oversampled/smote_{region}.csv' for region in regions]\n",
-    "\n",
-    "# 지역별 처리\n",
-    "for file_path, output_path in zip(file_paths, output_paths):\n",
-    "    # 데이터 로드\n",
-    "    data = pd.read_csv(file_path, index_col=0)\n",
-    "    data['cloudcover'] = data['cloudcover'].astype('int')\n",
-    "    data['lm_cloudcover'] = data['lm_cloudcover'].astype('int')\n",
-    "    X = data.drop(columns=['multi_class', 'binary_class'])\n",
-    "    y = data['multi_class']\n",
-    "\n",
-    "    # 불필요한 열 제거\n",
-    "    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)\n",
-    "\n",
-    "    # SMOTENC에서 사용할 범주형 변수 열 번호 설정\n",
-    "    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']\n",
-    "\n",
-    "    # sampling_strategy 설정\n",
-    "    count_class_2 = (y == 2).sum()\n",
-    "    sampling_strategy = {\n",
-    "        0: int(np.ceil(count_class_2 / 1000) * 500),\n",
-    "        1: int(np.ceil(count_class_2 / 1000) * 500),\n",
-    "        2: count_class_2\n",
-    "    }\n",
-    "\n",
-    "    # SMOTENC 적용\n",
-    "    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)\n",
-    "    X_resampled, y_resampled = smotenc.fit_resample(X, y)\n",
-    "\n",
-    "    # Resampled 데이터 생성\n",
-    "    lerp_data = X_resampled.copy()\n",
-    "    lerp_data['multi_class'] = y_resampled\n",
-    "\n",
-    "    # 제거변수 복구\n",
-    "    lerp_data['binary_class'] = lerp_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)\n",
-    "    lerp_data['hour_sin'] = np.sin(2 * np.pi * lerp_data['hour'] / 24)\n",
-    "    lerp_data['hour_cos'] = np.cos(2 * np.pi * lerp_data['hour'] / 24)\n",
-    "    lerp_data['month_sin'] = np.sin(2 * np.pi * lerp_data['month'] / 12)\n",
-    "    lerp_data['month_cos'] = np.cos(2 * np.pi * lerp_data['month'] / 12)\n",
-    "    lerp_data['ground_temp - temp_C'] = lerp_data['groundtemp'] - lerp_data['temp_C']\n",
-    "\n",
-    "    # 결과 저장\n",
-    "    lerp_data.to_csv(output_path, index = False)\n",
-    "    print(len(lerp_data[lerp_data['multi_class']==0]),'|',len(lerp_data[lerp_data['multi_class']==1]),'|',len(lerp_data[lerp_data['multi_class']==2]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SMOTENC(초기선형보간증강) + CTGAN(조건부증강)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'%pip install imbalanced-learn'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'''%pip install imbalanced-learn sdv optuna'''"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 소수범주당 증강 목표 20000개"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using device: cuda\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2025-01-20 14:56:59,102] A new study created in memory with name: no-name-b76e8d7c-ea9f-42f3-b88f-320929a7e5ac\n",
-      "[I 2025-01-20 14:57:16,217] Trial 0 finished with value: -37.318639001504366 and parameters: {'embedding_dim': 71, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 256, 'discriminator_steps': 1}. Best is trial 0 with value: -37.318639001504366.\n",
-      "[I 2025-01-20 14:57:36,855] Trial 1 finished with value: -90.68083127767693 and parameters: {'embedding_dim': 70, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 256, 'discriminator_steps': 2}. Best is trial 0 with value: -37.318639001504366.\n",
-      "[I 2025-01-20 14:58:03,444] Trial 2 finished with value: -13.074059044651209 and parameters: {'embedding_dim': 84, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 8, 'batch_size': 128, 'discriminator_steps': 1}. Best is trial 2 with value: -13.074059044651209.\n",
-      "[I 2025-01-20 14:58:54,752] Trial 3 finished with value: -12.274816765539791 and parameters: {'embedding_dim': 128, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 3 with value: -12.274816765539791.\n",
-      "[I 2025-01-20 14:59:09,155] Trial 4 finished with value: -34.44549346411927 and parameters: {'embedding_dim': 90, 'generator_dim': (64, 64), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 256, 'discriminator_steps': 1}. Best is trial 3 with value: -12.274816765539791.\n",
-      "[I 2025-01-20 14:59:50,114] Trial 5 finished with value: -9.00948546286418 and parameters: {'embedding_dim': 68, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 5 with value: -9.00948546286418.\n",
-      "[I 2025-01-20 15:01:11,009] Trial 6 finished with value: -6.388978160012437 and parameters: {'embedding_dim': 118, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 2}. Best is trial 6 with value: -6.388978160012437.\n",
-      "[I 2025-01-20 15:01:25,167] Trial 7 finished with value: -228.64770439962442 and parameters: {'embedding_dim': 108, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 256, 'discriminator_steps': 1}. Best is trial 6 with value: -6.388978160012437.\n",
-      "[I 2025-01-20 15:01:51,402] Trial 8 finished with value: -5.368727454151448 and parameters: {'embedding_dim': 114, 'generator_dim': (64, 64), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 1}. Best is trial 8 with value: -5.368727454151448.\n",
-      "[I 2025-01-20 15:02:11,569] Trial 9 finished with value: -278.60033287713617 and parameters: {'embedding_dim': 104, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 256, 'discriminator_steps': 2}. Best is trial 8 with value: -5.368727454151448.\n",
-      "[I 2025-01-20 15:03:05,202] Trial 10 finished with value: -11.579568231204782 and parameters: {'embedding_dim': 116, 'generator_dim': (64, 64), 'discriminator_dim': (64, 64), 'pac': 8, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 8 with value: -5.368727454151448.\n",
-      "[I 2025-01-20 15:04:54,530] Trial 11 finished with value: -51.98893561900591 and parameters: {'embedding_dim': 124, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 3}. Best is trial 8 with value: -5.368727454151448.\n",
-      "[I 2025-01-20 15:06:15,065] Trial 12 finished with value: -104.10651741905765 and parameters: {'embedding_dim': 112, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 2}. Best is trial 8 with value: -5.368727454151448.\n",
-      "[I 2025-01-20 15:08:05,216] Trial 13 finished with value: -71.59408383664889 and parameters: {'embedding_dim': 99, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 3}. Best is trial 8 with value: -5.368727454151448.\n",
-      "[I 2025-01-20 15:08:45,626] Trial 14 finished with value: -110.1562292208483 and parameters: {'embedding_dim': 119, 'generator_dim': (64, 64), 'discriminator_dim': (64, 64), 'pac': 8, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 8 with value: -5.368727454151448.\n",
-      "[I 2025-01-20 15:09:36,821] Trial 15 finished with value: -3.8151168836858163 and parameters: {'embedding_dim': 120, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 15 with value: -3.8151168836858163.\n",
-      "[I 2025-01-20 15:10:03,381] Trial 16 finished with value: -8.68366593294427 and parameters: {'embedding_dim': 101, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 1}. Best is trial 15 with value: -3.8151168836858163.\n",
-      "[I 2025-01-20 15:10:30,167] Trial 17 finished with value: -48.95835312594844 and parameters: {'embedding_dim': 93, 'generator_dim': (64, 64), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 1}. Best is trial 15 with value: -3.8151168836858163.\n",
-      "[I 2025-01-20 15:11:21,958] Trial 18 finished with value: -20.81247815272162 and parameters: {'embedding_dim': 108, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 15 with value: -3.8151168836858163.\n",
-      "[I 2025-01-20 15:11:49,059] Trial 19 finished with value: -50.99872228893688 and parameters: {'embedding_dim': 81, 'generator_dim': (64, 64), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 1}. Best is trial 15 with value: -3.8151168836858163.\n",
-      "[I 2025-01-20 15:13:09,572] Trial 20 finished with value: -5.639452046764817 and parameters: {'embedding_dim': 123, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 2}. Best is trial 15 with value: -3.8151168836858163.\n",
-      "[I 2025-01-20 15:14:30,274] Trial 21 finished with value: -23.57417977156469 and parameters: {'embedding_dim': 128, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 2}. Best is trial 15 with value: -3.8151168836858163.\n",
-      "[I 2025-01-20 15:15:22,032] Trial 22 finished with value: -0.6778059786818922 and parameters: {'embedding_dim': 122, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:16:13,320] Trial 23 finished with value: -32.5624320546421 and parameters: {'embedding_dim': 113, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:17:04,695] Trial 24 finished with value: -5.785384536972532 and parameters: {'embedding_dim': 121, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:17:56,139] Trial 25 finished with value: -8.738764219614195 and parameters: {'embedding_dim': 111, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:18:22,038] Trial 26 finished with value: -184.45992185820504 and parameters: {'embedding_dim': 105, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 8, 'batch_size': 128, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:19:13,003] Trial 27 finished with value: -2.6991205788981727 and parameters: {'embedding_dim': 115, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:20:04,624] Trial 28 finished with value: -44.592267621272406 and parameters: {'embedding_dim': 124, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:20:56,181] Trial 29 finished with value: -44.22943387558266 and parameters: {'embedding_dim': 117, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:21:47,508] Trial 30 finished with value: -29.557955872673972 and parameters: {'embedding_dim': 128, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:22:38,696] Trial 31 finished with value: -3.0604149741858233 and parameters: {'embedding_dim': 115, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:23:30,109] Trial 32 finished with value: -4.184301463771818 and parameters: {'embedding_dim': 121, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:24:21,165] Trial 33 finished with value: -56.76796024388123 and parameters: {'embedding_dim': 78, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:25:12,460] Trial 34 finished with value: -68.78895549007119 and parameters: {'embedding_dim': 109, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:25:26,435] Trial 35 finished with value: -7.698349027772178 and parameters: {'embedding_dim': 115, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 256, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:26:17,239] Trial 36 finished with value: -1.92703376893977 and parameters: {'embedding_dim': 125, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:27:09,507] Trial 37 finished with value: -342.3563728328815 and parameters: {'embedding_dim': 124, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:27:30,145] Trial 38 finished with value: -38.64880022938009 and parameters: {'embedding_dim': 65, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 256, 'discriminator_steps': 2}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:28:21,527] Trial 39 finished with value: -171.94890295218917 and parameters: {'embedding_dim': 126, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:29:42,180] Trial 40 finished with value: -2.797751148097547 and parameters: {'embedding_dim': 95, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 2}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:31:31,734] Trial 41 finished with value: -195.52351726339586 and parameters: {'embedding_dim': 95, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 3}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:32:52,135] Trial 42 finished with value: -66.50269881474058 and parameters: {'embedding_dim': 88, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 2}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:34:11,496] Trial 43 finished with value: -11.503285915156347 and parameters: {'embedding_dim': 89, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 2}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:35:03,425] Trial 44 finished with value: -198.7212272708481 and parameters: {'embedding_dim': 104, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:35:30,511] Trial 45 finished with value: -5.755500215208933 and parameters: {'embedding_dim': 99, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 256, 'discriminator_steps': 3}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:36:21,350] Trial 46 finished with value: -17.212335519290654 and parameters: {'embedding_dim': 74, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:37:42,167] Trial 47 finished with value: -47.30773434273214 and parameters: {'embedding_dim': 85, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 2}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:38:33,458] Trial 48 finished with value: -4.050649756572355 and parameters: {'embedding_dim': 118, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:39:53,817] Trial 49 finished with value: -85.45026059502085 and parameters: {'embedding_dim': 93, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 2}. Best is trial 22 with value: -0.6778059786818922.\n",
-      "[I 2025-01-20 15:40:45,481] A new study created in memory with name: no-name-9f81a759-8c1c-45e7-9717-966f9494aea6\n",
-      "[I 2025-01-20 15:42:36,221] Trial 0 finished with value: -75.5212527916712 and parameters: {'embedding_dim': 499, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 4, 'batch_size': 1024, 'discriminator_steps': 5}. Best is trial 0 with value: -75.5212527916712.\n",
-      "[I 2025-01-20 15:44:06,113] Trial 1 finished with value: -111.70948723752186 and parameters: {'embedding_dim': 281, 'generator_dim': (256, 256), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 512, 'discriminator_steps': 2}. Best is trial 0 with value: -75.5212527916712.\n",
-      "[I 2025-01-20 15:48:33,946] Trial 2 finished with value: -479.1722735079276 and parameters: {'embedding_dim': 468, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 256, 'discriminator_steps': 4}. Best is trial 0 with value: -75.5212527916712.\n",
-      "[I 2025-01-20 15:53:59,017] Trial 3 finished with value: -48.69174576070862 and parameters: {'embedding_dim': 219, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 256, 'discriminator_steps': 5}. Best is trial 3 with value: -48.69174576070862.\n",
-      "[I 2025-01-20 15:56:03,676] Trial 4 finished with value: -4.409888912338193 and parameters: {'embedding_dim': 137, 'generator_dim': (256, 256), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 3}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 15:56:37,777] Trial 5 finished with value: -471.9978221268116 and parameters: {'embedding_dim': 229, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 1024, 'discriminator_steps': 1}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 15:58:28,153] Trial 6 finished with value: -601.2484927626908 and parameters: {'embedding_dim': 478, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 1024, 'discriminator_steps': 5}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 16:00:31,897] Trial 7 finished with value: -695.4921399608821 and parameters: {'embedding_dim': 450, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 4, 'batch_size': 512, 'discriminator_steps': 3}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 16:04:59,739] Trial 8 finished with value: -47.389652192459366 and parameters: {'embedding_dim': 194, 'generator_dim': (256, 256), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 256, 'discriminator_steps': 4}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 16:07:34,586] Trial 9 finished with value: -1001.9812539137846 and parameters: {'embedding_dim': 355, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 4, 'batch_size': 256, 'discriminator_steps': 2}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 16:09:38,375] Trial 10 finished with value: -33.10530614059147 and parameters: {'embedding_dim': 130, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 3}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 16:11:41,364] Trial 11 finished with value: -362.128696252612 and parameters: {'embedding_dim': 128, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 3}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 16:13:12,305] Trial 12 finished with value: -328.82671400582115 and parameters: {'embedding_dim': 130, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 2}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 16:15:47,167] Trial 13 finished with value: -7.9286899630985275 and parameters: {'embedding_dim': 359, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 4}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 16:18:22,255] Trial 14 finished with value: -342.6117545823074 and parameters: {'embedding_dim': 354, 'generator_dim': (256, 256), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 4}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 16:20:57,741] Trial 15 finished with value: -67.35499346617792 and parameters: {'embedding_dim': 391, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 4}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 16:21:55,034] Trial 16 finished with value: -266.2126635115298 and parameters: {'embedding_dim': 302, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 1}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 16:24:30,585] Trial 17 finished with value: -35.730265204691946 and parameters: {'embedding_dim': 426, 'generator_dim': (256, 256), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 4}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 16:26:33,459] Trial 18 finished with value: -108.18797777120533 and parameters: {'embedding_dim': 259, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 3}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 16:27:45,561] Trial 19 finished with value: -71.26357253219594 and parameters: {'embedding_dim': 329, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 1024, 'discriminator_steps': 3}. Best is trial 4 with value: -4.409888912338193.\n",
-      "[I 2025-01-20 16:29:15,652] Trial 20 finished with value: -1.3630811306533475 and parameters: {'embedding_dim': 402, 'generator_dim': (256, 256), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 2}. Best is trial 20 with value: -1.3630811306533475.\n",
-      "[I 2025-01-20 16:30:44,754] Trial 21 finished with value: -488.94996822363987 and parameters: {'embedding_dim': 402, 'generator_dim': (256, 256), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 2}. Best is trial 20 with value: -1.3630811306533475.\n",
-      "[I 2025-01-20 16:32:13,977] Trial 22 finished with value: -716.9087223543194 and parameters: {'embedding_dim': 380, 'generator_dim': (256, 256), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 2}. Best is trial 20 with value: -1.3630811306533475.\n",
-      "[I 2025-01-20 16:34:15,782] Trial 23 finished with value: -1312.349106866347 and parameters: {'embedding_dim': 323, 'generator_dim': (256, 256), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 3}. Best is trial 20 with value: -1.3630811306533475.\n",
-      "[I 2025-01-20 16:35:45,149] Trial 24 finished with value: -658.7045880157874 and parameters: {'embedding_dim': 425, 'generator_dim': (256, 256), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 2}. Best is trial 20 with value: -1.3630811306533475.\n",
-      "[I 2025-01-20 16:36:41,995] Trial 25 finished with value: -649.0407497590966 and parameters: {'embedding_dim': 358, 'generator_dim': (256, 256), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 1}. Best is trial 20 with value: -1.3630811306533475.\n",
-      "[I 2025-01-20 16:39:18,618] Trial 26 finished with value: -137.14788624945254 and parameters: {'embedding_dim': 428, 'generator_dim': (128, 128), 'discriminator_dim': (256, 256), 'pac': 4, 'batch_size': 512, 'discriminator_steps': 4}. Best is trial 20 with value: -1.3630811306533475.\n",
-      "[I 2025-01-20 16:41:23,169] Trial 27 finished with value: -11.215443967140468 and parameters: {'embedding_dim': 172, 'generator_dim': (256, 256), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 3}. Best is trial 20 with value: -1.3630811306533475.\n",
-      "[I 2025-01-20 16:44:54,394] Trial 28 finished with value: -70.15458903694731 and parameters: {'embedding_dim': 301, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 256, 'discriminator_steps': 3}. Best is trial 20 with value: -1.3630811306533475.\n",
-      "[I 2025-01-20 16:46:43,597] Trial 29 finished with value: -77.00986664642153 and parameters: {'embedding_dim': 269, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 4, 'batch_size': 1024, 'discriminator_steps': 5}. Best is trial 20 with value: -1.3630811306533475.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processed and saved: ../data/data_oversampled/ctgan_incheon.feather\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2025-01-20 16:48:14,616] A new study created in memory with name: no-name-9673bd2f-608c-45e0-b8eb-39eeba0d0bbe\n",
-      "[I 2025-01-20 16:48:37,588] Trial 0 finished with value: -23.155852178206054 and parameters: {'embedding_dim': 72, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 0 with value: -23.155852178206054.\n",
-      "[I 2025-01-20 16:48:42,118] Trial 1 finished with value: -34.21490168723894 and parameters: {'embedding_dim': 90, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 256, 'discriminator_steps': 1}. Best is trial 0 with value: -23.155852178206054.\n",
-      "[I 2025-01-20 16:49:05,052] Trial 2 finished with value: -4.003410149026237 and parameters: {'embedding_dim': 118, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 2 with value: -4.003410149026237.\n",
-      "[I 2025-01-20 16:49:28,467] Trial 3 finished with value: -21.564028021550694 and parameters: {'embedding_dim': 106, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 2 with value: -4.003410149026237.\n",
-      "[I 2025-01-20 16:49:51,875] Trial 4 finished with value: -7.355484157065245 and parameters: {'embedding_dim': 112, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 2 with value: -4.003410149026237.\n",
-      "[I 2025-01-20 16:50:00,722] Trial 5 finished with value: -32.39901609359498 and parameters: {'embedding_dim': 74, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 256, 'discriminator_steps': 3}. Best is trial 2 with value: -4.003410149026237.\n",
-      "[I 2025-01-20 16:50:24,169] Trial 6 finished with value: -4.0750874978322935 and parameters: {'embedding_dim': 68, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 2 with value: -4.003410149026237.\n",
-      "[I 2025-01-20 16:50:30,904] Trial 7 finished with value: -5.8933791686324675 and parameters: {'embedding_dim': 117, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 256, 'discriminator_steps': 2}. Best is trial 2 with value: -4.003410149026237.\n",
-      "[I 2025-01-20 16:50:37,571] Trial 8 finished with value: -15.325497040087624 and parameters: {'embedding_dim': 128, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 8, 'batch_size': 256, 'discriminator_steps': 2}. Best is trial 2 with value: -4.003410149026237.\n",
-      "[I 2025-01-20 16:50:48,499] Trial 9 finished with value: -16.06789263895181 and parameters: {'embedding_dim': 112, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 1}. Best is trial 2 with value: -4.003410149026237.\n",
-      "[I 2025-01-20 16:51:11,461] Trial 10 finished with value: -6.933575610142412 and parameters: {'embedding_dim': 92, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 2 with value: -4.003410149026237.\n",
-      "[I 2025-01-20 16:51:48,391] Trial 11 finished with value: -4.1384946672555945 and parameters: {'embedding_dim': 64, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 2}. Best is trial 2 with value: -4.003410149026237.\n",
-      "[I 2025-01-20 16:52:38,821] Trial 12 finished with value: -9.416054623408765 and parameters: {'embedding_dim': 82, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 3}. Best is trial 2 with value: -4.003410149026237.\n",
-      "[I 2025-01-20 16:52:55,737] Trial 13 finished with value: -2.7694488886245567 and parameters: {'embedding_dim': 102, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 13 with value: -2.7694488886245567.\n",
-      "[I 2025-01-20 16:53:12,663] Trial 14 finished with value: -8.615367995825054 and parameters: {'embedding_dim': 100, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 13 with value: -2.7694488886245567.\n",
-      "[I 2025-01-20 16:53:29,586] Trial 15 finished with value: -6.889357108499393 and parameters: {'embedding_dim': 123, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 13 with value: -2.7694488886245567.\n",
-      "[I 2025-01-20 16:53:52,531] Trial 16 finished with value: -9.171953998759633 and parameters: {'embedding_dim': 98, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 13 with value: -2.7694488886245567.\n",
-      "[I 2025-01-20 16:54:09,458] Trial 17 finished with value: -57.35581165476122 and parameters: {'embedding_dim': 106, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 13 with value: -2.7694488886245567.\n",
-      "[I 2025-01-20 16:54:26,358] Trial 18 finished with value: -8.11141919896362 and parameters: {'embedding_dim': 119, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 13 with value: -2.7694488886245567.\n",
-      "[I 2025-01-20 16:54:49,299] Trial 19 finished with value: -36.89391231331456 and parameters: {'embedding_dim': 105, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 13 with value: -2.7694488886245567.\n",
-      "[I 2025-01-20 16:55:12,263] Trial 20 finished with value: -0.915130278465809 and parameters: {'embedding_dim': 85, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 16:55:35,365] Trial 21 finished with value: -9.14515742082317 and parameters: {'embedding_dim': 88, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 16:55:58,368] Trial 22 finished with value: -11.94829599012277 and parameters: {'embedding_dim': 81, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 16:56:15,325] Trial 23 finished with value: -1.5616654362533082 and parameters: {'embedding_dim': 82, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 16:56:32,320] Trial 24 finished with value: -2.062209737409747 and parameters: {'embedding_dim': 82, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 16:56:49,278] Trial 25 finished with value: -2.528799284383497 and parameters: {'embedding_dim': 80, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 16:57:06,209] Trial 26 finished with value: -5.408362529903892 and parameters: {'embedding_dim': 76, 'generator_dim': (64, 64), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 16:57:23,136] Trial 27 finished with value: -8.895873144737894 and parameters: {'embedding_dim': 88, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 16:57:29,847] Trial 28 finished with value: -67.16752180682067 and parameters: {'embedding_dim': 85, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 256, 'discriminator_steps': 2}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 16:57:46,817] Trial 29 finished with value: -2.3990267779936456 and parameters: {'embedding_dim': 94, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 16:58:09,819] Trial 30 finished with value: -1.0609161149494997 and parameters: {'embedding_dim': 77, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 16:58:32,799] Trial 31 finished with value: -40.89668749736999 and parameters: {'embedding_dim': 76, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 16:58:55,758] Trial 32 finished with value: -20.543485177832817 and parameters: {'embedding_dim': 71, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 16:59:18,740] Trial 33 finished with value: -48.817409667795275 and parameters: {'embedding_dim': 85, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 16:59:41,793] Trial 34 finished with value: -8.609796785683871 and parameters: {'embedding_dim': 80, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 16:59:52,805] Trial 35 finished with value: -0.9307100824718637 and parameters: {'embedding_dim': 70, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 1}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 17:00:03,767] Trial 36 finished with value: -18.096015461780212 and parameters: {'embedding_dim': 70, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 128, 'discriminator_steps': 1}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 17:00:08,288] Trial 37 finished with value: -47.453982612938844 and parameters: {'embedding_dim': 76, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 256, 'discriminator_steps': 1}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 17:00:31,733] Trial 38 finished with value: -317.9490947764603 and parameters: {'embedding_dim': 67, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 1}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 17:00:36,269] Trial 39 finished with value: -5.325056185063841 and parameters: {'embedding_dim': 74, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 256, 'discriminator_steps': 1}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 17:01:26,667] Trial 40 finished with value: -2.4975095593414944 and parameters: {'embedding_dim': 65, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 3}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 17:01:37,549] Trial 41 finished with value: -8.724928631283321 and parameters: {'embedding_dim': 85, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 1}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 17:01:54,575] Trial 42 finished with value: -9.957479554800914 and parameters: {'embedding_dim': 78, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 17:02:17,543] Trial 43 finished with value: -20.086840991896658 and parameters: {'embedding_dim': 90, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 17:02:28,489] Trial 44 finished with value: -123.24510747774687 and parameters: {'embedding_dim': 73, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 1}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 17:02:51,367] Trial 45 finished with value: -2.8224441691394726 and parameters: {'embedding_dim': 83, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 8, 'batch_size': 128, 'discriminator_steps': 3}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 17:03:08,278] Trial 46 finished with value: -61.541871320706946 and parameters: {'embedding_dim': 78, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 17:03:45,236] Trial 47 finished with value: -44.6086713907501 and parameters: {'embedding_dim': 71, 'generator_dim': (64, 64), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 64, 'discriminator_steps': 2}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 17:03:49,767] Trial 48 finished with value: -18.76213014994069 and parameters: {'embedding_dim': 69, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 4, 'batch_size': 256, 'discriminator_steps': 1}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 17:04:06,677] Trial 49 finished with value: -14.235673820573563 and parameters: {'embedding_dim': 92, 'generator_dim': (128, 128), 'discriminator_dim': (64, 64), 'pac': 8, 'batch_size': 128, 'discriminator_steps': 2}. Best is trial 20 with value: -0.915130278465809.\n",
-      "[I 2025-01-20 17:04:29,881] A new study created in memory with name: no-name-cd965ffc-fc7d-429e-ae05-208f0c3fad33\n",
-      "[I 2025-01-20 17:06:32,714] Trial 0 finished with value: -522.274136185342 and parameters: {'embedding_dim': 225, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 256, 'discriminator_steps': 3}. Best is trial 0 with value: -522.274136185342.\n",
-      "[I 2025-01-20 17:07:20,042] Trial 1 finished with value: -586.1407911119707 and parameters: {'embedding_dim': 480, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 1024, 'discriminator_steps': 4}. Best is trial 0 with value: -522.274136185342.\n",
-      "[I 2025-01-20 17:08:50,110] Trial 2 finished with value: -1338.898800006008 and parameters: {'embedding_dim': 383, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 256, 'discriminator_steps': 2}. Best is trial 0 with value: -522.274136185342.\n",
-      "[I 2025-01-20 17:09:46,877] Trial 3 finished with value: -595.5573560928374 and parameters: {'embedding_dim': 441, 'generator_dim': (128, 128), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 1024, 'discriminator_steps': 5}. Best is trial 0 with value: -522.274136185342.\n",
-      "[I 2025-01-20 17:10:15,266] Trial 4 finished with value: -186.13472727208838 and parameters: {'embedding_dim': 186, 'generator_dim': (256, 256), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 1024, 'discriminator_steps': 2}. Best is trial 4 with value: -186.13472727208838.\n",
-      "[I 2025-01-20 17:11:59,408] Trial 5 finished with value: -556.572362981219 and parameters: {'embedding_dim': 379, 'generator_dim': (128, 128), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 5}. Best is trial 4 with value: -186.13472727208838.\n",
-      "[I 2025-01-20 17:13:07,775] Trial 6 finished with value: -30.304752691466533 and parameters: {'embedding_dim': 214, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 3}. Best is trial 6 with value: -30.304752691466533.\n",
-      "[I 2025-01-20 17:14:32,975] Trial 7 finished with value: -31.549366617667648 and parameters: {'embedding_dim': 388, 'generator_dim': (128, 128), 'discriminator_dim': (256, 256), 'pac': 4, 'batch_size': 512, 'discriminator_steps': 4}. Best is trial 6 with value: -30.304752691466533.\n",
-      "[I 2025-01-20 17:15:12,970] Trial 8 finished with value: -345.73649432175523 and parameters: {'embedding_dim': 409, 'generator_dim': (128, 128), 'discriminator_dim': (128, 128), 'pac': 8, 'batch_size': 1024, 'discriminator_steps': 3}. Best is trial 6 with value: -30.304752691466533.\n",
-      "[I 2025-01-20 17:15:45,595] Trial 9 finished with value: -1021.6740667962498 and parameters: {'embedding_dim': 451, 'generator_dim': (128, 128), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 1}. Best is trial 6 with value: -30.304752691466533.\n",
-      "[I 2025-01-20 17:16:18,456] Trial 10 finished with value: -0.6514198871412 and parameters: {'embedding_dim': 270, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 1}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:16:51,307] Trial 11 finished with value: -4.8963478843156665 and parameters: {'embedding_dim': 277, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 1}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:17:24,454] Trial 12 finished with value: -153.22796082787534 and parameters: {'embedding_dim': 289, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 1}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:17:56,888] Trial 13 finished with value: -199.5670473579735 and parameters: {'embedding_dim': 299, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 1}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:18:47,371] Trial 14 finished with value: -140.37597916237385 and parameters: {'embedding_dim': 134, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 2}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:19:20,921] Trial 15 finished with value: -3561.1724538614976 and parameters: {'embedding_dim': 261, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 1}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:20:51,379] Trial 16 finished with value: -547.4278435153543 and parameters: {'embedding_dim': 334, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 256, 'discriminator_steps': 2}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:21:24,230] Trial 17 finished with value: -1821.0058166894948 and parameters: {'embedding_dim': 341, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 1}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:22:14,676] Trial 18 finished with value: -21.687672514288167 and parameters: {'embedding_dim': 253, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 2}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:23:12,549] Trial 19 finished with value: -110.57179286531195 and parameters: {'embedding_dim': 161, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 256, 'discriminator_steps': 1}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:24:39,667] Trial 20 finished with value: -880.2718942270703 and parameters: {'embedding_dim': 259, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 4}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:25:29,860] Trial 21 finished with value: -15.677402299300798 and parameters: {'embedding_dim': 247, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 2}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:26:20,084] Trial 22 finished with value: -443.87283814186236 and parameters: {'embedding_dim': 303, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 2}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:26:52,252] Trial 23 finished with value: -1813.9072516488602 and parameters: {'embedding_dim': 216, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 1}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:27:42,365] Trial 24 finished with value: -3066.9405889616755 and parameters: {'embedding_dim': 273, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 2}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:28:15,491] Trial 25 finished with value: -49.0184152543112 and parameters: {'embedding_dim': 343, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 1}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:29:06,215] Trial 26 finished with value: -1436.6956066386338 and parameters: {'embedding_dim': 233, 'generator_dim': (256, 256), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 512, 'discriminator_steps': 2}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:29:39,298] Trial 27 finished with value: -1622.8851329380343 and parameters: {'embedding_dim': 187, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 512, 'discriminator_steps': 1}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:31:10,397] Trial 28 finished with value: -610.6098245576701 and parameters: {'embedding_dim': 318, 'generator_dim': (256, 256), 'discriminator_dim': (256, 256), 'pac': 8, 'batch_size': 256, 'discriminator_steps': 2}. Best is trial 10 with value: -0.6514198871412.\n",
-      "[I 2025-01-20 17:31:48,784] Trial 29 finished with value: -227.41157626188772 and parameters: {'embedding_dim': 233, 'generator_dim': (256, 256), 'discriminator_dim': (128, 128), 'pac': 4, 'batch_size': 1024, 'discriminator_steps': 3}. Best is trial 10 with value: -0.6514198871412.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processed and saved: ../data/data_oversampled/ctgan_seoul.feather\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from imblearn.over_sampling import SMOTENC\n",
-    "import optuna\n",
-    "from ctgan import CTGAN\n",
-    "import torch\n",
-    "import warnings\n",
-    "\n",
-    "# 지역별 데이터 파일 경로\n",
-    "regions = ['busan', 'daegu', 'daejeon', 'gwangju', 'incheon', 'seoul']\n",
-    "file_paths = [f'../data/data_for_modeling/{region}_train.csv' for region in regions]\n",
-    "output_paths = [f'../data/data_oversampled/ctgan20000_{region}.csv' for region in regions]\n",
-    "\n",
-    "# GPU 사용 설정\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "print(f\"Using device: {device}\")\n",
-    "\n",
-    "# 경고 무시\n",
-    "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"optuna.distributions\")\n",
-    "\n",
-    "# 지역별 처리\n",
-    "for file_path, output_path in zip(file_paths, output_paths):\n",
-    "    # 데이터 로드\n",
-    "    data = pd.read_csv(file_path, index_col=0)\n",
-    "    X = data.drop(columns=['multi_class', 'binary_class'])\n",
-    "    y = data['multi_class']\n",
-    "\n",
-    "    # 불필요한 열 제거\n",
-    "    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)\n",
-    "\n",
-    "    # SMOTENC에서 사용할 범주형 변수 열 번호 설정\n",
-    "    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']\n",
-    "\n",
-    "    # sampling_strategy 설정\n",
-    "    count_class_0 = (y == 0).sum()\n",
-    "    count_class_1 = (y == 1).sum()\n",
-    "    count_class_2 = (y == 2).sum()\n",
-    "    sampling_strategy = {\n",
-    "        0: 500 if count_class_0 <= 500 else 1000,\n",
-    "        1: int(np.ceil(count_class_1 / 100) * 100),  # 백의 자리로 올림\n",
-    "        2: count_class_2\n",
-    "    }\n",
-    "\n",
-    "    # SMOTENC 적용\n",
-    "    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)\n",
-    "    X_resampled, y_resampled = smotenc.fit_resample(X, y)\n",
-    "\n",
-    "    # Resampled 데이터 생성\n",
-    "    lerp_data = X_resampled.copy()\n",
-    "    lerp_data['multi_class'] = y_resampled\n",
-    "\n",
-    "    # CTGAN에서 사용할 범주형 변수 열 이름 설정\n",
-    "    categorical_features = [\n",
-    "        col for col, dtype in zip(lerp_data.columns, lerp_data.dtypes) if dtype != 'float64'\n",
-    "    ]\n",
-    "\n",
-    "    # Optuna 목적 함수 정의\n",
-    "    def objective(trial):\n",
-    "        # 하이퍼파라미터 탐색 범위 설정\n",
-    "        embedding_dim = trial.suggest_int(\"embedding_dim\", 64, 128)\n",
-    "        generator_dim = trial.suggest_categorical(\"generator_dim\", [(64, 64), (128, 128)])\n",
-    "        discriminator_dim = trial.suggest_categorical(\"discriminator_dim\", [(64, 64), (128, 128)])\n",
-    "        pac = trial.suggest_categorical(\"pac\", [4, 8])\n",
-    "        batch_size = trial.suggest_categorical(\"batch_size\", [64, 128, 256])\n",
-    "        discriminator_steps = trial.suggest_int(\"discriminator_steps\", 1, 3)\n",
-    "\n",
-    "        # CTGAN 모델 생성\n",
-    "        ctgan = CTGAN(\n",
-    "            embedding_dim=embedding_dim,\n",
-    "            generator_dim=generator_dim,\n",
-    "            discriminator_dim=discriminator_dim,\n",
-    "            batch_size=batch_size,\n",
-    "            discriminator_steps=discriminator_steps,\n",
-    "            pac=pac\n",
-    "        )\n",
-    "\n",
-    "        # 범주 0 데이터 필터링\n",
-    "        data_0 = lerp_data[lerp_data['multi_class'] == 0]\n",
-    "\n",
-    "        # 모델 학습\n",
-    "        ctgan.fit(data_0, discrete_columns=categorical_features)\n",
-    "\n",
-    "        # 샘플 생성\n",
-    "        generated_data = ctgan.sample(len(data_0) * 2)\n",
-    "\n",
-    "        # 평가: 샘플의 연속형 변수 분포 비교\n",
-    "        real_visi = data_0['visi']\n",
-    "        generated_visi = generated_data['visi']\n",
-    "\n",
-    "        # 분포 간 차이(MSE) 계산\n",
-    "        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)\n",
-    "        return -mse\n",
-    "\n",
-    "    # Optuna로 최적화 수행\n",
-    "    study = optuna.create_study(direction=\"maximize\")\n",
-    "    study.optimize(objective, n_trials=50)\n",
-    "\n",
-    "    # 최적 하이퍼파라미터 출력\n",
-    "    best_params = study.best_params\n",
-    "\n",
-    "    # 최적 하이퍼파라미터로 CTGAN 학습 및 샘플 생성\n",
-    "    ctgan = CTGAN(\n",
-    "        embedding_dim=best_params[\"embedding_dim\"],\n",
-    "        generator_dim=best_params[\"generator_dim\"],\n",
-    "        discriminator_dim=best_params[\"discriminator_dim\"],\n",
-    "        batch_size=best_params[\"batch_size\"],\n",
-    "        discriminator_steps=best_params[\"discriminator_steps\"],\n",
-    "        pac=best_params[\"pac\"]\n",
-    "    )\n",
-    "\n",
-    "    # 범주 0 데이터로 최종 학습\n",
-    "    ctgan.fit(lerp_data[lerp_data['multi_class'] == 0], discrete_columns=categorical_features)\n",
-    "    generated_0 = ctgan.sample(20000)\n",
-    "\n",
-    "    # 범주 1 데이터 최적화 및 생성\n",
-    "    def objective_class1(trial):\n",
-    "        embedding_dim = trial.suggest_int(\"embedding_dim\", 128, 512)\n",
-    "        generator_dim = trial.suggest_categorical(\"generator_dim\", [(128, 128), (256, 256)])\n",
-    "        discriminator_dim = trial.suggest_categorical(\"discriminator_dim\", [(128, 128), (256, 256)])\n",
-    "        pac = trial.suggest_categorical(\"pac\", [4, 8])\n",
-    "        batch_size = trial.suggest_categorical(\"batch_size\", [256, 512, 1024])\n",
-    "        discriminator_steps = trial.suggest_int(\"discriminator_steps\", 1, 5)\n",
-    "\n",
-    "        ctgan = CTGAN(\n",
-    "            embedding_dim=embedding_dim,\n",
-    "            generator_dim=generator_dim,\n",
-    "            discriminator_dim=discriminator_dim,\n",
-    "            batch_size=batch_size,\n",
-    "            discriminator_steps=discriminator_steps,\n",
-    "            pac=pac\n",
-    "        )\n",
-    "\n",
-    "        data_1 = lerp_data[lerp_data['multi_class'] == 1]\n",
-    "        ctgan.fit(data_1, discrete_columns=categorical_features)\n",
-    "        generated_data = ctgan.sample(len(data_1) * 2)\n",
-    "\n",
-    "        real_visi = data_1['visi']\n",
-    "        generated_visi = generated_data['visi']\n",
-    "        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)\n",
-    "        return -mse\n",
-    "\n",
-    "    study_class1 = optuna.create_study(direction=\"maximize\")\n",
-    "    study_class1.optimize(objective_class1, n_trials=30)\n",
-    "\n",
-    "    best_params_class1 = study_class1.best_params\n",
-    "    ctgan = CTGAN(\n",
-    "        embedding_dim=best_params_class1[\"embedding_dim\"],\n",
-    "        generator_dim=best_params_class1[\"generator_dim\"],\n",
-    "        discriminator_dim=best_params_class1[\"discriminator_dim\"],\n",
-    "        batch_size=best_params_class1[\"batch_size\"],\n",
-    "        discriminator_steps=best_params_class1[\"discriminator_steps\"],\n",
-    "        pac=best_params_class1[\"pac\"]\n",
-    "    )\n",
-    "\n",
-    "    ctgan.fit(lerp_data[lerp_data['multi_class'] == 1], discrete_columns=categorical_features)\n",
-    "    generated_1 = ctgan.sample(20000 - int(np.ceil(count_class_1 / 100) * 100))\n",
-    "\n",
-    "    # 데이터 병합 및 저장\n",
-    "    well_generated0 = generated_0[(generated_0['visi'] >= 0) & (generated_0['visi'] < 100)]\n",
-    "    well_generated1 = generated_1[(generated_1['visi'] >= 100) & (generated_1['visi'] < 500)]\n",
-    "    smote_gan_data = pd.concat([lerp_data, well_generated0, well_generated1], axis=0)\n",
-    "    # 제거변수 복구\n",
-    "    smote_gan_data['binary_class'] = smote_gan_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)\n",
-    "    smote_gan_data['hour_sin'] = np.sin(2 * np.pi * smote_gan_data['hour'] / 24)\n",
-    "    smote_gan_data['hour_cos'] = np.cos(2 * np.pi * smote_gan_data['hour'] / 24)\n",
-    "    smote_gan_data['month_sin'] = np.sin(2 * np.pi * smote_gan_data['month'] / 12)\n",
-    "    smote_gan_data['month_cos'] = np.cos(2 * np.pi * smote_gan_data['month'] / 12)\n",
-    "    smote_gan_data['ground_temp - temp_C'] = smote_gan_data['groundtemp'] - smote_gan_data['temp_C']\n",
-    "\n",
-    "    filtered_data = smote_gan_data[smote_gan_data['multi_class'] != 2]\n",
-    "    original_class2 = data[data['multi_class'] == 2]\n",
-    "    final_data = pd.concat([filtered_data, original_class2], axis=0)\n",
-    "    final_data.reset_index(drop=True, inplace=True)\n",
-    "\n",
-    "    # 결과 저장\n",
-    "    final_data.to_csv(output_path, index = False)\n",
-    "    print(len(final_data[final_data['multi_class']==0]),'|',len(final_data[final_data['multi_class']==1]),'|',len(final_data[final_data['multi_class']==2]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 소수범주당 증강 목표 10000개"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using device: cuda\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2025-01-22 02:07:50,408] A new study created in memory with name: no-name-f35c7b28-3dae-4b56-9eb2-6e054e9a2682\n",
-      "/root/miniconda3/lib/python3.12/site-packages/torch/autograd/graph.py:825: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at ../aten/src/ATen/cuda/CublasHandlePool.cpp:135.)\n",
-      "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
-      "[W 2025-01-22 02:07:55,487] Trial 0 failed with parameters: {'embedding_dim': 96, 'generator_dim': (64, 64), 'discriminator_dim': (64, 64), 'pac': 8, 'batch_size': 64, 'discriminator_steps': 2} because of the following error: KeyboardInterrupt().\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/root/miniconda3/lib/python3.12/site-packages/optuna/study/_optimize.py\", line 197, in _run_trial\n",
-      "    value_or_values = func(trial)\n",
-      "                      ^^^^^^^^^^^\n",
-      "  File \"/tmp/ipykernel_386644/1364079751.py\", line 81, in objective\n",
-      "    ctgan.fit(data_0, discrete_columns=categorical_features)\n",
-      "  File \"/root/miniconda3/lib/python3.12/site-packages/ctgan/synthesizers/base.py\", line 50, in wrapper\n",
-      "    return function(self, *args, **kwargs)\n",
-      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"/root/miniconda3/lib/python3.12/site-packages/ctgan/synthesizers/ctgan.py\", line 408, in fit\n",
-      "    loss_d.backward()\n",
-      "  File \"/root/miniconda3/lib/python3.12/site-packages/torch/_tensor.py\", line 581, in backward\n",
-      "    torch.autograd.backward(\n",
-      "  File \"/root/miniconda3/lib/python3.12/site-packages/torch/autograd/__init__.py\", line 347, in backward\n",
-      "    _engine_run_backward(\n",
-      "  File \"/root/miniconda3/lib/python3.12/site-packages/torch/autograd/graph.py\", line 825, in _engine_run_backward\n",
-      "    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
-      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
-      "KeyboardInterrupt\n",
-      "[W 2025-01-22 02:07:55,488] Trial 0 failed with value None.\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[1], line 96\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;66;03m# Optuna로 최적화 수행\u001b[39;00m\n\u001b[1;32m     95\u001b[0m study \u001b[38;5;241m=\u001b[39m optuna\u001b[38;5;241m.\u001b[39mcreate_study(direction\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmaximize\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 96\u001b[0m \u001b[43mstudy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobjective\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     98\u001b[0m \u001b[38;5;66;03m# 최적 하이퍼파라미터 출력\u001b[39;00m\n\u001b[1;32m     99\u001b[0m best_params \u001b[38;5;241m=\u001b[39m study\u001b[38;5;241m.\u001b[39mbest_params\n",
-      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/optuna/study/study.py:475\u001b[0m, in \u001b[0;36mStudy.optimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m    373\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21moptimize\u001b[39m(\n\u001b[1;32m    374\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    375\u001b[0m     func: ObjectiveFuncType,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    382\u001b[0m     show_progress_bar: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    383\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    384\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Optimize an objective function.\u001b[39;00m\n\u001b[1;32m    385\u001b[0m \n\u001b[1;32m    386\u001b[0m \u001b[38;5;124;03m    Optimization is done by choosing a suitable set of hyperparameter values from a given\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    473\u001b[0m \u001b[38;5;124;03m            If nested invocation of this method occurs.\u001b[39;00m\n\u001b[1;32m    474\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 475\u001b[0m     \u001b[43m_optimize\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    476\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstudy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    477\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    478\u001b[0m \u001b[43m        \u001b[49m\u001b[43mn_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_trials\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    479\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    480\u001b[0m \u001b[43m        \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    481\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mIterable\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    482\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    483\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgc_after_trial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgc_after_trial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    484\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshow_progress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshow_progress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    485\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/optuna/study/_optimize.py:63\u001b[0m, in \u001b[0;36m_optimize\u001b[0;34m(study, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     62\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m n_jobs \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m---> 63\u001b[0m         \u001b[43m_optimize_sequential\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     64\u001b[0m \u001b[43m            \u001b[49m\u001b[43mstudy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     65\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     66\u001b[0m \u001b[43m            \u001b[49m\u001b[43mn_trials\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     67\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     68\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     69\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     70\u001b[0m \u001b[43m            \u001b[49m\u001b[43mgc_after_trial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     71\u001b[0m \u001b[43m            \u001b[49m\u001b[43mreseed_sampler_rng\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     72\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtime_start\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[43m            \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     74\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     75\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     76\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m n_jobs \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m:\n",
-      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/optuna/study/_optimize.py:160\u001b[0m, in \u001b[0;36m_optimize_sequential\u001b[0;34m(study, func, n_trials, timeout, catch, callbacks, gc_after_trial, reseed_sampler_rng, time_start, progress_bar)\u001b[0m\n\u001b[1;32m    157\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m    159\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 160\u001b[0m     frozen_trial \u001b[38;5;241m=\u001b[39m \u001b[43m_run_trial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstudy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    161\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    162\u001b[0m     \u001b[38;5;66;03m# The following line mitigates memory problems that can be occurred in some\u001b[39;00m\n\u001b[1;32m    163\u001b[0m     \u001b[38;5;66;03m# environments (e.g., services that use computing containers such as GitHub Actions).\u001b[39;00m\n\u001b[1;32m    164\u001b[0m     \u001b[38;5;66;03m# Please refer to the following PR for further details:\u001b[39;00m\n\u001b[1;32m    165\u001b[0m     \u001b[38;5;66;03m# https://github.com/optuna/optuna/pull/325.\u001b[39;00m\n\u001b[1;32m    166\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m gc_after_trial:\n",
-      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/optuna/study/_optimize.py:248\u001b[0m, in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m    241\u001b[0m         \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShould not reach.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    243\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    244\u001b[0m     frozen_trial\u001b[38;5;241m.\u001b[39mstate \u001b[38;5;241m==\u001b[39m TrialState\u001b[38;5;241m.\u001b[39mFAIL\n\u001b[1;32m    245\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m func_err \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    246\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(func_err, catch)\n\u001b[1;32m    247\u001b[0m ):\n\u001b[0;32m--> 248\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m func_err\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m frozen_trial\n",
-      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/optuna/study/_optimize.py:197\u001b[0m, in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m get_heartbeat_thread(trial\u001b[38;5;241m.\u001b[39m_trial_id, study\u001b[38;5;241m.\u001b[39m_storage):\n\u001b[1;32m    196\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 197\u001b[0m         value_or_values \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    198\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mTrialPruned \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    199\u001b[0m         \u001b[38;5;66;03m# TODO(mamu): Handle multi-objective cases.\u001b[39;00m\n\u001b[1;32m    200\u001b[0m         state \u001b[38;5;241m=\u001b[39m TrialState\u001b[38;5;241m.\u001b[39mPRUNED\n",
-      "Cell \u001b[0;32mIn[1], line 81\u001b[0m, in \u001b[0;36mobjective\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m     78\u001b[0m data_0 \u001b[38;5;241m=\u001b[39m lerp_data[lerp_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmulti_class\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m     80\u001b[0m \u001b[38;5;66;03m# 모델 학습\u001b[39;00m\n\u001b[0;32m---> 81\u001b[0m \u001b[43mctgan\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdiscrete_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcategorical_features\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;66;03m# 샘플 생성\u001b[39;00m\n\u001b[1;32m     84\u001b[0m generated_data \u001b[38;5;241m=\u001b[39m ctgan\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;28mlen\u001b[39m(data_0) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m)\n",
-      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/ctgan/synthesizers/base.py:50\u001b[0m, in \u001b[0;36mrandom_state.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m     49\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrandom_states \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 50\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     52\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m         \u001b[38;5;28;01mwith\u001b[39;00m set_random_states(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrandom_states, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_random_state):\n",
-      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/ctgan/synthesizers/ctgan.py:408\u001b[0m, in \u001b[0;36mCTGAN.fit\u001b[0;34m(self, train_data, discrete_columns, epochs)\u001b[0m\n\u001b[1;32m    406\u001b[0m     optimizerD\u001b[38;5;241m.\u001b[39mzero_grad(set_to_none\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m    407\u001b[0m     pen\u001b[38;5;241m.\u001b[39mbackward(retain_graph\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 408\u001b[0m     \u001b[43mloss_d\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    409\u001b[0m     optimizerD\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m    411\u001b[0m fakez \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mnormal(mean\u001b[38;5;241m=\u001b[39mmean, std\u001b[38;5;241m=\u001b[39mstd)\n",
-      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/torch/_tensor.py:581\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    571\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    572\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m    573\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m    574\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    579\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m    580\u001b[0m     )\n\u001b[0;32m--> 581\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    582\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m    583\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/torch/autograd/__init__.py:347\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    342\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m    344\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m    345\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m    346\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 347\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    348\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    349\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    350\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    351\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    352\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    353\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    354\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    355\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/lib/python3.12/site-packages/torch/autograd/graph.py:825\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[0;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    823\u001b[0m     unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[1;32m    824\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 825\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m    826\u001b[0m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    827\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[1;32m    828\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    829\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from imblearn.over_sampling import SMOTENC\n",
-    "import optuna\n",
-    "from ctgan import CTGAN\n",
-    "import torch\n",
-    "import warnings\n",
-    "\n",
-    "# 지역별 데이터 파일 경로\n",
-    "regions = ['busan', 'daegu', 'daejeon', 'gwangju', 'incheon', 'seoul']\n",
-    "file_paths = [f'../data/data_for_modeling/{region}_train.csv' for region in regions]\n",
-    "output_paths = [f'../data/data_oversampled/ctgan10000_{region}.csv' for region in regions]\n",
-    "\n",
-    "# GPU 사용 설정\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "print(f\"Using device: {device}\")\n",
-    "\n",
-    "# 경고 무시\n",
-    "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"optuna.distributions\")\n",
-    "\n",
-    "# 지역별 처리\n",
-    "for file_path, output_path in zip(file_paths, output_paths):\n",
-    "    # 데이터 로드\n",
-    "    data = pd.read_csv(file_path, index_col=0)\n",
-    "    X = data.drop(columns=['multi_class', 'binary_class'])\n",
-    "    y = data['multi_class']\n",
-    "\n",
-    "    # 불필요한 열 제거\n",
-    "    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)\n",
-    "\n",
-    "    # SMOTENC에서 사용할 범주형 변수 열 번호 설정\n",
-    "    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']\n",
-    "\n",
-    "    # sampling_strategy 설정\n",
-    "    count_class_0 = (y == 0).sum()\n",
-    "    count_class_1 = (y == 1).sum()\n",
-    "    count_class_2 = (y == 2).sum()\n",
-    "    sampling_strategy = {\n",
-    "        0: 500 if count_class_0 <= 500 else 1000,\n",
-    "        1: int(np.ceil(count_class_1 / 100) * 100),  # 백의 자리로 올림\n",
-    "        2: count_class_2\n",
-    "    }\n",
-    "\n",
-    "    # SMOTENC 적용\n",
-    "    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)\n",
-    "    X_resampled, y_resampled = smotenc.fit_resample(X, y)\n",
-    "\n",
-    "    # Resampled 데이터 생성\n",
-    "    lerp_data = X_resampled.copy()\n",
-    "    lerp_data['multi_class'] = y_resampled\n",
-    "\n",
-    "    # CTGAN에서 사용할 범주형 변수 열 이름 설정\n",
-    "    categorical_features = [\n",
-    "        col for col, dtype in zip(lerp_data.columns, lerp_data.dtypes) if dtype != 'float64'\n",
-    "    ]\n",
-    "\n",
-    "    # Optuna 목적 함수 정의\n",
-    "    def objective(trial):\n",
-    "        # 하이퍼파라미터 탐색 범위 설정\n",
-    "        embedding_dim = trial.suggest_int(\"embedding_dim\", 64, 128)\n",
-    "        generator_dim = trial.suggest_categorical(\"generator_dim\", [(64, 64), (128, 128)])\n",
-    "        discriminator_dim = trial.suggest_categorical(\"discriminator_dim\", [(64, 64), (128, 128)])\n",
-    "        pac = trial.suggest_categorical(\"pac\", [4, 8])\n",
-    "        batch_size = trial.suggest_categorical(\"batch_size\", [64, 128, 256])\n",
-    "        discriminator_steps = trial.suggest_int(\"discriminator_steps\", 1, 3)\n",
-    "\n",
-    "        # CTGAN 모델 생성\n",
-    "        ctgan = CTGAN(\n",
-    "            embedding_dim=embedding_dim,\n",
-    "            generator_dim=generator_dim,\n",
-    "            discriminator_dim=discriminator_dim,\n",
-    "            batch_size=batch_size,\n",
-    "            discriminator_steps=discriminator_steps,\n",
-    "            pac=pac\n",
-    "        )\n",
-    "\n",
-    "        # 범주 0 데이터 필터링\n",
-    "        data_0 = lerp_data[lerp_data['multi_class'] == 0]\n",
-    "\n",
-    "        # 모델 학습\n",
-    "        ctgan.fit(data_0, discrete_columns=categorical_features)\n",
-    "\n",
-    "        # 샘플 생성\n",
-    "        generated_data = ctgan.sample(len(data_0) * 2)\n",
-    "\n",
-    "        # 평가: 샘플의 연속형 변수 분포 비교\n",
-    "        real_visi = data_0['visi']\n",
-    "        generated_visi = generated_data['visi']\n",
-    "\n",
-    "        # 분포 간 차이(MSE) 계산\n",
-    "        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)\n",
-    "        return -mse\n",
-    "\n",
-    "    # Optuna로 최적화 수행\n",
-    "    study = optuna.create_study(direction=\"maximize\")\n",
-    "    study.optimize(objective, n_trials=50)\n",
-    "\n",
-    "    # 최적 하이퍼파라미터 출력\n",
-    "    best_params = study.best_params\n",
-    "\n",
-    "    # 최적 하이퍼파라미터로 CTGAN 학습 및 샘플 생성\n",
-    "    ctgan = CTGAN(\n",
-    "        embedding_dim=best_params[\"embedding_dim\"],\n",
-    "        generator_dim=best_params[\"generator_dim\"],\n",
-    "        discriminator_dim=best_params[\"discriminator_dim\"],\n",
-    "        batch_size=best_params[\"batch_size\"],\n",
-    "        discriminator_steps=best_params[\"discriminator_steps\"],\n",
-    "        pac=best_params[\"pac\"]\n",
-    "    )\n",
-    "\n",
-    "    # 범주 0 데이터로 최종 학습\n",
-    "    ctgan.fit(lerp_data[lerp_data['multi_class'] == 0], discrete_columns=categorical_features)\n",
-    "    generated_0 = ctgan.sample(10000)\n",
-    "\n",
-    "    # 범주 1 데이터 최적화 및 생성\n",
-    "    def objective_class1(trial):\n",
-    "        embedding_dim = trial.suggest_int(\"embedding_dim\", 128, 512)\n",
-    "        generator_dim = trial.suggest_categorical(\"generator_dim\", [(128, 128), (256, 256)])\n",
-    "        discriminator_dim = trial.suggest_categorical(\"discriminator_dim\", [(128, 128), (256, 256)])\n",
-    "        pac = trial.suggest_categorical(\"pac\", [4, 8])\n",
-    "        batch_size = trial.suggest_categorical(\"batch_size\", [256, 512, 1024])\n",
-    "        discriminator_steps = trial.suggest_int(\"discriminator_steps\", 1, 5)\n",
-    "\n",
-    "        ctgan = CTGAN(\n",
-    "            embedding_dim=embedding_dim,\n",
-    "            generator_dim=generator_dim,\n",
-    "            discriminator_dim=discriminator_dim,\n",
-    "            batch_size=batch_size,\n",
-    "            discriminator_steps=discriminator_steps,\n",
-    "            pac=pac\n",
-    "        )\n",
-    "\n",
-    "        data_1 = lerp_data[lerp_data['multi_class'] == 1]\n",
-    "        ctgan.fit(data_1, discrete_columns=categorical_features)\n",
-    "        generated_data = ctgan.sample(len(data_1) * 2)\n",
-    "\n",
-    "        real_visi = data_1['visi']\n",
-    "        generated_visi = generated_data['visi']\n",
-    "        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)\n",
-    "        return -mse\n",
-    "\n",
-    "    study_class1 = optuna.create_study(direction=\"maximize\")\n",
-    "    study_class1.optimize(objective_class1, n_trials=30)\n",
-    "\n",
-    "    best_params_class1 = study_class1.best_params\n",
-    "    ctgan = CTGAN(\n",
-    "        embedding_dim=best_params_class1[\"embedding_dim\"],\n",
-    "        generator_dim=best_params_class1[\"generator_dim\"],\n",
-    "        discriminator_dim=best_params_class1[\"discriminator_dim\"],\n",
-    "        batch_size=best_params_class1[\"batch_size\"],\n",
-    "        discriminator_steps=best_params_class1[\"discriminator_steps\"],\n",
-    "        pac=best_params_class1[\"pac\"]\n",
-    "    )\n",
-    "\n",
-    "    ctgan.fit(lerp_data[lerp_data['multi_class'] == 1], discrete_columns=categorical_features)\n",
-    "    generated_1 = ctgan.sample(10000 - int(np.ceil(count_class_1 / 100) * 100))\n",
-    "\n",
-    "    # 데이터 병합 및 저장\n",
-    "    well_generated0 = generated_0[(generated_0['visi'] >= 0) & (generated_0['visi'] < 100)]\n",
-    "    well_generated1 = generated_1[(generated_1['visi'] >= 100) & (generated_1['visi'] < 500)]\n",
-    "    smote_gan_data = pd.concat([lerp_data, well_generated0, well_generated1], axis=0)\n",
-    "    # 제거변수 복구\n",
-    "    smote_gan_data['binary_class'] = smote_gan_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)\n",
-    "    smote_gan_data['hour_sin'] = np.sin(2 * np.pi * smote_gan_data['hour'] / 24)\n",
-    "    smote_gan_data['hour_cos'] = np.cos(2 * np.pi * smote_gan_data['hour'] / 24)\n",
-    "    smote_gan_data['month_sin'] = np.sin(2 * np.pi * smote_gan_data['month'] / 12)\n",
-    "    smote_gan_data['month_cos'] = np.cos(2 * np.pi * smote_gan_data['month'] / 12)\n",
-    "    smote_gan_data['ground_temp - temp_C'] = smote_gan_data['groundtemp'] - smote_gan_data['temp_C']\n",
-    "\n",
-    "    filtered_data = smote_gan_data[smote_gan_data['multi_class'] != 2]\n",
-    "    original_class2 = data[data['multi_class'] == 2]\n",
-    "    final_data = pd.concat([filtered_data, original_class2], axis=0)\n",
-    "    final_data.reset_index(drop=True, inplace=True)\n",
-    "\n",
-    "    # 결과 저장\n",
-    "    final_data.to_csv(output_path, index = False)\n",
-    "    print(len(final_data[final_data['multi_class']==0]),'|',len(final_data[final_data['multi_class']==1]),'|',len(final_data[final_data['multi_class']==2]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 소수범주당 증강 목표 7000개"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from imblearn.over_sampling import SMOTENC\n",
-    "import optuna\n",
-    "from ctgan import CTGAN\n",
-    "import torch\n",
-    "import warnings\n",
-    "\n",
-    "# 지역별 데이터 파일 경로\n",
-    "regions = ['busan', 'daegu', 'daejeon', 'gwangju', 'incheon', 'seoul']\n",
-    "file_paths = [f'../data/data_for_modeling/{region}_train.csv' for region in regions]\n",
-    "output_paths = [f'../data/data_oversampled/ctgan7000_{region}.csv' for region in regions]\n",
-    "\n",
-    "# GPU 사용 설정\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "print(f\"Using device: {device}\")\n",
-    "\n",
-    "# 경고 무시\n",
-    "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"optuna.distributions\")\n",
-    "\n",
-    "# 지역별 처리\n",
-    "for file_path, output_path in zip(file_paths, output_paths):\n",
-    "    # 데이터 로드\n",
-    "    data = pd.read_csv(file_path, index_col=0)\n",
-    "    X = data.drop(columns=['multi_class', 'binary_class'])\n",
-    "    y = data['multi_class']\n",
-    "\n",
-    "    # 불필요한 열 제거\n",
-    "    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)\n",
-    "\n",
-    "    # SMOTENC에서 사용할 범주형 변수 열 번호 설정\n",
-    "    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']\n",
-    "\n",
-    "    # sampling_strategy 설정\n",
-    "    count_class_0 = (y == 0).sum()\n",
-    "    count_class_1 = (y == 1).sum()\n",
-    "    count_class_2 = (y == 2).sum()\n",
-    "    sampling_strategy = {\n",
-    "        0: 500 if count_class_0 <= 500 else 1000,\n",
-    "        1: int(np.ceil(count_class_1 / 100) * 100),  # 백의 자리로 올림\n",
-    "        2: count_class_2\n",
-    "    }\n",
-    "\n",
-    "    # SMOTENC 적용\n",
-    "    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)\n",
-    "    X_resampled, y_resampled = smotenc.fit_resample(X, y)\n",
-    "\n",
-    "    # Resampled 데이터 생성\n",
-    "    lerp_data = X_resampled.copy()\n",
-    "    lerp_data['multi_class'] = y_resampled\n",
-    "\n",
-    "    # CTGAN에서 사용할 범주형 변수 열 이름 설정\n",
-    "    categorical_features = [\n",
-    "        col for col, dtype in zip(lerp_data.columns, lerp_data.dtypes) if dtype != 'float64'\n",
-    "    ]\n",
-    "\n",
-    "    # Optuna 목적 함수 정의\n",
-    "    def objective(trial):\n",
-    "        # 하이퍼파라미터 탐색 범위 설정\n",
-    "        embedding_dim = trial.suggest_int(\"embedding_dim\", 64, 128)\n",
-    "        generator_dim = trial.suggest_categorical(\"generator_dim\", [(64, 64), (128, 128)])\n",
-    "        discriminator_dim = trial.suggest_categorical(\"discriminator_dim\", [(64, 64), (128, 128)])\n",
-    "        pac = trial.suggest_categorical(\"pac\", [4, 8])\n",
-    "        batch_size = trial.suggest_categorical(\"batch_size\", [64, 128, 256])\n",
-    "        discriminator_steps = trial.suggest_int(\"discriminator_steps\", 1, 3)\n",
-    "\n",
-    "        # CTGAN 모델 생성\n",
-    "        ctgan = CTGAN(\n",
-    "            embedding_dim=embedding_dim,\n",
-    "            generator_dim=generator_dim,\n",
-    "            discriminator_dim=discriminator_dim,\n",
-    "            batch_size=batch_size,\n",
-    "            discriminator_steps=discriminator_steps,\n",
-    "            pac=pac\n",
-    "        )\n",
-    "\n",
-    "        # 범주 0 데이터 필터링\n",
-    "        data_0 = lerp_data[lerp_data['multi_class'] == 0]\n",
-    "\n",
-    "        # 모델 학습\n",
-    "        ctgan.fit(data_0, discrete_columns=categorical_features)\n",
-    "\n",
-    "        # 샘플 생성\n",
-    "        generated_data = ctgan.sample(len(data_0) * 2)\n",
-    "\n",
-    "        # 평가: 샘플의 연속형 변수 분포 비교\n",
-    "        real_visi = data_0['visi']\n",
-    "        generated_visi = generated_data['visi']\n",
-    "\n",
-    "        # 분포 간 차이(MSE) 계산\n",
-    "        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)\n",
-    "        return -mse\n",
-    "\n",
-    "    # Optuna로 최적화 수행\n",
-    "    study = optuna.create_study(direction=\"maximize\")\n",
-    "    study.optimize(objective, n_trials=50)\n",
-    "\n",
-    "    # 최적 하이퍼파라미터 출력\n",
-    "    best_params = study.best_params\n",
-    "\n",
-    "    # 최적 하이퍼파라미터로 CTGAN 학습 및 샘플 생성\n",
-    "    ctgan = CTGAN(\n",
-    "        embedding_dim=best_params[\"embedding_dim\"],\n",
-    "        generator_dim=best_params[\"generator_dim\"],\n",
-    "        discriminator_dim=best_params[\"discriminator_dim\"],\n",
-    "        batch_size=best_params[\"batch_size\"],\n",
-    "        discriminator_steps=best_params[\"discriminator_steps\"],\n",
-    "        pac=best_params[\"pac\"]\n",
-    "    )\n",
-    "\n",
-    "    # 범주 0 데이터로 최종 학습\n",
-    "    ctgan.fit(lerp_data[lerp_data['multi_class'] == 0], discrete_columns=categorical_features)\n",
-    "    generated_0 = ctgan.sample(7000)\n",
-    "\n",
-    "    # 범주 1 데이터 최적화 및 생성\n",
-    "    def objective_class1(trial):\n",
-    "        embedding_dim = trial.suggest_int(\"embedding_dim\", 128, 512)\n",
-    "        generator_dim = trial.suggest_categorical(\"generator_dim\", [(128, 128), (256, 256)])\n",
-    "        discriminator_dim = trial.suggest_categorical(\"discriminator_dim\", [(128, 128), (256, 256)])\n",
-    "        pac = trial.suggest_categorical(\"pac\", [4, 8])\n",
-    "        batch_size = trial.suggest_categorical(\"batch_size\", [256, 512, 1024])\n",
-    "        discriminator_steps = trial.suggest_int(\"discriminator_steps\", 1, 5)\n",
-    "\n",
-    "        ctgan = CTGAN(\n",
-    "            embedding_dim=embedding_dim,\n",
-    "            generator_dim=generator_dim,\n",
-    "            discriminator_dim=discriminator_dim,\n",
-    "            batch_size=batch_size,\n",
-    "            discriminator_steps=discriminator_steps,\n",
-    "            pac=pac\n",
-    "        )\n",
-    "\n",
-    "        data_1 = lerp_data[lerp_data['multi_class'] == 1]\n",
-    "        ctgan.fit(data_1, discrete_columns=categorical_features)\n",
-    "        generated_data = ctgan.sample(len(data_1) * 2)\n",
-    "\n",
-    "        real_visi = data_1['visi']\n",
-    "        generated_visi = generated_data['visi']\n",
-    "        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)\n",
-    "        return -mse\n",
-    "\n",
-    "    study_class1 = optuna.create_study(direction=\"maximize\")\n",
-    "    study_class1.optimize(objective_class1, n_trials=30)\n",
-    "\n",
-    "    best_params_class1 = study_class1.best_params\n",
-    "    ctgan = CTGAN(\n",
-    "        embedding_dim=best_params_class1[\"embedding_dim\"],\n",
-    "        generator_dim=best_params_class1[\"generator_dim\"],\n",
-    "        discriminator_dim=best_params_class1[\"discriminator_dim\"],\n",
-    "        batch_size=best_params_class1[\"batch_size\"],\n",
-    "        discriminator_steps=best_params_class1[\"discriminator_steps\"],\n",
-    "        pac=best_params_class1[\"pac\"]\n",
-    "    )\n",
-    "\n",
-    "    ctgan.fit(lerp_data[lerp_data['multi_class'] == 1], discrete_columns=categorical_features)\n",
-    "    generated_1 = ctgan.sample(7000 - int(np.ceil(count_class_1 / 100) * 100))\n",
-    "\n",
-    "    # 데이터 병합 및 저장\n",
-    "    well_generated0 = generated_0[(generated_0['visi'] >= 0) & (generated_0['visi'] < 100)]\n",
-    "    well_generated1 = generated_1[(generated_1['visi'] >= 100) & (generated_1['visi'] < 500)]\n",
-    "    smote_gan_data = pd.concat([lerp_data, well_generated0, well_generated1], axis=0)\n",
-    "    # 제거변수 복구\n",
-    "    smote_gan_data['binary_class'] = smote_gan_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)\n",
-    "    smote_gan_data['hour_sin'] = np.sin(2 * np.pi * smote_gan_data['hour'] / 24)\n",
-    "    smote_gan_data['hour_cos'] = np.cos(2 * np.pi * smote_gan_data['hour'] / 24)\n",
-    "    smote_gan_data['month_sin'] = np.sin(2 * np.pi * smote_gan_data['month'] / 12)\n",
-    "    smote_gan_data['month_cos'] = np.cos(2 * np.pi * smote_gan_data['month'] / 12)\n",
-    "    smote_gan_data['ground_temp - temp_C'] = smote_gan_data['groundtemp'] - smote_gan_data['temp_C']\n",
-    "\n",
-    "    filtered_data = smote_gan_data[smote_gan_data['multi_class'] != 2]\n",
-    "    original_class2 = data[data['multi_class'] == 2]\n",
-    "    final_data = pd.concat([filtered_data, original_class2], axis=0)\n",
-    "    final_data.reset_index(drop=True, inplace=True)\n",
-    "\n",
-    "    # 결과 저장\n",
-    "    final_data.to_csv(output_path, index = False)\n",
-    "    print(len(final_data[final_data['multi_class']==0]),'|',len(final_data[final_data['multi_class']==1]),'|',len(final_data[final_data['multi_class']==2]))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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--- a/Analysis_code/best_deepgbm_model.pth
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-version https://git-lfs.github.com/spec/v1
-oid sha256:471ce03d818bdefc9631121537afd4b771d85d872f72d74634e5cee824de2b62
-size 988522
diff --git a/Analysis_code/best_model_f1.pth b/Analysis_code/best_model_f1.pth
deleted file mode 100644
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--- a/Analysis_code/best_model_f1.pth
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:12c6e86e039528b07c4cc6e90e3033da7b02d67c33539cbb81d280da281f46c3
-size 8999933
diff --git a/Analysis_code/deepgbm.py b/Analysis_code/deepgbm.py
deleted file mode 100644
index 9c3d96bad76eecb4885a9ce79faa32ceab14f9cf..0000000000000000000000000000000000000000
--- a/Analysis_code/deepgbm.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-class DeepGBM(nn.Module):
-    def __init__(self, num_features, cat_features, num_classes, d_main=128, d_hidden=64, n_blocks=4, dropout=0.2):
-        super(DeepGBM, self).__init__()
-
-        self.num_classes = num_classes
-        
-        # 연속형 변수 처리 (Linear)
-        self.num_linear = nn.Linear(num_features, d_main)
-
-        # 범주형 변수 처리 (Embedding)
-        self.cat_embedding = nn.ModuleList([
-            nn.Embedding(cat_size, d_main) for cat_size in cat_features
-        ])
-
-        # ResNet-like 블록
-        self.blocks = nn.ModuleList([
-            nn.Sequential(
-                nn.Linear(d_main, d_hidden),
-                nn.ReLU(),
-                nn.Dropout(dropout),
-                nn.Linear(d_hidden, d_main),
-                nn.ReLU()
-            ) for _ in range(n_blocks)
-        ])
-
-        if num_classes == 2:
-            self.output_layer = nn.Linear(d_main, 1)  # Binary classification
-        elif num_classes > 2:
-            self.output_layer = nn.Linear(d_main, num_classes)  # Multi classification
-
-    def forward(self, x_num, x_cat):  # 두 개의 입력을 받음
-        x_num = self.num_linear(x_num)
-
-        # 범주형 변수를 임베딩 후 합산
-        x_cat = [embed(x_cat[:, i]) for i, embed in enumerate(self.cat_embedding)]
-        x_cat = torch.stack(x_cat, dim=1).sum(dim=1)  
-        x = x_num + x_cat  # 연속형 + 범주형 결합
-        
-        for block in self.blocks:
-            x = x + block(x)  # Residual connection
-        x = self.output_layer(x)
-        
-        return x
diff --git a/Analysis_code/deeplearning_model_binary.ipynb b/Analysis_code/deeplearning_model_binary.ipynb
deleted file mode 100644
index 86fdbc4ca1c13efac70c5f13a75b0c6ae74c4a12..0000000000000000000000000000000000000000
--- a/Analysis_code/deeplearning_model_binary.ipynb
+++ /dev/null
@@ -1,3952 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 통일 데이터셋 변환 함수 정의"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
-    "import torch\n",
-    "from torch.utils.data import DataLoader, TensorDataset\n",
-    "import random\n",
-    "\n",
-    "# 데이터셋 준비 함수\n",
-    "def prepare_dataset(region, target = 'binary'):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../data/data_for_modeling/{region}_train.csv\"\n",
-    "    train_path = f'../data/data_oversampled/ctgan10000/ctgan10000_3_{region}.csv'\n",
-    "    test_path = f\"../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f\"{target}_class\"\n",
-    "\n",
-    "    # 데이터 로드\n",
-    "    region_dat = pd.read_csv(dat_path, index_col=0)\n",
-    "    region_train = pd.read_csv(train_path)\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2018]), :]\n",
-    "    region_test = pd.read_csv(test_path)\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    return X_train, X_val, X_test, y_train, y_val, y_test, categorical_cols, numerical_cols"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 데이터 변환 및 dataloader 생성 함수\n",
-    "def prepare_dataloader(region, target = 'binary', random_state = None):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../data/data_for_modeling/{region}_train.csv\"\n",
-    "    train_path = f'../data/data_oversampled/ctgan10000/ctgan10000_3_{region}.csv'\n",
-    "    test_path = f\"../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = pd.read_csv(dat_path, index_col=0)\n",
-    "    region_train = pd.read_csv(train_path)\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2018]), :]\n",
-    "    region_test = pd.read_csv(test_path)\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    # 연속형 변수와 범주형 변수 분리\n",
-    "    X_train_num = torch.tensor(X_train[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_train_cat = torch.tensor(X_train[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_val_num = torch.tensor(X_val[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_val_cat = torch.tensor(X_val[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_test_num = torch.tensor(X_test[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_test_cat = torch.tensor(X_test[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    # 레이블 변환\n",
-    "    if target == \"binary\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.float32)  # 이진 분류 → float32\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.float32)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32)\n",
-    "    elif target == \"multi\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.long)  # 다중 분류 → long\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.long)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.long)\n",
-    "    else:\n",
-    "        raise ValueError(\"target must be 'binary' or 'multi'\")\n",
-    "\n",
-    "    # TensorDataset 생성\n",
-    "    train_dataset = TensorDataset(X_train_num, X_train_cat, y_train_tensor)\n",
-    "    val_dataset = TensorDataset(X_val_num, X_val_cat, y_val_tensor)\n",
-    "    test_dataset = TensorDataset(X_test_num, X_test_cat, y_test_tensor)\n",
-    "\n",
-    "    # DataLoader 생성\n",
-    "    if random_state == None:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
-    "    else:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, generator=torch.Generator().manual_seed(random_state))\n",
-    "    val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n",
-    "    test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
-    "    \n",
-    "    return X_train, categorical_cols, numerical_cols, train_loader, val_loader, test_loader"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 통일 최적화 함수 정의"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "import optuna\n",
-    "from sklearn.metrics import f1_score\n",
-    "\n",
-    "# 모델을 GPU로 전송\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "\n",
-    "# 공통 Objective 함수\n",
-    "def objective(train_loader, val_loader, model_class, model_params):\n",
-    "\n",
-    "    # 모델 초기화\n",
-    "    model = model_class(**model_params).to(device)\n",
-    "\n",
-    "    # 손실 함수 및 옵티마이저 설정\n",
-    "    criterion = nn.BCELoss()\n",
-    "    optimizer = optim.AdamW(model.parameters(), lr=model_params[\"lr\"], weight_decay=model_params[\"weight_decay\"])\n",
-    "\n",
-    "    # 학습 루프\n",
-    "    epochs = 50  # 충분한 학습 기회 제공\n",
-    "    patience = 8  # Early stopping 기준 (8 epochs 동안 개선 없으면 종료)\n",
-    "    best_val_f1 = -float(\"inf\")  # 최대화 목표\n",
-    "    counter = 0  # 개선되지 않은 epoch 수\n",
-    "\n",
-    "    for epoch in range(epochs):\n",
-    "        model.train()\n",
-    "        train_loss = 0\n",
-    "\n",
-    "        for x_num_batch, x_cat_batch, y_batch in train_loader:\n",
-    "            x_num_batch, x_cat_batch, y_batch = (\n",
-    "                x_num_batch.to(device),\n",
-    "                x_cat_batch.to(device),\n",
-    "                y_batch.to(device)\n",
-    "            )\n",
-    "            optimizer.zero_grad()\n",
-    "            y_pred = model(x_num_batch, x_cat_batch)\n",
-    "            loss = criterion(y_pred, y_batch.unsqueeze(1))\n",
-    "            loss.backward()\n",
-    "            optimizer.step()\n",
-    "            train_loss += loss.item()\n",
-    "\n",
-    "        # Validation 평가\n",
-    "        model.eval()\n",
-    "        val_loss = 0\n",
-    "        y_pred_val, y_true_val = [], []\n",
-    "\n",
-    "        with torch.no_grad():\n",
-    "            for x_num_batch, x_cat_batch, y_batch in val_loader:\n",
-    "                x_num_batch, x_cat_batch, y_batch = (\n",
-    "                    x_num_batch.to(device),\n",
-    "                    x_cat_batch.to(device),\n",
-    "                    y_batch.to(device)\n",
-    "                )\n",
-    "                output = model(x_num_batch, x_cat_batch)\n",
-    "                val_loss += criterion(output, y_batch.unsqueeze(1)).item()\n",
-    "                y_pred_val.extend((output.cpu().numpy() > 0.5).astype(int))\n",
-    "                y_true_val.extend(y_batch.cpu().numpy())\n",
-    "\n",
-    "        # F1-score 계산\n",
-    "        val_f1 = f1_score(y_true_val, y_pred_val)\n",
-    "\n",
-    "        print(f\"Epoch {epoch+1}/{epochs} | Train Loss: {train_loss:.4f} | Validation Loss: {val_loss:.4f} | Validation F1: {val_f1:.4f}\")\n",
-    "\n",
-    "        # Early Stopping 체크\n",
-    "        if val_f1 > best_val_f1:\n",
-    "            best_val_f1 = val_f1\n",
-    "            counter = 0\n",
-    "        else:\n",
-    "            counter += 1\n",
-    "            if counter >= patience:\n",
-    "                print(f\"Early stopping at epoch {epoch+1}\")\n",
-    "                break\n",
-    "\n",
-    "    return best_val_f1  # 최종 F1-score 반환"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## FT-Transformer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 데이터셋 준비"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 시드 고정\n",
-    "seed = 42\n",
-    "random.seed(seed)\n",
-    "np.random.seed(seed)\n",
-    "torch.manual_seed(seed)\n",
-    "torch.cuda.manual_seed_all(seed)\n",
-    "\n",
-    "# DataLoader 생성\n",
-    "X_train, categorical_cols, numerical_cols, train_loader, val_loader, test_loader = prepare_dataloader(\"seoul\", target='binary', random_state = seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 모델 구현(하이퍼파라미터 최적화)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "[I 2025-02-02 15:41:08,871] A new study created in memory with name: no-name-251e54c9-5a4b-408c-a8d9-c1a826aa0507\n",
-      "[I 2025-02-02 15:41:24,977] Trial 0 finished with value: 0.7239199157007377 and parameters: {'d_token': 128, 'n_blocks': 4, 'attention_dropout': 0.24679835610086082, 'ffn_dropout': 0.21742508365045984, 'lr': 0.0007348118405270452, 'weight_decay': 0.00015930522616241006}. Best is trial 0 with value: 0.7239199157007377.\n",
-      "[I 2025-02-02 15:41:42,721] Trial 1 finished with value: 0.6823069403714564 and parameters: {'d_token': 192, 'n_blocks': 5, 'attention_dropout': 0.2545474901621302, 'ffn_dropout': 0.2550213529560302, 'lr': 0.0002014847788415866, 'weight_decay': 0.00011207606211860574}. Best is trial 0 with value: 0.7239199157007377.\n",
-      "[I 2025-02-02 15:42:00,849] Trial 2 finished with value: 0.7318982387475538 and parameters: {'d_token': 192, 'n_blocks': 5, 'attention_dropout': 0.3099085529881075, 'ffn_dropout': 0.3368209952651108, 'lr': 0.0006097839109531517, 'weight_decay': 2.5081156860452307e-05}. Best is trial 2 with value: 0.7318982387475538.\n",
-      "[I 2025-02-02 15:42:16,314] Trial 3 finished with value: 0.19511692592974142 and parameters: {'d_token': 256, 'n_blocks': 4, 'attention_dropout': 0.21951547789558387, 'ffn_dropout': 0.48466566117599996, 'lr': 0.0009239150319627245, 'weight_decay': 0.0004138040112561013}. Best is trial 2 with value: 0.7318982387475538.\n",
-      "[I 2025-02-02 15:42:31,589] Trial 4 finished with value: 0.19511692592974142 and parameters: {'d_token': 192, 'n_blocks': 4, 'attention_dropout': 0.34855307303338107, 'ffn_dropout': 0.21031655633456553, 'lr': 0.0008115595675970503, 'weight_decay': 3.292759134423613e-05}. Best is trial 2 with value: 0.7318982387475538.\n",
-      "[I 2025-02-02 15:42:46,768] Trial 5 finished with value: 0.7303424987940184 and parameters: {'d_token': 64, 'n_blocks': 4, 'attention_dropout': 0.49087538832936756, 'ffn_dropout': 0.43253984700833437, 'lr': 0.0008699593128513321, 'weight_decay': 0.0006161049539380963}. Best is trial 2 with value: 0.7318982387475538.\n",
-      "[I 2025-02-02 15:43:02,084] Trial 6 finished with value: 0.7247845919918905 and parameters: {'d_token': 128, 'n_blocks': 4, 'attention_dropout': 0.2975990992289793, 'ffn_dropout': 0.31660318690684464, 'lr': 0.000186788025710707, 'weight_decay': 0.00045443839603360173}. Best is trial 2 with value: 0.7318982387475538.\n",
-      "[I 2025-02-02 15:43:27,280] Trial 7 finished with value: 0.28465346534653463 and parameters: {'d_token': 192, 'n_blocks': 8, 'attention_dropout': 0.22236519310393127, 'ffn_dropout': 0.4960660809801552, 'lr': 0.0005918951335463647, 'weight_decay': 2.497073714505272e-05}. Best is trial 2 with value: 0.7318982387475538.\n",
-      "[I 2025-02-02 15:43:49,893] Trial 8 finished with value: 0.6748971193415638 and parameters: {'d_token': 128, 'n_blocks': 7, 'attention_dropout': 0.22221339552022712, 'ffn_dropout': 0.30753971856328177, 'lr': 0.00013057771348997234, 'weight_decay': 0.0005323617594751496}. Best is trial 2 with value: 0.7318982387475538.\n",
-      "[I 2025-02-02 15:44:07,744] Trial 9 finished with value: 0.6968566862013852 and parameters: {'d_token': 64, 'n_blocks': 5, 'attention_dropout': 0.41888185350141927, 'ffn_dropout': 0.39126724140656394, 'lr': 0.000771281194715635, 'weight_decay': 8.798929749689021e-05}. Best is trial 2 with value: 0.7318982387475538.\n",
-      "[I 2025-02-02 15:44:28,098] Trial 10 finished with value: 0.19511692592974142 and parameters: {'d_token': 256, 'n_blocks': 6, 'attention_dropout': 0.3554111493483545, 'ffn_dropout': 0.36590280160354616, 'lr': 0.00043660479644886074, 'weight_decay': 1.1952270129143866e-05}. Best is trial 2 with value: 0.7318982387475538.\n",
-      "[I 2025-02-02 15:44:45,702] Trial 11 finished with value: 0.6822519083969466 and parameters: {'d_token': 64, 'n_blocks': 5, 'attention_dropout': 0.48408856291704483, 'ffn_dropout': 0.42557494926138, 'lr': 0.00042673231508545695, 'weight_decay': 4.49074466433967e-05}. Best is trial 2 with value: 0.7318982387475538.\n",
-      "[I 2025-02-02 15:45:05,871] Trial 12 finished with value: 0.7150368033648791 and parameters: {'d_token': 64, 'n_blocks': 6, 'attention_dropout': 0.48739285247229314, 'ffn_dropout': 0.43103225611689927, 'lr': 0.0004896552590776571, 'weight_decay': 0.0009912861232764343}. Best is trial 2 with value: 0.7318982387475538.\n",
-      "[I 2025-02-02 15:45:23,603] Trial 13 finished with value: 0.6933638443935927 and parameters: {'d_token': 64, 'n_blocks': 5, 'attention_dropout': 0.4047001889260864, 'ffn_dropout': 0.32569556937329, 'lr': 0.00029920804271524077, 'weight_decay': 1.2615508659239553e-05}. Best is trial 2 with value: 0.7318982387475538.\n",
-      "[I 2025-02-02 15:45:41,530] Trial 14 finished with value: 0.7511987213638786 and parameters: {'d_token': 192, 'n_blocks': 5, 'attention_dropout': 0.32657703152441825, 'ffn_dropout': 0.4178174448017821, 'lr': 0.0003267555125994075, 'weight_decay': 0.00021351678009204976}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:46:04,378] Trial 15 finished with value: 0.7074829931972789 and parameters: {'d_token': 192, 'n_blocks': 7, 'attention_dropout': 0.31041768721719115, 'ffn_dropout': 0.2739122593366717, 'lr': 0.00030899938367208267, 'weight_decay': 0.00023496826834476444}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:46:24,868] Trial 16 finished with value: 0.6610328638497652 and parameters: {'d_token': 192, 'n_blocks': 6, 'attention_dropout': 0.32398593919255186, 'ffn_dropout': 0.3867121557282759, 'lr': 0.0002330123217423081, 'weight_decay': 8.506812879738529e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:46:42,870] Trial 17 finished with value: 0.6872202884137245 and parameters: {'d_token': 192, 'n_blocks': 5, 'attention_dropout': 0.39211245016802665, 'ffn_dropout': 0.34949985542474904, 'lr': 0.0005640304117528214, 'weight_decay': 0.0002277020852390507}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:47:05,644] Trial 18 finished with value: 0.712608473711077 and parameters: {'d_token': 192, 'n_blocks': 7, 'attention_dropout': 0.2800711256226658, 'ffn_dropout': 0.45887819655597584, 'lr': 0.00010363593787464723, 'weight_decay': 5.3707427835369724e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:47:26,042] Trial 19 finished with value: 0.7258145363408521 and parameters: {'d_token': 192, 'n_blocks': 6, 'attention_dropout': 0.36318783257719667, 'ffn_dropout': 0.3984033562225775, 'lr': 0.0003651849381739298, 'weight_decay': 1.8782195995924806e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:47:43,815] Trial 20 finished with value: 0.6697300402067777 and parameters: {'d_token': 256, 'n_blocks': 5, 'attention_dropout': 0.43274746581488754, 'ffn_dropout': 0.2824558846985788, 'lr': 0.0002556751337418656, 'weight_decay': 0.0002494683273647461}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:47:58,938] Trial 21 finished with value: 0.7178354500276091 and parameters: {'d_token': 64, 'n_blocks': 4, 'attention_dropout': 0.44984547464488966, 'ffn_dropout': 0.43243862220781226, 'lr': 0.0009982289684266161, 'weight_decay': 0.0009101904796088088}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:48:16,546] Trial 22 finished with value: 0.7169082125603865 and parameters: {'d_token': 64, 'n_blocks': 5, 'attention_dropout': 0.3266483061476531, 'ffn_dropout': 0.3566508287751997, 'lr': 0.0006592761670945311, 'weight_decay': 0.0006359195874655299}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:48:31,855] Trial 23 finished with value: 0.737927292457949 and parameters: {'d_token': 192, 'n_blocks': 4, 'attention_dropout': 0.27515293275689734, 'ffn_dropout': 0.4524271854297583, 'lr': 0.0005489202273938838, 'weight_decay': 0.00033027787368051153}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:48:49,727] Trial 24 finished with value: 0.6891252955082743 and parameters: {'d_token': 192, 'n_blocks': 5, 'attention_dropout': 0.27553431938378914, 'ffn_dropout': 0.45873360240139666, 'lr': 0.0005086245601961827, 'weight_decay': 0.000351934968810717}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:49:05,170] Trial 25 finished with value: 0.7389215162840364 and parameters: {'d_token': 192, 'n_blocks': 4, 'attention_dropout': 0.2837638019921456, 'ffn_dropout': 0.4641514065134212, 'lr': 0.0004026800729283218, 'weight_decay': 0.00011701373306653755}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:49:20,536] Trial 26 finished with value: 0.6642599277978339 and parameters: {'d_token': 192, 'n_blocks': 4, 'attention_dropout': 0.27251203104160765, 'ffn_dropout': 0.4607058172591405, 'lr': 0.0003601626181990028, 'weight_decay': 0.00014136037952166197}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:49:35,930] Trial 27 finished with value: 0.7280248190279214 and parameters: {'d_token': 192, 'n_blocks': 4, 'attention_dropout': 0.33730153869002266, 'ffn_dropout': 0.472794506350429, 'lr': 0.0003797203538970235, 'weight_decay': 0.0001573156600007119}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:49:51,312] Trial 28 finished with value: 0.6848891181021145 and parameters: {'d_token': 192, 'n_blocks': 4, 'attention_dropout': 0.24862055884209416, 'ffn_dropout': 0.4073050694157798, 'lr': 0.0002731917735846195, 'weight_decay': 0.0003171519637892753}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:50:06,623] Trial 29 finished with value: 0.6995412844036697 and parameters: {'d_token': 128, 'n_blocks': 4, 'attention_dropout': 0.29664078129288607, 'ffn_dropout': 0.4183212661013632, 'lr': 0.00044184677057511584, 'weight_decay': 6.641491095324641e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:50:21,934] Trial 30 finished with value: 0.7068676716917923 and parameters: {'d_token': 192, 'n_blocks': 4, 'attention_dropout': 0.3782977122856468, 'ffn_dropout': 0.4963551001157477, 'lr': 0.00020313715887341092, 'weight_decay': 0.0001853642529114672}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:50:39,805] Trial 31 finished with value: 0.1179245283018868 and parameters: {'d_token': 192, 'n_blocks': 5, 'attention_dropout': 0.3039279279944892, 'ffn_dropout': 0.44734530574119136, 'lr': 0.000664310381639209, 'weight_decay': 0.00012348127087389468}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:50:57,852] Trial 32 finished with value: 0.7144396551724138 and parameters: {'d_token': 192, 'n_blocks': 5, 'attention_dropout': 0.2612697301073756, 'ffn_dropout': 0.3787483984065484, 'lr': 0.0005416552465077905, 'weight_decay': 0.00017487733354466299}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:51:13,276] Trial 33 finished with value: 0.6918238993710691 and parameters: {'d_token': 192, 'n_blocks': 4, 'attention_dropout': 0.20051318674084254, 'ffn_dropout': 0.3370003290336566, 'lr': 0.0006598525178596913, 'weight_decay': 0.00030290392419648945}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:51:33,645] Trial 34 finished with value: 0.6428571428571429 and parameters: {'d_token': 256, 'n_blocks': 6, 'attention_dropout': 0.2855644129946276, 'ffn_dropout': 0.4817800237565608, 'lr': 0.00038807742512600733, 'weight_decay': 0.00010344315875246088}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:51:49,249] Trial 35 finished with value: 0.7339805825242719 and parameters: {'d_token': 192, 'n_blocks': 4, 'attention_dropout': 0.2399976537028991, 'ffn_dropout': 0.4445370590364987, 'lr': 0.00033171689577321723, 'weight_decay': 4.028888739382443e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:52:04,545] Trial 36 finished with value: 0.7280788177339902 and parameters: {'d_token': 192, 'n_blocks': 4, 'attention_dropout': 0.23554167653467295, 'ffn_dropout': 0.4467111904409484, 'lr': 0.00033857719057883365, 'weight_decay': 6.923538277736423e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:52:19,498] Trial 37 finished with value: 0.6766265060240964 and parameters: {'d_token': 128, 'n_blocks': 4, 'attention_dropout': 0.25646445799400547, 'ffn_dropout': 0.41569404568093943, 'lr': 0.0001694941500084373, 'weight_decay': 4.150908504110461e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:52:34,709] Trial 38 finished with value: 0.6599171652093879 and parameters: {'d_token': 192, 'n_blocks': 4, 'attention_dropout': 0.240636945885518, 'ffn_dropout': 0.47317587429145236, 'lr': 0.00023673773365563876, 'weight_decay': 2.865530584633714e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:52:50,248] Trial 39 finished with value: 0.7054108216432866 and parameters: {'d_token': 192, 'n_blocks': 4, 'attention_dropout': 0.20040957141100618, 'ffn_dropout': 0.4413156611762989, 'lr': 0.00029614995530728835, 'weight_decay': 0.00011737018375863745}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:53:08,355] Trial 40 finished with value: 0.669047619047619 and parameters: {'d_token': 192, 'n_blocks': 5, 'attention_dropout': 0.31579646247943044, 'ffn_dropout': 0.4991154146623525, 'lr': 0.0004745202231385376, 'weight_decay': 0.0004370471489831405}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:53:23,703] Trial 41 finished with value: 0.67472306143001 and parameters: {'d_token': 192, 'n_blocks': 4, 'attention_dropout': 0.26288915185936645, 'ffn_dropout': 0.4710451809725687, 'lr': 0.0006214025038700964, 'weight_decay': 2.064974994614282e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:53:41,812] Trial 42 finished with value: 0.3887903511883647 and parameters: {'d_token': 192, 'n_blocks': 5, 'attention_dropout': 0.34329097874915243, 'ffn_dropout': 0.23704077550252764, 'lr': 0.0007399242057813222, 'weight_decay': 1.6076979534676088e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:53:57,179] Trial 43 finished with value: 0.7377124483233808 and parameters: {'d_token': 256, 'n_blocks': 4, 'attention_dropout': 0.2905336075299809, 'ffn_dropout': 0.3728812523004836, 'lr': 0.00040811140436640766, 'weight_decay': 4.236150083191819e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:54:12,240] Trial 44 finished with value: 0.718544719555331 and parameters: {'d_token': 256, 'n_blocks': 4, 'attention_dropout': 0.28847655843654574, 'ffn_dropout': 0.4015845497202453, 'lr': 0.0004032728794886698, 'weight_decay': 3.40005553116306e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:54:27,624] Trial 45 finished with value: 0.7108050847457628 and parameters: {'d_token': 256, 'n_blocks': 4, 'attention_dropout': 0.23140734639114655, 'ffn_dropout': 0.3748198632542725, 'lr': 0.00033550308733082065, 'weight_decay': 5.907611414758871e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:54:42,893] Trial 46 finished with value: 0.7417148869016307 and parameters: {'d_token': 256, 'n_blocks': 4, 'attention_dropout': 0.29449206006179474, 'ffn_dropout': 0.41949021088844474, 'lr': 0.0004558422302910519, 'weight_decay': 4.5741857621694515e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:55:09,179] Trial 47 finished with value: 0.37842696629213485 and parameters: {'d_token': 256, 'n_blocks': 8, 'attention_dropout': 0.2954843610731864, 'ffn_dropout': 0.41487866519469907, 'lr': 0.0004629369824494006, 'weight_decay': 9.014365616038466e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:55:27,086] Trial 48 finished with value: 0.6156716417910447 and parameters: {'d_token': 256, 'n_blocks': 5, 'attention_dropout': 0.3300003159606516, 'ffn_dropout': 0.3887591993621017, 'lr': 0.0005205909284889117, 'weight_decay': 7.674171394833573e-05}. Best is trial 14 with value: 0.7511987213638786.\n",
-      "[I 2025-02-02 15:55:42,560] Trial 49 finished with value: 0.7027328499721138 and parameters: {'d_token': 256, 'n_blocks': 4, 'attention_dropout': 0.3121871568203331, 'ffn_dropout': 0.4263500985808496, 'lr': 0.0004191199421324702, 'weight_decay': 0.000225147272905659}. Best is trial 14 with value: 0.7511987213638786.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best trial: {'d_token': 192, 'n_blocks': 5, 'attention_dropout': 0.32657703152441825, 'ffn_dropout': 0.4178174448017821, 'lr': 0.0003267555125994075, 'weight_decay': 0.00021351678009204976}\n"
-     ]
-    }
-   ],
-   "source": [
-    "import optuna\n",
-    "from sklearn.metrics import accuracy_score, f1_score\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "from ft_transformer import FTTransformer\n",
-    "\n",
-    "# 모델을 GPU로 전송\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "\n",
-    "# 하이퍼파라미터 최적화 함수 정의\n",
-    "def objective(trial):\n",
-    "    d_token = trial.suggest_categorical(\"d_token\", [64, 128, 192, 256])\n",
-    "    n_blocks = trial.suggest_int(\"n_blocks\", 4, 8)\n",
-    "    attention_dropout = trial.suggest_float(\"attention_dropout\", 0.2, 0.5)  # Dropout 범위 증가\n",
-    "    ffn_dropout = trial.suggest_float(\"ffn_dropout\", 0.2, 0.5)\n",
-    "    lr = trial.suggest_float(\"lr\", 1e-4, 1e-3, log=True)\n",
-    "    weight_decay = trial.suggest_float(\"weight_decay\", 1e-5, 1e-3, log=True)  # Weight Decay 강화\n",
-    "\n",
-    "    # FT-Transformer 초기화(이진분류류)\n",
-    "    model = FTTransformer(\n",
-    "        num_features=len(numerical_cols),\n",
-    "        cat_cardinalities=[len(X_train[col].unique()) for col in categorical_cols],\n",
-    "        d_token=d_token,\n",
-    "        n_blocks=n_blocks,\n",
-    "        attention_dropout=attention_dropout,\n",
-    "        ffn_dropout=ffn_dropout,\n",
-    "        num_classes=2\n",
-    "    ).to(device)\n",
-    "\n",
-    "    # 손실 함수 및 옵티마이저 설정\n",
-    "    criterion = nn.BCELoss()\n",
-    "    optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
-    "\n",
-    "    # 학습 루프\n",
-    "    epochs = 8\n",
-    "    for epoch in range(epochs):\n",
-    "        model.train()\n",
-    "        for x_num_batch, x_cat_batch, y_batch in train_loader:\n",
-    "            x_num_batch, x_cat_batch, y_batch = (\n",
-    "                x_num_batch.to(device),\n",
-    "                x_cat_batch.to(device),\n",
-    "                y_batch.to(device)\n",
-    "            )\n",
-    "            optimizer.zero_grad()\n",
-    "            y_pred = model(x_num_batch, x_cat_batch)\n",
-    "            loss = criterion(y_pred, y_batch.unsqueeze(1))\n",
-    "            loss.backward()\n",
-    "            optimizer.step()\n",
-    "\n",
-    "    # Validation 평가\n",
-    "    model.eval()\n",
-    "    y_pred_val, y_true_val = [], []\n",
-    "    with torch.no_grad():\n",
-    "        for x_num_batch, x_cat_batch, y_batch in val_loader:\n",
-    "            x_num_batch, x_cat_batch, y_batch = (\n",
-    "                x_num_batch.to(device),\n",
-    "                x_cat_batch.to(device),\n",
-    "                y_batch.to(device)\n",
-    "            )\n",
-    "            output = model(x_num_batch, x_cat_batch)\n",
-    "            y_pred_val.extend((output.cpu().numpy() > 0.5).astype(int))\n",
-    "            y_true_val.extend(y_batch.cpu().numpy())\n",
-    "\n",
-    "    # f1-score 계산\n",
-    "    val_score = f1_score(y_true_val, y_pred_val)\n",
-    "    return val_score\n",
-    "\n",
-    "# Optuna 실행\n",
-    "sampler = optuna.samplers.TPESampler(seed=seed)\n",
-    "study = optuna.create_study(direction=\"maximize\", sampler=sampler)  # f1-score를 최대화\n",
-    "study.optimize(objective, n_trials=50)  # 50번의 탐색 시도\n",
-    "\n",
-    "# 최적의 하이퍼파라미터 출력\n",
-    "print(\"Best trial:\", study.best_trial.params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 모델 학습 루프"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best Hyperparameters: {'d_token': 192, 'n_blocks': 5, 'attention_dropout': 0.32657703152441825, 'ffn_dropout': 0.4178174448017821, 'lr': 0.0003267555125994075, 'weight_decay': 0.00021351678009204976}\n",
-      "Epoch 1/50, Train Loss: 0.1531, Validation Loss: 0.1658, Validation F1-Score: 0.6962\n",
-      "Epoch 2/50, Train Loss: 0.1036, Validation Loss: 0.2129, Validation F1-Score: 0.6684\n",
-      "Epoch 3/50, Train Loss: 0.0856, Validation Loss: 0.2259, Validation F1-Score: 0.6493\n",
-      "Epoch 4/50, Train Loss: 0.0789, Validation Loss: 0.1908, Validation F1-Score: 0.6861\n",
-      "Epoch 5/50, Train Loss: 0.0726, Validation Loss: 0.1832, Validation F1-Score: 0.6957\n",
-      "Epoch 6/50, Train Loss: 0.0674, Validation Loss: 0.1649, Validation F1-Score: 0.7127\n",
-      "Epoch 7/50, Train Loss: 0.0641, Validation Loss: 0.1444, Validation F1-Score: 0.7537\n",
-      "Epoch 8/50, Train Loss: 0.0640, Validation Loss: 0.1749, Validation F1-Score: 0.6901\n",
-      "Epoch 9/50, Train Loss: 0.0593, Validation Loss: 0.1693, Validation F1-Score: 0.6952\n",
-      "Epoch 10/50, Train Loss: 0.0597, Validation Loss: 0.1791, Validation F1-Score: 0.7131\n",
-      "Epoch 11/50, Train Loss: 0.0550, Validation Loss: 0.1743, Validation F1-Score: 0.7080\n",
-      "Epoch 12/50, Train Loss: 0.0550, Validation Loss: 0.1761, Validation F1-Score: 0.7167\n",
-      "Epoch 13/50, Train Loss: 0.0544, Validation Loss: 0.1967, Validation F1-Score: 0.6750\n",
-      "Epoch 14/50, Train Loss: 0.0539, Validation Loss: 0.1993, Validation F1-Score: 0.6651\n",
-      "Epoch 15/50, Train Loss: 0.0518, Validation Loss: 0.1751, Validation F1-Score: 0.7230\n",
-      "Early stopping at epoch 15\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 최적의 하이퍼파라미터 가져오기\n",
-    "best_params = study.best_trial.params\n",
-    "print(\"Best Hyperparameters:\", best_params)\n",
-    "\n",
-    "# FT-Transformer 초기화 (최적화된 하이퍼파라미터로 설정)\n",
-    "ft_transformer = FTTransformer(\n",
-    "    num_features=len(numerical_cols),\n",
-    "    cat_cardinalities=[len(X_train[col].unique()) for col in categorical_cols],\n",
-    "    d_token=best_params[\"d_token\"],\n",
-    "    n_blocks=best_params[\"n_blocks\"],\n",
-    "    attention_dropout=best_params[\"attention_dropout\"],\n",
-    "    ffn_dropout=best_params[\"ffn_dropout\"]\n",
-    ").to(device)\n",
-    "\n",
-    "# 손실 함수와 옵티마이저 재설정\n",
-    "criterion = nn.BCELoss()\n",
-    "optimizer_ft = optim.AdamW(ft_transformer.parameters(), lr=best_params[\"lr\"], weight_decay=best_params[\"weight_decay\"])\n",
-    "\n",
-    "# Early Stopping 설정\n",
-    "best_val_f1 = -float('inf')  # F1-Score는 최대화가 목표이므로 -inf로 초기화\n",
-    "patience = 8  # F1-Score가 개선되지 않는 Epoch 수\n",
-    "counter = 0  # 개선되지 않은 Epoch 수를 기록\n",
-    "\n",
-    "# 학습 루프\n",
-    "epochs = 50  # 최대 Epoch 수\n",
-    "for epoch in range(epochs):\n",
-    "    # Training Phase\n",
-    "    ft_transformer.train()\n",
-    "    train_loss = 0\n",
-    "    for x_num_batch, x_cat_batch, y_batch in train_loader:\n",
-    "        x_num_batch, x_cat_batch, y_batch = (\n",
-    "            x_num_batch.to(device),\n",
-    "            x_cat_batch.to(device),\n",
-    "            y_batch.to(device),\n",
-    "        )\n",
-    "        optimizer_ft.zero_grad()\n",
-    "        y_pred = ft_transformer(x_num_batch, x_cat_batch)\n",
-    "        loss = criterion(y_pred, y_batch.unsqueeze(1))\n",
-    "        loss.backward()\n",
-    "        optimizer_ft.step()\n",
-    "        train_loss += loss.item()\n",
-    "\n",
-    "    # Validation Phase\n",
-    "    ft_transformer.eval()\n",
-    "    val_loss = 0\n",
-    "    y_true_val, y_pred_val = [], []\n",
-    "    with torch.no_grad():\n",
-    "        for x_num_batch, x_cat_batch, y_batch in val_loader:\n",
-    "            x_num_batch, x_cat_batch, y_batch = (\n",
-    "                x_num_batch.to(device),\n",
-    "                x_cat_batch.to(device),\n",
-    "                y_batch.to(device),\n",
-    "            )\n",
-    "            y_pred = ft_transformer(x_num_batch, x_cat_batch)\n",
-    "            val_loss += criterion(y_pred, y_batch.unsqueeze(1)).item()\n",
-    "            y_true_val.extend(y_batch.cpu().numpy())\n",
-    "            y_pred_val.extend((y_pred.cpu().numpy() > 0.5).astype(int))\n",
-    "\n",
-    "    # 평균 Loss 계산\n",
-    "    train_loss /= len(train_loader)\n",
-    "    val_loss /= len(val_loader) \n",
-    "\n",
-    "    # F1-Score 계산\n",
-    "    val_f1 = f1_score(y_true_val, y_pred_val)\n",
-    "\n",
-    "    print(\n",
-    "        f\"Epoch {epoch+1}/{epochs}, Train Loss: {train_loss:.4f}, \"\n",
-    "        f\"Validation Loss: {val_loss:.4f}, Validation F1-Score: {val_f1:.4f}\"\n",
-    "    )\n",
-    "\n",
-    "    # Early Stopping 체크\n",
-    "    if val_f1 > best_val_f1:\n",
-    "        best_val_f1 = val_f1\n",
-    "        counter = 0\n",
-    "        # 최적 모델 저장\n",
-    "        torch.save(ft_transformer.state_dict(), \"best_model_f1.pth\")\n",
-    "    else:\n",
-    "        counter += 1\n",
-    "        if counter >= patience:\n",
-    "            print(f\"Early stopping at epoch {epoch+1}\")\n",
-    "            break"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 성능 평가"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test Accuracy: 0.9261415525114155\n",
-      "Test AUC: 0.9602353820488985\n",
-      "Test F1-Score: 0.55286800276434\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score\n",
-    "\n",
-    "# Test 데이터 성능 평가\n",
-    "ft_transformer.eval()\n",
-    "y_pred_test = []\n",
-    "y_true_test = []\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    for x_num_batch, x_cat_batch, y_batch in test_loader:\n",
-    "        x_num_batch, x_cat_batch, y_batch = (\n",
-    "            x_num_batch.to(device),\n",
-    "            x_cat_batch.to(device),\n",
-    "            y_batch.to(device),\n",
-    "        )\n",
-    "        output = ft_transformer(x_num_batch, x_cat_batch)\n",
-    "        y_pred_test.extend(output.cpu().numpy())\n",
-    "        y_true_test.extend(y_batch.cpu().numpy())\n",
-    "\n",
-    "# 연속형 예측값을 이진값으로 변환\n",
-    "y_pred_test_binary = (np.array(y_pred_test) > 0.5).astype(int)\n",
-    "\n",
-    "# 성능 지표 계산\n",
-    "print(\"Test Accuracy:\", accuracy_score(y_true_test, y_pred_test_binary))\n",
-    "print(\"Test AUC:\", roc_auc_score(y_true_test, y_pred_test))\n",
-    "print(\"Test F1-Score:\", f1_score(y_true_test, y_pred_test_binary))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ResNet-like"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 데이터셋 준비"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 시드 고정\n",
-    "seed = 42\n",
-    "random.seed(seed)\n",
-    "np.random.seed(seed)\n",
-    "torch.manual_seed(seed)\n",
-    "torch.cuda.manual_seed_all(seed)\n",
-    "\n",
-    "# DataLoader 생성\n",
-    "X_train, categorical_cols, numerical_cols, train_loader, val_loader, test_loader = prepare_dataloader(\"seoul\", target='binary', random_state = 42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 하이퍼파라미터 최적화"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2025-02-02 15:59:20,922] A new study created in memory with name: no-name-ecb8d2cb-843f-4b8f-ba41-9b6d94be0241\n",
-      "[I 2025-02-02 15:59:30,115] Trial 0 finished with value: 0.5192686357243319 and parameters: {'d_main': 128, 'd_hidden': 32, 'n_blocks': 8, 'dropout_first': 0.34044600469728353, 'dropout_second': 0.21242177333881365, 'lr': 0.00010994335574766199, 'weight_decay': 0.0008123245085588687}. Best is trial 0 with value: 0.5192686357243319.\n",
-      "[I 2025-02-02 15:59:35,646] Trial 1 finished with value: 0.5009784735812133 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.34474115788895177, 'dropout_second': 0.04184815819561255, 'lr': 0.0003839629299804173, 'weight_decay': 1.2562773503807034e-05}. Best is trial 0 with value: 0.5192686357243319.\n",
-      "[I 2025-02-02 15:59:41,174] Trial 2 finished with value: 0.19511692592974142 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.12602063719411183, 'dropout_second': 0.28466566117599995, 'lr': 0.00853618986286683, 'weight_decay': 0.0002661901888489054}. Best is trial 0 with value: 0.5192686357243319.\n",
-      "[I 2025-02-02 15:59:50,093] Trial 3 finished with value: 0.6725949878738885 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 8, 'dropout_first': 0.20351199264000677, 'dropout_second': 0.19875668530619459, 'lr': 0.0004201672054372534, 'weight_decay': 3.632486956676606e-05}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 15:59:59,156] Trial 4 finished with value: 0.5230160971476984 and parameters: {'d_main': 192, 'd_hidden': 32, 'n_blocks': 8, 'dropout_first': 0.1353970008207678, 'dropout_second': 0.058794858725743554, 'lr': 0.00012315571723666037, 'weight_decay': 9.462175356461487e-06}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:00:07,468] Trial 5 finished with value: 0.0 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 7, 'dropout_first': 0.12982025747190834, 'dropout_second': 0.2960660809801552, 'lr': 0.0035033984911586884, 'weight_decay': 3.945908811099999e-06}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:00:12,273] Trial 6 finished with value: 0.5830485304169515 and parameters: {'d_main': 128, 'd_hidden': 32, 'n_blocks': 3, 'dropout_first': 0.4452413703502375, 'dropout_second': 0.18698943804826737, 'lr': 0.0004589824181495653, 'weight_decay': 1.5512259126484753e-06}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:00:20,540] Trial 7 finished with value: 0.19511692592974142 and parameters: {'d_main': 192, 'd_hidden': 32, 'n_blocks': 7, 'dropout_first': 0.40431401944675904, 'dropout_second': 0.16838315927084888, 'lr': 0.003482846706526885, 'weight_decay': 3.0296104428212496e-05}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:00:28,039] Trial 8 finished with value: 0.6400343642611683 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout_first': 0.4630265895704372, 'dropout_second': 0.07478766874466249, 'lr': 0.000661859559718348, 'weight_decay': 0.0001847793417351927}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:00:36,993] Trial 9 finished with value: 0.0 and parameters: {'d_main': 192, 'd_hidden': 32, 'n_blocks': 8, 'dropout_first': 0.4214688307596458, 'dropout_second': 0.05597101766581075, 'lr': 0.006097025297491433, 'weight_decay': 4.149795789891592e-05}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:00:44,631] Trial 10 finished with value: 0.07984031936127745 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 6, 'dropout_first': 0.23874035656264875, 'dropout_second': 0.1191627229093386, 'lr': 0.0014138916628611978, 'weight_decay': 9.186066145996928e-05}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:00:52,142] Trial 11 finished with value: 0.6199059561128527 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 6, 'dropout_first': 0.2476670229366214, 'dropout_second': 0.10682736700049808, 'lr': 0.0006875192303983222, 'weight_decay': 0.00016773791183912636}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:00:58,558] Trial 12 finished with value: 0.5279538904899136 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.4992445333005462, 'dropout_second': 0.22957840083137454, 'lr': 0.00026708145837086683, 'weight_decay': 0.000544189514701203}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:01:06,648] Trial 13 finished with value: 0.0 and parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 7, 'dropout_first': 0.23905337219863312, 'dropout_second': 0.010075765480341559, 'lr': 0.0012557930846273358, 'weight_decay': 4.766194018548066e-05}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:01:12,778] Trial 14 finished with value: 0.5347852389594676 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.19123040647428097, 'dropout_second': 0.11858708726450355, 'lr': 0.00021863781499886916, 'weight_decay': 0.00013182468471438185}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:01:20,285] Trial 15 finished with value: 0.656 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 6, 'dropout_first': 0.29986491595140763, 'dropout_second': 0.23692395773958858, 'lr': 0.0008943218941938837, 'weight_decay': 0.0003586878521837277}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:01:28,462] Trial 16 finished with value: 0.5844036697247706 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 7, 'dropout_first': 0.3201820298115095, 'dropout_second': 0.2509034576046632, 'lr': 0.0012368375416860133, 'weight_decay': 0.0003933843039023355}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:01:35,404] Trial 17 finished with value: 0.0 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 5, 'dropout_first': 0.2794382614823519, 'dropout_second': 0.24944640643903046, 'lr': 0.002067442428463093, 'weight_decay': 1.7825601965382163e-05}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:01:40,304] Trial 18 finished with value: 0.657411188240098 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.185052145172123, 'dropout_second': 0.19465208459320812, 'lr': 0.0006509944715021355, 'weight_decay': 5.949055284431336e-06}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:01:45,253] Trial 19 finished with value: 0.6093471280770603 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.18071714680649648, 'dropout_second': 0.15224752924180684, 'lr': 0.00022031746107267636, 'weight_decay': 4.661801081342972e-06}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:01:50,794] Trial 20 finished with value: 0.597237775289287 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.1799367766346295, 'dropout_second': 0.18901655929349007, 'lr': 0.00044722710854316026, 'weight_decay': 1.1227386145926723e-06}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:01:55,660] Trial 21 finished with value: 0.5853000674308834 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 3, 'dropout_first': 0.27955578506271855, 'dropout_second': 0.2095950254957476, 'lr': 0.0007358925470348262, 'weight_decay': 5.031388037641803e-06}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:02:03,255] Trial 22 finished with value: 0.6617583369423993 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 6, 'dropout_first': 0.2073708534881274, 'dropout_second': 0.26298036974241923, 'lr': 0.0008285121463870491, 'weight_decay': 7.57796289720658e-05}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:02:08,817] Trial 23 finished with value: 0.32728632878249486 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 4, 'dropout_first': 0.20001601120798612, 'dropout_second': 0.2710526705672427, 'lr': 0.0020015023761662063, 'weight_decay': 7.328055550719538e-05}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:02:18,081] Trial 24 finished with value: 0.6281701131486539 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 8, 'dropout_first': 0.15734642099337648, 'dropout_second': 0.20467077434159242, 'lr': 0.0003676186285957462, 'weight_decay': 8.484877321047985e-06}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:02:25,099] Trial 25 finished with value: 0.5965045592705167 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.2170998437398996, 'dropout_second': 0.16083184686850863, 'lr': 0.0005365589635455455, 'weight_decay': 2.3038620151242498e-05}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:02:33,316] Trial 26 finished with value: 0.5584905660377358 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 7, 'dropout_first': 0.10927125312095436, 'dropout_second': 0.2613601094556424, 'lr': 0.00030485793870362976, 'weight_decay': 6.250253353769138e-05}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:02:40,952] Trial 27 finished with value: 0.19378427787934185 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 6, 'dropout_first': 0.15738248329924776, 'dropout_second': 0.18576686380696483, 'lr': 0.0009320900444178502, 'weight_decay': 2.1693900847788226e-06}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:02:46,415] Trial 28 finished with value: 0.4736171297476421 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.22284105915494618, 'dropout_second': 0.2275215422482748, 'lr': 0.00017006611735943575, 'weight_decay': 1.5091449536138288e-05}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:02:55,324] Trial 29 finished with value: 0.19511692592974142 and parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 8, 'dropout_first': 0.2649726067425218, 'dropout_second': 0.1424722036991971, 'lr': 0.0022738815322358146, 'weight_decay': 2.694620758307688e-05}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:03:00,544] Trial 30 finished with value: 0.45915354330708663 and parameters: {'d_main': 128, 'd_hidden': 32, 'n_blocks': 3, 'dropout_first': 0.35103917717404076, 'dropout_second': 0.20872104345048048, 'lr': 0.00010012078005753846, 'weight_decay': 0.00010485220093419964}. Best is trial 3 with value: 0.6725949878738885.\n",
-      "[I 2025-02-02 16:03:08,269] Trial 31 finished with value: 0.680594243268338 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 6, 'dropout_first': 0.3122622733458627, 'dropout_second': 0.22959715904464012, 'lr': 0.0009538992562057897, 'weight_decay': 0.0008351622710745379}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:03:15,309] Trial 32 finished with value: 0.6067335243553008 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.35611919589547497, 'dropout_second': 0.22615889083016483, 'lr': 0.0005685888485986714, 'weight_decay': 0.0009550197959107674}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:03:23,049] Trial 33 finished with value: 0.335 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 6, 'dropout_first': 0.3796369811324729, 'dropout_second': 0.2781657506788394, 'lr': 0.0016247278936636989, 'weight_decay': 4.528464672102566e-05}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:03:31,423] Trial 34 finished with value: 0.6100415251038127 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 7, 'dropout_first': 0.31457726271891395, 'dropout_second': 0.24629443338977997, 'lr': 0.0009916395396574313, 'weight_decay': 8.468038690363233e-06}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:03:40,440] Trial 35 finished with value: 0.5792826014079785 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 8, 'dropout_first': 0.1609595903458735, 'dropout_second': 0.29949544909552134, 'lr': 0.0003598006595715833, 'weight_decay': 0.000611366018985009}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:03:47,549] Trial 36 finished with value: 0.55859375 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.21368649308842594, 'dropout_second': 0.17650165280665778, 'lr': 0.0006969248287278233, 'weight_decay': 1.157759133419454e-05}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:03:55,811] Trial 37 finished with value: 0.5915492957746479 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 7, 'dropout_first': 0.26284056200741807, 'dropout_second': 0.19804880051449372, 'lr': 0.0004867907132712468, 'weight_decay': 2.508603520472002e-06}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:04:03,584] Trial 38 finished with value: 0.6141324454310025 and parameters: {'d_main': 192, 'd_hidden': 32, 'n_blocks': 6, 'dropout_first': 0.1421050626216277, 'dropout_second': 0.21908827524827684, 'lr': 0.00084224196723038, 'weight_decay': 0.00023303438320237155}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:04:09,139] Trial 39 finished with value: 0.0 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout_first': 0.10160616201392712, 'dropout_second': 0.1391621008584947, 'lr': 0.0032379140293698006, 'weight_decay': 6.269443823194281e-06}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:04:17,430] Trial 40 finished with value: 0.6051752921535893 and parameters: {'d_main': 192, 'd_hidden': 32, 'n_blocks': 7, 'dropout_first': 0.2935358343914325, 'dropout_second': 0.26846075784783446, 'lr': 0.0012398778873158685, 'weight_decay': 3.157930756708663e-06}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:04:25,135] Trial 41 finished with value: 0.6241379310344828 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 6, 'dropout_first': 0.3190351182045631, 'dropout_second': 0.23969591667705323, 'lr': 0.0008625263450648776, 'weight_decay': 0.00034440568782878036}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:04:32,838] Trial 42 finished with value: 0.591715976331361 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 6, 'dropout_first': 0.36771124174149844, 'dropout_second': 0.2864919167273482, 'lr': 0.0005796725863182776, 'weight_decay': 0.0006693113444106173}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:04:40,467] Trial 43 finished with value: 0.19511692592974142 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 6, 'dropout_first': 0.4014188726382219, 'dropout_second': 0.261409827181683, 'lr': 0.0016241904532108644, 'weight_decay': 0.00041403989414200593}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:04:47,372] Trial 44 finished with value: 0.6356275303643725 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.25708001150138227, 'dropout_second': 0.23696362420229689, 'lr': 0.0010726485646066052, 'weight_decay': 0.0002394830883280549}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:04:55,037] Trial 45 finished with value: 0.5898423817863397 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 6, 'dropout_first': 0.2939883848541707, 'dropout_second': 0.17573780024632835, 'lr': 0.0004283351035616762, 'weight_decay': 0.00015968844593287096}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:05:03,328] Trial 46 finished with value: 0.6297833102812356 and parameters: {'d_main': 256, 'd_hidden': 64, 'n_blocks': 7, 'dropout_first': 0.33761088583094895, 'dropout_second': 0.19784084013167183, 'lr': 0.0008029538747768709, 'weight_decay': 0.0007561108161069606}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:05:10,927] Trial 47 finished with value: 0.6577017114914425 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 6, 'dropout_first': 0.224510896201851, 'dropout_second': 0.22227813212160902, 'lr': 0.000623544967315617, 'weight_decay': 0.00010858010427724432}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:05:19,107] Trial 48 finished with value: 0.6053475935828877 and parameters: {'d_main': 128, 'd_hidden': 32, 'n_blocks': 7, 'dropout_first': 0.20179287097188797, 'dropout_second': 0.21472047222478008, 'lr': 0.0003075040219603553, 'weight_decay': 6.360962731072777e-05}. Best is trial 31 with value: 0.680594243268338.\n",
-      "[I 2025-02-02 16:05:26,114] Trial 49 finished with value: 0.6677727084197429 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout_first': 0.23962863264554043, 'dropout_second': 0.09087448546487817, 'lr': 0.0005968243273186192, 'weight_decay': 3.526800870958912e-05}. Best is trial 31 with value: 0.680594243268338.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best Hyperparameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 6, 'dropout_first': 0.3122622733458627, 'dropout_second': 0.22959715904464012, 'lr': 0.0009538992562057897, 'weight_decay': 0.0008351622710745379}\n"
-     ]
-    }
-   ],
-   "source": [
-    "import optuna\n",
-    "from sklearn.metrics import accuracy_score, f1_score\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "from resnet_like import ResNetLike  # ResNetLike 모델 불러오기\n",
-    "\n",
-    "# GPU 설정\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
-    "# Optuna 하이퍼파라미터 최적화 함수 정의\n",
-    "def objective(trial):\n",
-    "    # 하이퍼파라미터 탐색 공간 정의\n",
-    "    d_main = trial.suggest_categorical(\"d_main\", [64, 128, 192, 256])\n",
-    "    d_hidden = trial.suggest_categorical(\"d_hidden\", [32, 64, 128])\n",
-    "    n_blocks = trial.suggest_int(\"n_blocks\", 3, 8)  # ResNet 블록 수\n",
-    "    dropout_first = trial.suggest_float(\"dropout_first\", 0.1, 0.5)  # 첫 번째 Dropout\n",
-    "    dropout_second = trial.suggest_float(\"dropout_second\", 0.0, 0.3)  # 두 번째 Dropout\n",
-    "    lr = trial.suggest_float(\"lr\", 1e-4, 1e-2, log=True)  # 학습률\n",
-    "    weight_decay = trial.suggest_float(\"weight_decay\", 1e-6, 1e-3, log=True)  # L2 정규화\n",
-    "\n",
-    "    # 연속형 변수 + 범주형 변수 개수 반영하여 모델 입력 크기 설정\n",
-    "    input_dim = len(numerical_cols) + len(categorical_cols)\n",
-    "\n",
-    "    # 모델 초기화 및 GPU로 이동\n",
-    "    model = ResNetLike(\n",
-    "        input_dim=input_dim,\n",
-    "        d_main=d_main, \n",
-    "        d_hidden=d_hidden, \n",
-    "        n_blocks=n_blocks, \n",
-    "        dropout_first=dropout_first, \n",
-    "        dropout_second=dropout_second\n",
-    "    ).to(device)\n",
-    "\n",
-    "    # 손실 함수 및 옵티마이저 설정\n",
-    "    criterion = nn.BCELoss()\n",
-    "    optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
-    "\n",
-    "    # 학습 루프 (Epoch 설정)\n",
-    "    epochs = 8  # 빠른 탐색을 위해 Epoch을 줄임\n",
-    "    for epoch in range(epochs):\n",
-    "        model.train()\n",
-    "        for x_num_batch, x_cat_batch, y_batch in train_loader:  \n",
-    "            x_num_batch, x_cat_batch, y_batch = (\n",
-    "                x_num_batch.to(device),\n",
-    "                x_cat_batch.to(device),\n",
-    "                y_batch.to(device)\n",
-    "            )\n",
-    "\n",
-    "            optimizer.zero_grad()\n",
-    "            y_pred = model(x_num_batch, x_cat_batch)  # 두 개의 입력을 개별적으로 전달\n",
-    "            loss = criterion(y_pred, y_batch.unsqueeze(1))\n",
-    "            loss.backward()\n",
-    "            optimizer.step()\n",
-    "\n",
-    "    # Validation 평가\n",
-    "    model.eval()\n",
-    "    y_pred_val, y_true_val = [], []\n",
-    "    with torch.no_grad():\n",
-    "        for x_num_batch, x_cat_batch, y_batch in val_loader:  \n",
-    "            x_num_batch, x_cat_batch, y_batch = (\n",
-    "                x_num_batch.to(device),\n",
-    "                x_cat_batch.to(device),\n",
-    "                y_batch.to(device)\n",
-    "            )\n",
-    "\n",
-    "            output = model(x_num_batch, x_cat_batch)  # 두 개의 입력을 개별적으로 전달\n",
-    "            y_pred_val.extend((output.cpu().numpy() > 0.5).astype(int))\n",
-    "            y_true_val.extend(y_batch.cpu().numpy())\n",
-    "\n",
-    "    # f1-score 계산 (최적화 지표)\n",
-    "    val_score = f1_score(y_true_val, y_pred_val)\n",
-    "    return val_score\n",
-    "\n",
-    "# Optuna 실행 (최적화 시작)\n",
-    "sampler = optuna.samplers.TPESampler(seed=seed)\n",
-    "study = optuna.create_study(direction=\"maximize\", sampler=sampler)  # f1-score를 최대화\n",
-    "study.optimize(objective, n_trials=50)  # 50번의 탐색 시도\n",
-    "\n",
-    "# 최적의 하이퍼파라미터 출력\n",
-    "best_params = study.best_trial.params\n",
-    "print(\"Best Hyperparameters:\", best_params)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 모델 학습 루프"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/50, Train Loss: 0.5364, Validation Loss: 0.6911, Validation F1-Score: 0.6280\n",
-      "Epoch 2/50, Train Loss: 0.5197, Validation Loss: 0.7168, Validation F1-Score: 0.5862\n",
-      "Epoch 3/50, Train Loss: 0.5181, Validation Loss: 0.7134, Validation F1-Score: 0.6050\n",
-      "Epoch 4/50, Train Loss: 0.5160, Validation Loss: 0.6864, Validation F1-Score: 0.6337\n",
-      "Epoch 5/50, Train Loss: 0.5116, Validation Loss: 0.6981, Validation F1-Score: 0.6288\n",
-      "Epoch 6/50, Train Loss: 0.5077, Validation Loss: 0.6961, Validation F1-Score: 0.6237\n",
-      "Epoch 7/50, Train Loss: 0.5122, Validation Loss: 0.6937, Validation F1-Score: 0.6505\n",
-      "Epoch 8/50, Train Loss: 0.5121, Validation Loss: 0.6881, Validation F1-Score: 0.5837\n",
-      "Epoch 9/50, Train Loss: 0.5204, Validation Loss: 0.6984, Validation F1-Score: 0.5593\n",
-      "Epoch 10/50, Train Loss: 0.5312, Validation Loss: 0.8063, Validation F1-Score: 0.4385\n",
-      "Epoch 11/50, Train Loss: 0.5422, Validation Loss: 0.6892, Validation F1-Score: 0.6088\n",
-      "Epoch 12/50, Train Loss: 0.5183, Validation Loss: 0.6928, Validation F1-Score: 0.5327\n",
-      "Epoch 13/50, Train Loss: 0.5440, Validation Loss: 0.6985, Validation F1-Score: 0.4962\n",
-      "Epoch 14/50, Train Loss: 0.5612, Validation Loss: 0.7790, Validation F1-Score: 0.4641\n",
-      "Epoch 15/50, Train Loss: 0.5612, Validation Loss: 0.7481, Validation F1-Score: 0.5057\n",
-      "Early stopping at epoch 15\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "from sklearn.metrics import f1_score\n",
-    "\n",
-    "# 최적의 하이퍼파라미터 적용\n",
-    "best_model = ResNetLike(\n",
-    "    input_dim=len(numerical_cols) + len(categorical_cols),  # 입력 차원\n",
-    "    d_main=best_params[\"d_main\"],\n",
-    "    d_hidden=best_params[\"d_hidden\"],\n",
-    "    n_blocks=best_params[\"n_blocks\"],\n",
-    "    dropout_first=best_params[\"dropout_first\"],\n",
-    "    dropout_second=best_params[\"dropout_second\"]\n",
-    ").to(device)\n",
-    "\n",
-    "# 손실 함수 및 옵티마이저 설정\n",
-    "criterion = nn.BCEWithLogitsLoss()\n",
-    "optimizer = optim.AdamW(best_model.parameters(), lr=best_params[\"lr\"], weight_decay=best_params[\"weight_decay\"])\n",
-    "\n",
-    "# Early Stopping 설정\n",
-    "best_val_f1 = -float('inf')  # F1-Score 최대화 목표\n",
-    "patience = 8  # F1-Score가 개선되지 않는 Epoch 수\n",
-    "counter = 0\n",
-    "\n",
-    "# 모델 저장 폴더 생성\n",
-    "os.makedirs(\"./models\", exist_ok=True)\n",
-    "\n",
-    "# 모델 학습 루프\n",
-    "epochs = 50  # 최적의 하이퍼파라미터로 충분한 학습\n",
-    "for epoch in range(epochs):\n",
-    "    best_model.train()\n",
-    "    train_loss = 0\n",
-    "\n",
-    "    # Training Loop (x_batch 두 개를 나눠서 받음)\n",
-    "    for x_num_batch, x_cat_batch, y_batch in train_loader:\n",
-    "        x_num_batch, x_cat_batch, y_batch = (\n",
-    "            x_num_batch.to(device),\n",
-    "            x_cat_batch.to(device),\n",
-    "            y_batch.to(device)\n",
-    "        )\n",
-    "\n",
-    "        optimizer.zero_grad()\n",
-    "        y_pred = best_model(x_num_batch, x_cat_batch)  # 두 개의 입력을 개별적으로 전달\n",
-    "        loss = criterion(y_pred, y_batch.unsqueeze(1))  # y_batch 차원 조정\n",
-    "        loss.backward()\n",
-    "        optimizer.step()\n",
-    "\n",
-    "        train_loss += loss.item()\n",
-    "\n",
-    "    # Validation Loop\n",
-    "    best_model.eval()\n",
-    "    val_loss = 0\n",
-    "    y_pred_val, y_true_val = [], []\n",
-    "    with torch.no_grad():\n",
-    "        for x_num_batch, x_cat_batch, y_batch in val_loader:\n",
-    "            x_num_batch, x_cat_batch, y_batch = (\n",
-    "                x_num_batch.to(device),\n",
-    "                x_cat_batch.to(device),\n",
-    "                y_batch.to(device)\n",
-    "            )\n",
-    "\n",
-    "            output = best_model(x_num_batch, x_cat_batch)\n",
-    "            loss = criterion(output, y_batch.unsqueeze(1))  # 차원 맞춤\n",
-    "            val_loss += loss.item()\n",
-    "\n",
-    "            y_pred_val.extend((torch.sigmoid(output).cpu().numpy() > 0.5).astype(int))\n",
-    "            y_true_val.extend(y_batch.cpu().numpy())\n",
-    "\n",
-    "    # 평균 손실 계산\n",
-    "    train_loss /= len(train_loader)\n",
-    "    val_loss /= len(val_loader)\n",
-    "\n",
-    "    # f1-score 계산\n",
-    "    val_f1 = f1_score(y_true_val, y_pred_val)\n",
-    "\n",
-    "    print(\n",
-    "        f\"Epoch {epoch+1}/{epochs}, Train Loss: {train_loss:.4f}, \"\n",
-    "        f\"Validation Loss: {val_loss:.4f}, Validation F1-Score: {val_f1:.4f}\"\n",
-    "    )\n",
-    "\n",
-    "    # Early Stopping 체크\n",
-    "    if val_f1 > best_val_f1:\n",
-    "        best_val_f1 = val_f1\n",
-    "        counter = 0\n",
-    "        torch.save(best_model.state_dict(), \"./models/best_resnet_model.pth\")  # 모델 저장\n",
-    "    else:\n",
-    "        counter += 1\n",
-    "        if counter >= patience:\n",
-    "            print(f\"Early stopping at epoch {epoch+1}\")\n",
-    "            break  # 성능 개선이 없으면 학습 조기 종료"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 성능 평가"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test Accuracy: 0.9118721461187215\n",
-      "Test AUC: 0.867653555709893\n",
-      "Test F1-Score: 0.5113924050632911\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_1311698/1784587916.py:5: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
-      "  best_model.load_state_dict(torch.load(\"./models/best_resnet_model.pth\"))  # 모델 저장 경로 수정\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score\n",
-    "\n",
-    "# 저장된 최적 모델 불러오기\n",
-    "best_model.load_state_dict(torch.load(\"./models/best_resnet_model.pth\"))  # 모델 저장 경로 수정\n",
-    "best_model.to(device)\n",
-    "best_model.eval()\n",
-    "\n",
-    "# Test 데이터 평가\n",
-    "y_pred_test, y_true_test = [], []\n",
-    "with torch.no_grad():\n",
-    "    for x_num_batch, x_cat_batch, y_batch in test_loader:  # 3개 데이터 받기\n",
-    "        x_num_batch, x_cat_batch, y_batch = (\n",
-    "            x_num_batch.to(device),\n",
-    "            x_cat_batch.to(device),\n",
-    "            y_batch.to(device)\n",
-    "        )\n",
-    "\n",
-    "        output = best_model(x_num_batch, x_cat_batch)  # 두 개의 입력 전달\n",
-    "        y_pred_test.extend((torch.sigmoid(output).cpu().numpy() > 0.5).astype(int))\n",
-    "        y_true_test.extend(y_batch.cpu().numpy())\n",
-    "\n",
-    "# Test 성능 출력\n",
-    "print(\"Test Accuracy:\", accuracy_score(y_true_test, y_pred_test))\n",
-    "print(\"Test AUC:\", roc_auc_score(y_true_test, y_pred_test))\n",
-    "print(\"Test F1-Score:\", f1_score(y_true_test, y_pred_test))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## DeepGBM"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
-    "import torch\n",
-    "from torch.utils.data import DataLoader, TensorDataset\n",
-    "import random\n",
-    "\n",
-    "# 시드 고정\n",
-    "seed = 42\n",
-    "random.seed(seed)\n",
-    "np.random.seed(seed)\n",
-    "torch.manual_seed(seed)\n",
-    "torch.cuda.manual_seed_all(seed)\n",
-    "\n",
-    "# 데이터셋 불러오기\n",
-    "seoul = pd.read_csv('../data/data_for_modeling/seoul_train.csv', index_col=0)\n",
-    "seoul_train = pd.read_csv('../data/data_oversampled/ctgan10000/ctgan10000_3_seoul.csv')\n",
-    "seoul_val = seoul.loc[seoul['year'].isin([2018]),:]\n",
-    "seoul_test = pd.read_csv('../data/data_for_modeling/seoul_test.csv')\n",
-    "drop_col = ['binary_class','multi_class','visi','year']\n",
-    "target_col = 'binary_class'\n",
-    "\n",
-    "# 모든 데이터셋의 컬럼을 같은 순서로 정렬\n",
-    "common_columns = seoul_train.columns.to_list()  # 공통 변수 이름 \n",
-    "train_data = seoul_train[common_columns]\n",
-    "val_data = seoul_val[common_columns]\n",
-    "test_data = seoul_test[common_columns]\n",
-    "\n",
-    "# 설명변수와 반응변수 분리\n",
-    "X_train = train_data.drop(columns=drop_col)\n",
-    "y_train = train_data[target_col]\n",
-    "X_val = val_data.drop(columns=drop_col)\n",
-    "y_val = val_data[target_col]\n",
-    "X_test = test_data.drop(columns=drop_col)\n",
-    "y_test = test_data[target_col]\n",
-    "\n",
-    "# 범주형 변수와 연속형 변수 분리\n",
-    "categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "# 범주형 변수 인코딩\n",
-    "label_encoders = {}\n",
-    "for col in categorical_cols:\n",
-    "    le = LabelEncoder()\n",
-    "    le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "    label_encoders[col] = le\n",
-    "\n",
-    "# 각 데이터셋에 변환 적용\n",
-    "for col in categorical_cols:\n",
-    "    X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "    X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "    X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "# 연속형 변수 스케일링\n",
-    "scaler = StandardScaler()\n",
-    "scaler.fit(X_train[numerical_cols])  # Train 데이터만 사용\n",
-    "\n",
-    "# 각 데이터셋에 변환 적용\n",
-    "X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])  # Test 데이터는 변환만\n",
-    "\n",
-    "# 연속형 변수와 범주형 변수 분리\n",
-    "X_train_num = torch.tensor(X_train[numerical_cols].values, dtype=torch.float32)\n",
-    "X_train_cat = torch.tensor(X_train[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "X_val_num = torch.tensor(X_val[numerical_cols].values, dtype=torch.float32)\n",
-    "X_val_cat = torch.tensor(X_val[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "X_test_num = torch.tensor(X_test[numerical_cols].values, dtype=torch.float32)\n",
-    "X_test_cat = torch.tensor(X_test[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "y_train_tensor = torch.tensor(y_train.values, dtype=torch.float32)  # 이진 분류\n",
-    "y_val_tensor = torch.tensor(y_val.values, dtype=torch.float32)\n",
-    "y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32)\n",
-    "\n",
-    "# TensorDataset 생성\n",
-    "train_dataset = TensorDataset(X_train_num, X_train_cat, y_train_tensor)\n",
-    "val_dataset = TensorDataset(X_val_num, X_val_cat, y_val_tensor)\n",
-    "test_dataset = TensorDataset(X_test_num, X_test_cat, y_test_tensor)\n",
-    "\n",
-    "# DataLoader 생성\n",
-    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, generator=torch.Generator().manual_seed(seed))\n",
-    "val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n",
-    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "[I 2025-02-02 16:37:59,547] A new study created in memory with name: no-name-d43cf13c-34f9-4811-955c-28691341b8a5\n",
-      "[I 2025-02-02 16:38:12,621] Trial 0 finished with value: 0.6808309726156752 and parameters: {'d_main': 128, 'd_hidden': 32, 'n_blocks': 8, 'dropout': 0.34044600469728353, 'lr': 0.0005105903209394755, 'weight_decay': 1.0994335574766187e-05}. Best is trial 0 with value: 0.6808309726156752.\n",
-      "[I 2025-02-02 16:38:22,577] Trial 1 finished with value: 0.6743371685842922 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.21649165607921678, 'lr': 0.00040912205744437856, 'weight_decay': 1.9010245319870364e-05}. Best is trial 0 with value: 0.6808309726156752.\n",
-      "[I 2025-02-02 16:38:30,407] Trial 2 finished with value: 0.6716981132075471 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.34301794076057535, 'lr': 0.00014808945119975197, 'weight_decay': 1.3492834268013232e-05}. Best is trial 0 with value: 0.6808309726156752.\n",
-      "[I 2025-02-02 16:38:37,637] Trial 3 finished with value: 0.6577981651376147 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.29807076404450805, 'lr': 0.00010824018381500966, 'weight_decay': 0.000658628931758311}. Best is trial 0 with value: 0.6808309726156752.\n",
-      "[I 2025-02-02 16:38:48,942] Trial 4 finished with value: 0.6935050993022007 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 7, 'dropout': 0.4757995766256756, 'lr': 0.0007849235338159361, 'weight_decay': 0.0001569639638866114}. Best is trial 4 with value: 0.6935050993022007.\n",
-      "[I 2025-02-02 16:39:00,131] Trial 5 finished with value: 0.664714494875549 and parameters: {'d_main': 64, 'd_hidden': 64, 'n_blocks': 7, 'dropout': 0.2427013306774357, 'lr': 0.0001909565280104537, 'weight_decay': 0.00012172847081122418}. Best is trial 4 with value: 0.6935050993022007.\n",
-      "[I 2025-02-02 16:39:11,713] Trial 6 finished with value: 0.6680738786279683 and parameters: {'d_main': 256, 'd_hidden': 32, 'n_blocks': 7, 'dropout': 0.38274293753904687, 'lr': 0.0005358055009231865, 'weight_decay': 0.000348771262454593}. Best is trial 4 with value: 0.6935050993022007.\n",
-      "[I 2025-02-02 16:39:20,737] Trial 7 finished with value: 0.6916183447548762 and parameters: {'d_main': 256, 'd_hidden': 32, 'n_blocks': 4, 'dropout': 0.23007332881069884, 'lr': 0.0005365450324352025, 'weight_decay': 0.00018841476921545086}. Best is trial 4 with value: 0.6935050993022007.\n",
-      "[I 2025-02-02 16:39:30,545] Trial 8 finished with value: 0.6589769307923772 and parameters: {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.3090931317527976, 'lr': 0.00026763383567660797, 'weight_decay': 1.1241862095793047e-05}. Best is trial 4 with value: 0.6935050993022007.\n",
-      "[I 2025-02-02 16:39:40,338] Trial 9 finished with value: 0.6521958606764261 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.4022204554172195, 'lr': 0.00016935505549297937, 'weight_decay': 1.425475704402744e-05}. Best is trial 4 with value: 0.6935050993022007.\n",
-      "[I 2025-02-02 16:39:51,651] Trial 10 finished with value: 0.7065677966101694 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 7, 'dropout': 0.4820559747905796, 'lr': 0.0009630865624170769, 'weight_decay': 4.8723119722871314e-05}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:40:03,284] Trial 11 finished with value: 0.6976274608783443 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 7, 'dropout': 0.49897739440085004, 'lr': 0.0009903735540622498, 'weight_decay': 5.1544513708209705e-05}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:40:15,632] Trial 12 finished with value: 0.676424464192368 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 8, 'dropout': 0.48816208673575634, 'lr': 0.0009889782969316999, 'weight_decay': 4.3691424349158736e-05}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:40:26,111] Trial 13 finished with value: 0.6580773042616452 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.11054723376232306, 'lr': 0.000993563841007881, 'weight_decay': 5.090873160060654e-05}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:40:36,749] Trial 14 finished with value: 0.6780219780219781 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.4345281899318145, 'lr': 0.000703066044128388, 'weight_decay': 5.039872099693328e-05}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:40:48,268] Trial 15 finished with value: 0.5949908368967624 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 7, 'dropout': 0.4985994407528791, 'lr': 0.0003141126260425451, 'weight_decay': 2.805094210329636e-05}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:41:00,422] Trial 16 finished with value: 0.6973749380881624 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 8, 'dropout': 0.4405116343614309, 'lr': 0.0007296967058494006, 'weight_decay': 7.722238352424441e-05}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:41:10,955] Trial 17 finished with value: 0.6833073322932918 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.43765610083770745, 'lr': 0.0004012608786880134, 'weight_decay': 2.9186980345955912e-05}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:41:22,403] Trial 18 finished with value: 0.6659751037344398 and parameters: {'d_main': 192, 'd_hidden': 64, 'n_blocks': 7, 'dropout': 0.3905098603146417, 'lr': 0.000794490885070064, 'weight_decay': 8.035935542498882e-05}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:41:34,547] Trial 19 finished with value: 0.6765015806111696 and parameters: {'d_main': 128, 'd_hidden': 32, 'n_blocks': 8, 'dropout': 0.1618897082112804, 'lr': 0.0006218282540300562, 'weight_decay': 0.0003128762212736291}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:41:45,105] Trial 20 finished with value: 0.6280295047418335 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.4621158946002487, 'lr': 0.0004064042869034392, 'weight_decay': 2.7603261798192472e-05}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:41:57,182] Trial 21 finished with value: 0.6953405017921147 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 8, 'dropout': 0.4255974822311327, 'lr': 0.0009993172418888458, 'weight_decay': 8.324553715242003e-05}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:42:09,218] Trial 22 finished with value: 0.6933911159263272 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 8, 'dropout': 0.4553663047983129, 'lr': 0.0007358139178301945, 'weight_decay': 6.406934888466268e-05}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:42:20,691] Trial 23 finished with value: 0.7056345444971037 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 7, 'dropout': 0.4990207539511106, 'lr': 0.0008344830972076081, 'weight_decay': 0.0001172549162060698}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:42:32,369] Trial 24 finished with value: 0.677765843179377 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 7, 'dropout': 0.4963004424730474, 'lr': 0.0008635172258202799, 'weight_decay': 0.00011965822367646233}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:42:42,922] Trial 25 finished with value: 0.6868686868686869 and parameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.4096404328164892, 'lr': 0.0006168881273294814, 'weight_decay': 0.00027785748995414627}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:42:54,551] Trial 26 finished with value: 0.6903846153846154 and parameters: {'d_main': 192, 'd_hidden': 128, 'n_blocks': 7, 'dropout': 0.37583885242041665, 'lr': 0.0008818680724435528, 'weight_decay': 3.8281345408950636e-05}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:43:05,183] Trial 27 finished with value: 0.6951156812339332 and parameters: {'d_main': 256, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.4733076998075283, 'lr': 0.0006434339896205424, 'weight_decay': 0.00020042363494450496}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:43:16,533] Trial 28 finished with value: 0.6837294332723949 and parameters: {'d_main': 64, 'd_hidden': 32, 'n_blocks': 7, 'dropout': 0.45810275836541026, 'lr': 0.00022850161272551156, 'weight_decay': 0.00010873128761655598}. Best is trial 10 with value: 0.7065677966101694.\n",
-      "[I 2025-02-02 16:43:25,562] Trial 29 finished with value: 0.6750392464678179 and parameters: {'d_main': 128, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.33103658661547813, 'lr': 0.00046154770212266305, 'weight_decay': 0.000644143910687572}. Best is trial 10 with value: 0.7065677966101694.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best Hyperparameters: {'d_main': 128, 'd_hidden': 128, 'n_blocks': 7, 'dropout': 0.4820559747905796, 'lr': 0.0009630865624170769, 'weight_decay': 4.8723119722871314e-05}\n"
-     ]
-    }
-   ],
-   "source": [
-    "import optuna\n",
-    "from sklearn.metrics import f1_score\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "from deepgbm import DeepGBM  # .py 파일에서 DeepGBM 불러오기\n",
-    "\n",
-    "# GPU 설정\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
-    "# 하이퍼파라미터 최적화 함수 정의\n",
-    "def objective(trial):\n",
-    "    d_main = trial.suggest_categorical(\"d_main\", [64, 128, 192, 256])\n",
-    "    d_hidden = trial.suggest_categorical(\"d_hidden\", [32, 64, 128])\n",
-    "    n_blocks = trial.suggest_int(\"n_blocks\", 3, 8)  # ResNet 블록 개수\n",
-    "    dropout = trial.suggest_float(\"dropout\", 0.1, 0.5)  # Dropout 비율\n",
-    "    lr = trial.suggest_float(\"lr\", 1e-4, 1e-3, log=True)  # 학습률\n",
-    "    weight_decay = trial.suggest_float(\"weight_decay\", 1e-5, 1e-3, log=True)  # 정규화\n",
-    "\n",
-    "    # DeepGBM 모델 초기화 (x_num, x_cat을 따로 받는 구조)\n",
-    "    model = DeepGBM(\n",
-    "        num_features=len(numerical_cols),\n",
-    "        cat_features=[len(X_train[col].unique()) for col in categorical_cols],\n",
-    "        d_main=d_main,\n",
-    "        d_hidden=d_hidden,\n",
-    "        n_blocks=n_blocks,\n",
-    "        dropout=dropout\n",
-    "    ).to(device)\n",
-    "\n",
-    "    # 손실 함수 및 옵티마이저 설정\n",
-    "    criterion = nn.BCELoss()\n",
-    "    optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
-    "\n",
-    "    # 학습 루프\n",
-    "    epochs = 8  # 빠른 탐색을 위해 Epoch 수 조정\n",
-    "    for epoch in range(epochs):\n",
-    "        model.train()\n",
-    "        for x_num_batch, x_cat_batch, y_batch in train_loader:\n",
-    "            x_num_batch, x_cat_batch, y_batch = (\n",
-    "                x_num_batch.to(device),\n",
-    "                x_cat_batch.to(device),\n",
-    "                y_batch.to(device)\n",
-    "            )\n",
-    "\n",
-    "            optimizer.zero_grad()\n",
-    "            y_pred = model(x_num_batch, x_cat_batch)  # 두 개의 입력 사용\n",
-    "            loss = criterion(y_pred, y_batch.unsqueeze(1))\n",
-    "            loss.backward()\n",
-    "            optimizer.step()\n",
-    "\n",
-    "    # Validation 평가\n",
-    "    model.eval()\n",
-    "    y_pred_val, y_true_val = [], []\n",
-    "    with torch.no_grad():\n",
-    "        for x_num_batch, x_cat_batch, y_batch in val_loader:\n",
-    "            x_num_batch, x_cat_batch, y_batch = (\n",
-    "                x_num_batch.to(device),\n",
-    "                x_cat_batch.to(device),\n",
-    "                y_batch.to(device)\n",
-    "            )\n",
-    "            output = model(x_num_batch, x_cat_batch)\n",
-    "            y_pred_val.extend((output.cpu().numpy() > 0.5).astype(int))\n",
-    "            y_true_val.extend(y_batch.cpu().numpy())\n",
-    "\n",
-    "    # f1-score 계산\n",
-    "    val_score = f1_score(y_true_val, y_pred_val)\n",
-    "    return val_score\n",
-    "\n",
-    "# Optuna 최적화 실행\n",
-    "sampler = optuna.samplers.TPESampler(seed=seed)\n",
-    "study = optuna.create_study(direction=\"maximize\", sampler=sampler)\n",
-    "study.optimize(objective, n_trials=50)  # 50번의 탐색 시도\n",
-    "\n",
-    "# 최적의 하이퍼파라미터 출력\n",
-    "best_params = study.best_trial.params\n",
-    "print(\"Best Hyperparameters:\", best_params)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 모델 학습 루프"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/50, Train Loss: 0.2099, Validation Loss: 0.1996, Validation F1-Score: 0.6706\n",
-      "Epoch 2/50, Train Loss: 0.1449, Validation Loss: 0.2015, Validation F1-Score: 0.6869\n",
-      "Epoch 3/50, Train Loss: 0.1205, Validation Loss: 0.1908, Validation F1-Score: 0.6717\n",
-      "Epoch 4/50, Train Loss: 0.1006, Validation Loss: 0.1875, Validation F1-Score: 0.6871\n",
-      "Epoch 5/50, Train Loss: 0.0901, Validation Loss: 0.1842, Validation F1-Score: 0.6961\n",
-      "Epoch 6/50, Train Loss: 0.0845, Validation Loss: 0.1994, Validation F1-Score: 0.6975\n",
-      "Epoch 7/50, Train Loss: 0.0778, Validation Loss: 0.1846, Validation F1-Score: 0.6903\n",
-      "Epoch 8/50, Train Loss: 0.0747, Validation Loss: 0.2222, Validation F1-Score: 0.6944\n",
-      "Epoch 9/50, Train Loss: 0.0697, Validation Loss: 0.2332, Validation F1-Score: 0.6693\n",
-      "Epoch 10/50, Train Loss: 0.0680, Validation Loss: 0.2238, Validation F1-Score: 0.6992\n",
-      "Epoch 11/50, Train Loss: 0.0630, Validation Loss: 0.2649, Validation F1-Score: 0.6868\n",
-      "Epoch 12/50, Train Loss: 0.0613, Validation Loss: 0.2403, Validation F1-Score: 0.6968\n",
-      "Epoch 13/50, Train Loss: 0.0565, Validation Loss: 0.2490, Validation F1-Score: 0.6834\n",
-      "Epoch 14/50, Train Loss: 0.0566, Validation Loss: 0.2333, Validation F1-Score: 0.6932\n",
-      "Epoch 15/50, Train Loss: 0.0530, Validation Loss: 0.2409, Validation F1-Score: 0.6930\n",
-      "Epoch 16/50, Train Loss: 0.0506, Validation Loss: 0.2452, Validation F1-Score: 0.6746\n",
-      "Epoch 17/50, Train Loss: 0.0487, Validation Loss: 0.2174, Validation F1-Score: 0.6831\n",
-      "Epoch 18/50, Train Loss: 0.0467, Validation Loss: 0.2583, Validation F1-Score: 0.7254\n",
-      "Epoch 19/50, Train Loss: 0.0452, Validation Loss: 0.2625, Validation F1-Score: 0.7012\n",
-      "Epoch 20/50, Train Loss: 0.0452, Validation Loss: 0.2315, Validation F1-Score: 0.7137\n",
-      "Epoch 21/50, Train Loss: 0.0428, Validation Loss: 0.2830, Validation F1-Score: 0.6999\n",
-      "Epoch 22/50, Train Loss: 0.0422, Validation Loss: 0.2739, Validation F1-Score: 0.6999\n",
-      "Epoch 23/50, Train Loss: 0.0401, Validation Loss: 0.2810, Validation F1-Score: 0.7142\n",
-      "Epoch 24/50, Train Loss: 0.0391, Validation Loss: 0.2781, Validation F1-Score: 0.7056\n",
-      "Epoch 25/50, Train Loss: 0.0373, Validation Loss: 0.2804, Validation F1-Score: 0.6988\n",
-      "Epoch 26/50, Train Loss: 0.0373, Validation Loss: 0.2887, Validation F1-Score: 0.7086\n",
-      "Early stopping at epoch 26\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 최적 하이퍼파라미터 적용\n",
-    "best_model = DeepGBM(\n",
-    "    num_features=len(numerical_cols),\n",
-    "    cat_features=[len(X_train[col].unique()) for col in categorical_cols],\n",
-    "    d_main=best_params[\"d_main\"],\n",
-    "    d_hidden=best_params[\"d_hidden\"],\n",
-    "    n_blocks=best_params[\"n_blocks\"],\n",
-    "    dropout=best_params[\"dropout\"]\n",
-    ").to(device)\n",
-    "\n",
-    "# 손실 함수 및 옵티마이저 설정\n",
-    "criterion = nn.BCELoss()\n",
-    "optimizer = optim.AdamW(best_model.parameters(), lr=best_params[\"lr\"], weight_decay=best_params[\"weight_decay\"])\n",
-    "\n",
-    "# Early Stopping 설정\n",
-    "best_val_f1 = -float(\"inf\")  # F1-Score 최대화 목표\n",
-    "patience = 8  # F1-Score가 개선되지 않는 Epoch 수\n",
-    "counter = 0\n",
-    "\n",
-    "# 학습 루프\n",
-    "epochs = 50  # 충분한 학습\n",
-    "for epoch in range(epochs):\n",
-    "    best_model.train()\n",
-    "    train_loss = 0\n",
-    "    for x_num_batch, x_cat_batch, y_batch in train_loader:\n",
-    "        x_num_batch, x_cat_batch, y_batch = (\n",
-    "            x_num_batch.to(device),\n",
-    "            x_cat_batch.to(device),\n",
-    "            y_batch.to(device),\n",
-    "        )\n",
-    "        optimizer.zero_grad()\n",
-    "        y_pred = best_model(x_num_batch, x_cat_batch)  # 두 개의 입력 사용\n",
-    "        loss = criterion(y_pred, y_batch.unsqueeze(1))\n",
-    "        loss.backward()\n",
-    "        optimizer.step()\n",
-    "        train_loss += loss.item()\n",
-    "\n",
-    "    # Validation 평가\n",
-    "    best_model.eval()\n",
-    "    val_loss = 0\n",
-    "    y_true_val, y_pred_val = [], []\n",
-    "    with torch.no_grad():\n",
-    "        for x_num_batch, x_cat_batch, y_batch in val_loader:\n",
-    "            x_num_batch, x_cat_batch, y_batch = (\n",
-    "                x_num_batch.to(device),\n",
-    "                x_cat_batch.to(device),\n",
-    "                y_batch.to(device),\n",
-    "            )\n",
-    "            y_pred = best_model(x_num_batch, x_cat_batch)\n",
-    "            val_loss += criterion(y_pred, y_batch.unsqueeze(1)).item()\n",
-    "            y_true_val.extend(y_batch.cpu().numpy())\n",
-    "            y_pred_val.extend((y_pred.cpu().numpy() > 0.5).astype(int))\n",
-    "\n",
-    "    # 평균 Loss 계산\n",
-    "    train_loss /= len(train_loader)\n",
-    "    val_loss /= len(val_loader)\n",
-    "\n",
-    "    # F1-Score 계산\n",
-    "    val_f1 = f1_score(y_true_val, y_pred_val)\n",
-    "\n",
-    "    print(\n",
-    "        f\"Epoch {epoch+1}/{epochs}, Train Loss: {train_loss:.4f}, \"\n",
-    "        f\"Validation Loss: {val_loss:.4f}, Validation F1-Score: {val_f1:.4f}\"\n",
-    "    )\n",
-    "\n",
-    "    # Early Stopping 체크\n",
-    "    if val_f1 > best_val_f1:\n",
-    "        best_val_f1 = val_f1\n",
-    "        counter = 0\n",
-    "        torch.save(best_model.state_dict(), \"best_deepgbm_model.pth\")  # 최적 모델 저장\n",
-    "    else:\n",
-    "        counter += 1\n",
-    "        if counter >= patience:\n",
-    "            print(f\"Early stopping at epoch {epoch+1}\")\n",
-    "            break"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 성능 평가"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test Accuracy: 0.8901826484018265\n",
-      "Test AUC: 0.8314176113850111\n",
-      "Test F1-Score: 0.440046565774156\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score\n",
-    "\n",
-    "# 저장된 최적 모델 불러오기\n",
-    "best_model.load_state_dict(torch.load(\"best_deepgbm_model.pth\", weights_only=True))\n",
-    "best_model.to(device)\n",
-    "best_model.eval()\n",
-    "\n",
-    "# Test 데이터 평가\n",
-    "y_pred_test, y_true_test = [], []\n",
-    "with torch.no_grad():\n",
-    "    for x_num_batch, x_cat_batch, y_batch in test_loader:\n",
-    "        x_num_batch, x_cat_batch, y_batch = (\n",
-    "            x_num_batch.to(device),\n",
-    "            x_cat_batch.to(device),\n",
-    "            y_batch.to(device),\n",
-    "        )\n",
-    "        output = best_model(x_num_batch, x_cat_batch)\n",
-    "        y_pred_test.extend((output.cpu().numpy() > 0.5).astype(int))\n",
-    "        y_true_test.extend(y_batch.cpu().numpy())\n",
-    "\n",
-    "# Test 성능 출력\n",
-    "print(\"Test Accuracy:\", accuracy_score(y_true_test, y_pred_test))\n",
-    "print(\"Test AUC:\", roc_auc_score(y_true_test, y_pred_test))\n",
-    "print(\"Test F1-Score:\", f1_score(y_true_test, y_pred_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## TabNet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting pytorch-tabnet\n",
-      "  Downloading pytorch_tabnet-4.1.0-py3-none-any.whl.metadata (15 kB)\n",
-      "Requirement already satisfied: numpy>=1.17 in /root/miniconda3/lib/python3.12/site-packages (from pytorch-tabnet) (1.26.4)\n",
-      "Requirement already satisfied: scikit_learn>0.21 in /root/miniconda3/lib/python3.12/site-packages (from pytorch-tabnet) (1.5.2)\n",
-      "Requirement already satisfied: scipy>1.4 in /root/miniconda3/lib/python3.12/site-packages (from pytorch-tabnet) (1.15.0)\n",
-      "Requirement already satisfied: torch>=1.3 in /root/miniconda3/lib/python3.12/site-packages (from pytorch-tabnet) (2.5.1)\n",
-      "Requirement already satisfied: tqdm>=4.36 in /root/miniconda3/lib/python3.12/site-packages (from pytorch-tabnet) (4.66.4)\n",
-      "Requirement already satisfied: joblib>=1.2.0 in /root/miniconda3/lib/python3.12/site-packages (from scikit_learn>0.21->pytorch-tabnet) (1.4.2)\n",
-      "Requirement already satisfied: threadpoolctl>=3.1.0 in /root/miniconda3/lib/python3.12/site-packages (from scikit_learn>0.21->pytorch-tabnet) (3.5.0)\n",
-      "Requirement already satisfied: filelock in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (3.16.1)\n",
-      "Requirement already satisfied: typing-extensions>=4.8.0 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (4.12.2)\n",
-      "Requirement already satisfied: networkx in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (3.4.1)\n",
-      "Requirement already satisfied: jinja2 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (3.1.4)\n",
-      "Requirement already satisfied: fsspec in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (2024.9.0)\n",
-      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (12.4.127)\n",
-      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (12.4.127)\n",
-      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (12.4.127)\n",
-      "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (9.1.0.70)\n",
-      "Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (12.4.5.8)\n",
-      "Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (11.2.1.3)\n",
-      "Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (10.3.5.147)\n",
-      "Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (11.6.1.9)\n",
-      "Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (12.3.1.170)\n",
-      "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (2.21.5)\n",
-      "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (12.4.127)\n",
-      "Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (12.4.127)\n",
-      "Requirement already satisfied: triton==3.1.0 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (3.1.0)\n",
-      "Requirement already satisfied: setuptools in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (69.5.1)\n",
-      "Requirement already satisfied: sympy==1.13.1 in /root/miniconda3/lib/python3.12/site-packages (from torch>=1.3->pytorch-tabnet) (1.13.1)\n",
-      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /root/miniconda3/lib/python3.12/site-packages (from sympy==1.13.1->torch>=1.3->pytorch-tabnet) (1.3.0)\n",
-      "Requirement already satisfied: MarkupSafe>=2.0 in /root/miniconda3/lib/python3.12/site-packages (from jinja2->torch>=1.3->pytorch-tabnet) (3.0.1)\n",
-      "Downloading pytorch_tabnet-4.1.0-py3-none-any.whl (44 kB)\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.5/44.5 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-      "\u001b[?25hInstalling collected packages: pytorch-tabnet\n",
-      "Successfully installed pytorch-tabnet-4.1.0\n",
-      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
-      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "'''%pip install pytorch-tabnet'''"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 데이터셋 준비"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
-    "import torch\n",
-    "import random\n",
-    "\n",
-    "# 시드 고정\n",
-    "seed = 42\n",
-    "random.seed(seed)\n",
-    "np.random.seed(seed)\n",
-    "torch.manual_seed(seed)\n",
-    "torch.cuda.manual_seed_all(seed)\n",
-    "\n",
-    "# 데이터셋 불러오기\n",
-    "X_train, X_val, X_test, y_train, y_val, y_test, categorical_cols, numerical_cols = prepare_dataset(\"seoul\", target = \"binary\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2025-02-02 17:45:02,478] A new study created in memory with name: no-name-30a8858a-054c-46b2-8eb6-150ef0380bd0\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.51162 | valid_accuracy: 0.6097  |  0:00:01s\n",
-      "epoch 1  | loss: 0.35934 | valid_accuracy: 0.80126 |  0:00:02s\n",
-      "epoch 2  | loss: 0.38031 | valid_accuracy: 0.88516 |  0:00:03s\n",
-      "epoch 3  | loss: 0.32778 | valid_accuracy: 0.83447 |  0:00:04s\n",
-      "epoch 4  | loss: 0.2564  | valid_accuracy: 0.86701 |  0:00:05s\n",
-      "epoch 5  | loss: 0.25241 | valid_accuracy: 0.8984  |  0:00:06s\n",
-      "epoch 6  | loss: 0.23158 | valid_accuracy: 0.90228 |  0:00:07s\n",
-      "epoch 7  | loss: 0.2121  | valid_accuracy: 0.89772 |  0:00:08s\n",
-      "epoch 8  | loss: 0.21777 | valid_accuracy: 0.9024  |  0:00:09s\n",
-      "epoch 9  | loss: 0.21399 | valid_accuracy: 0.89167 |  0:00:10s\n",
-      "epoch 10 | loss: 0.20104 | valid_accuracy: 0.91804 |  0:00:11s\n",
-      "epoch 11 | loss: 0.19229 | valid_accuracy: 0.91256 |  0:00:12s\n",
-      "epoch 12 | loss: 0.18519 | valid_accuracy: 0.90354 |  0:00:13s\n",
-      "epoch 13 | loss: 0.17756 | valid_accuracy: 0.90354 |  0:00:14s\n",
-      "epoch 14 | loss: 0.18859 | valid_accuracy: 0.90925 |  0:00:15s\n",
-      "epoch 15 | loss: 0.18694 | valid_accuracy: 0.90856 |  0:00:16s\n",
-      "epoch 16 | loss: 0.17478 | valid_accuracy: 0.91792 |  0:00:17s\n",
-      "epoch 17 | loss: 0.16563 | valid_accuracy: 0.92146 |  0:00:18s\n",
-      "epoch 18 | loss: 0.15951 | valid_accuracy: 0.90879 |  0:00:19s\n",
-      "epoch 19 | loss: 0.15968 | valid_accuracy: 0.90457 |  0:00:20s\n",
-      "epoch 20 | loss: 0.15753 | valid_accuracy: 0.91769 |  0:00:21s\n",
-      "epoch 21 | loss: 0.15174 | valid_accuracy: 0.91473 |  0:00:22s\n",
-      "epoch 22 | loss: 0.15183 | valid_accuracy: 0.92546 |  0:00:23s\n",
-      "epoch 23 | loss: 0.14962 | valid_accuracy: 0.90811 |  0:00:24s\n",
-      "epoch 24 | loss: 0.15239 | valid_accuracy: 0.91347 |  0:00:25s\n",
-      "epoch 25 | loss: 0.16776 | valid_accuracy: 0.91347 |  0:00:26s\n",
-      "epoch 26 | loss: 0.16609 | valid_accuracy: 0.92043 |  0:00:27s\n",
-      "epoch 27 | loss: 0.15284 | valid_accuracy: 0.92523 |  0:00:28s\n",
-      "epoch 28 | loss: 0.16267 | valid_accuracy: 0.92511 |  0:00:29s\n",
-      "epoch 29 | loss: 0.15016 | valid_accuracy: 0.92763 |  0:00:30s\n",
-      "Stop training because you reached max_epochs = 30 with best_epoch = 29 and best_valid_accuracy = 0.92763\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:45:33,592] Trial 0 finished with value: 0.6752049180327869 and parameters: {'n_d': 24, 'n_a': 64, 'n_steps': 8, 'gamma': 1.5986584841970366, 'lambda_sparse': 0.0002051338263087451, 'momentum': 0.07083786293111903}. Best is trial 0 with value: 0.6752049180327869.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.51572 | valid_accuracy: 0.7105  |  0:00:00s\n",
-      "epoch 1  | loss: 0.31607 | valid_accuracy: 0.8145  |  0:00:01s\n",
-      "epoch 2  | loss: 0.26815 | valid_accuracy: 0.85731 |  0:00:02s\n",
-      "epoch 3  | loss: 0.24246 | valid_accuracy: 0.8129  |  0:00:03s\n",
-      "epoch 4  | loss: 0.22795 | valid_accuracy: 0.88653 |  0:00:04s\n",
-      "epoch 5  | loss: 0.21193 | valid_accuracy: 0.88105 |  0:00:05s\n",
-      "epoch 6  | loss: 0.20544 | valid_accuracy: 0.91039 |  0:00:06s\n",
-      "epoch 7  | loss: 0.19844 | valid_accuracy: 0.89247 |  0:00:07s\n",
-      "epoch 8  | loss: 0.18712 | valid_accuracy: 0.91929 |  0:00:08s\n",
-      "epoch 9  | loss: 0.177   | valid_accuracy: 0.91427 |  0:00:09s\n",
-      "epoch 10 | loss: 0.17666 | valid_accuracy: 0.91438 |  0:00:09s\n",
-      "epoch 11 | loss: 0.17366 | valid_accuracy: 0.90982 |  0:00:10s\n",
-      "epoch 12 | loss: 0.17071 | valid_accuracy: 0.91324 |  0:00:11s\n",
-      "epoch 13 | loss: 0.18261 | valid_accuracy: 0.9137  |  0:00:12s\n",
-      "epoch 14 | loss: 0.17591 | valid_accuracy: 0.9266  |  0:00:13s\n",
-      "epoch 15 | loss: 0.16704 | valid_accuracy: 0.91872 |  0:00:14s\n",
-      "epoch 16 | loss: 0.16162 | valid_accuracy: 0.92317 |  0:00:15s\n",
-      "epoch 17 | loss: 0.16996 | valid_accuracy: 0.93105 |  0:00:16s\n",
-      "epoch 18 | loss: 0.17147 | valid_accuracy: 0.9234  |  0:00:17s\n",
-      "epoch 19 | loss: 0.15642 | valid_accuracy: 0.92626 |  0:00:17s\n",
-      "epoch 20 | loss: 0.14691 | valid_accuracy: 0.93242 |  0:00:18s\n",
-      "epoch 21 | loss: 0.15126 | valid_accuracy: 0.92934 |  0:00:19s\n",
-      "epoch 22 | loss: 0.14708 | valid_accuracy: 0.92831 |  0:00:20s\n",
-      "epoch 23 | loss: 0.13999 | valid_accuracy: 0.91758 |  0:00:21s\n",
-      "epoch 24 | loss: 0.13341 | valid_accuracy: 0.93139 |  0:00:22s\n",
-      "epoch 25 | loss: 0.12963 | valid_accuracy: 0.92957 |  0:00:23s\n",
-      "epoch 26 | loss: 0.129   | valid_accuracy: 0.93174 |  0:00:24s\n",
-      "epoch 27 | loss: 0.12625 | valid_accuracy: 0.9218  |  0:00:25s\n",
-      "epoch 28 | loss: 0.12765 | valid_accuracy: 0.93059 |  0:00:26s\n",
-      "\n",
-      "Early stopping occurred at epoch 28 with best_epoch = 20 and best_valid_accuracy = 0.93242\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:46:00,402] Trial 1 finished with value: 0.682062298603652 and parameters: {'n_d': 8, 'n_a': 56, 'n_steps': 7, 'gamma': 1.7080725777960455, 'lambda_sparse': 0.00010994335574766199, 'momentum': 0.3882648423431778}. Best is trial 1 with value: 0.682062298603652.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.41085 | valid_accuracy: 0.83607 |  0:00:00s\n",
-      "epoch 1  | loss: 0.20081 | valid_accuracy: 0.88893 |  0:00:01s\n",
-      "epoch 2  | loss: 0.14401 | valid_accuracy: 0.91039 |  0:00:01s\n",
-      "epoch 3  | loss: 0.12132 | valid_accuracy: 0.92454 |  0:00:02s\n",
-      "epoch 4  | loss: 0.11407 | valid_accuracy: 0.92945 |  0:00:03s\n",
-      "epoch 5  | loss: 0.10872 | valid_accuracy: 0.93653 |  0:00:03s\n",
-      "epoch 6  | loss: 0.10113 | valid_accuracy: 0.9403  |  0:00:04s\n",
-      "epoch 7  | loss: 0.10132 | valid_accuracy: 0.93596 |  0:00:04s\n",
-      "epoch 8  | loss: 0.09937 | valid_accuracy: 0.93607 |  0:00:05s\n",
-      "epoch 9  | loss: 0.09028 | valid_accuracy: 0.93219 |  0:00:06s\n",
-      "epoch 10 | loss: 0.08605 | valid_accuracy: 0.94326 |  0:00:06s\n",
-      "epoch 11 | loss: 0.07732 | valid_accuracy: 0.94292 |  0:00:07s\n",
-      "epoch 12 | loss: 0.07636 | valid_accuracy: 0.94132 |  0:00:07s\n",
-      "epoch 13 | loss: 0.07348 | valid_accuracy: 0.93607 |  0:00:08s\n",
-      "epoch 14 | loss: 0.06838 | valid_accuracy: 0.93779 |  0:00:09s\n",
-      "epoch 15 | loss: 0.06585 | valid_accuracy: 0.94829 |  0:00:09s\n",
-      "epoch 16 | loss: 0.06846 | valid_accuracy: 0.94178 |  0:00:10s\n",
-      "epoch 17 | loss: 0.06709 | valid_accuracy: 0.9468  |  0:00:11s\n",
-      "epoch 18 | loss: 0.06514 | valid_accuracy: 0.95194 |  0:00:11s\n",
-      "epoch 19 | loss: 0.06273 | valid_accuracy: 0.94304 |  0:00:12s\n",
-      "epoch 20 | loss: 0.06262 | valid_accuracy: 0.94406 |  0:00:12s\n",
-      "epoch 21 | loss: 0.06462 | valid_accuracy: 0.94646 |  0:00:13s\n",
-      "epoch 22 | loss: 0.0566  | valid_accuracy: 0.93607 |  0:00:14s\n",
-      "epoch 23 | loss: 0.05701 | valid_accuracy: 0.94098 |  0:00:14s\n",
-      "epoch 24 | loss: 0.05561 | valid_accuracy: 0.94167 |  0:00:15s\n",
-      "epoch 25 | loss: 0.05261 | valid_accuracy: 0.94326 |  0:00:15s\n",
-      "epoch 26 | loss: 0.05365 | valid_accuracy: 0.94441 |  0:00:16s\n",
-      "\n",
-      "Early stopping occurred at epoch 26 with best_epoch = 18 and best_valid_accuracy = 0.95194\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:46:17,235] Trial 2 finished with value: 0.7617430673457838 and parameters: {'n_d': 56, 'n_a': 16, 'n_steps': 4, 'gamma': 1.1834045098534338, 'lambda_sparse': 0.0004059611610484307, 'momentum': 0.21465500833657278}. Best is trial 2 with value: 0.7617430673457838.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.44898 | valid_accuracy: 0.81632 |  0:00:00s\n",
-      "epoch 1  | loss: 0.26007 | valid_accuracy: 0.84087 |  0:00:01s\n",
-      "epoch 2  | loss: 0.23179 | valid_accuracy: 0.88482 |  0:00:02s\n",
-      "epoch 3  | loss: 0.19977 | valid_accuracy: 0.92466 |  0:00:03s\n",
-      "epoch 4  | loss: 0.16338 | valid_accuracy: 0.90148 |  0:00:04s\n",
-      "epoch 5  | loss: 0.15101 | valid_accuracy: 0.92979 |  0:00:05s\n",
-      "epoch 6  | loss: 0.13924 | valid_accuracy: 0.93596 |  0:00:06s\n",
-      "epoch 7  | loss: 0.13496 | valid_accuracy: 0.93779 |  0:00:07s\n",
-      "epoch 8  | loss: 0.12218 | valid_accuracy: 0.93813 |  0:00:08s\n",
-      "epoch 9  | loss: 0.11479 | valid_accuracy: 0.93379 |  0:00:09s\n",
-      "epoch 10 | loss: 0.11308 | valid_accuracy: 0.94429 |  0:00:09s\n",
-      "epoch 11 | loss: 0.11463 | valid_accuracy: 0.94441 |  0:00:10s\n",
-      "epoch 12 | loss: 0.10708 | valid_accuracy: 0.94429 |  0:00:11s\n",
-      "epoch 13 | loss: 0.11045 | valid_accuracy: 0.94441 |  0:00:12s\n",
-      "epoch 14 | loss: 0.10571 | valid_accuracy: 0.94874 |  0:00:13s\n",
-      "epoch 15 | loss: 0.10262 | valid_accuracy: 0.94429 |  0:00:14s\n",
-      "epoch 16 | loss: 0.09813 | valid_accuracy: 0.94817 |  0:00:15s\n",
-      "epoch 17 | loss: 0.10055 | valid_accuracy: 0.94281 |  0:00:16s\n",
-      "epoch 18 | loss: 0.09636 | valid_accuracy: 0.94509 |  0:00:17s\n",
-      "epoch 19 | loss: 0.09546 | valid_accuracy: 0.93436 |  0:00:17s\n",
-      "epoch 20 | loss: 0.09955 | valid_accuracy: 0.94247 |  0:00:18s\n",
-      "epoch 21 | loss: 0.10707 | valid_accuracy: 0.93653 |  0:00:19s\n",
-      "epoch 22 | loss: 0.10258 | valid_accuracy: 0.94144 |  0:00:20s\n",
-      "\n",
-      "Early stopping occurred at epoch 22 with best_epoch = 14 and best_valid_accuracy = 0.94874\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:46:38,544] Trial 3 finished with value: 0.7623080995235575 and parameters: {'n_d': 32, 'n_a': 24, 'n_steps': 7, 'gamma': 1.139493860652042, 'lambda_sparse': 0.0003839629299804173, 'momentum': 0.15288111888453979}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.40326 | valid_accuracy: 0.78071 |  0:00:00s\n",
-      "epoch 1  | loss: 0.21984 | valid_accuracy: 0.89646 |  0:00:01s\n",
-      "epoch 2  | loss: 0.19744 | valid_accuracy: 0.89703 |  0:00:01s\n",
-      "epoch 3  | loss: 0.17036 | valid_accuracy: 0.93801 |  0:00:02s\n",
-      "epoch 4  | loss: 0.1596  | valid_accuracy: 0.92774 |  0:00:03s\n",
-      "epoch 5  | loss: 0.14734 | valid_accuracy: 0.94053 |  0:00:03s\n",
-      "epoch 6  | loss: 0.1261  | valid_accuracy: 0.90765 |  0:00:04s\n",
-      "epoch 7  | loss: 0.13111 | valid_accuracy: 0.94361 |  0:00:04s\n",
-      "epoch 8  | loss: 0.12254 | valid_accuracy: 0.93185 |  0:00:05s\n",
-      "epoch 9  | loss: 0.11484 | valid_accuracy: 0.93893 |  0:00:06s\n",
-      "epoch 10 | loss: 0.12082 | valid_accuracy: 0.92146 |  0:00:06s\n",
-      "epoch 11 | loss: 0.11907 | valid_accuracy: 0.91347 |  0:00:07s\n",
-      "epoch 12 | loss: 0.10386 | valid_accuracy: 0.93904 |  0:00:07s\n",
-      "epoch 13 | loss: 0.09116 | valid_accuracy: 0.94521 |  0:00:08s\n",
-      "epoch 14 | loss: 0.08679 | valid_accuracy: 0.93596 |  0:00:09s\n",
-      "epoch 15 | loss: 0.0809  | valid_accuracy: 0.9363  |  0:00:09s\n",
-      "epoch 16 | loss: 0.07893 | valid_accuracy: 0.93573 |  0:00:10s\n",
-      "epoch 17 | loss: 0.07731 | valid_accuracy: 0.93984 |  0:00:10s\n",
-      "epoch 18 | loss: 0.07423 | valid_accuracy: 0.93744 |  0:00:11s\n",
-      "epoch 19 | loss: 0.07371 | valid_accuracy: 0.93151 |  0:00:12s\n",
-      "epoch 20 | loss: 0.06917 | valid_accuracy: 0.94053 |  0:00:12s\n",
-      "epoch 21 | loss: 0.06548 | valid_accuracy: 0.93219 |  0:00:13s\n",
-      "\n",
-      "Early stopping occurred at epoch 21 with best_epoch = 13 and best_valid_accuracy = 0.94521\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:46:52,266] Trial 4 finished with value: 0.7433155080213903 and parameters: {'n_d': 32, 'n_a': 56, 'n_steps': 4, 'gamma': 1.5142344384136117, 'lambda_sparse': 0.0015304852121831463, 'momentum': 0.028115660960799115}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.34256 | valid_accuracy: 0.83813 |  0:00:00s\n",
-      "epoch 1  | loss: 0.19041 | valid_accuracy: 0.92409 |  0:00:00s\n",
-      "epoch 2  | loss: 0.15125 | valid_accuracy: 0.91781 |  0:00:01s\n",
-      "epoch 3  | loss: 0.13014 | valid_accuracy: 0.92911 |  0:00:01s\n",
-      "epoch 4  | loss: 0.11999 | valid_accuracy: 0.93059 |  0:00:02s\n",
-      "epoch 5  | loss: 0.10766 | valid_accuracy: 0.92763 |  0:00:03s\n",
-      "epoch 6  | loss: 0.09502 | valid_accuracy: 0.92934 |  0:00:03s\n",
-      "epoch 7  | loss: 0.09133 | valid_accuracy: 0.93482 |  0:00:04s\n",
-      "epoch 8  | loss: 0.08396 | valid_accuracy: 0.93345 |  0:00:04s\n",
-      "epoch 9  | loss: 0.08269 | valid_accuracy: 0.9274  |  0:00:04s\n",
-      "epoch 10 | loss: 0.08525 | valid_accuracy: 0.93447 |  0:00:05s\n",
-      "epoch 11 | loss: 0.07883 | valid_accuracy: 0.9363  |  0:00:05s\n",
-      "epoch 12 | loss: 0.07277 | valid_accuracy: 0.94258 |  0:00:06s\n",
-      "epoch 13 | loss: 0.06871 | valid_accuracy: 0.94292 |  0:00:06s\n",
-      "epoch 14 | loss: 0.06777 | valid_accuracy: 0.93756 |  0:00:07s\n",
-      "epoch 15 | loss: 0.06583 | valid_accuracy: 0.9363  |  0:00:07s\n",
-      "epoch 16 | loss: 0.06456 | valid_accuracy: 0.94155 |  0:00:08s\n",
-      "epoch 17 | loss: 0.06711 | valid_accuracy: 0.94155 |  0:00:08s\n",
-      "epoch 18 | loss: 0.06725 | valid_accuracy: 0.93824 |  0:00:09s\n",
-      "epoch 19 | loss: 0.06817 | valid_accuracy: 0.93539 |  0:00:09s\n",
-      "epoch 20 | loss: 0.06471 | valid_accuracy: 0.94224 |  0:00:10s\n",
-      "epoch 21 | loss: 0.06237 | valid_accuracy: 0.94167 |  0:00:10s\n",
-      "\n",
-      "Early stopping occurred at epoch 21 with best_epoch = 13 and best_valid_accuracy = 0.94292\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:47:03,493] Trial 5 finished with value: 0.6940024479804161 and parameters: {'n_d': 40, 'n_a': 16, 'n_steps': 3, 'gamma': 1.9488855372533331, 'lambda_sparse': 0.00853618986286683, 'momentum': 0.32527496576541987}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.54642 | valid_accuracy: 0.73162 |  0:00:00s\n",
-      "epoch 1  | loss: 0.34982 | valid_accuracy: 0.775   |  0:00:01s\n",
-      "epoch 2  | loss: 0.33705 | valid_accuracy: 0.82009 |  0:00:02s\n",
-      "epoch 3  | loss: 0.36378 | valid_accuracy: 0.84658 |  0:00:03s\n",
-      "epoch 4  | loss: 0.26389 | valid_accuracy: 0.87466 |  0:00:04s\n",
-      "epoch 5  | loss: 0.26708 | valid_accuracy: 0.8879  |  0:00:05s\n",
-      "epoch 6  | loss: 0.2209  | valid_accuracy: 0.91564 |  0:00:06s\n",
-      "epoch 7  | loss: 0.20343 | valid_accuracy: 0.90183 |  0:00:07s\n",
-      "epoch 8  | loss: 0.20446 | valid_accuracy: 0.90457 |  0:00:08s\n",
-      "epoch 9  | loss: 0.18992 | valid_accuracy: 0.91724 |  0:00:09s\n",
-      "epoch 10 | loss: 0.1826  | valid_accuracy: 0.9226  |  0:00:10s\n",
-      "epoch 11 | loss: 0.18397 | valid_accuracy: 0.92546 |  0:00:11s\n",
-      "epoch 12 | loss: 0.16944 | valid_accuracy: 0.92203 |  0:00:12s\n",
-      "epoch 13 | loss: 0.16622 | valid_accuracy: 0.92477 |  0:00:13s\n",
-      "epoch 14 | loss: 0.15952 | valid_accuracy: 0.92306 |  0:00:14s\n",
-      "epoch 15 | loss: 0.15906 | valid_accuracy: 0.92717 |  0:00:15s\n",
-      "epoch 16 | loss: 0.15773 | valid_accuracy: 0.90457 |  0:00:16s\n",
-      "epoch 17 | loss: 0.15543 | valid_accuracy: 0.92386 |  0:00:17s\n",
-      "epoch 18 | loss: 0.15167 | valid_accuracy: 0.92534 |  0:00:18s\n",
-      "epoch 19 | loss: 0.14806 | valid_accuracy: 0.92306 |  0:00:19s\n",
-      "epoch 20 | loss: 0.14644 | valid_accuracy: 0.92717 |  0:00:20s\n",
-      "epoch 21 | loss: 0.14616 | valid_accuracy: 0.93014 |  0:00:21s\n",
-      "epoch 22 | loss: 0.1509  | valid_accuracy: 0.93082 |  0:00:22s\n",
-      "epoch 23 | loss: 0.15154 | valid_accuracy: 0.91792 |  0:00:23s\n",
-      "epoch 24 | loss: 0.14669 | valid_accuracy: 0.92511 |  0:00:24s\n",
-      "epoch 25 | loss: 0.1441  | valid_accuracy: 0.92591 |  0:00:25s\n",
-      "epoch 26 | loss: 0.14843 | valid_accuracy: 0.92934 |  0:00:26s\n",
-      "epoch 27 | loss: 0.15011 | valid_accuracy: 0.91861 |  0:00:27s\n",
-      "epoch 28 | loss: 0.16114 | valid_accuracy: 0.92854 |  0:00:28s\n",
-      "epoch 29 | loss: 0.15804 | valid_accuracy: 0.92728 |  0:00:29s\n",
-      "Stop training because you reached max_epochs = 30 with best_epoch = 22 and best_valid_accuracy = 0.93082\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:47:34,042] Trial 6 finished with value: 0.6926977687626775 and parameters: {'n_d': 24, 'n_a': 8, 'n_steps': 8, 'gamma': 1.4401524937396013, 'lambda_sparse': 0.00017541893487450815, 'momentum': 0.2031189949433954}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.48696 | valid_accuracy: 0.68527 |  0:00:00s\n",
-      "epoch 1  | loss: 0.29365 | valid_accuracy: 0.82511 |  0:00:01s\n",
-      "epoch 2  | loss: 0.25789 | valid_accuracy: 0.86975 |  0:00:02s\n",
-      "epoch 3  | loss: 0.20216 | valid_accuracy: 0.8992  |  0:00:02s\n",
-      "epoch 4  | loss: 0.1853  | valid_accuracy: 0.92511 |  0:00:03s\n",
-      "epoch 5  | loss: 0.17174 | valid_accuracy: 0.91199 |  0:00:04s\n",
-      "epoch 6  | loss: 0.16428 | valid_accuracy: 0.92386 |  0:00:05s\n",
-      "epoch 7  | loss: 0.14458 | valid_accuracy: 0.92466 |  0:00:05s\n",
-      "epoch 8  | loss: 0.13132 | valid_accuracy: 0.93379 |  0:00:06s\n",
-      "epoch 9  | loss: 0.12207 | valid_accuracy: 0.92534 |  0:00:07s\n",
-      "epoch 10 | loss: 0.12534 | valid_accuracy: 0.92021 |  0:00:07s\n",
-      "epoch 11 | loss: 0.11432 | valid_accuracy: 0.92295 |  0:00:08s\n",
-      "epoch 12 | loss: 0.1261  | valid_accuracy: 0.9274  |  0:00:09s\n",
-      "epoch 13 | loss: 0.11539 | valid_accuracy: 0.92432 |  0:00:09s\n",
-      "epoch 14 | loss: 0.10864 | valid_accuracy: 0.93402 |  0:00:10s\n",
-      "epoch 15 | loss: 0.10136 | valid_accuracy: 0.93733 |  0:00:11s\n",
-      "epoch 16 | loss: 0.09272 | valid_accuracy: 0.9379  |  0:00:12s\n",
-      "epoch 17 | loss: 0.09546 | valid_accuracy: 0.93847 |  0:00:12s\n",
-      "epoch 18 | loss: 0.10111 | valid_accuracy: 0.9339  |  0:00:13s\n",
-      "epoch 19 | loss: 0.09387 | valid_accuracy: 0.93071 |  0:00:14s\n",
-      "epoch 20 | loss: 0.09709 | valid_accuracy: 0.93573 |  0:00:15s\n",
-      "epoch 21 | loss: 0.09374 | valid_accuracy: 0.92717 |  0:00:15s\n",
-      "epoch 22 | loss: 0.09057 | valid_accuracy: 0.93185 |  0:00:16s\n",
-      "epoch 23 | loss: 0.09584 | valid_accuracy: 0.91849 |  0:00:17s\n",
-      "epoch 24 | loss: 0.10278 | valid_accuracy: 0.93425 |  0:00:17s\n",
-      "epoch 25 | loss: 0.08888 | valid_accuracy: 0.93276 |  0:00:18s\n",
-      "\n",
-      "Early stopping occurred at epoch 25 with best_epoch = 17 and best_valid_accuracy = 0.93847\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:47:53,089] Trial 7 finished with value: 0.6980392156862745 and parameters: {'n_d': 8, 'n_a': 64, 'n_steps': 5, 'gamma': 1.662522284353982, 'lambda_sparse': 0.0004201672054372534, 'momentum': 0.21282652825934623}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.95852 | valid_accuracy: 0.55479 |  0:00:01s\n",
-      "epoch 1  | loss: 0.44115 | valid_accuracy: 0.49543 |  0:00:02s\n",
-      "epoch 2  | loss: 0.42561 | valid_accuracy: 0.80183 |  0:00:03s\n",
-      "epoch 3  | loss: 0.53733 | valid_accuracy: 0.74589 |  0:00:04s\n",
-      "epoch 4  | loss: 0.31668 | valid_accuracy: 0.86473 |  0:00:06s\n",
-      "epoch 5  | loss: 0.22462 | valid_accuracy: 0.84155 |  0:00:07s\n",
-      "epoch 6  | loss: 0.22422 | valid_accuracy: 0.88379 |  0:00:08s\n",
-      "epoch 7  | loss: 0.19977 | valid_accuracy: 0.88619 |  0:00:09s\n",
-      "epoch 8  | loss: 0.20833 | valid_accuracy: 0.82386 |  0:00:10s\n",
-      "epoch 9  | loss: 0.20295 | valid_accuracy: 0.89646 |  0:00:12s\n",
-      "epoch 10 | loss: 0.17829 | valid_accuracy: 0.89555 |  0:00:13s\n",
-      "epoch 11 | loss: 0.18023 | valid_accuracy: 0.92374 |  0:00:14s\n",
-      "epoch 12 | loss: 0.17958 | valid_accuracy: 0.89441 |  0:00:15s\n",
-      "epoch 13 | loss: 0.17927 | valid_accuracy: 0.89737 |  0:00:16s\n",
-      "epoch 14 | loss: 0.17239 | valid_accuracy: 0.92078 |  0:00:18s\n",
-      "epoch 15 | loss: 0.15836 | valid_accuracy: 0.91176 |  0:00:19s\n",
-      "epoch 16 | loss: 0.16379 | valid_accuracy: 0.93002 |  0:00:20s\n",
-      "epoch 17 | loss: 0.15064 | valid_accuracy: 0.92352 |  0:00:21s\n",
-      "epoch 18 | loss: 0.15415 | valid_accuracy: 0.9113  |  0:00:22s\n",
-      "epoch 19 | loss: 0.15037 | valid_accuracy: 0.92705 |  0:00:24s\n",
-      "epoch 20 | loss: 0.146   | valid_accuracy: 0.91735 |  0:00:25s\n",
-      "epoch 21 | loss: 0.14947 | valid_accuracy: 0.92637 |  0:00:26s\n",
-      "epoch 22 | loss: 0.14708 | valid_accuracy: 0.92066 |  0:00:27s\n",
-      "epoch 23 | loss: 0.15086 | valid_accuracy: 0.92386 |  0:00:29s\n",
-      "epoch 24 | loss: 0.13848 | valid_accuracy: 0.92774 |  0:00:30s\n",
-      "\n",
-      "Early stopping occurred at epoch 24 with best_epoch = 16 and best_valid_accuracy = 0.93002\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:48:24,135] Trial 8 finished with value: 0.6751457339692634 and parameters: {'n_d': 40, 'n_a': 16, 'n_steps': 10, 'gamma': 1.7751328233611146, 'lambda_sparse': 0.007568292060167618, 'momentum': 0.3589826666667831}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.2977  | valid_accuracy: 0.8282  |  0:00:00s\n",
-      "epoch 1  | loss: 0.13881 | valid_accuracy: 0.91792 |  0:00:01s\n",
-      "epoch 2  | loss: 0.12205 | valid_accuracy: 0.92854 |  0:00:01s\n",
-      "epoch 3  | loss: 0.10262 | valid_accuracy: 0.93196 |  0:00:02s\n",
-      "epoch 4  | loss: 0.09549 | valid_accuracy: 0.93824 |  0:00:02s\n",
-      "epoch 5  | loss: 0.0872  | valid_accuracy: 0.93413 |  0:00:03s\n",
-      "epoch 6  | loss: 0.08352 | valid_accuracy: 0.9387  |  0:00:03s\n",
-      "epoch 7  | loss: 0.07734 | valid_accuracy: 0.93916 |  0:00:04s\n",
-      "epoch 8  | loss: 0.07214 | valid_accuracy: 0.94441 |  0:00:04s\n",
-      "epoch 9  | loss: 0.07138 | valid_accuracy: 0.94623 |  0:00:05s\n",
-      "epoch 10 | loss: 0.0698  | valid_accuracy: 0.93984 |  0:00:05s\n",
-      "epoch 11 | loss: 0.06457 | valid_accuracy: 0.94007 |  0:00:06s\n",
-      "epoch 12 | loss: 0.0611  | valid_accuracy: 0.94144 |  0:00:06s\n",
-      "epoch 13 | loss: 0.06191 | valid_accuracy: 0.94429 |  0:00:07s\n",
-      "epoch 14 | loss: 0.06013 | valid_accuracy: 0.94178 |  0:00:07s\n",
-      "epoch 15 | loss: 0.05827 | valid_accuracy: 0.94212 |  0:00:08s\n",
-      "epoch 16 | loss: 0.05663 | valid_accuracy: 0.93573 |  0:00:08s\n",
-      "epoch 17 | loss: 0.05921 | valid_accuracy: 0.94075 |  0:00:09s\n",
-      "\n",
-      "Early stopping occurred at epoch 17 with best_epoch = 9 and best_valid_accuracy = 0.94623\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:48:33,712] Trial 9 finished with value: 0.729776247848537 and parameters: {'n_d': 40, 'n_a': 64, 'n_steps': 3, 'gamma': 1.1959828624191453, 'lambda_sparse': 0.00012315571723666037, 'momentum': 0.13687882899767312}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.64705 | valid_accuracy: 0.76416 |  0:00:01s\n",
-      "epoch 1  | loss: 0.29235 | valid_accuracy: 0.88938 |  0:00:02s\n",
-      "epoch 2  | loss: 0.17376 | valid_accuracy: 0.89977 |  0:00:03s\n",
-      "epoch 3  | loss: 0.13063 | valid_accuracy: 0.92146 |  0:00:04s\n",
-      "epoch 4  | loss: 0.12107 | valid_accuracy: 0.92922 |  0:00:06s\n",
-      "epoch 5  | loss: 0.1248  | valid_accuracy: 0.92374 |  0:00:07s\n",
-      "epoch 6  | loss: 0.10799 | valid_accuracy: 0.93916 |  0:00:08s\n",
-      "epoch 7  | loss: 0.10338 | valid_accuracy: 0.9226  |  0:00:09s\n",
-      "epoch 8  | loss: 0.11006 | valid_accuracy: 0.94372 |  0:00:10s\n",
-      "epoch 9  | loss: 0.09386 | valid_accuracy: 0.93893 |  0:00:12s\n",
-      "epoch 10 | loss: 0.09263 | valid_accuracy: 0.9347  |  0:00:13s\n",
-      "epoch 11 | loss: 0.09154 | valid_accuracy: 0.9403  |  0:00:14s\n",
-      "epoch 12 | loss: 0.09004 | valid_accuracy: 0.93824 |  0:00:15s\n",
-      "epoch 13 | loss: 0.08856 | valid_accuracy: 0.93733 |  0:00:17s\n",
-      "epoch 14 | loss: 0.09239 | valid_accuracy: 0.93539 |  0:00:18s\n",
-      "epoch 15 | loss: 0.08891 | valid_accuracy: 0.92751 |  0:00:19s\n",
-      "epoch 16 | loss: 0.08882 | valid_accuracy: 0.93596 |  0:00:20s\n",
-      "\n",
-      "Early stopping occurred at epoch 16 with best_epoch = 8 and best_valid_accuracy = 0.94372\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:48:55,252] Trial 10 finished with value: 0.7336574824419233 and parameters: {'n_d': 64, 'n_a': 32, 'n_steps': 10, 'gamma': 1.0179618756148194, 'lambda_sparse': 0.0015695029402008024, 'momentum': 0.2791963586919666}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.50981 | valid_accuracy: 0.76267 |  0:00:00s\n",
-      "epoch 1  | loss: 0.24604 | valid_accuracy: 0.8508  |  0:00:01s\n",
-      "epoch 2  | loss: 0.22524 | valid_accuracy: 0.87432 |  0:00:02s\n",
-      "epoch 3  | loss: 0.19477 | valid_accuracy: 0.904   |  0:00:03s\n",
-      "epoch 4  | loss: 0.17533 | valid_accuracy: 0.9274  |  0:00:03s\n",
-      "epoch 5  | loss: 0.15164 | valid_accuracy: 0.9339  |  0:00:04s\n",
-      "epoch 6  | loss: 0.14632 | valid_accuracy: 0.92443 |  0:00:05s\n",
-      "epoch 7  | loss: 0.15291 | valid_accuracy: 0.92968 |  0:00:06s\n",
-      "epoch 8  | loss: 0.13765 | valid_accuracy: 0.93505 |  0:00:07s\n",
-      "epoch 9  | loss: 0.13135 | valid_accuracy: 0.93242 |  0:00:07s\n",
-      "epoch 10 | loss: 0.1237  | valid_accuracy: 0.94007 |  0:00:08s\n",
-      "epoch 11 | loss: 0.12006 | valid_accuracy: 0.93333 |  0:00:09s\n",
-      "epoch 12 | loss: 0.11845 | valid_accuracy: 0.93664 |  0:00:10s\n",
-      "epoch 13 | loss: 0.12189 | valid_accuracy: 0.93607 |  0:00:11s\n",
-      "epoch 14 | loss: 0.11624 | valid_accuracy: 0.93345 |  0:00:12s\n",
-      "epoch 15 | loss: 0.11998 | valid_accuracy: 0.9355  |  0:00:12s\n",
-      "epoch 16 | loss: 0.12215 | valid_accuracy: 0.93311 |  0:00:13s\n",
-      "epoch 17 | loss: 0.1115  | valid_accuracy: 0.93231 |  0:00:14s\n",
-      "epoch 18 | loss: 0.11118 | valid_accuracy: 0.92888 |  0:00:15s\n",
-      "\n",
-      "Early stopping occurred at epoch 18 with best_epoch = 10 and best_valid_accuracy = 0.94007\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:49:11,078] Trial 11 finished with value: 0.714828897338403 and parameters: {'n_d': 64, 'n_a': 32, 'n_steps': 6, 'gamma': 1.2765899964395564, 'lambda_sparse': 0.0005836712223868827, 'momentum': 0.16881777729239383}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.54443 | valid_accuracy: 0.68653 |  0:00:00s\n",
-      "epoch 1  | loss: 0.19888 | valid_accuracy: 0.92934 |  0:00:01s\n",
-      "epoch 2  | loss: 0.15859 | valid_accuracy: 0.92763 |  0:00:02s\n",
-      "epoch 3  | loss: 0.14323 | valid_accuracy: 0.91747 |  0:00:03s\n",
-      "epoch 4  | loss: 0.13449 | valid_accuracy: 0.93539 |  0:00:03s\n",
-      "epoch 5  | loss: 0.11473 | valid_accuracy: 0.93219 |  0:00:04s\n",
-      "epoch 6  | loss: 0.11382 | valid_accuracy: 0.93687 |  0:00:05s\n",
-      "epoch 7  | loss: 0.10431 | valid_accuracy: 0.93676 |  0:00:06s\n",
-      "epoch 8  | loss: 0.0963  | valid_accuracy: 0.93927 |  0:00:07s\n",
-      "epoch 9  | loss: 0.09182 | valid_accuracy: 0.93744 |  0:00:08s\n",
-      "epoch 10 | loss: 0.09092 | valid_accuracy: 0.93094 |  0:00:08s\n",
-      "epoch 11 | loss: 0.09416 | valid_accuracy: 0.93527 |  0:00:09s\n",
-      "epoch 12 | loss: 0.08802 | valid_accuracy: 0.93676 |  0:00:10s\n",
-      "epoch 13 | loss: 0.08599 | valid_accuracy: 0.93676 |  0:00:11s\n",
-      "epoch 14 | loss: 0.0855  | valid_accuracy: 0.93607 |  0:00:12s\n",
-      "epoch 15 | loss: 0.08633 | valid_accuracy: 0.93128 |  0:00:12s\n",
-      "epoch 16 | loss: 0.08624 | valid_accuracy: 0.93904 |  0:00:13s\n",
-      "\n",
-      "Early stopping occurred at epoch 16 with best_epoch = 8 and best_valid_accuracy = 0.93927\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:49:25,330] Trial 12 finished with value: 0.6885245901639344 and parameters: {'n_d': 56, 'n_a': 24, 'n_steps': 6, 'gamma': 1.017145473653846, 'lambda_sparse': 0.0004630932602208763, 'momentum': 0.12821396325505197}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.39021 | valid_accuracy: 0.8605  |  0:00:00s\n",
-      "epoch 1  | loss: 0.20744 | valid_accuracy: 0.90434 |  0:00:01s\n",
-      "epoch 2  | loss: 0.16345 | valid_accuracy: 0.91587 |  0:00:02s\n",
-      "epoch 3  | loss: 0.14347 | valid_accuracy: 0.93059 |  0:00:02s\n",
-      "epoch 4  | loss: 0.13227 | valid_accuracy: 0.92534 |  0:00:03s\n",
-      "epoch 5  | loss: 0.1338  | valid_accuracy: 0.92477 |  0:00:04s\n",
-      "epoch 6  | loss: 0.13381 | valid_accuracy: 0.93059 |  0:00:05s\n",
-      "epoch 7  | loss: 0.13119 | valid_accuracy: 0.92705 |  0:00:05s\n",
-      "epoch 8  | loss: 0.12643 | valid_accuracy: 0.9218  |  0:00:06s\n",
-      "epoch 9  | loss: 0.11881 | valid_accuracy: 0.93219 |  0:00:07s\n",
-      "epoch 10 | loss: 0.11992 | valid_accuracy: 0.93847 |  0:00:07s\n",
-      "epoch 11 | loss: 0.11171 | valid_accuracy: 0.93174 |  0:00:08s\n",
-      "epoch 12 | loss: 0.10676 | valid_accuracy: 0.93824 |  0:00:09s\n",
-      "epoch 13 | loss: 0.10288 | valid_accuracy: 0.93847 |  0:00:09s\n",
-      "epoch 14 | loss: 0.10689 | valid_accuracy: 0.94315 |  0:00:10s\n",
-      "epoch 15 | loss: 0.10905 | valid_accuracy: 0.93002 |  0:00:11s\n",
-      "epoch 16 | loss: 0.1084  | valid_accuracy: 0.93345 |  0:00:12s\n",
-      "epoch 17 | loss: 0.10403 | valid_accuracy: 0.93699 |  0:00:12s\n",
-      "epoch 18 | loss: 0.10135 | valid_accuracy: 0.9379  |  0:00:13s\n",
-      "epoch 19 | loss: 0.09833 | valid_accuracy: 0.93174 |  0:00:14s\n",
-      "epoch 20 | loss: 0.097   | valid_accuracy: 0.93779 |  0:00:14s\n",
-      "epoch 21 | loss: 0.09527 | valid_accuracy: 0.94007 |  0:00:15s\n",
-      "epoch 22 | loss: 0.09524 | valid_accuracy: 0.93733 |  0:00:16s\n",
-      "\n",
-      "Early stopping occurred at epoch 22 with best_epoch = 14 and best_valid_accuracy = 0.94315\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:49:42,178] Trial 13 finished with value: 0.7233333333333334 and parameters: {'n_d': 48, 'n_a': 40, 'n_steps': 5, 'gamma': 1.24080793750091, 'lambda_sparse': 0.0008657857585898337, 'momentum': 0.25667540149110746}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.64359 | valid_accuracy: 0.75297 |  0:00:00s\n",
-      "epoch 1  | loss: 0.29294 | valid_accuracy: 0.75856 |  0:00:01s\n",
-      "epoch 2  | loss: 0.28205 | valid_accuracy: 0.83973 |  0:00:02s\n",
-      "epoch 3  | loss: 0.25178 | valid_accuracy: 0.89749 |  0:00:03s\n",
-      "epoch 4  | loss: 0.23547 | valid_accuracy: 0.9105  |  0:00:04s\n",
-      "epoch 5  | loss: 0.21306 | valid_accuracy: 0.88995 |  0:00:05s\n",
-      "epoch 6  | loss: 0.18191 | valid_accuracy: 0.91644 |  0:00:06s\n",
-      "epoch 7  | loss: 0.17079 | valid_accuracy: 0.92158 |  0:00:07s\n",
-      "epoch 8  | loss: 0.1593  | valid_accuracy: 0.92066 |  0:00:08s\n",
-      "epoch 9  | loss: 0.15135 | valid_accuracy: 0.92705 |  0:00:09s\n",
-      "epoch 10 | loss: 0.14464 | valid_accuracy: 0.93379 |  0:00:11s\n",
-      "epoch 11 | loss: 0.14458 | valid_accuracy: 0.9347  |  0:00:12s\n",
-      "epoch 12 | loss: 0.14334 | valid_accuracy: 0.93242 |  0:00:13s\n",
-      "epoch 13 | loss: 0.14632 | valid_accuracy: 0.92854 |  0:00:14s\n",
-      "epoch 14 | loss: 0.13872 | valid_accuracy: 0.93014 |  0:00:14s\n",
-      "epoch 15 | loss: 0.13632 | valid_accuracy: 0.92774 |  0:00:15s\n",
-      "epoch 16 | loss: 0.1355  | valid_accuracy: 0.92466 |  0:00:16s\n",
-      "epoch 17 | loss: 0.12837 | valid_accuracy: 0.92043 |  0:00:18s\n",
-      "epoch 18 | loss: 0.13153 | valid_accuracy: 0.92272 |  0:00:19s\n",
-      "epoch 19 | loss: 0.138   | valid_accuracy: 0.92568 |  0:00:20s\n",
-      "\n",
-      "Early stopping occurred at epoch 19 with best_epoch = 11 and best_valid_accuracy = 0.9347\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:50:02,919] Trial 14 finished with value: 0.7204301075268817 and parameters: {'n_d': 24, 'n_a': 8, 'n_steps': 8, 'gamma': 1.3427992882663162, 'lambda_sparse': 0.00030304348224437444, 'momentum': 0.08946331230660341}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.3892  | valid_accuracy: 0.6855  |  0:00:00s\n",
-      "epoch 1  | loss: 0.16781 | valid_accuracy: 0.90982 |  0:00:01s\n",
-      "epoch 2  | loss: 0.13605 | valid_accuracy: 0.92409 |  0:00:02s\n",
-      "epoch 3  | loss: 0.12093 | valid_accuracy: 0.92021 |  0:00:02s\n",
-      "epoch 4  | loss: 0.11758 | valid_accuracy: 0.92979 |  0:00:03s\n",
-      "epoch 5  | loss: 0.10738 | valid_accuracy: 0.93276 |  0:00:04s\n",
-      "epoch 6  | loss: 0.10967 | valid_accuracy: 0.93139 |  0:00:05s\n",
-      "epoch 7  | loss: 0.10541 | valid_accuracy: 0.93676 |  0:00:05s\n",
-      "epoch 8  | loss: 0.09431 | valid_accuracy: 0.94064 |  0:00:06s\n",
-      "epoch 9  | loss: 0.09774 | valid_accuracy: 0.94269 |  0:00:07s\n",
-      "epoch 10 | loss: 0.08963 | valid_accuracy: 0.94521 |  0:00:07s\n",
-      "epoch 11 | loss: 0.0905  | valid_accuracy: 0.94212 |  0:00:08s\n",
-      "epoch 12 | loss: 0.08551 | valid_accuracy: 0.94543 |  0:00:09s\n",
-      "epoch 13 | loss: 0.08468 | valid_accuracy: 0.94258 |  0:00:10s\n",
-      "epoch 14 | loss: 0.07902 | valid_accuracy: 0.94463 |  0:00:10s\n",
-      "epoch 15 | loss: 0.08157 | valid_accuracy: 0.94258 |  0:00:11s\n",
-      "epoch 16 | loss: 0.07662 | valid_accuracy: 0.94874 |  0:00:12s\n",
-      "epoch 17 | loss: 0.07576 | valid_accuracy: 0.94281 |  0:00:12s\n",
-      "epoch 18 | loss: 0.07322 | valid_accuracy: 0.946   |  0:00:13s\n",
-      "epoch 19 | loss: 0.07388 | valid_accuracy: 0.94132 |  0:00:14s\n",
-      "epoch 20 | loss: 0.07447 | valid_accuracy: 0.94155 |  0:00:15s\n",
-      "epoch 21 | loss: 0.07069 | valid_accuracy: 0.93927 |  0:00:15s\n",
-      "epoch 22 | loss: 0.06996 | valid_accuracy: 0.94372 |  0:00:16s\n",
-      "epoch 23 | loss: 0.07028 | valid_accuracy: 0.94463 |  0:00:17s\n",
-      "epoch 24 | loss: 0.07007 | valid_accuracy: 0.94406 |  0:00:17s\n",
-      "\n",
-      "Early stopping occurred at epoch 24 with best_epoch = 16 and best_valid_accuracy = 0.94874\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:50:21,362] Trial 15 finished with value: 0.7595072308516336 and parameters: {'n_d': 56, 'n_a': 24, 'n_steps': 5, 'gamma': 1.1232319960378998, 'lambda_sparse': 0.0023793537960598537, 'momentum': 0.2415450242787598}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.52921 | valid_accuracy: 0.646   |  0:00:00s\n",
-      "epoch 1  | loss: 0.30409 | valid_accuracy: 0.75308 |  0:00:01s\n",
-      "epoch 2  | loss: 0.24622 | valid_accuracy: 0.85925 |  0:00:02s\n",
-      "epoch 3  | loss: 0.24997 | valid_accuracy: 0.92089 |  0:00:03s\n",
-      "epoch 4  | loss: 0.2286  | valid_accuracy: 0.92089 |  0:00:04s\n",
-      "epoch 5  | loss: 0.20017 | valid_accuracy: 0.92317 |  0:00:05s\n",
-      "epoch 6  | loss: 0.17618 | valid_accuracy: 0.9145  |  0:00:06s\n",
-      "epoch 7  | loss: 0.16827 | valid_accuracy: 0.92237 |  0:00:07s\n",
-      "epoch 8  | loss: 0.16989 | valid_accuracy: 0.91221 |  0:00:08s\n",
-      "epoch 9  | loss: 0.16782 | valid_accuracy: 0.92934 |  0:00:09s\n",
-      "epoch 10 | loss: 0.16692 | valid_accuracy: 0.92945 |  0:00:09s\n",
-      "epoch 11 | loss: 0.1592  | valid_accuracy: 0.93025 |  0:00:10s\n",
-      "epoch 12 | loss: 0.15833 | valid_accuracy: 0.93208 |  0:00:11s\n",
-      "epoch 13 | loss: 0.15862 | valid_accuracy: 0.93037 |  0:00:12s\n",
-      "epoch 14 | loss: 0.15143 | valid_accuracy: 0.93105 |  0:00:13s\n",
-      "epoch 15 | loss: 0.15252 | valid_accuracy: 0.93562 |  0:00:14s\n",
-      "epoch 16 | loss: 0.14167 | valid_accuracy: 0.92922 |  0:00:15s\n",
-      "epoch 17 | loss: 0.14061 | valid_accuracy: 0.92945 |  0:00:16s\n",
-      "epoch 18 | loss: 0.13331 | valid_accuracy: 0.93299 |  0:00:17s\n",
-      "epoch 19 | loss: 0.12791 | valid_accuracy: 0.94326 |  0:00:18s\n",
-      "epoch 20 | loss: 0.12528 | valid_accuracy: 0.93744 |  0:00:18s\n",
-      "epoch 21 | loss: 0.12422 | valid_accuracy: 0.93539 |  0:00:19s\n",
-      "epoch 22 | loss: 0.12001 | valid_accuracy: 0.93413 |  0:00:20s\n",
-      "epoch 23 | loss: 0.1228  | valid_accuracy: 0.9403  |  0:00:21s\n",
-      "epoch 24 | loss: 0.1167  | valid_accuracy: 0.94292 |  0:00:22s\n",
-      "epoch 25 | loss: 0.11684 | valid_accuracy: 0.94247 |  0:00:23s\n",
-      "epoch 26 | loss: 0.11316 | valid_accuracy: 0.94463 |  0:00:24s\n",
-      "epoch 27 | loss: 0.10914 | valid_accuracy: 0.93847 |  0:00:25s\n",
-      "epoch 28 | loss: 0.10715 | valid_accuracy: 0.93916 |  0:00:26s\n",
-      "epoch 29 | loss: 0.10973 | valid_accuracy: 0.93619 |  0:00:27s\n",
-      "Stop training because you reached max_epochs = 30 with best_epoch = 26 and best_valid_accuracy = 0.94463\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:50:49,085] Trial 16 finished with value: 0.723961297666477 and parameters: {'n_d': 48, 'n_a': 40, 'n_steps': 7, 'gamma': 1.3942734108004724, 'lambda_sparse': 0.0009265347442301014, 'momentum': 0.30312923567815764}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.37941 | valid_accuracy: 0.86963 |  0:00:00s\n",
-      "epoch 1  | loss: 0.18481 | valid_accuracy: 0.92477 |  0:00:01s\n",
-      "epoch 2  | loss: 0.14904 | valid_accuracy: 0.92968 |  0:00:01s\n",
-      "epoch 3  | loss: 0.13069 | valid_accuracy: 0.91963 |  0:00:02s\n",
-      "epoch 4  | loss: 0.11683 | valid_accuracy: 0.93196 |  0:00:03s\n",
-      "epoch 5  | loss: 0.10633 | valid_accuracy: 0.93288 |  0:00:03s\n",
-      "epoch 6  | loss: 0.09759 | valid_accuracy: 0.94144 |  0:00:04s\n",
-      "epoch 7  | loss: 0.09288 | valid_accuracy: 0.93242 |  0:00:04s\n",
-      "epoch 8  | loss: 0.08709 | valid_accuracy: 0.93858 |  0:00:05s\n",
-      "epoch 9  | loss: 0.08528 | valid_accuracy: 0.94132 |  0:00:06s\n",
-      "epoch 10 | loss: 0.08487 | valid_accuracy: 0.93801 |  0:00:06s\n",
-      "epoch 11 | loss: 0.0881  | valid_accuracy: 0.9339  |  0:00:07s\n",
-      "epoch 12 | loss: 0.08164 | valid_accuracy: 0.93721 |  0:00:07s\n",
-      "epoch 13 | loss: 0.07956 | valid_accuracy: 0.94235 |  0:00:08s\n",
-      "epoch 14 | loss: 0.07609 | valid_accuracy: 0.94692 |  0:00:09s\n",
-      "epoch 15 | loss: 0.07549 | valid_accuracy: 0.94578 |  0:00:09s\n",
-      "epoch 16 | loss: 0.06854 | valid_accuracy: 0.9411  |  0:00:10s\n",
-      "epoch 17 | loss: 0.06796 | valid_accuracy: 0.93938 |  0:00:10s\n",
-      "epoch 18 | loss: 0.06769 | valid_accuracy: 0.93858 |  0:00:11s\n",
-      "epoch 19 | loss: 0.0698  | valid_accuracy: 0.94189 |  0:00:12s\n",
-      "epoch 20 | loss: 0.06964 | valid_accuracy: 0.94543 |  0:00:12s\n",
-      "epoch 21 | loss: 0.06632 | valid_accuracy: 0.93059 |  0:00:13s\n",
-      "epoch 22 | loss: 0.06611 | valid_accuracy: 0.93961 |  0:00:13s\n",
-      "\n",
-      "Early stopping occurred at epoch 22 with best_epoch = 14 and best_valid_accuracy = 0.94692\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:51:03,377] Trial 17 finished with value: 0.7446457990115322 and parameters: {'n_d': 32, 'n_a': 24, 'n_steps': 4, 'gamma': 1.1229176751032102, 'lambda_sparse': 0.003743309561243855, 'momentum': 0.1774269738540521}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.624   | valid_accuracy: 0.69977 |  0:00:01s\n",
-      "epoch 1  | loss: 0.27796 | valid_accuracy: 0.81005 |  0:00:02s\n",
-      "epoch 2  | loss: 0.2435  | valid_accuracy: 0.9113  |  0:00:03s\n",
-      "epoch 3  | loss: 0.22003 | valid_accuracy: 0.9153  |  0:00:04s\n",
-      "epoch 4  | loss: 0.21892 | valid_accuracy: 0.91906 |  0:00:05s\n",
-      "epoch 5  | loss: 0.20274 | valid_accuracy: 0.90479 |  0:00:06s\n",
-      "epoch 6  | loss: 0.17367 | valid_accuracy: 0.91781 |  0:00:07s\n",
-      "epoch 7  | loss: 0.16973 | valid_accuracy: 0.91062 |  0:00:08s\n",
-      "epoch 8  | loss: 0.16077 | valid_accuracy: 0.9234  |  0:00:10s\n",
-      "epoch 9  | loss: 0.14759 | valid_accuracy: 0.93105 |  0:00:11s\n",
-      "epoch 10 | loss: 0.14162 | valid_accuracy: 0.93151 |  0:00:12s\n",
-      "epoch 11 | loss: 0.14227 | valid_accuracy: 0.93505 |  0:00:13s\n",
-      "epoch 12 | loss: 0.13655 | valid_accuracy: 0.93653 |  0:00:14s\n",
-      "epoch 13 | loss: 0.13343 | valid_accuracy: 0.93231 |  0:00:15s\n",
-      "epoch 14 | loss: 0.13448 | valid_accuracy: 0.92454 |  0:00:16s\n",
-      "epoch 15 | loss: 0.12931 | valid_accuracy: 0.93333 |  0:00:17s\n",
-      "epoch 16 | loss: 0.12407 | valid_accuracy: 0.9411  |  0:00:18s\n",
-      "epoch 17 | loss: 0.12118 | valid_accuracy: 0.93413 |  0:00:19s\n",
-      "epoch 18 | loss: 0.12116 | valid_accuracy: 0.92979 |  0:00:21s\n",
-      "epoch 19 | loss: 0.12082 | valid_accuracy: 0.93505 |  0:00:22s\n",
-      "epoch 20 | loss: 0.12163 | valid_accuracy: 0.93539 |  0:00:23s\n",
-      "epoch 21 | loss: 0.11732 | valid_accuracy: 0.93756 |  0:00:24s\n",
-      "epoch 22 | loss: 0.11412 | valid_accuracy: 0.93573 |  0:00:25s\n",
-      "epoch 23 | loss: 0.10798 | valid_accuracy: 0.92945 |  0:00:26s\n",
-      "epoch 24 | loss: 0.10756 | valid_accuracy: 0.94395 |  0:00:27s\n",
-      "epoch 25 | loss: 0.10692 | valid_accuracy: 0.94212 |  0:00:28s\n",
-      "epoch 26 | loss: 0.10502 | valid_accuracy: 0.93425 |  0:00:29s\n",
-      "epoch 27 | loss: 0.1035  | valid_accuracy: 0.94155 |  0:00:30s\n",
-      "epoch 28 | loss: 0.10731 | valid_accuracy: 0.94247 |  0:00:31s\n",
-      "epoch 29 | loss: 0.10359 | valid_accuracy: 0.93824 |  0:00:33s\n",
-      "Stop training because you reached max_epochs = 30 with best_epoch = 24 and best_valid_accuracy = 0.94395\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:51:37,278] Trial 18 finished with value: 0.7384123601491742 and parameters: {'n_d': 16, 'n_a': 16, 'n_steps': 9, 'gamma': 1.1342940581443615, 'lambda_sparse': 0.0003186419211415119, 'momentum': 0.12004229695216845}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.35191 | valid_accuracy: 0.7661  |  0:00:00s\n",
-      "epoch 1  | loss: 0.22988 | valid_accuracy: 0.88995 |  0:00:01s\n",
-      "epoch 2  | loss: 0.19959 | valid_accuracy: 0.91279 |  0:00:01s\n",
-      "epoch 3  | loss: 0.17566 | valid_accuracy: 0.92203 |  0:00:02s\n",
-      "epoch 4  | loss: 0.14589 | valid_accuracy: 0.9274  |  0:00:03s\n",
-      "epoch 5  | loss: 0.12829 | valid_accuracy: 0.93881 |  0:00:03s\n",
-      "epoch 6  | loss: 0.11731 | valid_accuracy: 0.93699 |  0:00:04s\n",
-      "epoch 7  | loss: 0.11036 | valid_accuracy: 0.93927 |  0:00:04s\n",
-      "epoch 8  | loss: 0.11062 | valid_accuracy: 0.93858 |  0:00:05s\n",
-      "epoch 9  | loss: 0.09907 | valid_accuracy: 0.9403  |  0:00:06s\n",
-      "epoch 10 | loss: 0.09521 | valid_accuracy: 0.94486 |  0:00:06s\n",
-      "epoch 11 | loss: 0.08668 | valid_accuracy: 0.94304 |  0:00:07s\n",
-      "epoch 12 | loss: 0.08419 | valid_accuracy: 0.93744 |  0:00:07s\n",
-      "epoch 13 | loss: 0.07734 | valid_accuracy: 0.94018 |  0:00:08s\n",
-      "epoch 14 | loss: 0.0732  | valid_accuracy: 0.93779 |  0:00:09s\n",
-      "epoch 15 | loss: 0.07284 | valid_accuracy: 0.93368 |  0:00:09s\n",
-      "epoch 16 | loss: 0.07146 | valid_accuracy: 0.93995 |  0:00:10s\n",
-      "epoch 17 | loss: 0.07099 | valid_accuracy: 0.94018 |  0:00:10s\n",
-      "epoch 18 | loss: 0.06689 | valid_accuracy: 0.94064 |  0:00:11s\n",
-      "\n",
-      "Early stopping occurred at epoch 18 with best_epoch = 10 and best_valid_accuracy = 0.94486\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:51:49,153] Trial 19 finished with value: 0.7410187667560322 and parameters: {'n_d': 48, 'n_a': 48, 'n_steps': 4, 'gamma': 1.313378794087976, 'lambda_sparse': 0.0006934858536024428, 'momentum': 0.030467246352504904}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.45767 | valid_accuracy: 0.78322 |  0:00:00s\n",
-      "epoch 1  | loss: 0.18894 | valid_accuracy: 0.86495 |  0:00:01s\n",
-      "epoch 2  | loss: 0.14092 | valid_accuracy: 0.93185 |  0:00:02s\n",
-      "epoch 3  | loss: 0.12462 | valid_accuracy: 0.93527 |  0:00:03s\n",
-      "epoch 4  | loss: 0.11243 | valid_accuracy: 0.92637 |  0:00:03s\n",
-      "epoch 5  | loss: 0.09825 | valid_accuracy: 0.92557 |  0:00:04s\n",
-      "epoch 6  | loss: 0.09195 | valid_accuracy: 0.93916 |  0:00:05s\n",
-      "epoch 7  | loss: 0.08764 | valid_accuracy: 0.93779 |  0:00:06s\n",
-      "epoch 8  | loss: 0.0824  | valid_accuracy: 0.94007 |  0:00:07s\n",
-      "epoch 9  | loss: 0.08075 | valid_accuracy: 0.93653 |  0:00:08s\n",
-      "epoch 10 | loss: 0.0789  | valid_accuracy: 0.93779 |  0:00:08s\n",
-      "epoch 11 | loss: 0.08072 | valid_accuracy: 0.94098 |  0:00:09s\n",
-      "epoch 12 | loss: 0.0745  | valid_accuracy: 0.94189 |  0:00:10s\n",
-      "epoch 13 | loss: 0.07463 | valid_accuracy: 0.94258 |  0:00:11s\n",
-      "epoch 14 | loss: 0.07059 | valid_accuracy: 0.94201 |  0:00:12s\n",
-      "epoch 15 | loss: 0.07092 | valid_accuracy: 0.94121 |  0:00:12s\n",
-      "epoch 16 | loss: 0.06723 | valid_accuracy: 0.93916 |  0:00:13s\n",
-      "epoch 17 | loss: 0.06698 | valid_accuracy: 0.94155 |  0:00:14s\n",
-      "epoch 18 | loss: 0.06673 | valid_accuracy: 0.94372 |  0:00:15s\n",
-      "epoch 19 | loss: 0.06547 | valid_accuracy: 0.94132 |  0:00:16s\n",
-      "epoch 20 | loss: 0.06276 | valid_accuracy: 0.93801 |  0:00:16s\n",
-      "epoch 21 | loss: 0.06269 | valid_accuracy: 0.94098 |  0:00:17s\n",
-      "epoch 22 | loss: 0.05956 | valid_accuracy: 0.94281 |  0:00:18s\n",
-      "epoch 23 | loss: 0.05975 | valid_accuracy: 0.94475 |  0:00:19s\n",
-      "epoch 24 | loss: 0.0656  | valid_accuracy: 0.93904 |  0:00:20s\n",
-      "epoch 25 | loss: 0.0627  | valid_accuracy: 0.94315 |  0:00:20s\n",
-      "epoch 26 | loss: 0.06008 | valid_accuracy: 0.94452 |  0:00:21s\n",
-      "epoch 27 | loss: 0.06152 | valid_accuracy: 0.9411  |  0:00:22s\n",
-      "epoch 28 | loss: 0.0625  | valid_accuracy: 0.93756 |  0:00:23s\n",
-      "epoch 29 | loss: 0.05881 | valid_accuracy: 0.94361 |  0:00:24s\n",
-      "Stop training because you reached max_epochs = 30 with best_epoch = 23 and best_valid_accuracy = 0.94475\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:52:13,807] Trial 20 finished with value: 0.7259343148357871 and parameters: {'n_d': 56, 'n_a': 8, 'n_steps': 6, 'gamma': 1.010033289129168, 'lambda_sparse': 0.00024163285968083325, 'momentum': 0.21395506234129033}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.40211 | valid_accuracy: 0.76473 |  0:00:00s\n",
-      "epoch 1  | loss: 0.18057 | valid_accuracy: 0.92203 |  0:00:01s\n",
-      "epoch 2  | loss: 0.15386 | valid_accuracy: 0.9121  |  0:00:02s\n",
-      "epoch 3  | loss: 0.14088 | valid_accuracy: 0.9226  |  0:00:02s\n",
-      "epoch 4  | loss: 0.13926 | valid_accuracy: 0.93082 |  0:00:03s\n",
-      "epoch 5  | loss: 0.12709 | valid_accuracy: 0.93025 |  0:00:04s\n",
-      "epoch 6  | loss: 0.12436 | valid_accuracy: 0.93573 |  0:00:05s\n",
-      "epoch 7  | loss: 0.11683 | valid_accuracy: 0.93276 |  0:00:05s\n",
-      "epoch 8  | loss: 0.10997 | valid_accuracy: 0.93733 |  0:00:06s\n",
-      "epoch 9  | loss: 0.10217 | valid_accuracy: 0.93539 |  0:00:07s\n",
-      "epoch 10 | loss: 0.09985 | valid_accuracy: 0.92991 |  0:00:07s\n",
-      "epoch 11 | loss: 0.09574 | valid_accuracy: 0.93596 |  0:00:08s\n",
-      "epoch 12 | loss: 0.09224 | valid_accuracy: 0.93813 |  0:00:09s\n",
-      "epoch 13 | loss: 0.08921 | valid_accuracy: 0.93094 |  0:00:10s\n",
-      "epoch 14 | loss: 0.08516 | valid_accuracy: 0.93082 |  0:00:10s\n",
-      "epoch 15 | loss: 0.08748 | valid_accuracy: 0.93813 |  0:00:11s\n",
-      "epoch 16 | loss: 0.08493 | valid_accuracy: 0.93345 |  0:00:12s\n",
-      "epoch 17 | loss: 0.08119 | valid_accuracy: 0.93493 |  0:00:12s\n",
-      "epoch 18 | loss: 0.0818  | valid_accuracy: 0.93824 |  0:00:13s\n",
-      "epoch 19 | loss: 0.07903 | valid_accuracy: 0.93744 |  0:00:14s\n",
-      "epoch 20 | loss: 0.07899 | valid_accuracy: 0.93037 |  0:00:14s\n",
-      "epoch 21 | loss: 0.07789 | valid_accuracy: 0.93756 |  0:00:15s\n",
-      "epoch 22 | loss: 0.07829 | valid_accuracy: 0.93813 |  0:00:16s\n",
-      "epoch 23 | loss: 0.07602 | valid_accuracy: 0.93539 |  0:00:17s\n",
-      "epoch 24 | loss: 0.07435 | valid_accuracy: 0.93493 |  0:00:17s\n",
-      "epoch 25 | loss: 0.07385 | valid_accuracy: 0.93904 |  0:00:18s\n",
-      "epoch 26 | loss: 0.07141 | valid_accuracy: 0.94155 |  0:00:19s\n",
-      "epoch 27 | loss: 0.06869 | valid_accuracy: 0.93847 |  0:00:20s\n",
-      "epoch 28 | loss: 0.06624 | valid_accuracy: 0.93938 |  0:00:20s\n",
-      "epoch 29 | loss: 0.067   | valid_accuracy: 0.93174 |  0:00:21s\n",
-      "Stop training because you reached max_epochs = 30 with best_epoch = 26 and best_valid_accuracy = 0.94155\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:52:35,793] Trial 21 finished with value: 0.7229437229437229 and parameters: {'n_d': 56, 'n_a': 24, 'n_steps': 5, 'gamma': 1.1633563297268914, 'lambda_sparse': 0.0032084615724975586, 'momentum': 0.2449184141264009}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.41115 | valid_accuracy: 0.72078 |  0:00:00s\n",
-      "epoch 1  | loss: 0.17945 | valid_accuracy: 0.88881 |  0:00:01s\n",
-      "epoch 2  | loss: 0.14713 | valid_accuracy: 0.91324 |  0:00:02s\n",
-      "epoch 3  | loss: 0.13612 | valid_accuracy: 0.92352 |  0:00:02s\n",
-      "epoch 4  | loss: 0.12366 | valid_accuracy: 0.93242 |  0:00:03s\n",
-      "epoch 5  | loss: 0.1203  | valid_accuracy: 0.92979 |  0:00:04s\n",
-      "epoch 6  | loss: 0.11271 | valid_accuracy: 0.93265 |  0:00:04s\n",
-      "epoch 7  | loss: 0.10493 | valid_accuracy: 0.93447 |  0:00:05s\n",
-      "epoch 8  | loss: 0.10303 | valid_accuracy: 0.9371  |  0:00:06s\n",
-      "epoch 9  | loss: 0.10188 | valid_accuracy: 0.93927 |  0:00:07s\n",
-      "epoch 10 | loss: 0.10548 | valid_accuracy: 0.93653 |  0:00:07s\n",
-      "epoch 11 | loss: 0.09854 | valid_accuracy: 0.93721 |  0:00:08s\n",
-      "epoch 12 | loss: 0.09401 | valid_accuracy: 0.93756 |  0:00:09s\n",
-      "epoch 13 | loss: 0.09029 | valid_accuracy: 0.94189 |  0:00:09s\n",
-      "epoch 14 | loss: 0.0828  | valid_accuracy: 0.9411  |  0:00:10s\n",
-      "epoch 15 | loss: 0.08584 | valid_accuracy: 0.93916 |  0:00:11s\n",
-      "epoch 16 | loss: 0.08464 | valid_accuracy: 0.94247 |  0:00:12s\n",
-      "epoch 17 | loss: 0.08248 | valid_accuracy: 0.93527 |  0:00:12s\n",
-      "epoch 18 | loss: 0.08326 | valid_accuracy: 0.94247 |  0:00:13s\n",
-      "epoch 19 | loss: 0.07868 | valid_accuracy: 0.94247 |  0:00:14s\n",
-      "epoch 20 | loss: 0.08013 | valid_accuracy: 0.94349 |  0:00:15s\n",
-      "epoch 21 | loss: 0.07806 | valid_accuracy: 0.94053 |  0:00:15s\n",
-      "epoch 22 | loss: 0.07372 | valid_accuracy: 0.94064 |  0:00:16s\n",
-      "epoch 23 | loss: 0.0723  | valid_accuracy: 0.94132 |  0:00:17s\n",
-      "epoch 24 | loss: 0.07422 | valid_accuracy: 0.93733 |  0:00:17s\n",
-      "epoch 25 | loss: 0.08092 | valid_accuracy: 0.92854 |  0:00:18s\n",
-      "epoch 26 | loss: 0.07504 | valid_accuracy: 0.94018 |  0:00:19s\n",
-      "epoch 27 | loss: 0.07517 | valid_accuracy: 0.94167 |  0:00:20s\n",
-      "epoch 28 | loss: 0.06732 | valid_accuracy: 0.93938 |  0:00:20s\n",
-      "\n",
-      "Early stopping occurred at epoch 28 with best_epoch = 20 and best_valid_accuracy = 0.94349\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:52:57,022] Trial 22 finished with value: 0.7173043974871502 and parameters: {'n_d': 64, 'n_a': 24, 'n_steps': 5, 'gamma': 1.0961922879933907, 'lambda_sparse': 0.0016155377070117766, 'momentum': 0.17094765515429317}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.41821 | valid_accuracy: 0.84372 |  0:00:00s\n",
-      "epoch 1  | loss: 0.20584 | valid_accuracy: 0.87728 |  0:00:01s\n",
-      "epoch 2  | loss: 0.16455 | valid_accuracy: 0.92226 |  0:00:01s\n",
-      "epoch 3  | loss: 0.13399 | valid_accuracy: 0.92466 |  0:00:02s\n",
-      "epoch 4  | loss: 0.11364 | valid_accuracy: 0.93276 |  0:00:03s\n",
-      "epoch 5  | loss: 0.10375 | valid_accuracy: 0.93584 |  0:00:03s\n",
-      "epoch 6  | loss: 0.10204 | valid_accuracy: 0.93345 |  0:00:04s\n",
-      "epoch 7  | loss: 0.09306 | valid_accuracy: 0.92717 |  0:00:04s\n",
-      "epoch 8  | loss: 0.09096 | valid_accuracy: 0.93368 |  0:00:05s\n",
-      "epoch 9  | loss: 0.09034 | valid_accuracy: 0.93676 |  0:00:06s\n",
-      "epoch 10 | loss: 0.08428 | valid_accuracy: 0.93231 |  0:00:06s\n",
-      "epoch 11 | loss: 0.08575 | valid_accuracy: 0.94269 |  0:00:07s\n",
-      "epoch 12 | loss: 0.0817  | valid_accuracy: 0.94189 |  0:00:07s\n",
-      "epoch 13 | loss: 0.07896 | valid_accuracy: 0.93676 |  0:00:08s\n",
-      "epoch 14 | loss: 0.07372 | valid_accuracy: 0.9395  |  0:00:09s\n",
-      "epoch 15 | loss: 0.07335 | valid_accuracy: 0.93813 |  0:00:09s\n",
-      "epoch 16 | loss: 0.07094 | valid_accuracy: 0.93436 |  0:00:10s\n",
-      "epoch 17 | loss: 0.06639 | valid_accuracy: 0.92808 |  0:00:10s\n",
-      "epoch 18 | loss: 0.06913 | valid_accuracy: 0.93425 |  0:00:11s\n",
-      "epoch 19 | loss: 0.07044 | valid_accuracy: 0.93333 |  0:00:12s\n",
-      "\n",
-      "Early stopping occurred at epoch 19 with best_epoch = 11 and best_valid_accuracy = 0.94269\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:53:09,553] Trial 23 finished with value: 0.732409381663113 and parameters: {'n_d': 48, 'n_a': 32, 'n_steps': 4, 'gamma': 1.242298444344926, 'lambda_sparse': 0.002445878111790842, 'momentum': 0.24208438843370517}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.4941  | valid_accuracy: 0.69349 |  0:00:00s\n",
-      "epoch 1  | loss: 0.22395 | valid_accuracy: 0.88105 |  0:00:01s\n",
-      "epoch 2  | loss: 0.18918 | valid_accuracy: 0.90365 |  0:00:02s\n",
-      "epoch 3  | loss: 0.15504 | valid_accuracy: 0.91655 |  0:00:03s\n",
-      "epoch 4  | loss: 0.14261 | valid_accuracy: 0.92785 |  0:00:04s\n",
-      "epoch 5  | loss: 0.14548 | valid_accuracy: 0.92763 |  0:00:05s\n",
-      "epoch 6  | loss: 0.12783 | valid_accuracy: 0.93082 |  0:00:06s\n",
-      "epoch 7  | loss: 0.12422 | valid_accuracy: 0.92614 |  0:00:07s\n",
-      "epoch 8  | loss: 0.11942 | valid_accuracy: 0.94007 |  0:00:08s\n",
-      "epoch 9  | loss: 0.11568 | valid_accuracy: 0.94018 |  0:00:09s\n",
-      "epoch 10 | loss: 0.10884 | valid_accuracy: 0.93687 |  0:00:09s\n",
-      "epoch 11 | loss: 0.1049  | valid_accuracy: 0.93345 |  0:00:10s\n",
-      "epoch 12 | loss: 0.1031  | valid_accuracy: 0.93128 |  0:00:11s\n",
-      "epoch 13 | loss: 0.09914 | valid_accuracy: 0.93607 |  0:00:12s\n",
-      "epoch 14 | loss: 0.10053 | valid_accuracy: 0.94201 |  0:00:13s\n",
-      "epoch 15 | loss: 0.09411 | valid_accuracy: 0.93836 |  0:00:14s\n",
-      "epoch 16 | loss: 0.09745 | valid_accuracy: 0.93493 |  0:00:15s\n",
-      "epoch 17 | loss: 0.09518 | valid_accuracy: 0.93938 |  0:00:16s\n",
-      "epoch 18 | loss: 0.09151 | valid_accuracy: 0.93881 |  0:00:17s\n",
-      "epoch 19 | loss: 0.08873 | valid_accuracy: 0.9371  |  0:00:18s\n",
-      "epoch 20 | loss: 0.08906 | valid_accuracy: 0.92979 |  0:00:18s\n",
-      "epoch 21 | loss: 0.08373 | valid_accuracy: 0.93733 |  0:00:19s\n",
-      "epoch 22 | loss: 0.0792  | valid_accuracy: 0.93562 |  0:00:20s\n",
-      "\n",
-      "Early stopping occurred at epoch 22 with best_epoch = 14 and best_valid_accuracy = 0.94201\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:53:30,887] Trial 24 finished with value: 0.7180910099889012 and parameters: {'n_d': 56, 'n_a': 16, 'n_steps': 7, 'gamma': 1.0929277549886702, 'lambda_sparse': 0.005229229230684199, 'momentum': 0.28408237254426966}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.34691 | valid_accuracy: 0.82911 |  0:00:00s\n",
-      "epoch 1  | loss: 0.18238 | valid_accuracy: 0.88413 |  0:00:00s\n",
-      "epoch 2  | loss: 0.14372 | valid_accuracy: 0.91416 |  0:00:01s\n",
-      "epoch 3  | loss: 0.12771 | valid_accuracy: 0.93185 |  0:00:02s\n",
-      "epoch 4  | loss: 0.11084 | valid_accuracy: 0.93128 |  0:00:02s\n",
-      "epoch 5  | loss: 0.0963  | valid_accuracy: 0.93539 |  0:00:03s\n",
-      "epoch 6  | loss: 0.09497 | valid_accuracy: 0.93379 |  0:00:03s\n",
-      "epoch 7  | loss: 0.08821 | valid_accuracy: 0.92751 |  0:00:04s\n",
-      "epoch 8  | loss: 0.08247 | valid_accuracy: 0.9371  |  0:00:04s\n",
-      "epoch 9  | loss: 0.08307 | valid_accuracy: 0.93425 |  0:00:05s\n",
-      "epoch 10 | loss: 0.07629 | valid_accuracy: 0.93311 |  0:00:05s\n",
-      "epoch 11 | loss: 0.06965 | valid_accuracy: 0.93196 |  0:00:06s\n",
-      "epoch 12 | loss: 0.0691  | valid_accuracy: 0.93219 |  0:00:06s\n",
-      "epoch 13 | loss: 0.0746  | valid_accuracy: 0.94247 |  0:00:07s\n",
-      "epoch 14 | loss: 0.06728 | valid_accuracy: 0.92877 |  0:00:07s\n",
-      "epoch 15 | loss: 0.06603 | valid_accuracy: 0.93744 |  0:00:08s\n",
-      "epoch 16 | loss: 0.06397 | valid_accuracy: 0.93584 |  0:00:08s\n",
-      "epoch 17 | loss: 0.0613  | valid_accuracy: 0.9282  |  0:00:09s\n",
-      "epoch 18 | loss: 0.06263 | valid_accuracy: 0.93436 |  0:00:09s\n",
-      "epoch 19 | loss: 0.05693 | valid_accuracy: 0.9363  |  0:00:10s\n",
-      "epoch 20 | loss: 0.05689 | valid_accuracy: 0.93356 |  0:00:10s\n",
-      "epoch 21 | loss: 0.0577  | valid_accuracy: 0.93813 |  0:00:11s\n",
-      "\n",
-      "Early stopping occurred at epoch 21 with best_epoch = 13 and best_valid_accuracy = 0.94247\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:53:42,283] Trial 25 finished with value: 0.7230769230769231 and parameters: {'n_d': 32, 'n_a': 24, 'n_steps': 3, 'gamma': 1.4670609710214029, 'lambda_sparse': 0.0012766533397569697, 'momentum': 0.15133588150594873}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.46262 | valid_accuracy: 0.79795 |  0:00:00s\n",
-      "epoch 1  | loss: 0.23106 | valid_accuracy: 0.8282  |  0:00:01s\n",
-      "epoch 2  | loss: 0.18701 | valid_accuracy: 0.86221 |  0:00:02s\n",
-      "epoch 3  | loss: 0.17376 | valid_accuracy: 0.89543 |  0:00:03s\n",
-      "epoch 4  | loss: 0.15922 | valid_accuracy: 0.92911 |  0:00:03s\n",
-      "epoch 5  | loss: 0.14441 | valid_accuracy: 0.93413 |  0:00:04s\n",
-      "epoch 6  | loss: 0.1343  | valid_accuracy: 0.94189 |  0:00:05s\n",
-      "epoch 7  | loss: 0.13649 | valid_accuracy: 0.9363  |  0:00:06s\n",
-      "epoch 8  | loss: 0.1306  | valid_accuracy: 0.93322 |  0:00:07s\n",
-      "epoch 9  | loss: 0.1252  | valid_accuracy: 0.93002 |  0:00:07s\n",
-      "epoch 10 | loss: 0.12126 | valid_accuracy: 0.93699 |  0:00:08s\n",
-      "epoch 11 | loss: 0.12    | valid_accuracy: 0.93071 |  0:00:09s\n",
-      "epoch 12 | loss: 0.11542 | valid_accuracy: 0.93151 |  0:00:10s\n",
-      "epoch 13 | loss: 0.11732 | valid_accuracy: 0.93447 |  0:00:11s\n",
-      "epoch 14 | loss: 0.1122  | valid_accuracy: 0.93425 |  0:00:11s\n",
-      "\n",
-      "Early stopping occurred at epoch 14 with best_epoch = 6 and best_valid_accuracy = 0.94189\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:53:54,775] Trial 26 finished with value: 0.7342036553524804 and parameters: {'n_d': 40, 'n_a': 16, 'n_steps': 6, 'gamma': 1.1950236581346791, 'lambda_sparse': 0.0023400383905811677, 'momentum': 0.19507415200606232}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.46816 | valid_accuracy: 0.77009 |  0:00:00s\n",
-      "epoch 1  | loss: 0.23461 | valid_accuracy: 0.87968 |  0:00:01s\n",
-      "epoch 2  | loss: 0.19834 | valid_accuracy: 0.89281 |  0:00:02s\n",
-      "epoch 3  | loss: 0.1735  | valid_accuracy: 0.93459 |  0:00:02s\n",
-      "epoch 4  | loss: 0.16171 | valid_accuracy: 0.92626 |  0:00:03s\n",
-      "epoch 5  | loss: 0.15309 | valid_accuracy: 0.92135 |  0:00:04s\n",
-      "epoch 6  | loss: 0.14653 | valid_accuracy: 0.91655 |  0:00:04s\n",
-      "epoch 7  | loss: 0.15426 | valid_accuracy: 0.92991 |  0:00:05s\n",
-      "epoch 8  | loss: 0.1395  | valid_accuracy: 0.93231 |  0:00:06s\n",
-      "epoch 9  | loss: 0.13185 | valid_accuracy: 0.93037 |  0:00:07s\n",
-      "epoch 10 | loss: 0.12759 | valid_accuracy: 0.93607 |  0:00:07s\n",
-      "epoch 11 | loss: 0.11971 | valid_accuracy: 0.93447 |  0:00:08s\n",
-      "epoch 12 | loss: 0.11881 | valid_accuracy: 0.93984 |  0:00:09s\n",
-      "epoch 13 | loss: 0.11442 | valid_accuracy: 0.9355  |  0:00:10s\n",
-      "epoch 14 | loss: 0.10914 | valid_accuracy: 0.93242 |  0:00:10s\n",
-      "epoch 15 | loss: 0.11241 | valid_accuracy: 0.93744 |  0:00:11s\n",
-      "epoch 16 | loss: 0.12271 | valid_accuracy: 0.93607 |  0:00:12s\n",
-      "epoch 17 | loss: 0.11658 | valid_accuracy: 0.93276 |  0:00:12s\n",
-      "epoch 18 | loss: 0.11444 | valid_accuracy: 0.93584 |  0:00:13s\n",
-      "epoch 19 | loss: 0.10761 | valid_accuracy: 0.92774 |  0:00:14s\n",
-      "epoch 20 | loss: 0.10325 | valid_accuracy: 0.93596 |  0:00:15s\n",
-      "\n",
-      "Early stopping occurred at epoch 20 with best_epoch = 12 and best_valid_accuracy = 0.93984\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:54:10,273] Trial 27 finished with value: 0.7083563918096292 and parameters: {'n_d': 56, 'n_a': 32, 'n_steps': 5, 'gamma': 1.3594035424449564, 'lambda_sparse': 0.0004512588696281365, 'momentum': 0.09526450591717059}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.34807 | valid_accuracy: 0.87489 |  0:00:00s\n",
-      "epoch 1  | loss: 0.17781 | valid_accuracy: 0.92797 |  0:00:01s\n",
-      "epoch 2  | loss: 0.13428 | valid_accuracy: 0.92694 |  0:00:01s\n",
-      "epoch 3  | loss: 0.12184 | valid_accuracy: 0.93984 |  0:00:02s\n",
-      "epoch 4  | loss: 0.10939 | valid_accuracy: 0.93333 |  0:00:03s\n",
-      "epoch 5  | loss: 0.09965 | valid_accuracy: 0.93893 |  0:00:03s\n",
-      "epoch 6  | loss: 0.09667 | valid_accuracy: 0.94087 |  0:00:04s\n",
-      "epoch 7  | loss: 0.09394 | valid_accuracy: 0.9403  |  0:00:04s\n",
-      "epoch 8  | loss: 0.0925  | valid_accuracy: 0.93744 |  0:00:05s\n",
-      "epoch 9  | loss: 0.08903 | valid_accuracy: 0.94189 |  0:00:06s\n",
-      "epoch 10 | loss: 0.08595 | valid_accuracy: 0.9339  |  0:00:06s\n",
-      "epoch 11 | loss: 0.0921  | valid_accuracy: 0.9363  |  0:00:07s\n",
-      "epoch 12 | loss: 0.09322 | valid_accuracy: 0.93938 |  0:00:07s\n",
-      "epoch 13 | loss: 0.08945 | valid_accuracy: 0.93447 |  0:00:08s\n",
-      "epoch 14 | loss: 0.09022 | valid_accuracy: 0.92991 |  0:00:09s\n",
-      "epoch 15 | loss: 0.08631 | valid_accuracy: 0.92854 |  0:00:09s\n",
-      "epoch 16 | loss: 0.08414 | valid_accuracy: 0.92957 |  0:00:10s\n",
-      "epoch 17 | loss: 0.08354 | valid_accuracy: 0.93584 |  0:00:10s\n",
-      "\n",
-      "Early stopping occurred at epoch 17 with best_epoch = 9 and best_valid_accuracy = 0.94189\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:54:21,479] Trial 28 finished with value: 0.7279529663281668 and parameters: {'n_d': 16, 'n_a': 40, 'n_steps': 4, 'gamma': 1.0692005047122888, 'lambda_sparse': 0.0006747716169120555, 'momentum': 0.22245931557841697}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.59666 | valid_accuracy: 0.79943 |  0:00:01s\n",
-      "epoch 1  | loss: 0.45964 | valid_accuracy: 0.69498 |  0:00:02s\n",
-      "epoch 2  | loss: 0.38141 | valid_accuracy: 0.89121 |  0:00:03s\n",
-      "epoch 3  | loss: 0.25975 | valid_accuracy: 0.86758 |  0:00:04s\n",
-      "epoch 4  | loss: 0.22724 | valid_accuracy: 0.89589 |  0:00:05s\n",
-      "epoch 5  | loss: 0.20368 | valid_accuracy: 0.91701 |  0:00:06s\n",
-      "epoch 6  | loss: 0.18894 | valid_accuracy: 0.89874 |  0:00:07s\n",
-      "epoch 7  | loss: 0.1854  | valid_accuracy: 0.92295 |  0:00:08s\n",
-      "epoch 8  | loss: 0.16135 | valid_accuracy: 0.91849 |  0:00:10s\n",
-      "epoch 9  | loss: 0.15377 | valid_accuracy: 0.91119 |  0:00:11s\n",
-      "epoch 10 | loss: 0.1584  | valid_accuracy: 0.89829 |  0:00:12s\n",
-      "epoch 11 | loss: 0.15127 | valid_accuracy: 0.93128 |  0:00:13s\n",
-      "epoch 12 | loss: 0.13917 | valid_accuracy: 0.93562 |  0:00:14s\n",
-      "epoch 13 | loss: 0.13326 | valid_accuracy: 0.93881 |  0:00:15s\n",
-      "epoch 14 | loss: 0.12677 | valid_accuracy: 0.93025 |  0:00:16s\n",
-      "epoch 15 | loss: 0.1305  | valid_accuracy: 0.93596 |  0:00:17s\n",
-      "epoch 16 | loss: 0.12925 | valid_accuracy: 0.9266  |  0:00:18s\n",
-      "epoch 17 | loss: 0.13814 | valid_accuracy: 0.93379 |  0:00:20s\n",
-      "epoch 18 | loss: 0.13049 | valid_accuracy: 0.93619 |  0:00:21s\n",
-      "epoch 19 | loss: 0.12173 | valid_accuracy: 0.93162 |  0:00:22s\n",
-      "epoch 20 | loss: 0.1178  | valid_accuracy: 0.93356 |  0:00:23s\n",
-      "epoch 21 | loss: 0.11358 | valid_accuracy: 0.93653 |  0:00:24s\n",
-      "\n",
-      "Early stopping occurred at epoch 21 with best_epoch = 13 and best_valid_accuracy = 0.93881\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:54:46,785] Trial 29 finished with value: 0.7102702702702702 and parameters: {'n_d': 48, 'n_a': 24, 'n_steps': 9, 'gamma': 1.5343779318950561, 'lambda_sparse': 0.00016300834886699597, 'momentum': 0.2649384617298923}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.58329 | valid_accuracy: 0.81941 |  0:00:00s\n",
-      "epoch 1  | loss: 0.29285 | valid_accuracy: 0.86336 |  0:00:01s\n",
-      "epoch 2  | loss: 0.22696 | valid_accuracy: 0.91461 |  0:00:02s\n",
-      "epoch 3  | loss: 0.18988 | valid_accuracy: 0.90925 |  0:00:03s\n",
-      "epoch 4  | loss: 0.18638 | valid_accuracy: 0.90811 |  0:00:04s\n",
-      "epoch 5  | loss: 0.16789 | valid_accuracy: 0.93516 |  0:00:05s\n",
-      "epoch 6  | loss: 0.16765 | valid_accuracy: 0.88025 |  0:00:06s\n",
-      "epoch 7  | loss: 0.15636 | valid_accuracy: 0.9355  |  0:00:07s\n",
-      "epoch 8  | loss: 0.14617 | valid_accuracy: 0.92957 |  0:00:08s\n",
-      "epoch 9  | loss: 0.14327 | valid_accuracy: 0.92363 |  0:00:09s\n",
-      "epoch 10 | loss: 0.14005 | valid_accuracy: 0.92203 |  0:00:09s\n",
-      "epoch 11 | loss: 0.14772 | valid_accuracy: 0.93185 |  0:00:10s\n",
-      "epoch 12 | loss: 0.1399  | valid_accuracy: 0.93927 |  0:00:11s\n",
-      "epoch 13 | loss: 0.12887 | valid_accuracy: 0.94258 |  0:00:12s\n",
-      "epoch 14 | loss: 0.12591 | valid_accuracy: 0.93779 |  0:00:13s\n",
-      "epoch 15 | loss: 0.13704 | valid_accuracy: 0.93516 |  0:00:14s\n",
-      "epoch 16 | loss: 0.12821 | valid_accuracy: 0.9347  |  0:00:15s\n",
-      "epoch 17 | loss: 0.12955 | valid_accuracy: 0.9355  |  0:00:16s\n",
-      "epoch 18 | loss: 0.1195  | valid_accuracy: 0.93881 |  0:00:17s\n",
-      "epoch 19 | loss: 0.11225 | valid_accuracy: 0.93116 |  0:00:18s\n",
-      "epoch 20 | loss: 0.11565 | valid_accuracy: 0.93413 |  0:00:19s\n",
-      "epoch 21 | loss: 0.10814 | valid_accuracy: 0.93984 |  0:00:19s\n",
-      "\n",
-      "Early stopping occurred at epoch 21 with best_epoch = 13 and best_valid_accuracy = 0.94258\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:55:07,377] Trial 30 finished with value: 0.7237781438769907 and parameters: {'n_d': 64, 'n_a': 8, 'n_steps': 7, 'gamma': 1.2706701528592617, 'lambda_sparse': 0.00020591233059887278, 'momentum': 0.3242256066504274}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.38446 | valid_accuracy: 0.83938 |  0:00:00s\n",
-      "epoch 1  | loss: 0.18263 | valid_accuracy: 0.9105  |  0:00:01s\n",
-      "epoch 2  | loss: 0.14896 | valid_accuracy: 0.92329 |  0:00:01s\n",
-      "epoch 3  | loss: 0.13453 | valid_accuracy: 0.92032 |  0:00:02s\n",
-      "epoch 4  | loss: 0.12652 | valid_accuracy: 0.92911 |  0:00:03s\n",
-      "epoch 5  | loss: 0.12708 | valid_accuracy: 0.93413 |  0:00:03s\n",
-      "epoch 6  | loss: 0.11877 | valid_accuracy: 0.94018 |  0:00:04s\n",
-      "epoch 7  | loss: 0.10601 | valid_accuracy: 0.93299 |  0:00:04s\n",
-      "epoch 8  | loss: 0.09763 | valid_accuracy: 0.93105 |  0:00:05s\n",
-      "epoch 9  | loss: 0.09013 | valid_accuracy: 0.9403  |  0:00:06s\n",
-      "epoch 10 | loss: 0.09183 | valid_accuracy: 0.93938 |  0:00:06s\n",
-      "epoch 11 | loss: 0.09007 | valid_accuracy: 0.93756 |  0:00:07s\n",
-      "epoch 12 | loss: 0.08186 | valid_accuracy: 0.94304 |  0:00:07s\n",
-      "epoch 13 | loss: 0.08119 | valid_accuracy: 0.93699 |  0:00:08s\n",
-      "epoch 14 | loss: 0.08249 | valid_accuracy: 0.94361 |  0:00:09s\n",
-      "epoch 15 | loss: 0.0798  | valid_accuracy: 0.9411  |  0:00:09s\n",
-      "epoch 16 | loss: 0.07149 | valid_accuracy: 0.93699 |  0:00:10s\n",
-      "epoch 17 | loss: 0.07026 | valid_accuracy: 0.9379  |  0:00:10s\n",
-      "epoch 18 | loss: 0.06834 | valid_accuracy: 0.93447 |  0:00:11s\n",
-      "epoch 19 | loss: 0.06959 | valid_accuracy: 0.93573 |  0:00:12s\n",
-      "epoch 20 | loss: 0.06952 | valid_accuracy: 0.94155 |  0:00:12s\n",
-      "epoch 21 | loss: 0.0714  | valid_accuracy: 0.93961 |  0:00:13s\n",
-      "epoch 22 | loss: 0.06892 | valid_accuracy: 0.93562 |  0:00:13s\n",
-      "\n",
-      "Early stopping occurred at epoch 22 with best_epoch = 14 and best_valid_accuracy = 0.94361\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:55:21,705] Trial 31 finished with value: 0.7443064182194618 and parameters: {'n_d': 32, 'n_a': 24, 'n_steps': 4, 'gamma': 1.1322179042515514, 'lambda_sparse': 0.004321765994323485, 'momentum': 0.17628878892831942}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.3318  | valid_accuracy: 0.86998 |  0:00:00s\n",
-      "epoch 1  | loss: 0.18827 | valid_accuracy: 0.91884 |  0:00:01s\n",
-      "epoch 2  | loss: 0.15179 | valid_accuracy: 0.91564 |  0:00:01s\n",
-      "epoch 3  | loss: 0.12275 | valid_accuracy: 0.92534 |  0:00:02s\n",
-      "epoch 4  | loss: 0.10362 | valid_accuracy: 0.92842 |  0:00:02s\n",
-      "epoch 5  | loss: 0.09638 | valid_accuracy: 0.93676 |  0:00:03s\n",
-      "epoch 6  | loss: 0.08977 | valid_accuracy: 0.93733 |  0:00:03s\n",
-      "epoch 7  | loss: 0.08381 | valid_accuracy: 0.93927 |  0:00:04s\n",
-      "epoch 8  | loss: 0.07998 | valid_accuracy: 0.93995 |  0:00:04s\n",
-      "epoch 9  | loss: 0.0772  | valid_accuracy: 0.93938 |  0:00:05s\n",
-      "epoch 10 | loss: 0.07595 | valid_accuracy: 0.9403  |  0:00:05s\n",
-      "epoch 11 | loss: 0.07569 | valid_accuracy: 0.93368 |  0:00:06s\n",
-      "epoch 12 | loss: 0.07672 | valid_accuracy: 0.94452 |  0:00:06s\n",
-      "epoch 13 | loss: 0.07309 | valid_accuracy: 0.93733 |  0:00:07s\n",
-      "epoch 14 | loss: 0.06878 | valid_accuracy: 0.93276 |  0:00:07s\n",
-      "epoch 15 | loss: 0.0674  | valid_accuracy: 0.94132 |  0:00:07s\n",
-      "epoch 16 | loss: 0.07026 | valid_accuracy: 0.94315 |  0:00:08s\n",
-      "epoch 17 | loss: 0.06721 | valid_accuracy: 0.94247 |  0:00:08s\n",
-      "epoch 18 | loss: 0.06498 | valid_accuracy: 0.93356 |  0:00:09s\n",
-      "epoch 19 | loss: 0.06452 | valid_accuracy: 0.93801 |  0:00:09s\n",
-      "epoch 20 | loss: 0.06448 | valid_accuracy: 0.94167 |  0:00:10s\n",
-      "\n",
-      "Early stopping occurred at epoch 20 with best_epoch = 12 and best_valid_accuracy = 0.94452\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:55:32,520] Trial 32 finished with value: 0.7527975584944049 and parameters: {'n_d': 24, 'n_a': 16, 'n_steps': 3, 'gamma': 1.1745885111041938, 'lambda_sparse': 0.0055991752089406465, 'momentum': 0.19256679452155392}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.41156 | valid_accuracy: 0.8645  |  0:00:00s\n",
-      "epoch 1  | loss: 0.19191 | valid_accuracy: 0.91039 |  0:00:00s\n",
-      "epoch 2  | loss: 0.15266 | valid_accuracy: 0.91918 |  0:00:01s\n",
-      "epoch 3  | loss: 0.14502 | valid_accuracy: 0.92705 |  0:00:01s\n",
-      "epoch 4  | loss: 0.13591 | valid_accuracy: 0.93573 |  0:00:02s\n",
-      "epoch 5  | loss: 0.12342 | valid_accuracy: 0.94041 |  0:00:02s\n",
-      "epoch 6  | loss: 0.1181  | valid_accuracy: 0.93493 |  0:00:03s\n",
-      "epoch 7  | loss: 0.11095 | valid_accuracy: 0.93893 |  0:00:03s\n",
-      "epoch 8  | loss: 0.10741 | valid_accuracy: 0.93927 |  0:00:04s\n",
-      "epoch 9  | loss: 0.10457 | valid_accuracy: 0.9363  |  0:00:04s\n",
-      "epoch 10 | loss: 0.1003  | valid_accuracy: 0.93744 |  0:00:05s\n",
-      "epoch 11 | loss: 0.09707 | valid_accuracy: 0.94406 |  0:00:05s\n",
-      "epoch 12 | loss: 0.09323 | valid_accuracy: 0.9411  |  0:00:06s\n",
-      "epoch 13 | loss: 0.09063 | valid_accuracy: 0.93779 |  0:00:06s\n",
-      "epoch 14 | loss: 0.08585 | valid_accuracy: 0.93664 |  0:00:07s\n",
-      "epoch 15 | loss: 0.08433 | valid_accuracy: 0.94292 |  0:00:07s\n",
-      "epoch 16 | loss: 0.08005 | valid_accuracy: 0.93961 |  0:00:08s\n",
-      "epoch 17 | loss: 0.07712 | valid_accuracy: 0.93596 |  0:00:08s\n",
-      "epoch 18 | loss: 0.07722 | valid_accuracy: 0.94144 |  0:00:09s\n",
-      "epoch 19 | loss: 0.07351 | valid_accuracy: 0.94212 |  0:00:09s\n",
-      "\n",
-      "Early stopping occurred at epoch 19 with best_epoch = 11 and best_valid_accuracy = 0.94406\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:55:42,705] Trial 33 finished with value: 0.7219069239500567 and parameters: {'n_d': 16, 'n_a': 16, 'n_steps': 3, 'gamma': 1.1913150299734105, 'lambda_sparse': 0.00619159825684386, 'momentum': 0.22821635612221458}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.31715 | valid_accuracy: 0.86689 |  0:00:00s\n",
-      "epoch 1  | loss: 0.17315 | valid_accuracy: 0.91826 |  0:00:00s\n",
-      "epoch 2  | loss: 0.1247  | valid_accuracy: 0.92979 |  0:00:01s\n",
-      "epoch 3  | loss: 0.1067  | valid_accuracy: 0.93584 |  0:00:01s\n",
-      "epoch 4  | loss: 0.09597 | valid_accuracy: 0.93436 |  0:00:02s\n",
-      "epoch 5  | loss: 0.0925  | valid_accuracy: 0.9339  |  0:00:03s\n",
-      "epoch 6  | loss: 0.08782 | valid_accuracy: 0.93573 |  0:00:03s\n",
-      "epoch 7  | loss: 0.08932 | valid_accuracy: 0.9371  |  0:00:04s\n",
-      "epoch 8  | loss: 0.08297 | valid_accuracy: 0.93299 |  0:00:04s\n",
-      "epoch 9  | loss: 0.07792 | valid_accuracy: 0.93071 |  0:00:04s\n",
-      "epoch 10 | loss: 0.07672 | valid_accuracy: 0.93162 |  0:00:05s\n",
-      "epoch 11 | loss: 0.07467 | valid_accuracy: 0.93128 |  0:00:05s\n",
-      "epoch 12 | loss: 0.07016 | valid_accuracy: 0.93162 |  0:00:06s\n",
-      "epoch 13 | loss: 0.06865 | valid_accuracy: 0.93105 |  0:00:06s\n",
-      "epoch 14 | loss: 0.0669  | valid_accuracy: 0.93447 |  0:00:07s\n",
-      "epoch 15 | loss: 0.06509 | valid_accuracy: 0.92854 |  0:00:07s\n",
-      "\n",
-      "Early stopping occurred at epoch 15 with best_epoch = 7 and best_valid_accuracy = 0.9371\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:55:50,919] Trial 34 finished with value: 0.728437654016757 and parameters: {'n_d': 24, 'n_a': 16, 'n_steps': 3, 'gamma': 1.0580799204136884, 'lambda_sparse': 0.0021396821263543835, 'momentum': 0.19302606110846154}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.47847 | valid_accuracy: 0.85811 |  0:00:00s\n",
-      "epoch 1  | loss: 0.26958 | valid_accuracy: 0.89235 |  0:00:01s\n",
-      "epoch 2  | loss: 0.23715 | valid_accuracy: 0.91336 |  0:00:01s\n",
-      "epoch 3  | loss: 0.21046 | valid_accuracy: 0.91998 |  0:00:02s\n",
-      "epoch 4  | loss: 0.19762 | valid_accuracy: 0.92717 |  0:00:02s\n",
-      "epoch 5  | loss: 0.18819 | valid_accuracy: 0.90685 |  0:00:03s\n",
-      "epoch 6  | loss: 0.17835 | valid_accuracy: 0.91735 |  0:00:04s\n",
-      "epoch 7  | loss: 0.16374 | valid_accuracy: 0.9266  |  0:00:04s\n",
-      "epoch 8  | loss: 0.15243 | valid_accuracy: 0.91884 |  0:00:05s\n",
-      "epoch 9  | loss: 0.14681 | valid_accuracy: 0.93253 |  0:00:05s\n",
-      "epoch 10 | loss: 0.14221 | valid_accuracy: 0.92352 |  0:00:06s\n",
-      "epoch 11 | loss: 0.13792 | valid_accuracy: 0.92443 |  0:00:07s\n",
-      "epoch 12 | loss: 0.13522 | valid_accuracy: 0.94064 |  0:00:07s\n",
-      "epoch 13 | loss: 0.12487 | valid_accuracy: 0.92546 |  0:00:08s\n",
-      "epoch 14 | loss: 0.12912 | valid_accuracy: 0.93493 |  0:00:08s\n",
-      "epoch 15 | loss: 0.11491 | valid_accuracy: 0.93847 |  0:00:09s\n",
-      "epoch 16 | loss: 0.10628 | valid_accuracy: 0.9355  |  0:00:10s\n",
-      "epoch 17 | loss: 0.10426 | valid_accuracy: 0.93744 |  0:00:10s\n",
-      "epoch 18 | loss: 0.1046  | valid_accuracy: 0.9387  |  0:00:11s\n",
-      "epoch 19 | loss: 0.10161 | valid_accuracy: 0.91941 |  0:00:11s\n",
-      "epoch 20 | loss: 0.0975  | valid_accuracy: 0.93402 |  0:00:12s\n",
-      "\n",
-      "Early stopping occurred at epoch 20 with best_epoch = 12 and best_valid_accuracy = 0.94064\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:56:03,769] Trial 35 finished with value: 0.7161572052401747 and parameters: {'n_d': 24, 'n_a': 8, 'n_steps': 4, 'gamma': 1.9739236222012595, 'lambda_sparse': 0.001147872267664542, 'momentum': 0.15342249904964475}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.33235 | valid_accuracy: 0.85137 |  0:00:00s\n",
-      "epoch 1  | loss: 0.17157 | valid_accuracy: 0.92466 |  0:00:00s\n",
-      "epoch 2  | loss: 0.1364  | valid_accuracy: 0.93402 |  0:00:01s\n",
-      "epoch 3  | loss: 0.11848 | valid_accuracy: 0.92671 |  0:00:01s\n",
-      "epoch 4  | loss: 0.11056 | valid_accuracy: 0.93493 |  0:00:02s\n",
-      "epoch 5  | loss: 0.09953 | valid_accuracy: 0.93562 |  0:00:02s\n",
-      "epoch 6  | loss: 0.09568 | valid_accuracy: 0.93893 |  0:00:03s\n",
-      "epoch 7  | loss: 0.08778 | valid_accuracy: 0.94292 |  0:00:03s\n",
-      "epoch 8  | loss: 0.08715 | valid_accuracy: 0.94018 |  0:00:04s\n",
-      "epoch 9  | loss: 0.08656 | valid_accuracy: 0.93961 |  0:00:04s\n",
-      "epoch 10 | loss: 0.08587 | valid_accuracy: 0.94635 |  0:00:05s\n",
-      "epoch 11 | loss: 0.0755  | valid_accuracy: 0.94064 |  0:00:05s\n",
-      "epoch 12 | loss: 0.07821 | valid_accuracy: 0.94041 |  0:00:06s\n",
-      "epoch 13 | loss: 0.07822 | valid_accuracy: 0.94132 |  0:00:06s\n",
-      "epoch 14 | loss: 0.0733  | valid_accuracy: 0.93779 |  0:00:07s\n",
-      "epoch 15 | loss: 0.07769 | valid_accuracy: 0.94189 |  0:00:07s\n",
-      "epoch 16 | loss: 0.07335 | valid_accuracy: 0.93642 |  0:00:08s\n",
-      "epoch 17 | loss: 0.06728 | valid_accuracy: 0.94053 |  0:00:08s\n",
-      "epoch 18 | loss: 0.06699 | valid_accuracy: 0.93916 |  0:00:09s\n",
-      "\n",
-      "Early stopping occurred at epoch 18 with best_epoch = 10 and best_valid_accuracy = 0.94635\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:56:13,542] Trial 36 finished with value: 0.7486631016042781 and parameters: {'n_d': 32, 'n_a': 16, 'n_steps': 3, 'gamma': 1.222807949141764, 'lambda_sparse': 0.00971405536579209, 'momentum': 0.0627213067269411}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.60726 | valid_accuracy: 0.65959 |  0:00:00s\n",
-      "epoch 1  | loss: 0.32327 | valid_accuracy: 0.78071 |  0:00:01s\n",
-      "epoch 2  | loss: 0.26496 | valid_accuracy: 0.87352 |  0:00:02s\n",
-      "epoch 3  | loss: 0.23906 | valid_accuracy: 0.8903  |  0:00:02s\n",
-      "epoch 4  | loss: 0.22749 | valid_accuracy: 0.9016  |  0:00:03s\n",
-      "epoch 5  | loss: 0.21507 | valid_accuracy: 0.92237 |  0:00:04s\n",
-      "epoch 6  | loss: 0.20581 | valid_accuracy: 0.91005 |  0:00:04s\n",
-      "epoch 7  | loss: 0.19077 | valid_accuracy: 0.90445 |  0:00:05s\n",
-      "epoch 8  | loss: 0.19085 | valid_accuracy: 0.92443 |  0:00:06s\n",
-      "epoch 9  | loss: 0.18194 | valid_accuracy: 0.92215 |  0:00:07s\n",
-      "epoch 10 | loss: 0.17387 | valid_accuracy: 0.92203 |  0:00:07s\n",
-      "epoch 11 | loss: 0.16277 | valid_accuracy: 0.92249 |  0:00:08s\n",
-      "epoch 12 | loss: 0.16102 | valid_accuracy: 0.91918 |  0:00:09s\n",
-      "epoch 13 | loss: 0.15843 | valid_accuracy: 0.92317 |  0:00:09s\n",
-      "epoch 14 | loss: 0.15387 | valid_accuracy: 0.90925 |  0:00:10s\n",
-      "epoch 15 | loss: 0.15106 | valid_accuracy: 0.93105 |  0:00:11s\n",
-      "epoch 16 | loss: 0.14664 | valid_accuracy: 0.92454 |  0:00:11s\n",
-      "epoch 17 | loss: 0.14664 | valid_accuracy: 0.925   |  0:00:12s\n",
-      "epoch 18 | loss: 0.14471 | valid_accuracy: 0.93151 |  0:00:13s\n",
-      "epoch 19 | loss: 0.13268 | valid_accuracy: 0.93356 |  0:00:13s\n",
-      "epoch 20 | loss: 0.13501 | valid_accuracy: 0.91427 |  0:00:14s\n",
-      "epoch 21 | loss: 0.13932 | valid_accuracy: 0.93664 |  0:00:15s\n",
-      "epoch 22 | loss: 0.13938 | valid_accuracy: 0.93196 |  0:00:15s\n",
-      "epoch 23 | loss: 0.13442 | valid_accuracy: 0.93025 |  0:00:16s\n",
-      "epoch 24 | loss: 0.14439 | valid_accuracy: 0.93082 |  0:00:17s\n",
-      "epoch 25 | loss: 0.13891 | valid_accuracy: 0.93288 |  0:00:18s\n",
-      "epoch 26 | loss: 0.12943 | valid_accuracy: 0.93048 |  0:00:18s\n",
-      "epoch 27 | loss: 0.1252  | valid_accuracy: 0.93493 |  0:00:19s\n",
-      "epoch 28 | loss: 0.13083 | valid_accuracy: 0.94053 |  0:00:20s\n",
-      "epoch 29 | loss: 0.12587 | valid_accuracy: 0.9371  |  0:00:20s\n",
-      "Stop training because you reached max_epochs = 30 with best_epoch = 28 and best_valid_accuracy = 0.94053\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:56:34,902] Trial 37 finished with value: 0.71326362135388 and parameters: {'n_d': 8, 'n_a': 32, 'n_steps': 5, 'gamma': 1.8874432378189303, 'lambda_sparse': 0.0003092858649558244, 'momentum': 0.1132397079238248}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.53619 | valid_accuracy: 0.57557 |  0:00:00s\n",
-      "epoch 1  | loss: 0.31732 | valid_accuracy: 0.90674 |  0:00:01s\n",
-      "epoch 2  | loss: 0.29928 | valid_accuracy: 0.83345 |  0:00:02s\n",
-      "epoch 3  | loss: 0.25874 | valid_accuracy: 0.87089 |  0:00:03s\n",
-      "epoch 4  | loss: 0.25692 | valid_accuracy: 0.87705 |  0:00:04s\n",
-      "epoch 5  | loss: 0.26225 | valid_accuracy: 0.88995 |  0:00:05s\n",
-      "epoch 6  | loss: 0.23708 | valid_accuracy: 0.90034 |  0:00:06s\n",
-      "epoch 7  | loss: 0.22675 | valid_accuracy: 0.90114 |  0:00:07s\n",
-      "epoch 8  | loss: 0.22047 | valid_accuracy: 0.91632 |  0:00:08s\n",
-      "epoch 9  | loss: 0.2018  | valid_accuracy: 0.91724 |  0:00:08s\n",
-      "epoch 10 | loss: 0.19331 | valid_accuracy: 0.90959 |  0:00:09s\n",
-      "epoch 11 | loss: 0.19135 | valid_accuracy: 0.925   |  0:00:10s\n",
-      "epoch 12 | loss: 0.17713 | valid_accuracy: 0.92511 |  0:00:11s\n",
-      "epoch 13 | loss: 0.16346 | valid_accuracy: 0.92089 |  0:00:12s\n",
-      "epoch 14 | loss: 0.15398 | valid_accuracy: 0.92249 |  0:00:13s\n",
-      "epoch 15 | loss: 0.15063 | valid_accuracy: 0.91792 |  0:00:14s\n",
-      "epoch 16 | loss: 0.14622 | valid_accuracy: 0.93116 |  0:00:15s\n",
-      "epoch 17 | loss: 0.13301 | valid_accuracy: 0.93059 |  0:00:16s\n",
-      "epoch 18 | loss: 0.13113 | valid_accuracy: 0.92477 |  0:00:17s\n",
-      "epoch 19 | loss: 0.13038 | valid_accuracy: 0.9242  |  0:00:17s\n",
-      "epoch 20 | loss: 0.12463 | valid_accuracy: 0.92397 |  0:00:18s\n",
-      "epoch 21 | loss: 0.12433 | valid_accuracy: 0.92683 |  0:00:19s\n",
-      "epoch 22 | loss: 0.12402 | valid_accuracy: 0.92432 |  0:00:20s\n",
-      "epoch 23 | loss: 0.11884 | valid_accuracy: 0.91963 |  0:00:21s\n",
-      "epoch 24 | loss: 0.11529 | valid_accuracy: 0.93253 |  0:00:22s\n",
-      "epoch 25 | loss: 0.10892 | valid_accuracy: 0.92603 |  0:00:23s\n",
-      "epoch 26 | loss: 0.11023 | valid_accuracy: 0.92877 |  0:00:24s\n",
-      "epoch 27 | loss: 0.11299 | valid_accuracy: 0.92922 |  0:00:25s\n",
-      "epoch 28 | loss: 0.11595 | valid_accuracy: 0.92523 |  0:00:26s\n",
-      "epoch 29 | loss: 0.11495 | valid_accuracy: 0.93425 |  0:00:26s\n",
-      "Stop training because you reached max_epochs = 30 with best_epoch = 29 and best_valid_accuracy = 0.93425\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:57:02,460] Trial 38 finished with value: 0.700312174817898 and parameters: {'n_d': 40, 'n_a': 16, 'n_steps': 7, 'gamma': 1.5873343488710119, 'lambda_sparse': 0.0030518355038422927, 'momentum': 0.2357369293781781}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.48542 | valid_accuracy: 0.65194 |  0:00:01s\n",
-      "epoch 1  | loss: 0.29712 | valid_accuracy: 0.73813 |  0:00:02s\n",
-      "epoch 2  | loss: 0.30147 | valid_accuracy: 0.80537 |  0:00:03s\n",
-      "epoch 3  | loss: 0.2837  | valid_accuracy: 0.84132 |  0:00:04s\n",
-      "epoch 4  | loss: 0.28887 | valid_accuracy: 0.81906 |  0:00:05s\n",
-      "epoch 5  | loss: 0.25672 | valid_accuracy: 0.89772 |  0:00:06s\n",
-      "epoch 6  | loss: 0.2351  | valid_accuracy: 0.86438 |  0:00:07s\n",
-      "epoch 7  | loss: 0.22729 | valid_accuracy: 0.87534 |  0:00:08s\n",
-      "epoch 8  | loss: 0.21406 | valid_accuracy: 0.8887  |  0:00:09s\n",
-      "epoch 9  | loss: 0.20939 | valid_accuracy: 0.90103 |  0:00:10s\n",
-      "epoch 10 | loss: 0.19016 | valid_accuracy: 0.92158 |  0:00:11s\n",
-      "epoch 11 | loss: 0.17315 | valid_accuracy: 0.91712 |  0:00:12s\n",
-      "epoch 12 | loss: 0.17264 | valid_accuracy: 0.92534 |  0:00:13s\n",
-      "epoch 13 | loss: 0.15628 | valid_accuracy: 0.921   |  0:00:14s\n",
-      "epoch 14 | loss: 0.1504  | valid_accuracy: 0.91096 |  0:00:15s\n",
-      "epoch 15 | loss: 0.15163 | valid_accuracy: 0.89943 |  0:00:16s\n",
-      "epoch 16 | loss: 0.14703 | valid_accuracy: 0.92249 |  0:00:17s\n",
-      "epoch 17 | loss: 0.14098 | valid_accuracy: 0.93208 |  0:00:18s\n",
-      "epoch 18 | loss: 0.13393 | valid_accuracy: 0.93459 |  0:00:19s\n",
-      "epoch 19 | loss: 0.13433 | valid_accuracy: 0.94098 |  0:00:20s\n",
-      "epoch 20 | loss: 0.1287  | valid_accuracy: 0.92021 |  0:00:21s\n",
-      "epoch 21 | loss: 0.13776 | valid_accuracy: 0.9347  |  0:00:22s\n",
-      "epoch 22 | loss: 0.14637 | valid_accuracy: 0.93699 |  0:00:23s\n",
-      "epoch 23 | loss: 0.15078 | valid_accuracy: 0.90959 |  0:00:24s\n",
-      "epoch 24 | loss: 0.15007 | valid_accuracy: 0.92432 |  0:00:25s\n",
-      "epoch 25 | loss: 0.14907 | valid_accuracy: 0.92477 |  0:00:26s\n",
-      "epoch 26 | loss: 0.14999 | valid_accuracy: 0.92477 |  0:00:27s\n",
-      "epoch 27 | loss: 0.15182 | valid_accuracy: 0.91062 |  0:00:28s\n",
-      "\n",
-      "Early stopping occurred at epoch 27 with best_epoch = 19 and best_valid_accuracy = 0.94098\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:57:31,273] Trial 39 finished with value: 0.7067498581962564 and parameters: {'n_d': 16, 'n_a': 8, 'n_steps': 8, 'gamma': 1.4138397892874734, 'lambda_sparse': 0.005707090935690004, 'momentum': 0.3896524847355101}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.31977 | valid_accuracy: 0.87443 |  0:00:00s\n",
-      "epoch 1  | loss: 0.14673 | valid_accuracy: 0.91553 |  0:00:01s\n",
-      "epoch 2  | loss: 0.11759 | valid_accuracy: 0.91701 |  0:00:01s\n",
-      "epoch 3  | loss: 0.10481 | valid_accuracy: 0.92203 |  0:00:01s\n",
-      "epoch 4  | loss: 0.1001  | valid_accuracy: 0.93573 |  0:00:02s\n",
-      "epoch 5  | loss: 0.0901  | valid_accuracy: 0.93653 |  0:00:02s\n",
-      "epoch 6  | loss: 0.08319 | valid_accuracy: 0.93699 |  0:00:03s\n",
-      "epoch 7  | loss: 0.07801 | valid_accuracy: 0.93311 |  0:00:04s\n",
-      "epoch 8  | loss: 0.074   | valid_accuracy: 0.93687 |  0:00:04s\n",
-      "epoch 9  | loss: 0.07202 | valid_accuracy: 0.94155 |  0:00:05s\n",
-      "epoch 10 | loss: 0.07003 | valid_accuracy: 0.94007 |  0:00:05s\n",
-      "epoch 11 | loss: 0.0669  | valid_accuracy: 0.93082 |  0:00:06s\n",
-      "epoch 12 | loss: 0.066   | valid_accuracy: 0.94189 |  0:00:06s\n",
-      "epoch 13 | loss: 0.06317 | valid_accuracy: 0.93995 |  0:00:07s\n",
-      "epoch 14 | loss: 0.06402 | valid_accuracy: 0.94041 |  0:00:07s\n",
-      "epoch 15 | loss: 0.06095 | valid_accuracy: 0.9387  |  0:00:08s\n",
-      "epoch 16 | loss: 0.05869 | valid_accuracy: 0.93596 |  0:00:08s\n",
-      "epoch 17 | loss: 0.05896 | valid_accuracy: 0.94338 |  0:00:09s\n",
-      "epoch 18 | loss: 0.05731 | valid_accuracy: 0.93676 |  0:00:09s\n",
-      "epoch 19 | loss: 0.05453 | valid_accuracy: 0.93938 |  0:00:10s\n",
-      "epoch 20 | loss: 0.05415 | valid_accuracy: 0.94406 |  0:00:10s\n",
-      "epoch 21 | loss: 0.05257 | valid_accuracy: 0.94189 |  0:00:11s\n",
-      "epoch 22 | loss: 0.0537  | valid_accuracy: 0.9476  |  0:00:11s\n",
-      "epoch 23 | loss: 0.05393 | valid_accuracy: 0.93973 |  0:00:12s\n",
-      "epoch 24 | loss: 0.05513 | valid_accuracy: 0.94007 |  0:00:12s\n",
-      "epoch 25 | loss: 0.05287 | valid_accuracy: 0.93847 |  0:00:13s\n",
-      "epoch 26 | loss: 0.05136 | valid_accuracy: 0.94521 |  0:00:13s\n",
-      "epoch 27 | loss: 0.04965 | valid_accuracy: 0.93687 |  0:00:14s\n",
-      "epoch 28 | loss: 0.05255 | valid_accuracy: 0.93836 |  0:00:14s\n",
-      "epoch 29 | loss: 0.04759 | valid_accuracy: 0.94372 |  0:00:15s\n",
-      "Stop training because you reached max_epochs = 30 with best_epoch = 22 and best_valid_accuracy = 0.9476\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:57:46,859] Trial 40 finished with value: 0.7402376910016978 and parameters: {'n_d': 24, 'n_a': 48, 'n_steps': 3, 'gamma': 1.1667191264583925, 'lambda_sparse': 0.00010306424305630605, 'momentum': 0.1985963870918099}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.33659 | valid_accuracy: 0.81541 |  0:00:00s\n",
-      "epoch 1  | loss: 0.16148 | valid_accuracy: 0.921   |  0:00:00s\n",
-      "epoch 2  | loss: 0.13764 | valid_accuracy: 0.9355  |  0:00:01s\n",
-      "epoch 3  | loss: 0.11773 | valid_accuracy: 0.93858 |  0:00:01s\n",
-      "epoch 4  | loss: 0.10368 | valid_accuracy: 0.93904 |  0:00:02s\n",
-      "epoch 5  | loss: 0.08849 | valid_accuracy: 0.93642 |  0:00:02s\n",
-      "epoch 6  | loss: 0.08623 | valid_accuracy: 0.93916 |  0:00:03s\n",
-      "epoch 7  | loss: 0.08374 | valid_accuracy: 0.94406 |  0:00:03s\n",
-      "epoch 8  | loss: 0.08064 | valid_accuracy: 0.94155 |  0:00:04s\n",
-      "epoch 9  | loss: 0.07317 | valid_accuracy: 0.94543 |  0:00:05s\n",
-      "epoch 10 | loss: 0.07671 | valid_accuracy: 0.94452 |  0:00:05s\n",
-      "epoch 11 | loss: 0.07501 | valid_accuracy: 0.94509 |  0:00:05s\n",
-      "epoch 12 | loss: 0.07546 | valid_accuracy: 0.9387  |  0:00:06s\n",
-      "epoch 13 | loss: 0.07465 | valid_accuracy: 0.9355  |  0:00:07s\n",
-      "epoch 14 | loss: 0.06778 | valid_accuracy: 0.94315 |  0:00:07s\n",
-      "epoch 15 | loss: 0.0673  | valid_accuracy: 0.94498 |  0:00:07s\n",
-      "epoch 16 | loss: 0.07388 | valid_accuracy: 0.93379 |  0:00:08s\n",
-      "epoch 17 | loss: 0.07704 | valid_accuracy: 0.93961 |  0:00:08s\n",
-      "\n",
-      "Early stopping occurred at epoch 17 with best_epoch = 9 and best_valid_accuracy = 0.94543\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:57:56,146] Trial 41 finished with value: 0.749475890985325 and parameters: {'n_d': 32, 'n_a': 16, 'n_steps': 3, 'gamma': 1.228568193669895, 'lambda_sparse': 0.00877988970783116, 'momentum': 0.04866248413822538}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.33777 | valid_accuracy: 0.8629  |  0:00:00s\n",
-      "epoch 1  | loss: 0.16269 | valid_accuracy: 0.88311 |  0:00:00s\n",
-      "epoch 2  | loss: 0.13035 | valid_accuracy: 0.92454 |  0:00:01s\n",
-      "epoch 3  | loss: 0.10987 | valid_accuracy: 0.929   |  0:00:02s\n",
-      "epoch 4  | loss: 0.10458 | valid_accuracy: 0.92694 |  0:00:02s\n",
-      "epoch 5  | loss: 0.09602 | valid_accuracy: 0.93322 |  0:00:03s\n",
-      "epoch 6  | loss: 0.0927  | valid_accuracy: 0.93938 |  0:00:03s\n",
-      "epoch 7  | loss: 0.08976 | valid_accuracy: 0.92763 |  0:00:04s\n",
-      "epoch 8  | loss: 0.08873 | valid_accuracy: 0.9371  |  0:00:04s\n",
-      "epoch 9  | loss: 0.08053 | valid_accuracy: 0.93881 |  0:00:05s\n",
-      "epoch 10 | loss: 0.07759 | valid_accuracy: 0.93459 |  0:00:05s\n",
-      "epoch 11 | loss: 0.0775  | valid_accuracy: 0.93002 |  0:00:06s\n",
-      "epoch 12 | loss: 0.08402 | valid_accuracy: 0.92968 |  0:00:06s\n",
-      "epoch 13 | loss: 0.08826 | valid_accuracy: 0.93322 |  0:00:06s\n",
-      "epoch 14 | loss: 0.08031 | valid_accuracy: 0.93539 |  0:00:07s\n",
-      "\n",
-      "Early stopping occurred at epoch 14 with best_epoch = 6 and best_valid_accuracy = 0.93938\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:58:03,957] Trial 42 finished with value: 0.7324937027707809 and parameters: {'n_d': 32, 'n_a': 24, 'n_steps': 3, 'gamma': 1.2742240557233138, 'lambda_sparse': 0.007683519076360558, 'momentum': 0.011013535481353275}. Best is trial 3 with value: 0.7623080995235575.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.31833 | valid_accuracy: 0.76621 |  0:00:00s\n",
-      "epoch 1  | loss: 0.15936 | valid_accuracy: 0.90491 |  0:00:01s\n",
-      "epoch 2  | loss: 0.11391 | valid_accuracy: 0.92192 |  0:00:01s\n",
-      "epoch 3  | loss: 0.09934 | valid_accuracy: 0.9339  |  0:00:02s\n",
-      "epoch 4  | loss: 0.09147 | valid_accuracy: 0.93516 |  0:00:02s\n",
-      "epoch 5  | loss: 0.08413 | valid_accuracy: 0.93539 |  0:00:03s\n",
-      "epoch 6  | loss: 0.08536 | valid_accuracy: 0.9355  |  0:00:04s\n",
-      "epoch 7  | loss: 0.08246 | valid_accuracy: 0.9339  |  0:00:04s\n",
-      "epoch 8  | loss: 0.08266 | valid_accuracy: 0.94703 |  0:00:05s\n",
-      "epoch 9  | loss: 0.07919 | valid_accuracy: 0.94132 |  0:00:06s\n",
-      "epoch 10 | loss: 0.07745 | valid_accuracy: 0.93584 |  0:00:06s\n",
-      "epoch 11 | loss: 0.07453 | valid_accuracy: 0.93938 |  0:00:07s\n",
-      "epoch 12 | loss: 0.07515 | valid_accuracy: 0.93584 |  0:00:07s\n",
-      "epoch 13 | loss: 0.0729  | valid_accuracy: 0.93836 |  0:00:08s\n",
-      "epoch 14 | loss: 0.07833 | valid_accuracy: 0.9395  |  0:00:08s\n",
-      "epoch 15 | loss: 0.07302 | valid_accuracy: 0.93984 |  0:00:09s\n",
-      "epoch 16 | loss: 0.06809 | valid_accuracy: 0.93973 |  0:00:10s\n",
-      "\n",
-      "Early stopping occurred at epoch 16 with best_epoch = 8 and best_valid_accuracy = 0.94703\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:58:14,556] Trial 43 finished with value: 0.7668341708542713 and parameters: {'n_d': 32, 'n_a': 16, 'n_steps': 4, 'gamma': 1.0637727829168055, 'lambda_sparse': 0.0067730056400632, 'momentum': 0.04700564113367184}. Best is trial 43 with value: 0.7668341708542713.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.33348 | valid_accuracy: 0.88847 |  0:00:00s\n",
-      "epoch 1  | loss: 0.17942 | valid_accuracy: 0.88014 |  0:00:01s\n",
-      "epoch 2  | loss: 0.13183 | valid_accuracy: 0.91712 |  0:00:01s\n",
-      "epoch 3  | loss: 0.11399 | valid_accuracy: 0.92568 |  0:00:02s\n",
-      "epoch 4  | loss: 0.10637 | valid_accuracy: 0.93619 |  0:00:03s\n",
-      "epoch 5  | loss: 0.10003 | valid_accuracy: 0.93219 |  0:00:03s\n",
-      "epoch 6  | loss: 0.09927 | valid_accuracy: 0.93082 |  0:00:04s\n",
-      "epoch 7  | loss: 0.09199 | valid_accuracy: 0.9355  |  0:00:04s\n",
-      "epoch 8  | loss: 0.08574 | valid_accuracy: 0.93938 |  0:00:05s\n",
-      "epoch 9  | loss: 0.08799 | valid_accuracy: 0.93299 |  0:00:06s\n",
-      "epoch 10 | loss: 0.0914  | valid_accuracy: 0.93699 |  0:00:06s\n",
-      "epoch 11 | loss: 0.0822  | valid_accuracy: 0.93539 |  0:00:07s\n",
-      "epoch 12 | loss: 0.07794 | valid_accuracy: 0.9379  |  0:00:07s\n",
-      "epoch 13 | loss: 0.07885 | valid_accuracy: 0.93048 |  0:00:08s\n",
-      "epoch 14 | loss: 0.07626 | valid_accuracy: 0.94121 |  0:00:09s\n",
-      "epoch 15 | loss: 0.07289 | valid_accuracy: 0.94087 |  0:00:09s\n",
-      "epoch 16 | loss: 0.06912 | valid_accuracy: 0.93014 |  0:00:10s\n",
-      "epoch 17 | loss: 0.07172 | valid_accuracy: 0.9371  |  0:00:10s\n",
-      "epoch 18 | loss: 0.0686  | valid_accuracy: 0.94075 |  0:00:11s\n",
-      "epoch 19 | loss: 0.0701  | valid_accuracy: 0.94007 |  0:00:12s\n",
-      "epoch 20 | loss: 0.06486 | valid_accuracy: 0.94281 |  0:00:12s\n",
-      "epoch 21 | loss: 0.0695  | valid_accuracy: 0.94189 |  0:00:13s\n",
-      "epoch 22 | loss: 0.06483 | valid_accuracy: 0.93984 |  0:00:13s\n",
-      "epoch 23 | loss: 0.07076 | valid_accuracy: 0.9379  |  0:00:14s\n",
-      "epoch 24 | loss: 0.07237 | valid_accuracy: 0.93413 |  0:00:15s\n",
-      "epoch 25 | loss: 0.06788 | valid_accuracy: 0.93699 |  0:00:15s\n",
-      "epoch 26 | loss: 0.06267 | valid_accuracy: 0.93459 |  0:00:16s\n",
-      "epoch 27 | loss: 0.06219 | valid_accuracy: 0.93733 |  0:00:16s\n",
-      "epoch 28 | loss: 0.06379 | valid_accuracy: 0.94212 |  0:00:17s\n",
-      "\n",
-      "Early stopping occurred at epoch 28 with best_epoch = 20 and best_valid_accuracy = 0.94281\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:58:32,364] Trial 44 finished with value: 0.7125645438898451 and parameters: {'n_d': 40, 'n_a': 16, 'n_steps': 4, 'gamma': 1.0553647445745977, 'lambda_sparse': 0.004260884118394998, 'momentum': 0.09213632154541491}. Best is trial 43 with value: 0.7668341708542713.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.37053 | valid_accuracy: 0.85308 |  0:00:00s\n",
-      "epoch 1  | loss: 0.18977 | valid_accuracy: 0.91861 |  0:00:01s\n",
-      "epoch 2  | loss: 0.16849 | valid_accuracy: 0.92203 |  0:00:02s\n",
-      "epoch 3  | loss: 0.14414 | valid_accuracy: 0.93105 |  0:00:02s\n",
-      "epoch 4  | loss: 0.12881 | valid_accuracy: 0.93642 |  0:00:03s\n",
-      "epoch 5  | loss: 0.1242  | valid_accuracy: 0.93368 |  0:00:04s\n",
-      "epoch 6  | loss: 0.10982 | valid_accuracy: 0.93836 |  0:00:04s\n",
-      "epoch 7  | loss: 0.10474 | valid_accuracy: 0.93847 |  0:00:05s\n",
-      "epoch 8  | loss: 0.0969  | valid_accuracy: 0.94167 |  0:00:06s\n",
-      "epoch 9  | loss: 0.09312 | valid_accuracy: 0.93995 |  0:00:06s\n",
-      "epoch 10 | loss: 0.09079 | valid_accuracy: 0.93927 |  0:00:07s\n",
-      "epoch 11 | loss: 0.08816 | valid_accuracy: 0.93858 |  0:00:08s\n",
-      "epoch 12 | loss: 0.09465 | valid_accuracy: 0.94326 |  0:00:09s\n",
-      "epoch 13 | loss: 0.0861  | valid_accuracy: 0.94269 |  0:00:09s\n",
-      "epoch 14 | loss: 0.08523 | valid_accuracy: 0.94167 |  0:00:10s\n",
-      "epoch 15 | loss: 0.08406 | valid_accuracy: 0.94532 |  0:00:11s\n",
-      "epoch 16 | loss: 0.08572 | valid_accuracy: 0.93676 |  0:00:11s\n",
-      "epoch 17 | loss: 0.08231 | valid_accuracy: 0.93836 |  0:00:12s\n",
-      "epoch 18 | loss: 0.08121 | valid_accuracy: 0.94075 |  0:00:13s\n",
-      "epoch 19 | loss: 0.07751 | valid_accuracy: 0.94155 |  0:00:13s\n",
-      "epoch 20 | loss: 0.08151 | valid_accuracy: 0.94132 |  0:00:14s\n",
-      "epoch 21 | loss: 0.08475 | valid_accuracy: 0.93916 |  0:00:15s\n",
-      "epoch 22 | loss: 0.08126 | valid_accuracy: 0.94372 |  0:00:15s\n",
-      "epoch 23 | loss: 0.0778  | valid_accuracy: 0.93539 |  0:00:16s\n",
-      "\n",
-      "Early stopping occurred at epoch 23 with best_epoch = 15 and best_valid_accuracy = 0.94532\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:58:49,459] Trial 45 finished with value: 0.7474960463890353 and parameters: {'n_d': 24, 'n_a': 8, 'n_steps': 5, 'gamma': 1.0055647304698914, 'lambda_sparse': 0.00637503128174428, 'momentum': 0.14768674158804773}. Best is trial 43 with value: 0.7668341708542713.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.32825 | valid_accuracy: 0.87911 |  0:00:00s\n",
-      "epoch 1  | loss: 0.15042 | valid_accuracy: 0.92374 |  0:00:01s\n",
-      "epoch 2  | loss: 0.11754 | valid_accuracy: 0.91769 |  0:00:01s\n",
-      "epoch 3  | loss: 0.10857 | valid_accuracy: 0.92911 |  0:00:02s\n",
-      "epoch 4  | loss: 0.09521 | valid_accuracy: 0.92751 |  0:00:02s\n",
-      "epoch 5  | loss: 0.09114 | valid_accuracy: 0.93516 |  0:00:03s\n",
-      "epoch 6  | loss: 0.0875  | valid_accuracy: 0.93311 |  0:00:04s\n",
-      "epoch 7  | loss: 0.08057 | valid_accuracy: 0.93162 |  0:00:04s\n",
-      "epoch 8  | loss: 0.08178 | valid_accuracy: 0.92922 |  0:00:05s\n",
-      "epoch 9  | loss: 0.07724 | valid_accuracy: 0.93493 |  0:00:06s\n",
-      "epoch 10 | loss: 0.0752  | valid_accuracy: 0.92763 |  0:00:06s\n",
-      "epoch 11 | loss: 0.07963 | valid_accuracy: 0.93059 |  0:00:07s\n",
-      "epoch 12 | loss: 0.0779  | valid_accuracy: 0.93653 |  0:00:07s\n",
-      "epoch 13 | loss: 0.07947 | valid_accuracy: 0.93482 |  0:00:08s\n",
-      "epoch 14 | loss: 0.07601 | valid_accuracy: 0.93858 |  0:00:09s\n",
-      "epoch 15 | loss: 0.08031 | valid_accuracy: 0.94235 |  0:00:09s\n",
-      "epoch 16 | loss: 0.07341 | valid_accuracy: 0.93162 |  0:00:10s\n",
-      "epoch 17 | loss: 0.07114 | valid_accuracy: 0.93733 |  0:00:10s\n",
-      "epoch 18 | loss: 0.06835 | valid_accuracy: 0.93995 |  0:00:11s\n",
-      "epoch 19 | loss: 0.06766 | valid_accuracy: 0.93984 |  0:00:12s\n",
-      "epoch 20 | loss: 0.06208 | valid_accuracy: 0.9395  |  0:00:12s\n",
-      "epoch 21 | loss: 0.06305 | valid_accuracy: 0.93094 |  0:00:13s\n",
-      "epoch 22 | loss: 0.06895 | valid_accuracy: 0.9411  |  0:00:14s\n",
-      "epoch 23 | loss: 0.06489 | valid_accuracy: 0.93721 |  0:00:14s\n",
-      "\n",
-      "Early stopping occurred at epoch 23 with best_epoch = 15 and best_valid_accuracy = 0.94235\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:59:04,514] Trial 46 finished with value: 0.7174034695019585 and parameters: {'n_d': 40, 'n_a': 24, 'n_steps': 4, 'gamma': 1.1521457363834404, 'lambda_sparse': 0.0018429919548465087, 'momentum': 0.3563162284282082}. Best is trial 43 with value: 0.7668341708542713.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.45096 | valid_accuracy: 0.75845 |  0:00:00s\n",
-      "epoch 1  | loss: 0.25648 | valid_accuracy: 0.79201 |  0:00:01s\n",
-      "epoch 2  | loss: 0.22042 | valid_accuracy: 0.83664 |  0:00:02s\n",
-      "epoch 3  | loss: 0.20895 | valid_accuracy: 0.89041 |  0:00:03s\n",
-      "epoch 4  | loss: 0.19748 | valid_accuracy: 0.90228 |  0:00:03s\n",
-      "epoch 5  | loss: 0.18834 | valid_accuracy: 0.9145  |  0:00:04s\n",
-      "epoch 6  | loss: 0.17576 | valid_accuracy: 0.92979 |  0:00:05s\n",
-      "epoch 7  | loss: 0.16898 | valid_accuracy: 0.93094 |  0:00:06s\n",
-      "epoch 8  | loss: 0.1635  | valid_accuracy: 0.92854 |  0:00:07s\n",
-      "epoch 9  | loss: 0.1589  | valid_accuracy: 0.93447 |  0:00:08s\n",
-      "epoch 10 | loss: 0.15276 | valid_accuracy: 0.92409 |  0:00:08s\n",
-      "epoch 11 | loss: 0.14862 | valid_accuracy: 0.92808 |  0:00:09s\n",
-      "epoch 12 | loss: 0.1534  | valid_accuracy: 0.93276 |  0:00:10s\n",
-      "epoch 13 | loss: 0.14868 | valid_accuracy: 0.93288 |  0:00:11s\n",
-      "epoch 14 | loss: 0.14521 | valid_accuracy: 0.9371  |  0:00:11s\n",
-      "epoch 15 | loss: 0.16208 | valid_accuracy: 0.94155 |  0:00:12s\n",
-      "epoch 16 | loss: 0.14509 | valid_accuracy: 0.94224 |  0:00:13s\n",
-      "epoch 17 | loss: 0.13395 | valid_accuracy: 0.93447 |  0:00:14s\n",
-      "epoch 18 | loss: 0.12431 | valid_accuracy: 0.93653 |  0:00:15s\n",
-      "epoch 19 | loss: 0.12072 | valid_accuracy: 0.94247 |  0:00:15s\n",
-      "epoch 20 | loss: 0.12043 | valid_accuracy: 0.93801 |  0:00:16s\n",
-      "epoch 21 | loss: 0.12123 | valid_accuracy: 0.93162 |  0:00:17s\n",
-      "epoch 22 | loss: 0.12482 | valid_accuracy: 0.93493 |  0:00:18s\n",
-      "epoch 23 | loss: 0.1135  | valid_accuracy: 0.94555 |  0:00:19s\n",
-      "epoch 24 | loss: 0.11734 | valid_accuracy: 0.93014 |  0:00:19s\n",
-      "epoch 25 | loss: 0.11518 | valid_accuracy: 0.93459 |  0:00:20s\n",
-      "epoch 26 | loss: 0.11282 | valid_accuracy: 0.93801 |  0:00:21s\n",
-      "epoch 27 | loss: 0.1146  | valid_accuracy: 0.9363  |  0:00:22s\n",
-      "epoch 28 | loss: 0.11062 | valid_accuracy: 0.93619 |  0:00:23s\n",
-      "epoch 29 | loss: 0.10881 | valid_accuracy: 0.93231 |  0:00:23s\n",
-      "Stop training because you reached max_epochs = 30 with best_epoch = 23 and best_valid_accuracy = 0.94555\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:59:29,003] Trial 47 finished with value: 0.7406199021207178 and parameters: {'n_d': 24, 'n_a': 32, 'n_steps': 6, 'gamma': 1.3148856617955356, 'lambda_sparse': 0.0005546394043418927, 'momentum': 0.26455657959525036}. Best is trial 43 with value: 0.7668341708542713.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.37015 | valid_accuracy: 0.69977 |  0:00:00s\n",
-      "epoch 1  | loss: 0.25558 | valid_accuracy: 0.79418 |  0:00:01s\n",
-      "epoch 2  | loss: 0.22682 | valid_accuracy: 0.86941 |  0:00:01s\n",
-      "epoch 3  | loss: 0.20077 | valid_accuracy: 0.88128 |  0:00:02s\n",
-      "epoch 4  | loss: 0.16969 | valid_accuracy: 0.90285 |  0:00:02s\n",
-      "epoch 5  | loss: 0.14816 | valid_accuracy: 0.90913 |  0:00:03s\n",
-      "epoch 6  | loss: 0.1425  | valid_accuracy: 0.91313 |  0:00:04s\n",
-      "epoch 7  | loss: 0.12966 | valid_accuracy: 0.91792 |  0:00:04s\n",
-      "epoch 8  | loss: 0.11946 | valid_accuracy: 0.91027 |  0:00:05s\n",
-      "epoch 9  | loss: 0.1147  | valid_accuracy: 0.92477 |  0:00:06s\n",
-      "epoch 10 | loss: 0.11491 | valid_accuracy: 0.92249 |  0:00:06s\n",
-      "epoch 11 | loss: 0.1068  | valid_accuracy: 0.91941 |  0:00:07s\n",
-      "epoch 12 | loss: 0.10981 | valid_accuracy: 0.90468 |  0:00:07s\n",
-      "epoch 13 | loss: 0.10048 | valid_accuracy: 0.93151 |  0:00:08s\n",
-      "epoch 14 | loss: 0.09698 | valid_accuracy: 0.92842 |  0:00:08s\n",
-      "epoch 15 | loss: 0.09362 | valid_accuracy: 0.92831 |  0:00:09s\n",
-      "epoch 16 | loss: 0.08679 | valid_accuracy: 0.92808 |  0:00:10s\n",
-      "epoch 17 | loss: 0.08355 | valid_accuracy: 0.9266  |  0:00:10s\n",
-      "epoch 18 | loss: 0.08247 | valid_accuracy: 0.9242  |  0:00:11s\n",
-      "epoch 19 | loss: 0.08347 | valid_accuracy: 0.92671 |  0:00:11s\n",
-      "epoch 20 | loss: 0.08235 | valid_accuracy: 0.93128 |  0:00:12s\n",
-      "epoch 21 | loss: 0.08107 | valid_accuracy: 0.93265 |  0:00:13s\n",
-      "epoch 22 | loss: 0.07809 | valid_accuracy: 0.93151 |  0:00:13s\n",
-      "epoch 23 | loss: 0.08124 | valid_accuracy: 0.92968 |  0:00:14s\n",
-      "epoch 24 | loss: 0.0735  | valid_accuracy: 0.93059 |  0:00:14s\n",
-      "epoch 25 | loss: 0.07251 | valid_accuracy: 0.9242  |  0:00:15s\n",
-      "epoch 26 | loss: 0.07171 | valid_accuracy: 0.93059 |  0:00:16s\n",
-      "epoch 27 | loss: 0.07134 | valid_accuracy: 0.92991 |  0:00:16s\n",
-      "epoch 28 | loss: 0.07244 | valid_accuracy: 0.92968 |  0:00:17s\n",
-      "epoch 29 | loss: 0.06894 | valid_accuracy: 0.93174 |  0:00:17s\n",
-      "\n",
-      "Early stopping occurred at epoch 29 with best_epoch = 21 and best_valid_accuracy = 0.93265\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 17:59:47,384] Trial 48 finished with value: 0.6995926680244399 and parameters: {'n_d': 32, 'n_a': 16, 'n_steps': 4, 'gamma': 1.7151786447984039, 'lambda_sparse': 0.004674209747462921, 'momentum': 0.2814624393570832}. Best is trial 43 with value: 0.7668341708542713.\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.3458  | valid_accuracy: 0.79475 |  0:00:00s\n",
-      "epoch 1  | loss: 0.18822 | valid_accuracy: 0.87991 |  0:00:01s\n",
-      "epoch 2  | loss: 0.14911 | valid_accuracy: 0.91187 |  0:00:02s\n",
-      "epoch 3  | loss: 0.13122 | valid_accuracy: 0.91918 |  0:00:02s\n",
-      "epoch 4  | loss: 0.12069 | valid_accuracy: 0.92877 |  0:00:03s\n",
-      "epoch 5  | loss: 0.11757 | valid_accuracy: 0.93596 |  0:00:04s\n",
-      "epoch 6  | loss: 0.10929 | valid_accuracy: 0.93276 |  0:00:05s\n",
-      "epoch 7  | loss: 0.106   | valid_accuracy: 0.93299 |  0:00:05s\n",
-      "epoch 8  | loss: 0.11179 | valid_accuracy: 0.92683 |  0:00:06s\n",
-      "epoch 9  | loss: 0.1022  | valid_accuracy: 0.93276 |  0:00:07s\n",
-      "epoch 10 | loss: 0.09173 | valid_accuracy: 0.93881 |  0:00:07s\n",
-      "epoch 11 | loss: 0.09782 | valid_accuracy: 0.9371  |  0:00:08s\n",
-      "epoch 12 | loss: 0.09728 | valid_accuracy: 0.93436 |  0:00:09s\n",
-      "epoch 13 | loss: 0.09453 | valid_accuracy: 0.93527 |  0:00:09s\n",
-      "epoch 14 | loss: 0.08701 | valid_accuracy: 0.93413 |  0:00:10s\n",
-      "epoch 15 | loss: 0.08408 | valid_accuracy: 0.93881 |  0:00:11s\n",
-      "epoch 16 | loss: 0.09053 | valid_accuracy: 0.94064 |  0:00:12s\n",
-      "epoch 17 | loss: 0.08787 | valid_accuracy: 0.93505 |  0:00:12s\n",
-      "epoch 18 | loss: 0.08235 | valid_accuracy: 0.94235 |  0:00:13s\n",
-      "epoch 19 | loss: 0.07899 | valid_accuracy: 0.94098 |  0:00:14s\n",
-      "epoch 20 | loss: 0.07536 | valid_accuracy: 0.93687 |  0:00:14s\n",
-      "epoch 21 | loss: 0.07645 | valid_accuracy: 0.93687 |  0:00:15s\n",
-      "epoch 22 | loss: 0.07528 | valid_accuracy: 0.94201 |  0:00:16s\n",
-      "epoch 23 | loss: 0.07414 | valid_accuracy: 0.93779 |  0:00:17s\n",
-      "epoch 24 | loss: 0.07399 | valid_accuracy: 0.93756 |  0:00:17s\n",
-      "epoch 25 | loss: 0.07219 | valid_accuracy: 0.93596 |  0:00:18s\n",
-      "epoch 26 | loss: 0.07177 | valid_accuracy: 0.93505 |  0:00:19s\n",
-      "\n",
-      "Early stopping occurred at epoch 26 with best_epoch = 18 and best_valid_accuracy = 0.94235\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n",
-      "[I 2025-02-02 18:00:07,052] Trial 49 finished with value: 0.7167694896242288 and parameters: {'n_d': 48, 'n_a': 24, 'n_steps': 5, 'gamma': 1.1028661256752443, 'lambda_sparse': 0.003345545315056804, 'momentum': 0.20788217904522843}. Best is trial 43 with value: 0.7668341708542713.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best Hyperparameters: {'n_d': 32, 'n_a': 16, 'n_steps': 4, 'gamma': 1.0637727829168055, 'lambda_sparse': 0.0067730056400632, 'momentum': 0.04700564113367184}\n"
-     ]
-    }
-   ],
-   "source": [
-    "import optuna\n",
-    "from pytorch_tabnet.tab_model import TabNetClassifier\n",
-    "from sklearn.metrics import f1_score\n",
-    "\n",
-    "# Optuna 최적화 함수\n",
-    "def objective(trial):\n",
-    "    # 하이퍼파라미터 탐색 범위 설정\n",
-    "    n_d = trial.suggest_int(\"n_d\", 8, 64, step=8)\n",
-    "    n_a = trial.suggest_int(\"n_a\", 8, 64, step=8)\n",
-    "    n_steps = trial.suggest_int(\"n_steps\", 3, 10)\n",
-    "    gamma = trial.suggest_float(\"gamma\", 1.0, 2.0)\n",
-    "    lambda_sparse = trial.suggest_float(\"lambda_sparse\", 0.0001, 0.01, log=True)\n",
-    "    momentum = trial.suggest_float(\"momentum\", 0.01, 0.4)\n",
-    "\n",
-    "    # TabNet 모델 생성\n",
-    "    model = TabNetClassifier(\n",
-    "        n_d=n_d, n_a=n_a, n_steps=n_steps,\n",
-    "        gamma=gamma, lambda_sparse=lambda_sparse,\n",
-    "        momentum=momentum,\n",
-    "        seed=42\n",
-    "    )\n",
-    "\n",
-    "    # 모델 학습\n",
-    "    model.fit(\n",
-    "        X_train.to_numpy(), y_train.to_numpy(),\n",
-    "        eval_set=[(X_val.to_numpy(), y_val.to_numpy())],\n",
-    "        eval_name=['valid'],\n",
-    "        eval_metric=['balanced_accuracy'],\n",
-    "        max_epochs=25,\n",
-    "        patience=6,\n",
-    "        batch_size=1024,\n",
-    "        virtual_batch_size=128,\n",
-    "        verbose=0\n",
-    "    )\n",
-    "\n",
-    "    # Validation 데이터로 F1-score 계산\n",
-    "    y_pred_val = model.predict(X_val.to_numpy())\n",
-    "    val_score = f1_score(y_val.to_numpy(), y_pred_val)\n",
-    "\n",
-    "    return val_score  # 최적화를 F1-score 기준으로 진행\n",
-    "\n",
-    "# Optuna 실행\n",
-    "sampler = optuna.samplers.TPESampler(seed=seed)\n",
-    "study = optuna.create_study(direction=\"maximize\", sampler=sampler)\n",
-    "study.optimize(objective, n_trials=50)  # 50번 탐색 실행\n",
-    "\n",
-    "# 최적 하이퍼파라미터 출력\n",
-    "best_params = study.best_trial.params\n",
-    "print(\"Best Hyperparameters:\", best_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 모델 학습 루프"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.31833 | valid_balanced_accuracy: 0.82671 |  0:00:00s\n",
-      "epoch 1  | loss: 0.15936 | valid_balanced_accuracy: 0.87756 |  0:00:01s\n",
-      "epoch 2  | loss: 0.11391 | valid_balanced_accuracy: 0.87688 |  0:00:01s\n",
-      "epoch 3  | loss: 0.09934 | valid_balanced_accuracy: 0.82004 |  0:00:02s\n",
-      "epoch 4  | loss: 0.09147 | valid_balanced_accuracy: 0.82121 |  0:00:03s\n",
-      "epoch 5  | loss: 0.08413 | valid_balanced_accuracy: 0.7819  |  0:00:03s\n",
-      "epoch 6  | loss: 0.08536 | valid_balanced_accuracy: 0.85017 |  0:00:04s\n",
-      "epoch 7  | loss: 0.08246 | valid_balanced_accuracy: 0.76575 |  0:00:04s\n",
-      "epoch 8  | loss: 0.08266 | valid_balanced_accuracy: 0.88493 |  0:00:05s\n",
-      "epoch 9  | loss: 0.07919 | valid_balanced_accuracy: 0.82698 |  0:00:06s\n",
-      "epoch 10 | loss: 0.07745 | valid_balanced_accuracy: 0.80953 |  0:00:06s\n",
-      "epoch 11 | loss: 0.07453 | valid_balanced_accuracy: 0.81522 |  0:00:07s\n",
-      "epoch 12 | loss: 0.07515 | valid_balanced_accuracy: 0.85778 |  0:00:07s\n",
-      "epoch 13 | loss: 0.0729  | valid_balanced_accuracy: 0.82253 |  0:00:08s\n",
-      "epoch 14 | loss: 0.07833 | valid_balanced_accuracy: 0.86493 |  0:00:09s\n",
-      "epoch 15 | loss: 0.07302 | valid_balanced_accuracy: 0.8062  |  0:00:09s\n",
-      "epoch 16 | loss: 0.06809 | valid_balanced_accuracy: 0.82516 |  0:00:10s\n",
-      "\n",
-      "Early stopping occurred at epoch 16 with best_epoch = 8 and best_valid_balanced_accuracy = 0.88493\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
-      "  warnings.warn(wrn_msg)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Successfully saved model at ./models/tabnet_model.zip\n",
-      "Validation F1-Score: 0.7668\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:454: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
-      "  saved_state_dict = torch.load(f, map_location=self.device)\n",
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
-      "  warnings.warn(f\"Device used : {self.device}\")\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pytorch_tabnet.tab_model import TabNetClassifier\n",
-    "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n",
-    "import numpy as np\n",
-    "\n",
-    "# 최적 하이퍼파라미터 적용\n",
-    "best_model = TabNetClassifier(\n",
-    "    n_d=best_params[\"n_d\"],\n",
-    "    n_a=best_params[\"n_a\"],\n",
-    "    n_steps=best_params[\"n_steps\"],\n",
-    "    gamma=best_params[\"gamma\"],\n",
-    "    lambda_sparse=best_params[\"lambda_sparse\"],\n",
-    "    momentum=best_params[\"momentum\"],\n",
-    "    seed=42  # 시드 고정\n",
-    ")\n",
-    "\n",
-    "# Early Stopping 설정\n",
-    "best_val_f1 = -float(\"inf\")  # F1-score는 최대화가 목표\n",
-    "patience = 8  # F1-score가 개선되지 않는 Epoch 수\n",
-    "counter = 0\n",
-    "\n",
-    "# 모델 학습\n",
-    "best_model.fit(\n",
-    "    X_train.to_numpy(), y_train.to_numpy(),\n",
-    "    eval_set=[(X_val.to_numpy(), y_val.to_numpy())],\n",
-    "    eval_name=['valid'],\n",
-    "    eval_metric=['balanced_accuracy'],  # TabNet은 F1-score를 지원하지 않음 (balanced_accuracy 사용)\n",
-    "    max_epochs=50,  # 최대 Epoch\n",
-    "    patience=patience,  # Early stopping 적용\n",
-    "    batch_size=1024,\n",
-    "    virtual_batch_size=128\n",
-    ")\n",
-    "\n",
-    "# 학습이 끝난 후 모델 저장 (경로 명확히 지정)\n",
-    "best_model.save_model(\"./models/tabnet_model\")\n",
-    "\n",
-    "# 저장된 모델 불러오기\n",
-    "best_model.load_model(\"./models/tabnet_model.zip\")\n",
-    "\n",
-    "# Validation 데이터 평가\n",
-    "y_pred_val = best_model.predict(X_val.to_numpy())\n",
-    "val_f1 = f1_score(y_val.to_numpy(), y_pred_val)\n",
-    "\n",
-    "print(f\"Validation F1-Score: {val_f1:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 성능 평가"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test Accuracy: 0.8888127853881278\n",
-      "Test AUC: 0.8782745963906583\n",
-      "Test F1-Score: 0.46775956284153003\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda3/lib/python3.12/site-packages/pytorch_tabnet/abstract_model.py:454: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
-      "  saved_state_dict = torch.load(f, map_location=self.device)\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score\n",
-    "\n",
-    "# 저장된 모델 불러오기\n",
-    "loaded_model = TabNetClassifier()\n",
-    "loaded_model.load_model(\"./models/tabnet_model.zip\")\n",
-    "\n",
-    "# Test 데이터 평가\n",
-    "y_pred_test = loaded_model.predict(X_test.to_numpy())\n",
-    "\n",
-    "# 성능 평가 지표 계산\n",
-    "print(\"Test Accuracy:\", accuracy_score(y_test, y_pred_test))\n",
-    "print(\"Test AUC:\", roc_auc_score(y_test, y_pred_test))\n",
-    "print(\"Test F1-Score:\", f1_score(y_test, y_pred_test))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Analysis_code/deeplearning_model_multi.ipynb b/Analysis_code/deeplearning_model_multi.ipynb
deleted file mode 100644
index 7711169cbf84ff911b53871586cfa43239980a3b..0000000000000000000000000000000000000000
--- a/Analysis_code/deeplearning_model_multi.ipynb
+++ /dev/null
@@ -1,2676 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 통합 적용 모델 구성"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 통일 데이터셋 변환 함수 정의"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'%pip install torch torchvision scikit-learn'"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'''%pip install torch torchvision scikit-learn'''"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import numpy as np\n",
-    "import random\n",
-    "\n",
-    "# Python 및 Numpy 시드 고정\n",
-    "seed = 42\n",
-    "random.seed(seed)\n",
-    "np.random.seed(seed)\n",
-    "\n",
-    "# PyTorch 시드 고정\n",
-    "torch.manual_seed(seed)\n",
-    "torch.cuda.manual_seed(seed)\n",
-    "torch.cuda.manual_seed_all(seed)  # Multi-GPU 환경에서 동일한 시드 적용\n",
-    "\n",
-    "# PyTorch 연산의 결정적 모드 설정\n",
-    "torch.backends.cudnn.deterministic = True  # 실행마다 동일한 결과를 보장\n",
-    "torch.backends.cudnn.benchmark = True  # 성능 최적화를 활성화 (가능한 한 빠른 연산 수행)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
-    "import torch\n",
-    "from torch.utils.data import DataLoader, TensorDataset\n",
-    "import random\n",
-    "\n",
-    "# 전처리 함수\n",
-    "def preprocessing(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df['lm_cloudcover'] = df['lm_cloudcover'].astype('int')\n",
-    "    df['cloudcover'] = df['cloudcover'].astype('int')\n",
-    "    return df\n",
-    "\n",
-    "# 데이터셋 준비 함수\n",
-    "def prepare_dataset(region, data_sample='pure', target='multi', fold=3):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    return X_train, X_val, X_test, y_train, y_val, y_test, categorical_cols, numerical_cols"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
-    "import torch\n",
-    "from torch.utils.data import DataLoader, TensorDataset\n",
-    "import random\n",
-    "\n",
-    "# 전처리 함수\n",
-    "def preprocessing(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df['lm_cloudcover'] = df['lm_cloudcover'].astype('int')\n",
-    "    df['cloudcover'] = df['cloudcover'].astype('int')\n",
-    "    return df\n",
-    "\n",
-    "# 데이터 변환 및 dataloader 생성 함수\n",
-    "def prepare_dataloader(region, data_sample='pure', target='multi', fold=3, random_state=None):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    # 연속형 변수와 범주형 변수 분리\n",
-    "    X_train_num = torch.tensor(X_train[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_train_cat = torch.tensor(X_train[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_val_num = torch.tensor(X_val[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_val_cat = torch.tensor(X_val[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_test_num = torch.tensor(X_test[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_test_cat = torch.tensor(X_test[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    # 레이블 변환\n",
-    "    if target == \"binary\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.float32)  # 이진 분류 → float32\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.float32)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32)\n",
-    "    elif target == \"multi\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.long)  # 다중 분류 → long\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.long)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.long)\n",
-    "    else:\n",
-    "        raise ValueError(\"target must be 'binary' or 'multi'\")\n",
-    "\n",
-    "    # TensorDataset 생성\n",
-    "    train_dataset = TensorDataset(X_train_num, X_train_cat, y_train_tensor)\n",
-    "    val_dataset = TensorDataset(X_val_num, X_val_cat, y_val_tensor)\n",
-    "    test_dataset = TensorDataset(X_test_num, X_test_cat, y_test_tensor)\n",
-    "\n",
-    "    # DataLoader 생성\n",
-    "    if random_state == None:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
-    "    else:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, generator=torch.Generator().manual_seed(random_state))\n",
-    "    val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n",
-    "    test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
-    "    \n",
-    "    return X_train, categorical_cols, numerical_cols, train_loader, val_loader, test_loader"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 사용자 정의 성능지표 함수"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import confusion_matrix\n",
-    "from sklearn.utils.class_weight import compute_class_weight\n",
-    "\n",
-    "def calculate_csi(Y_test, pred):\n",
-    "\n",
-    "    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경\n",
-    "    # 혼동 행렬에서 H, F, M 추출\n",
-    "    H = (cm[0, 0] + cm[1, 1])\n",
-    "    \n",
-    "    F = (cm[1, 0] + cm[2, 0] +\n",
-    "         cm[0, 1] + cm[2, 1])\n",
-    "    \n",
-    "    M = (cm[0, 2] + cm[1, 2])\n",
-    "    \n",
-    "    # CSI 계산\n",
-    "    CSI = H / (H + F + M + 1e-10)\n",
-    "    return CSI\n",
-    "\n",
-    "def eval_metric_csi(y_true, pred_prob):\n",
-    "\n",
-    "    pred = np.argmax(pred_prob, axis=1)\n",
-    "    y_true = y_true\n",
-    "    y_pred = pred\n",
-    "    csi = calculate_csi(y_true, y_pred)\n",
-    "    return -1*csi\n",
-    "\n",
-    "def sample_weight(y_train):\n",
-    "    class_weights = compute_class_weight(\n",
-    "        class_weight='balanced',\n",
-    "        classes=np.unique(y_train),  # 고유 클래스\n",
-    "        y=y_train                   # 학습 데이터 레이블\n",
-    "    )\n",
-    "    sample_weights = np.array([class_weights[label] for label in y_train])\n",
-    "\n",
-    "    return sample_weights"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 통일 하이퍼파라미터 최적화 함수 정의"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'%pip install --upgrade ipywidgets'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'''%pip install --upgrade ipywidgets'''"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import optuna\n",
-    "from sklearn.metrics import accuracy_score, f1_score\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "from ft_transformer import FTTransformer\n",
-    "from resnet_like import ResNetLike\n",
-    "from deepgbm import DeepGBM\n",
-    "\n",
-    "# Optuna의 Trial 로그 숨기기 (WARNING 레벨 이상만 출력)\n",
-    "optuna.logging.set_verbosity(optuna.logging.WARNING)\n",
-    "\n",
-    "# 모델을 GPU로 전송\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "\n",
-    "# 하이퍼파라미터 최적화 함수 정의\n",
-    "def objective(trial, model_choose, region, data_sample='pure', target='multi', n_folds=3, random_state=None):\n",
-    "\n",
-    "    val_scores = []\n",
-    "\n",
-    "    # fold별로 반복\n",
-    "    for fold in range(1, n_folds+1):\n",
-    "        X_train, categorical_cols, numerical_cols, train_loader, val_loader, _ = prepare_dataloader(region, data_sample=data_sample, target=target, fold=fold, random_state=random_state)\n",
-    "\n",
-    "        if model_choose == \"ft_transformer\":\n",
-    "            d_token = trial.suggest_categorical(\"d_token\", [64, 128, 192, 256])\n",
-    "            n_blocks = trial.suggest_int(\"n_blocks\", 4, 8)\n",
-    "            attention_dropout = trial.suggest_float(\"attention_dropout\", 0.2, 0.5)\n",
-    "            ffn_dropout = trial.suggest_float(\"ffn_dropout\", 0.2, 0.5)\n",
-    "            lr = trial.suggest_float(\"lr\", 1e-4, 1e-3, log=True)\n",
-    "            weight_decay = trial.suggest_float(\"weight_decay\", 1e-5, 1e-3, log=True)\n",
-    "\n",
-    "            # FT-Transformer 초기화(다중분류: 3개 범주)\n",
-    "            model = FTTransformer(\n",
-    "                num_features=len(numerical_cols),\n",
-    "                cat_cardinalities=[len(X_train[col].unique()) for col in categorical_cols],\n",
-    "                d_token=d_token,\n",
-    "                n_blocks=n_blocks,\n",
-    "                attention_dropout=attention_dropout,\n",
-    "                ffn_dropout=ffn_dropout,\n",
-    "                num_classes=3\n",
-    "            ).to(device)\n",
-    "\n",
-    "        elif model_choose == 'resnet_like':\n",
-    "            # 하이퍼파라미터 탐색 공간 정의\n",
-    "            d_main = trial.suggest_categorical(\"d_main\", [64, 128, 192, 256])\n",
-    "            d_hidden = trial.suggest_categorical(\"d_hidden\", [32, 64, 128])\n",
-    "            n_blocks = trial.suggest_int(\"n_blocks\", 3, 8)  # ResNet 블록 수\n",
-    "            dropout_first = trial.suggest_float(\"dropout_first\", 0.1, 0.5)  # 첫 번째 Dropout\n",
-    "            dropout_second = trial.suggest_float(\"dropout_second\", 0.0, 0.3)  # 두 번째 Dropout\n",
-    "            lr = trial.suggest_float(\"lr\", 1e-4, 1e-2, log=True)  # 학습률\n",
-    "            weight_decay = trial.suggest_float(\"weight_decay\", 1e-6, 1e-3, log=True)  # L2 정규화\n",
-    "\n",
-    "            # 연속형 변수 + 범주형 변수 개수 반영하여 모델 입력 크기 설정\n",
-    "            input_dim = len(numerical_cols) + len(categorical_cols)\n",
-    "\n",
-    "            # 모델 초기화 및 GPU로 이동\n",
-    "            model = ResNetLike(\n",
-    "                input_dim=input_dim,\n",
-    "                d_main=d_main, \n",
-    "                d_hidden=d_hidden, \n",
-    "                n_blocks=n_blocks, \n",
-    "                dropout_first=dropout_first, \n",
-    "                dropout_second=dropout_second,\n",
-    "                num_classes=3\n",
-    "            ).to(device)\n",
-    "\n",
-    "        elif model_choose == 'deepgbm':\n",
-    "            d_main = trial.suggest_categorical(\"d_main\", [64, 128, 192, 256])\n",
-    "            d_hidden = trial.suggest_categorical(\"d_hidden\", [32, 64, 128])\n",
-    "            n_blocks = trial.suggest_int(\"n_blocks\", 3, 8)  # ResNet 블록 개수\n",
-    "            dropout = trial.suggest_float(\"dropout\", 0.1, 0.5)  # Dropout 비율\n",
-    "            lr = trial.suggest_float(\"lr\", 1e-4, 1e-3, log=True)  # 학습률\n",
-    "            weight_decay = trial.suggest_float(\"weight_decay\", 1e-5, 1e-3, log=True)  # 정규화\n",
-    "\n",
-    "            # DeepGBM 모델 초기화 (x_num, x_cat을 따로 받는 구조)\n",
-    "            model = DeepGBM(\n",
-    "                num_features=len(numerical_cols),\n",
-    "                cat_features=[len(X_train[col].unique()) for col in categorical_cols],\n",
-    "                d_main=d_main,\n",
-    "                d_hidden=d_hidden,\n",
-    "                n_blocks=n_blocks,\n",
-    "                dropout=dropout,\n",
-    "                num_classes=3\n",
-    "            ).to(device)\n",
-    "\n",
-    "        # 손실 함수 및 옵티마이저 설정\n",
-    "        if target == 'binary':\n",
-    "            criterion = nn.BCEWithLogitsLoss()  # 이진 분류용\n",
-    "        elif target == 'multi':\n",
-    "            criterion = nn.CrossEntropyLoss()   # 다중 분류용\n",
-    "\n",
-    "        # 가중치 조정\n",
-    "        optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
-    "\n",
-    "        # 학습 설정\n",
-    "        epochs = 50  # epoch 증가\n",
-    "        patience = 8  # Early Stopping 기준 (8 epoch 동안 개선 없으면 중지)\n",
-    "        best_val_score = 0  \n",
-    "        counter = 0  \n",
-    "\n",
-    "        for epoch in range(epochs):\n",
-    "            model.train()\n",
-    "            for x_num_batch, x_cat_batch, y_batch in train_loader:\n",
-    "                x_num_batch, x_cat_batch, y_batch = (\n",
-    "                    x_num_batch.to(device),\n",
-    "                    x_cat_batch.to(device),\n",
-    "                    y_batch.to(device)\n",
-    "                )\n",
-    "                optimizer.zero_grad()\n",
-    "                y_pred = model(x_num_batch, x_cat_batch)\n",
-    "\n",
-    "                # 손실 계산 (이진 분류 | 다중 분류)\n",
-    "                if target == 'binary':\n",
-    "                    loss = criterion(y_pred, y_batch.float())\n",
-    "                elif target == 'multi':\n",
-    "                    loss = criterion(y_pred, y_batch)\n",
-    "\n",
-    "                loss.backward()\n",
-    "                optimizer.step()\n",
-    "\n",
-    "            # Validation 평가\n",
-    "            model.eval()\n",
-    "            y_pred_val, y_true_val = [], []\n",
-    "            with torch.no_grad():\n",
-    "                for x_num_batch, x_cat_batch, y_batch in val_loader:\n",
-    "                    x_num_batch, x_cat_batch, y_batch = (\n",
-    "                        x_num_batch.to(device),\n",
-    "                        x_cat_batch.to(device),\n",
-    "                        y_batch.to(device)\n",
-    "                    )\n",
-    "                    output = model(x_num_batch, x_cat_batch)\n",
-    "\n",
-    "                    if target == 'binary':\n",
-    "                        pred = (torch.sigmoid(output) >= 0.5).long()\n",
-    "                    elif target == 'multi':\n",
-    "                        pred = output.argmax(dim=1)\n",
-    "\n",
-    "                    y_pred_val.extend(pred.cpu().numpy())  \n",
-    "                    y_true_val.extend(y_batch.cpu().numpy())\n",
-    "\n",
-    "            # csi-score 계산 (다중클래스용)\n",
-    "            val_csi = calculate_csi(y_true_val, y_pred_val)  \n",
-    "\n",
-    "            # Optuna Pruning 적용 (조기 종료)\n",
-    "            trial.report(val_csi, epoch)\n",
-    "            if trial.should_prune():\n",
-    "                raise optuna.exceptions.TrialPruned()\n",
-    "\n",
-    "            # Early Stopping 체크\n",
-    "            if val_csi > best_val_score:\n",
-    "                best_val_score = val_csi\n",
-    "                counter = 0  # 개선되었으므로 카운터 초기화\n",
-    "            else:\n",
-    "                counter += 1  # 개선되지 않으면 카운터 증가\n",
-    "\n",
-    "            if counter >= patience:\n",
-    "                break  # Early Stopping 발동\n",
-    "\n",
-    "        val_scores.append(best_val_score)\n",
-    "\n",
-    "        # 모든 fold에서 평균 성능을 반환\n",
-    "        avg_val_score = sum(val_scores) / len(val_scores)\n",
-    "        return avg_val_score"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 통일 최적화 + soft voting 함수 정의"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import optuna\n",
-    "from sklearn.metrics import accuracy_score, f1_score\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "from ft_transformer import FTTransformer\n",
-    "from resnet_like import ResNetLike\n",
-    "from deepgbm import DeepGBM\n",
-    "import copy\n",
-    "import os\n",
-    "\n",
-    "# 모델을 GPU로 전송\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "\n",
-    "def fold_voting(model_choose, region, data_sample='pure', target='multi', n_folds=3, random_state = None):\n",
-    "\n",
-    "    # Optuna 실행\n",
-    "    sampler = optuna.samplers.TPESampler(seed=seed)\n",
-    "    study = optuna.create_study(direction=\"maximize\", sampler=sampler)\n",
-    "    study.optimize(lambda trial: objective(trial, model_choose=model_choose, region=region, data_sample=data_sample, target=target, n_folds=n_folds, random_state=random_state), n_trials=50, show_progress_bar=True)\n",
-    "\n",
-    "    # 최적의 하이퍼파라미터 가져오기\n",
-    "    best_params = study.best_trial.params\n",
-    "    print(f\"### Best Params (All Folds): {best_params} ###\")\n",
-    "\n",
-    "    model_paths = []\n",
-    "\n",
-    "    for fold in range(1, n_folds + 1):\n",
-    "        X_train, categorical_cols, numerical_cols, train_loader, val_loader, _ = prepare_dataloader(region=region, data_sample=data_sample, target=target, fold=fold, random_state=seed)\n",
-    "\n",
-    "        # 구현모델 선택\n",
-    "        if model_choose == 'ft_transformer':\n",
-    "            # FT-Transformer 초기화 (최적화된 하이퍼파라미터로 설정)\n",
-    "            model = FTTransformer(\n",
-    "                num_features=len(numerical_cols),\n",
-    "                cat_cardinalities=[len(X_train[col].unique()) for col in categorical_cols],\n",
-    "                d_token=best_params[\"d_token\"],\n",
-    "                n_blocks=best_params[\"n_blocks\"],\n",
-    "                attention_dropout=best_params[\"attention_dropout\"],\n",
-    "                ffn_dropout=best_params[\"ffn_dropout\"],\n",
-    "                num_classes=3\n",
-    "            ).to(device)\n",
-    "        elif model_choose == 'resnet_like':\n",
-    "            # ResNet-Like 초기화 (최적화된 하이퍼파라미터로 설정)\n",
-    "            model = ResNetLike(\n",
-    "                input_dim=len(numerical_cols) + len(categorical_cols),  # 입력 차원\n",
-    "                d_main=best_params[\"d_main\"],\n",
-    "                d_hidden=best_params[\"d_hidden\"],\n",
-    "                n_blocks=best_params[\"n_blocks\"],\n",
-    "                dropout_first=best_params[\"dropout_first\"],\n",
-    "                dropout_second=best_params[\"dropout_second\"],\n",
-    "                num_classes=3\n",
-    "            ).to(device)\n",
-    "        elif model_choose == 'deepgbm':\n",
-    "            # DeepGBM 초기화 (최적화된 하이퍼파라미터로 설정)\n",
-    "            model = DeepGBM(\n",
-    "                num_features=len(numerical_cols),\n",
-    "                cat_features=[len(X_train[col].unique()) for col in categorical_cols],\n",
-    "                d_main=best_params[\"d_main\"],\n",
-    "                d_hidden=best_params[\"d_hidden\"],\n",
-    "                n_blocks=best_params[\"n_blocks\"],\n",
-    "                dropout=best_params[\"dropout\"],\n",
-    "                num_classes=3\n",
-    "            ).to(device)\n",
-    "\n",
-    "        # 손실 함수 및 옵티마이저 설정\n",
-    "        if target == 'binary':\n",
-    "            criterion = nn.BCEWithLogitsLoss()  # 이진 분류용\n",
-    "        elif target == 'multi':\n",
-    "            criterion = nn.CrossEntropyLoss()   # 다중 분류용\n",
-    "        optimizer_ft = optim.AdamW(model.parameters(), lr=best_params[\"lr\"], weight_decay=best_params[\"weight_decay\"])\n",
-    "\n",
-    "        # Early Stopping 설정\n",
-    "        best_csi = -float('inf')  # CSI-Score는 최대화가 목표이므로 -inf로 초기화\n",
-    "        patience = 10  # F1-Score가 개선되지 않는 Epoch 수\n",
-    "        counter = 0  # 개선되지 않은 Epoch 수를 기록\n",
-    "        best_model = None\n",
-    "\n",
-    "        # 학습 루프\n",
-    "        epochs = 50  # 최대 Epoch 수\n",
-    "        for epoch in range(epochs):\n",
-    "            # Training Phase\n",
-    "            model.train()\n",
-    "            for x_num_batch, x_cat_batch, y_batch in train_loader:\n",
-    "                x_num_batch, x_cat_batch, y_batch = (\n",
-    "                    x_num_batch.to(device),\n",
-    "                    x_cat_batch.to(device),\n",
-    "                    y_batch.to(device),\n",
-    "                )\n",
-    "                optimizer_ft.zero_grad()\n",
-    "                y_pred = model(x_num_batch, x_cat_batch)\n",
-    "                \n",
-    "                # 손실 계산 (이진 분류 | 다중 분류)\n",
-    "                if target == 'binary':\n",
-    "                    loss = criterion(y_pred.squeeze(-1), y_batch.float())\n",
-    "                elif target == 'multi':\n",
-    "                    loss = criterion(y_pred, y_batch)\n",
-    "\n",
-    "                loss.backward()\n",
-    "                optimizer_ft.step()\n",
-    "\n",
-    "            # Validation Phase\n",
-    "            model.eval()\n",
-    "            y_true_val, y_pred_val = [], []\n",
-    "            with torch.no_grad():\n",
-    "                for x_num_batch, x_cat_batch, y_batch in val_loader:\n",
-    "                    x_num_batch, x_cat_batch, y_batch = (\n",
-    "                        x_num_batch.to(device),\n",
-    "                        x_cat_batch.to(device),\n",
-    "                        y_batch.to(device),\n",
-    "                    )\n",
-    "                    y_pred = model(x_num_batch, x_cat_batch)\n",
-    "\n",
-    "                    if target == 'binary':\n",
-    "                        pred = (torch.sigmoid(y_pred) >= 0.5).long()\n",
-    "                    elif target == 'multi':\n",
-    "                        pred = y_pred.argmax(dim=1)\n",
-    "                    y_true_val.extend(y_batch.cpu().numpy())\n",
-    "                    y_pred_val.extend(pred.cpu().numpy())  # 가장 높은 확률의 클래스 선택\n",
-    "\n",
-    "            # CSI-Score 계산\n",
-    "            val_csi = calculate_csi(y_true_val, y_pred_val)\n",
-    "\n",
-    "            # Early Stopping 체크\n",
-    "            if val_csi > best_csi:\n",
-    "                best_csi = val_csi\n",
-    "                counter = 0\n",
-    "                best_model = copy.deepcopy(model)\n",
-    "            else:\n",
-    "                counter += 1\n",
-    "                if counter >= patience:\n",
-    "                    print(f\"Early stopping at epoch {epoch+1}\")\n",
-    "                    break\n",
-    "                    \n",
-    "        # 모델 저장 경로 설정\n",
-    "        save_dir = f\"./save_model/{model_choose}/{data_sample}\"\n",
-    "        os.makedirs(save_dir, exist_ok=True)  # 폴더 없으면 자동 생성\n",
-    "\n",
-    "        # 모델 저장\n",
-    "        model_path = f\"./save_model/{model_choose}/{data_sample}/{region}_fold{fold}.pth\"\n",
-    "        torch.save(best_model, model_path)\n",
-    "        model_paths.append(model_path)\n",
-    "        print(f\"Saving model to {model_path}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 사용자 soft voting 정의 함수"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "\n",
-    "# 모델을 GPU로 전송\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "\n",
-    "# Soft Voting 앙상블\n",
-    "def pred_fold(region, model_choose, data_sample, fold, target='multi'):\n",
-    "    _, _, _, _, _, y_test, _, _ = prepare_dataset(region=region, data_sample=data_sample, target=target)\n",
-    "    _, _, _, _, _, test_loader = prepare_dataloader(region=region, data_sample=data_sample, target=target, random_state=seed)\n",
-    "\n",
-    "    folder_path = f'./save_model/{model_choose}/{data_sample}'\n",
-    "    model_paths = [path for path in glob.glob(f'{folder_path}/*.pth') if f'{region}' in path]\n",
-    "\n",
-    "    model = torch.load(model_paths[fold-1], weights_only=False).to(device)\n",
-    "    model.eval()\n",
-    "\n",
-    "    test_preds = []\n",
-    "    with torch.no_grad():\n",
-    "        for x_num_batch, x_cat_batch, _ in test_loader:\n",
-    "            output = model(x_num_batch.to(device), x_cat_batch.to(device))\n",
-    "            output = torch.softmax(output, dim=1)\n",
-    "            test_preds.extend(output.cpu().numpy())\n",
-    "\n",
-    "    # 최종 예측 (Soft Voting)\n",
-    "    final_preds = np.argmax(test_preds, axis=1)\n",
-    "\n",
-    "    return y_test, final_preds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "\n",
-    "# 모델을 GPU로 전송\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "\n",
-    "# Soft Voting 앙상블\n",
-    "def soft_voting(region, model_choose, data_sample, target='multi'):\n",
-    "    _, _, _, _, _, y_test, _, _ = prepare_dataset(region=region, data_sample=data_sample, target=target)\n",
-    "    _, _, _, _, _, test_loader = prepare_dataloader(region=region, data_sample=data_sample, target=target, random_state=seed)\n",
-    "\n",
-    "    folder_path = f'./save_model/{model_choose}/{data_sample}'\n",
-    "    model_paths = [path for path in glob.glob(f'{folder_path}/*.pth') if f'{region}' in path]\n",
-    "\n",
-    "    if target == 'multi':\n",
-    "        test_probs = np.zeros((len(y_test), 3))\n",
-    "    elif target == 'binary':\n",
-    "        test_probs = np.zeros((len(y_test), 2))\n",
-    "\n",
-    "    for _, path in enumerate(model_paths):\n",
-    "        model = torch.load(path, weights_only=False).to(device)\n",
-    "        model.eval()\n",
-    "\n",
-    "        test_preds = []\n",
-    "        with torch.no_grad():\n",
-    "            for x_num_batch, x_cat_batch, _ in test_loader:\n",
-    "                output = model(x_num_batch.to(device), x_cat_batch.to(device))\n",
-    "                output = torch.softmax(output, dim=1)\n",
-    "                test_preds.extend(output.cpu().numpy())\n",
-    "\n",
-    "        test_probs += np.array(test_preds) / len(model_paths)\n",
-    "\n",
-    "    # 최종 예측 (Soft Voting)\n",
-    "    final_preds = np.argmax(test_probs, axis=1)\n",
-    "\n",
-    "    return y_test, test_probs, final_preds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 모델 별 K-fold + Soft Voting 진행"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for model_choose in ['ft_transformer','resnet_like','deepgbm']:\n",
-    "    for region in ['seoul','busan','daejeon','daegu','incheon','gwangju']:\n",
-    "        for data_sample in ['pure','smote','ctgan7000','ctgan10000','ctgan20000']:\n",
-    "            fold_voting(model_choose=model_choose, region=region, data_sample=data_sample, random_state=seed)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "230879b7c5e641c8b8b61492ae92c3e4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.29807076404450805, 'lr': 0.00010824018381500966, 'weight_decay': 0.000658628931758311} ###\n",
-      "Early stopping at epoch 22\n",
-      "Saving model to ./save_model/deepgbm/pure/seoul_fold1.pth\n",
-      "Early stopping at epoch 50\n",
-      "Saving model to ./save_model/deepgbm/pure/seoul_fold2.pth\n",
-      "Early stopping at epoch 24\n",
-      "Saving model to ./save_model/deepgbm/pure/seoul_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "53d2758c71964eb28e446dae5138a3e9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 256, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.18531333425244892, 'lr': 0.00019904391652517882, 'weight_decay': 7.803511669278675e-05} ###\n",
-      "Early stopping at epoch 12\n",
-      "Saving model to ./save_model/deepgbm/smote/seoul_fold1.pth\n",
-      "Early stopping at epoch 14\n",
-      "Saving model to ./save_model/deepgbm/smote/seoul_fold2.pth\n",
-      "Early stopping at epoch 19\n",
-      "Saving model to ./save_model/deepgbm/smote/seoul_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1a34a1880a7c43a69835c3f95784175e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 32, 'n_blocks': 8, 'dropout': 0.10718475024592788, 'lr': 0.0009432177379597497, 'weight_decay': 4.308729113230509e-05} ###\n",
-      "Early stopping at epoch 13\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/seoul_fold1.pth\n",
-      "Early stopping at epoch 19\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/seoul_fold2.pth\n",
-      "Early stopping at epoch 18\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/seoul_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3ec2807729c641f59163b26756414b28",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 256, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.14063926069511057, 'lr': 0.0004059290878693028, 'weight_decay': 0.0008509845719526007} ###\n",
-      "Early stopping at epoch 14\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/seoul_fold1.pth\n",
-      "Early stopping at epoch 18\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/seoul_fold2.pth\n",
-      "Early stopping at epoch 26\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/seoul_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4d7a3ceee7854891aa2cfef6265ef833",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 32, 'n_blocks': 8, 'dropout': 0.34044600469728353, 'lr': 0.0005105903209394755, 'weight_decay': 1.0994335574766187e-05} ###\n",
-      "Early stopping at epoch 35\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/seoul_fold1.pth\n",
-      "Early stopping at epoch 48\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/seoul_fold2.pth\n",
-      "Early stopping at epoch 13\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/seoul_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d941a8b45f5b47aba5e38e89ba96e5bc",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 32, 'n_blocks': 8, 'dropout': 0.4651768944020654, 'lr': 0.0005440940639887522, 'weight_decay': 1.0703736319022912e-05} ###\n",
-      "Early stopping at epoch 18\n",
-      "Saving model to ./save_model/deepgbm/pure/busan_fold1.pth\n",
-      "Early stopping at epoch 21\n",
-      "Saving model to ./save_model/deepgbm/pure/busan_fold2.pth\n",
-      "Early stopping at epoch 26\n",
-      "Saving model to ./save_model/deepgbm/pure/busan_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cd7785fd4688437bad84b4cd3dd17402",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 256, 'd_hidden': 32, 'n_blocks': 7, 'dropout': 0.36241152483468775, 'lr': 0.0005993036540724003, 'weight_decay': 0.0006194094729468491} ###\n",
-      "Early stopping at epoch 22\n",
-      "Saving model to ./save_model/deepgbm/smote/busan_fold1.pth\n",
-      "Early stopping at epoch 15\n",
-      "Saving model to ./save_model/deepgbm/smote/busan_fold2.pth\n",
-      "Early stopping at epoch 20\n",
-      "Saving model to ./save_model/deepgbm/smote/busan_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4236916937fa4d299507f7bf67480433",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 32, 'n_blocks': 5, 'dropout': 0.32589216101245205, 'lr': 0.000870943966836362, 'weight_decay': 0.0004755258531422745} ###\n",
-      "Early stopping at epoch 32\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/busan_fold1.pth\n",
-      "Early stopping at epoch 41\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/busan_fold2.pth\n",
-      "Early stopping at epoch 18\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/busan_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a58c944a170f4a27939007d870df4d3b",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 64, 'n_blocks': 5, 'dropout': 0.22554979058504504, 'lr': 0.0007872290481937401, 'weight_decay': 1.3478651710155104e-05} ###\n",
-      "Early stopping at epoch 30\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/busan_fold1.pth\n",
-      "Early stopping at epoch 15\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/busan_fold2.pth\n",
-      "Early stopping at epoch 17\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/busan_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "794104daa0814654b5a7374e63bad90b",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 256, 'd_hidden': 32, 'n_blocks': 4, 'dropout': 0.23007332881069884, 'lr': 0.0005365450324352025, 'weight_decay': 0.00018841476921545086} ###\n",
-      "Early stopping at epoch 34\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/busan_fold1.pth\n",
-      "Early stopping at epoch 21\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/busan_fold2.pth\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/busan_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "516dd122b4cf443eaa6514976e0ba235",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 32, 'n_blocks': 8, 'dropout': 0.34044600469728353, 'lr': 0.0005105903209394755, 'weight_decay': 1.0994335574766187e-05} ###\n",
-      "Early stopping at epoch 18\n",
-      "Saving model to ./save_model/deepgbm/pure/daejeon_fold1.pth\n",
-      "Early stopping at epoch 20\n",
-      "Saving model to ./save_model/deepgbm/pure/daejeon_fold2.pth\n",
-      "Early stopping at epoch 25\n",
-      "Saving model to ./save_model/deepgbm/pure/daejeon_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9e3bf1f1058f4dcfa1254f06dcb0fe38",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 64, 'd_hidden': 128, 'n_blocks': 5, 'dropout': 0.2994658107096196, 'lr': 0.0005068273287449525, 'weight_decay': 2.0471105563346853e-05} ###\n",
-      "Early stopping at epoch 16\n",
-      "Saving model to ./save_model/deepgbm/smote/daejeon_fold1.pth\n",
-      "Early stopping at epoch 16\n",
-      "Saving model to ./save_model/deepgbm/smote/daejeon_fold2.pth\n",
-      "Early stopping at epoch 21\n",
-      "Saving model to ./save_model/deepgbm/smote/daejeon_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d188977daf5147fe8ed6dffd4d233c06",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 256, 'd_hidden': 32, 'n_blocks': 7, 'dropout': 0.38274293753904687, 'lr': 0.0005358055009231865, 'weight_decay': 0.000348771262454593} ###\n",
-      "Early stopping at epoch 31\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/daejeon_fold1.pth\n",
-      "Early stopping at epoch 23\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/daejeon_fold2.pth\n",
-      "Early stopping at epoch 16\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/daejeon_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "76f8c4fd7727459ba4d87aada9cbcf87",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 32, 'n_blocks': 3, 'dropout': 0.37335751073629203, 'lr': 0.0005518803643548146, 'weight_decay': 0.0006506083261092598} ###\n",
-      "Early stopping at epoch 42\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/daejeon_fold1.pth\n",
-      "Early stopping at epoch 26\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/daejeon_fold2.pth\n",
-      "Early stopping at epoch 33\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/daejeon_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c2f82c3c6d6a4cf5933e6dd18eede561",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 64, 'n_blocks': 7, 'dropout': 0.498315839448228, 'lr': 0.000982377661956639, 'weight_decay': 5.1544513708209705e-05} ###\n",
-      "Early stopping at epoch 44\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/daejeon_fold1.pth\n",
-      "Early stopping at epoch 21\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/daejeon_fold2.pth\n",
-      "Early stopping at epoch 39\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/daejeon_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "485ab1b95b0446348036e7f85f06670b",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 192, 'd_hidden': 128, 'n_blocks': 4, 'dropout': 0.44295645200373956, 'lr': 0.0006995754135310067, 'weight_decay': 7.588529866409616e-05} ###\n",
-      "Early stopping at epoch 12\n",
-      "Saving model to ./save_model/deepgbm/pure/daegu_fold1.pth\n",
-      "Early stopping at epoch 13\n",
-      "Saving model to ./save_model/deepgbm/pure/daegu_fold2.pth\n",
-      "Early stopping at epoch 18\n",
-      "Saving model to ./save_model/deepgbm/pure/daegu_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8bf0047f247b4a5caf0f2d12383850cc",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 256, 'd_hidden': 128, 'n_blocks': 3, 'dropout': 0.34301794076057535, 'lr': 0.00014808945119975197, 'weight_decay': 1.3492834268013232e-05} ###\n",
-      "Early stopping at epoch 25\n",
-      "Saving model to ./save_model/deepgbm/smote/daegu_fold1.pth\n",
-      "Early stopping at epoch 24\n",
-      "Saving model to ./save_model/deepgbm/smote/daegu_fold2.pth\n",
-      "Early stopping at epoch 23\n",
-      "Saving model to ./save_model/deepgbm/smote/daegu_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cf4d89937031420cab15ecba54ffc850",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 256, 'd_hidden': 128, 'n_blocks': 6, 'dropout': 0.4749441191638353, 'lr': 0.0007742129275419718, 'weight_decay': 0.0001276488991046795} ###\n",
-      "Early stopping at epoch 17\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/daegu_fold1.pth\n",
-      "Early stopping at epoch 39\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/daegu_fold2.pth\n",
-      "Early stopping at epoch 48\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/daegu_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c0df822671e74384a66b4b0395e6ab81",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 256, 'd_hidden': 32, 'n_blocks': 8, 'dropout': 0.32191381712732553, 'lr': 0.0006025357742197527, 'weight_decay': 1.6123356366734284e-05} ###\n",
-      "Early stopping at epoch 25\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/daegu_fold1.pth\n",
-      "Early stopping at epoch 40\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/daegu_fold2.pth\n",
-      "Early stopping at epoch 23\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/daegu_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9492d14036d64bb7bb2eb8c3d2bee972",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 128, 'n_blocks': 7, 'dropout': 0.49897739440085004, 'lr': 0.0009903735540622498, 'weight_decay': 5.1544513708209705e-05} ###\n",
-      "Early stopping at epoch 29\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/daegu_fold1.pth\n",
-      "Early stopping at epoch 20\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/daegu_fold2.pth\n",
-      "Early stopping at epoch 18\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/daegu_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5c8a303b51f94df288ff928629f0d728",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 32, 'n_blocks': 8, 'dropout': 0.34044600469728353, 'lr': 0.0005105903209394755, 'weight_decay': 1.0994335574766187e-05} ###\n",
-      "Early stopping at epoch 15\n",
-      "Saving model to ./save_model/deepgbm/pure/incheon_fold1.pth\n",
-      "Early stopping at epoch 33\n",
-      "Saving model to ./save_model/deepgbm/pure/incheon_fold2.pth\n",
-      "Early stopping at epoch 22\n",
-      "Saving model to ./save_model/deepgbm/pure/incheon_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6d66e1cd2b2b4aebac519cc9f774853b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 256, 'd_hidden': 32, 'n_blocks': 6, 'dropout': 0.4453655419744991, 'lr': 0.0006995754135310067, 'weight_decay': 7.03362294158028e-05} ###\n",
-      "Early stopping at epoch 28\n",
-      "Saving model to ./save_model/deepgbm/smote/incheon_fold1.pth\n",
-      "Early stopping at epoch 17\n",
-      "Saving model to ./save_model/deepgbm/smote/incheon_fold2.pth\n",
-      "Early stopping at epoch 14\n",
-      "Saving model to ./save_model/deepgbm/smote/incheon_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "189a4560776e4601b2490693d9456b91",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.29807076404450805, 'lr': 0.00010824018381500966, 'weight_decay': 0.000658628931758311} ###\n",
-      "Early stopping at epoch 35\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/incheon_fold1.pth\n",
-      "Early stopping at epoch 28\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/incheon_fold2.pth\n",
-      "Early stopping at epoch 41\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/incheon_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "04414670ee404a1f958e74d114044570",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 64, 'n_blocks': 4, 'dropout': 0.4656646404627707, 'lr': 0.000725954595588046, 'weight_decay': 9.156653323796898e-05} ###\n",
-      "Early stopping at epoch 14\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/incheon_fold1.pth\n",
-      "Early stopping at epoch 25\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/incheon_fold2.pth\n",
-      "Early stopping at epoch 38\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/incheon_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c03845943e074a83a880a4080d779cfa",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 64, 'd_hidden': 32, 'n_blocks': 3, 'dropout': 0.29122487178013184, 'lr': 0.0008644753350143602, 'weight_decay': 0.0006848083277415096} ###\n",
-      "Early stopping at epoch 39\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/incheon_fold1.pth\n",
-      "Early stopping at epoch 32\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/incheon_fold2.pth\n",
-      "Early stopping at epoch 44\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/incheon_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4d15a078f9ea42279a268e574300d613",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 192, 'd_hidden': 32, 'n_blocks': 7, 'dropout': 0.4268924061648935, 'lr': 0.00031827191626512363, 'weight_decay': 7.927166314511625e-05} ###\n",
-      "Early stopping at epoch 41\n",
-      "Saving model to ./save_model/deepgbm/pure/gwangju_fold1.pth\n",
-      "Early stopping at epoch 26\n",
-      "Saving model to ./save_model/deepgbm/pure/gwangju_fold2.pth\n",
-      "Early stopping at epoch 18\n",
-      "Saving model to ./save_model/deepgbm/pure/gwangju_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "718bf72a28814022bd53ea88c94a39da",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 64, 'n_blocks': 3, 'dropout': 0.49840679574448543, 'lr': 0.0007932730569025867, 'weight_decay': 5.2371870545865264e-05} ###\n",
-      "Early stopping at epoch 34\n",
-      "Saving model to ./save_model/deepgbm/smote/gwangju_fold1.pth\n",
-      "Early stopping at epoch 34\n",
-      "Saving model to ./save_model/deepgbm/smote/gwangju_fold2.pth\n",
-      "Early stopping at epoch 22\n",
-      "Saving model to ./save_model/deepgbm/smote/gwangju_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4470573fa1844b6ebf6e59f2ff9d865c",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 64, 'd_hidden': 128, 'n_blocks': 7, 'dropout': 0.3593531534642382, 'lr': 0.0007435971973093597, 'weight_decay': 0.00039310742030944726} ###\n",
-      "Early stopping at epoch 35\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/gwangju_fold1.pth\n",
-      "Early stopping at epoch 22\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/gwangju_fold2.pth\n",
-      "Early stopping at epoch 29\n",
-      "Saving model to ./save_model/deepgbm/ctgan7000/gwangju_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2f0a9966e1b04380b4abf79e45a022a7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 32, 'n_blocks': 3, 'dropout': 0.3613681971081546, 'lr': 0.0008491548609780148, 'weight_decay': 0.0004487641324799225} ###\n",
-      "Early stopping at epoch 29\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/gwangju_fold1.pth\n",
-      "Early stopping at epoch 30\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/gwangju_fold2.pth\n",
-      "Early stopping at epoch 42\n",
-      "Saving model to ./save_model/deepgbm/ctgan10000/gwangju_fold3.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f3db776ffbfc436c89040085d212e367",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### Best Params (All Folds): {'d_main': 128, 'd_hidden': 32, 'n_blocks': 6, 'dropout': 0.30240965980622486, 'lr': 0.0008659736510641475, 'weight_decay': 0.0003723756511903005} ###\n",
-      "Early stopping at epoch 32\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/gwangju_fold1.pth\n",
-      "Early stopping at epoch 31\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/gwangju_fold2.pth\n",
-      "Early stopping at epoch 14\n",
-      "Saving model to ./save_model/deepgbm/ctgan20000/gwangju_fold3.pth\n"
-     ]
-    }
-   ],
-   "source": [
-    "for region in ['seoul','busan','daejeon','daegu','incheon','gwangju']:\n",
-    "    for data_sample in ['pure','smote','ctgan7000','ctgan10000','ctgan20000']:\n",
-    "        fold_voting(model_choose='deepgbm', region=region, data_sample=data_sample, random_state=seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 모델별 지역 성능(원데이터)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### FT-Transformer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### seoul ###\n",
-      "Test Accuracy: 0.910958904109589\n",
-      "Test F1-Score: 0.48826800257083774\n",
-      "Test CSI: 0.34453781512602144\n",
-      "### busan ###\n",
-      "Test Accuracy: 0.9679223744292238\n",
-      "Test F1-Score: 0.5245668459304146\n",
-      "Test CSI: 0.4158004158003294\n",
-      "### daejeon ###\n",
-      "Test Accuracy: 0.9182648401826484\n",
-      "Test F1-Score: 0.566496524475418\n",
-      "Test CSI: 0.31021194605006647\n",
-      "### daegu ###\n",
-      "Test Accuracy: 0.9768264840182649\n",
-      "Test F1-Score: 0.7999244404273532\n",
-      "Test CSI: 0.26181818181808664\n",
-      "### incheon ###\n",
-      "Test Accuracy: 0.9181506849315069\n",
-      "Test F1-Score: 0.6680714652935209\n",
-      "Test CSI: 0.5138983050847109\n",
-      "### gwangju ###\n",
-      "Test Accuracy: 0.9440639269406392\n",
-      "Test F1-Score: 0.5373981558893273\n",
-      "Test CSI: 0.4691224268688549\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score\n",
-    "\n",
-    "for region in ['seoul','busan','daejeon','daegu','incheon','gwangju']:\n",
-    "    y_test, test_probs, final_preds = soft_voting(region=region, model_choose='ft_transformer', data_sample='pure')\n",
-    "\n",
-    "    # 성능 지표 계산\n",
-    "    print(f\"### {region} ###\")\n",
-    "    print(\"Test Accuracy:\", accuracy_score(y_test, final_preds))\n",
-    "    '''print(\"Test AUC:\", roc_auc_score(y_test, test_probs, multi_class=\"ovr\"))  # 다중분류 AUC'''\n",
-    "    print(\"Test F1-Score:\", f1_score(y_test, final_preds, average=\"macro\"))  # 다중분류에서는 macro-F1 사용\n",
-    "    print(\"Test CSI:\", calculate_csi(y_test, final_preds))\n",
-    "    '''print(confusion_matrix(y_test, final_preds))'''"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### ResNet-like"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### seoul ###\n",
-      "Test Accuracy: 0.9075342465753424\n",
-      "Test F1-Score: 0.47993840811230576\n",
-      "Test CSI: 0.32217573221754625\n",
-      "### busan ###\n",
-      "Test Accuracy: 0.968607305936073\n",
-      "Test F1-Score: 0.586245547831333\n",
-      "Test CSI: 0.45652173913034455\n",
-      "### daejeon ###\n",
-      "Test Accuracy: 0.9060502283105023\n",
-      "Test F1-Score: 0.6242613500628746\n",
-      "Test CSI: 0.3560250391236028\n",
-      "### daegu ###\n",
-      "Test Accuracy: 0.9698630136986301\n",
-      "Test F1-Score: 0.7948471717999764\n",
-      "Test CSI: 0.25212464589227984\n",
-      "### incheon ###\n",
-      "Test Accuracy: 0.9184931506849315\n",
-      "Test F1-Score: 0.6724661948944141\n",
-      "Test CSI: 0.5188679245282669\n",
-      "### gwangju ###\n",
-      "Test Accuracy: 0.9299086757990868\n",
-      "Test F1-Score: 0.5255850318041286\n",
-      "Test CSI: 0.4387568555758283\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score\n",
-    "\n",
-    "for region in ['seoul','busan','daejeon','daegu','incheon','gwangju']:\n",
-    "    y_test, test_probs, final_preds = soft_voting(region=region, model_choose='resnet_like', data_sample='pure')\n",
-    "\n",
-    "    # 성능 지표 계산\n",
-    "    print(f\"### {region} ###\")\n",
-    "    print(\"Test Accuracy:\", accuracy_score(y_test, final_preds))\n",
-    "    '''print(\"Test AUC:\", roc_auc_score(y_test, test_probs, multi_class=\"ovr\"))  # 다중분류 AUC'''\n",
-    "    print(\"Test F1-Score:\", f1_score(y_test, final_preds, average=\"macro\"))  # 다중분류에서는 macro-F1 사용\n",
-    "    print(\"Test CSI:\", calculate_csi(y_test, final_preds))\n",
-    "    '''print(confusion_matrix(y_test, final_preds))'''"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### DeepGBM"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### seoul ###\n",
-      "Test Accuracy: 0.9140410958904109\n",
-      "Test F1-Score: 0.48377287268662217\n",
-      "Test CSI: 0.33066666666663724\n",
-      "### busan ###\n",
-      "Test Accuracy: 0.9658675799086758\n",
-      "Test F1-Score: 0.5404572118702554\n",
-      "Test CSI: 0.3983903420522337\n",
-      "### daejeon ###\n",
-      "Test Accuracy: 0.9071917808219178\n",
-      "Test F1-Score: 0.5336211256730917\n",
-      "Test CSI: 0.2701974865349847\n",
-      "### daegu ###\n",
-      "Test Accuracy: 0.9742009132420091\n",
-      "Test F1-Score: 0.4604475684598693\n",
-      "Test CSI: 0.2416107382549525\n",
-      "### incheon ###\n",
-      "Test Accuracy: 0.9174657534246575\n",
-      "Test F1-Score: 0.6883413153781813\n",
-      "Test CSI: 0.5147651006711064\n",
-      "### gwangju ###\n",
-      "Test Accuracy: 0.9417808219178082\n",
-      "Test F1-Score: 0.6238007071539367\n",
-      "Test CSI: 0.4539614561027351\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score\n",
-    "\n",
-    "for region in ['seoul','busan','daejeon','daegu','incheon','gwangju']:\n",
-    "    y_test, test_probs, final_preds = soft_voting(region=region, model_choose='deepgbm', data_sample='pure')\n",
-    "\n",
-    "    # 성능 지표 계산\n",
-    "    print(f\"### {region} ###\")\n",
-    "    print(\"Test Accuracy:\", accuracy_score(y_test, final_preds))\n",
-    "    '''print(\"Test AUC:\", roc_auc_score(y_test, test_probs, multi_class=\"ovr\"))  # 다중분류 AUC'''\n",
-    "    print(\"Test F1-Score:\", f1_score(y_test, final_preds, average=\"macro\"))  # 다중분류에서는 macro-F1 사용\n",
-    "    print(\"Test CSI:\", calculate_csi(y_test, final_preds))\n",
-    "    '''print(confusion_matrix(y_test, final_preds))'''"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 지역별 모델 성능 비교"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### seoul"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### ft_transformer ###\n",
-      "Test Accuracy: 0.910958904109589\n",
-      "Test F1-Score: 0.48826800257083774\n",
-      "Test CSI: 0.34453781512602144\n",
-      "### resnet_like ###\n",
-      "Test Accuracy: 0.9075342465753424\n",
-      "Test F1-Score: 0.47993840811230576\n",
-      "Test CSI: 0.32217573221754625\n",
-      "### deepgbm ###\n",
-      "Test Accuracy: 0.9140410958904109\n",
-      "Test F1-Score: 0.48377287268662217\n",
-      "Test CSI: 0.33066666666663724\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score\n",
-    "\n",
-    "for model_choose in ['ft_transformer','resnet_like','deepgbm']:\n",
-    "    y_test, test_probs, final_preds = soft_voting(region='seoul', model_choose=model_choose, data_sample='pure')\n",
-    "\n",
-    "    # 성능 지표 계산\n",
-    "    print(f\"### {model_choose} ###\")\n",
-    "    print(\"Test Accuracy:\", accuracy_score(y_test, final_preds))\n",
-    "    '''print(\"Test AUC:\", roc_auc_score(y_test, test_probs, multi_class=\"ovr\"))  # 다중분류 AUC'''\n",
-    "    print(\"Test F1-Score:\", f1_score(y_test, final_preds, average=\"macro\"))  # 다중분류에서는 macro-F1 사용\n",
-    "    print(\"Test CSI:\", calculate_csi(y_test, final_preds))\n",
-    "    '''print(confusion_matrix(y_test, final_preds))'''"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### busan"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### ft_transformer ###\n",
-      "Test Accuracy: 0.9679223744292238\n",
-      "Test F1-Score: 0.5245668459304146\n",
-      "Test CSI: 0.4158004158003294\n",
-      "### resnet_like ###\n",
-      "Test Accuracy: 0.968607305936073\n",
-      "Test F1-Score: 0.586245547831333\n",
-      "Test CSI: 0.45652173913034455\n",
-      "### deepgbm ###\n",
-      "Test Accuracy: 0.9658675799086758\n",
-      "Test F1-Score: 0.5404572118702554\n",
-      "Test CSI: 0.3983903420522337\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score\n",
-    "\n",
-    "for model_choose in ['ft_transformer','resnet_like','deepgbm']:\n",
-    "    y_test, test_probs, final_preds = soft_voting(region='busan', model_choose=model_choose, data_sample='pure')\n",
-    "\n",
-    "    # 성능 지표 계산\n",
-    "    print(f\"### {model_choose} ###\")\n",
-    "    print(\"Test Accuracy:\", accuracy_score(y_test, final_preds))\n",
-    "    '''print(\"Test AUC:\", roc_auc_score(y_test, test_probs, multi_class=\"ovr\"))  # 다중분류 AUC'''\n",
-    "    print(\"Test F1-Score:\", f1_score(y_test, final_preds, average=\"macro\"))  # 다중분류에서는 macro-F1 사용\n",
-    "    print(\"Test CSI:\", calculate_csi(y_test, final_preds))\n",
-    "    '''print(confusion_matrix(y_test, final_preds))'''"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### daejeon"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### ft_transformer ###\n",
-      "Test Accuracy: 0.9182648401826484\n",
-      "Test F1-Score: 0.566496524475418\n",
-      "Test CSI: 0.31021194605006647\n",
-      "### resnet_like ###\n",
-      "Test Accuracy: 0.9060502283105023\n",
-      "Test F1-Score: 0.6242613500628746\n",
-      "Test CSI: 0.3560250391236028\n",
-      "### deepgbm ###\n",
-      "Test Accuracy: 0.9071917808219178\n",
-      "Test F1-Score: 0.5336211256730917\n",
-      "Test CSI: 0.2701974865349847\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score\n",
-    "\n",
-    "for model_choose in ['ft_transformer','resnet_like','deepgbm']:\n",
-    "    y_test, test_probs, final_preds = soft_voting(region='daejeon', model_choose=model_choose, data_sample='pure')\n",
-    "\n",
-    "    # 성능 지표 계산\n",
-    "    print(f\"### {model_choose} ###\")\n",
-    "    print(\"Test Accuracy:\", accuracy_score(y_test, final_preds))\n",
-    "    '''print(\"Test AUC:\", roc_auc_score(y_test, test_probs, multi_class=\"ovr\"))  # 다중분류 AUC'''\n",
-    "    print(\"Test F1-Score:\", f1_score(y_test, final_preds, average=\"macro\"))  # 다중분류에서는 macro-F1 사용\n",
-    "    print(\"Test CSI:\", calculate_csi(y_test, final_preds))\n",
-    "    '''print(confusion_matrix(y_test, final_preds))'''"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### daegu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### ft_transformer ###\n",
-      "Test Accuracy: 0.9768264840182649\n",
-      "Test F1-Score: 0.7999244404273532\n",
-      "Test CSI: 0.26181818181808664\n",
-      "### resnet_like ###\n",
-      "Test Accuracy: 0.9698630136986301\n",
-      "Test F1-Score: 0.7948471717999764\n",
-      "Test CSI: 0.25212464589227984\n",
-      "### deepgbm ###\n",
-      "Test Accuracy: 0.9742009132420091\n",
-      "Test F1-Score: 0.4604475684598693\n",
-      "Test CSI: 0.2416107382549525\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score\n",
-    "\n",
-    "for model_choose in ['ft_transformer','resnet_like','deepgbm']:\n",
-    "    y_test, test_probs, final_preds = soft_voting(region='daegu', model_choose=model_choose, data_sample='pure')\n",
-    "\n",
-    "    # 성능 지표 계산\n",
-    "    print(f\"### {model_choose} ###\")\n",
-    "    print(\"Test Accuracy:\", accuracy_score(y_test, final_preds))\n",
-    "    '''print(\"Test AUC:\", roc_auc_score(y_test, test_probs, multi_class=\"ovr\"))  # 다중분류 AUC'''\n",
-    "    print(\"Test F1-Score:\", f1_score(y_test, final_preds, average=\"macro\"))  # 다중분류에서는 macro-F1 사용\n",
-    "    print(\"Test CSI:\", calculate_csi(y_test, final_preds))\n",
-    "    '''print(confusion_matrix(y_test, final_preds))'''"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### incheon"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### ft_transformer ###\n",
-      "Test Accuracy: 0.9181506849315069\n",
-      "Test F1-Score: 0.6680714652935209\n",
-      "Test CSI: 0.5138983050847109\n",
-      "### resnet_like ###\n",
-      "Test Accuracy: 0.9184931506849315\n",
-      "Test F1-Score: 0.6724661948944141\n",
-      "Test CSI: 0.5188679245282669\n",
-      "### deepgbm ###\n",
-      "Test Accuracy: 0.9174657534246575\n",
-      "Test F1-Score: 0.6883413153781813\n",
-      "Test CSI: 0.5147651006711064\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score\n",
-    "\n",
-    "for model_choose in ['ft_transformer','resnet_like','deepgbm']:\n",
-    "    y_test, test_probs, final_preds = soft_voting(region='incheon', model_choose=model_choose, data_sample='pure')\n",
-    "\n",
-    "    # 성능 지표 계산\n",
-    "    print(f\"### {model_choose} ###\")\n",
-    "    print(\"Test Accuracy:\", accuracy_score(y_test, final_preds))\n",
-    "    '''print(\"Test AUC:\", roc_auc_score(y_test, test_probs, multi_class=\"ovr\"))  # 다중분류 AUC'''\n",
-    "    print(\"Test F1-Score:\", f1_score(y_test, final_preds, average=\"macro\"))  # 다중분류에서는 macro-F1 사용\n",
-    "    print(\"Test CSI:\", calculate_csi(y_test, final_preds))\n",
-    "    '''print(confusion_matrix(y_test, final_preds))'''"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### gwangju"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score\n",
-    "\n",
-    "for model_choose in ['ft_transformer','resnet_like','deepgbm']:\n",
-    "    y_test, test_probs, final_preds = soft_voting(region='gwangju', model_choose=model_choose, data_sample='pure')\n",
-    "\n",
-    "    # 성능 지표 계산\n",
-    "    print(f\"### {model_choose} ###\")\n",
-    "    print(\"Test Accuracy:\", accuracy_score(y_test, final_preds))\n",
-    "    '''print(\"Test AUC:\", roc_auc_score(y_test, test_probs, multi_class=\"ovr\"))  # 다중분류 AUC'''\n",
-    "    print(\"Test F1-Score:\", f1_score(y_test, final_preds, average=\"macro\"))  # 다중분류에서는 macro-F1 사용\n",
-    "    print(\"Test CSI:\", calculate_csi(y_test, final_preds))\n",
-    "    '''print(confusion_matrix(y_test, final_preds))'''"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 성능비교 시각화 plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Region</th>\n",
-       "      <th>Model</th>\n",
-       "      <th>CSI</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>0.344538</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>0.322176</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>0.330667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>0.415800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>0.456522</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>0.398390</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>0.310212</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>0.356025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>0.270197</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>0.261818</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>0.252125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>0.241611</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>0.513898</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>0.518868</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>0.514765</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>0.469122</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>0.438757</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>0.453961</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Region           Model       CSI\n",
-       "0     seoul  ft_transformer  0.344538\n",
-       "1     seoul     resnet_like  0.322176\n",
-       "2     seoul         deepgbm  0.330667\n",
-       "3     busan  ft_transformer  0.415800\n",
-       "4     busan     resnet_like  0.456522\n",
-       "5     busan         deepgbm  0.398390\n",
-       "6   daejeon  ft_transformer  0.310212\n",
-       "7   daejeon     resnet_like  0.356025\n",
-       "8   daejeon         deepgbm  0.270197\n",
-       "9     daegu  ft_transformer  0.261818\n",
-       "10    daegu     resnet_like  0.252125\n",
-       "11    daegu         deepgbm  0.241611\n",
-       "12  incheon  ft_transformer  0.513898\n",
-       "13  incheon     resnet_like  0.518868\n",
-       "14  incheon         deepgbm  0.514765\n",
-       "15  gwangju  ft_transformer  0.469122\n",
-       "16  gwangju     resnet_like  0.438757\n",
-       "17  gwangju         deepgbm  0.453961"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score\n",
-    "\n",
-    "performance = []\n",
-    "\n",
-    "for region in ['seoul','busan','daejeon','daegu','incheon','gwangju']:\n",
-    "    for model_choose in ['ft_transformer','resnet_like','deepgbm']:\n",
-    "        y_test, test_probs, final_preds = soft_voting(region=region, model_choose=model_choose, data_sample='pure')\n",
-    "\n",
-    "        # 성능 지표 계산\n",
-    "        csi = calculate_csi(y_test, final_preds)\n",
-    "\n",
-    "        # 데이터 저장\n",
-    "        performance.append({\"Region\": region, \"Model\": model_choose, \"CSI\": csi})\n",
-    "\n",
-    "# DataFrame으로 변환\n",
-    "df_perf = pd.DataFrame(performance)\n",
-    "df_perf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# 그래프 크기 설정\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "\n",
-    "# 바 그래프 그리기\n",
-    "sns.barplot(data=df_perf, x=\"Region\", y=\"CSI\", hue=\"Model\")\n",
-    "\n",
-    "# 그래프 꾸미기\n",
-    "plt.title(\"Performance Comparison by Region and Model\")\n",
-    "plt.xlabel(\"Region\")\n",
-    "plt.ylabel(\"CSI Score\")\n",
-    "plt.legend(title=\"Model\")\n",
-    "plt.xticks(rotation=45)  # 지역명이 길 경우 회전\n",
-    "\n",
-    "# 그래프 출력\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## TabNet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'%pip install pytorch-tabnet'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'''%pip install pytorch-tabnet'''"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 데이터셋 준비"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
-    "import torch\n",
-    "import random\n",
-    "\n",
-    "# 시드 고정\n",
-    "seed = 42\n",
-    "random.seed(seed)\n",
-    "np.random.seed(seed)\n",
-    "torch.manual_seed(seed)\n",
-    "torch.cuda.manual_seed_all(seed)\n",
-    "\n",
-    "# 데이터셋 불러오기\n",
-    "X_train, X_val, X_test, y_train, y_val, y_test, categorical_cols, numerical_cols = prepare_dataset(\"seoul\", target = \"multi\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Early stopping occurred at epoch 31 with best_epoch = 23 and best_valid_csi_metric = 0.49728\n",
-      "\n",
-      "Early stopping occurred at epoch 15 with best_epoch = 7 and best_valid_csi_metric = 0.46986\n",
-      "\n",
-      "Early stopping occurred at epoch 23 with best_epoch = 15 and best_valid_csi_metric = 0.52999\n",
-      "\n",
-      "Early stopping occurred at epoch 30 with best_epoch = 22 and best_valid_csi_metric = 0.5258\n",
-      "\n",
-      "Early stopping occurred at epoch 15 with best_epoch = 7 and best_valid_csi_metric = 0.49212\n",
-      "\n",
-      "Early stopping occurred at epoch 16 with best_epoch = 8 and best_valid_csi_metric = 0.50329\n",
-      "\n",
-      "Early stopping occurred at epoch 18 with best_epoch = 10 and best_valid_csi_metric = 0.48949\n",
-      "\n",
-      "Early stopping occurred at epoch 23 with best_epoch = 15 and best_valid_csi_metric = 0.53168\n",
-      "Stop training because you reached max_epochs = 50 with best_epoch = 48 and best_valid_csi_metric = 0.53687\n",
-      "\n",
-      "Early stopping occurred at epoch 23 with best_epoch = 15 and best_valid_csi_metric = 0.55556\n",
-      "\n",
-      "Early stopping occurred at epoch 19 with best_epoch = 11 and best_valid_csi_metric = 0.52191\n",
-      "\n",
-      "Early stopping occurred at epoch 29 with best_epoch = 21 and best_valid_csi_metric = 0.50078\n",
-      "\n",
-      "Early stopping occurred at epoch 17 with best_epoch = 9 and best_valid_csi_metric = 0.47917\n",
-      "\n",
-      "Early stopping occurred at epoch 31 with best_epoch = 23 and best_valid_csi_metric = 0.48897\n",
-      "\n",
-      "Early stopping occurred at epoch 25 with best_epoch = 17 and best_valid_csi_metric = 0.57122\n",
-      "\n",
-      "Early stopping occurred at epoch 24 with best_epoch = 16 and best_valid_csi_metric = 0.51626\n",
-      "\n",
-      "Early stopping occurred at epoch 15 with best_epoch = 7 and best_valid_csi_metric = 0.54084\n",
-      "\n",
-      "Early stopping occurred at epoch 31 with best_epoch = 23 and best_valid_csi_metric = 0.52264\n",
-      "\n",
-      "Early stopping occurred at epoch 21 with best_epoch = 13 and best_valid_csi_metric = 0.54005\n",
-      "\n",
-      "Early stopping occurred at epoch 23 with best_epoch = 15 and best_valid_csi_metric = 0.52377\n",
-      "\n",
-      "Early stopping occurred at epoch 20 with best_epoch = 12 and best_valid_csi_metric = 0.5151\n",
-      "\n",
-      "Early stopping occurred at epoch 10 with best_epoch = 2 and best_valid_csi_metric = 0.50446\n",
-      "\n",
-      "Early stopping occurred at epoch 16 with best_epoch = 8 and best_valid_csi_metric = 0.54733\n",
-      "\n",
-      "Early stopping occurred at epoch 12 with best_epoch = 4 and best_valid_csi_metric = 0.52098\n",
-      "\n",
-      "Early stopping occurred at epoch 23 with best_epoch = 15 and best_valid_csi_metric = 0.5317\n",
-      "\n",
-      "Early stopping occurred at epoch 32 with best_epoch = 24 and best_valid_csi_metric = 0.549\n",
-      "\n",
-      "Early stopping occurred at epoch 19 with best_epoch = 11 and best_valid_csi_metric = 0.50556\n",
-      "\n",
-      "Early stopping occurred at epoch 16 with best_epoch = 8 and best_valid_csi_metric = 0.52058\n",
-      "\n",
-      "Early stopping occurred at epoch 20 with best_epoch = 12 and best_valid_csi_metric = 0.51613\n",
-      "\n",
-      "Early stopping occurred at epoch 31 with best_epoch = 23 and best_valid_csi_metric = 0.51721\n",
-      "\n",
-      "Early stopping occurred at epoch 29 with best_epoch = 21 and best_valid_csi_metric = 0.54202\n",
-      "\n",
-      "Early stopping occurred at epoch 27 with best_epoch = 19 and best_valid_csi_metric = 0.5661\n",
-      "\n",
-      "Early stopping occurred at epoch 30 with best_epoch = 22 and best_valid_csi_metric = 0.52038\n",
-      "\n",
-      "Early stopping occurred at epoch 33 with best_epoch = 25 and best_valid_csi_metric = 0.54363\n",
-      "\n",
-      "Early stopping occurred at epoch 21 with best_epoch = 13 and best_valid_csi_metric = 0.53012\n",
-      "\n",
-      "Early stopping occurred at epoch 24 with best_epoch = 16 and best_valid_csi_metric = 0.54863\n",
-      "\n",
-      "Early stopping occurred at epoch 23 with best_epoch = 15 and best_valid_csi_metric = 0.50951\n",
-      "\n",
-      "Early stopping occurred at epoch 27 with best_epoch = 19 and best_valid_csi_metric = 0.53793\n",
-      "\n",
-      "Early stopping occurred at epoch 31 with best_epoch = 23 and best_valid_csi_metric = 0.54465\n",
-      "\n",
-      "Early stopping occurred at epoch 17 with best_epoch = 9 and best_valid_csi_metric = 0.5\n",
-      "\n",
-      "Early stopping occurred at epoch 14 with best_epoch = 6 and best_valid_csi_metric = 0.56151\n",
-      "\n",
-      "Early stopping occurred at epoch 16 with best_epoch = 8 and best_valid_csi_metric = 0.51922\n",
-      "\n",
-      "Early stopping occurred at epoch 18 with best_epoch = 10 and best_valid_csi_metric = 0.51718\n",
-      "\n",
-      "Early stopping occurred at epoch 21 with best_epoch = 13 and best_valid_csi_metric = 0.51779\n",
-      "\n",
-      "Early stopping occurred at epoch 10 with best_epoch = 2 and best_valid_csi_metric = 0.52174\n",
-      "\n",
-      "Early stopping occurred at epoch 19 with best_epoch = 11 and best_valid_csi_metric = 0.50699\n",
-      "\n",
-      "Early stopping occurred at epoch 41 with best_epoch = 33 and best_valid_csi_metric = 0.54059\n",
-      "\n",
-      "Early stopping occurred at epoch 37 with best_epoch = 29 and best_valid_csi_metric = 0.56875\n",
-      "\n",
-      "Early stopping occurred at epoch 35 with best_epoch = 27 and best_valid_csi_metric = 0.53787\n",
-      "\n",
-      "Early stopping occurred at epoch 12 with best_epoch = 4 and best_valid_csi_metric = 0.51343\n",
-      "Best Hyperparameters: {'n_d': 48, 'n_a': 32, 'n_steps': 6, 'gamma': 1.3427992882663162, 'lambda_sparse': 0.0010676916501410128, 'momentum': 0.1377411909968035}\n"
-     ]
-    }
-   ],
-   "source": [
-    "import optuna\n",
-    "from pytorch_tabnet.tab_model import TabNetClassifier\n",
-    "from pytorch_tabnet.metrics import Metric\n",
-    "from sklearn.metrics import f1_score\n",
-    "import warnings\n",
-    "\n",
-    "# 경고 메시지 숨기기\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "\n",
-    "# TabNet에서 사용할 사용자 정의 Metric\n",
-    "class CSIMetric(Metric):\n",
-    "    def __init__(self):\n",
-    "        self._name = \"csi_metric\"\n",
-    "        self._maximize = True\n",
-    "\n",
-    "    def __call__(self, y_true, y_pred_prob):\n",
-    "        y_pred = np.argmax(y_pred_prob, axis=1)\n",
-    "        return calculate_csi(y_true, y_pred)\n",
-    "\n",
-    "# Optuna 최적화 함수\n",
-    "def objective(trial):\n",
-    "    # 하이퍼파라미터 탐색 범위 설정\n",
-    "    n_d = trial.suggest_int(\"n_d\", 8, 64, step=8)\n",
-    "    n_a = trial.suggest_int(\"n_a\", 8, 64, step=8)\n",
-    "    n_steps = trial.suggest_int(\"n_steps\", 3, 10)\n",
-    "    gamma = trial.suggest_float(\"gamma\", 1.0, 2.0)\n",
-    "    lambda_sparse = trial.suggest_float(\"lambda_sparse\", 0.0001, 0.01, log=True)\n",
-    "    momentum = trial.suggest_float(\"momentum\", 0.01, 0.4)\n",
-    "\n",
-    "    # TabNet 모델 생성\n",
-    "    model = TabNetClassifier(\n",
-    "        n_d=n_d, n_a=n_a, n_steps=n_steps,\n",
-    "        gamma=gamma, lambda_sparse=lambda_sparse,\n",
-    "        momentum=momentum,\n",
-    "        verbose=0,\n",
-    "        seed=seed\n",
-    "    )\n",
-    "\n",
-    "    # 모델 학습\n",
-    "    model.fit(\n",
-    "        X_train.to_numpy(), y_train.to_numpy(),\n",
-    "        eval_set=[(X_val.to_numpy(), y_val.to_numpy())],\n",
-    "        eval_name=['valid'],\n",
-    "        eval_metric=['csi_metric'],\n",
-    "        max_epochs=50,\n",
-    "        patience=8,\n",
-    "        batch_size=512,\n",
-    "        virtual_batch_size=128\n",
-    "    )\n",
-    "\n",
-    "    # Validation 데이터로 F1-score 계산\n",
-    "    y_pred_val = model.predict(X_val.to_numpy())\n",
-    "    csi_score = calculate_csi(y_val.to_numpy(), y_pred_val)\n",
-    "\n",
-    "    return csi_score  # 최적화를 F1-score 기준으로 진행\n",
-    "\n",
-    "# Optuna 실행\n",
-    "sampler = optuna.samplers.TPESampler(seed=seed)\n",
-    "study = optuna.create_study(direction=\"maximize\", sampler=sampler)\n",
-    "study.optimize(objective, n_trials=50)  # 50번 탐색 실행\n",
-    "\n",
-    "# 최적 하이퍼파라미터 출력\n",
-    "best_params = study.best_trial.params\n",
-    "print(\"Best Hyperparameters:\", best_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 모델 학습 루프"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0  | loss: 0.65117 | valid_csi_metric: 0.2587  |  0:00:01s\n",
-      "epoch 1  | loss: 0.33195 | valid_csi_metric: 0.39111 |  0:00:02s\n",
-      "epoch 2  | loss: 0.24615 | valid_csi_metric: 0.44093 |  0:00:03s\n",
-      "epoch 3  | loss: 0.1948  | valid_csi_metric: 0.46779 |  0:00:04s\n",
-      "epoch 4  | loss: 0.18316 | valid_csi_metric: 0.50305 |  0:00:05s\n",
-      "epoch 5  | loss: 0.16839 | valid_csi_metric: 0.4462  |  0:00:06s\n",
-      "epoch 6  | loss: 0.16348 | valid_csi_metric: 0.45389 |  0:00:08s\n",
-      "epoch 7  | loss: 0.15119 | valid_csi_metric: 0.4276  |  0:00:09s\n",
-      "epoch 8  | loss: 0.15715 | valid_csi_metric: 0.50084 |  0:00:10s\n",
-      "epoch 9  | loss: 0.14863 | valid_csi_metric: 0.48189 |  0:00:11s\n",
-      "epoch 10 | loss: 0.15017 | valid_csi_metric: 0.48625 |  0:00:12s\n",
-      "epoch 11 | loss: 0.14889 | valid_csi_metric: 0.52342 |  0:00:13s\n",
-      "epoch 12 | loss: 0.14121 | valid_csi_metric: 0.52754 |  0:00:15s\n",
-      "epoch 13 | loss: 0.13633 | valid_csi_metric: 0.46671 |  0:00:16s\n",
-      "epoch 14 | loss: 0.1357  | valid_csi_metric: 0.14583 |  0:00:17s\n",
-      "epoch 15 | loss: 0.12973 | valid_csi_metric: 0.15389 |  0:00:18s\n",
-      "epoch 16 | loss: 0.13033 | valid_csi_metric: 0.39516 |  0:00:19s\n",
-      "epoch 17 | loss: 0.11944 | valid_csi_metric: 0.57122 |  0:00:20s\n",
-      "epoch 18 | loss: 0.11868 | valid_csi_metric: 0.5     |  0:00:21s\n",
-      "epoch 19 | loss: 0.11035 | valid_csi_metric: 0.48313 |  0:00:23s\n",
-      "epoch 20 | loss: 0.10546 | valid_csi_metric: 0.50574 |  0:00:24s\n",
-      "epoch 21 | loss: 0.14626 | valid_csi_metric: 0.37308 |  0:00:25s\n",
-      "epoch 22 | loss: 0.14373 | valid_csi_metric: 0.17938 |  0:00:26s\n",
-      "epoch 23 | loss: 0.11781 | valid_csi_metric: 0.43773 |  0:00:27s\n",
-      "epoch 24 | loss: 0.10846 | valid_csi_metric: 0.37634 |  0:00:28s\n",
-      "epoch 25 | loss: 0.10376 | valid_csi_metric: 0.1566  |  0:00:29s\n",
-      "epoch 26 | loss: 0.10205 | valid_csi_metric: 0.21463 |  0:00:31s\n",
-      "epoch 27 | loss: 0.09992 | valid_csi_metric: 0.4367  |  0:00:32s\n",
-      "\n",
-      "Early stopping occurred at epoch 27 with best_epoch = 17 and best_valid_csi_metric = 0.57122\n",
-      "Successfully saved model at ./models/tabnet_model.zip\n",
-      "Validation CSI-Score: 0.5712\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pytorch_tabnet.tab_model import TabNetClassifier\n",
-    "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score\n",
-    "import numpy as np\n",
-    "\n",
-    "# 최적 하이퍼파라미터 적용\n",
-    "best_model = TabNetClassifier(\n",
-    "    n_d=best_params[\"n_d\"],\n",
-    "    n_a=best_params[\"n_a\"],\n",
-    "    n_steps=best_params[\"n_steps\"],\n",
-    "    gamma=best_params[\"gamma\"],\n",
-    "    lambda_sparse=best_params[\"lambda_sparse\"],\n",
-    "    momentum=best_params[\"momentum\"],\n",
-    "    seed=seed  # 시드 고정\n",
-    ")\n",
-    "\n",
-    "# Early Stopping 설정\n",
-    "best_val_f1 = -float(\"inf\")  # F1-score는 최대화가 목표\n",
-    "patience = 10  # F1-score가 개선되지 않는 Epoch 수\n",
-    "counter = 0\n",
-    "\n",
-    "# 모델 학습\n",
-    "best_model.fit(\n",
-    "    X_train.to_numpy(), y_train.to_numpy(),\n",
-    "    eval_set=[(X_val.to_numpy(), y_val.to_numpy())],\n",
-    "    eval_name=['valid'],\n",
-    "    eval_metric=['csi_metric'],\n",
-    "    max_epochs=50,  # 최대 Epoch\n",
-    "    patience=patience,  # Early stopping 적용\n",
-    "    batch_size=512,\n",
-    "    virtual_batch_size=128\n",
-    ")\n",
-    "\n",
-    "# 학습이 끝난 후 모델 저장 (경로 명확히 지정)\n",
-    "best_model.save_model(\"./models/tabnet_model\")\n",
-    "\n",
-    "# 저장된 모델 불러오기\n",
-    "best_model.load_model(\"./models/tabnet_model.zip\")\n",
-    "\n",
-    "# Validation 데이터 평가\n",
-    "y_pred_val = best_model.predict(X_val.to_numpy())\n",
-    "val_csi = calculate_csi(y_val.to_numpy(), y_pred_val)\n",
-    "\n",
-    "print(f\"Validation CSI-Score: {val_csi:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 성능 평가"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test Accuracy: 0.8820776255707763\n",
-      "Test F1-Score: 0.4866900629111339\n",
-      "Test CSI-Score: 0.29052197802195806\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import accuracy_score, f1_score\n",
-    "\n",
-    "# 저장된 모델 불러오기\n",
-    "loaded_model = TabNetClassifier()\n",
-    "loaded_model.load_model(\"./models/tabnet_model.zip\")\n",
-    "\n",
-    "# Test 데이터 평가\n",
-    "y_pred_test = loaded_model.predict(X_test.to_numpy())\n",
-    "\n",
-    "# 성능 평가 지표 계산\n",
-    "print(\"Test Accuracy:\", accuracy_score(y_test, y_pred_test))\n",
-    "print(\"Test F1-Score:\", f1_score(y_test, y_pred_test, average='macro'))\n",
-    "print(\"Test CSI-Score:\", calculate_csi(y_test, y_pred_test))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Analysis_code/final_test/final.ipynb b/Analysis_code/final_test/final.ipynb
deleted file mode 100644
index c45372d7e9ed40e1418f4c4b06b6908acaf93a30..0000000000000000000000000000000000000000
--- a/Analysis_code/final_test/final.ipynb
+++ /dev/null
@@ -1,1143 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
-    "import torch\n",
-    "from torch.utils.data import DataLoader, TensorDataset\n",
-    "import random\n",
-    "from collections import Counter\n",
-    "import sys\n",
-    "sys.path.append('../../../../../../../../mnt/workspace/LightGBM/python-package')\n",
-    "from lightgbm import LGBMClassifier\n",
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.inspection import permutation_importance\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "from xgboost import XGBClassifier\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "import sys\n",
-    "sys.path.append('../')\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "import optuna\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import random\n",
-    "from ft_transformer import FTTransformer\n",
-    "from resnet_like import ResNetLike\n",
-    "from deepgbm import DeepGBM\n",
-    "from pytorch_tabnet.tab_model import TabNetClassifier\n",
-    "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Python 및 Numpy 시드 고정\n",
-    "seed = 42\n",
-    "random.seed(seed)\n",
-    "np.random.seed(seed)\n",
-    "\n",
-    "# PyTorch 시드 고정\n",
-    "torch.manual_seed(seed)\n",
-    "torch.cuda.manual_seed(seed)\n",
-    "torch.cuda.manual_seed_all(seed)  # Multi-GPU 환경에서 동일한 시드 적용\n",
-    "\n",
-    "# PyTorch 연산의 결정적 모드 설정\n",
-    "torch.backends.cudnn.deterministic = True  # 실행마다 동일한 결과를 보장\n",
-    "torch.backends.cudnn.benchmark = True  # 성능 최적화를 활성화 (가능한 한 빠른 연산 수행)\n",
-    "\n",
-    "# 전처리 함수\n",
-    "def preprocessing(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df['lm_cloudcover'] = df['lm_cloudcover'].astype('int')\n",
-    "    df['cloudcover'] = df['cloudcover'].astype('int')\n",
-    "    return df\n",
-    "\n",
-    "# 데이터셋 준비 함수\n",
-    "def prepare_dataset(region, data_sample='pure', target='multi', fold=3):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    return X_train, X_val, X_test, y_train, y_val, y_test, categorical_cols, numerical_cols\n",
-    "\n",
-    "# 데이터 변환 및 dataloader 생성 함수\n",
-    "def prepare_dataloader(region, data_sample='pure', target='multi', fold=3, random_state=None):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    # 연속형 변수와 범주형 변수 분리\n",
-    "    X_train_num = torch.tensor(X_train[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_train_cat = torch.tensor(X_train[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_val_num = torch.tensor(X_val[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_val_cat = torch.tensor(X_val[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_test_num = torch.tensor(X_test[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_test_cat = torch.tensor(X_test[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    # 레이블 변환\n",
-    "    if target == \"binary\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.float32)  # 이진 분류 → float32\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.float32)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32)\n",
-    "    elif target == \"multi\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.long)  # 다중 분류 → long\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.long)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.long)\n",
-    "    else:\n",
-    "        raise ValueError(\"target must be 'binary' or 'multi'\")\n",
-    "\n",
-    "    # TensorDataset 생성\n",
-    "    train_dataset = TensorDataset(X_train_num, X_train_cat, y_train_tensor)\n",
-    "    val_dataset = TensorDataset(X_val_num, X_val_cat, y_val_tensor)\n",
-    "    test_dataset = TensorDataset(X_test_num, X_test_cat, y_test_tensor)\n",
-    "\n",
-    "    # DataLoader 생성\n",
-    "    if random_state == None:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
-    "    else:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, generator=torch.Generator().manual_seed(random_state))\n",
-    "    val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n",
-    "    test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
-    "    \n",
-    "    return X_train, categorical_cols, numerical_cols, train_loader, val_loader, test_loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "# 디바이스 설정 (CUDA 사용 가능하면 GPU로, 아니면 CPU로)\n",
-    "import glob\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
-    "def calculate_csi(Y_test, pred):\n",
-    "\n",
-    "    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경\n",
-    "    # 혼동 행렬에서 H, F, M 추출\n",
-    "    H = (cm[0, 0] + cm[1, 1])\n",
-    "    \n",
-    "    F = (cm[1, 0] + cm[2, 0] +\n",
-    "         cm[0, 1] + cm[2, 1])\n",
-    "\n",
-    "    M = (cm[0, 2] + cm[1, 2])\n",
-    "    \n",
-    "    # CSI 계산\n",
-    "    CSI = H / (H + F + M + 1e-10)\n",
-    "    return CSI\n",
-    "\n",
-    "def csi_metric(y_true, pred):\n",
-    "    y_pred_binary = np.argmax(pred, axis=1)\n",
-    "    score = calculate_csi(y_true, y_pred_binary)\n",
-    "    return 'CSI', score, True  # higher_better=True\n",
-    "\n",
-    "\n",
-    "def eval_metric_csi(y_true, pred_prob):\n",
-    "\n",
-    "    pred = np.argmax(pred_prob, axis=1)\n",
-    "    y_true = y_true\n",
-    "    y_pred = pred\n",
-    "    csi = calculate_csi(y_true, y_pred)\n",
-    "    return -1*csi\n",
-    "\n",
-    "\n",
-    "from sklearn.metrics import matthews_corrcoef, accuracy_score\n",
-    "\n",
-    "def multiclass_mcc(y_val, y_pred):\n",
-    "    \"\"\"\n",
-    "    다중 분류에서도 sklearn의 matthews_corrcoef를 그대로 사용할 수 있음.\n",
-    "    \"\"\"\n",
-    "    return matthews_corrcoef(y_val, y_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "# 디바이스 설정 (CUDA 사용 가능하면 GPU로, 아니면 CPU로)\n",
-    "import glob\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "\n",
-    "def calculate_csi(Y_test, pred):\n",
-    "\n",
-    "    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경\n",
-    "    # 혼동 행렬에서 H, F, M 추출\n",
-    "    H = (cm[0, 0] + cm[1, 1])\n",
-    "    \n",
-    "    F = (cm[1, 0] + cm[2, 0] +\n",
-    "         cm[0, 1] + cm[2, 1])\n",
-    "\n",
-    "    M = (cm[0, 2] + cm[1, 2])\n",
-    "    \n",
-    "    # CSI 계산\n",
-    "    CSI = H / (H + F + M + 1e-10)\n",
-    "    return CSI\n",
-    "\n",
-    "# Soft Voting 앙상블\n",
-    "def get_proba(region, model_choose, data_sample, fold, target='multi'):\n",
-    "    _, _, _, _,val_loader , test_loader = prepare_dataloader(region=region, data_sample=data_sample, target=target,fold=fold ,random_state=120)\n",
-    "\n",
-    "    folder_path = f'../save_model/{model_choose}/{data_sample}'\n",
-    "    model_paths = [path for path in glob.glob(f'{folder_path}/*.pth') if f'{region}' in path]\n",
-    "\n",
-    "    model = torch.load(model_paths[fold-1], weights_only=False).to(device)\n",
-    "    model.eval()\n",
-    "\n",
-    "    test_preds = []\n",
-    "\n",
-    "\n",
-    "    with torch.no_grad():\n",
-    "        for x_num_batch, x_cat_batch, _ in test_loader:\n",
-    "            output = model(x_num_batch.to(device), x_cat_batch.to(device))\n",
-    "            output = torch.softmax(output, dim=1)\n",
-    "            test_preds.extend(output.cpu().numpy())\n",
-    "\n",
-    "\n",
-    "    return test_preds\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul = pd.read_csv(\"../../data/data_for_modeling/seoul_train.csv\")\n",
-    "df_seoul_test = pd.read_csv(\"../../data/data_for_modeling/seoul_test.csv\")\n",
-    "\n",
-    "df_busan = pd.read_csv(\"../../data/data_for_modeling/busan_train.csv\")\n",
-    "df_busan_test = pd.read_csv(\"../../data/data_for_modeling/busan_test.csv\")\n",
-    "\n",
-    "df_daegu = pd.read_csv(\"../../data/data_for_modeling/daegu_train.csv\")\n",
-    "df_daegu_test = pd.read_csv(\"../../data/data_for_modeling/daegu_test.csv\")\n",
-    "\n",
-    "df_daejeon = pd.read_csv(\"../../data/data_for_modeling/daejeon_train.csv\")\n",
-    "df_daejeon_test = pd.read_csv(\"../../data/data_for_modeling/daejeon_test.csv\")\n",
-    "\n",
-    "df_incheon = pd.read_csv(\"../../data/data_for_modeling/incheon_train.csv\")\n",
-    "df_incheon_test = pd.read_csv(\"../../data/data_for_modeling/incheon_test.csv\")\n",
-    "\n",
-    "df_gwangju = pd.read_csv(\"../../data/data_for_modeling/gwangju_train.csv\")\n",
-    "df_gwangju_test = pd.read_csv(\"../../data/data_for_modeling/gwangju_test.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocessing_df(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df['year'] = df['year'].astype('int')\n",
-    "    df['month'] = df['month'].astype('int')\n",
-    "    df['hour'] = df['hour'].astype('int')\n",
-    "    df['binary_class'] = df['binary_class'].astype('int')\n",
-    "    df['multi_class'] = df['multi_class'].astype('int')\n",
-    "\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',\n",
-    "       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',\n",
-    "       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',\n",
-    "       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',\n",
-    "       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',\n",
-    "       'month_sin', 'month_cos','multi_class']]\n",
-    "    return df\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul_test= preprocessing_df(df_seoul_test).copy()\n",
-    "df_busan_test= preprocessing_df(df_busan_test).copy()\n",
-    "df_daegu_test= preprocessing_df(df_daegu_test).copy()\n",
-    "df_gwangju_test= preprocessing_df(df_gwangju_test).copy()\n",
-    "df_daejeon_test= preprocessing_df(df_daejeon_test).copy()\n",
-    "df_incheon_test= preprocessing_df(df_incheon_test).copy()\n",
-    "\n",
-    "df_seoul= preprocessing_df(df_seoul).copy()\n",
-    "df_busan= preprocessing_df(df_busan).copy()\n",
-    "df_daegu= preprocessing_df(df_daegu).copy()\n",
-    "df_gwangju= preprocessing_df(df_gwangju).copy()\n",
-    "df_daejeon= preprocessing_df(df_daejeon).copy()\n",
-    "df_incheon= preprocessing_df(df_incheon).copy()\n",
-    "\n",
-    "df_seoul_test.drop(columns=['year'], inplace=True)\n",
-    "df_busan_test.drop(columns=['year'], inplace=True)\n",
-    "df_daegu_test.drop(columns=['year'], inplace=True)\n",
-    "df_daejeon_test.drop(columns=['year'], inplace=True)\n",
-    "df_incheon_test.drop(columns=['year'], inplace=True)\n",
-    "df_gwangju_test.drop(columns=['year'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import joblib\n",
-    "\n",
-    "lgb_seoul= joblib.load('../save_model/LGB_optima/lgb_seoul_smote.pkl')\n",
-    "lgb_busan= joblib.load('../save_model/LGB_optima/lgb_busan_smote.pkl')\n",
-    "lgb_incheon= joblib.load('../save_model/LGB_optima/lgb_incheon_smote.pkl')\n",
-    "lgb_daegu= joblib.load('../save_model/LGB_optima/lgb_daegu_smote.pkl')\n",
-    "lgb_daejeon= joblib.load('../save_model/LGB_optima/lgb_daejeon_smote.pkl')\n",
-    "lgb_gwangju= joblib.load('../save_model/LGB_optima/lgb_gwangju_smote.pkl')\n",
-    "\n",
-    "xgb_seoul= joblib.load('../save_model/XGB_optima/xgb_seoul_smote.pkl')\n",
-    "xgb_busan= joblib.load('../save_model/XGB_optima/xgb_busan_ctgan20000.pkl')\n",
-    "xgb_incheon= joblib.load('../save_model/XGB_optima/xgb_incheon_smote.pkl')\n",
-    "xgb_daegu= joblib.load('../save_model/XGB_optima/xgb_daegu_smote.pkl')\n",
-    "xgb_daejeon= joblib.load('../save_model/XGB_optima/xgb_daejeon_smote.pkl')\n",
-    "xgb_gwangju= joblib.load('../save_model/XGB_optima/xgb_gwangju_smote.pkl')\n",
-    "\n",
-    "lgb_seoul_1= lgb_seoul[0]\n",
-    "lgb_seoul_2= lgb_seoul[1]\n",
-    "lgb_seoul_3= lgb_seoul[2]\n",
-    "\n",
-    "lgb_busan_1= lgb_busan[0]\n",
-    "lgb_busan_2= lgb_busan[1]\n",
-    "lgb_busan_3= lgb_busan[2]\n",
-    "\n",
-    "lgb_incheon_1= lgb_incheon[0]\n",
-    "lgb_incheon_2= lgb_incheon[1]\n",
-    "lgb_incheon_3= lgb_incheon[2]\n",
-    "\n",
-    "lgb_daegu_1= lgb_daegu[0]\n",
-    "lgb_daegu_2= lgb_daegu[1]\n",
-    "lgb_daegu_3= lgb_daegu[2]\n",
-    "\n",
-    "lgb_daejeon_1= lgb_daejeon[0]\n",
-    "lgb_daejeon_2= lgb_daejeon[1]\n",
-    "lgb_daejeon_3= lgb_daejeon[2]\n",
-    "\n",
-    "lgb_gwangju_1= lgb_gwangju[0]\n",
-    "lgb_gwangju_2= lgb_gwangju[1]\n",
-    "lgb_gwangju_3= lgb_gwangju[2]\n",
-    "\n",
-    "\n",
-    "xgb_seoul_1= xgb_seoul[0]\n",
-    "xgb_seoul_2= xgb_seoul[1]\n",
-    "xgb_seoul_3= xgb_seoul[2]\n",
-    "\n",
-    "xgb_busan_1= xgb_busan[0]\n",
-    "xgb_busan_2= xgb_busan[1]\n",
-    "xgb_busan_3= xgb_busan[2]\n",
-    "\n",
-    "xgb_incheon_1= xgb_incheon[0]\n",
-    "xgb_incheon_2= xgb_incheon[1]\n",
-    "xgb_incheon_3= xgb_incheon[2]\n",
-    "\n",
-    "xgb_daegu_1= xgb_daegu[0]\n",
-    "xgb_daegu_2= xgb_daegu[1]\n",
-    "xgb_daegu_3= xgb_daegu[2]\n",
-    "\n",
-    "xgb_daejeon_1= xgb_daejeon[0]\n",
-    "xgb_daejeon_2= xgb_daejeon[1]\n",
-    "xgb_daejeon_3= xgb_daejeon[2]\n",
-    "\n",
-    "xgb_gwangju_1= xgb_gwangju[0]\n",
-    "xgb_gwangju_2= xgb_gwangju[1]\n",
-    "xgb_gwangju_3= xgb_gwangju[2]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **Soft Voting**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "voting = []\n",
-    "mcc = []\n",
-    "accuracy = []\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm', 'vap_pressure',\n",
-       "       'dewpoint_C', 'loc_pressure', 'sea_pressure', 'solarRad', 'snow_cm',\n",
-       "       'cloudcover', 'lm_cloudcover', 'low_cloudbase', 'groundtemp', 'O3',\n",
-       "       'NO2', 'PM10', 'PM25', 'month', 'hour', 'ground_temp - temp_C',\n",
-       "       'hour_sin', 'hour_cos', 'month_sin', 'month_cos', 'multi_class'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_seoul_test.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CSI score of soft(test) : 0.3248062015503624\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "probas = []\n",
-    "\n",
-    "# 1 Fold\n",
-    "test_preds  = get_proba('seoul', 'deepgbm', 'ctgan20000', 1)\n",
-    "probas.append(test_preds)\n",
-    "test_preds  = get_proba('seoul', 'resnet_like', 'smote', 1)\n",
-    "probas.append(test_preds)\n",
-    "# probas.append(xgb_seoul_1.predict_proba(df_seoul_test.iloc[:,:-1]))\n",
-    "\n",
-    "# 2 Fold\n",
-    "test_preds  = get_proba('seoul', 'deepgbm', 'ctgan20000', 2)\n",
-    "probas.append(test_preds)\n",
-    "test_preds  = get_proba('seoul', 'resnet_like', 'smote', 2)\n",
-    "probas.append(test_preds)\n",
-    "# probas.append(xgb_seoul_2.predict_proba(df_seoul_test.iloc[:,:-1]))\n",
-    "\n",
-    "# 3 Fold\n",
-    "test_preds  = get_proba('seoul', 'deepgbm', 'ctgan20000', 3)\n",
-    "probas.append(test_preds)\n",
-    "test_preds  = get_proba('seoul', 'resnet_like', 'smote', 3)\n",
-    "probas.append(test_preds)\n",
-    "# probas.append(xgb_seoul_3.predict_proba(df_seoul_test.iloc[:,:-1]))\n",
-    "\n",
-    "voting.append(calculate_csi(df_seoul_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "mcc.append(multiclass_mcc(df_seoul_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "accuracy.append(accuracy_score(df_seoul_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "\n",
-    "\n",
-    "print(\"CSI score of soft(test) :\", calculate_csi(df_seoul_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[   2,   11,    0],\n",
-       "       [   6,  417,   58],\n",
-       "       [   7,  789, 7470]])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion_matrix(df_seoul_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CSI score of soft(test) : 0.46608315098458075\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "probas = []\n",
-    "\n",
-    "# 1 Fold\n",
-    "test_preds  = get_proba('busan', 'deepgbm', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "test_preds  = get_proba('busan', 'resnet_like', 'ctgan10000', 1)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "test_preds  = get_proba('busan', 'deepgbm', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "test_preds  = get_proba('busan', 'resnet_like', 'ctgan10000', 2)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "\n",
-    "# 3 Fold\n",
-    "test_preds  = get_proba('busan', 'deepgbm', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "test_preds  = get_proba('busan', 'resnet_like', 'ctgan10000', 3)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "\n",
-    "voting.append(calculate_csi(df_busan_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "mcc.append(multiclass_mcc(df_busan_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "accuracy.append(accuracy_score(df_busan_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "\n",
-    "print(\"CSI score of soft(test) :\", calculate_csi(df_busan_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  11,   13,    0],\n",
-       "       [  11,  202,   68],\n",
-       "       [   2,  150, 8303]])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion_matrix(df_busan_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CSI score of hard(test) : 0.572269457161506\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "\n",
-    "# 1 Fold\n",
-    "probas = []\n",
-    "test_preds = get_proba('incheon', 'deepgbm', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('incheon', 'resnet_like', 'smote', 1)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('incheon', 'ft_transformer', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "probas.append(lgb_incheon_1.predict_proba(df_incheon_test.iloc[:,:-1]))\n",
-    "probas.append(xgb_incheon_1.predict_proba(df_incheon_test.iloc[:,:-1]))\n",
-    "\n",
-    "# 2 Fold\n",
-    "test_preds = get_proba('incheon', 'deepgbm', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('incheon', 'resnet_like', 'smote', 2)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('incheon', 'ft_transformer', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "probas.append(lgb_incheon_2.predict_proba(df_incheon_test.iloc[:,:-1]))\n",
-    "probas.append(xgb_incheon_2.predict_proba(df_incheon_test.iloc[:,:-1]))\n",
-    "\n",
-    "# 3 Fold\n",
-    "test_preds = get_proba('incheon', 'deepgbm', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('incheon', 'resnet_like', 'smote', 3)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('incheon', 'ft_transformer', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "probas.append(lgb_incheon_3.predict_proba(df_incheon_test.iloc[:,:-1]))\n",
-    "probas.append(xgb_incheon_3.predict_proba(df_incheon_test.iloc[:,:-1]))\n",
-    "\n",
-    "\n",
-    "\n",
-    "voting.append(calculate_csi(df_incheon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "mcc.append(multiclass_mcc(df_incheon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "accuracy.append(accuracy_score(df_incheon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "\n",
-    "\n",
-    "print(\"CSI score of hard(test) :\", calculate_csi(df_incheon_test.iloc[:,-1],mode(np.argmax(probas, axis=2), axis=0).mode[0]))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  87,   74,   21],\n",
-       "       [  22,  788,  395],\n",
-       "       [   2,  140, 7231]])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion_matrix(df_incheon_test.iloc[:,-1],mode(np.argmax(probas, axis=2), axis=0).mode[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CSI score of soft(test) : 0.2852112676055334\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "probas= []\n",
-    "\n",
-    "# 1 Fold\n",
-    "test_preds = get_proba('daegu', 'deepgbm', 'smote', 1)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('daegu', 'ft_transformer', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "# 2 Fold\n",
-    "test_preds = get_proba('daegu', 'deepgbm', 'smote', 2)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('daegu', 'ft_transformer', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "# 3 Fold\n",
-    "test_preds = get_proba('daegu', 'deepgbm', 'smote', 3)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('daegu', 'ft_transformer', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "voting.append(calculate_csi(df_daegu_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "mcc.append(multiclass_mcc(df_daegu_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "accuracy.append(accuracy_score(df_daegu_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "\n",
-    "print(\"CSI score of soft(test) :\", calculate_csi(df_daegu_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[   1,    0,    0],\n",
-       "       [   1,   80,   47],\n",
-       "       [   2,  153, 8476]])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion_matrix(df_daegu_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CSI score of soft(test) : 0.31884057971011603\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "probas = []\n",
-    "\n",
-    "# 1 Fold\n",
-    "test_preds = get_proba('daejeon', 'deepgbm', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('daejeon', 'ft_transformer', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "test_preds = get_proba('daejeon', 'deepgbm', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('daejeon', 'ft_transformer', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "\n",
-    "# 3 Fold\n",
-    "test_preds = get_proba('daejeon', 'deepgbm', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('daejeon', 'ft_transformer', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "voting.append(calculate_csi(df_daejeon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "mcc.append(multiclass_mcc(df_daejeon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "accuracy.append(accuracy_score(df_daejeon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "\n",
-    "\n",
-    "print(\"CSI score of soft(test) :\", calculate_csi(df_daejeon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  15,   23,   15],\n",
-       "       [  10,  337,  271],\n",
-       "       [   0,  433, 7656]])"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion_matrix(df_daejeon_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CSI score of soft(test) : 0.4759725400457121\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "probas = []\n",
-    "\n",
-    "# 1 Fold\n",
-    "test_preds = get_proba('gwangju', 'deepgbm', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('gwangju', 'ft_transformer', 'pure', 1)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "test_preds = get_proba('gwangju', 'deepgbm', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('gwangju', 'ft_transformer', 'pure', 2)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "\n",
-    "# 3 Fold\n",
-    "test_preds = get_proba('gwangju', 'deepgbm', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "test_preds = get_proba('gwangju', 'ft_transformer', 'pure', 3)\n",
-    "probas.append(test_preds)\n",
-    "\n",
-    "voting.append(calculate_csi(df_gwangju_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "mcc.append(multiclass_mcc(df_gwangju_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "accuracy.append(accuracy_score(df_gwangju_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))\n",
-    "\n",
-    "\n",
-    "print(\"CSI score of soft(test) :\", calculate_csi(df_gwangju_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  10,   12,    8],\n",
-       "       [   2,  406,  235],\n",
-       "       [   0,  201, 7886]])"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion_matrix(df_gwangju_test.iloc[:,-1], np.argmax(np.mean(probas, axis=0), axis=1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "plt.figure(figsize=(10,5))\n",
-    "sns.barplot(x=['Seoul', 'Busan', 'Incheon', 'Daegu', 'Daejeon', 'Gwangju'], y=voting)\n",
-    "plt.title('CSI Score of Voting Model')\n",
-    "plt.ylabel('CSI')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.4078715882283252"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(voting)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0.3248062015503624,\n",
-       " 0.46608315098458075,\n",
-       " 0.5763157894736463,\n",
-       " 0.2852112676055334,\n",
-       " 0.31884057971011603,\n",
-       " 0.4759725400457121]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "voting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0.5106142349456536,\n",
-       " 0.640202275543952,\n",
-       " 0.709448778435959,\n",
-       " 0.45579515959653394,\n",
-       " 0.453960121993875,\n",
-       " 0.6218724605270242]"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mcc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0.9005707762557078,\n",
-       " 0.9721461187214612,\n",
-       " 0.9264840182648402,\n",
-       " 0.9768264840182649,\n",
-       " 0.9141552511415525,\n",
-       " 0.9477168949771689]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "accuracy"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Analysis_code/find_reason/ busan_trend.ipynb b/Analysis_code/find_reason/ busan_trend.ipynb
deleted file mode 100644
index 1bbc656fd226cec2d9a974801cbe74ab0e372ced..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/ busan_trend.ipynb	
+++ /dev/null
@@ -1,157 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "26f60e36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 분석에 필요한 라이브러리 임포트\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from scipy.spatial import distance\n",
-    "\n",
-    "\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "28456d07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "busan = pd.read_feather(\"../../data/data_for_modeling/df_busan.feather\")\n",
-    "feature = ['hm','PM10','PM25','multi_class','year','month','hour']\n",
-    "busan = busan[feature]\n",
-    "busan = busan.loc[busan['year'].isin([2018,2019,2020,2021,2022,2023]),:]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "84183ace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['hm', 'PM10', 'PM25', 'multi_class', 'year', 'month', 'hour'], dtype='object')"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "busan.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "5b942b62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 월별로 시간의 흐름에 따라 PM10, PM25 변수의 변화 추이 시각화\n",
-    "\n",
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "# 월별 평균 PM10, PM25 계산 함수\n",
-    "def get_monthly_trend(df, variable):\n",
-    "    # 'year', 'month' 컬럼을 기준으로 월별 평균, Q1, Q3 계산\n",
-    "    monthly = df.groupby(['year', 'month'])[variable].agg(['mean', \n",
-    "                                                           lambda x: x.quantile(0.25), \n",
-    "                                                           lambda x: x.quantile(0.75)]).reset_index()\n",
-    "    monthly.columns = ['year', 'month', 'mean', 'q1', 'q3']\n",
-    "    # 날짜 컬럼 생성 (월의 첫날로)\n",
-    "    monthly['date'] = pd.to_datetime(monthly['year'].astype(str) + '-' + monthly['month'].astype(str).str.zfill(2) + '-01')\n",
-    "    monthly = monthly.sort_values('date')\n",
-    "    return monthly\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
-    "variables_to_analyze = ['hm', 'PM25']\n",
-    "\n",
-    "for i, var in enumerate(variables_to_analyze):\n",
-    "    if var in busan.columns:\n",
-    "        trend = get_monthly_trend(busan, var)\n",
-    "        ax = axes[i]\n",
-    "        # 평균값\n",
-    "        ax.plot(trend['date'], trend['mean'], 'o-', color='blue', label='평균값')\n",
-    "        # Q1, Q3\n",
-    "        ax.plot(trend['date'], trend['q1'], 's--', color='orange', alpha=0.7, label='Q1')\n",
-    "        ax.plot(trend['date'], trend['q3'], 's--', color='purple', alpha=0.7, label='Q3')\n",
-    "        # 선형 트렌드선\n",
-    "        if len(trend) > 1:\n",
-    "            z = np.polyfit(range(len(trend)), trend['mean'], 1)\n",
-    "            p = np.poly1d(z)\n",
-    "            ax.plot(trend['date'], p(range(len(trend))), \"r--\", linewidth=1, alpha=0.6)\n",
-    "            slope = z[0]\n",
-    "            ax.text(trend['date'].iloc[0], trend['mean'].max(), \n",
-    "                    f'월별 변화율: {slope:.4f}/month', fontsize=10, color='darkred')\n",
-    "        ax.set_title(f'{var} 변화 추이', fontsize=14)\n",
-    "        ax.set_xlabel('날짜', fontsize=12)\n",
-    "        ax.set_ylabel(var, fontsize=12)\n",
-    "        ax.grid(True, linestyle='--', alpha=0.7)\n",
-    "        ax.legend()\n",
-    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
-    "        plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n",
-    "    else:\n",
-    "        axes[i].text(0.5, 0.5, f'{var} 컬럼 없음', ha='center', va='center', fontsize=12)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e149d5dc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Analysis_code/find_reason/ daegu_trend.ipynb b/Analysis_code/find_reason/ daegu_trend.ipynb
deleted file mode 100644
index 391514049ae2121ae22f516cdf848bbc1a0ef872..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/ daegu_trend.ipynb	
+++ /dev/null
@@ -1,157 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "26f60e36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 분석에 필요한 라이브러리 임포트\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from scipy.spatial import distance\n",
-    "\n",
-    "\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "28456d07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "daegu = pd.read_feather(\"../../data/data_for_modeling/df_daegu.feather\")\n",
-    "feature = ['hm','PM10','PM25','multi_class','year','month','hour']\n",
-    "daegu = daegu[feature]\n",
-    "daegu = daegu.loc[daegu['year'].isin([2018,2019,2020,2021,2022,2023]),:]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "84183ace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['hm', 'PM10', 'PM25', 'multi_class', 'year', 'month', 'hour'], dtype='object')"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "daegu.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "5b942b62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 월별로 시간의 흐름에 따라 PM10, PM25 변수의 변화 추이 시각화\n",
-    "\n",
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "# 월별 평균 PM10, PM25 계산 함수\n",
-    "def get_monthly_trend(df, variable):\n",
-    "    # 'year', 'month' 컬럼을 기준으로 월별 평균, Q1, Q3 계산\n",
-    "    monthly = df.groupby(['year', 'month'])[variable].agg(['mean', \n",
-    "                                                           lambda x: x.quantile(0.25), \n",
-    "                                                           lambda x: x.quantile(0.75)]).reset_index()\n",
-    "    monthly.columns = ['year', 'month', 'mean', 'q1', 'q3']\n",
-    "    # 날짜 컬럼 생성 (월의 첫날로)\n",
-    "    monthly['date'] = pd.to_datetime(monthly['year'].astype(str) + '-' + monthly['month'].astype(str).str.zfill(2) + '-01')\n",
-    "    monthly = monthly.sort_values('date')\n",
-    "    return monthly\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
-    "variables_to_analyze = ['hm', 'PM25']\n",
-    "\n",
-    "for i, var in enumerate(variables_to_analyze):\n",
-    "    if var in daegu.columns:\n",
-    "        trend = get_monthly_trend(daegu, var)\n",
-    "        ax = axes[i]\n",
-    "        # 평균값\n",
-    "        ax.plot(trend['date'], trend['mean'], 'o-', color='blue', label='평균값')\n",
-    "        # Q1, Q3\n",
-    "        ax.plot(trend['date'], trend['q1'], 's--', color='orange', alpha=0.7, label='Q1')\n",
-    "        ax.plot(trend['date'], trend['q3'], 's--', color='purple', alpha=0.7, label='Q3')\n",
-    "        # 선형 트렌드선\n",
-    "        if len(trend) > 1:\n",
-    "            z = np.polyfit(range(len(trend)), trend['mean'], 1)\n",
-    "            p = np.poly1d(z)\n",
-    "            ax.plot(trend['date'], p(range(len(trend))), \"r--\", linewidth=1, alpha=0.6)\n",
-    "            slope = z[0]\n",
-    "            ax.text(trend['date'].iloc[0], trend['mean'].max(), \n",
-    "                    f'월별 변화율: {slope:.4f}/month', fontsize=10, color='darkred')\n",
-    "        ax.set_title(f'{var} 월별 변화 추이', fontsize=14)\n",
-    "        ax.set_xlabel('날짜', fontsize=12)\n",
-    "        ax.set_ylabel(var, fontsize=12)\n",
-    "        ax.grid(True, linestyle='--', alpha=0.7)\n",
-    "        ax.legend()\n",
-    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
-    "        plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n",
-    "    else:\n",
-    "        axes[i].text(0.5, 0.5, f'{var} 컬럼 없음', ha='center', va='center', fontsize=12)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e149d5dc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Analysis_code/find_reason/ gwangju_trend.ipynb b/Analysis_code/find_reason/ gwangju_trend.ipynb
deleted file mode 100644
index 37a66f6d17d36e0c5ad4fee1fe1a61967639e587..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/ gwangju_trend.ipynb	
+++ /dev/null
@@ -1,136 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "26f60e36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 분석에 필요한 라이브러리 임포트\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from scipy.spatial import distance\n",
-    "\n",
-    "\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "28456d07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gwangju = pd.read_feather(\"../../data/data_for_modeling/df_gwangju.feather\")\n",
-    "feature = ['hm','PM10','PM25','multi_class','year','month','hour']\n",
-    "gwangju = gwangju[feature]\n",
-    "gwangju = gwangju.loc[gwangju['year'].isin([2018,2019,2020,2021,2022,2023]),:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "5b942b62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 월별로 시간의 흐름에 따라 PM10, PM25 변수의 변화 추이 시각화\n",
-    "\n",
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "# 월별 평균 PM10, PM25 계산 함수\n",
-    "def get_monthly_trend(df, variable):\n",
-    "    # 'year', 'month' 컬럼을 기준으로 월별 평균, Q1, Q3 계산\n",
-    "    monthly = df.groupby(['year', 'month'])[variable].agg(['mean', \n",
-    "                                                           lambda x: x.quantile(0.25), \n",
-    "                                                           lambda x: x.quantile(0.75)]).reset_index()\n",
-    "    monthly.columns = ['year', 'month', 'mean', 'q1', 'q3']\n",
-    "    # 날짜 컬럼 생성 (월의 첫날로)\n",
-    "    monthly['date'] = pd.to_datetime(monthly['year'].astype(str) + '-' + monthly['month'].astype(str).str.zfill(2) + '-01')\n",
-    "    monthly = monthly.sort_values('date')\n",
-    "    return monthly\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
-    "variables_to_analyze = ['hm', 'PM25']\n",
-    "\n",
-    "for i, var in enumerate(variables_to_analyze):\n",
-    "    if var in gwangju.columns:\n",
-    "        trend = get_monthly_trend(gwangju, var)\n",
-    "        ax = axes[i]\n",
-    "        # 평균값\n",
-    "        ax.plot(trend['date'], trend['mean'], 'o-', color='blue', label='평균값')\n",
-    "        # Q1, Q3\n",
-    "        ax.plot(trend['date'], trend['q1'], 's--', color='orange', alpha=0.7, label='Q1')\n",
-    "        ax.plot(trend['date'], trend['q3'], 's--', color='purple', alpha=0.7, label='Q3')\n",
-    "        # 선형 트렌드선\n",
-    "        if len(trend) > 1:\n",
-    "            z = np.polyfit(range(len(trend)), trend['mean'], 1)\n",
-    "            p = np.poly1d(z)\n",
-    "            ax.plot(trend['date'], p(range(len(trend))), \"r--\", linewidth=1, alpha=0.6)\n",
-    "            slope = z[0]\n",
-    "            ax.text(trend['date'].iloc[0], trend['mean'].max(), \n",
-    "                    f'월별 변화율: {slope:.4f}/month', fontsize=10, color='darkred')\n",
-    "        ax.set_title(f'{var} 월별 변화 추이', fontsize=14)\n",
-    "        ax.set_xlabel('날짜', fontsize=12)\n",
-    "        ax.set_ylabel(var, fontsize=12)\n",
-    "        ax.grid(True, linestyle='--', alpha=0.7)\n",
-    "        ax.legend()\n",
-    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
-    "        plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n",
-    "    else:\n",
-    "        axes[i].text(0.5, 0.5, f'{var} 컬럼 없음', ha='center', va='center', fontsize=12)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e9b71ce7",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Analysis_code/find_reason/ incheon_trend.ipynb b/Analysis_code/find_reason/ incheon_trend.ipynb
deleted file mode 100644
index b767250447669b29634ff0cb544a63f87e3ac090..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/ incheon_trend.ipynb	
+++ /dev/null
@@ -1,157 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "26f60e36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 분석에 필요한 라이브러리 임포트\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from scipy.spatial import distance\n",
-    "\n",
-    "\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "28456d07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "incheon = pd.read_feather(\"../../data/data_for_modeling/df_incheon.feather\")\n",
-    "feature = ['hm','PM10','PM25','multi_class','year','month','hour']\n",
-    "incheon = incheon[feature]\n",
-    "incheon = incheon.loc[incheon['year'].isin([2018,2019,2020,2021,2022,2023]),:]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "84183ace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['hm', 'PM10', 'PM25', 'multi_class', 'year', 'month', 'hour'], dtype='object')"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "incheon.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "5b942b62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 월별로 시간의 흐름에 따라 PM10, PM25 변수의 변화 추이 시각화\n",
-    "\n",
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "# 월별 평균 PM10, PM25 계산 함수\n",
-    "def get_monthly_trend(df, variable):\n",
-    "    # 'year', 'month' 컬럼을 기준으로 월별 평균, Q1, Q3 계산\n",
-    "    monthly = df.groupby(['year', 'month'])[variable].agg(['mean', \n",
-    "                                                           lambda x: x.quantile(0.25), \n",
-    "                                                           lambda x: x.quantile(0.75)]).reset_index()\n",
-    "    monthly.columns = ['year', 'month', 'mean', 'q1', 'q3']\n",
-    "    # 날짜 컬럼 생성 (월의 첫날로)\n",
-    "    monthly['date'] = pd.to_datetime(monthly['year'].astype(str) + '-' + monthly['month'].astype(str).str.zfill(2) + '-01')\n",
-    "    monthly = monthly.sort_values('date')\n",
-    "    return monthly\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
-    "variables_to_analyze = ['hm', 'PM25']\n",
-    "\n",
-    "for i, var in enumerate(variables_to_analyze):\n",
-    "    if var in incheon.columns:\n",
-    "        trend = get_monthly_trend(incheon, var)\n",
-    "        ax = axes[i]\n",
-    "        # 평균값\n",
-    "        ax.plot(trend['date'], trend['mean'], 'o-', color='blue', label='평균값')\n",
-    "        # Q1, Q3\n",
-    "        ax.plot(trend['date'], trend['q1'], 's--', color='orange', alpha=0.7, label='Q1')\n",
-    "        ax.plot(trend['date'], trend['q3'], 's--', color='purple', alpha=0.7, label='Q3')\n",
-    "        # 선형 트렌드선\n",
-    "        if len(trend) > 1:\n",
-    "            z = np.polyfit(range(len(trend)), trend['mean'], 1)\n",
-    "            p = np.poly1d(z)\n",
-    "            ax.plot(trend['date'], p(range(len(trend))), \"r--\", linewidth=1, alpha=0.6)\n",
-    "            slope = z[0]\n",
-    "            ax.text(trend['date'].iloc[0], trend['mean'].max(), \n",
-    "                    f'월별 변화율: {slope:.4f}/month', fontsize=10, color='darkred')\n",
-    "        ax.set_title(f'{var} 월별 변화 추이', fontsize=14)\n",
-    "        ax.set_xlabel('날짜', fontsize=12)\n",
-    "        ax.set_ylabel(var, fontsize=12)\n",
-    "        ax.grid(True, linestyle='--', alpha=0.7)\n",
-    "        ax.legend()\n",
-    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
-    "        plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n",
-    "    else:\n",
-    "        axes[i].text(0.5, 0.5, f'{var} 컬럼 없음', ha='center', va='center', fontsize=12)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e149d5dc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Analysis_code/find_reason/ seoul_trend.ipynb b/Analysis_code/find_reason/ seoul_trend.ipynb
deleted file mode 100644
index 173def5e11f934b134edfe6cb7c7246a81fa8f0c..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/ seoul_trend.ipynb	
+++ /dev/null
@@ -1,157 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "26f60e36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 분석에 필요한 라이브러리 임포트\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from scipy.spatial import distance\n",
-    "\n",
-    "\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "28456d07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "seoul = pd.read_feather(\"../../data/data_for_modeling/df_seoul.feather\")\n",
-    "feature = ['hm','PM10','PM25','multi_class','year','month','hour']\n",
-    "seoul = seoul[feature]\n",
-    "seoul = seoul.loc[seoul['year'].isin([2018,2019,2020,2021,2022,2023]),:]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "84183ace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['hm', 'PM10', 'PM25', 'multi_class', 'year', 'month', 'hour'], dtype='object')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "seoul.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "5b942b62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 월별로 시간의 흐름에 따라 PM10, PM25 변수의 변화 추이 시각화\n",
-    "\n",
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "# 월별 평균 PM10, PM25 계산 함수\n",
-    "def get_monthly_trend(df, variable):\n",
-    "    # 'year', 'month' 컬럼을 기준으로 월별 평균, Q1, Q3 계산\n",
-    "    monthly = df.groupby(['year', 'month'])[variable].agg(['mean', \n",
-    "                                                           lambda x: x.quantile(0.25), \n",
-    "                                                           lambda x: x.quantile(0.75)]).reset_index()\n",
-    "    monthly.columns = ['year', 'month', 'mean', 'q1', 'q3']\n",
-    "    # 날짜 컬럼 생성 (월의 첫날로)\n",
-    "    monthly['date'] = pd.to_datetime(monthly['year'].astype(str) + '-' + monthly['month'].astype(str).str.zfill(2) + '-01')\n",
-    "    monthly = monthly.sort_values('date')\n",
-    "    return monthly\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
-    "variables_to_analyze = ['hm', 'PM25']\n",
-    "\n",
-    "for i, var in enumerate(variables_to_analyze):\n",
-    "    if var in seoul.columns:\n",
-    "        trend = get_monthly_trend(seoul, var)\n",
-    "        ax = axes[i]\n",
-    "        # 평균값\n",
-    "        ax.plot(trend['date'], trend['mean'], 'o-', color='blue', label='평균값')\n",
-    "        # Q1, Q3\n",
-    "        ax.plot(trend['date'], trend['q1'], 's--', color='orange', alpha=0.7, label='Q1')\n",
-    "        ax.plot(trend['date'], trend['q3'], 's--', color='purple', alpha=0.7, label='Q3')\n",
-    "        # 선형 트렌드선\n",
-    "        if len(trend) > 1:\n",
-    "            z = np.polyfit(range(len(trend)), trend['mean'], 1)\n",
-    "            p = np.poly1d(z)\n",
-    "            ax.plot(trend['date'], p(range(len(trend))), \"r--\", linewidth=1, alpha=0.6)\n",
-    "            slope = z[0]\n",
-    "            ax.text(trend['date'].iloc[0], trend['mean'].max(), \n",
-    "                    f'월별 변화율: {slope:.4f}/month', fontsize=10, color='darkred')\n",
-    "        ax.set_title(f'{var} 월별 변화 추이', fontsize=14)\n",
-    "        ax.set_xlabel('날짜', fontsize=12)\n",
-    "        ax.set_ylabel(var, fontsize=12)\n",
-    "        ax.grid(True, linestyle='--', alpha=0.7)\n",
-    "        ax.legend()\n",
-    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
-    "        plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n",
-    "    else:\n",
-    "        axes[i].text(0.5, 0.5, f'{var} 컬럼 없음', ha='center', va='center', fontsize=12)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e149d5dc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Analysis_code/find_reason/daejeon_trend.ipynb b/Analysis_code/find_reason/daejeon_trend.ipynb
deleted file mode 100644
index be316c4db6040bc5184791a7f81a441a64b4986c..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/daejeon_trend.ipynb
+++ /dev/null
@@ -1,157 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "26f60e36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 분석에 필요한 라이브러리 임포트\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from scipy.spatial import distance\n",
-    "\n",
-    "\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "28456d07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "daejeon = pd.read_feather(\"../../data/data_for_modeling/df_daejeon.feather\")\n",
-    "feature = ['hm','PM10','PM25','multi_class','year','month','hour']\n",
-    "daejeon = daejeon[feature]\n",
-    "daejeon = daejeon.loc[daejeon['year'].isin([2018,2019,2020,2021,2022,2023]),:]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "84183ace",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['hm', 'PM10', 'PM25', 'multi_class', 'year', 'month', 'hour'], dtype='object')"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "daejeon.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "5b942b62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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l6NevHwDHJHa+uukDjhuNc889F1VVVa73Y926dZg6daqrro899hhOnTqFdevWYcOGDfjXv/6FrKws7N27121fmZmZPnuSOJ988tYjhRBCCCGESBfHcaivr8eWLVtwzz33YN68eZgyZYrXba+66ircc889+PLLL3HNNde4lpeXl+O7777DL7/8EtCxf/rpJ7z66qt49913PRq3AeCWW26B3W5H3759oVQqcfr0aaxZswbXX389du7c6bUnSGtz5szBiBEjwLIsFAoFNBoN3n//fVRVVeGdd95x3X+0TrqsXr3a6770ej1Gjx6NxYsXe21Uz8zMxMKFC93mm9NoNAHff5w+fdojOeQcxvXYsWNuc2D42jfP8z6HmFIqlV4n+Xbef2i1WhiNRp/lbGlpwZAhQ3D69Gmkp6dj9uzZ+Oijj3DBBRe4er8/8MADqKqqwrfffosNGzbgT3/6EywWC2pra90SRHT/QQiJVJTAIISQbuBt4uaUlBQAQGNjo8c6b0/dxMTEeJ2ALSYmxvX0Unta91hweu+995CamopRo0YBABYvXuzR86L1RNxO8fHxKCoq6vCYre3evRv5+fl+bTto0CDExcUhLy/P6/r2Jg9v67777sNNN92ElJQU17wihYWFePrpp2E2mz2SRX369HGbILw9ubm5ePfddzFixIh2f9z36dMHt956K2699VaIooimpiaPbYYNG4affvoJjz76qMe6devWISsry+NpM0IIIYQQIk133XUX7r77bvA8DwBISkrC//3f/+Ghhx7y+RqtVotrrrkGb731llsCY+XKlejXrx8mTZrk9/HLyspw7bXX4pprrvHZ++Lqq692+3vMmDG44oorMHjwYDz44INYvHgxhg4d6vMY3h52+umnn2CxWDBmzBgAjh4gy5Yt67C8DMMEPB/DunXr/BryCQBycnKQnp7uNnl6a/369fN72Nm77roLF1xwAR544AHceOONSEtLQ1lZGV5//XXs2LED//rXvzxe429Ph5iYGLz55pvo169fu/dWaWlpuP76612Tvjc0NHj0bhk2bBjWrFnj9fXr1q2DWq3G2LFj/SoXIYQEEyUwCCEkQjh/QDpvWryta8s5/0OwyGQyt8ntXn75ZTz11FOuv/fu3Yt9+/a1u4/s7Gy/JpfOzs7Gli1b/OrWPmjQIBgMBvA87/FjnmVZn++PN3/605+g0+mwYsUKPPjgg+B5Hnq9HjNnzsSWLVs6/VRRfX096uvrMWDAAFgsloAn5KupqUF6ejpiYmIAOHrIXHXVVThy5IjbpOd1dXX45JNPcMcdd3SqnIQQQgghJPI88MADWLhwIRiGQUxMDLKzs/3qLXDzzTdjxIgROHr0KAYMGAAAeP/997F48WK/j200GnHppZciOzsbb7/9dsBlv/POO7F06VL8+OOP7SYwvGl7/3H//ffjlltucf1dVFSE//3vfz6HNgKAKVOmYPPmzR0eKyUlBevWrUNSUlKH2yYmJqKioiIo9x8zZ87Ehg0b8Mgjj+CFF16AzWaDTqfDueeei02bNrkeHgtUU1MTqqur0bdvX4iiGPD9R11dHZKSkpCQkAAAWLRoEV588UV8//33bsP9Wq1WvP3227jqqqtc9yqEEBJOlMAghBCCpqYmmEwmvP7662BZ1jUxXVujRo3CjBkz2t3Xnj17vHaDbs1sNiMvLw9//PFHQJNQy2QyfPvtt9DpdD670vtj3rx5mDdvHj777DNcddVVqK6udrtx6oy7774bH374YZf2sXz5cjz44IMAgLlz52LGjBm49NJL8d5776GgoABHjx7FLbfcgoyMDPzlL3/p0rEIIYQQQkjkyMjICLjxH3CfzPuf//wnNm3ahKKiIlx33XV+vZ7jOCxYsAANDQ34/ffffQ511B6NRoOUlBSYzWa/X2M0GtHc3IwHH3wQgiD4vP/Izc3FI4880m4y5+uvv+7w2DzPY/To0di0aVNAvQhkMhm2bt2KhoYGXHLJJX6/rq3p06dj8+bN2L59O8aPH4/du3e7honqrH/+85944oknurSPJUuWuObmKCgowA033IBrrrkG77//PmbMmIGysjLcc889aG5uDunE7oQQ0h5KYBBCCMGVV16JjRs3+rXtpZdeirVr1/pcH0ivkI4SHd689NJLSE9PbzeBER8fj4ULF3bYrdv55JQgCKiurkZ5eTmKi4tx/Phx15NI/vrggw/wwQcf+Fw/duxYTJs2Df/85z/92h/Lsvjqq6/wl7/8Beeffz7MZjPkcjmuvPJKvPLKK/T0EyGEEEIIAeBohP7b3/6G5cuX47333sMll1zi9/BGN998M7Zt24Zt27b5PbxSWwaDAXV1dT6He/Xm7rvvxjvvvOPXtsOGDWu3F3h8fDzi4+M73I9Go+nU/ceHH36II0eOdJjAuPbaazscItd5/yGKIurq6lBRUYFTp07h+PHjsNvtAT3c9fjjj+Pxxx/3ud45ZPAXX3zh9z7ffPNN9OrVC9deey0aGxvBMAzOP/98bN261eswyIQQEg6UwCCEEILvv//er+0efvhhrFq1qsPt6uvr2705MJlMAByT1oVCQkICPv74Y7dlr7zyCp5//nnXTYPdbofBYADguJmRyWRITk5G7969kZub69ZturtotVq89tprePnll1FTU4PExMSQvWeEEEIIIUSarr76atx7771YuXIlPv/8c3z22Wd+ve6RRx7BRx99hO+++w6DBg3q9PEfeughxMXFeZ3jwpe3337br+Gq3nnnHdx0000dbmcwGNrthcHzPMxmc0h/S7ftjb1mzRrcc889ruGvOI5DS0sLAGDgwIFgGAbJycnIzMxEbm4uCgoKQlY2f8nlcjzxxBN47LHHUFNTg7i4OGg0mu4uFiGkh6MEBiGEELzyyiv461//6le370svvbTd9VdffTU+/fTTDveTk5ODcePG+V3Grpo3bx6GDx8OQRDAMAxkMhnUajViY2ORnJyMxMREj67pref/6E4ymczvp+gIIYQQQkjP4pzM++6770ZSUhIuvPDCDl/z8ccf44knnsCDDz6I5ORkHDhwwG09wzAYNGiQ21wP8+bNw5w5c9C3b18wDIPjx4/jjTfewI4dO/DZZ58hLi7O7zL/97//xfXXX4+mpqYOtx02bFi76x944AE8/fTTHe4nNTXV56TcoTBt2jSsXLnS7f5DpVIhJiYGSUlJSE5O9phL4z//+U/YytcelmWRlpbW3cUghBAAlMAghJCwUigUYBjG63wL3tYplcp2t/e1XC4P7ON9/fr1mDVrFl577bUOy99R1/KdO3figQceCNkYqTKZDLW1tX5NUseyrKsre3p6OiUBCCGEEEJIxPH1u94bpVLptRfBzTffjNdffx0333yzR6O4UqkEy7JuE2H//PPPAIAVK1b4/N1eWlqKzMxM1996vR5///vfUVFRAY7j0KtXL0ybNg1vvvlmwPN3/PDDD+jbty+++uqrdreTy+VISUlpd5udO3fi6quvxieffBJQGfwlk8lgMBj8uv9gGAY5OTmQyWTQ6/VdmrePEEKIAyUwCCEkjDIzMyEIgt/rJk6c6HN7X8M+TZo0CXa7PaByMQzj8zit2e12lJWVIT093WeShGVZGAwGlJaWdrg/nU4X8FwTM2fOxEMPPeTXpHcMw6CmpgZJSUkBHcMpkJvJcO6rO/ZPCCGEEEJCI5B5GY4dO+Z1ubOnsTeLFi3CokWL3Ja99dZbeOutt/wvJOD3nBX+YBgGPM9DFMV2J+jmOA4VFRVISUmBSqXyug3LsjAajX7df6jVaiQnJwdUVmdPCn8n3d65cydGjx4d0DGcOvMwWnv7CiW6/yCEhAsjiqLY3YUghBDSvV555RXcf//9sFgsfm3/ww8/YMaMGV7X3XHHHXjzzTf9SqLMnj0bGzZsCKishBBCCCGEEGlbs2YNrrvuOjQ3N/u1/bvvvosbbrjB67pnn30Wy5Ytg9Vq7XA//fr185kEIoQQEpkogUEIIYQQQgghhBBCCCGEkIjDdrwJIYQQQgghhBBCCCGEEEJIeFECgxBCCCGEEEIIIYQQQgghESeiJvF+7733cOutt+Lo0aPIyclxLT98+DBuueUWGAwGMAyDZcuWYe7cua71drsdf/nLX7Bx40YAwIwZM/Dcc89BqVT6dVxBEFBeXo7Y2Nh2J48ihBBCCCGEAKIoorm5Gb169QLL0jNRAN1TEEIIIYQQ4q9A7iciJoGxbNky7NixAwkJCeA4zrXcYrFgzpw5ePvttzF16lRUVlZi6tSp6Nu3L4YPHw4AeOSRR2C1WrF//34AwJ133omHH34YzzzzjF/HLi8vR+/evYNfKUIIIYQQQqJYSUkJsrKyursYEYHuKQghhBBCCAmMP/cTEZHAEAQBGRkZ+Oabb5Cfn++2bsOGDRg1ahSmTp0KAEhPT8d9992H9957Dy+++CIEQcCHH36IAwcOuLI1y5cvx6BBg7BixQrIZLIOjx8bGwvA8YbFxcUFuXb+4TgOu3fvxqhRoyCXR0RYiJ8odtJEcZMuip00Udyki2InTaGOW1NTE3r37u36HU26/56CrlXpothJE8VNuih20kWxkyaKm3SFMnaB3E9ExFnDsixuu+02r+s2btzoSl44TZ06FS+99BIAYM+ePejVqxf0er1rfVxcHLKzs7Fr1y4UFBR47NNqtcJqtbr+bm5uBgBotVpotVpXmViWhSAIEATBrawsy4LneYii2OFymUwGhmHcepU4lwMAz/MAHN1mRo4cCb1eD4ZhXMud5HI5RFF0W84wDGQymUcZfS0Pd506Wh4tdXLGLjY2FgqFIirq1Fq0xKltnQRBcMWNYZioqFM0xslbnViWxejRoxETE+MaokPqdYrGOLUtuyiKGDVqFBISEiCKYlTUqb3l0VQn5/dcfHx8u3WVUp3aljEa68TzvNv3XLDr5FxOQyWd5Xwv4uLiuiWBIYoixowZg/j4eIqLxFDspIniJl0UO+mi2EkTxU26whE7f/YbEQmM9pSXl2P27Nluy3r37o3CwkLXem9dtZ3beEtgrFixAo899pjH8t27d0On0wEAUlJSkJ+fj6KiItTU1Li2ycrKQlZWFo4dOwaDweBanpeXh9TUVBw4cABms9m1fODAgdDr9di9e7fbzejw4cOhVCqxY8cOtzKMHTsWNpsN+/btcy2TyWQoKCiAwWDAkSNHXMs1Gg1GjBiB2tpa1/sBAPHx8Rg0aBDKy8tRWlrqWk51ojpRnc7WqampCcePH4+qOkVjnHzVyW63Y+fOnVFVp2iMk7c6JSYmoqysLKrqFI1xojpJv07FxcUhrdPu3btBIgvDMG4PdRHpoNhJE8VNuih20kWxkyaKm3RFSuwYsfWjVhEgJycHGzduRN++fQEAs2bNwt/+9je3JIYgCK4nzlatWoVvv/0Wq1atctvPddddh+nTp+PPf/6zxzHa9sBwdlmpq6tzPS0V7ifyeJ7Hnj17MHr0aMjlcnrKUEJ1csZu5MiRUKlUUVGn1qIlTm3rZLfbsWvXLowcOdK1X6nXKRrj5K1Ooihi165dGDFihOu1Uq9TNMapbdl5nsfevXsxevRoV/mlXqf2lkdTnZzfc6NGjYJSqYyKOrUtYzTWyWazYffu3a7vuWDXqaGhAUlJSTAYDN02BGukaWpqQnx8fLe9JzQ8g3RR7KSJ4iZdFDvpothJE8VNukI9hJS/v50j/qxRqVSwWCxuy8xmM1QqFRiG8breuY1Go/G5T5VK5bFcLpd7BMN5U9eW8+bN3+W+gtx6uSiKrqFsvG3va7mvMga6PBR16mh5tNRJFEW3htRoqFNr0VonZ9xar5d6naIxTm2XcxznSmT7+5kd6XVqr4zRVCdnI2s01amzy6VWJ1EUXf8fLXXyZ7nU6+Ttey7UdSLdq21SjEgHxU6aKG7SRbGTLoqdNFHcpCsSYhfxdx5ZWVk4ffq027LWs5N7W992m2DheR52uz2o+3RyPt1msVh63A2hUqn0euNNCCGEEEIIIYQQQgghoRTKNl8p60p7tUKh8PmQVKAivqV84sSJ+N///ofbb7/dteyXX37BxIkTAQAjR47E8ePH0djY6BqTq6mpCUeOHMHo0aODUgZRFFFZWYnGxsag7M/XMdRqNU6fPt3jJrRhWRa5ublQKpXdXRRCCCGEEEIIIYQQQkgPEI42Xynranu1Xq9Henp6l9u6I34ODKPRiMGDB+ODDz7A1KlTUVlZicmTJ+Ojjz7C+PHjAQB33XUXrFYrXnvtNTAMgzvuuAOiKOLVV1/165gdjblVUVGBxsZGpKamQqvVhiTBIIoiBEEAy7I9KoEhCALKy8uhUCiQnZ0tybqLougaskyK5e+pKG7SRbGTJoqbdFHspCnUcevu+R4iUXe/J3StShfFTpoobtJFsZMuip00RXLcwtHmK2Wdba8WRREmkwnV1dXQ6/XIyMjw2EbSc2AolUooFArX3zqdDmvXrsVtt92GlpYWCIKAxx57zJW8AICnn34ad999N4YMGQIAOPfcc/Gvf/0rKOXhed51IiclJQVln960ziP1tIslJSUF5eXl4DjOLfZSQr1HpIniJl0UO2miuEkXxU6aKG49D8Vcuih20kRxky6KnXRR7KQpEuMWrjZfKetKe7Vzburq6mqkpqZ2aTipiJt44NixY+jTp4/bshEjRmDr1q3Yu3cv9u/fj0WLFrmtV6vVeOONN3D48GEcPnwYb7/9ts8JvAPlHP9Mq9UGZX/tMRqNIT9GJHJ+iEXCpDCdwfM8duzYIdny91QUN+mi2EkTxU26KHbSRHHreSjm0kWxkyaKm3RR7KSLYidNkRq3cLb5SllX2qud721X5xeJuARGpOppvSLCid5bQgghhBBCCCGEEEJIuFG7ZOgE672lBAYhhBBCCCGEEEIIIYQQQiJOxM2BEc14Hti8GaioADIygMmTgS4M/0UIIYQQQgghhBBCCCGkG1Gbb2hRD4wwWb0ayMkBpk8HFi1y/Dcnx7HcSafTdUvZZs+ejb1794Zs/5s2bcL8+fNDtv/uJpPJMHbs2C5NRkPCj+ImXRQ7aaK4SRfFTpoobj0PxVy6KHbSRHGTLoqddFHspCna4+ZPm293CFZ7r6/26nC291IPjDBYvRqYNw9oNXE7AKCszLH8iy+AK64ABEEAywYvp1RbW4vp06ejrq4OHMchNTUVFosFFRUV6Nu3LzZt2oT4+HjY7Xa3yVTuvPNObNiwwes+m5ubcdVVV+H55593LVu6dClWt7oqBUFAfX09Dh48iLS0NNhsti5P1hLpbDZb0CaOJ+FDcZMuip00Udyki2InTRS3nodiLl0UO2miuEkXxU66KHbSFK1x86fNd+7c4B6zJ7b3UgKjE0QRMJn825bngTvv9DyRnfthGOCuu4CZMwGLxQydTof25jfRatHu+taSk5Oxf/9+vPLKK6isrMQ//vEP7NmzB3fffTc2bNiAcePGgeM4FBUVub3uX//6l899/vzzz3jhhRfcli1fvhzLly93/W0ymZCRkdFtPUrCjed57Nu3D2PHjoVcTpeUVFDcpItiJ00UN+mi2EkTxa3noZhLF8VOmihu0kWxky6KnTRJKW6haPOdNavj4aQitb33ySefhNFohE6ng9ls7rb23sg+ayKUyQTExARnX6IIlJYCej0DoOOdtrQAwThPlEol9uzZAwCYNm2a369rampCampqu9t88sknmDlzJmJavUk//PADBg4ciBEjRuDTTz/tTJEJIe0w1hhhbbL6XK+KU0GXovN7OxJZ/IkbADoHCCGEkB6GvtcJIYSQ4AlFm298fMfbUntv+yiB0UPZbDZce+21EAQBBw8e9Pt1ZWVlyMvL87n++PHjWLZsGX788Ue35TNnzsSaNWs6W1xCSDuMNUasXrQapjrfjwlok7S44F8XYP2d6zvcbu7Hc+lGN4L4E19V7JkERrPvBgw6BwghhJDo4u9vQPpeJ4QQQnqGaG3vpQRGJ2i1jsyYPzZtAi66qOPt1q0TMWaMCVqtFkw7fYa0Wj8L2QGLxYKbbroJGRkZ+POf/+z36w4fPowZM2Z4Xffzzz/jhhtuwFtvvYWBAwcGp6ASEa0TEUW7aImbtckKU50JcpUcco3nxzpn5mCqM6GlosWv7axN1oi/yY2W2PnDn/gaa40AAHWcOqLPgZ4Ut2hDsZMmilvPQzGXrs7Ezt/fgFL4bSdVdM1JF8VOuih20iSVuIWmzReYMqXj4wZDKNp7N2/ejNtvv71b23spgdEJDON/t57zzgOyshyTt3gbE41hHOvPO4+BTBbcH5WHDh3C1VdfjYaGBvA8j7Vr10KpVEIURSxatAgzZszA7NmzXWOXPfvss1i5cqXr9eXl5dBqtdDr9QCAEydOoE+fPvjxxx/x8MMPY8GCBXjkkUdw8OBBPPnkkzh8+DBWr16NkSNHupVDqVRCrVYHtW6RRC6Xo6CgoLuLQQIUjXGTa+RQ6pSuvwVegLHKCM7KwW60o/jnYlgaLFDoFOBtPBQ6BdTx7tcmZ+XCXeyARWPs/OGMr7XFCluTzbWcs3KwGhw9LxiGgVwthzLGcR7wNh6mWpPPc0AZq3T13mi9v5CUv4fGLRpQ7KSJ4tbzUMylq6uxc/5GMDeYIVPJoNSe/T0ohd92UkXXnHRR7KSLYidNUopbaNp8O54DIxA9sb2XEhghJpMBL73kmHmeYdxPaGdHixdfBFhWBMfxkMlk7fbACMTgwYOxb9++DrcrKChAYmIi7r//ftx///2u5bfccgvOOeccXH/99QCAoUOH4ptvvkFOTo7b65cuXYqLLroI7733nuvE5TjONTFP3759sWrVqqDUKRKJogiDwYD4+PigxY6EXk+Im8AJaCptgsAJEDgBJ78/CXO9GdYmK1g5C12aziOBIQU9IXbtMdWYYKo1AWe+TwROAG/jIUIEZ+YcjRitEhjtnQOxWbEeCYxQ6elxkzKKnTRR3Hoeirl0BSN21iYr6o/XAwAyx2UGs3jEB7rmpItiJ10UO2mK1rj52+Yb7M4n4WzvvfDCC/HWW2+5kiHd1d7LhuUoPdzcucAXXwCZbX5HZmU5ls+d6/jbYrGEtBxffPEFpk+fjkGDBmHgwIEYPHgw/u///g+33Xab2zhnp06dCmi/X331FUpLS12TtZSUlLh1O7rssstw9OjR4FQiAvE8jyNHjoDn+e4uCglAtMZNxNlvTEbGQJuihSZJA2WcEr3G9IIyTglNkgbaFK2rkVtqojV2/krITUB8n3hoU7Su+CpiFFDqHLFVaBSubVkF2+45oNAq2jlScPX0uEkZxU6aKG49D8VcuoIRO4EXglgi4g+65qSLYiddFDtpiua4+dvmG0qhbu91Jim6s72XemCEydy5wJw5wObNQEUFkJEBTJ4c/CycL++88w7efvttrFy50jVemclkwocffogpU6Zg7969SExMBABccMEF2LhxY0D7F0UR4plUI8/zEISzP6DLy8td2TlCSGhZDVY0FjVCnaiGPluPhNwE2Iw2WBotGDR3EEq2lUCtV7sNNSVChN1o78ZSk0DFpMa4/t9mtKGlyjFIZ0xajFts5Sq5X+cAIYQQQqTN+QADI4ueJ1sJIYQQKejONt+e0t5LPTDCSCYDpk0DFi50/Dec89d88803uPfee90mW9Fqtbj55psxZMgQ7Nixw7XceXLefPPNmDlzpmv5888/j/T0dJ/H+Nvf/oa+ffti2rRprmU7d+5ERUUFVq9eHdwKEUK8srXYwNt4CJx/T+EJgoC6Y3WoOVQDu5mSGJHM3GAGZwnuONYCL8DcYIatxdbxxoQQQgiJWAzrSFyIgpdBuAkhhBASUt3V5huO9t5HHnkE/fr169b2XkpgRBCWDV04Lr74Yrz66qsoLCx0LbNarfjggw9w5MgRt8l0nOPRjRo1Cr1793YtP++889qdnOXpp5/GiRMn8PPPPwNwjIt211134c0338RHH32E33//Pci1igwMw0Cj0UTVOH49QTTGjTNzMDeYIXACWBkLm9EGm9EGzsx5bOdcZzfbIdgFCHYBDScbIPKRf9MbjbHrCG/nYThlQMXuCkfC4Uz82sa3dWx9bdN6u8biRtQeroWhxOB1u2DqiXGLFhQ7aaK49TwUc+nqauxa/wYU7AKsLdaQf68TuuakjGInXRQ7aaK4hU442nufeOIJHD9+vFvbe2lcnwjBMAy0Wm3I9n/TTTchLi4ON9xwA2pqaiCKIliWxdSpU/Hzzz8jISHBte2QIUMwffp0qFTeJ3ZlGAbff/+9W3auT58+eOCBB/DUU0/BYrFgwoQJuOSSSzBq1CgsWbIEBQUFmDdvHl5//XWcd955Iatnd5DJZBgxYkR3F4MEKJripopTQZukhanOBKvBCpEXIfACLI1n59XRJmkRkxHj2o6zcm6vtzZZwVt5WFusUMZG9tBC0RQ7f6jiVOCtPERehEwlg91kh93k3ltGl+yYUMvabHWLbWtezwHRMQm4ud4MVZzKtZ3z/4Opp8UtmlDspIni5umjjz7CnXfeiezsbNcylUqFbdu2QSaToaKiAosXL0ZpaSkEQcDtt9+OW265pRtLHBiKuXR1NnatfwM2FDbAORWapcHi6pERqu91QteclFHspItiJ00Ut9AJdXtvTk4Oli1bhhdeeKFb23sZURQj/3HbEGtqakJ8fDwMBgPi4uLc1lksFhQVFSE3N7fdbFRXiaLomsk9WjKSGzduxKxZs1x/19fXQyaTIT4+3m27cL3HoSIIAmpra5GcnBzSXjQkuKItbsYaI+pP1OPHh34EK2dxwb8ugExxts+iKk4FXYoOxhojrE1Wj9c3lTZhy/ItEEURo24YhaFXDw1n8QMSbbHriKHEgLWL10KwC5jy0BTE58R7bONsmPAW29bbtD0HeBuP9Xeth8AJmP74dMRkxLi2C7aeFrdoQrGTplDHrb3fz5Hq3//+NzZu3IiPPvrI6/qJEyfi9ttvxzXXXIPm5mbMnj0bjzzyCC666CK/9t/d7wldq9LVldgZa4xoKmvChns3uJad//z5rgdSQvW9TuiakzKKnXRR7KQpUuMm9fbIcPDWXu1vey/Q/nscyG9n6oERQaxWa1RNdt36ZAbgmjQm2giCgMLCQiQmJkbUBzFpX7TFTZeiQ83BGih1SiQNSELKwBSf23m7iU3MT4TIi/jthd+w/+P9SOqfhIzRGaEudqdEW+w6sn/VfsiVcmRNy0LuzNx2t/WngaLtOdCroBeq91XD2mRF9rnZ7byya3pa3KIJxU6aKG6B2bdvH3iexzXXXAMAiI2NxeOPP47XXnvN7wRGd6OYS1dXYqdL0cFUa4JS50hYzHpmFpL6JYGV0zkQanTNSRfFTroodtJEcZO2tu3V3dHeGz2t5YQQ0sPVHasDACT1T+rU63Nn5KLmcA2OfnUUm/6xCTOWz3DrxeFET/KFT0NhA0q2lgAMMGzRsJAcI31EOqr3VaNybyX6XdQvJMcghJBIt3HjRkydOtVt2eTJkzFv3jyIoui1h7TVaoXVerbnW1NTEwDHuMAc5xjOj2VZsCwLQRAgCIJrW+dynufRukO8r+UymQwMw7j223o5APA873oNz/Nuy1uTy+WubZwYhoFMJvMoo6/l4ayTP8ujpU6t6xBonZrLmyGKIlKGpSChXwIECICAbq9Ta9ESp7airU7RGCdvdXL+vyAIbvuXcp2iMU7eyt76+NFSp/aWR0udWn/PRVKdnK91Tm7dGsMwHsu6c3kgQlGW1usD2Y/z79a/jwFHPAKpJyUwCCEkSujSdI7eF4O9977wx8DLB+K3F36DTCXD6kWrvW6jTdJi7sdzKYkRBvs+2gcAyJ6cDX0ffUiOkTY8DQBQvb/aZyMdIYREu/LycvTp08dtmUajgVqtRnV1NdLS0jxes2LFCjz22GMey3fv3g2dzvEdmZKSgvz8fBQVFaGmpsa1TVZWFrKysnDs2DEYDAbX8ry8PKSmpuLAgQMwm82u5QMHDoRer8fu3bvdbryHDx8OpVKJHTt2QBRFNDY2YteuXSgoKIDNZsO+fftc28pkMhQUFMBgMODIkSNu9RwxYgRqa2vdJoCMj4/HoEGDUF5ejtLSUtfycNaptbFjx0Ztnfbu3euKnVwuD7hOtnIbLFYLGuwNrmN0d52iMU5t6zRq1ChwHIddu3a5fj9JvU7RGCdvdXKO/15XV4dTp05FRZ2iMU7e6iSKIoxGIwBETZ2A6ItT2zqVlJS4vudSU1Mjqk4xMTHged51XgGOpIlWqwXHcW4Pq8hkMmg0GtjtdthsNtdyuVwOtVoNq9Xq1kivVCqhVCphsVjcyqhSqaBQKGA2m92SOGq1GnK5HCaTya1xX6PRgGVZtzICgE6ngyAIbu8LwzDQ6XTgeR4Wy9l5UTtbJ5vNBp7nYTKZOlUnp8OHD3vEyduDAb7QHBiInDkwLBYL1Gp1j2s8kvqYczzP49ixY+jfv78ro0siH8XNu/qT9fh8/ueQq+SQazxz3JyZA2flMP/z+UjM755h4XpK7ERRxMFPD+Lo2qOY/exsxGWGZjx1gRfw5cIvwZk5XPjyhdDn6ENynJ4St2hEsZOmUMetu+d76IyVK1di2bJlyM7ORl1dHfr27YulS5diwoQJWLx4McaPH4+bbrrJ7TXZ2dn45ZdfkJvrOYSftx4YvXv3Rl1dnes9CedThoIg4Pjx4+jXrx8UCoVreWv0NGhk1slut7tix7JswHX69Z+/4vTm09Dn6dFnWh/0ntQbMSkxFKcQ14lhGBw9ehR9+/Z1DYki9TpFY5y81UkQBJw8eRJ9+/Z1a3+Rcp2iMU7eyu78rhs4cCAYhomKOrW3PFrqxHGc63tOLpdHTJ0sFgtKSkqQk5Pj0R5JPTDgusasVitUKpXr8zKQ/VgsFhQXF6N3795u77FMJkNTUxP0er1f9xOUwEBkJDB6MnqPCYkczgSGWq+GUqeEzWgDb+OhSdAAAGxGGyyNlm5NYPQ0vI2HTBnaRuPyneWI7RWLmPSYHpdEJ4R0jhQTGEajETzPIy4uDqIoYt26dbj++uuxbds2vPjiixg4cCDuuOMOt9ekpKRg//79SE9P73D/UnxPSHT49q5v0VjY6Pp75oqZSB2a2n0FIoQQQiSA2iNDL1iTeNPMKeFgqQGaT/r+Z6mBKIqw2WxdzrKR8BMEAaWlpV67SJHIFW1xMzeYwVm4jjcMgM1oQ82hGjQUNkTUZ1O0xa4joU5eAECvMb0QmxEb0uRFT4tbNKHYSRPFzZNOp3PdHDEMg4svvhhz5szBt99+i6ysLJw+fdpte7PZjJaWFqSmSqMhmGIuXV2NXf9L+mPAnAFQxjom8uaswf1NSLyja066KHbSRbGTpqiOmx9tvlIWKe3VlMAINUsNsG0RsGW+73/bFgGWGrexxoLFZrNhxYoVGDlyJEaOHIlhw4bhr3/9K1paWrxu/95770GlUqG4uDjoZYlWUf1BHMWiLW573t+DL676AsfXHQ/aPhVaBRiWgciL4MyRcyMcbbFrSxRF/PH6H6jYXdHtPxKCKdrjFs0odtJEcfMPz/OQy+WYOHEifvnlF7d1mzZtQkFBgWtomEhHMZeursYuf3Y+Ri8ejdjMWACO3psk9Oiaky6KnXRR7KQpauMWQJtvKISrzTcU7dWBksavcSmzNwHWOoBVAQq95z9W5Vhvbwr6oQVBwBVXXIHi4mJs2bIFe/bswY4dO6DT6TB9+nS3SV4AYNmyZfj888+RkJDgMYYcIYEy1hhRf7Le5z9jjbHjnRC/1R6thSiI0KUFb2JthmGg0DrG0Lab7EHbL3HX9lo5tvYYDn1+CD888AMq91aG7Vop/KEQm1dsRt3xurAcjxBCwq2srMztN+6XX36J9evX44orrsCUKVNgt9uxatUqAEBzczMeffRRjyGlCIlkzl6bgj3KGogIIYSQSERtvmHjOUMr8R9vaWclC8iUZ/+UqQC5xst2gud+fO1XFth4bKtWrUJ9fT2++eYb17AgKpUKjz76KA4dOoQXXngBS5cudZRCEJCRkYFvvvkG+fn5AR2HkLaMNUasXrQapjqTz220SVrM/XgudCnBa3DvqazNVrSUOzLsSf2TgrpvhVYBW7ONEhgh4u1aaSptAm/lodKrsPaGtWG7Vsq2l6F0WykS8xOR1C+45xEhhESC9evX49lnn4VKpQIADBgwAD/++CMyMjIAAGvWrMGSJUvw1FNPged5LF68GPPnz+/OIhPSoeaKZlibrIjLjINc5bi9pyGkCCGEkC4KZ5tvgO29QM9r86UERldsbueGJmksMOzRs383HgQYLx1eRA5gHU84y+VnwvHbjd6zc9O+Dqh4K1euxN133+11TPN77rkH1113netkZlkWt912W0D7Jw4syyIlJUUywwuEg7XJClOdCXKVHHKN58cMZ+ZgqjPB2mTttgRGNMWt/ng9ACAmIwaqWFVQ9ukcMophGQicALPBDI1RExFDSUVT7NpeK1aDFSIvQqaSQd9HD8EuhO1aSRuehtJtpajcW4khC4YEff/RFLeehmInTRQ3TzfeeCNuvPFGn+v79OmD7777LowlCi6KuXR1JXYnN5zE4S8Oo+9FfcEqHK+nIaTCg6456aLYSRfFTpokGbcgt/m6eGvzDbC9Fwhvm6+rvbobSejMiW4Mw0CtVgd1AtXdu3ejoKDA67oxY8bg5MmTPsdFI/5jWRb5+fnS+iAOE7lGDqVOCZlKBlbGQqlTQqlTek1qhFs0xa3umGPIn6QBXX9qXhWngjZJC87KwdJoAW/nIXACbE02WBot4KwctElaqOKCkyjpjGiKnZNcI4dCp4Cp3gRWziI2MxbqeHVYr5X0EekAgNrDtSFp+IjGuPUUFDtporj1PBRz6epK7JrLmwHArQcGJTDCg6456aLYSRfFTpoobsEXrjbfULRXd0b3tyJK2eTP21nZ5qLUDwHkXp6e5YyAzQBRFGG1WKBSqcCc825QimcwGJCWluZ1nUKhQEJCApqamhATExOU4/VUgiCgqKgIubm59GHshQgRdUfqYDfbkTo0FQqNouMXhUE0xa32aC2A4AwfpUvRYe7Hc2FtsgJw3AB/e+e3EHkRs56eBU2iBqo4VbcO/RVNsWvNUm8BZ+LAyBnEpseG/fixmbHQJGpgrjej9kgt0oZ7//7orGiNW09AsZMmilvPQzGXrq7EzpnAiO0Vi9ShqciZnoO4zLhQFJO0QdecdFHspItiJ02SjFsQ23zdSKzNVxRFWK1WR3t1NyYxJHLWRCiZup1/yjYbs77/nTkBXJOo+NpngGJjY1FVVeV1nd1uR2NjI5KTkwPeL3EnCAJqamogCDRZnlfimQmgRcBYHTkTd0dL3ERRRN3RMz0wgjT/hS5Fh8T8RCTmJyJlUAom/XUSzn/+fKSPSEdifmK3z1sSLbFrTYSIpjJHN9KYtBiw8vB/PTMMg9ThqQCAqv3evzu6Ihrj1lNQ7KSJ4tbzUMylq7OxE0XRNQ9abK9YJOQlIGNUBnSpNMdcONA1J10UO+mi2EmTJOMW5DbfdvfbCeFs842ESb+pB0a48ObAlgfB0KFD8ccffyAvL89j3a5du5CXlwelsu1FR3oyY43R9eS9N5158p5hGCT2T0T9sXqY682Iz47vajFJKyIvYujCoag7VoeEvISQHCN/tjQneQqlUFwrsb1iYawyIia9+3rFpQ1Pw6mfT6FqbxVwTbcVg5CQCsX1Swgh3cFcZwZv48GwDCUtCCGEkO5Cbb4hRwmMUFPEAaokwFoHCD5ullVJju2CPFTpwoUL8dJLL2HBggUe3XxeeOEFLFq0KLgHJJJmrDFi9aLVMNWZfG6jTdJi7sdzA27YUcerwSpYCHYBFoMFMoWsq8UlZ7ByFgMuHdDdxehRQnGtMGCgTdJCm6QNVjE7JX1EOhgZA1bOQhTFbh/nkpBgC+V3HSGEhJtz+Chdug6snEVjcSPqjtUhJiMGacOCOxQkIYQQQtoIpM03yHpamy8lMEJNnQJM/NhzhvnWFHGAKhlKuz2oh16yZAk++eQT3H777Xj22Weh0+lgs9nwzDPPYN++fXj33eCMu9bTsSyLrKws6Yzj54O1yQpTnQlyldzrxMGcmYOpzgRrk9XvRh1jjRHmejPUejWUsUqYqk1oLm9GTGr3z7sSLXELB87CoWx7GVoqWzBkwZDuLk63xy4U1wpn9t4l09fyUNGl6jDvP/MgVwf/50F3x410XjTFLhTXb6SKprgR/1DMpauzsWs9/wUAlO8ox96Ve5E7M5cSGGFA15x0Ueyki2InTVEbN3/bfNUpQT90ONt8I6EnByUwwkGd0uHJyiD4J4RcLsf69evxxBNPYNy4cZDJZCgvL8fw4cOxbds26HTeb8yVSiUUisiYaFkKnB/E0UKukUOp834uclb/GlNVcSpok7So3FsJ3spDk6SBQquAwAkw15mhilFBl6qDKk4VzKIHJFriVra9DDEZMYjLigvZ0/K8nce2Z7cBAPpd3M/n+REukRK7YFwrylglLI0WgHFcN95iqE3ShvVaCUXyAoicuJHARWPsnNcvb+fBylm3a8/f6zfSRWPcSPso5tLV2dilDk3F6CWjXT04ZUpHL2feFuSu/cQruuaki2InXRQ7aYrquPnR5hsK4WrzZRiGEhjkLFEUYbFYoFarg9oIqdPp8NRTT+Gpp54CABw/fhznnXceDh06hIkTJ3p9zbFjx4J2/J6A53kcO3YM/fv3h0xGQyMBjkmgZz41E9/f/z0YGYPZz86GKlaFTf/YBMMpA0bdMAr55+d369Ot0RA3gROw5aktEOwCLnnzEtfTd8GmilVBm6yFqdaExuJGpA5JDclx/BWJsRMhgrfykKsC+1qtP1aPxL6JkCllmLliJhRazx8S3TUeP2fhgprMiMS4Ef9Ea+xsRhtqDtVAm6QN2RxC3Sla40Z8o5hLV2djF5cVh7iss8NSUAIjvOiaky6KnXRR7KSJ4hYa4WjzDVV7daAogRFBeD70PzT79euHTz/9FAsXLsTatWsxZEj3DwcjdaIowmAwQBTF7i5KSHBWrlNzVlTsqoBSp0TWxCxkjMwAAEy8byIUWkVENBRFQ9waihog2AUoY5WIyQjtsFz6PD1MtSY0FDZ0ewIj0mInCiIaihtgabQgdbD/740oitj/yX7IVXIMXTQ0YoZ64G08Nj6wEY1FjbjiwyugjAnO0xaRFjfiv2iNnanWBIiO/8b3iQcri64u7dEaN+IbxVy6ghU7mYoSGOFE15x0Ueyki2InTRS38AhVm2842qs7QgmMHmjcuHE4efJkdxcjqIw1RlibfEyYg+57glmqBEGAsdoIS6MFtmYb4rPjA2rE5O08in8qBgDkn5fvWp46tHsbvqNN3bE6AEBS/6SQZ8IT8hJQvr0cjUWNIT2OJDEAb+UhciLqT9Qjvk+8Xy8r2VqCptNNUOgUGHBZ5EzELlPKwFk4CJyA6gPVyDonSrv6kh4vLjMOxiojAMBisECbqA3o9fTbgxDSnURBxKnNpxDbKxaJ+YlgWMb10BElMAghhJCeKRrbfAFKYJAoYKwxYvWi1TDVmXxuo03SYu7Hc6khwU92ox1Np89OQtRc3ozEvol+v77s9zLYmm3QJmuRMSrD6zaiIIJhu6/7WTSoO3o2gRFqCbmOXjMNhQ0hP5aUiKKI5opmKGOVsJvtsJvsaC5z/N3R6w785wAAYMBlA7p9XpG20oanoamkCVX7qiiBQaIWK2cRkxGDlooWWOoDS2DQbw9CSHcz1Zrw6z9/BStnseDLBQCoBwYhhBBColN09ZWXOJWq+yY0ljJrkxWmOhPkKjnUerXHP7lKDlOdqd2nJLuCZVnk5eWBZaPjcuLMHEx1JgicAIVOAUbGgDNzaK5o9nsfJzc4sr25M3M9khTWZit+feFXfH3z1xA4IahlD0Q0xM3VA2NAGBIYZ4b9MpwydGvcgMiJHWfmYG22wlBsQNPpJsT2ioXACzDVdvx5U7KtBIZTBkfvizmR0/vCKW24Yziryr2VQdtnpMSNBC4aY8eZOdiMNsg1cgjcmeu22QrO7N8E3t3928Mf0Rg30j6KuXR1JnbN5Y7f5rp0nev3Ns2BEV50zUkXxU66KHbSRHGTtkhor6YeGBGCYZiAZoEnnuQaOZQ6JQReAMMybkPqcFb/GiQ6g2VZpKZKf2gkVZwK2iQtTHUmmOvMrkZqhU4Bu9EOU40JcZlxUMW1/8Hlev9ZBnmz8jzWKzQKVOysgNVgRcXuCmQWZIakPh2RetxsLTY0lzluXJP6hT6BoUvTQaFVwG6yo6m0CfocfciP6Ut3x671tcK38BA4xzkv8iJUsSqY682wNllhrjUD+Z6vj/TeFwCQOiwVYICm002wNFqg1qu7vM/ujhvpvGiKXevrt7miGayMhSiKEGyCozeVTgltkrbD7zon528PwPH913oejVD+9vBHNMWN+IdiLl2diZ0zgRHbK9a1TJ+jx7kPntthT1ASHHTNSRfFTroodtJEcZOuSGmvptRXhBBFESaTiSa06SKb0YbK3ZVoqWgJ2zF5nsfevXsjYlKbrtCl6DD347mY//l89J7QGxljMnDRqxfhmnXXoN/F/ZA+Mh1Drx7a4VAYrIzFtL9Pw+UrL0dMuufE0qycRc70HABA4cbCUFTFL1KPW91xR++LmIwYvxvauoJhGExZNgWXvXuZ33M8hEp3x671tXL+8+cjY0wGcmflYv7n83Ht99di5A0jkT4yHbve3eW1twrDMDjn7nOQPSU7oua+aE0Vq4I+Vw8AqNpXFZR9dnfcSOdFU+yc1+/lKy9HQl4C4vvEY9yd49D3wr6Y9vdpmP/5/E4N+9RS1YKKnRXtDikVbtEUN+Ifirl0dSZ23hIY6ng1ek/sjbRhaUEvI/FE15x0Ueyki2InTRQ36YqU9mrqgRFBBKF7h2WJBobTBjAyBnJ1+E5tURRhNpu7/WIOBl2KDqyMBW/joYxRInd6LuRqOQpuK8DWp7ei6IciDF04FKrYjhvM23tiO29mHo6uOYry7eWwNlv92l+wST1uSf2TMOWRKeCt4fsBECmTsEdC7HQpOuhSdLA0WKDUKRGXGYfEfMc8MTOemIGf//4zhv+/4WDl3p8TSMxPxKT7J4WzyAFLH5GOxsJGVO6tRJ8pfbq8v0iIG+mcaIudLkUHW4sNSp0SqjgVpiydAlbBuvXcDJSzR1xjcSO0SYFNBh4q0RY30jGKuXR1JnbeEhgkvOiaky6KnXRR7KSJ4iZtkdBeTQmMMDDWGNsdA1kVp4I2OTJudqXMZrLB1mwDGMewR6Rzao/WAgDi+8S7EkG9J/WGPlePlsoWNJxsQPrIdK+vba5ohkwp67DxRp+jhz5Pj8bCRpzadAr9L+4f3Er0AEqdstuG3yJnWZsdn+2th2pQxigx+9nZYBjG4/O/7RAzqjhVRE7wa6wxQpemQ/LgZOhSdag/We+2PlLLTYi/jNVGAI7h8ZxjxndFyuAUVO2rgsiLEPju/4FPCIl+zgRGXGacaxln5VD2exkETkDujNzuKhohhBDSY/jT5kv3zl1HCYwQM9YYsXrR6naHFNAmaXHFqiuAEOQwbDYbnnvuOXz66acAHN22LrzwQjzyyCOIiTk7vM+//vUvvPPOO2AYBlarFQUFBXjqqaeQmSmdBlJjpaMxQpushVwlB8/x3T7ZsBS5Jobuf3ZeBYZhMOG+CdAkatrtLbHvw304veU0xt4yFv0u6tfucfJm5WHXW7tQ9EMRJTAkwm6y4+jao2gub8Y595zTpaeVo4Wt2QYAHteFM3mxetFqNFc2Q+RFyNVyNJU2Qa6WQ5OgASNjoE3Sdmq4mlBq+71V+L3nUG+RWG5CAmGsOpPASD17DvN2Hs1lzZ2a40eulkOukYMzc7AYLJCr6Cc2ISR0BF5AS6VjyNzWPTDsJju2PbsNYICc6Tn0W40QQggJIX/bfEN179yT2nxpDowQszZZYaozQa6SQ61Xe/yTq+Qw1ZlgbbJCre76JKmtCYKAK664AsXFxdiyZQv27NmDHTt2QKfTYfr06TCbza5tL730Umzfvh179+7FgQMHkJOTg0suuSSo5QklzsLBarBC4AWo9Wo0VzajYmcF6o7WwW62h/TYMpkMAwcOhEzW9Sc4I8GwhcMw+9nZHmPz6/vo201eWJutKP21FBCB5IHJHR4nZ2oOWDmL+uP1aDzV2NViB0zKcTPVmbDvo30o31ke1uOychYH/nMAxT8Vw1xn7vgFIRJJsfPWA8O1rsmKpvImNJc3w1xnhmAXIPIiOAsHlV7l9vkfSQL53gpEJMWNBCYaY+fqgXEmgdFU1oT/Xvtf/PDgDwE9/GBrscHcYIbNaINcI4fACTBWGcGZu3cCbyA640baRzGXroBjJwKTH5qMUYtHQZOkObsfZ48yEfQgVxjQNSddFDvpothJU7TGLVT3zv4IZ5tvsNurO4MSGF3AWTif/3ib+7j0MpUMco3c459MJYMoimAYBnK5HAzD+NxnoFatWoX6+nq88cYbrsybSqXCo48+iry8PLzwwguubXNzc10npFwux2OPPYbCwkKUl4e3gbQzVHEqcBYOouB4wtlusoO38eBtPKwGKyz1FmiTtCGb6JhhGOj1+qh5wkmmlCF5YDL0ffRe14uiiMo9lR4Z5uKfiyFwAvR5eiTkJXR4HFWcCv0u7ofh/294u/NlhIqU41ZzsAYHPz2I/av2h/W4MqUMcb0dwxQ0FDWE9ditRVLs+l7QFzNXzET/S7z3IpIpZVBoHUPaGauNYOUs4rLioI5TQ66J7Ce05Ro5FDoFGJaBwAtQ6pRQ6pSdLnckxY0EJhpj11LleHJZl+ZIYMRmxIJVsLC12FC1v+OJ61VxKmiTtGipbEH1/mo0FDWAYRgInABznRl2iz2kvz38EY1xI+2jmEtXoLFj5SwyCzIxcM5At9e0HhKv7f0oCT665qSLYiddFDtpkmLcgtnm29F+OyNcbb6t26u7U2S3nkS4z+d/7nNdxtgMTHt0muvvmoM1YFjPYAucAFbBQhRFGI1GaLVarL1xrdfs3MKvFwZUvpUrV+Luu+/2epLdc889uO6667B06VKvrzWZTGAYBklJSV7XRxJznRmJfRMh5ouY8cQMaFMcY3EdXn0YJ749gbjecZj97OyQDXXCcRx2796NUaNGQS6P/ktq93u7cXTNUfS9sC8KbisA4EhqnNxwEgCQf16+3/savXh0SMroDynHzdswX+GSkJsAQ7EBDYUN3TYHRyTFTpukbXfOF4ZhoO+jR2NRIwROACNjXA2mUmBrsqH2SC1kShnSRqaBQed/tERS3EhgojF2bXtgMCyDrAlZOLn+JEq2lSBjVEa7r9el6HDZe5fhf7f+D3aTHePvGo/kQcnYcN8G2I12TPrbJGSMzujWYdaiMW6kfRRz6QpW7Fg5CzAAxDMJDOn85JAkuuaki2InXRQ7aZJi3ILZ5tuatzbfQNt7gfC1+YqiCJPJBK1W261JDOqBEUHaZuW6avfu3SgoKPC6bsyYMTh58iRaWlo81h08eBBXXXUVHn30UahU3ffkoL/UejX6XtAXAy8fiKxzspCYn4jE/ESMvWUstMlaWOotqD9e3/GOuoDno+MJp7LtZfjj9T9QsbvC5zZZ47MAACc3nHQ9wVp/oh6GYgNYBYucqTnhKGpQSDVurgTGgPAnMPS5egBAQ2H39cAApBU7mVKGhPwEsApH7wuZXDrdZpUxSoCBq1dbV0kpbsRdtMVu2qPTMPvZ2UgZlOJalj0pGwBQ+mspRKHj32S1R2rBMAwS8hMw4LIBSO6fjKnLpuLyf1+O/PPyI2KOmGiLG+kYxVy6Aoldxa4KnNp8CqZa9x7RDMO4emFQD4zwoGtOuih20kWxkyaKW3CFs8032O3VnSGNtFeEmv/5fJ/r2mbeUoakQKFTeGxnN9phMVjcll327mVBKZ/BYEBaWprXdQqFAgkJCWhqanJ1Nbr//vvx4YcfoqqqCosXL8Zdd90VlHKEWkx6DCbcO8HjglLFqjDw8oE48MkB7F+1H70n9PaaESVnlf1RhpPrT0KhUfh8+jR1aCoS+yeicncltr+8HSP/PBL7P94Pm9GGXuN6oaWqBSqzyq+GG2ONEaZaE6r2VcFUY0LfC/u6rVfF+bcfKTPWGNsdD9E5/IhzG4ETULW3Crydh0wpQ/3J+rC+T87hwRqLGsNyvEh38vuTEOwCMsdnttsTQx2v7vCJ7kjEsAwUWgXsRjvsLXbIE+lnA4kOmkQNNIkat2WpQ1OhjFXCarCi+mA10oZ5/w0FOH7EH//fcQBAvwv7uZ5GyhwnnYnwCCHSdeSrI6jcVYlxd4zz6P0sU8rAW4Pz4AEhhBDSU1Gbb2SRTEuE3W7H8uXLsXr1atjtdqSlpeGNN97AgAGOiYYrKiqwePFilJaWQhAE3H777bjllltCWia52v+3j2EZsKxnhxeGZTy64ASy3/bExsaiqqoKeXl5HuvsdjsaGxuRnHx2suVnn30Wzz77LOrq6vD3v/8df/7zn7Fy5cqglCUcvHVlGjBnAI59fQxNJU04tekUcqblhL9gElJ3tOOhiYw1RpT+VoqaAzWo2FmBI2uOoLm8GSIvormiGYUbCqFN0mLux3PbbVQ31hixetFqNJc3o6m0CWCAXW/vAiM7G0d/9iNlzveg7XwirTknTndOFs1ZOTSXNoNhGXx7x7cAwvs+JeQ6EhgtFS2wm+yu+R16qkOfH0JLRQv0ufp2ExhSptQpYTfaYTPaPBp8CYkmrJxF5vhMFG0sQsnWknYTGPXH69FwsgGsgkXeLM/fWYQQEkrN5c0AgNhesR7rqAcGIYQQ0nXU5htZJDOE1MMPP4zDhw/j999/x6FDh/DYY4/hyiuvhN1uBwBceeWVWLRoEfbu3Ytt27bh3//+N9atW9fNpT6LM3OwGW0e/zjz2claNJrgNgwNHToUf/zxh9d1u3btQl5eHpRKpce6pKQkvPTSS/jvf/8Lg8EQ1DIFU+3RWvz6/K9oKmvyuY1Sp8SgKweBkTFoqfTsOhUMMpkMw4cPh0wmnWFhvOEsHAynHPFub2gia5MVnJmDKl4FVs6Cs3BIG5YGfZ4eMRkxkKvkMNWZ2u1V4NyPqc4EVZwKylglWBkLESLUejXUerXf++msSIib8z2Qq+Suerf+J1fJYaw1wlhrdG3DylmwchaqeFVY3qe2VHEqaJO1YBVsyK6pjkRC7JxszTYAZ4Za8sGfz/9I5Cw3Iz8zMXGDuUvljqS4kcBEW+zqjtdh9/u7cXrraY91bsNItdNV+vg6R++L7MnZHhN1V+6txNZntrrmhuou0RY30jGKuXQFEjuBE2Cscszj4y2BMWbJGEz62yTXHD8kdOiaky6KnXRR7KQp2uPWHff84WzzDXZ7dWdIogeGKIp4/fXXUVxc7Jo1fcqUKTj33HPx3XffITs7GzzP45prrgHgyEI9/vjjeO2113DRRRd1Z9EdjX1JWpjqTOCs3k9cbZIWqjiV12xdVyxcuBAvvfQSFixY4JHxe+GFF7Bo0SKfr7VarbDZbBE9Rt2BTw6gYmcFWDmL8XeO97ld/0v6I/vcbMSkx4SsLN4+FKSm/mQ9REGEJknj15PksZmxaCxshK3FBlbOIj4rHgBgg83nue6NXCNHbK9YGE4ZYGu2QZ+td60LZD+dESlxk2vkUOqUECECrdrLRFGEaBDBgIFMLYNSp4SxxghWzkKTqIFS5yh/qN+ntmY9MwuaRA1YWfflwCMhdqIgwmZ0JDDaNmA6l/n7+R9J2pZbsAsQOAFWgxXmBjMYhul0uSMhbqRzoil2tYdrcWT1EfSe1NuVsHBKG5GGYdcOQ++JvX1OUsfbeZTvKAcA9Luon8f6xuJGnN58GtYmq8fQLuEWTXEj/qGYS5e/sWupagFExxOc6gS1x/reE3sHu2ikHXTNSRfFTroodtIUjXHrznv+cLb5Bru9ujMkkcCoqqqCUqlEYmKi2/IhQ4bgjz/+wLFjxzB16lS3dZMnT8a8efMgiqJHIK1WK6zWs08rNzU5nuDnOA4c5zjhWJYFy7IQBMHRiHjmH+AYqsjbU3nelmuTHUO7tB3zrDXnE81GoxFarfeG40CO6Vx+00034ZNPPsFtt92GZ599FjqdDna7HU8//TT27duHd955B6IowmazoaamBpmZjnGbGxsbcfPNN2PevHlITEwM6LiBcO7D+Y/jOPA8D5lMBkEQIAiC27atl9cdrUPZjjKwLIshC4Z4bO+MH8/zgBxQJ6vBcZzb8tbll8lkYBjGFf/WywHPyYZaL+d5Hrt27cLo0aOhUqkgiqLb9m3L3tHy1ueerzq1Lnuw6lR3tA4iRCT0TXB7jVwud6sTzzmOo9AooE5Uw26yg+d4yEQZGDiuNVEUwXO81+vJWSfnfgBAnaiG4bTBlaV2DkvUdj9diVNrcrkcHMdhx44dGD16tGu/4Y4Tz50tl91iR+2hWtcyBo6n3jmbo652kx0pQ1Og76NHTFoMwAKCKLh9NoXr3FMlqCCIAiAg6NdT2zh5q5Moivjjjz9csQt1nHzVydpsdW3Dqli3dTKZDNpkLS774DLYmmxnl8tlEEURAu8oizJOCXWio/EhUj4jtMlaXLHqCpgbzAAc59WGezeAs3CY+vBUxGTGQBmnhCpBBY7jfMapbdmdn5UFBQWu8oerTsE696TwWR6KOjljN2bMGCiVSsnXqamiCYIoQJ2khiAI7nFigIFXDnT9SPdWJ5lChovfvBhl28sQnxcPjuPc6pQ2Kg2CKKBqfxWsLVYodcpuOffsdjt27tzp+qwMdpzaLifdj+d57NixA2PHjoVcLonbPHJGILFzDh8VkxHjM9FKwoOuOemi2EkXxU6aojVuuhQd5n48t8N5TUMx3PeSJUvwySef4Pbbb3e1+dpsNjzzzDPYt28f3n33XQCAzWZDdXU1srKyADjafJcsWeJq8/WH0WiETte9PTslcdbEx8ejpaUFjY2N0Ov1ruUnTpyA1WqF0WhEnz593F6j0WigVqtRXV3tManJihUr8Nhjj3kcZ/fu3a6ApKSkID8/H6WlpbDZbDCZTOB5HkqlEkqlEhaLxe3mUqVSQaFQwGw2u91EqtVqx4mqdZ+1XaPRgGVZGI2O7r/O/QOORiyz2ezalmEY6HQ68DwPi+VsIoRlWWi1WnAc55aQkclk0Gg0EEURX3zxBZ5++mkUFBRALpejoqICQ4cOxYYNGwA4TsKamhrMmzcPLS0tUKkcPUHmz5+Pe+65BwC81kkul8NkMrVbJyedTtdunUwmE2w2Gw4cOACNRoMRI0agtrYWhYWFru3j4+MxaNAglJeXo7S0FCfeOYGmxib0md4HMekxOHnyJGpqalzbZ2VlISsrC8eOHXPrEpUsT4YWWtQoatzKM3DgQOj1euzevdstrsOHD4dSqcSOHTvc6jR27FjYbDbs27cPoiiisbERe/fuxbhx42AwGHDkyBG398WfOjk5z72ioiK/6pSXl4fU1FQcOHCgS3WyHbFB4AU0sA2udTKZDAUFBW51MpWaYLFYoIEG2l5amC1mmHkzzI1myBVyqBgVODuH/fv3Q9ug9VknU6kJnN3R+GG2mgE1wDVxqCmsQXL/ZDBgYLFY3PbTlTg5OevU1NSExsZG7Nq1CwzDdEucTKVnr3tDvQEcw4G3O/6Wy+UQ4WjEYsDAarPC0GhAvD4erJJ1JF5NAG/mwZkc76NUz7324uStTkOGDIHNZnPFrrvqZKmxQOAFKHVK7Nqzy2udDp867FGnxsZGHD/iGH4GDYCmOvLipE5U40DhAdeyhAsTMKxgGPT99Th07BDQAOBU+3FqWydRFF3fD1I99yItTuGqk/N77tSpU+jXr5/k63Rs9zEYGg2oaKpAUVFRp+J05MQRmDVmVO+s9loni8ICa40Vp347hbxped1y7p06dcrtey7Ycdq9ezcIIeHX3vwXAFB7pBamWhOS+ifRMFKEEEJIiOlSdN0yb6tcLsf69evxxBNPYNy4cZDJZCgvL8fw4cOxbds2V/t2TU0N5syZA6PRCLVaDZZlsWjRIslN4s2IXX2MPkz+/Oc/QxRFvPbaa9BoNPjuu+9w00034cILL4QgCBg/fjxuuukmt9dkZ2fjl19+QW5urttybz0wevfujbq6OsTFxQE4+zSayWRCcXExcnNzXcNXdaY3REdvsyiKMJlM0Gq1Xp+kCdYxT5w4gfPOOw8fffQRJk6cGPT9B8K5D4vFgqKiImRnZ0Oj0Xg8TWisMcLWbHMtrz9Rj23PbANYYPrj05HcPxmaJE2HT4MW/ViErSu2Qq1XY9oT0yBTnB17j5WxUMeroUpw79bVk3pg/LT0J9QcrsG0x6chdViqa3nbp0EbTjbgy6u/hCZBA4VW4Rj2yFknMLCb7DA3mHHlf65EQn6Czzq13Y/dZEfNwRqIEJE6xHH8tvsJ5lO7dru923tgNJxswOqFqx1zWWjlgAiIvOh8M2E32dFS1eJI+KXqoIxVuj4fnPuwG+2wNFqw4IsFSMhLCMu5ZzfbsevNXWgua8Z5/zwPIsQunXtS7YFRd7QOPzzwA3QpOlz81sVdqpMUPiO6Wqee2gOjuaoZlsazDx84J3jjeR4QHb1wdCm6iK5TtPXAWH/XejQUNWDyQ5ORNT7L67lXsaMCpzefRv85/ZGQl+Bazlt4KHVKVw9dX3Xa8/4eHP3qKHKn52LCvRO65dyz2Wwh7YHR0NCApKQkGAwG1+/nnq6pqQnx8fHd9p44e5dG29ONPUEgsfvj9T9wYt0JDJ4/GCOuG+Gx/qdHfkLl7kqcc+85yJ2e62UPJFjompMuip10UeykKVLj5myPbN3mK2XHjx/Heeedh1WrVrm1+XaF80FEnU7XqZ6f7b3Hgfx2jpyzpgNvvPEGVqxYgcmTJ8Nms+Hcc8/F//3f/6GhoQHNzc1uPROczGaz14lGVCoVVCrP8cfkcrnHhcSyLBiGcf1z8hW0QJd72y5Y+/a2vF+/fvj000+xcOFCrF27FkOGDAnq/gPV+r2Vy+Wum2LnzbSxxoi1162Fqc7kek1zRTM4EwdlrBLr71gPbZJjmC5vGU/n/ow1Rvz6zK+o3lcNgRPw6d5Poda7Xzjt7cfXB6xzubMRwfn/3rZ31qmry31NeuRreUdld5r9zGxYGi1Q6BSQyd331bpOMrnMFXuGYVzDRrXdXiaXeb2enHVqux+lTglNkga8je9wP/7Wqb3lzvNOJnPffzjj1Pp9ZhkWYAC02hUv413zTLAy1rFNq/K3rofz/8Nx7sliZCjfXg7OzKG5rBnx2fFe6xqsOLVdznGc19h1pU7+Lm99PN7s6B2jilN1uU6dKXu4PyPaW+5vnZznajTVqb3lxhoj1ly7xu07rK223z2RWieGYVz/L/U4mWvNYBkW8ZnxPut06pdTKNlSAl2qDin9U1zLt724DS2VLSi4rQApg1M89u8sS/aEbBxfexwVOyscY9V302eEt8/KUMeJEBJag68cjIzRGT7n95MpzyRIrZE7lyEhhBBCgq+9Nl+pk8ydh0qlwt///nf8/e9/dy27/fbbMWHCBJSUlOD06dNu25vNZrS0tCA1NRVSEa7xxMaNG4eTJ0+G5VhdZW2ywlRnglwlh1wjh91sh2ATwCpY6PvoIQoiTHUmWJus7XbZsjZZYa43Q5emg7HKCLvRjvje8WBkjsY0zsz5tR9vZDIZxo4d6/PGX0raJnXaw5m9j33ta7k/+9Gl6lwN+IHuJ1CRFDd/3stgvd/BwDAM9Ll61B6qRUNhg88ERqhESuySByZj5oqZrs+RaCaKIsq2l6HuWB0GXznYNU9NICIlbuHU9jusra5894RTNMXOZrTB1uKYl6a9oVWyJ2WjZEsJSraWYMR1I8AwDEy1JpRtL3P0nIltfxLC5EHJUMYoYWu2ofZoLVIGeSY7Qi2a4kb8QzGXrkBip0vVtfv55Upg2CiBEWp0zUkXxU66KHbSRHELn1C0+Xb3/BeA27O+0tLc3Iy1a9fioosuwsSJE/HLL7+4rd+0aZNrmAqpaN39n7iTa+SOJ/QTNNDn6BGXGQdNosZrg1B7dGk6KGMcjQ7OiTWVOmXA+2nLZrN1vFGUUMWpoE3SgrNysDRaPP5xVg7aJC1UcZ69nDraj7XJCmujNaD9dEV3x631e1B/vB6VeyphOG1wey91yTroknVdfr+DzTmkSkNhQ1iP69TdsQMAZYwSqUNTu6VhMtwYhsHOt3bi0GeHUH+ivtP7iYS4dQfnd1jbf1397gmnaImdqcbRG0YVp4Jc7fv97zW2F2RKGVoqWtBY3AgAOLH+BCACqcNTEd+7/cQtK2ORMTYDCX0TuvUp6GiJG/EfxVy6ghU7SmCEF11z0kWxky6KnTRR3KQrEtqrJXPnzPO8K1NXVlaG66+/Hvfccw8SExMxZcoU2O12rFq1Ctdccw2am5vx6KOP4r777gva8cMxVYjZbIZOp4PVYEBLeTnEMyeIMjYWMZmZYM/Uv+7QIcTn50N+ZhgsY1UVRJ5HTK9ern21XVZ/7Bhis7Kg0Go7Xb6aAweQNHAg2FZDBjSXlsLScLYBMzYrC+oER8Omua4OdpMJcb17u+1HFASYqqthaWyEyHGw8jyaq6vx7WOPYeBll2HQokU+y8DKWJ8T1vmDYRjEZsai4WQDWipbEJMW4xqip7N4nse+ffsibiy/QGxesRmcmcPw/zccSf2S2t1Wl6LD3I/nwtpk9bmNKk7V4dPE7e3HbrLj5IaTyByXGbKnkiMhbq3fgz9e/QOVeyox/Nrh6DO1j2sbZ2Kiq+93sCXknklgFIU/gREJseuJkvonwVRtQt2xOqQNTwv49T09bpYmCxqLGxGXFQdtYue/i7tDNMUuvk88Ll95OSwGz6FHW5Or5UgfnY6y38pQsq0E8b3jcfI7x5NM/S7s59exzrn7nC7/xuiKaIob8Q/FXLr8jZ2p1oSin4qgz9EjsyDT6zYyFSUwwoWuOemi2EkXxU6aIj1uEpkeuts426s7I1jvbeSdNT4sX74ca9euhd1uh0ajwe23345rr70WgKNRes2aNViyZAmeeuop8DyPxYsXY/78+V0+rkLhGCbDZDJ5nU8j2DiLBU0lJUjo1w/yMxNCGysr0Xz6NOLPTEYuiiIQ6AnQwWuaSkqg0GqhSUryvazNhJWAI2ERm5UV0DENRUVg5XLo8/MhUyhgMBgQq1Ri0iOP4Mdbb4WppgZj7rorsPp5se/tt7HrX/8Cw7JQ6lMg2GYByIAmSYOmkibwNh52sx2qGEcDschb8Mu9N6Px5GFAFDHgqqswYdky15jthz76CD/eeSdis7Ndx5CpVFiwaZPr75aKCny3eDFaSkshCgJG3n47Rt5yy9n15eXY9OCDqNm7FxBFaFNTMeXpp5E2erRH+f87Zw6GXHcd+l95ZZffi/aIgojK3ZXgzBxG/nlku9tuWLIEvSZNwtA//SkoDea6FJ3X/ex6ZxdO/XwKxmojss/NDsp8K5HK+R7IVDIodUok9U9CYn6i1+0iiT5XDwBoLGyEKIpRHSNfyneWo6WyBSmDU1wJnWiWPCAZJVtKUHu0truLIjm8jUdjUSMEToCxyii5BEY0YRgGmkQNNInt/6Yz1hih76NH0Q9FOL7uOFg5i6ayJqjiVdCmaGGsMXb4udydyQtCSHSqP1GPfR/sQ0LfBN8JDAUlMAghhBB/hLvNtycymRw94J3vdWdJJoGxbNkyLFu2zOf6Pn364Lvvvgv6cWUyGfR6PaqrqwEAWq02JA11oijCarWC4zhAowEniuDOTEzOxMTAWFMD1Zm/7YIAi9XqCp7FaoPI8YChxbU/q8UKS0M9musbAAbgrVYorFbwPsabs9ntEGw2MK0mQ7fZ7bDU1sJkNDr2yXGwWCyQ8TysBgOMVVU+66NNTQNv58Bb7WBblQsAzEYTdBm9YOd5WO121NTUQKvVIiU7G33nzEFjm7HabNU/wF5tAHSXQpPkaHToKAZF332HvW+9hYVbtkAVH48d/3oPmx58AtrUv4MB4xo2QuTPJlgspz+Cftb5uOKrz8HbbPh6wQLsef11jLrtNgCAwHHIvegiXPzRR27H4rizcxCsvfJKjLz9dgy+5hrYmpvx+ezZiMvORt5FF0EUBHw+ezbGP/ggLlq50lXO1ZdcgsUnTrj1jjHX16N861Zc8p//tFvPYGgqawJn5iBTydzmMrC1tODA++9j9B13uJbxNhsEuz3kZRp4xUAcX3cctYdqUbm7EhmjM0J+zO5ma3Z0p3QOcRbp9H30YFjGNb+MNqnnNcgW/1SMU7+cwqgbR/WIBEZSf0cyu/5YfY9NWnWGKIpoPOVIXgCArcUGQRAkNcRlT2OsMWL1otUw1hgdw0eJwIlvT0DkRagT1Phy4Zcek6+3x26yw2KwIDaj8z1ICSEEAJrLmwGg3R7p1AODEEII8U+42nylzNleLZPJAnpvRFGEyWRCdXU19Hp9l+c/kUwCozulp6cDgOuEDgVRFGGz2aCQy2GqqoLaYIBMrYbIcbA0Njp6KhQVAQBaamqgFUWwCgUEXoCxsh4iZwKY4612KACsEoxM5TjBRDMa2dOQqbyPk2+pr4fdZALTqkFF5HkoY2MhP5OFNNXVwaBUuoaygo/smcALKDtyGqLdAlHkwJTUtdmAg1ixCwqdBjKlEqLdDn7fPuz75ReYa2tx4ZnGfdfmtnoI1mrwdgs4CwdNUsdZ0X1vvolJjz8OVbyjQT7v4suxaemT4JpPQanrh6SBSWBw9sIT7C3gjScx7GZHzw+ZUokpzzyDrxcscCUw2iOTyVCzbx8Ensfga64B4Bj6a9Ljj2PPa68h76KL0FJRgZbycgw+03MIAHLPPx/qxETUHzni1gvj2OefI/eii6AIQwa47qgjPon9Et2eFjXX1mL700+7JTDCRZukRb+L+uHoV0ex76N9SB+VHpIvkUiaQMo5qaxUEhgypQxx2XEQOAFWgzXsCYxIiJ0rZh1M5hstEvITwLAMzPVmmOvM0CYHHvNIiFu4mWvN4EycI3HOAIJdgK3FBnWcuruLFpBoid3h/x6GpdGC3Om50OfovW7jnHxdoVFAnaCGvcUOURDBKBjE946HwAl+T75+estp/Prcr0gZmoIZT8wIQY3aFy1xI/6jmEuXP7FrKmsC0H4CI+ucLMSkx0DfRx+sopF20DUnXRQ76aLYSVOkxi0cbb5S5myvViqVnWqX0+v1rve4KyiB4QeGYZCRkYHU1FTYw/D0eZNajf3vvIOGo0ehjI9Hzvnno9/ll7vWr7nnHsx46SXE9+mDxtON+PWhlwFYocs5z7WNpXIrbPUHINP0hsiLkMuOY9ZrryJl4ECvx/ztk0+QOGgQ+l9xxdlly5dDlpWF7AsvBAB8fcstuOy//4Um4eyTxgc/+gjHPvsMAMAqlRh0zTVIHTMdO57ZAb55LwRbBXQ5l7kdi7fy4Kx2nHNHPnjTafBNTdCkpGDyk09Cn5/vUTZ11ny0VLZAsPJQxilhN56NAWfmPLYHgFM//IALP/zQbZk8ph8s1fshj+3jsb2l+iBkutyzyRkAif37w1RdDWN1NXSpqV6PAwByuRwFBQXY8fzz6D11qtu6zMmTsXbePIiiiJiMDKj0eux/7z0Mu+EGAMCR//wHlvp6JAwY4Pa6w6tWYfzSpa667HntNWiSk1G2ZQsYlsWkxx9HfF4eNt56K2wtLYjt3RsXvP++Wzl3vPAC9r7xBhiGAatQYPzSpRi0cCEAoLGoCOuuvRZZkydj/7//A7uRg61iICx/HQN1QgJ+f/ppHFy5EqbqaqwcORIDr74a4x94AABQunkz9r75JjiTCaIoYsLDD7c7b0lnDZ4/GCfWn0D98XqUbS9D1ngfQ5V1kjNukUJqCQwAuODFC7pliJRIiZ1zXhIpxawr5Co54nPi0VjYiNqjtchOzu74Ra1fHyFxCydRFMEqWMd3QK8YMCwDmUIGVsbCZrT5/A6LNNEUu+Kfi9FY2IjUoak+ExhOco0cqYNTARawGqywm+xQ69WO2Fn9i50+Vw+BE1BzoAZ2kx0Kbde6TgcimuJG/EMxly5/Y+dPD4ykfkkdzmlHgoOuOemi2EkXxU6aIjlu4W7z7UkUCkXQEleUwAiATCYLWcZQFEWc3LwZRR9/DABgFQooFQqoVSrU/v47qrdsgWC3Y8w998BWWQmlTAa1Wg2lTAlLtQBr2Wq0HN7o2p9ga0HcgPlQxEyCpdECBVMMBQC12vsTn0JTE1ir1W290NSEYx98gOpt2wAAjQcOQKVQuLY58dVXOPHxx5i7Zg1UcXGwNjVh9cUXg71ZA0ulBTA3QTDXAWfa1BsPrIS1Zh9EUYTIi9h8LNY1Rqs3Y++7D/mXL4JSdzZpwbAMLI3uE29qk7SuiY4Bx9BHrFwOZasJZlRxKqj0abDUFXu8HgA4Uz2Ucalu+wEcc3w0FRW1m8AQRREGgwHNZWWIz8lxW6fQaCBTq2GqroYuLQ2XffEF1lx2GYo3bIBcrcap77/HFWvXupW1qaQE9ceOoc+sWY46y2Q4sWYNLvrwQ5z35psw19fjk3PPhS49HRd//DHic3Jw6KOPsPnBB3HBu+8CAHa/9hqOfvYZFm7ZAm1KCgzFxfjywguhTkxE7vnng2EYVPz+OzInTULugtdgKDJALV+HXx9/HNNfeAHj//Y3DLzqKnxy7rn40549bnUq37oVC376CXG9e6OxsBAfjR2LPrNnQ5uS4vM96gx1vBoDLhuAQ58fwr6P9iFzXGZQe2E44xYfH9/tXQR5Ow/e6ujm39Wn+Xke2LwZqKgAMjKAyZOBUD3o0F3ju0dK7JxJp7afG9EseUAyGgsb0XCyAdmTAktgRErcwkkdr0ZifiJaqlog8o7vPsEuwG46+8O47XdYJIqm2JmqHWOw6lL9m1eIYR31VceroY4PvNdMXGYcYnrFoKW8BRW7KwK+broimuJG/EMxly5/Y9dS7hiat70EBgkfuuaki2InXRQ7aZJC3ELZ5itlkRI7SmBECJ7nUatUYvorr0Aud4Rl9SWXYOBttyF72jR8f+utKNu2DWXbtsHQZo4IRcIYxOZOgkKnQGNhIxiWQXxOPBgwsBkdDWyJg4dCneB7jPaYzExsXroUf/zzn65lpqoqXPLJJ66G9JKff3Z7Td2RI+gzcyZUcXEAAFVcHLImT0bDscMAHJMQm8q2wlSxF/LYwUidsAQAYDPa0FKyF3G9D+KqH8/OW/KCWo17LJ7JhYFXDgSrYJE+Mh0Ft7tnbBuOHcam+2/FF7OfAwBMWLYMGePHQ94mUaNL0WHMreeg9Bcrpjzr6NGx94O9AANMun8S9rx6ChDtHkNByNVq2M9MOAOGQemmTfjk3HNhrquDvm9fnLN0KVILCnDkyBFYGhqQ5KWHS+t9JA8diiHXX4/fly8HAIy5+24ktnnNkU8+Qf9588DKz16e8bm5rl4OmsREpAwbhl4TJrgSJv2uvBK/Pfmka/vtTz2Fy7/6ypVUiM/JweTly/H7ihXIPf/8M9VhMP7hR7Hm/30NABhx843Y+tB9HuVva/iSJYjr3RsAoM/LQ9qYMajcsQN5Z3rqBNPAKwbi+P+Ow1BsQMnWEmSfG7yGH57nceTIEYwdO9Z1zXUX3sYjdXgqbC22Lj2du3o1cNddQGnp2WVZWcBLLwFz5wahoD6Eez6ESImd1OYtCYaBVwzEgDkDOtVwEilxCweb0QaGYaBL0WHux3NdvXW8UcWp/JpHoTtFS+xsRpsr8ehvAiMYMsdl4uiaoyjbXhbWBEa0xI34j2IuXb5iZ6wxur5DeCsPQ4kBAMBZONSfrPf6HWKuN6OhqAFKnRLJA5PDV4keiK456aLYSRfFTpoobtIVKbGjsyaSMYyrUXD266+7Fn9/661QnkkatCbYBJhqHQ3l2mStW6PaOQ8vhz4/0eehxt1/P8bdf39AxRswbx4+mzkTythYJA0ZgrqDB3Hsiy9w3vurceibrQAAZco5sPIXwg50atJSgRNQ8UcFlDolhl49FIlt6pCYPwn5F+5zW2aqqXFNgN6aTC5Al6pHYn4iVLEqtJS3gJWzSMhLQExGAoyVlR6vsZvNrjlA+s+bh35XXAFVXBxEUUThunX472WXYcGmTY79q1Rej8uZzVBoNBB4Hp/PmgW5RoP/t3MnWIUCmx54AB+NG4drf/8dylhHg+Dhjz/GrNdec9uHrs14cXKNBkmDB7v+Vmg04MxmAIDVYIClvh5po0a5vSbz3HPx3Y03uv7WpqZCsDFIHZYKU50J+vxMmOvrPcrfljrRPQba1FSYa2o6fF1nqGJVGHTlILRUtrgmEI5GSp0SM5+c2aV9rF4NzJsHiKL78rIyx/Ivvgh+EoO38fjx4R9hOGXAnH/PgUITvqFROqt1Q4A3/jYmi4LoShBH+tPzwUSTEHdMFEX8+vyvaC5rxuSlkxGfHe92ThX/XIzTW06j74V90WtMr24sac9jqnH8RlLGKsP6eeVMYFTsqHDMpcFG5lNnhJDIYqwxYvWi1TDVOT67eCuPptImMDIGX/35KwCOXnxzP57r9j1TfaAa257dhtThqV3+fUkIIYQQEgkogRGBbM3N2Pb444jJyMCJNWtw8uuv3dbH5+ZCm+z5NE3jvjeAuhMAgNpfWciUMoiCY8iKj8cvw3W7d7qenPemo4Y9UXBvGdXn52Ph1q049NFH+PLCCzHtn//Eol9/hbnx7I05b+UAOaBL07mSF4YDr4BrqYRRocfKkSNd2yYOHIg3+/TB2HvvxZi7HJNpl20vQ92eNWBgQMaYq3yWrTVNcjI4sxm2lhYoY2Jcy5tLShCT5ZhHQZOkAcMyEDgB5nozYrOyULl9u8e+mktKEHvmNa2HeWIYBvkXX4y+c+ageP16YMIExGRloen0abfX281m2FtaoE1Nxekff4SxshJ/PnQIsjMToM/95ht8NnMmDn/yCUYsWYK6w4dhNRiQOXFih/Vkld6f+mZ8JIlEUQTTujscw0CbrMWMf8yAKIpoPHnSs/XbHwwDURACf52fhiwY4jo3rc3ez08pPMEcSjzv6HnhLXyiCDAMcPfdwJw5wRtOyhmT+hP1sDRacGrTKST2PZvcisSYtG0I8MZbQ4A3NqMNOPN+K3U9pwcGOcvXd+bxdcdR/FMx5Bo5eBvvsb7mcA3Kfi+DLlVHCYwwa6lyDL2iSwvvZ5M2WQtRFNFc0YyT3590+6wEIvPzkhDS/axNVpjqTJCr5JBr5BBFEao4FQRegDJGCc7MwVRngrXJ6vYZwioc9wLevoMIIYQQQqSIEhgRgmEYaDQaMAwDuUaDAWcmfvbm66uuwsCFCz2SEfqRt8Bw2gBzrRmQMUgZlQ7OzMHSaIGGfR+m6mqfCQxnw17dH89BkTob8pi+HtvIUhaC59wb6mIzMzH+b3/Db088geE33wxjZSUqt++HrW4rFFqFY1x/OaDWnx3SKX7o/8HSaMHln8/36FFx4N//RsXvv7v+VmgVUGiNAGfwe7x9hmGQMX48SjdtQt5FF7mWl/zyCyafGWaJlbHQJGtgqjbBWG1ErwkT8PN990HgeddE3vVHj0KmVLoSGN4IPA+ZQgGlRgP9hAnYfGaia6fSTZuQXlAAhmVhbWpCQr9+ruSFs6zJQ4bA0tAAwDF5t3Oi7c5SxsYiplcvVO3ahbTRo13Ly7duRWqbXhmty+GxLELG/gtmo3Nrra85qdu82X3YqLZEESgpcWw3bVrXj9c6Js0VzeBMHNbdvs6tJ0JnYuKvzsaubUNAW74aArxRaBSYuWIm7CY7WHn3zAXSXUq2leD0ltPoPbF3QMO6RdM15+tzyW62u8YnT8hPgCres3dO+oh0nFh3ApV7PXv9RapoiZ2x2gjA/+GjfE2yHsjk68YaI9ZctwY1h2pga7Hhm5u/gSZR47ZNqD4voyVuxH8Uc+lqL3ZyjfzswxIx7us4q+fnkUzp+A1PCYzQo2tOuih20kWxkyaKm3RFSuwogREhZDIZRowYAQAQBQHbHnsMzSUlXpMYIsdBrde7LePMHJqP/hu2+gMAJ4MgAlU/so5Hr0VAM6S3xwTTrTkb9hhGhFIng1LvPocEZ+bAWXNhNzp+JP/y17+i6NtvXeXTZWTg02nToEtLg1KfBgg22I0iBE4ARMdwCRaDBZYGC2zl74E3lmDNpa+4fmA7sTIZxrVKAqSPTMf1f3zpM5njy+g778TWRx5B5rnnQhUXhyOffQa70YjerVpvY9JiHAmMKiNSpuUgvaAA259+GucsXQrebsemv/0No+64w7V9c1kZdGlprrkpjn35JYrXr8fk5csRk5EBURQh2O04tGoVBl9zjaMnzaOPYux9jnkl+syciS1Ll+LwJ5+4khSVO3bg5DffYP733wMADn/yCS7/6quA6upNwf334/tbb8UVX38NXWoqGouKsPnBBzHz1VfdtrO12HyO369OSIC1sdGjJ0u4Oc9NMIC10QpljBKapLONP4E0OrfW+prrbsU/F2PnWzuROS4T59x9TsCvr6gI7nYdaZ0IUCeoYbKZwLCMK1HZ2Zj4q6uxc2sIaMNbQ4A3rJxF6tDUTpdByupP1OP05tOQq+UBJTAi6ZrrKm/JMMEuoLm8GaychTJO6dqu7TWQOsxx3jSdboKl0eKW4I9U0RI7ZwIjJq397zRVnAraJC1MdSafnwn+Tr7uPFe0yVpokjRQxancfvuE8vMyWuJG/Ecxl65gxk6ucnwvUQIj9Oiaky6KnXRR7KSJ4iZdkRI7SmBECEEQUFtbi+TkZFgbGlCzbx9uae+R6jNa32Tbm8qgzr4BdlsvmOvNYDRyxPaKdT3Zp0nq+MaYkTFQqBVeG/da38RPfeYZTH3mGa/7MNYYUbdoNWp2fwtRECGTs7AYLDCcMkDkRSgsZUgq+Cvm//dmv2/WA8309bviCjSXlODjCRPAsCx06em4/KuvXMMr8XY7Tn3zOJRpV7saNC54/31svPVWvD9kCERBQN85c1Bw39lJrYvXr8cfzz4LmcrRYJEwYAAW/PgjtGlpqK6uRnJyMuasWYPvlyzB9qeegsDzGL54MQbMnw8AUMXH48r167HpgQew/amnwLAs1ImJuOjDD5HQty/Kf/0VCp0OKUOHutVFrlK5jukkUyrdenIAgKLVEFfDb7oJvN2OT6dOdWyvUuHcJ59En5mOcXBZhQKsQokvF36JmF4xuPjVix37bHUcZUwMhi1ejA9GjYI+Px/z1q93bNNm6CqZSuWxLBQEuwBbiw28jUdcVpzbGOL+Njq77a/VNRfo3CzBZjFYYGu2dfpGMyMj8O143tEjo6LCsXzy5MCHl5Jr5NCyWljqLBA4we1zozMx8Vckxa4nShrgmI+m7lhdQK+Lxrg5k2GiKKL2cC0gOr6X4/vE+xySURWrgj5Pj8bCRlTtq0KfKX3CXOrARUvsRt0wCgMvH9jhHBShmHxdk6TpcuI0UNESN+I/irl0+RO7xlONkKvl0CZr2+2ZTkNIhQ9dc9JFsZMuip00UdykK1JiRwmMCCEIAgoLC5GYmAhlXBwYmQzvt2nIbm3i3/+OAfPmud1k/3zvJlT8tgpqjRbKWAEKjRxoYWAzM/hs+tMYc9ddGH7TTe2WQ6ZNR8PeNwBGBYETwCpYMCzjmkvj93/swYXvv97uPpxlWn3lLjQV1eHcJ2eh96TeOPLfIzi+7jhMhXmwlb2Kz6a/5/X18Tk5uOLrr3Hyu5PImpAFdXznnk4dfeedGH3nnd7rqVCg4MFXcODjA64xsTWJibj000997m/YjTdiWKtJsJ04jnPFLr5PH8z77juf+4jPycGl//mP13WiIGDaP//psbzXhAlY8MMPbsvOe+stj+3+fPCg29+jbrsNo267zeuxYjMzcdF/NmHzPzaDlbNg5SzisrNx49GjbtvNePFFzHjxxXaPe8G773o9RrBpEjWwNFjA23gYq42ISe9ar5DW11x3f4HaWhyTQStjO5cImjwZyMpyTNjdXmelV18F8vKAHTscc2a0zpFmZQEvvRT4RN8KrSORxpk5xzwrYehWGIzY2S121B93TFqfNiwt4Nc3Fjei+mA14nvHI2144K+XsqR+jgSG4bQBdrPd78mQI+maC7am0ibYWmxg5AwS+yZC4NufFyhteBoaCxtRubdSMgmMaIgdwzDQJmn92laXopP8vBTREjfiP4q5dHUUO97Ow1hlBBi49UT2xtkDQ7CFbo464kDXnHRR7KSLYidNFDfpipTYUQIjAskUCtx86pTf2ztvsvPm3g+rcAkGzBmA0YtHd/xCL2L6LoRi+HWo2lcF0cpDmaRBYn4ibEYbLI0WjH94vl/70SRqkH/ZfFTsqsCAywZArVdj7C1jUb6jHErd7Zj414noM9l3o03Vvir88eof2PfhPly+8vKQjDEfkx4DTZLG78a3UMucNCmsx3M+PZ3UPymsx+0shmUQ2ysWjcWNaK5ohi5V1+FTtFJhN9oBwOdwXh2RyRzJh3nzPNcxzNmJvL/4Ali7FrDZPLcrK3O8/osvAktiyNQysHIWAifAXGeGNtm/xsHuxspYxzj2DDqVeKnaX4Vdb+1C73N797gEhiZRA22yFqZaE+pP1HcqARRtnEMJKWOVkKvljkne25E2PA1H1xxF1b6qcBSPRAjezsPcYAbEjoexIoSQ1pw9whQaBWTy9rvM0hwYhBBCCIk2lMCIIrbmM09xt2kEDbRxzlRncky+DcDcYAbPBf7jl5WxmPiXiRAF0dXIrIxRYuDlA7F/1X7sX7UfvSf29tn9+eSGkwCArIlZIZsgN3d6LnKn54Zk35HIWGN0Gw6j9LdS2Iw2qGJVqD9ZH/BwGN1Bm6JFc0UzeGtwemF40/Z9aisU75O12XG8ziYwAEfS4YsvgMWLgTNzwgNw9Kx48UVHz4s77gC2bPH+emeS4+67gTlz/B9OigGDmIwYWJusXifGjlTOXi8QAYETIFMENn6W8/NWFdvxGPjRKGlAEky1JtQdraMEBgB1vDqg3oKpQ1IhU8mgTdaCs3CQq6Vz7UiV3WTH7y//jpi0GIy4bkS3JMA5CwdDsQGsgoUuTQcG0ZGEJ4SElnOuHIETIFPJXElyzux9+Dm1Xo3RN42W1O8yQgghhJD20K+aCMEwDOLj47s0/IprGJozjaBFPxbhyFdHMGzRMGSNz/JrH6Ioorms+UyhAAiAud7sc9zmjrRtIBhw2QAcXXsUzWXNKP65GHkz87zWo2RbCQAg/7z8Th03nIIRu1Az1hixetFqx2TYZzQWNUIURBirjNj+ynbXXCmRnMRgmDO9MIrO9sLoyr7axs3b+9RWKN6nttduZ82dC+zeDfzjH8D06cAjj7jPbfH448CMGb5fL4pASYljboxW8913KCYjBrEZsV0qeyC6es1xZg4tVS0QOMfQChaDBQqNwmdDgDddHfZL6pIGJKFka0lA82BI4bMyXBRaBa785MqAE2fdJRpiZ6w2omRLCZSxSoy8fmS3lEGpUwKMY04n3sq7hnkJlWiIGwkMxVy6vMWu9VyH5nqz43eLCFgaLa5ttElaVy9AJ4VWgQGXDQhb2Xsyuuaki2InXRQ7aaK4SVekxI4SGBFCJpNh0KBBXdqH6ynuMw1qjcWNaCxsROH3hX4nMMx1ZtiNdrAKFnGZcQDj+BEcSMMeb+PRXN6M+D6eJ7hCq8DgeYOx5/092P/xfuRMzfHoYVH8SzEEu4D4nHgk9k30+7jdJRixCzVrkxWmOhPkKjnkGjk4CweGZcDKWWhTteAtPEx1JlibrBGbwHCeg3KNHIyMAWfmYCgxdDq55i1ubd8nb2UIxfsUzKf5naPPzZ7tmYSorPRvHxUV/m3n63MhkM+LzujsNde6IcDWYjubwKi3gNc5epp5awjwxtlLp8f2wOifBIZlwNv976Enhc/KQHFm7ux3r1YJRsa4lndEKskLIDpiZ6w2AkCXEt9d4TwnWAULzsTBVGuCWq8O6edlNMSNBIZiLl3eYuecV7CxuBEb/7YRYIALXrzANf8YEJqewcR/dM1JF8VOuih20kRxk65IiR0lMCKEIAgoLy9Hr169Oj0pStunuPPPy8eR/x5B+R/lMNeboUn0PeFb64Y9XZoOAi9AFEVABKwGR+OMvw17lXsrsenxTUgZkoJZT83yWN/v4n449s0x9BrbC5yVg1Lu3gDtHD4qf3Z+yDN8W5/ditrDtZj010lIHpjcqX0EI3bhItfIodQpYTfbwcpZKGOVUMWoYGNs4KyhbXTurNbnprOMyhglBLUAhmXAWTm/z83W2oub833yJhTvU1xWHHg7D3VC5yasb62oyPHfXC+jo2Vk+LePjrbzFhMAEHgBlgYLZEoZEnITAo6Jvzp7zTkbAqxNVnx///euJxiHXzscfaY65uTxtyGgp/fASB6YjPmfz3eNs+0PKX1WdqT1NdBU2gTeykOXpnPrReV3MqzZCmWMstufaGlPNMSupaoFAKBLC29DX9vPS4ZhIHACTLVne/p15jvMH9EQNxIYirl0+YqdLkWH6gPVUOqUSOyX6PewjTWHasDbeKQMSZFUwlxq6JqTLoqddFHspIniJl2REjtKYEQIQRBQWlqK9PT0zicw2jzFHZcVh+RByag9XIuiH4sweN5gn69t3bDnjSiKUMer/WrYK9teBgCI7xPvdb1cJcclb1ziteGrobABjYWNYOUscqbndHisrrI0WGCqMaGlqqVLCYyuxi7c5Go5tKnaiJnAvD0dnZtA554+i6S4nXP3OUHbV3sJjMmTHXNilJU5hotqi2Ec6ydPbv8YvmJy6pdT2PfRPqjiVLjw5QtD9kRgV2KnS9FBk6iBwAmuJJVcLUdifmC9vYIxb4mUsTIWCLA9JJKuua5yXgOmOhPW37keAidgxpMz3J7u7+hzSRRFfH//96g7VoeLX7/Y0esxQkVD7LqrB0bbz8uy7WXY9fYu6HP0mPyQ48M2VE9QR0PcSGAo5tLVXuxMtSawchapw1L93t8PS3+AyIuY8/4caJO1wS4uOYOuOemi2EkXxU6aKG7SFSmxowRGFHFN4t3qieC82XmoPVyLwo2FGHTlIJ9PeAq8YxLbto14vI3HgU8PoPS3UlzwwgUdlkEURZT/UQ4AyByX6XM7X0/t1p+sBytnkXlOZliGZnE2ZBirjCE/ViRRxaigipHO0De6FJ3Pxh1RFCEKXlrjeyCrFSh3XH5eExgyGfDSS8C8eY5kReskhvOj4cUX/ZvA21tM9H30KPm1BMZKI8q2lyG+t/ckZnezNFiAM3WPz4nvVC8KV8I4RL1MpEQUxYjuPRAquhQdzPVmyFVyqFJUyDonK6D3gWEYx3ehCFTtrYroBEY0cCYwYtJiwn7s1p+XyhglDnx8AOZ6M+J7xwfUi4kQ0jMNmT8EAy4bAN4WwLCNShk4MxfQUI+EEEIIIZGK0l5RQhRFjF4yGiNvGAlNwtmhovpM7gO5Wo7msmbUHKrx+frin4ux9sa1OPjZQbflrILFqV9Ooel0E05vPd1hORoKG2CuM0OulnfYxdlYY0TRT0X4YekPqDlUg/qT9UjIS8DUR6ciZ1oOjDWhTyo4h5JwNmwQaanYVYH1d63H8XXHg75vnuNhM9lgbba6hguKdKdOOZISWi2QkuJ9m7lzgS++ADLb5BezshzL587t/PFZOYthC4cBAA5/eRg2Y2S+b85J2rXJWlz08kUYcGngE11OuG8CJj88OWKTNOFQe6QW3937HX565KfuLkq3cU5intg/sVNJnLThju/Jqn1VQS0X8eR8UCHcQ0i1pUvVQa1XQxREGE4burUshBDpkKvkAT3c5UyO8lZKYBBCCCFE+qgHRoRgWRYpKSmd7o7DMAzyZ+d7LJer5cienI3C7wtR+H0hUod4dj0WeAEHPz0I3sZ7TKjNMAzyZuVh/6r9KPy+ELnTvTzW3Yqz90XayLR2nyo01hjx5aIvUbm7EoJdwIFPDniM/69N0mLux3NDOjGdsweGc2zszuhq7MJNFETYLXbI1XLJlNkXY7URjUWNOPzFYfQ9v29AT7J2FDdjtRHNpc0AHL2aUgb5yAh0kanWhG/v/BbqBDUufvXiLu2ruNjx39zcsz0qvJk7F5gzB/jmG+Dyyx3Lfv/d/zky2pMzLQcHPz+I5tJmHF171JXQCKauXnPmOjMAQJPke16gjiQP6NyQc9FErpGj/ng95Go5REEEw7bfgC+1z0p/OBMYSf2TOvX6tBFp2L9qP6r2VUV0T5ZoiJ253nHdd9ck3k4Mw2Da49OgS9X5nGspWKIhbiQwFHPp8hW7zn43uBIYAfTaIIGja066KHbSRbGTJoqbdEVK7OjMiRAsyyI/Pz8kJ0T++fnIHJ+J7MnZXtcX/1yMlooWqOJU6HdRP4/1ebPyAAao3l+N5ormdo/lnP+iveGjAMDaZIW5zgxdqg6snIXd5GhQV+vVUOvVkKvkMNWZ2p33IBhcQ0h1oQdGKGMXbJyZg6nehKo9VajYWQGb0Qab0QbOHJkTeHckb1YetClamOvNOPHdiYBe217c7GY7WipaIHACBE6A3WQP2ftkbbbC1myDranrvRWc81/k5HS8rUzmSGIMH+74e/PmLh8eAMCwDIZf49jp0TVHXXNFBFNXr7m0EWmY9fQsjLpxVJBL1rPE946HXC0HZ+H8epJcSp+V/qo72rUERlK/JMjVctiabWgsbgxiyYIrGmJ3+b8vx5z350TEUF0JuQkhT14A0RE3EhiKuXT5it3vL/2Ob+/6FuU7ywPaHyUwwoOuOemi2EkXxU6aKG7SFSmxozMnQgiCgJMnT0IQhE693mKwoGJ3hdcGkOQByZjy8BT0GtPL87hnel8AwKArB0Gu9uyUo03WImO049Hswu8LfZbBXG9G/fF6AECvsZ7H8kaXpnONP99wsgGG0wYotArINeHpHOQcC9tUbYLobVZjP3Q1duGgilNBm6QFZ+VgrjND4ARABCyNFlgaLeCsHLRJWsmN58/KWQxZMAQAcPiLwwHdpHmLm/N9sjRaYDfa3RIYoXqfvM1d01ntTeDty/Tpjv/+FMRRgHpP6o34nHjYTXYc+e+R4O34jK5ec0qdEimDUxCfHY9vbv0GX1z9heOa8JO12Ypj3xzza1i9aMawDBL7O+ZNcvZEaI8UPisDYW22oqXC0XuvswkMVs4iZYijd1ckDyMVDbFjWAbaZK1HT9NoFg1xI4GhmEuXt9iJoojKPZVoLGwM+LNLpqIERjjQNSddFDvpothJE8VNuiIldj3nLi7CCYKAmpqaTp8Qdcfq8PMjP+P3f/0e0Os66n3hlDc7DwBQ9EMRBN57GVVxKkx7bBpGXD/CbR6O9jAM4/E0ZDiH0NAkaaBN1SKxXyLsJnun9tHV2IWDLkWHuR/PxfzP52PcHeOQMSYDoxaPwvzP57v+hXq4rlDpbC8Mb3Fzvk8D5gxAxpgMjL9rPDLGZCBjdAbmvD8nJO+Tc34NZUz3JDBmzHD898cfu3x4F4ZhMOK6Eeh/aX/0v7R/8HZ8RrCuOYVWgZaKFtiNjgSVv1oqWrDzzZ3Y/e7uLh0/Gjgb7muP1na4rRQ+KwOh1Clx4csXYuL9EwMal7yttBFn5sHYG9kJjGiKXXcTRRF7Vu7B93/9HqZaU8iOQ3HreSjm0uUtdi0VLTDXmR3J7gCHMqUeGOFB15x0Ueyki2InTRQ36YqU2NEcGFHCn6e4myuaUbixEDlTcxCfHe9X7wunrPFZUMWpYK43o2JXBTILPIeIYuUsMkZnuHpr+EudqIZCp4DdaEdMekxAr+0qVsZizrtzwnrM7qJL0Tka3kVHw1vasDQk5id2d7G6zNkL449X/+jUXBhtydVy1B+rh1KnxIjrRqD+RD1szTao9Wro++iDV/AzujuBMWUKwLLAsWNAWZnn5N6dlVmQ6fVzIhKc3HASvJ1H1vgsaBI0MNWaYK43Q5us9ev1zmGxgtFrRuqcCQx/emBEG4ZloM/RQ5+j79J+MkZloGFaQ8ReL9Hg9NbTKNlagl4FvTqcyyscGIZB5e5KNJxsQO2RWmSf632IT0JIz+bsmZc0MCng37b9L+4PyyQL4rPjQ1E0QgghhJCwogRGlHA1qPloBDXWGLHtuW2o3FUJw2kDhl49FI1FjWgsboRcLUevgvaHfGLlLPpd3A+WRgtiM2KDWnYGDJL6J8FutkMdp+74BaRLnPOYxPYKbhy7U96sPOxduReGEgMOfnYQWROyPLZRxan86jlR/HMxBLsAfZ4eCfkJ0CRpYGu2wVxnjsoEhl4PjB4N7NjhGEbq2mu7XAwXY43RNY+NwAkewx/4G5NgO7z6MJrLmhHfOx6apLMJDH85E8Zdeeq+s3jeMV9JRYVj0vXJkx3zmXQHY40RMqUMNqMNNYdqUHOoxjVkBdB98ZUafY4eE++b2OF2ra8nb+j99q32SC1Obz4NTZImIhIYAJA0IIkSGISQdjkTGGnD0gJ+bc60nCCXhhBCCCGk+1ACI0KwLPv/2TvvMDeqs2/fM+rS9u71rnvvlWaMMR0SQjAloaU3QhJISA8pJCR5Qxp8SUgvJISOEyBU04zpuIB77157e1WXZr4/jkcr7aq3lczc16VLu6PRzBk9c87MnN9TaGpqSrsoSrxJUGe7k5VXr6T3UC8DRwc49s4xtj68FUmSUAIKQX+Qxz7xWMLUOLOvnh3zs7bNbRx56wjNS5qpmVqTcvsNJgMG0wjNwh1HVdW00ldlart8M9AicraXjMpvtEsucXe76drThbfXy+u/ej3qOvZqe8Q5HstuB9eIugYTz52IJEnYq+307u9NaYI7FbIlYAwMQMfxLD6pCBgg6mBkW8DQxp3+o/24Ol0YjAbstZERDkNtkiyZ9DlVVXF3Clvaqm1YK4Vomop9RyoCY+VKuPFGOHx4cFlTE9x5J6xYkdemhOzr6nTRd6QP2Sjz8IcfjhCpku1zxYiqqrx919tUjKtgwjkTMFpyezsV/nvHIt3+lAzFbjtnmxMAR13hCDw102rY/eRuOrYnTr+WLsVuN53U0W1evAy1naqqtG1qA6B+TuoChk5+0Ptc8aLbrnjRbVec6HYrXgrFdrqAUSBoJ0S6xPMI9vZ5cXW6sFZa8fZ6CfqCyLKMpVysG3AHcHW68PZ50574OPDyAXY/tZuAN5CSgBFwB1Jangv2PreXTfdtonFRI4uvX5zy9zO1XT4JeAKhidpsR9KMJN4+L0pAwVZli1oAPto5Hstuy3+4nIOvHGT0ySKdy8TzJjL6pNHUTEtdmEsGS7mFigkVGQtK+/eL98pKKE8xW8BZZ8HPf57dOhjauCMbZRSfguJXMNlNoRQImYw7mfQ5v8tPwCPGF3u1HVuVqNcTb2J4KCMRgbFyJVx+Oahq5PIjR8Tyhx/Or4ih2ddoMVI3s27Y56n0uWJk4NgAe57eg2yUmXjexIy3p6oqvQd66W/pp/m05mGfh//eyY5x2aTYbedsFQJGSX3hCPfaNaV7TzdBfzAnThzFbjed1NFtXrwMtV3f4T48PR4MZgPVU6tT3p6zzRlKj5lsikyd1NH7XPGi26540W1XnOh2K14KxXa69FUgBINBtm3bRjCYXqG1ZDyCTTYTjgYHslFm4NgAZocZs8McdTIkFqqq0rG9g3fufgf1+Eyaqqq0vN0CkHQOb0uZBXu1nYA3gKfHM+wV8AawV9uxlOVhglACV5uL/pb+tL6eqe3yiaqqzP2YKK6cjZRFhYbRZhTntNWIyWaKe47HspvRamTCORNCk9PNpzUz+aLJlDWVDdtGNph2yTQuvPNCpl48NaPtaOmjxo1L/bunnw5GoxBBtO1kC1uVDVu1Ddkg4+n2pDXuDCWTPqcJeCaHCaPViL3aHrE8GUJRM3mKwAgGReTFUPECBpfddJNYL99ofW7oK5U+V4xoNT8qJ1ZmZeK573AfT33xKV7/5esE/bF/n1R+72xS7LYrxAiMkoYSLGUWlIBC997unOyj2O2mkzq6zYuXaLYbu2wsTac1pXWd2fzAZlZ9bRX7XsjyjZ1OBHqfK1502xUvuu2KE91uxUuh2E6PwCgQVFWlt7c3JAqkSrJpaBy1DgZaBlACCu3b26mdVpvSfgKeAC/c8gJBb5DRJ42mdnotPft6cHW4MFgMSYc4O2odrLh3RUHk89YmNLQJjlTJ1Hb5xGQzMeOyGSPdjJzibHPSd7iP0tGlcb1th9pNe08njVghkE79C42SEjjpJHjtNZFGKp1txKOsqYz23nZcnS5KGkswWU0ZbS+TPhdKH3U88qK0sZSKCRUpeScmqjmUbdasiUwbNRRVhUOHxHpnnpmXJg1vAypBTxCjNfZtRTGNlYno3CEEDK2IeaaUNZVhrbDi6fHQuaOTulnDo1pGkmK2nd/tD0VNFZKAIUkS1dOqaXmrhY7tHWml30xEMdtNJz10mxcvQ21X3lzOaV9NXB8pFlpqw4A3f1Ht70X0Ple86LYrXnTbFSe63YqXQrGdLmCcIEy+aDIN8xoSTqYYLUYs5Ra8vd60CmabbCbGnD6Gfc/vY8+ze6idXsuRt44A0DCvIZQeJhkctY6CKDiqTXK72lxp18HQKRxUVJSAQt/hPizllqQny/e/tJ9tj2xj+mXTI4q8BjwBuvd2E/AGGDV/VK6anTGZCBgg6mBoAsYnPpG9dgGYHebQ5Kyr3UV5c4o5rrKIlirKVi0EjDGnj0m5gO6My2cw5vQxeTuOo0ezu162UVWVYxuOoQQUGhY0YDCObD2jfKBFYGRLwJAkibo5dRx8+SDH3j0WU8DQ0gCWNZchoV+rkkFzTjCXmjHZMxNPs03N1Bo6d3SiBvUHOR0dnewim0SihaBP93TV0dHR0dHRKX50AeMEoenk5PORVU6oxNvnDXkgp0rjokZ2PLaDPU/vYcI5E9jz7B58Th9lTWV07enKW+REtrBV25BkUdDc0+1J+3cpBrr3diMZJEpHlaYkNhUTjloH7k43vn4fXbu6qJ2ZXJTR3lV76T3QG8qVrtF3pI/nvvEctiobH7z7g1lv79Nffhq/08/Sby+lYlxF2tvJhoDx4x8LAUNVIds6nq3KhqfHE/KEHim0CAwtdVQ6VIytoGJsRZZalJhRSepmya6XbSRJQkVMwCoB5YQXMMJT/qSTlzwaznYnjloHPqePA6sP0HRK07DPPd0evP1eZIOM0WbEUeMg6A/GTTmlA55uD5JBKqjoC43pK6Yz44oZSTtOONudBRG5qqOjk1u0lLrlY8vTdqzS7vN1AUNHR0dHR0fnREAXMAoEWZaZMGFCXqq6G0yGtCfvnO1OXrr1JVo3tqL4Fe59/714ujwAuDpcvPn/3sRebWfFvSuK5iFaNsjYamy42lwMtA6kLGDk03aZsv6v62nb2MYpXzklIsrgREKSJKomVdG+pZ2AO0DPvh4cDcPPxXC79R/tp21TG0gw4ZwJEetp54O7240SVJAN2bXzwNEB/E4/sjGz7WYqYJx2GpjNoij0rl0wZUpGzRmGVi/C5/ShKEpG28qkz008fyJ1s+uiemIXagTW0qXQ1CRsEy1qU5LE50uX5r9tGgajgUAggOJXIEZwXzGNlfHo3teN4lcwl5opaci8KLSz3cnKq1cycGyA3oO9HF1/lCNvHUGSxbkY9AVxd7nxDfgwmAxYK6xYy6343f5QKquK8RUZtyMexWy7hnkNfGjlh/A5R1Y8jUYq4752nmhRZNEYev9VzHbTSQ/d5sVLuO32r97Phr9soPn0Zk7/xulpbU9LIaULGLlF73PFi2674kW3XXGi2614KRTb6QJGgSDLMnV16eW81tJ3mEvNVE2sCk16DCXgjp4DNdbyaHj7vLg73diqbbg73Pj6fBhtRmSTjL3GTsAdwNXpwtvnLRoBA0RebFebC2ebk9rpqdUFycR2+Wbg6AAApaNKR7gluSH8XC5pLKF7TzfOVieqog6bsA63297n9gIwasGoYbUQrOVWJFlCVVQ8PZ6MPPeHoioqfpcfyKyegqqKAtyQvoBhs8Gpp8Lq1SIKI1sCRrhNLBUWTDYTfqefoDf9B+pM+py1XEz+hvPkF5/EeczJRb+7KCkv7d1P78ZoM9J0clPcmg/ZwmCAO++Eyy+Pvc4dd4j18o1mXy11m6/fhyRLUa8rxTRWxqPvcB9IIn1UNgQvb58XV6cLU4kJk8NE0BtENsmYS8y4Olz0t/QLYUgGR70DR4ODoC+IqqgoikLQE6RrT1dOIwyK3XaSLGEptYx0M2KiqipqUI0raGjnidFijFq0Pdr9V7HbTSc5hkbmGDHS098T+l+PzCkOwvtr68ZWAKonpx/lp0dg5Ad9nC1edNsVL7rtipN4dtOjjAubQulzuoBRIASDQTZv3sysWbMwpDgL5Xf6een7LwFw5corMciR37eUWbBX23F1umIWcrNX27GUJf9wX1JfgrfXCyrUzapDNsoYTGK/xVgsrmpSFWpQTWsyMhPb5ZOgL4irQ3hulozK3Gu4kIh1jlvKLLg73QwcG6BuVl3EOa7Zbcb0Gex7ToQvTDh3wrBtS7KErcqGq8OFu8udVQHD5/RxPPNORgJGdzf09Ym/x45Nvz1nnSUEjBdegM9+Nv3tQHSbmB3iGLWbk1THHY1s9zm/y0/AIyb/Ek0Cq6rK23e9DSpc+q9L8yJgAKxYAT/9KXzzm8M/mz9ffJ5PhtpX8SsiDV+vJ1Tca6h9i2WsTMT45eNpOqUp6+nQTDaTGGvaXSh+BWerE3enG0mSMNlNVIyrIOgP4u0ZvLm3VdroP9KPf8CPUqGk1Z+S4USxXSGy++ndbL5vM2OXjWX+J+YnXN9oM4bG0qEMvf/S7XbiMzQyR1VVPB4PVqs1JLAWW2T0exWtv86cMZP2Le0A1M1Of7IgJGBk4DCikxh9nC1edNsVL7rtipNYdksnylgnvxRKn9MFjAJBVVXcbndaVd29/WIyw2g1hkSEcBy1DlbcuyKriqZslLFWWPG7/AR9QUy2wiqMmSoLPrkg7e9mYrt8MtA6ACqY7KacTXKNFLHOcVVVWfenddir7Mz96NyIc1yz29F1R3F3ubGUWWLWkrFWWYWA0emGydlrt29ATIAarcaMUkhp6aPq68Gegb6yfDl8//vZqYORi3FHI5M+t+XBLZgcJsadOS40CWivtuNqc+Hp9iT8vt/pz4rolA4DIoCKM86Az30O/H745Cdh/Xp4/HG4+OL8tWWofTfes5EDqw8w5eIpTP3AVGC4fYtlrEwGk82Uk+ueo86BrdqGucSM3+nH2+ulpLEESZK48DcXhorPh9OytoV1f1yHbJTp3tOdkxv7YrbdG3e8QdAXZNZVsyhvLh/p5gxDNsm4u9x0bO/I+raL2W46yTE0MkdVVfx9fqxlQsAo1sjo9yJaf+3a3YXf6cfkMFE1sSrt7VVNrmL2NbMpayrLYit1hqKPs8WLbrviRbddcRLLbulEGevkl0Lpc7qAcQKgTYLGm0xz1Dqy3tkrxlcgG2UkCi9nvM5wtPRRJaNKCjLPf6bEOsfP/b9zY6ZVg8H0UeOWj4spItir7XTRhbvLnZ3GHieZvpsMmda/0Dj5ZJFKqr0dtmyBWbMy295Qm6iqSu/BXto2tTHhnAl5i17QUAIKG+/ZCCqMWTImtFyrcxLP60MjXDDOtG5Jqjz+uHj/5CfhqqvE31u3ws9+Bl/6EpxzjrBfvgi3b9WkKo6uPYrRasxowuW9TrhnvaXUQv28egLuAJ4eD7ZqW9TftmpiFb5+Hzse3cEbv36D8+84/4RNE5gOR946gq/fx8wrZ450U6JSM60GgK7dXSgBJalxJeAJRE2NqPPeRYvMUVQFg9+AyWFClsS5VIyR0e9l2ja3ASLCPd79ayKqJlbp12MdHR0dnaIhlShjnfcmevWUEwAtjYVWJDdfGIyGE068UJUTV8XvP9oPnHjpoxIR/vCnBBRa1rZEfD7pgkk0L2lm4nkTY24jlQnuVNAEDFNJZpNQ2RIwzGY4/XityBdfzGxb0ZAkidU/XM26P66jfVt79neQAE+PB1QRQWYpH4xCChVqT0KgCo23Zfkdbw8ehHffBVmGiy4aXH7LLaKA9/798H//l9cmRVA1sYrmJc0n/GRJ68ZWVn19FVsf2ZqX/SVbKG3ex+ZRM70Gv8vPO/94J7eNKiL8bn+oz9prs5f+L5uUNpZiLjWj+BW693Un9Z2+I320bW6j/1h/jluno6OTb9o2HRcwMkgfpaOjo6Ojo6NzoqELGAWCwWBg2rRpaeUTC3lx51nAOJHw9Hh49JOP8vCHHk45LCoT2+WT/hYx0VHa+N70zA36gjx909Os+voqtv1nG737e2mwNGCrtDHjihkE/UGc7c6o3x1z+hgWXb+I5tOas9om2SBTMaGC8jGZpTXJloABog4GiDoYuaB+dj0w+ICeLM52J117uuja0xWyXe/+3tCyWLYLRxOgrJXWiCgkLS1PMgKGFoGR7/RR//ufeD/1VKipGVxeUgK//rX4+2c/gz178tqsEE2nNHH6N0+PKwQWy1gZj/at7XRs66Bnf89INyUC2Siz5BtLaDq1iSnvnxLqF0NfyfSTaBSr7Zxt4njNJeaYHl0jjSRJVE8VhXqTSSMV8ARCY5W1zBp33WK1m076eLo9eA976T+ki1vFhsFgYMqkKXRsE+NA/Zz6jLYX8AboOdBD78HebDRPJwb6OFu86LYrXnTbFSfJ2M3n9DHQOhB65tYpDAqlz+kppAoESZKoqKhI67sjMaEWcEcP4Yq1vNAxl5pxd7hRFRVPtyfkkZ0Mmdgun4xdOhZHrYPaGbUj3ZQRwdPrYe/zexloGeCxTzxGWVMZsilSw41VHKpuVh11s7LvCVc/p54L77ww4+3s3y/esyFgLF8u3levBkURHv/ZpG52Hfue30frptakv5Otwl7uTjHpN7SOQDoRGJbS/NaR0dJHRatzcdllcO65sGqVSCX1v/9lVr8kVxTLWBmPzp2dAFRPqc76tjO9rqqKyv4X97P14djRIekWwCtW22kChr2uMKMvNGqm1XB07VE6tncw9eKpcdftO9KH4lewVFhQVRWfU4xJ0c6TYrWbTgaoEHQH8Rl9eFo30LvjERSfh5UX3sGEi85n6U9+gul4sax/LV7MBx5+mPKxYwFY/5vf4O3t5dRbbgltbuiyh847j2U//zl1c+em3cS/Tp3KNW++iTXs3Hz5W99i50MPhf4/4/bbmbJiBQBb//1vWtetY/mvfhWxnaDPx7o772TPo4/i7upCDQRQFYWyceOY/YlPMP3qq9Nu40ggSRIVlRUs/fZS2re2UzGuIqPtde/t5rmvP0fJqBIu/lMei2S9x9DH2eJFt13xotuuOEnGbt5eL32H+7DX2rGUWvC7/flpnE5cCqXP6REYBUIgEODtt98mEEhdAMjnhJqlzIK92k7AK3JyD30FvAHs1faiKxItG+TQpKY24ZEsmdgun9TNqmPG5TPeswKGt8+L0WrE5DAhyRJ9LX30d/VjsBqwVlgxWoyh4lDFRjYjMBYuhNJS6O4WKYuyjRaB0bWri4AnuT4TXtjLWmHFUm7BJ/uwlFtSsp0mgNirIyczSxpKqJxYmVR0UkgwzmPE28DAYERMNAFDkuA3vwGTCZ58clDsyDeqqsb1limWsTIWqqrSuSP7Aka2rqvh/cRSYUEJKBgsYnzLdIwrVts5W8X1vKS+sFMn1kwVYVXxIjAsZRbMDjPeHi9KQMFkNSU8T4rVbjrpY7Qa8fv9+PqO0rP1HspmXI9j+vf44P9exjFqFKs+97nQuorfj+IfnBhQg0GUIefK0GVBny/iO0N58StfYcu//hW57OabI5YFvd5h+znjpz/lU7t3h16aeKG1M+jzDdvXE9dcQ+++fVzyn//wia1b+eTOnXxq927O/s1vePePf2TdnXfGbGcqbPzzn/nH7NncPXcuD194If1HjsRd39ffzxPXXsvfZ83i7zNn8toPfzgsutvvdvPq97/PvxYu5J/z5/O36dPZt2oV69avo3Z2LXOumROKFH3yox/ll0YjqqKEvh/0+3nt1lu5e+5c/jZjBg8sX07Xjh0R+zCYhJdk5zv/5fkvfjEbP0XW2HbvvTz18Y+PdDOygj7OFi+67YoX3XbFSTJ2U4LiWicbZfqP9tO2uQ13d3brkOqkTqH0OT0Co4AIBoNpfa9hfgMGiyHjNDTJ4Kh1sOLeFXEnQCxllqwXDM8HjnoHrnYXzjZnqKhmsqRrO538IkkSVROr6N7bTTAQxN/nx2v34pgoztdYxaGUgELHjg48PZ6I4s+FgKoORmCMG5f59oxGOOMMeOIJMWk+f37m2wzHUefAXmfH1eaifVs7o+aPSr5txwt7ubvdqKqacpHSWBEYtdNrueCOC5JqQ9PJTTjqHHkVaVetAp8PJkyA6dOjrzN1Ktx8s6iD8aUvgcUCXV0wahQsXQq5jvb0DfhYec1KVEXlQ//5UMxCxMU8VjrbnHj7vMhGmcoJlVnbbravq0abEd+AD2erE4PFQO3MWgxGcQJkUgCvGG2nOSQ46qL/dsEgrFkDR4/mr69Eo3pKNdXTqqmZWoMSVJANw/uPo9bBuLPGIZtkGhY0sPj6xcPWiXaeFKPddFJHi8BRFRVVUQm6j2BxTEKlEgggGwzM+tjHuE8rdJUCm//2Nw4+9xwA7Zs2xV1XVRTUIeecGgyy+7//pe/AAQC8vYMpjXY+8ggvf+MbMbd39m9/G/Oznt27Wfz1r2OvjXTMqZ4+nUmXXELPkJyK6+68k74DB4ZFcsRj3zPP8O6f/sRVr7yCpbyc7Q8+yKMrVnDtm2/G/M6zn/kMNbNn87577iHo8/H4lVfyzu9/z/zPfx4AJRDgkQsvZMzy5Vz12msYLSKayu/10vrOOxHbOvLqq7g7OlCDQVRFQToeFvvKLbfQd+AA17z5JkarlUMvv8yjl13GRzZswGASddUMFjGY9e1+iam/ujvpY84FG373O2Zcdx2WsjIgsRBWbOjjbPGi26540W1XnMSzW8AdwO/0owQUlICC3+1H8Sv0Hugd5oCok38Koc/pAsYJQM3UmpD3Xj5w1DqKUqBIhKPOQTvtDLQOjHRTso6330v71nbKmsooG1020s0ZUWSTTOWkypCnazKFXQPeAM9/83kAGh9uxGjJztC57s/raHm7hZlXzmTCORPS2saxY+DxiFRPY7KkrSxfLgSMF18Uk+LZpn52Pfue30fbpraUBAwQE+WduzoJ+ANUN1RDCqmSYkVgpIKjzhFzMjRXhKePipca6pZb4M9/hgMH4IIwPaapCe68E8IcWrOOyTFYiN7b500pDV+xoEVfVEyoCHm2ZotsX1ftNXYGWgcIeoK42lzv2dpHAW8AySDhqB/+265cCTfeCIcPDy7LR1+Jhslu4ryfnxd3nd5DvRzbcAyzw8xJN5xE5fjsiWg6xYsWweXqdBHwBoSnvwQKzfh6ViG3v0vpmLl4Oo/wyrd+FDetknfAx4bf/I5t9z4wuKynm+azzmfahz+CyWHmlW/elLBNr9xyC2//4heh/13HjjHvC19g/PEL0zt33RX6bMpllzHlssvibm/zP/4RdflZv/kNqz77WRpPO42aWbMw2e14urtpef113B0dXHh35KR934ED9OzenbD94Wz84x9Z8sMfYikXTmLTrryS9XfcQds771A3b96w9d1dXRx59VUuuuceAAxmM2fcfjuPX3llSMDY8q9/YSkv57Tvfx9nu5O+PiG0+jw+9v5rLyWHSxg1dxSqqvD8F2/k/ff9m31PPhnah6qqvPv73/Pp/fsxWkUdnOYzzqDp9NPZ/8wzTHz/+8W+TQb8Ay0Evf2MXrIkpePONm///OeMO//8kICho6Ojo6OjEX4v43P6UAIKAU8AS5mFgCuAt8+Lu8vNvuf3xd3GiThHqROJLmDo6BxHm5RMNYVUMdC5o5M1t62hfFw5F/3morzuu1A8XMOxllmpmlRFf09/UqmATHYTBouBoDeIu8tN6ajsTAY625wMHB0g6E9fzdbSRzU1iRRC2UAr5P3yyxAIiKiMbKLVwWjbnFohbyCiDkbAG8BsSz6V0/yPz2fyRZOx10QXMLQUD1IBFZBQFCEmQfT0UeE88wx0dg5ffuQIXH45PPxw7iZmJUnCUm7B0y3S2ZyQAsbx+hf5dBhIF9kgU1JfQu+BXrwDXkp5bwoYi69fzKLPLgqFo2usXCn6xJCsLnnpK+kycGwAS5mF2hm1uniRJbZv387cuXP59re/zfe//30Ajh49yqc+9SkOHz6MoijccMMNfC4s7VKhMTSCKxgI8tRPnkI5qDBu6e8Y2PM4Pbv+whs//A9TrriCmR/5yLB0RiBqTW3817so5pMJlgxebBTX8+z536vsf7EVo82IHEh83T79ttuY9bGPhf5/4aabsFZWUtrUBIAc5SZw/W9/y8Y//QkAg8XC/BtuiNhGNJpOP53r1q6l7d136dq+nYDLRcno0Sz98Y+pmDhx2PqpRF5oHHj+eS4ckhKradky9q9aFVXAOPTSS4w65ZSIY6yaMgVXWxvOtjYcdXXsuP9+Ftx447D6Xn63n55DPRy67xAV4yrwtb+IQa7BUtUUsQ9XaysGsxlbVVXE8uqZMzn29tuDAobZgPvIq9gaTwFE2qk/jRnDqd/7Hht+9zvUYJCpV17Jwptu4plPfYquHTswORyc/ZvfMOqkk0Lb3f/ss6z59rfx9fejBoNM+/CHOe3WW0PH+Kfx41ly6628dfvtAJhLSznvT3+idvZsDjz/PC/dfDMDLS385wMfoHrGDC55+GEABlpaePiCC+g7eBAUhQkXX8yy228vqHswHR0dHZ3cE34v8+r/vUrXni4Wfm4hjQsbUVWVt+96m413b2TV11bhaHBgdgx//k+3zp9OcaELGCOIs90ZeuBQVZXmkmZ69/eGbtySVRE7dnQgyRJlTWWYbFmawXwPouXI1nJmJ4vBYGDOnDkYcjQrH36eRCOZ86T/aD9A3r1wC8nDdSi2KhuWcktSD0qSJGGrsjFwdAB3Z/YEDN+AyOlsLkm/nkI2619ozJ0LlZWiDsa6dXDyydnbNkDjokbO/OGZ1E5PvR5LeXM5zlYnRqORgCs1AcNWZYs5sf7Cd1+gc3sny36wjLqZsQu2H37zMAFPgLqZdTGFkGzy1lvQ1gZlZUL8i0UwKPpaNFRVRG7cdBNccknuBMSQgNHrifp5rsfKXCObZKwVVqomVyVeuQDQbu79A35Uhk9YpkIx2S7RNdPosHDjjY5h4gXkr6/EIuAJ0Hekj6qJw8+x0YtH84G/fAC/K/m0K8Vkt5Hgxhtv5KyzzsIflsrmsssu44YbbuCaa66hv7+fc889lzFjxnDRRfl1/kiF8AguVVUZN83M5tV/4aC7hNGLm1HGNWOtrKRtwwaOvfUWit/Pwi9/OWIb3j4vAZ8Df+fjBPvfCS1X/QPYx6/AVLmEgDeAw9STVht3PvQQbRs2AODp7o74bPejj7Jr5UqRpqmsDG9fHyvf9z7KxoxhjOZREcbzX/oSB559Nul9L7r5ZuZ8+tMpt9k3MIBsNGJ2RN5jlzY30xEjldZASwulzc3Dlpc2NdG3bx+Oujra3n0Xo83Gk9d9mPZXNiCbS3GMuwhJnoDJYsJWYcNs9+PseAnTlG8OG8/M5eX4Bgbw9PREFELv2b0bv8fLSy8Jp6G6CgOuI69RvejLqEEVg8mEq62N9o0b+djGjaiqyn8uvphHLryQJT/8IePOO4/u3bv57wc/yMc3bwagfeNGnv74x/ngY4/RsHAhfpeLJ6+9lle/+12W/uQnYseqytZ77uGa11/HXFrK9vvv56mPfISPbNjA2LPP5qPvvMOfxo3j0sceo3LSpFB7W159lcuffZbmZcvwu1zct2QJe594IiTAFAv6OFu86LYrXnTbFSfx7Kbdy8gmGbPDTO202tD98OLrF7P1wa34nX7cnW4spZbImm/uQKjOny5g5IZC6XO6gDFCDPW6AfHQET6ZmqyK+Oadb9J3qI+zfnwW9XPqc9bmE52ypjJqptdQMa4i5e+azbkp6BvtPBlKMufJwFGRFitbE+/JUAwerpKcvJeXrfq4gNGVvSJShSpgyDKceSb85z+iDka2BQxruTXl1FEakixhr7PjbHOKibws1VLWQlUT2XfLg1vo2tnF0luW5kXA+N//xPsFF0C8YWbNmkihcCiqCocOifXOPDOrTQxhrbDSSy+enugCBuRurMwH8z46j7kfmUuGWkDeMNlNIpVMQCHoyTxnaTHYLplrpke203V4BRD9mpmPvhKNgWMDPP6ZxzGYDFz+wOVR68gYrUaM1tRu3YvBbiPBI488Qn19PRMmTAgVJNy4cSPBYJBrrrkGgNLSUn74wx9y1113FbSAMZQJ555O714HjYsbWfiZhax8//uZ9vnPM+bMM1l1/fUcee01jrz2Gr1DakSYKhdSOn5JVM9Gn9NHwBugasYsrJWxI4BKRo9mzbe/HZlCqrWV9993H2PPOQcQUQrhdG7fztizzw6lF7KUldG0dCkdmzeHBIxt//43B597jrHnncfZ/+//hb578KWXWPuLX7BCu1gCv7Za+bIn9nUoGu2bN/PktdeG/j/1u99l1Mknh1I0hWO0WvG7oo8x3p6ehN/xdHbyxm23segbP6ar7V2Mxnb6Nt2BVHUVsnEMthob7oP3Yh9zAZJhuNOFyWZj2lVX8eJNN3HOXXdhtNnY/8wzvPvvlbzjuZB/ikAWxrKOT0kmVOcYgr4gslFGVRRO+c53kGQZCZh48cXsf+YZxp0nUthVTpqEyeHA2dqKo76et3/xCxZ99as0LFwo9m23c87vf8/fpk3jlFtuwWQX90Enf/ObmEvFM8a0D3+Ypz/+cXz9/aFl0Rhz9tk0L1sW2u6Uyy/n8Jo1RSdggD7OFjO67YoX3XbFSSK7aaJ9eJYMSZaw19rxWX34+n1IkjTsXiWTOn86yVEIfU4XMEYIb58XV6cLo8WI0WZEVVX6+/opLStFkqSUVERf//FJ0CRS4ejEpmZaDefefm7K3wsGg6xdu5ZFixZhzHKunaHnyVCSPU/6W0QERsmokqy2LxaaN3hBerhqhS6j9Ll4aJ778SbGUsU/ILw+MxEwtALe2RQwQNTB+M9/RB2Mb30ru9tOF79b/F6SLOH3+HH3uLFV2RLaTvvulge3YK+2M/l9k4dF3mj2TSRgaOOtpTQ/Rby1+heJnuePHk1ue8mulw7WcjFp4+2N7v2ey7EyX0iSlFLdlZEgvD/IZpmAMxC6jqRLsdgu/JqJDH2H+jBajZQ1iYnRgDuA+5gLC15cMQQMjVz2lWg46h2Y7Cb8Tj89+3uomiS8zvqO9NF7oJemU5tSTq1SLHbLNy6Xi+9973usWrWKPx1PWwTw3HPPsez4hKrG0qVLufzyy4c5GWl4vV683sExr6+vD4BAIBASRmRZRpZlFEVBUQbTmWnLg8FgRFqnWMsNBoO4XwlEXvM0b7hgMEgwGOSQ/xDn/fY8zGYzqirirxRFIRAIsPw3v8FoFM8dz37ucxjsdgKBQKgwo6qqKOpgGyUkJEkS21FVFn/zR5SMrURRlKjHtOjmmznpa1+LekxaG1UgePz3MRgMTL38ch44+2wMdjvVM2fSuWULOx9+mCteeCHUtmlXXcXy//f/MBgMqKoaam/4fsKXBwIBJEnCYDAMa2O05ZXTpnHd+vURx+Rqbyfg8YSOVduXb2AAg8UybDmAZDIR6OoKtUHD73JhsFrFeSTLLLj5ZkrHTkRV38FQ0oR9/AX073sNv20UknIYX+8+KiZ+BG+vj2Bg8JgMx9t+9l138eZPf8p9p59O0OfD13g6j3d+AStdoX3O5X62q5ewad1Mmv6rsuJK0R5LTU2o7bLFQuW0aRHnqtFmwzswgKW6mvaNG5lz/fWh8z8QCGCprsbR2EjH9u00zJ8PgG3UqIjjtVZX4+7sRLaJeyvN5uF2slRURNjJWlND165doe3Esl8++1MyywHefvttFixYEFonlXOvEI9JGyPCl5+IxxQMBtmwYQMLFiwIjVHFfkwnop2itT0YDLJ+/XoWL14can+xH1O85SfKMfn9ftavX8+CBQswmUxRj2nJt5bg7/djq7WF2qldB8uay1D8CpYKS+heRbs3U1WVYCAY+o5up+wek9/vZ926daFrXTaPKVpq01joTzMjjNFmxOwwo6gKBr8Bk8OELIkLaDIqoriRzu+Emk7+0c4Tb58X2Sxjsg6mCkvmPAmlkMpTBEYheIMPJVqhS7/Lj0fxhC589mp7RDhiOFrh5/dCBAYM1sFYvRr++U9RIDyb9UtcHS52PLYDn9PHyV+MH+JhKbNgLjHTvrUdS5kFo9WIElTw9fpC3v7xbAfganex7eFtmBwmprx/yrDPQwJGZwIBYyB/gvGBA7Bxo4iISeT8OyrJgJZk10sHa4UQMGKlkCpmgr4gskku6NzcQ8c4EJOPSkDB0+3BWmlN2E9OFIw2I0F/kKA3iGyQI7y0jMbkPLRy2VeiIUkS1VOrObb+GB07OkICxub7NnNg9QGmfGAKCz+9ML+NOkH5yU9+wjXXXENjY2PE8paWFsaOHRuxzGazYbVaaWtro75+eJTzT3/6U2699dZhyzds2IDjeOqh2tpaJk6cyL59+2hvbw+t09TURFNTEzt37qS3tze0fMKECdTV1bF582bc7sFr0rRp06ioqGDDhg0RD6Nz5szBbDazdu1aVFWlp6eH9evXM3faNF679VZcRiOv/elPSH/5C5Is09DQgNfrpd9kYsv+/UgHD6J0iIdVv99P91t/INh/AEkS4oVskFECCkpQ4d8nf4v5d/+Dplmz0j4mn9fLO++8g6miQhzTxInM/N3vOPDkk7z89a8z/otf5NLnn8fe0MDatWtp3b+fgba2kBjn8/nYuHEj2779bTzHbzbvnjePYDCI1+vFOmYMv29uZtx113Hx7bfT0dHB3r17OfLAA3hbW5n/3e8yffp0WlpaOBx2szrUTqqq4nM6ObBrF+OnTg0d0/61a5FtNjo6OobZqd3rxbd3b+gc0OzUtW8fppoagsEghooKDns8HNy0CZfLJa6dxmqUQD9I0Lv9n1jGX4okiQmATcfTVa1buxZ7aSlz586lp78f00UXMeWiiwgG4bLLFnIaN3GAUwGQUJjDQ/yOV+mRxnPTV/2MHrs2tJ3mceNoamri2LFjdHd0sHbt2pCdAHbu2AHt7bi9XrZt20bJ9OkR557b5WLr9u2UT5uGCmzevBlbmL0B3G43245v1+f1snXrVs6YNk38hvv309PVxdq1a7HZbMydOxen00lHe3uoLeXl5UnZKZf9KZzwc0/DYDAwf/58AoEA69evD90jaMeknXsaxXJMixcvpre3l+3bt4eWn4jHZLGI+6HOzk4OHDhwQhzTiWinaMekqipOp0j7faIcE5x4dhp6TIcOHQrdo9TV1cU+pplNbNu2LXRMrsMugoEgkiThM/giov1LSoSDrtvlZtOmTdi77bqdcnBMBw4cCNlOkqSsHlM0x4BY6AJGgeDt8eLt8qI4FGTz8LQBsQh6gygB8dCRySSoziBKUAGVqOkbRhJvv5eO7R0YzAbq59UjJekGrASVUF2PfNXAKARv8KFEK3S5adMmZs+ejcEoZuXj1ROxVSc3wZ0sWsoiyEx81ASMceOy0Kgwtm8XE+c+H3z0o2JZNuuXqIrK9v9sRzJILPz0wrhpURy1DmZ+aCbbHtlG7cxaFnx2Aa/c+wqzl86mZnKNKCCdoBaMFjmj2XEoyURghAvG+RhvtYwYp50G1QlSZS1dKuxz5Ej0yCdJEp/Hq6ORKVWTq2g+vZnyMeW528kI8dbv3uLYhmPM/+R8xi0bN9LNicrQMQ5ExJDBbMBgSTzGnWhofdlgjVRdS0qgoQF6Wkeur8SiZlqNEDC2dzDlfVPoO9zHgZfFpMqEsyfkv0EnIHv27OGRRx5hw/F6DOH09PQwderUYcutViuuGCmDvvWtb/GVr3wl9H9fXx/Nzc3Mnz+fsuMpkTTP3vHjx0cIJNryKVOmRI1WmDVr1jDvNYD5x73ehy5ftGhRyCt1wYIFGCSJqZddwVQp8kQ3HPfI+9+HP8zZN99MaXMz3Xu72cQmTCYTtQu/wNH1R1GBhvkNGE1GvANePD0e7PLfmdTQwKjjXhOxjmn7t77Fwi9/mcYlS4YdU+ndd9O0bBmywRBq+ynnnw/nn8/v7r6bi370I7zt7Rx99VWsO3YwbuxY2jo7WbRoEQaDAZvNxqJFi1gUVgdjqJfhlrvvpvXttwGoqamhqqoK5/3309vfz5QpwomhsbGRhoaGYW0PP6YDp5xCcNcumDo1ZKfdO3ey5Ec/oqamZpidemtqePAPf0AJBkN26tqxg3cdDmqP139oPu00RkkSVbNns9O+EyQI9B9DMtVisBiQPS4CB1bSte8R1KDKvu+Ka+qOG25g5sc+BnPnho4JYPVqiZ5WFzN4jKf5EQCTeIFuxtHNeFDh6FETHs9iABYuWoTxeBqIhoYGbB4PixYtivgNpkydStnYsfQsWUJ5Zyfl5eWhc8/Z2sra/n6WXHIJFpsNCZg9ezYVYfUt3kH0m0WTJwOwyW5nxowZgJgkGTduHIf27GHRokWhSf+SkhKqq6tDbdGWJ2OniLZnsT8NXa6de0MxGo3DIjCACDsV2zGVl5dHLD8Rj0mLwKiurqa2drAuXzEf04lop2jHpF3rTqRjCl9+oh5TbW1tRARGssfUXdnNTuNOAMpKy/C7/QR9QYw2I0aTEb/Pj81uY/bs2VROrMzrMZ2Idop2TGPHjqWjoyMiAiNbxxReiy4RuoBRIPQe6sXT78Ff48doTt4s3n4xUSEb5dAEhU76vPKzVzj82mFOvflUxp4xNvEX8oizTYgQQV+QoCeYdB5sV7sLNagim+SYk7fZphC8waMRXugyEAhg77ZTObEyqdQaDXMbWHT9orRqpEQj4A1QObES34APk8OU+AvRthGAgwfF39mMwFi5Eq64Irf1Sxx1Dux1dlxtLtq3tcetiaEEFVreasHsMDPrw7OomVZD0weamLhoYtJpUTThSYukGUoyETZ+pz9U/yAfEW9a+qiLL068rsEgxKXLLxcTsENtp6rwq1/lNmXbuGXjCnZyP1M6d3Ti6fZEzQ1fSISPce9l3F1u3B1ukKCkLjJ1oiTBd78L13xh+Pe0AJs77sh/ekOAmqliQrRzRycAm+/fDCqMPmU0lRNi1x3QSZ4bb7yR2267DWuUOgUWiwVPlNoJbrcbmy36/ZPFYgl58YZjNBqHXZ+0sPqhxCqIGGt5rOuetlySJPY+vZfN926if+udyPRFDc9XAwEcNTUYjcaIideed/+K1PEuSGbaVxuQTTKqoqIGVewzm6maNCl0HLGOSQ0EQFWHtdVgMDDheL0FgNVf/zr7nnoq1D7HqFE8cs45OOrrKRs7lopJkzDZ7UiyHLEto9HI4x/6EO3vvoshii1lg4GTvvnNiDaefccdkevEaHv48oU33sjrP/gBzWecgaWsjO0PPkjA5WLc2WcjHV8n3E7VkybRsHgxb/3sZ5zy7W8T9Pt57TvfYf4XvxiaHFhwww28cNNNnPuXh5AkiaDzKJ6W56Hk4xjMBuqW/wpZkvE5hZfppQ9dwd8mVfORdeuQtd8g7Lc9uv0IH+FjrOHLuBGTEfO4lw1cjR0XRvw4cdDWZgz9dkPtF81ORqORRV/+Mg+fdx5jli+nYdEiVJ+PF2+4gXmf/zzWsOLmhijnuyRJoWW26mqcR45QM21aKE3FUJtKMdqSjJ2Gtj0a6fanRMvD02Bl2vZCOSaItF8ybSz2Y8rGsRbaMZ2Idhqa6iva8ljrJ2p7IRxTusuL5Zi0NELhE+Dh67u73Bx6/RD2GjtNJzcNHofRELK3JEn0H+7H0+OhYnwFplpTaLnBOHwc1u2UvWOKdq3LxjGlkuFAFzAKBKNZqIeKX0m8chjh9S8KObVFsWAwG1AVNSQWJPUdgyHkFZYr1KCKp3vwodrb701awDCXmjnt66eFCh7lA80bPFYaqZH0cNVI1W4V4yqyJl4AmB1mLrjjgoy2cfiwqDdiNkNjVTv098Ve2VQG1trYnx8nn/VL6mbVsf+F/bRtaosrYBxdfxR3lxtLmYWmk5uQDFLKfS4bERiaYGy0GnMeodXfL+qPQHICBghR6eGHhf3C+54maOQ7p/9Q8jFWZgtnuzMUyeB3+uncKSaUJaNE156u91QkAxSX7QLeAM42J7IsU9ZUFjVl1vnni75y1VUiykxj1Cj4zW+yE2WWKs52J7JJTFp27e5i11O72PXULlCh+bRmnO3OlM+5YrJbPnj66adxuVxcdtllUT9vamrioOYVcBy3283AwAB1dXX5aGLGaDY/uPog3p4e+g/s5Ma+9sRfPE7AHcDXewSl5CowjsVcYaV8bDkBd4CAN8D7H7gCW3VV4g0leb+57PbbWXb77XHX2fyPf0Rd3rl1K5c9/TTl2Q5BDWPypZfSf+gQ9556KpIs42ho4IOPPhoSL4J+P49feSXn/v73OI57LF7w97/z3PXX8/eZM1EVhUmXXMLim28ObXPsOeew8KabePKqi3EeHUAyWiid+lEMpdNC6U0lImuzGSyWiN/0zZ/8hN2PPYbi9+Py21jHDaxHFCE34mEmj/Ek/8fZPEcJTlZxDqNG1dLmcERsx2A2YzBFOtEYzGbk48tqZs7kon//m+duuAFvTw+qojDzIx/hlO98J6JthiGFPQ0WS2gbAAtvuolnPvUpShobufDuu8V+o3xn6LJiQB9nixfddsWLbrviJJHdeg/2su4P6ygfWx4hYGiEapkqKkpAwef0YbKbkqqHqZMZhdLnikbA6Ovr45ZbbmH16tVIkoTD4eDWW2/lnHPOAWDbtm187nOfo7e3F0mS+O53v8uKkXgCTRODxQCqSAmVCvlMZ/JewFEnJgcGWgdS+p7P54vpnZcNPL0eVEXMKJePLU8ph7nZYWbs0vxGkxgM8OtfCy/+WIyUh2s4ubZbrtHSR82b3o78xtXg7Yy9sqUaTrs3oYiRz/ol9bPr2f/Cflo3tcZdb+8qkZ9x3PJxyEYZVVXpa+uja3MXalBl8oWTE+4rYQRGjYjGiVfoPlwwzjWrVomJ1YkTYdq05L+3YoUQl9asEYLFqFGwdSvccAN85ztw6aVCPMwVqqrid/pjXpOKoc85252svHplSPTyu/0MtAwgm2Qe+8RjgDiPVty7oihEjN1P72bPqj1MPG8ik86flPgLMSgG2/kGfDiPCQcEa7U1bn9esQIaG2H/fnEtCgbhZz8bOfFCO+d6D/Wi+BRWXr0SVVExOUw8e/OzaZ9zxWC3fLFv3z4OHz7MvHnzQsuOHTsGCHHjl7/8JV/72tcivvPyyy+HioQWCz6fj4pxFchGG0oQ/j5rVsx1T/vBD5h6+eURNXQUapD67wPJjK8XOluOR2cYJP7z/jtZ/JUvM+fTn47bhsopU3jqox/FXBo9dem4c89l+a9/ndTxyCbTsEl2gOqZM3nonHOiRmAAlI8bxwotF2MGLPjSl1jwpS9F/cxgMvHB//wnYpmtqoqLH3gg7jbnfOpTTLzkqlC/VxETM4qiEPQMPgtqdYu+PCQy6NTvfpdTv/tdQIxdPxkH0vEUknY6eYqf4KSOIMJ2jfUKS5fCmQORzzfTr756WNuuCEvNBTDmzDO59s03Yx7LJ3fsGLbs03v2DNtP+L4qJ00atu+Z113HzOuui7mfQkYfZ4sX3XbFi2674iSe3TRnwaHP2kPr/AV8AZSAgrfPi8EkrnPvlTp/I0kh9LmiETCuvPJKli1bxoYNG5BlmXXr1nHxxRfz+uuvU19fzyWXXMKf//xnli1bxrFjx1i2bBmTJk1izpw5I930uGhqoaIq+L1+vP1efE5f0ipiSUMJ8z4xD5MtvRQ0OpGU1IvJDq1mRDIEg0E2btzIokWLkk5nkyruLjdKQKGksQRziVkozoHkz5ORwHs8DbssgxIWWFRaCv/4x8hMEoWTjt3atrTh7nQz+qTRSUfA5BJNwJg+qU+IF7IFDFEuKkG3+Nzfl1DAyGf9krrZwqO1a1cXAU8g6m/q6fFw5K0jAEw8byJwPO/p6vW0/rsVR40jKQEjUQRGSUNJwoiYklElLL1laSiNVC4JTx+VauCUwRApLp1xBvzrX/DGGyI645FHstbMCJxtTh7/9OPIRpkrHr5iWMRXPsbKbODt8+LqdGG0GDHajAR8AWSjjLXCirXCSsAdwNXpwtvnLQoBw93lpmtnF2Wjy9IWMIrFdlpRP8kgUVJfItK+hRF+zVRVaGsTf196qYjIWLUKrr02b80NEX7OlY0WuX3dHW4kWaJ8bDkSUlrnXLHYLV9cf/31XH/99RHLfvCDHxAIBLjtttuEAOv38+9//5trrrmG/v5+vv/973NzmPd8oaPZfP7c+chGEw1n/T8u+fsl2Guii/ca4TV0Xvx+DQMtYqK7rLmMZd9bBqRWQ2f5L3/J8l/+MrODOc6Ma65hxjXXDFt+8f33Z2X7I0Wmtdk0wlNIAvQxmjf5jNjm8Uf9r30lMOJOQycq+jhbvOi2K1502xUnieymOQsOFSKGXi93P7WbbSu30XxaM/M+Pi/0nWJ4LitWCqXPFY070QsvvMDnP//5wZykCxeyYMEC1q1bx7PPPsv8+fNZtkzcYDc0NHDzzTfzt7/9bSSbHBdNRQx4A3h6PCheBSWo4OsX+U4D3kBSKqKjzsH0S6cz6YL0vSp1BtEiMFJJIZVLtPPEXmPHXmdHlmU8PZ7QK5nz5PCbhzny9pGQop0PgkH44Q/F3z/6kUiF84Xj+cbHjh158SJd1vx4Da/9/DUGjqUWoRONg68c5PHPPM7aP65Nexv794v3kEe9wQZGx/BXNFEjBvmsX+Koc2CvtaMGVTq2d0RdZ9+L+1CDKtVTqyOKQ9tG2ZAkCXenG0/v8JzlQ0kUgZEMllKRwqrplByGMCD6zxNPiL+TTR8VD1mGP/0JjEZR3+SxxzLfZjQsZRZURRV1elKMJixEjDYjZocZ1a8iG2VsVTbMDjNGW3E9KFVPFRXgtTRYJzK1M2qpn1OPo86Bt98bcb0ces0cGACtLvNHPyren3kmUnDPN0abkfLmcspGl2Ept2CvteOocRTdOVdMmEymUCFJSZL473//yz//+U9mz57NySefzJVXXskV8cJJCxSDyUBpk4h+6Nnfk9R3HLUOyprK8PX5MDvMmB0iPW3VxCqqJlbpEwM5wFHroHJCJTsf30nL2y1Y66xUTqxM+TdfsQL++c/hy+2lBhYthDOWFP81WUdHR0fnxEYTKKJF8jtqHaFrY/WUaswOMwaLQb9HeY9RNE9Ep5xyCr/61a+49dZbARHS/dprr/GHP/yB22+/PSReaCxbtow777wz6ra8Xi9e7+Bkbl+fyBsfCAQIBIR3nla8RFEUlLCnWW15MBgcVm092nKtUI22XQ17jZ1L/30p7m4xqda2vY3nf/g8oyaP4qzbzkIJKpjLzFgqLQQCAYxGI6qqEgwO3oBqRVSGtjHW8lwfk5YPLbyN8ZYX4jFZqi0oqsJA6wB+vz+kLsY7Jm1bwWAw68dkqbTwgX9+AF+fD0mWkGWZgbYB2ra04ah1UDOtBmuFFUetI+Yxbfj7BvoO97HsB8tomNeQFzvde6/Czp0y1dUq118fpLxcZtYsmd/9TmXzZomDBwM0No78uRd+XMnYyVppxdPnob+tn7IxZRmdewMdA/S19IUKs6ZzTHv2yIDM6NFiHRUi9ilJIAEqKqgqwWAAwooNRmv70qUyTU0qR46Aqg53/ZckldGj4dRTgyhK5naqmVFD67uteHo9w9Y3Go3UTK+heWkz9fPqQ4USAWSLjKPBwcDRAdp3tNO4sDFufzr926cz0DpAaVMpgUAgpp0kSQrZdaTGvddfh/Z2I+XlKkuXSlkZ92bPlvnylxV+/nOZL3xB5YwzgpSXZ/eYMIJslkUNgk4n5aPLI9oY3q5Cvj4FA1p/UlFUkV9VRcVkF5OcobEjEIwo3lmox1Q+vhxFVeg/0o+334vBNuiGm+z1SWuTts5IH5OGdu75PD4MJgOWSguX3ncpAadIxaKlXhQHJbZjKjFhqbSwa1cAMFJSonLeeRIOh0prq8S6dQHmz8/vMQUDwdB5BWBymKiZXiNy/KpKRL8J334y5174eJPtYxq6vBj5Tlguf4CxY8fyzDPPjFBrskvl+Er6DvbRs7+HxkWNSX2nZ39PRJSht8+Lqqp6nb0c4u50s+/5fSDDuK+PS3s7Q0uBPPAAVL8r07YRgj5dwNDR0dHRKWy09PiJnLi1zzXBQ+e9Q9EIGHfffTcXXnghb7zxBlOnTuXee+/lnnvuoampiZaWFs4999yI9Zubm9m7d2/Ubf30pz8NCSHhbNiwAYdDKHe1tbVMnDiRffv20d4+WPyuqamJpqYmdu7cSW9vb2j5hAkTqKurY/Pmzbjdg0Vgp02bRkVFBRs2bIh4eJ0zZw7WKiub924GwGPwoBpVAt4A1lFWNm3aBN3AAfGguHjxYnp7e9m+fXtoGzabjfE14zl26BitA62YSsTkSnl5OdOnT6elpYXDYcns83FMZrOZtWsjPcoXLVqEz+dj48aNoWXxjmnu3Ll0dHRE2C9fx7Tj4A6xvgqvv/A6i5YuSnhMqqrS29vLu+++y0knnZTVY2o71oaqCu9f7Zjeevgt9j6+l6qFVYyrH8eEygmUUhr1mMrLyjmy/QhKQGFP2x4Orz2ccztNmTKdH/wgCMhceeUhduxoCR3TrFleNm2y8uc/7+d97+sY0XOvr6+P3t5e1q9fjyRJSdmp29NNX08f777xLr5qX0bnXsu7LfT29BI0iuNI55g2b54BlKGq4vwJBAIMDPQgqUFUZAwGmTL1MAHJhtcXZNemTXhN3QnPvW99q5UbbqhHzGIMTlpIkoqqwg037GTDhu7s2GkxXHzjxfj9/ojlmp1MDSaMZxjppJPOtZ3YbDZmzpxJIBDAaXbS29PLuufWMVA6EPeYWvtbaXe2w47Ydtp/3378+/ws/cpSeit7hx1TsC3I2y++jaXegq3BlrSdhh5Tov70l780A6M57bQ+TKZyDh/Ozri3YsVm7rlnCocOWfn859u46y571sfyfl8/vh4fm9Zu4vTRp0f0J1VVcR13eS/k65PnqIjoCQaCOHucKBYFZHD73Viw4Pf7cblcbNq0CXu3vSiuuV6TF1vAxoENB+gyd4WWJ3t90q5zBw4cYPLkyQVxTNq5d3TPUZ686UlGXTiK6oXVoWNqa2sbfkxTpnP48GG2rN3CO++UAjOprvZjNps5+WQXL7zg4O9/byEYbMnrMbkOu3C5XPgNfiwlFgL+AANhueoVn4IJE93d3ezu3B3TThrauXfgwIGI61y2j2nDhg3oFB6awFQxroIDqw/Qva876e+WjS7j9G+djrvbzbo/rEPxKwS9wYJIm3mi0n+0HwB7rR2jOf3f+Z13Iv+fMwfadh53xNIFjJwy0kVNddJHt13xotuuOIlnt3gRGOFoNTJ0ASO/FEKfk9RwV6sCRlEUfve73/HlL3+ZYDDIVVddxZ133kltbS3nnHMO3/jGNyJEDEVRQl5pQ72GokVgNDc309nZSVlZGZB/z0kloNDyVgslDSVUTaqK2CfE9tpde9da9jyzh1lXz2LGlTNCywvZGzTRMY10VMkrP3kFg8XA3I/OpbS+dESP6eArB1n7+7VMvWQqM6+ciSzLtKxv4cXvvYi92s77//J+DAZDzGNydbj478f/i2yUueyBy5ANcnLeoEF45RWJo0ehqcnA6aerQHLHdP/9Bq69FqqrVXbtClJaOnhM3/mOyk9+IvHhDyv8619K0Z17b975Jnuf38vsq2cz68OzMjr31v95Pbue2MXMK2Yy76Pz0jqmsWMNtLRIrH1pBwtdV6GaKlANNqSu4xNltkYkdwuqGgDJQPC0B6F0KlKwH4N8/FjVsLZLMrKlAsVczSOPqHzlKzKHDw+On01NKr/6lcKll6oFYaetD2/l3X++S/OSZpZ8fUnGY8Sbv36TA6sPMP8T85lyyZRhx7T+z+vZ/th2pl82nTnXzcnJMSmKwrx5BrZskfjnPxWuuy5229M59556Ci6+2IAsq7zxBjidEocPBxk1Ck4/XcVgyOyYnvvac3Tu6mTpt5cy5rQxWRvL+4714e0dvG5rEWnBYBBUMJeZcdQ6snLude/pZuVVK7FUWEJRFwASEpIk4R0QqYkuu/8yKidWFsU1941fvcGhNYeYdfUspl8xffCYCuCam+wxOdudoajVECq8dvtrdO/rpnJSJRf+5kIMJkNSx/TIIxIf/rCBJUtUXnlF4ne/U/jCF2TOOEPl+eeDebVT955uHvnwI1grrFhKLKioEdv2O/14e71c9sBlVIyviHlMQ9uYazt1d3dTXV1Nb29v6P75vU5fXx/l5eUF8Zu0bmpl68NbGbVgFNMumZbSd1VV5aErHsJgNnDhby7MKP2iTnz2rNrDW//vLRrmN7D8h8vT3s6nPw1/+cvg/2vXguf5Vzm45iALPrOAqRdPzUJrdXR0dHR0csPqH66m5e0WTvrSSUw8d2LM9Tw9Hva9uA97tZ2xZ4zNYwt1ckEq985F405z7bXXsmvXLp577jnGjBnDrbfeypw5c3j77bexWCx4PJE50N1uNxaLJWrIs8ViwWIZHpZkNBqHFSTRHuqGEkt9irU8VqETbblqUKmYU0F5eXkohclQJEkath3fgE94j1fYkm57vo4pmeXRjileG/NxTGd+78yoy2Otr3mmlpeL/PzZPKb9L+zHP+BH8Smhdepm1mE0GfF0efB0eCgdVRrzmAaODiBLMqUNpZgtkUp2rGN67DEjN94IYc6cNDVJ3HmncVjtiqFtD4bVvvjqVyUqKyP3cf75Ej/5CTz3nHz8u8Rse7zl2Tj3AAYGBkJ9LtYxhS931DqQJRlvjze0TrrnXtAdRJbkUAhkqm0PBIy0tIi/x40zwlYRKyGFCU3YR4PBjNS/D4JejFt+IIp5S0aQJEQCqiFYqpFPu5crrqhlxQr485/h+uuhshL275eiHlc27KSqKpIiYTCLbfndftbfvZ4J50ygYmzFsHX7+vqonlyNLMn07usNtSGaPfpb+tmzag/lY8oZv3x8zLY7ah2irkaXO+oxefu9yJIcdbzNxrinqjIPPyyzZYtI/3X++fHPsXTOvfe/Hz70IXjgAYklS8DvBxDrNzWJQqBaP0/nmGyVNmRJxj/gH9bG8LEylWNytjt59LpHQ4XYo2GvtrPi3hWh/KeZjBFa8VQJCVmKfi2WJAmD0RDxvUK+5tZOq+XQmkN07epK6/o09DqX72Nytjv5zzX/GXYOuDpceHu9SLKEqqj4+/2Ya81JHZMWQNLQIMb/Cy8U6772moTLZUS7f86HnQxGQ+i8gkGxTCOUOk+WU7KfJElRr3O5PiadkSO8r9bPrqd+dn1a25EkiSsevAJJ1lNH5RqtrlpJQwk9PT3D+muyDI3AcDqheVEjtiobleMrs9BSnWiE9zk91VpxoduueNFtV5wkstuca+cw8byJoRTbsbBWWJl+6fS46+hkl0Lpc0VRxHv37t08+eSTPPfcc5x55plMmDCBu+++m/PPP5+77rqLpqYmDh48GPGdQ4cO0RSqbFv4BINBtm/fPsxDLxFanrhEYVY6uSNd2yXC1eHi6PqjAEw4d0JoudFipGpKFQBtm9vibqO/RYSll4wqSWqfK1fC5ZdHihcAR46I5StXxv/+/ffDzp1QXQ033DD881NPhZIS6OiAkc48kY7dbNUibZC7y51gzcRk2ncPHBDvDgdUadf4oBt8faAe95YNuMDgAMdYMNoh4AXnQfAcBckCporIl2wRAodf1AUyGOCqq8SmurvFw3Au2PXkLv77kf+y+YHNoWUH1xxkx3938MpPX4nwCIZB25WNFTOMA0cH8Lv8Mbffva+bbQ9vY/dTu2OuA2CrEvaNNVHu6z+el7M0fl7OdFi5UuSv1n5vVYWFCxP3uXTQghX9Q36yZPt5PKwVVkB4xgwl3bHS2+fF1enCaDFirbAOexktRlydrqyHEQfcAbz9Xrz9XnxOX+gVcKeX9z8YhJdegvvuE+9ZvmTEpXpqNfYae2gMS5VcXeeSJdo5ACIyQTbKlDaV4uv3pXQOHDsm3hsaxPuECTBpEgQC8OKL2T6C5Ai4AxHnWubn3MjaTSf/ZGLzoD/Ilge30LKuRTgV6OJFXtAEDFudLW3bBQKw+fgt1HGdGacTxp81ngWfWkDdrLpsNVdnCPo4W7zotitedNsVJ4nsVjmhkqZTmnDU6QW5C41C6XNFIWD09fXR2NgY8vzTmD17Nt3d3Zx22mmsXr064rPVq1dz2mmn5bOZGeM86GTHYzto3dSa9He0CTUtD5xOdlAVFZ/TN6Jt2Pv8XlChdlZtKMpCQ/Ooa9uUQMA4nld36PejEQzCjTeKidOhaMtuuin2pFtk9AWURtmlyQRnnSX+fvbZhE0qOLQJ7mwIGN7+4zkeHen13f37xfv48SCZy8BSDYoX/N2gBEANgr9HvAhC+SyY+DGQDKD4xXpGR+TLMHxys7wc6o87cO7cmVZTE2IwG/D0eCLO5z3P7gGEeBdL5beUWTjrx2dx6T2XRqT6GYq7U9gr0eRtIvuGRKcsj7eZCoepEAzCD34Q/bNk+nkiaqbV0Hx6M2VN2U+dYrQZMTvMw15GW3a9wC1lFuzVdgLeAH1H+ji24Rgd2zrw9Hjw9HgIeAPYq+0JC8yFowlUy5fD1VeL93HjciNQRaN6SjWX/P0STrrhpPzsMEdo54AkSyLC0ChTPqY8FHmTCq3Hb7U0AQPgggvE+9NPZ6GxKRB+zmnnWfgrnXNOR0fD2+cdnn4tCr0He9n4r428/svX89Cq4sbZ7qRrT1fMl7M9eY+PgaPHIzDqk3M2isbOneDxCKeWWbOOtzFHTic6Ojo6OjojTffeblrWtYz4nJ1OfimK2O+5c+dSWlrKr3/9a2688UZkWWbPnj38+c9/5q9//Svz5s3je9/7HqtXr2bZsmUcO3aMX/ziF9xzzz0j3fSU6NnUw7ENx5h2ybSkQ75DAoYegZE1Dqw5wOu/fJ36ufUsvzX9XLSZoKoqe1eJ4qMTzxue/69udh1bHthC2yZR5DvWBG/ooSiJCIw1a4ZPoEa2CQ4dEuudeebwzxNFX2icfz489hg88wx861sJm1VQVE2sYtHnFyUlCCXCVmXDUe8IeRKnyr594n38eMBaC6fdKyInOtfC7j9C2WSY/vXBL5jKxOeOuyDgFlEZSTJlipjs27EDFi1Kq7lxqZstPAO7dnUR8ARwtjvp3NGJJEuMP2t83O/Wz0k8VmoRFYlyeCeKsAmJTlkcbxMJh5IkBIVLLhERMZmSaT9PxMTzJkYds4oJR62DFfeuwNvnZffTu9n2yDaaTmli/ifnh9axlFmSnjTXBKqhNtYEqocfZlh6vmxzIoXXK0GFjm0dqIqKpdxC6ejSuBFYsdAiMOrDhpDzz4ff/lYIGFr/ywfh51wsUjnndHQ03v3Xu2x9cCtTL5nKgk8tiLtu9x5R7Fur7bPjsR0ceesIE8+bqOeYDsPZ7mTl1StTSmsYd3ttQmkoGVUCHem16d13xfucOYMORAMDEPAE8A34kE0y1vL07jd1dHR0dHRyjaqq7HpyF5ZSC82nNSMb4/vav/KzVxhoGeDs/zubupl6lOF7haIQMAwGA0888QTf+c53mDdvHgaDAbvdzu23386SJUsAeOyxx/j85z/PwMAAiqJw6623cvLJJ49wy5NHkiRK6krw4sXVHvuGeCiaR3AuUpq8V7GWW1GDKs7W5FyXJEnCZrNldYKobVMbzlYnJruJ5tOah31eM60G2Sjj6nTh7nRjr4k+MTv76tk0L2mmenJ1wn0ePZpc26Ktl0z0hcZ554n3116D/v746+aSdOxmr7Ez+cLJWdn/0m8tzej7moAxbtzxBdZa8erbLqIpHOOgdMhEsr9PRGDYm0BOfhJ+6lQxoZ2rCAxHnQN7rR1Xu4uO7R20rBPFPRoXN2KrHB41kartUo3A8HR5ogqDoRRSWfSCzrWgMJRM+nmmZGus7DnQg6qqlI4qxWjJzW2Mo9aBo9aB0SI8/qsmVVE1sSrl7eRboEqEqqr4Xf6UI79ycZ1LF9kgY6+14+3zUjmhMu02DU0hBaKPmc0iwm3XLiHe5gvtnMsmhWQ3nfww1OYlDcKBpWd/T8Lvdu3pAgiNdX1H+mh9t5Wa6TW5aWyREp7SLloEYMAdCKU1TKZPf/AfH2SgdQBbjQ2bM73+qtW/mDuXUH0fpxO2/3c7m/69iYkXTCz6CLxCRR9nixfddsWLbrviJJ7d/C4/6/6wDoArV16ZcFuWMgsDLQNZTyGsE51C6XNFIWAA1NTU8Mc//jHm53PnzuXVV1/NY4uyi8FgYNYps3j52ZdDnjiJCPqDBDwiL7KeQip7aDn3XO2uuNENGgaDgblz52a1DVr6nLHLxkadoDNajCy/bTkV4yriTkRVjKugYlxFUvscNSq5tu3dO/h3MCgmVh96SExuV1XFj74AkWN8wgSxnZdegosvTm6/2SYXdssnEREY4fh7xbspUQofBXy9Iu2UtSHumtok3o4dKTczKVwdLkpGldCzv4fdz+zm0KuH8Dl91M6opWtP1zDP43Dbubvc7Hh8B74BX8yH86QjMCptVE6sxFZtQ/EroYLiICZ+c1FzKN+CQrL9PNn1oqEqxyfJh/xO2epz7k43SkDJKN1Gsmi1PKyV6Xmu5lugisexd4/x6s9epaypjHNvPzel7+Z6vHS2O2M+gKiqyr5V+wj6BvOalY0ug6bMIkuiCRglJbB0KTz/vIjCyKeAkQuK/TqnkzpDba7dA/bs60l4TxsegQGDYr0+ORAdLaVdNALe5OvWyEZZjGmQdn/VIjDmzRMOQiAEDEOjuI8JevU88blCH2eLF912xYtuu+Iknt20ew2j1YjBlNirS79HyS+F0ueKRsA40VEUBbfkRlXV5CMwVJj3iXn4Bnxx87/rpIa9xg4SBH1BPD2eqB7g4SiKQkdHBzU1NchydsrKzPzQTKyVVsYvj50+J9uhckuXQlOTSGsSzVtY45Zb4K234KKL4LbbIifngkFYtSpxOpTzzoM//EHUwRgpASNdu3Xu7GTg2AB1s+pCHvsjQUwBwzEW6pdB2bTEG+nfJd4tNSDFvhxMnSrecxGBoaVi6N7XjavdxdF1YqZeMkis/tFqJEkalooh3HaqqrLt4W1IssTCTy+MEB00QhEYCexlMBu44I4Lon+owtLvLMU34MtqxFs+BIVwEvVzSRKfL00zQKjnQA9PffEprOVWLv3XpRGfZWOsVFUVJaAA0N/Sn1ZURCqEBIw0U72NZMTLUOw1dnz9Prr3dKMElISh2eHk4jqnES8dixJUcLW7CHgCSAYJe60QITMtLqyqgzUw6odkoTv/fCFgPPMMfOlLGe1mxMml3XQKk6E2rxhbAZJ4wPd0e2JeB5WgQs++HmAwAkOfHEiMz+mj91Av5c3ladc008ikv4ZHYGhihtNJ6J4oXADWyS76OFu86LYrXnTbFSfx7Jaqo6B2j6JlSNDJLYXS5/TeXiAoisKx/mOoqHj7vEl57hjMBqZfOp25180d8VCeEwnZKIc8tZOJhlEUhb1796IoStbaUN5czoJPLqByQmXa2+g/2s+Ox3fQtjl+oW8NgwHuvDP6Z5IkXpdeCkajqGHxuc8N9yzu60uu6PD554v3Z55Jqmk5IV27rf3jWl77+Wt07upMe9/uLjePf+ZxVn19VdrbiClg1C6B6V+FhrNjfznoFnUwkEENgLcbAk6xPAqaF/LOnfHFrXTQUjFYK6zIRhnZKGOwGHDUO7BV2jBajKFUDBrhtrNV2bCUW1AVNWqKDFVVk04hFQ9Jlmg6uYkJZ09IaeI3EUuXwujRcfYrQXNz+oLCUML7eazLxh13pJ/OyFJqARU8vSINVziZjpUBdwBvvxcloKAEFNydbnxOHwF38p6uqaIJGImE7FjkW6CKR2ljKeYSM0FfMKl0MuHk4jqnEZ6OxVphxVphxVIhHkqcx5wE3AEUv4LRYhT53J2+Ya9Uz4HubvAfL5sxVMDQCnm/+KIoilvM5NJuOoXJUJsbzAZKR4tcnfH6fd+hPoK+IEabMVQ3TauZoAsYsena04Wvz0f71va0vr9/9X5e//XrHH7jcNr99dgxIchKEsyeLQp5gy5g5At9nC1edNsVL7rtipN4dtPuNcxlSQoYpbqTRT4plD6nCxgFhNFmDEVSpFIHQyf7OOrF3X+ydTBGis33b2bV11cxcGxg2GftW9pZ/6f1bHlwS9LbW7FCFJR1DEnZ29Qklq9cCevWiRzh0dDmK2+6SURjxGL5cjFBumvX4ER8saB5L2qT4ung7fMycHSA/pb+tL7f3w+dx/WTYQJGPExlYKkWaaP8PSCpoATA1yn+V7zi8yHppyZMEPZyOqGlJa0mJ8RcZsZeY6esuYxRC0ZROb4Ss8McNb90OJIkhdJddO/tjrrO+/7wPs79+blJ55dXVRVVybJSEwODAc46K/pnmsCQiaAQDa2fRxNOfvGLzApKh+qDqNnziLGUWbBX2wl4A7i73CEBI+AR/we8AezV9qzWJtHINAJDi3iJRbYFqnhIkkTVFOFZ3bkzfQE2V2jpWAxmAwMtA/QfEeOjyWGidHQpFeMqCHpFZOTQV6rngJY+qrISLEO+MmsWNDaC2y1Se+noFDuhNFJxBAyt/oVWwBv0CIxkUHzHH+TTvGVo29TG/hf2h37/dNAiLiZPFvfvuoCho6Ojo1NspBuBod+jvLfQU0gVGPYaO/2H+nG2Oylrip/D3tPrwdnqxFZli1nEWSc9HHUO2re0J12PJFt07+1m6yNbmXTBJOpn1ydc/+j6o3Rs66B1U2uoUKOGNjmuedIly4oV8OMfw/r1ovjsBz8oJte0CdSuLvDFmZdMJqd7eTmceiq88opII/XZz6bUxBFFi86Jlu4kWbz9xz0M0qylsH+/eK+qgrKhw4S/DwwOkKPMeFtr4bR7xToA+/8NrS9B44XQfHzW2lQm1gvDbBZCye7dog5GvIiBdJGQqJ6SuNh8NConVHJs/bGoEwCSJKVUHHfD3zew64ldzPzQTGZeMTO03NnmpH1rO456B7XTa+NsITV6e+F//xN/V1YKz3CNpiYhXmQiKMRixQpROHrNGpG+6M9/Fh7na9bAV76S/nZlo4y51Iyv34en15MVUcFR62DFvSvw9nnp2t3Fqz8brHe19NtLqRhfMaxOSjZQVZWmU5twd7rTThdnMMA3vgFf/OLwz3IlUMWjeko1x9Yfo3NnJ5MvmpyfnaaI5tGMBKWjS7GUWfD2ejn/V+fHjaJK5RzQ0kc1RCn/I0kiSvDvfxdRguemVi5ER6fgqBxfyaFXDtG9L7rIDzBu2TgqJ1Si+Ac960LpGfr09AyxqJpcRecOIQirqEikFhHff/T4vXpD+jWdwutfgC5g6Ojo6OgUH5oQkWyqZku5LmC8F9EFjAJBkiTKy8sZ//nxGC1GypvLE36nZW0Lb97xJqMWjuLMH5yZ+0a+h6ibVYcSVEJh9/HQbJeNNF67n9nNwZcPgkpSAkbd7Do6tnXQtqmNiedOjPhMeygqbUx8DOH4fLB5s/j7S18S3vfhZCun+3nnjayAka7dQhEYXelHYIQ8DErTEzBipo8CePvzojj34t+BY8zwz621gwJF5XzofBsUH5ROHL5uGFOnCgFj587YEQP5YqjttHzdWgHSTJANMkFvcJh927e18/ovX6duTh1n/zhOeq4U+eUvhWgxfbrIYf3aa6LvjBoVKRzmAoNhUGScPx9mzoT//ldEWS1cmP52LeUWIWD0eCKuZZmMlZoI5epwReQZlwxSzupgSJIUszB8KmzbJt6t1siURLkUqGKhiYQdOzpS+l42r3OJCHrERFvN1BosZRZ8TjFe2qptWbO1FoExNH2UxgUXCAHj6adFVFKxkk+76RQG0WxeN7uOye+bTP2c2PeVslGmcnxk2lJLmQVtPj5RAfD3IgF3AIPVINIpKODp8mCwGFJKaadFUJeOKk27v4bXv4DoAkYqRcV1UkMfZ4sX3XbFi2674iSe3VKdH6mZVsO8T8xL6PStkx0Kpc/pAkaBYDAYmD59ekrfSTXMSicxznYn3j4vlRMrQ2lpwr26o3l4pmO78H1pBH1Bdj2xC7/LT830GpztzoTepPWz69n64FbaNrUNe7jUIjBKR6UmYGzZIkSMioroE+TZyul+/vnwve+JYqmBgKitkU/StZvmAZxJCqlM+64mYIwbN+QDVRmMrjAlYXdHs3h3HY6/HqIOxhNPiAiMkWao7bS+2rO/Z1hx4rbNbbSsbaFmeg1NJ8fJ5XOcWAKVlg4pmwW829rgV78Sf992m4h0iRW1lGumTYOrr4Z77oEf/AAefzz9bVkrrPQf7sfbG+kRk26fC2eol03f4b6MtpdrurvhH/8Qfz/2GPzmN+K3vfZasTxfkRcamoDRf7gfn9OXdNHZbNguWRrmNYj6KTm8P9YEjGgRGADnnAOyLK6Hhw6JNF/xCAYHo5nyIT4mSz7tplMYRLN57fTatCIHbdU2PvzfDyPJ+gRROFpaQ1eni4A3gCRJBANBnB3O0JiaTEo7JaCEUgaXNJSk3V/jRWA46hxMPH8ijrrsRijqDKKPs8WLbrviRbddcRLPbuPOHEfl+Mqka1aWN5cn5fStkx0Kpc/pAkaBoCgKLS0tNDY2Jl3VXZtQ0wWM7OBsd7Ly6pVxUwPZq+2suHdFhLCQju2i7cvX78PZ5kQ2yrz0/Zei7msoNdNqkI0yrg4XzlZnKARdVVUGjgqvrlRTSK1bJ94XLIhe5FfL6X7kSPSCzpIkPk+U033hQpECqasL3noLTjstpWZmTDp2g8EUUtmIwEh3MjxmBIa/f9AoxiQEDNvxXFCeY6IWhhz7kjB1qnjfuTO1tuaCobYraSgR9YMkcHW4IlIxtG1uY9sj25hw7oTUBIwhAlUo7VeaUTPR+OlPxQTDokVw6aVZ22zafO97cN99IqXVm2/CySentx2t8KtWP0Ij3T4XzoRzJtB8WjPbHtnG1oe25lTACPqCqIqK0Zr+rdKf/wwuF8yZIybFt2wRAobbPTIT3NZyK2POGIOj1oESSL4IWzZslwq59u5JJGBUVcFJJ8Ebb4gowU9+Mva2Vq4U6RYPh+nATU1w5535ja6JRr7tpjPypGPzgWMDbH5gM7XTa5l43mA0piRJORUSi5XwtIa7n97Ntke24ahzMOOKGTTME4NKMintnG1OUEWUhLXSmpbt3O5Bx5JoERjlzeWc9IXMIwl1YqOPs8WLbrviRbddcRLPbqWjSlN2vNXJH4XS5/TeXiAoisLhw4cZaBtg+6Pb2fF4YjfnXEyovZfx9nlxdbowWoxYK6xYyi2YHWYsFRasFVaMFiOuTtcwD2DNdoqS/GTQ0H1ZK6wE/AFko4yjwRFzX0MxWo1UTRYpNVo3tUZsP+AOgAQl9akJGOvXi/dYKWQMBjExA8MFjlRyuhsMYkIPRJ7xfJOO3aCwIjCGCxhh0RdxxIgQlhqY8Q1Y8GuQ4htsyhTxnqsIjIA7gM/pG/aKlophqO0kSeJ9v38fl9132bA80ppImKw3R8i+OY7AOHgQ7rpL/P2Tn0QXC/PN5Mlw3XXi7+9/P/3t1M6sZczSMTjqIydv0u1z4UiShNlhpnam8CYeGuWRTQ6sOcBDVzzE6h+tTuv7fr+IuAC46SZh42nTxP/bt2enjemw5GtLmPexeSGhKRmyYbtEpDIGZIpWAyNWCikQaaRApJGKxcqVcPnlkeIFCIH/8svF5yNJPuymU1jEsnnAE6BjR0coOjeczp2d7HtuH3tW7clXM4seR62DygmV7HlmD2aHmffd9T5mXDaDqolVVE2sSqoej5bq1dHgQJKktPrrli0iAqymBhobj7ctTMDQyT36OFu86LYrXnTbFSfZtJuqqnTu7KRlXQtKUD8Pck2h9DldwCgwXO0uNvxlA9v/m3h2Q08hlRuMNiMmu4mO7R107uzEaDZidpgx2rIfsGS0iW3LBpmgO4hskilrKktpX3Wz6wDhaa6hPaDaa+yh/LfJokVgxMuBv2IFPPzw8GLOTU1iebJep+efL96ffTalJo4ojloHiz6/iFO+fIpIc5IGRqsRR70j7cLAsQWMHvFuSjIXpCRB3elQMi7hDLoWgbFvX/wi7qmipWIIeAN4ejzDXgFvIKlUDLYqW1SvbU1o0iJnEqGt5+n2RNg307olQ/nRj8TveOaZg0JeIfDd74p0bs88A6++mnj9aEy9eCpLvr6E0YtzUO39OPWz67nsvss45/9y9+N5ukUESbrX2JUrxcR2XR1cdZVYpgkYu3aJ1Hk6kWPAQOsA7dva6d7TnfIYkAqJIjBg8Pq0alV0WwWDIvIi2mVAW3bTTWI9HZ2R5p1/vMOqr65i9zO7h33WtVukSo1WY+adu9/h+e88T/u29py3sdjwO/1wvK+nc2/g6faAlHqq13C09FFz5w7exoULGKqq4nP6hkVE6ujo6OjoFBIHXznI/tX78fQmeb1S4dmvPsvqH6wOORrqnPjoKaQKDHvt8fQ0nW5URY2bdzYXOdmzTaHmhU6EJEkYjAaCviABTwCDKbeN7jsiPOctZRaMZiM+f/KDcP3senY/tTsizUnl+ErO/fm5+N3+lNrh9w8+DC1YEH/dFSvgkksys+9554n3t94SueIrK+OvXwgYrUYmXzg5o23MvGImM6+YmdZ3VRX27xd/DxMwfL3i3ZT9fJCjRkFJCQwMwJ49ouh0NghPxRCLZFIxxCLVCAxrhfBKVwIKvn5faNI0FPGWBcF4505RIBjgxz8ujOgLjQkT4OMfF6mPvv99eO65kW5RJFsf3spA6wATz5tI9eTqnO5Lm/DRzolU+fWvxfv114sC3gBjxgwW896/HyZNykJD08A34KNrTxf1c+pHvBhb+BjQsraFdX9cR9WUKpZ8bUlonUzGgGgkI2AsXiyuSd3d0dMcrlkzPPIiHFUV9TPWrBm52jY6OhoV4yoAUStqKFqtN62eVMRnu7to29jGwLGBtOponMho9wVGqxGDyYASUJBkKemaIRPOmcDYM8YS8KSvZmsFvLX6FxApYLg73Tz68UeRjTIf+s+H0t6Pjo6Ojo5OLnn3n+8ycHSAc24/J6kocUmWMJeY8fX78PZ5035e0yku9AiMAkGWZWpra3HUOJAMEmpQTZhjP9sewdlm5UpRZHj5clEcdvly8f9Ip1RIFu13jVcTAwZtl0kuuJKGEiSDRGlj6l5Y9XPqWfHvFSy+fnFomdFqpGZaDaPmJ1lx+zjbtoHXC2VlMHFi4vUNBjExc9VV4j1VcaqpCWbMAEURxbzzSTbsNhJ0dUH/8QwQw4p4aymkzBXJb9B1BA79B47FN4AkDaaRynYdDEetI5R2Idpr6MRlNNt5+7288rNXeOpLT0VETqQagSEbZern1TP65NEE/YOu09kUjL//fSHuvv/9+a/9kgzf+Q6YTKJPrk4vexKqIrw+w8lGnzvy1hH2PL0HV0f8cTkbZCJgvPGGqCNiNgsBQ0OWB6OZRiqNVNAf5D/X/YcXb3kxVEA2EbkeL7UxwFIqUjdWjK2IOwZkipZCKp6AYTAMiuzR0kgdPZrcvpJdLxcU63VOJ31i2bxifAUAPft6Iparqkr3nm4gegRGSMRPkNL0vUioFmGpmee+9RwPXfFQVIEoHgazIfQbp9NfwyMwNMIFDNkktqUEFFQlvahhnfjo42zxotuueNFtV5zEs5t2n5GKs6B+j5I/CqXP6T2+QJBlmYkTJ2IwGkKews72+MlLJ104iRlXzqCsKcl0MXlkpPJCB4Pw0kuiEO1LL2WWusFRJ54A3B3uuHn1NNsN7czOdidde7qiv3Z3hQQoALPDTMO8hrQmRyVZypoHrVb/Yv58MdGWD7QJonzXwYhlt2To3tvNgZcP5LSAcCy09FGjRg16dYewNUD9mVA+K/kNDuyBPX+Do4kNkOs6GMkSzXYmu4mWt1ro2dcTKmCvBJRQjYRkIzAAzvrRWZxxyxkRosfcj8zl5JtOpmrS8AmeZNDGpv/7P7j/frHsttvS2lTOGTsWPvUp8fd3vwsvvpjamNqxvYP7P3g/z3wl8pzKpM9paDeollILe5/fy4vffzFnedvd3UL8SkfA0KIvrrlmeJ2Fka6DYTAZKB8norQ6dnQk9Z1s2C4Zwu2bK4JBaDuecTFeDQwYTCP14IPD+0A88SOcUan5EWSVfNlNp3CIZfOKsRUgibRF4ekZnK1O/E4/slGmfMzw6E19ciA2EbUIVXHP0XOgJ+3tpdpfVXVQwIgVgWG0DEZnhztl6GQPfZwtXnTbFS+67YqTWHZTFVWkZYSUUsbq9yj5o1D6nN7jCwRFUdizZw+KooQmzhN5Rk48dyJzr5ubUe7UXJCrvNCJxIlsR3yYS0XdC1VR43r7httOw9nuZOXVK3noioeGve6/5H4evuphuvd2hx5+AGRDZt1RVdXQ9rY/up1dT+5KOedtMvUvsk14HYw0S0qkRTS7JcvWR7by2s9fo2VtS1r7fv47z/PMV56h92BvSt8LBuF//xN/V1ZG6UNVC2D6zdB0cfIbtTeJd1ecXCjH0TzHsx2BkSrRbCcb5NCkbPde4U2qRbHJRjnj/Pn1c+qZcPYE7DXJRXKEEz42fetbYpnNJlJxFSrf/raohbFmDZx1VmpjqjaZM7TAdiZ9TiM0wV1uYeDYAMfWH6NzR2fa24uHNn7aKlOrVXPwIDzyiPj7xhuHfz7SAgZA9RSRfqtzZ3K/XTZslwwh+2ax3sVQOjpE1J8kQW2CjDjaGLtjR2Qf+POf4Re/iP9dSYLmZpFacaTIl910CodYNjdajZQ0lACRaaS09FEV4yuQjcPvQ/XJgdiER2aWjxX3H8lGYKiqygvffYE37ngj5NCUan/dvx/6+kSkn3ZdgUEBw++HYNijftCnCxi5QB9nixfddsWLbrviJJbdhjr2Jot+j5I/CqXP6QJGgaAoCu3t7ULAOJ4qwdkWPwKjUEklL3SyJBInshnxEXAH8Dl9+J1+LGUWlIBC35G+mPUkwm2n4e3z4up0YbQYsVZYsVZYsVSIbQ20DqAGxEy9b8CHzzn8FXCnlg+392Avj33iMZ6+8WlUVWXL/VtY+/u1KQsYWgRGovoX2eSMM8TD18GD8KtfZR45kyzR7JYsmmd+ovRisejZ20PXri5IIXBG6wO33ir+37o1SynZbI3i3d8/mIIqBoUSgRHLdlr6C21Cxl5j5wN/+wDn/vzclKOUVFVFCWR+gY41Nnk8uY1Gy5S33opeuDiZMVXLW+p3+iM8PjPpcwBKUAnd4FrKLKHow1xFQnl7xM1wqhEYv/2tGMPOOisyrYdGIQgYNVNrAJIWfzK1XbLkQ8DQ6l/U1gqRLhYrV8JnPjN8+eHDYvmTTw5+f+jwov1/xx0jW/crX3bTKRzi2TyURipskt3ZKp41otW/gMG+qBfIHE54BIZWY6T3QHKOKd5eL63vtLLvhX0YLGKQSLW/avUvZs4UaR81HGEZ99weGckgBiRdwMgN+jhbvOi2K1502xUnseymXU9NdlNUZ4pY6AJG/iiUPqcLGAWIVsg7XgqpoD9I585O+o/256tZSZPtvNCJxImHHspOxIelzIK92k7AG8DT48HT40GSJRHSNuDH2+fFXm1PaWLFaDNidpgxmA30H+nH2epElmWsFVaMNiOSLIX2Ff4KeAMp7ctR58Dd7cbV7qJ7T3dokk/ztkuGYHDwYSifERhPPz042fPVrxZHrRRblfDITlSnJhqqOlgbINkcj0kLdP5+UFIsBmmwgvW4G7LrSNxVCyUCIxaVE8QEjJbPW5IlkVs/xbRP2x/dzkOXP8S6P4uQpKAvyP6X9tOyriWivkYichWNlmu0dkcjmXabHKbQhMnQKIxM8A344Pj+zSXmnAoYqqrSdFoTjSc1ppR+bGAA/vQn8fdNN0VfpxAEjOqpIgKje093VoS6bJEPAUOrfxEvfVS8vqthNgvR/5FHYPToyM+qquDhh2HFiszbq6OTLUKFvMPqYExfMZ3LH7icOdfMifodfXIgNmPPGMs5PzuHmVfOTFnA0J7f7NV2DKb0VM5o9S9AjE2acOp0ijoboAsYOjo6OjqFiTZ3ZSoxJVgzEv0e5b1HHN8znZFi4nkTaTqlKe7ks6vDxbM3P4vRauSKh67IY+sSk2y+52TWSzQBKEnw6U9Db5znhfCIjzPPjL2eo9bBintXDBsAj7x1hNLGUsqayrCUWVIuJqoEFdq3thP0BpFkibLmMkwlJqztVi6484JQ2PlQUtmX0Wqkeko1Hds62POsyEtjq7JhtCbfxXfsAJdLeG5Nnpz01zJCm5gfal9tYr5QJ4C0CU2tQHQq+J3+iEnYRCTTB266CS65BAzvfAOch2DeT6BiCietTQABAABJREFUdvKNsjeBp12kkSqfHnM17bxoa4OeHqioSH4X+UDzIO3e042qqmnXhjFajAR9wZB9XZ0uXv/l6ymPt6lEo8Ubm/JNpu2WJAlruRV3lxtPryettFvRCC/uJhtkykYLAcPb68Xb781q3QRJkjjphpNS/t7dd4vr0aRJ8L73RV9Hi2Tq7BTpjGpqMmhompQ2lmJymPA7/fQe7A2JfyONFrGTjwiMeDUsEvUBAJ9P2HDFCjH+rlkj6to8/zx84hOFee3SeW8zevFoTHYTtTMic6eZ7CZM9uiTBpZSC0joBaCjYC23hiIONccUV4cL34Av4f3dwDFRq6tkVPKORkPRnI7C61+AuC90OER6KU3ACLgDuoCho6Ojo1OQpOvA1LioEUu5JZQaV+fERxcwCgRZlmlqakKWZUrqSyipj39Dq4Vym0uTzxGXL5YuhaYmMQkdbdJVksTnyeSFTmYiLZ54EU4yER+OWscw0UBLSxOLcNtFw9nuJOgNYrAYqJ5Sjclmwuf0IZtkyseWJ9x+stTNrosQMEoaU3so0upfzJ+fn5QXKU3M56A9iewWj0wiMDQPA4PFkJTXXUqTyebjnujGFOvi2Juga0PCOhhlZWLS79gxEYVxUurzu1khlu0qxlYgyRLePi/uTjftW9vp3ttN4+JG6mbWJb39ofbVbJZsxIxGtqPR8kU22m2psAgBIyyNXSZ9DsIEjOPXPaPViL3GjqvDRf+RfizTcjfpHY9gUPS/I0fgpz8Vy268EWIdpt0uCqUfOCCiME4/PX9t1ZAkiarJVbS+00rHjo6EAkamtkuWc356jogISU97TIpkBIxU+4DBIMS8HTuEgKF5Ro80+bKbTuEQz+ZVk6pSjkisn1PPhx/9cNoOAe8VzA5z6HrUe7B3mEg0lJCAEeaslmp/jRWBAZECxpilYwh4AphsqXm26iSHPs4WL7rtihfddsVJLLtVT65m6S1LU0ofBVA3q466Wck/4+ukT6H0OV3AKBC0EyJZwvOuFhoGA9x5p/Cgj0WyeaGzObGXbGRIPJSAMmxgTWS7kvoSZFnGYDHk7OHB2e7EWmENeYABGEyGUC2AZKI58l3Ae6S901Ptc+FoNTDcne6UPf1DfTfJyfCkJ9JaFBh9XMAwlSXdHgBsqRXyLhQBYygGs4HKiZWoiihof/jNwxx8+SCWcktKAoa1UnhUhgSMNAXjbEaj5ZNstFvzSg1PIZVJnwOonVHLZfdfRsAzmCattKkUV4eLviN91EzLXihD0BdECSoYrca4/XvlSiFWhI9lkgSVCQIapk0bWQHD2e6kYW4DleMrsVXZQtcKjaHXjExtlwqpPrykipZCKp6AkW4fmD9fvG/YMCjEjyT5tJtOYZCKzVvWtbD1oa2MPnk00y+NHn0pybpwEYv9L+3HN+CjcVEjJQ0lNJ7UiLfPG0rZFI+Bo8MjMFKxXU+PKOINsQUMEALG6Z9dlNQ2ddJDH2eLF912xYtuu+Iklt2sFVaaTtbtWcgUSp/TBYwCIRgMsnPnTqZMmYIsy+x4bAeudhezr54dNaw7XY/gfLFiBfzrX3DttZHLTSa4//7kUyskO4lQWytScWQa8RELT6+HDX/dQMf2Dt73+/chGwYnWMJtZ4iiykiShKMutbRTqeBsd7Ly6pU4252iMOPx36BnXw87/yeKFdir7ay4d0VcESPfBbxH2js9kd3ioXnoB31B/E5/Sv3Q7xTF4JOdDE+2DzQ19A92gFQFjNpToWwq2EcnXHXKFFi9emQLecez3Xm/PC804awJEJrglCza+p5uT0gMgdQFDC0aLZZQl42xKRdkI4qufk495lJzRPqoTPqc2K+E2WHG7Bi0Q9noMrp3d0eIGtngwJoDvHnHmzQubmTZ95ZFXSdWCjxVheuuA5st9rVu2jR45pmRqYOhXTNcna6Y6wy9ZmRqu0JCi8CIVwMj3T4we7Zwzmhvh5aW4bUx8s2JZDed5Ehk8/6Wfjp2dFAxtoKO7R20b2lPqV6aziA7HttB164uHHUOShpKWHz94qS/Gy0CI5X+unGjeB8zJrpgHi5g6OQWfZwtXnTbFS+67YqTbNst6AvSc6CHgCdA/ew4N/Y6GVMofU6PuSoQVFWlt7c35M299cGt7Hh0B8626HeemkdwNnN+Z5sJE8R7dTX8/vcinYbfDzNmJL8NbRIhFpIEzc1w112D/w/9HJKP+IiFyW7i6LqjDBwdoOXtlojPwm0XjhJQ8Lv8+Jy+Ya+AO3uTbd4+L65OFyabCUuZBdkoIxtlbFU2USzcYsTV6Ypb3EhRhMco5C8CY6S902PZLRkMZgMnfekkzvjeGUl52g3dr73OnnR9E60PxPLk1frAaYu16IsSkFPUps2VUDpRFPROQCEU8o5nu3Bvea2GRSpFmAEs5YM5vz09nrTHWy0aLRrZGptyQXi70x1TZ1w+gyVfW0L9nMGbyUz6XCwWfGoBK+5dwZT3TcnaNkGIVxBbtEqmyHO8QucjWchbu2YYLUasFdZhr2jXjFzYbiieHg8vfu9F3rjjjZztA5JLIZVuH7DZBm2rXVNHknzYTaewSGTzbf/Zxhu/eoODrxyke083MFg/KhZv3PEGz3/neVwdsUXP9yKZRMNr9SjCBYxU+mus+hca4QKGElTwu/0iPZ9O1tHH2eJFt13xotuuOIllt7bNbexfvZ/+lv6Utudsd/LsV55lzW1rstlMnSgUSp/TBYwCxV4rvFZjChgDhVsDQ2PTJvG+eDF87nNw4YXi/3vuSX4bBoOYJIhG+CSCVvB5qLdjWVl2CkEbTAYmnCcUmZ1PJJ65NZea8fZ66drVxcCxATw9nohXwBvAXm3PaqFSo81IyagSHPUOKidWYq+zY3aYMdoST2bv2gUDA2LyRZugzjXJTswXmne6xsRzJzJ68eiUBYxR80dxyV8vienVPZSkJ8GDx4vBmKIXhc8WWgHikYzASAYloIQEjFQjMGSDjK1ysA5GJuPtySdHX97UVLhF6kG0K9qY2tg4cu3e98I+3vrdW7SsGxSRZaOck9zsWu0Oa0V0US+VFHjRGEkBQ8NoMyLJEj6nD0kejG5J5pqRC9xdbo5tOMbRdbktCpOMgAGx+0CivhueRkpHp9CoHC/Eiu593SEBI1FdjNZNrbRtbEur7teJTDTnBlVVGTg2kPAB/4I7LuDKR65MWH8oFvHqX0CkgPHi917k4Ssf5tBrh9Lal46Ojo6OTi7Z9dQuXv/F6xx5+0hK39Pm0vwuXaR/r6CnkCpQHHUOuvd042yPLmCkmkd/JNDCm2fPFu/XXgtPPAH//jf88IexC5wOJZYHflOTmLjVJhFWrBAFn9esgfvugz/9SaSWuvTSjA4jxOQLJ7PtkW20vtNKf0s/pY2xCyV3bu+kemo1skFm+W3Lo06CJVOXIlVK6kogjTpGWv2LefPAmKdRIbxWiiRF92QuRO/0kWDFCvj2t+HHP45cHtEH2jMUMNrWQO9mqFsG5bHDpDSBa9cuEblTaLXTlIDCs199NjQxA4Mpv1Khfl49fqcf2ShnNN7ed594X7IEbrtNpEQbNUoIc4V+boePqddcI1Li/Pa38MEPJvd9JagQ8AQiUj5lQuvGVvY9v4+S+hIaFzZmZZuxSCRgZJoCTxMw9u0DjwesiYOfckJ/Sz/uTjfWSiuVEyoj0iPmm1A/K8vtfY1WAyNeCimN8D6QbN+dP184augChk4hUjG+AoBjG46hBlWQBkWNWFhKLbja4kfyvtdQgsqwlKBKUGHlNSvxO/1c8vdLIlIoRiNVB5hwUonAMJjEfrSoDx0dHR0dnUIi3WwHZocZJEAVzxGaA6LOiYsuYBQIsiwzYcKEUFX3RBEYoxaMwmg1FnSuNy0CQxMwPvABKC0VRedefTV5z/rf/la8f+xj8NGPxp9EMBhEwedFi4RQsnu32Fc2iqQ66hw0Lmqk5e0Wdj21iwWfFMUihtou6Avyzj/ewWgxMvva2TmfaMsG+a5/oaF5uA4tgguihkouvbyH2i1V+g730bW7i5KGkqwWD45FR4d4v/hiuOqqKH3AXAX1y8GW5vnW+Sa0rgZLbVwBY/x4IXK5XCI/fHNzervLhHi2k40yfrc/9L+51JzWJMGpXz419PeEsydQOaGS8jGpi0NaxNl11+WmGH2u0cbU979fiMKvvpqcgHF0/VFe+sFLVE6s5IJfXwBk3ue0yTNLeeTN7as/f5Wu3V0s+94yykanWP8lBu7u4+nHYtwIZ5oCr74eysuht1dcp2bNSqeVmWOrtuHudOPp9tC2qY3yseVR+0umtkuGkH2zGJk4FJ8POjvF34kiMDS0PpAshRSBkQ+76RQW8WzubHeiBBR8Tl9oWcmoEvqOiBSUsRxrtD6pCxiDaOIFDDo3yAaRvtXv9NNzoCehgDGUZPtrIABbtoi/k4nAMFh0ASOX6ONs8aLbrnjRbVecxLJbuikZJVnCUmrB2+fF26cLGLmkUPqcLmAUCLIsU1c36DqvFX12tUfPN9u4sLGgJ8ZVdVDAmDNHvNvtcNll8I9/iEm9ZASMo0fhoYfE31/60uDEQCJKSuDKK+Hvf4e//S07AgbApAsn0fJ2C/ue28fc6+ZiMBuG2U4rwG6vsTPtg9Oys+Mco0Vg5Kv+RTjhHq4tLfD1r8cunJpNhtotVfa/tJ8tD2xh0kWTUhIw3v3XuxzbcIypl0xl3LJxSX1HUeDxx8Xf118/mI4tgvLp4pUutuPFZlxx8uIAJpOob7Nzp3iNlIARzXbOdifePi/WCitdu7oAsFRa6Npz/O80o54qJ1SmleZh0yaR5sFkgiuuSPnrBcXSpULAePnl5NY3l5qFN0zP4IRXpn0u1gR33+E+BloG6DvclzUBI1EERqaFziVJRGG8+aZIIzViAkaFjZrpNfTsEwXwunZ1YS4xYyox4e5000VXaF0jRnr6e0L/ZzuKMB8CRlubeDcaoSp+1py00Tyi9++H7u7oBXbzRaZ9Tqf4iHd9XHn1SlydLnoP9IZSLZhLzDx0hbjRtlfbWXHvimH9OiRg9OsChob2W5jspojItfKx5fQd6qP3QG/M57Q9q/aw74V9jFs2jkkXTAotT7a/7tgBXq9wChs/Pvo6EQKGQxcwcok+zhYvuu2KF912xUksu2VS39dcZg4JGDq5o1D6nC5ZFgjBYJB3332X4PGKn9rDQ6wIjELnyBHx4G4wDKbKAOGFDPDggyJtRiL+9CfhabRkSfLihcYnPjG4r4GB1L4bi8aFjdjr7PgGfBxYcwCItJ2nx8OWB4Vb1JyPzMFoKXyNUFFGLgJDQ/Nwvfpq+NSnxDIt9U6uGNrnUkVLS6TVWUiWvkN9dO3qivDeS8S6dULcKSmBs85KaXfJY09OwICRr4MRzXba5MxDVzzE1ge3cnTdUdq2tHFs/TEeuuIhHrriIVZevTJmWr5oqKqa0QP/v/8t3t/3vtxNluaLM84Q7+vWJTeeWsvFxL+n1xPKBZ5pnwtNcA+5uS1rEqJF/5HUCr/F3ddx4SWWgJGNAu2FUAcDxO9ZN6uOksYSkIR407u/lydueCLUdx68/EH++f5/8uDlD6bdnxKRDwEjPH1UrhyIKith3Djxt5bmZaTItM/pFB+xbO7t8+LqdGG0GLGUW5CNMrJRRAxYK6wYLUZcndHTROkRGMPRJluGeotWjKsAoGd/T8zvdu/ppn1z+7BnvGT7qzauzJkTexzTU0jlD32cLV502xUvuu2Kk1h2i3VNTQb9HiU/FEqf0wWMAkFVVdxud2iiR0shFSsCo2d/D/1H+wu2WI0WfTFtGljC5iLOPFN4pfb0wJNPxt+Gzwd/+IP4+wtfSL0NS5bA5Mni5l2L4sgUSZaY9eFZzP3oXBoXCc+qcNtt/PdGAu4AVZOrGHfmuOzsNEkC7gA+p2/YK+AOxP3e3r3Q1yfsNCN25qC8cdVV4v3ZZwfTJuWCoX0uVWzVg0WeUyFUEDqFegqPPSbezz8/sj9FEHCCEt/WcdEEDHfi8BetDsbOxPXsc0I024VPzthqbGJyxiDjqHMknJyJxoGXD/DQ5Q+x5qdrOPzmYVrWtuB3JS86KcqggHHttSkdXkEyZgyMHQvBILz+euL1tTRPil8JjUGZ9rlYE9yagNF3uC+t7Q5FVVWaTmti9MmjQ/08GloKPMeQIIRkC7SPtIARfs3wu/3YKm1UjK9ANstIRgm/y4/RYsRaYcVaYcVgN4T+TrU/JUM+BAytgHcy9S8yoVDSSGXa53SKj0Q2N9pEn5aNMiWjSigfW47ZYcZoi+1wo08ODKd8bDnn/OwcTrnplIjlIQHjQE/M7w4cE14AJQ0lEcuT7a9aAe9Y9S9ATyGVT/RxtnjRbVe86LYrTqLZTQkooWfsdCIwtHsUTQTRyQ2F0ud0AaNAqRhbwXm/Oo8L7rwg6ucv3PIC//vM/7I2YZNthhbw1pBl4WUPosZBPFauFJMNo0alVwtBkgajMP72t9S/H4uJ505kxuUzQh7GGqqq4u0VD3fzPzkfSXPDzTGWMgv2ajsBbwBPj2fYK+ANYK+2x5wU0qIv5s4VqW5GmqlTRSRIICAmAQuVdCMwMhEwLrkkzkqbfwwvXwptr6TUnhC2RtFp/APgjz+ujHQERjyMNiP2ajuyUUYNqhitxoSTM9Ew2U0EfUHcXW7evPNNVt+6GldHdEE5Gi+/LOq6lJeLCIwTAS0KY82axOsaLUaMVvGbe3qTCLdLgBIYLJiaawFDkiROuuEkzrjljIQ30itWDOYgv+EGePFFUZg7mWvWSAkY8a4ZQW+QkoYSqiZXIRtkjDYjJpsJv8uPbJMxOUxp9adkCHqDofblCk3ASLb+RboUioChoxMNW5WNqilVlDWVJXWvaimzgCQEaR2ByWaidkYtdbMi0ylotbL6DvWhBKP/Xv1HRbTgUAEjWbQIjFj1L2CIgGHWBQwdHR0dncIkvC6XyZH6ZNT45eOZ/8n5ealJqjPyFH5+m/coBrOB6snVUT9TVTWtSdB8MrSAdzjXXQe33w5PPAFdXbFTq2jFuz/7WTCneZgf+Qh85zvwyivCW1ybeM0FkiSx9NtL6dnfE/LAygeOWgcr7l0R1zMuXq5yrf7FSKWPisZVVwlh5d574XOfG+nWRMdeLaKk3N1ulKASkQM5HqmGSO7bJwRBWYaLLoqzor9XvJtKk9ruMAxmsNaBuxVch8Acu2D1SEdgJEJLlwDCPo6a1PP0hwtU6Yy3WvHuK64Aa/QsREXH0qVCeE62DoalwkLgmJgkLx2V5nl5HM0GSMPtoNW9GAlBX1UHi6l+9rPRr3mxCBcwVHUw9VSuSeaa4e5087/P/Q+AviN99B/tR7JJVFRU5KxdJ3/pZBZ/fnFOPXt0AUNHR0y+m2zJTxJMPG8iky6clDfHnGKmpKEEo9VIwBOgv6Wf8ubIeylVUXG1uULrpkIwKK6/b7wh/o93vQkXMCrHV9J8enNen010dHR0dHSSwWQzsfSWpcJZKsn5lHCaTxuBgpw6I4YuYBQIBoOBadOmYYiXMPs4AU8ANSge8NPJE5cPhhbwDmfWLOE19O67oj5FtAnqDRvg1VdFoc3PfCb9djQ2wgUXiHRV//gH/OQn6W8rnIG2Afa9sI9DrxxiwacX0GBpoHd/b+jhztnuzGpx00Q4ah1p70+LwBiJAt6x+PCHRTHvNWvg0KHcFIpOpc9Fw1phRZIlVEVE3mgT3olIdTJcK959+ulQHV3TFIQEjNjCQ0JsTULA8LQCsasKa0Lg/v2ikGTMtFY5IhnbORocuNpcmB3pjZFa6iAtqgqSt5nHM5i27kRIH6WhRWC88UZydreWW3Eec4Z+w0z6nKXcwuUPXI6334skR06ilY4W4oiv34e3z5uxB3/AK66xRpsx4YTdkSPQ2yuuVZqwlywTJ4rvOZ1iO01NGTQ6RRJdMyKKd1vF76C4FNSACjmM1JONuQ0MDq+BkUs0AWP7dnC7wZbc5SHrZHqd0yk+cmHzXPfLYqR1Yys9B3qomVYT4XAmSRKTLpyEbJJDUYjhuDpcKAEF2Shjr7FHfBbPditXwo03ishOjcsvh//3/6JH/IULGOPOHJf3tLbvJfRxtnjRbVe86LYrTqLZzWA20HRyHh+CdNKiUPqcLmAUCJI03LPx0OuHaN/SzuiTR1M/e/BpW/Pglk1yKCy4kPD7Yds28Xcs76DrrhMCxj33RBcwtOiLK64QKaQy4ROfEALG3XfDD38oJowywdnuZOU1K2l9p5WgL8iuJ3ehKiqWMkvoIc9ebWfFvSvyKmKkg6oWZgRGU5Pw9n75ZXjgAfjqV7O/j2h9LqXvyxLWSivuTjeuTldSAoYSUAh4RD2AZCfDH31UvMdNH6Wqg2mfTGVJbTcqU24Aox2M8c/bhgYoLYX+ftizJ/+1U5KxXcWYCsqbyodNdieLpcyCZJBCYrHBYkh6vP3f/0RdmeZmcR5nBU97/NRepjKw1mZpZ9GZMgXq6qCtDd5+W4hq8WiY34Cj3hGqh5FJn5MkCZPdhMk+fPbcaDFSPrYcg9mAtz9zAePgKwd58443aVzcyLLvLYu77ubN4n3KlNQjBU0mmDRJTHJv355fASMVHLUOnK1O/C4/3j5vKPqsGMlXBEZjI9TWQnu7cOg46aTc7i8WmV7ndIoP3eb54eCrB9n95G5mfnjmsIj5+Z+YH/N7Wv0LR71j2P1JLNutXCnEiqHBaS0tYnm0mkvhAoZObtH7XPGi26540W1XnOTCbn6XX0ThS8TMYKOTOYXS53SXmgIhEAjw9ttvEwgMFuFtebuFHY/uoH1Le8S6mge3pdRSkOHcO3YIEaOsTBR+jcZVV4mUOK++KopIh9PZKVIHQXrFu4dy8cXCc72lRRSGzhRvnxd3pxt7jciz7+3z4up24Wp3YSm35KS4aa44cAC6u8VE2qzYDvcjglYrRTsXsk20PpcqCz+zkDO+dwaljcmlxwl4AjjqHaE88ono7obVq8XfH/hAvA33Dz7ZZiJgWGsTihcgUt2MZB2MZG2XrngB4iJtqxwUpVIpKqalj7rmGjHOZYynHV67Gl65IvbrtavFejlEkgajMJJJIzXnmjks+doSaqcLYSUbfS4WF/32Is7/1fmhdFKZ4OkWNTuSiXDUBIx0x8+RLuSdLOYyM36/Pyv1TGKx+oerefXnr+Ltz921M18ChiQVRhqpXPY5ncIkkc0D7gA+p2/YK+COfY74XX5e+b9XeOGWF0a8eGOhEEoHmmIqX7/bj63KFjV9VDTbBYMi8iLaz64tu+kmsV44QwUMVVVRAnoNk1ygj7PFi2674kW3XXESzW69h3rZ/9J+Ond1prXNts1tPHvzs6z9/dpsNVMnCoXS53QBo4AIDrn7dNSJu09nW6T7jCZgmEoKoOJyFMILeMfSVxob4eyzxd///nfkZ3/9q0jBMn8+nHpq5u0xmwfTuPz975lvT8NR78BgMSAZJCSDRGlTKZYSS06Km+YKLfpi9uz064zkissvF9EyGzbkbnJvaJ9LlebTmhm9eHTSaYrMJWY+8JcPcPn9lyc1uf700+KhdMYM4akdE5+WPqoE5PycfyNdByOW7dKZnImFlkYKkk/X19kpIr4gi+mj/H3g7QTZAqaK4S/ZIj5PUHw9G6RSyDsa6fa5oxuO8tZv32Lfi/vS23EKeHrEJL21InHxkhNdwND6k8FoQA2qeLo9afenuPvxBmh5u4WDLx9MK/9tsmgppHItYEBhCBiQ+XVOp/iIZnNLmQV7tZ2AV9QlGvoKeAPYq+1RI9hkk8yhVw/R+m4rfpc/H4dQ8GhCa7TfS1VV3F1u2ja3Dfus6eQmPnj3B2NG9w213Zo1kWmjhu9LpFodek0OFzD2vbiP+z9wP6t/tDrOEelkgj7OFi+67YqXdG3nbHfStacr5svZroeu5ZKhdmtZ28Lrv3ydHY+m5xWpXYeLwXm42CmE8bJ4Zlrfg9hrRZqGoYNo6KY5BY/gfBKvgHc4114Lq1YJb+VbbhFiRzAId90lPv/iF7NX1PTjH4c77xTpeDo6oKYm821KsoS9xs5A6wCyWQ4JTsWEJmAUUv0LjepqOO88MRl8331w660j3aL8o6WPiht9AdmpfwGg+GHv38F1BGZ+RxT2jsFIRmBEQ5uccXW6CHijT67GmpyJRd2sOjp3CG+QZAWMhx4SEWjz5sHMmUnvKjkMttgRMkp+btq0lFivvgqBQOKUfEpQIegNRk39lApdu7rY88weVEVl/PLxMddTVTXjyMRUBAzteneiCRhD+5MSVFCCCj6nD1e7C9kkp9yf4hFKjWmUc+oEoEVg5LoGBhSOgKGjAyIV3Ip7V8R9wLeUWaKmPjWYDKHC1N4+b9q1pU4k4kVgePu8/Pej/wUJrnjwiqi1MJKNED16NLn2DF0vXMAwmET6y6Bv5CcedHR0dEYSZ7uTlVevxNXpirlOsaQCP1EIZZdJ85lCFzDeW+gCRgGjDZqu9sgBtmx0GTOunDGs+FuhoEVgRCvgHc6KFXD99cKD++23RY7oJ54QaY2qqkQh52wxd66YpF+3TkR83HhjdrZbOroUVVUJWoNQeNm8EqIV8C6k+hfhXH31oIDxgx9kT9DKFs42J+1b2zGXmmlc2JjVbft88NRT4u+49S8AjCVQvxzMGQoYkhGOPQ8BF3iOgmNszFVHOgJjKJlMzsRi3sfmMenCSbRtbktaMNbSR113XdK7SR3XAQh6oGQSSPmtgzR7NpSXi8LV774bX/w8+OpBXv3Zq9TNquPsn5yd0X41u8a6ue3Z38Ort78KErzvd+/LaF/ubjdARAqxaASDsHWr+DtTAUOrG1UoDO1PwUCQR7/2KMYBI4uvX8yoBaNS7k/xCLdvrlJjulyiNg3kNwJj48bkxD4dnVzjqHWk3WfNZeaQgFE6Krm0mScy8SIwrOVWLOUWvL1eeg/1ZpSTO9k6gEPXCxcwZJOIatMFDB0dnfc63j4vrk4XRosxqsNMwB0IpQLXBYz8oD0DpJqSUUO7DgfcAYL+YEi01zkx0R+nCgSDwcCcOXMiqrprHv2udleEV2nFuAoqxlWMRDOTItkIjJIS+OAHRY2De+4RAsZvfiM++9SnwJa4JnJKfPzjQsD429/gS1/KzmS4wWigYlwFSlApyHok8Qgv4F2IERggJu5tNti1S7R10aLsbTtan0uV1k2tvHnHmzQsaEhKwDj85mG2PLCF+rn1zPvovLjrrl4tJtvq6pIoAFsyDqZ/Jel2x0SSwN4EfTtFFEYcAWMkIzBi2S6TyZlYlNSXUFI/PFd1NPbuFZEJspxdATYCNQDu43lwBvZC6eQc7Sg6BoMo3v3EE6IORryxw1xiBnUwoiGTPpdIwDCXmuk71IckSygBBdmYfhqiZCMw9u4V6Q5tNpgwIb19aUJgS4vo72WZl/DIGuH9SVVVLv7lxVQ0VOQk+jP08FKWO89uLX2U1Zqf33nSJHGfMzAgxsmsR2QlQTauczrFRa5sbi234morjvpu+cA/IFJpxZpwKR9bTtvGNnr290QIGE9+8UksZRZOu/k0bFWRDzrRbLd0KTQ1wZEj0etgSJL4XIuO1AgXMIwW8bivCxi5QR9nixfddsVLprYz2owxowljRfLrZE40u2kRGMlmOxiKyWFCkiVURcXX7xt2bdXJDoUyXuo1MAoI85AiBPYaO0jihrNYHhh6ekQuVkgsYMCgl/I//wk/+xk895y4Gb/++uy37aqrwGIR3pDZTumQSbHgkeLwYZFOy2hMzlYjQUnJYPqk++7L/vaH9rlk0XJn+l1+fE4f3Xu7k8qd6Wxz0rWri4FjAwn38dhj4v3ii7NUCDpZbKPFuytO0mUGBYyODujqynGbopCu7ZIhPDdq5+5O2re2J2VfreD82WeLOj85IRB27vi6RaRMnkm2kLe1XAgA3t7B61e6dkskYNiqbBitRlRFpf9of1r7CO2rR+wrkYCh1b+YMUMIO+lQUTEYDVAo6dhiUT2+Om3vqEQksm820ASM+nqQvO3Qvyf2y9Oe8f5kWUR/wsimkcrlWKlTmOTC5nqKhkGUgBKqBRJrzNIczXoP9IaW+QZ89O7vpW1jGyZH9LSKQ21nMIgUuNHQ/KbuuGP4NUgTMDweQh/qAkbu0MfZ4kW3XfGi2644GWq30DNAmg5SkiSFxA/9HiW3FEKf0yMwCoRgMMjatWtZtGgRxuN5BmSjjK3ShrvLjbPNGZoMcrY5UQJKaMKmkNCiL8aMEWlGEtHfLx7ye3vhm98UyywWkdpo3Ljstq2qCi69FO6/H370I7jyShFyvXRpepNPWhFTVVXp7+untKwUSZKyXtw0V2jRFzNnCo/UQuWqq+CBB4Tdbr89/YnCoUTrc8kQnjsz6AvSd6iPY+8co33L4IRXrNyZIQ+DBJOAqjpY/yJh+igQKZ9kc3YKeNubxHsCAaOkREzSt7SINFKnnJL5rpMlXdslQ7h9A+4A/S1iMrx8bHnIqz+afVV1MH1U1op3R0PxgSSLdF+KT9jJPiaHOxxOeCFvVY0dzaYJAN5+r6ihoCpp2y3RBLckSZQ1l9G1q4u+w32UN6eXTk1VVZqXNOPuckcUcY9GpgW8NaZNE7UZtm+HxYsz21auyGWfg/wIGFr9i2nj2+G1q0Xh+1hYquG0e8Fam9E+588XUVkbNuR4XIhBru2mU3jkyub65MAgkixxzu3nxK0HogkYPQd6Qss0cd1WZQtFRYQTy3YrVsDdd8NHPhK5flOTEC9WrBi+f0fY7acvIO5dFJ+S+OB0UkYfZ4sX3XbFi2674iSa3TKNwADx/ODt9er3KDmkUPqc3tsLnOW3LcdSasFSPvhQv/6v6zn82mEWXb+IyRflN31IIpJNHwWwciV86EPDQ6I9Hrj8cnj44eg35Zkw+fjP9d//iheIB4A770x+X0OLm6qqit/lx6N4QmmkslncNFdo9S9ylj7K0w7+vtifm8qSmhy64ALhpdzSIiZMzzwzay1Mi/DcmZZSCwNHhUe8pcyCJEtxc2cmK2C8+66IZLLZhDd/Qnb+BtpegcnXw+iL0jquEEkKGCDS34yEgJFLInKjWow420S0hSRLWCusw+wbDIrz8tVXhQe91SqE0pwQdIPBAWXTQFXA3QK+LvD1gCm5NFfZYMECcW52doraDTNmRF/PXGoWtYFUUfDUWJL+LUcyE9ylo0vp2tVF/5H0IzAkSWLx55NTEbIpYLz0UuEV8o7G/hf3s++5fUw4ewITz5uYte363fG9mbOBJmCMa+wT4oVsAUMUkSroFp/7+7IiYIBeyFun+LGUWURUuFf34pdkidrp8ceG8jFCRO/dPxiBoUXfOupTT3WpCRLNzSJiPZEDls0mnAtUFbxBce3V06Lo6OjoDNK1pwtvr5eShhJKG/XaTiOFr/94Ee8MUtROvWQqAU+Akob8PQ/rjAy6gFHgRPMi1Tp5Jiplrki2gHcwKAppR8vnqnHTTcL7PFse9ytXwm23DV9+5Ehqgkm04qabNm1i9uzZGIyisdksbportAiMnBTw9mTPw9Vigcsug7/+VaToGWkBQ8NoM2JymDCYDaiKisFsCHnUxXpITPYCraWPOu88sNuTaIwmFGVjElsTMNxH4rvXI9JIvfhi4ae+SQejzYjBYghFXZgcppCnpWbflSvFOHY4TOuRJFi1Ksviq6lM9BdvJyhhniVGh4jGMDrE56b8FFAwm+HUU+GFF0QaqVgChmyQsZRa8PZ58fR4KClJ//xMRsAoaxLH33c4jnCaRbIpYEBxCBiuDhcdWzuwVdqyKmDMvGIm01dMRwnkzkNYSyFVo11yDDbRd6KhZMeDK1zASDCc6ugUNPM+No8Fn1pQdPXeRoqKsRWAqKnk6fVgLbeGBIySUalfC59+WryvWCEikxMhSeL+0ekEv2Rm1MJRMdNW6ejo6LwXcXe5QRXPDSWNJUjo17eRYPENi/F0e9K6NmpMOn9SFlukU8joAkYR4u3PLE9cLkk2AmPNmshJv6GoqvBAz5bHfTzBRJtUSEUwCS9uGggEsHfbqZxYWRQhjOEe4wDz5uVgJ/7serhefbUQMB5+GH77WzGBWghISMhmmaAnSNAXjJoSIJxkIzC09FFa/Y+E+HrEezYmsG2jRIeQDKLegim2R4pWgHjnzsx3W4hEFIIeMq/6zDNwzReGjyludw4iyKy1QuxLFNFkqcnSDhNzxhmDAsbnPhd7PUv5cQGj10NJU/o3ph/8xwfx9nlFbagYZEPACHgDKAEFk90Ud6LO6x08799LAkbd7Dq2sIXWja2oqprVyUzZICMbclfwR4vAqMlfN2HmTDCZRH2wAweynxpTRydfGEx6kVuNvsN9HN1wlLLRZYxaMCrqOkarkdnXzMZeYw/9dlrEbumo1Dx9VVXccwCcf37y33M4hIARMNk48wdnprRPHR0dnRMZn8uH4h98uPP2epGNctGkAj+RaFyYq8KROiciehHvAsFgMLBo0aJhVd2793Wz/i/r2bZyW2iZf0CkWshVMc10UdVBASNRBMbRJOvOJrteIlIRTFIllu0KkZUrxQTK8uWi7giINF4rV+Zoh5qHq8E86CludEQXNf4/e+cdJ1dZtv/vmTN9trdstqR30kkIBEKoAooCISjE9r52RF71VdSfBcReUAEVERT0FSmCARFQ6RBCDamQ3pPdzfbd2Z1ezu+PZ860nb5ndmezc30+85ndM2dOmec8z3nOfd33daXA6tWiXL63F265RRh6v/iiIGNyPjSN2k31oVHJiVTIhMA4flzIe0kSXHpphgfhCzWmoSLDL6SAziCC5Wfen5K8gIiR90hXYIxUn5OQkE2RqioViiJ8dNJVkA3n+hwCcy0MHoTdv4L+d6B0euxLkmHrN6Bzo4Y7TY5oI+9Uv0PDsgYmr56MscQ4rHYzWA2U1JfEkkpxKJ9UTsXUirD2eC44+spR/n7133n5+6kdyvfsAb9feD01Nua8OyBCYOzbJ7ZZiFDbrnZ2LXqzHu+Al77DfaN9WFlBJTBqoznzgBPcJyJjqMYwGgWJAaMjIzWW5idFaINim+cfXbu72HzXZvY8nnryM//q+Uy7YBoGq6h8SCchlazt9uyBo0dFRfLq1Zkfpyo75XBk/p0iskexz41dFNtu7CLXtlOlwL0DXoL+YPjl6nbh7nPj9/jHhBT4WEW++pzH7qFrTxf9x/Izny+icMbLwk8XH0fwer1YLLGBXWenkz3/2EPljErmrpkLRCowCk1C6sgRYcptMEQCm8kwMXHCUs7rpUO+CZNEbVdoWL9eZIbHBxtbW/PnORJG/05hOFyxAHTZO4bLsvDqeOIJ+Na3Isuz9S+JhxbtVjqxFFudDXN5+vPSm4XsVCoCQ5WPOuMMqKvL4AAUJUpCSiMJoQylqNQKjH37IBgE3QhS4iPV5+oW1KH4hUSYisHBSDA0EbSuIAvDvgscR8CbYHLW9h/o2QJ922H+t8FcP3SdDH1nMsGKFWKsb2mBw4dh6tTE6y35xJLw34qi5LXdypvLueT2S4a1DXevG0h/f42WjxpuEUJzs9Ard7ng0KGIV1OhQW272lNqaXu7jfZt7VROrdRk22/d8RZeh5f5V8/P2YA9HcIERnQFhs8OjqMhCbb87HfJEti6VRAYefPHSYGxMD8pQlvko83tLXa2/2U7sknmjC+foem2xxpyfQ4zlhoxV5pTVmAkajtVPmrVqgxlRUOIJzCU0ANAUQZMexTH2bGLYtuNXeTSdqoU+P5/72frvVvDy0+7/jQmLJwAjA0p8LGM6Hbz2D20bW7DUmUJ//654OBzB9l6z1YmnzOZlV9ZqdWhFhGHQhgvixUYBYJAIMD27dsJxKXs2urE4OnscIr1vIGwgV6hVWCo1Rdz54rAViqsWiWCz8nm0JIkgjqrVmlzbPkkTJK1XSEhnYQW5CFjPLxzJwQ8Ykfe3KRd1q8X5EU8VP+SXCpItGo3U6kJS4UlowfCc793LmsfXJvyBq0SGBnLR/kHIo04Qh4IKqZMEX3d7RYB+5HCSPY5nU4XQ15A5lnyWlWQhWEPaQyVzRn6Wd25MLgPOl+Fly6HDVfCK1fFvl5dJ/xpNIDVCsuWib9fTl2sEEau7dZ3uI83fv0Gu/+Rf40ld58gMMwVqQlJlcBIJ5eYCXS6CBlYqDJS0W03YZEYv9q3t2u2/da3Wjn68lH87vyVoKgeGNXRBIYSuhYlmSE6cRphNI28x8L8pAhtka82D/qCHNt4jLa3tb6xjT1k6mcW8AboeLeDoxuPArDq/63iiv+7gpo5iXXskrWdKh918cXZHadKYAwOKjx81cM8eNmDYT+pIrRDcZwduyi23djFcNrOVmsj6AtitBnDL71ZT9X0KqqmVxXJizwivt36j/Xz2i9e483fvjms7aoVM8V7XP5QKONlkcAocFhrRaqNx+7B7/FHZGokwiXJhYJMDbxBZNTfdpv4Oz7uq/5/663aGXiPNGFSaMinhFZaKFFBoaAr66+r5EvCTeebfEkBv8uP1+GNeXkcnpy1MwMBePJJePZZ8X/G8lFqNr6hBHQaFdXZ98CO78Ge21OuptfD9JCP78nmg5Gofb0OL36Xn0ytbrSqIBMH5ABHiCUqmz3084BDEFg6IxAM+ZdURF46U8R3RiNEy0ilQjAQxOfy5bwf+3E7B58+yPHXUgxiUVAUJWy0ni2yJTCG63+hYiz5YKgEbMeODk1MtxVFCT9wZFLJlts+EkhIBVzgGwTFD65WGDws+lkg+/tUKowmgVFEEVohOjigpNINHAfItAJjsH2Q577xHG/c+kbOv5nbDS+9JP7Oxv8CIgSG0ymhBBVQCCfBFVFEEUWMZ/Qd6QNAkkVwyNnlHMWjGb/INCEgHYoExvhBkcAocBisBvQWES1zdjrRGXSc8qFTmHP5nIIrAc7UwFvFmjVCtiheP7ypSXs5o5EmTAoNI+05EgN9CZROE3/7s58cjCr5kgCqdqbf48fd58bd58bV66L3YC8d2zvwOX1Za2eq3iSXXhohYi6+OMPKEp0BJpwLNRpKOgT90P0W9G5Lu6oqd/PXvw7fl6QQkKh9o19+j5+6yVaqJ5pGlhC1h7S2LRPBmETqRpKhZJq4JnwDgsTI0XcmE2RCYBx89iAPXf4Qr/3itZz3o05GM+lTO/++k4fXPsz2v2zPaV+uXhG8tlSm/r3GM4FRObWSkokl1C+px+tI7/2TDgFPgIBXDBz50hweGBASXQDV9WVCMiroEcRf0C9e3h7w9YnlpmrNKtoWLRJjQksLdGpTAFVEESOOcN9UMvP8OpmRacCldGKpMIV1+3F05GZEsWGDGLsaGyN+OpkiWkJKZxCP/OpYW0QRRRQxnmGuMGOptjBxqcg2c3Vrm7xSRGbQShq/SGCMHxQ9MAoIiQxRJEnCVmej/0g/jg4HZU1lLPxIBiUOo4BsKjBUrFkDl10mJuhtbSJjedWq/BAJKmHyxS/GBsQbG4fnowCJ266QMNKeI2GomayKJLJcvfasM1zzSb7k0m6qdmb8DfKl772E/ZidUz50CnPXzB1SfurqdfHS917CXGHmnJvOCS9P5k2iymOlJfMs9TD3f7M+j5SwNol3T6eQ/5ITP6SvXw8vvCD+/vOfxWu4viSZIl99Lln7RsNUZqJkg421a0VgMrrt8kaIppKPiobeBtZmoevvPCoIRH1+SqHPPFOc7/79kfE7HgabqBRUKxtyabdsCAyjzUjAG8B+PLdKk0wqMAYHhVcFZB9QSoaxQGCobSfpJC79/aWaJVGo7asz6JBN+enXqnxUaSnYqmph5f2iGmn3L6F/l/hQtsKpt4qLWkO/mNJSmDFDeAVt2QLveY8mm80YhT4/KUJ75KPNdXodBqsBn9OHx+4ZdrbkWEamARedXkdpUyn9h/vZdOcm7MfsTDl3Cgs/nPxBKb7tVP+Liy7K3m8pmsCoNenxOXxFAiNPKI6zYxfFthu7GE7bqV5OXbu7qJ5VTdXMKq0Oq4g0iG43rSswvPbxnWCRbxTCeFmswCgQ6PV6li9fjj6BNokqI+XozC17J1sEAiKT+oEHMs+o9ngiEjLZaoLLsjC6veYa8Z7PfrFmjTCcff75iBHe448PL9iaqu0KBSMuoWUIZbj67ODtFtJRSkBktno6s8pwzRf5Mpx2s9XawjqZ6uuUD56C0Wak450OrDVDXRY9/R569/fSs68nvGxUvUlSwVAmJKkURUirJIBKvAwOxi4fji9Jpsh3n0vUvtEvW60tTIjWxsU481FBBkQqMBLJR8XDXA+mSkFcKLlLN6VDebnILofkFVAqEeDud+fcbtkQGGVNYkyxt+RGYHj6QlJGKQiMnTvFe3091CSWMs8aKoGxa1fi8WC0Ed92WlaAqsFAU5kpb5WlqnzUBNV+yFwLpdOF5JpapSRJgqwtna4ZeaFitGSkxsL8pAhtkc82VwP2asBhvEI9/0y8CCumVADQtqkNR7sjpYRTorZT/S+ylY+CuAoMY7ECI18ojrNjF8W2G7vQqu1q5tQw/+r5NJzaoNGRFZEK8e2mdQWG3+0v3ufyhEIZL4sERoFAURT6+voSaqSGjby7nHjsHgZaBxLKNuRCPMRDlbI591xYt068T5mSPhi5a5fYX1UVNBT4+C/L4rxOPVX8rwajckWqtisUjLiEljmU4Vp1KphqYP5N4v/znoFVj8JZD4v/MwgSpSNfIDfyRet2m7J6CjqDjv7D/fTs7xnyuSq5EP3Aq4k8VsAt5E+0hCRFqjCcLUN3OcrES6H0uTVr4M47xd9Tp4pqlEOH8lR9YmkAa0P6CgwVJTOhbJ7wv8gj0slIqZ4Gnj5Pzu2WC4HhaHdkPYFVFIXms5ppPL0RS3VyCSlVLjFj+Sh3JwwcSP5ydzJzpuh2vb3Q1ZXVYY8IErWdoigMtA0M2wcjm/bNFSqBUV8f94F/QLyr/kH2/JTAjBaBUShjZREjh3y2eVGiQeC0609j1bdXUTUjfcZuxeSKmP9L6kuSrhvfdsePw7vvgk4HF1yQ/XFGExiyQUzwi4Ed7VEcZ8cuim03djGctlOCxfYeLcS3m1YVGAarIexnopIiRWiLQhkvxwSBEQgEWLFiBYsXL455lZWV8a9//QuAXbt2sXr1ahYvXsySJUtYn8/03zwgEAiwe/fuhK7u86+ezxX3XcGCdQs49PwhnvjsE2y6c1PMOrkSD/HbWLt2aEA1k4xqVT5qwYLsS5xHC2qliHrsuSJV2xUSRjxjPOgDby8YSqH+fGh8L9ScBmUzsspwTUW+qLjppuzJF63bzVhipHllMwAHnj4w5PNEBIYm8lgH/wQvXwGHH8j0UDODJWRO4xzKsIy2L0kh9Tk12HzKKXmuIJv5WTjt96LvFBDSERim8khGjMfpyandsglwm8pNQrZKgYG2gaz2I0kSy69dztnfOjvlRDor/wt3J7y6Dl65Kvnr1XVYdZ1Mniy+UogyUon63DM3PMMTn3mCzl3DM3bwOUSVUD4JDFVCagiBMe//wcLvQn0oOqhWOmmM0SIwCmmsLGJkkM82N5WZkHQSPmf+KvvGAqpnVtO0oilM0CeDo9OBJEt4Hd7wK+gL0nOgh54DPUMq6+PbTq2+OO00kSCWLWIIDFORwMgXiuPs2EWx7cYuhtN2m/+wmcc+/hj7/rUPAPtxOye2niiOjyOA+HbTqgJDkiQWrFvAkk8tQW8qVlTlA4UyXo6J1pVlmTfeeCNmmdvtZvr06Zxxxhm43W4uu+wy7r77blavXs2JEydYvXo1M2bMYGE2hgwFimgz0XAnjwqCDltDn/QZ1ZIkMqovuyxxgC5bA+9CgHqs6rGPB6xZI9pz7VqYPh3+8If8eY7Q9ap4r1goSIxhIJl/icEAPh/86U/wsY+J/0cT098znSMvHeHoy0dZ+qmlMTfQRASGJvJYvn7xrrXPQbgC49iQj0bVFL7AoAZGw9I0o414fxklAMH8yX2olU87dsDddwtT9+gxxWA1oNPrCPqDePpzy4jJhsCQJImypjK693RjP24fkv2qBVQCI6P7nc8Onm7QmRIbqQdc4nOfnTlzajl8WBAYmhrA5wm2CTa693TTvr2dCQty7wCTzppE0xlNeX1wHCIhpUIlBIM+aP133isw9u0TsnslyZOwiyiiYLHqm6vQGXR5k3o7meDodLB+3XoG2wfpP9IfXv7ct58LV0NYq62suX/NEM80FcORj4JYAqP61GqMJcawL1URRRRRxHhF35E+XD2u8Fj89Fefxufw8d473kt5c/koH934wpzL59B4WiM1s4evyXvKBzUyJiyioDEmKjAS4aGHHuL888+noqKCp59+miVLlrB69WoA6uvr+cpXvsI999wzykepPeKDoFpJuQw3ozoXA+/RxngkMCAScF24MM8Z450bxXvNSvHud8LhB2HXL3MSeVf9S154Ae6/X7xv3w5lZfDKK/C1r2l36LmibkEdtgk2fE4fxzbGBv4TZRho4k3iC2n9GzSecFmbQG+NSKtEYdRM4QsQI0JgeHpASSPTo/rOBD3g6xMvVyv0bYWB/Vn5zmSDV14BVQrzM58ZWv0nSVLYTyJXAuP8H5/P+//wfurm12W0fmmjIEwHWrKrwPB7/Hgd3rSlsVlVYKiQLRGvhehXFKkxFoy8o1G/SJQztG9rH/a2dLIOgyV/gbWkElIqyuZA5SKoOSMvJiR1dUJeU1Fg2zbNN19EESMC2SiPe/LC5/Sx5597OPLykZTreewenN1ODDYDpU2l6PQ6dHod1hor5gozepMeZ7czqRyX3w/PPiv+1oLAWH7tcs77/nnUztXW36eIIoooYqzBflQ8O5dPFs/Oqnelq9uV9DtF5Ae1c2uZdv60sARwEUWkw5iowEiE3//+9/z0pz8F4Nlnnw2TFypWr17NbaruTBw8Hg8eT2TCaLeLQczv9+P3Cy15nU6HTqcjGAwSDEYCR+ryQCAQE+RItlyWxWRf3W70ciBcghMMBjGZRHapoigxpTl+j5+dD+zE0elAURSCShC9VU8gEGDDBjkj4uHFFwOce66U9Jza2jLjso4fD6AouiHntGOHDEjMn6/g98eyJfHnqkKv1w85V0mSkGV5yDEmWz6cdpozRwH0HD8OPT0KVVXp2ynRcrXt1P2P5jllcu21tIi2qq8P4o/SLte0nZwnwL4PJIlgxXJ0wSA6nQHlyAMowQDB5mvAXJvTOZ1zTuw53XuvxJVXytx6KyxbFmDtWoVXXpFoa4PGRl0o+D/0nIBwu/n9fs3aaealMxloGaBsSlnMcaoPqXqrPmb5rbfKXHWVBChAJDAgSeK8f/GLIIqioH4lvp10nl5QFHTGcm2vvfKlsOI+kKRQ+0Xa44wzoKlJpqUFFCVRMEOhuVnijDP8RF9+mfSnaCS79iRJwmw2h9su43PKQ386cUL0p5qaAH5/7PrZnFOqdlK2fwfF1UHwlO9A+bzE56SvRHf6fegCgwSCoWP3OdBtuwGJINLinxKwTELRV6I2Sqb3p2TLH31U4kMf0g25BlpalFD1n8TllwepP7Uen8uHzqjDZBJGzdm0k8FiQGfUhfpB7D06UTvVza/D6/RirbOG18/knA69eIg3f/0mDcsaOPe75yZsp95eORwMnzVLXN8prz3EPUFRlHBgXJJAQkJBLJcUhUDAz6xZQUDHrl2x99HhtpOK4dxz1fuc+lsHg0Gq51UTVIJ07enC6/RitBo1nxtpdU5ijqOjvp7Icm8/UscLSOZqdPXnElzwPbFcHVs1nu8tWqTQ2qpj06YAK1YowzqnQABeeUXixAmJxkYdZ54ZRJKG9idFUWLuc1qfU/zyIkYfkiRhsVjGPdGQLzg6HWy+azPGUiOTz56cdn2DxYDepMfV5UI2yZhKIpWEfk9s/4luuzffFJ5IFRWwfHluxxpNYBSRPxT73NhFse3GLnJtO4/dg7vPDRCutrDWWOk/0o+zy6n5cRYRi3z2OVePC0enA0ulJewhXIR2KJTxckwSGDt27KC3t5dVobTk1tZWLrzwwph1mpubOXjwYMLv//jHP+bmm28esnzLli3YQrO92tpapk+fzqFDh+jsjOg7NzU10dTUxN69e+nvj5QET5s2jbq6Ot555x1crgh7O2fOHCoqKtiyZUvMw+jChQsxGo1s2hTrZQHgcrnYHmXMoJN0HHziIF63F7/Nj7vPzaHWQ/je8dHWtijlb6Vi48aDTJmiS3pOEyc2ZbQdu30P/f0NMefU16enrW0ZAHPnBoac07Jly/B6vTHnJMsyy5cvp7+/n91R6aYWi4VFixbR1dUV037l5eXMnTuX1tZWjkcxNsNtp/r6JZw4YeL11wd573tLM26nROe0bdu2gjgnFcmuvePHlwF6gsHjbNrUmpd26tn7BHJ/H07jdI5t3xc+p35/BcrAAVo2PcWg+RRNzqmhAW644VR+/nMD//3f8D//E6SnJ1Ll0NgY5AtfOMA55/TGnNPg4CAej4fNmzdr207Lmph7+Vx27drFvk37wsv9Dj8Gm4GOvo6Y6+n88+fwyCMVrFsXxOOJlMM0Nir88pcKzc1vEX35xbfT9I6DGBQHFYbyEb32rruukm9+cxaSlDhh+Vvfgu3bc+9P6a69hoaGcNtpdU6QfX9qa1sOyDgcB9i0KWLerll/mjkJd/de3G4X+3efICA705zTdPbu2hU+p2ZvIzX6E5g8HbzTYsLlOpz2nDJpp0AAvvCFJSjKUM1SRZGQJIUvfQnOOKMLZZmCHj37O/dTXl6OLMscP348b+00/cLp9FT00B5op31Te8bndOLtE/T39WOwi0qARO3U1yfuuQ0Nbnbv3hppp2TXXh14vV7cbjtBndCNN5stWMxmBgcdBL125KCTfTt2MHFiHVDJjh1eNm2KmCUMp51UaHXPPXr0aLidOjo6cEkuvN1edr64k8XvXZxTf2r5TwuuEy5qzqjhzCvOzMs5HTo0Hyihvp7wOZm9h5nc8xtkWwNl9efmfb43YcIJoIlnn+1mxYqDOZ/TPff08qtfTaGjIxIInTgxyP/8T+Q+p/ano0ePxtzntD6nLSNt6lFEWsiyzKJFmT0bZIuOdzvY8/geypvLWfiRMVRyrSHChqNZevYYS4zIxtQlz9Ftp8pHXXhhpMoxWxQJjJFBPvtcEflFse3GLnJtu/6jYu5jm2BDbxaDq6VaVEMXCYz8I77dDr90GGOJkQkLJ4QlvXLFjgd2cODfB1jw4QXMvzqbMvkiMkGhjJeSMto24jnguuuuY9q0aXzlK18B4IILLuDrX/96DIkRDAbD2WrxLFGiCozm5ma6u7spKxPlS6NRgdHd3U1dXV14O9F48rNP4uhwhLe96luraDytkQ0bZM49N/1v9uyzqSswFEXHlClKyozqpibYvz+A0Rh7Ti++KHHhhTLTpins3z8y2aDRxz6cdrr8ch1PPqnj179W+MIXcq/A6O7uprq6GqPROOrnFH2Mia69979f5t//lrj77gD/9V9xGePuTgKe3pj19XKonYIB0JeBuTZ9xvjmG1D6d6FM/zRKw/si57TrV3DiOZRJH0SZvE6zcwKZU0+F7duTVzI89FCQK64Qf+v1ooKpo6OD6upqdDrdqLfT6tVBXn5Zx+c/H2TNGoXVq3XIcpr+pCjoNl4JShDdyv9DMVZqc+052wl6+yLLpdCxq5n9oevgscd0fPnLujhfEgWfT+K00+D55/2Yop7xtazA6OzspLKyEp1ON6LtFH/tnXKKzN69Es895w8bWudyTknbyb4DZdu3UUy1BJffnfU5SccfQ3f4T0hVSwmccpNmWfAvvSRxwQXpJ5rPPRfk7LPFMQaDQXp6eqirqwv/H3/s8efkHfCy4y87MFWamH/N/CHrx7eTu8eNd8BLIK4SUCeL60Rv08fojUef05Y/bmHvP/cy54o5nPrJUxO20513ynzhC3DppUEefTQYXp702nMcQtmwFsVQAQSRHIdBZ0Iqmy0qMHwOJF8fgZUP0umaSUODDklSsNsDmM2RYyyECozu7m5qa2vR6/XhZW/e/iaHnj/E3CvmsvSTS3PqTy985wU6dnRwxlfOYOo5U/NyTlOnyhw/LrKaTz01tLznLXTv/hCpdAbSslvF+u5ecJ+Astmaz/cefjjIhz4kM326wne/G0xZIZjsnB55JMgHPyiFSOPk9zn1GP1+P52dneH7nNbn1NvbS3V1Nf39/eH583iH3W6nvLx81H6TYDBIV1cXNTU14fujVjj26jFe+fEr1Myt4cKfXZj+Cychjr12jFd+9ArVs6t5zy3vSbpez4EeHr7qYcwVZoy2oUS/1+HF3efmqoevomq6cOiObrszz9Tx+uvCq+6Tn8ztWP/xD7j8clixAn77ibc59Nwh5l8znzmXz8ltg0UkRD77XBH5RbHtxi5ybbu9T+7l7TvfpmF5A6tvFAouO+7fwTsPvMP0i6dz2nWn5euQiyC23RS/wt+u/BsAax9ai8E6PCnZbX/Zxs6/7WTW+2dx6mdO1eJwi4hCPsfLbObOY64Cw+Fw8PDDD7Nz587wMpPJhNvtjlnP5XKFpSriYTKZwnJN0dDr9WFpGRXqQ1081Ie3TJfHbzd+ud/vx37ffdRNnoxkNqM3GIQj8bJlUFFBlcWF0X6UoCQTlGRKnN3IA2WsWlVBc2OQrhYPXgwEEHImKiRJaOyfc46MehrJzum22yTWrk14mIDET34CpihTYvXY1aZYsEBCktKfa8xWJSnh8mTHmO3ydO20cCE8+SS8846U9BjTLff7/Rw5coTa2tqCOKd0x94aKrpobpZjs7rcnfDah9F7uodsQyI0WJiqYeX9YK5NeYzS/G8jdb8BVctiUsd0pTOg/XlwHolZPtxzCgSgqyv6aCMQ2eDwla/IrFkT8fxQFCXcbtHb06KdFEWhd18vRzYcYcknloSDp6nOqadHrHP55TrOPz/5ucYs9w0AiujohjJtrj13J7rXP4LO0w3eXrEPYwUYyggfeeg6WLu2liuuEN44bW3C86KpSWL5cnjzTfj61/X85jdJjj3ZOcUh0Tn5/X4OHTpEdXV1xmN2vvqT6oHR0KBPmCU57HGvfzcSElL5PHS53J/qTofDf4a+HciKF/RDjaRzGfc6OhJ+NATt7Tr0IRNvn8fH4cOHqampQa/XZ9ROA70DHHzmIOZKM4s/unjI+tHn6uh08OiHH8XZLTKogv4gkizFzAOSmabq9Xq8di86SYetWnyWqJ0iBt7ivFIduwpJkpAIiv4UcCNIViEjJUkSSBJ6WU99vY6KCujrkzh0SD/EJDyXdkp0LLmMEfH3OXV5w9IGjrxwhI53xAWRS3/yDfrQSTqsldZwW2l5TooS6af19VHnGnSJsdMYSlxxHka36YtgKIWVfw2bE2k13+vuFusfOCDx0Y+Kv5ua4Lbb9KxZk/6cAgH48pd1STzPYu9z0U2Z6D6n9Ry2iMJBMBjk4MGDVFVVaf6AqVYdJPNtGA9QvQizrcDIBGrbQRVvvinaLlf/C4itwAj4AvicPnwu3/APtIgY5LPPFZFfFNtu7CLXtlMrMFT/C4h4YBQrMPKP6Hbz2sX9VJIl9JbhzyfV+7K7351mzSJyQaGMl2PuyePBBx/kvPPOo6Ym4lTf1NTE0aNHY9Y7duwYTU2ZySIVCszHjiH19YmnVL8fvF6YNg0qKqgbOMDEw8+j0+soaSil9P/ehRPnIX/4w/z+xhaOffYHKEAQHT4MOLDxLenHAPzjkjuRf9YrCBH19d73CqfVXbuEi7XRyBqTgZe/beB/b2ngLdd89PiYzzsEdQbcQQPv/tMAZxsirsNOJ+h0vLNVD8gsXDj29CPHo5G3SmA0NMR94LODpxt0phhj2TACLvG5zx4mMJLCWA4TE2SmlU4X74MHsj7uVNiwIXJeiRBtQn/OOZruOvH+Agov/+BlPP0e6hfV03haY9rvdId4o+rqbHYUhPrzRFA0gdl2Toi+DnRmwA7IYKgQn8ddB7I89De97z649FL47W9h5UpYt06bQys0uN2gqrDkzcR7YI94L8sxY9LSCJaJ4GqD3i1Qu1KTw8rGyH3P43vYfPdmJp09CXlVduXBaqAsk2CRapqqN+npP9aP3+WncnolxhKR/ep3+cOmqfEEBhDWxFVNxxNBvVdkZeANwojdHRqk/AHwhzQ9AhG5HkmC2bPhjTdE1u0VV8CqVRHStRAxYeEEZr1/FhMW5d4B1AcYY+nQLGUt0NsLvlDMri7aB94fMnnXC9N3rJNANgrS1tUK1vTjdqZYvx6uu27o8pYWQn4xDCEx4rFhAxl5no3Ufa6I8Qd1HFZllMYj1HuSel/JB557TiIYhFNOEY9cuSKawNCHEtAC3kCKbxRRRBFFnNwobSildn4t1TMjD9yqhFTRxHtkoSYEGEuMmvgqFJMsxgfGHIFx55138pOf/CRm2cqVK3nyySe5Lurp8KWXXmLlSm0CNSOFtnXraFy2bEiWLYD7nIvY2zGR6edPYtpHFwhywyrY4ks+Us3z/Z/hJ9/z4h70YUQMBg0NcPvtsMTUDL0l4gne6xXkiJrC19UlSii8XvD5OMvn45Lqhbx1fD5f/bSd69vuJKjAU0+C8hAc7oUpT90hIip33AH79nHJE7AUifPfMsAbHxG1ym+/DU89JbLsjUZBmkyeDJddJvb/t7/FEioGA6xeDSYT7NsHAwOxn9XWQlmZOE63O7JclsNZkrlAJTDeeUf8JCe7h5fHE6lUGEJgqJAtoE9ifBQc5g3BNlX8yJ5u8PYLokMDtLVlt14gAC+9JLFxYzUOh8Q552gbJNTpdUw5dwp7HtvDgacP0HhaIy//8GX8bj/LPreMssbY0jhFibRLFDebHsZymPNl7Q48GrIFjIC3B1Bir4k018H73gff/jb84Afw6U+LYG9PT6RKo9CDsplCzeo2GoXRpuZQFLAPk8CQJKg+DY7/A7rf1IzAWLVKBFaE7GDi3TY1ifWOvChKgt39bmxkZ6qWDYGhQm/RY7QZCfqCIBEj3xFvmhoNd29qAkNRIhUYGRMYhjJRsWTfA8GofXu7QQp1AlM1GMpYvz5CkNx+u3iJDP30we3RgqXKMqwybUVR8Axk38bZQDVdr6wkRtIO36B4N5SId50eSqZD/y6w79aMwAgE4ItfTNxP1HnHl74kpkepxsVs73NFFKE1VJLRM+BBCSpIupN80pwA8RUYgUBsFWr8/MbvSnzPSbYc4Omnxe86nOoLiCUwVP+NIoFRRBFFjGfMuWwOcy6LfaaqmFzBgo8soHRi6Sgd1fhEOCFAowSmYpLF+MCYIjA2b95MT08P5513XszytWvXcuONN/LSSy+xevVqTpw4wS233MJ99903SkeaPSRJory8PCn7aJtYgV9vYcBpGJrqa7Vy3g2n8tIgfO97kcX//qM6+X1f8h2vWkVIhBkQvMEPfi3+vv7GShoqfwk+H23f8vL7O/z8/i0vT3XK1NcDl11GsLef2//sI4CP933IB83N4ssVFTBzZpgYCac/gpjtHzwYWa6+zjhDRBeefhqiTC0BuOoquOACEd25667oHw6mToWvf138f/PNITmdKPLjIx8Rae2vvgqHDollRiPo9cyeOguDYRaK3c6Jf+xi4iRDhHAxmwXpAtDXJ55I1G3qdGG2I13bFRLUQI7RCFVVaVYOuMDTBZZ6kDLUJPT2w86fQM1KaLx0KCOkt4ClAdwdIsNVIwIj02zw48fh738XwaLjx2VgJpCfIOH090xnz2N7aH2rFVevi853O/EOeFGCQ6NYg4ORLpJVBUa+IYcCucHsM1K++114/XV49lk49VTBW6oY7u9dKH1OJTAmTMgT+elqFdngshFKpua+nfrzwNoE1cs1OzRZFm24di1DjNzV3+LWW8V6KiHgsXtoKG/Iqt1yITBAkBj0pQ4SxSNcgVGZmMBoaREVN3o9zMmUTzLXCtm9t/8XXC2R5ad8C0qmiL8NZax/qpa1a4cGubPJ0M8n8tXnAp6AIJrIP4FRXx/3gd8u3vVRhHLZnAiBUX8+WkCryolsqp5UFMpYWcTIIZ9tbioN9VFFeDiE/x9HUAMjxhIj69cLcjK6f6vzm4tWmbBWW3F2O5MS59Zqa8y4J0kSZWXlPPNMkcAodDg6HeH5SSAQQO6X6T3YG5bhM5WZElZ6FlFYKN4jxy60bDtrjZX5HyqaPscjepxLhFzGueh2CycEaDSXKFZg5BeFMl6OKQLjD3/4A9dee+2QH81ms/H444/z+c9/nsHBQYLBIDfffDMrVqwYpSPNHrIsM3fu3KSf2+rE4NCxowPPgCdhR1claFRs3Jj95PeNNyAYFHH7hiYdhLJlv/FLeHQjbNkGn/iE8I2QZs7k4H540SNi/Q0fhbBI/vTp4pUIJpNIz06Gz3wmlvjw+aA0xIjPmCF0GNTlUZUogCipUKtM1G2oqVC9vXD4cMx3DbLMnDmz8O1owXvnPTA56jiqq+FHPxJ///CHYLdHPpMkuOEGmD4d+emnmbtxY4TcMBph8WJBuPT0CBc9lRQxGsWPdfHFYjvbtonjVD83GERphM0GLpcomYgmY4Y5YKiZmRMnZrApV6uolHB3QtWSzHbQ9Rr0vQMBDzS9P/E6C38gPBW0kjwifTa4iq99LfHyfAQJy5vLqZ5dTfeebg49dyhSJpnAzFGtvjCbYy/ntAi4AZ0IcOcDKoER8AJBsa9MvyoL6ahnn40lL2D4v3e68XKkEE1g5AWyGaZcI8jE4fSXkmnipTHWrBFtmCiAc+utkbY1l4vryNvvzbrdcs3O15vF7+VzZqb3rSgKk1ZNwt3rxlKVQEKPSPXFrFliyM4YOgP4+kQVkyrnpbeGJfW0ytDPJ1L1uaA/SNfuLnoP9jL7A7Oz2q76kCEb5bDEidZISmDEV2BApNJJrXzSAFpVTmRT9aSiUMbKIkYO+WxznV6HwWbA5/DhsSd+FjnZMefyOTSe1sgr20q55rOpSGcba+5fk1XwR5ZlgsG5tLSI+WB0X84FKoHhdILOUCQwtIKj08H6devDflsqNrM5/Hcyv60iCgvFe+TYRS5t53P6kHRS+BmhiORINs5FI5dxLrrd1Gc8zSowSosERj5RKOPlmOq9d9xxR9LPFi1axMaNG0fwaLRFMBiktbWVhoaGGFMUlfnUW/Qs+q9FvPXbt/jHJ/7BBT+5AIid/KpB0HnzhCrUhg3ZH8err4r3ePUtkwn++leRSf2vf8Gdd8K110YKJebN0zCwogbrE6G8XDhvJ0OqaOj73idecVj4V/jrjjk8cOZv+Mb/RhEn0U8ln/qUkK7y+SLkSMjMNNjYSM/kyVSVlKALBMQ6llAAzOeDzs5YMkaWIwTG3/8eiYKquO46cY4vvgiPPRb72fLl4lj6++GnP40lTQwG+J//Edv/178Eo6XXx5Aqra3NTOAE7yk9BJuiiJGyMihHnHOfF8xGkOrB3wWyD5xHwZiBtlFnqA+mkqkxZ6ORlBkyyQa/+GL4979HNkjYeFojbZvb2H7f9jCBMdg+iKtXVDSo/Tfa/yIrjurYY3D4r6LaZeZntTnoaEh6IXOjBCDoDXliZIZAAG68MfFnw/29k42XIw01MJo3AsNUDVMK20BkzRrRhp/+NNx7rxhi//GP2DZVKzBcfS6OHTtGY2Njxu2WawWGOhn2OrwZSZ1IksTya1NXqGQtH6WiL6QLVTJNVLS52kR1Wwhjwdsg1RxloHWA/3z5PwCUNZfFtFW67CzvoBek/FVfQKyBdwymrBNVFpYoqSiVwHAcBr8roel9tsilciIRou9z8YivelJRKGNlESOHfLe5qcyE3+UPz2nGG8qayrBNLOOrV6afTx46ZMsqsBMMBnnoITtQwTnnRB4lcoVKYCgKBCgSGFoh2m9Lb9GjKAperxejUei4p/PbKqJwULxHjl3k0nYHnjnAlj9sYfrF0zntutNiPhtoG8DR7qB8UnnSRKbxhPhxLh65jnPR7aZWNGqVDGGuMDPvg/MwlZlQFGXUKwVONhTKeDmmCIyTGcFgkOPHj1NfXx++IOKZT5/Tx2DbILJJ5uGrHgZimc/OTrGtK68UBMbrr4sEflMWY0IyAgOEmdxPfyom5V/5Cpx9Njz+uPistlYELMeirr3wwZDYttMAtiTEyezkWaXBefPY73SyLJF/yYQJydP+QejsqJUiauVIeUhWaflyIckVTX6ouk8GA5x2WuxnXq+QtgJR+XHsWOznDQ20tjYzmz2sddwPd0cdx9y58Mn3g1+BPx0SgWuAoCwyhz+qQI0enuuG524BW12E/Dj/fFi2DPbugPufEEn6czqh5O8iIq9G3DZsiMhwhSS8mDFDXKD9/eL4431RsrjxpMsGr6oSvE4yaB0kdHQ62PT7TZzYciIiGyXB+g+vD6+j9t+uLnHjz8r/AsAXcpDWZ1O2kSV0RlEBEMiOwMhnUDbReDkaSBoYLUT4ndD+AjiOwKzPa7ppWRYKgPfeK4ag+PuAGpwOBoIc2XeEiRMn5p3A0Jv1yEaZgDegmdRJ7gRGiOmvXCj6UMmJmD6bi4dPKs31fCDdHMV+3E7AE+CRDz0SY26bLjurclolVz92NT5XZpUyuSAp0WhtHOpzYaoSJLu7Cwb2iTYbJnKpnEiGNWvgppvE1CEa8VVPKgplrCxi5JDvNr/k9kuQTfK4DgzkY34TCMCLLyo88IB4DrnwwuEfZ0xFr8VC9ezqosa7hgj7bSlBnD4nNpsNnST6XCq/rSIKB8V75NhFLm3Xf0Q8NyfyunvzN2/Ssb2DM75yBlPOmaLloY5phMe5YBAJKSYhLJdxLrrdGlc0Yq40UzKhJP0XMzlWs55FH12kybaKGIpCGS+LBEYBIxHzqdPrMFgNmCvMQ5hPtQLjzDMFodDZKby0M/UyDwbhtdfE38m+c/31Qj7qmWdgyZKIbv9//gNTphS22WgyqEbeqnnqiEKnE3Xi5gSB4Zqa5BFtqxUuvzz5dj/84YSLW/8OL7Oaxe9bxXt+HlVtotMB3SBLcHktKEYIKOALgtMEBjsMHoHGCqhfBPrqyHfVFLGOt2HAA7oKONoFvjYRMVKf3h58MGIg3/8O+Oxw++PQNA0efTRy8am45BJxjvv2Cd8TgyFiCl9VBZ8PBWHvuy9MfqwxGLjslwZeN67mqLOGqcEDLG9oQTbp+c8LRhZioJNa2mhAj486OvBhiHm1tmZmzJgOHrsHd68bU4UJr11kGMhGOTxpiu6/3d0iuJe1/4VKYBi08RKJQSDke6GTQTGI/yU5sjwNxoPhbF4lpAJu6N0qMsKNFcPfnhKA/b8X/W/SWjDXDX+bUVBJHDVYHA3ZKGOwGvA4PPgHs5vsrrh+BYs/vjhh9k8yqL4XsknG5/Th6HQg6aSUfhh+t5+gP4jBZkgamMuZwOjdJt4rFgofkqmx43OmGfobN0bUCxNpro/0vTd6jmKpsuDsFMkWica4VNlZkk5KKK2nFZJKSCXDpA8JyTbbJE32n41fTCZQJfkuuEBIeo4UiVVEEcC4l9448PQBDrysw0ATPlL7w2U6v4l4acio0r0/+5l4rhrOuC7L4vHC7YbSec0seG9z7hsroogiihjj6D8qnpsrJlcM+cxaLRhfZ1dyyaTxioAvQPv2dkylJqpnaWfWWd5cTnnz0BjGaCRqFTF2ML5noWMEKvPZs78HnV5H0B8MP+xHM59qBUZdnejo69fDyy9nTmDs3ClsHqzW5CpNOh188IOCwPDFJUwWitlotlAJjD17InYUJytaW8X7xMYExInbB5YaaOyGgBO8PSCbwFgJLreQ05jigQvfCyUJAjvlR+GD02HKh2HK1UM//+1vBUvm9cLGT4GjEywD4rP3vlekcEdXjKhRvcpKOPfc2M+iy4pcLnHhhipZZJ+PMz+9nDMnAeu3wd+egWCQBa3weeBFzuFBrmEibXyLH8YcogcTP/7x7Rw+DNJPf4LB3oUfPfsw8laZkUU//CDv+cIs2LwZ3norVr5ryhRYsUI8KW7ciL7TTZ3jIPpKM4Eqmf3HzMhmmTK9C0kJ4paDuAJ+cDnp6qgAdLlXYGhJYBjKhHSRpxuCHrFtA6D4RTUOiM8NZam2oplsSiEjrwSGfS+880ORDX76vcPfnqEUyudB37vQ/RY0DpXTGw5SERgATWc0EfAF8MvZERh6sz7jgJmpLNY0VSfrRDWAEjHojjdNVXH0laO8cdsbNCxvYPWNq4d8HgjAu++Kv7MmMBb/RMhIlSf+YqYePr/9rXjFY7TvvXqLHqveirvXjd/jjyEjCiELNWml1PF/Como2rMifj8ADRdrfgzJKgQbG7Mnn154QbxffTVcc422x1lEEUWkxtu/fxtnWwAjH0hLYGQyv1m/Xozf8WN/R4c247rNJqalDkfu2ygiNQbbBvH5fShlSsQLsogiiigoKIoSJjDKJw99brbWhAiMFJ4P4xWefg9KQMHd5yboD6LT5y/7PkLoR5Zlk6jl6HTg6nFRUl8S9mAs4uRCkcAoEOh0Ompra1OW4wQ8Qrc06AsO+UxRIh4YNTVC3kklML7xjcyOQZWPWrFCJLonPIYA3Hxz4s8KxWw0WzQ1CdWm/n7YvTu1xUYiZNJ2hQKVwGhoSPChuRZW3i8qI7pehwN/hLJZMPcG8A3AO9+DoB8Cg0O/63dC7xbxd+2ZyQ9ArTiZMAe6+sF1BFgkWLe6JBnhNTWC4EiGT386+Wdr1ohXIMAEt49fzfJxrFVcmO1M4Cd8I1x7YcSLhMI778C3vgUrWUU5/ZHP7T5uu97G7Q2wpjkgnggHBiISYDqd6DwOBzz2GMbuQWZ3H0AelFGCCgf070Mn65h67GVKnO0E/EECngDWH27Fav4MsJLl3o3w5UdiZbRmzICPfETs4667Yn1Njm6CZTIYy2HrVuF7Em0Y39QkfleHQwwQ0fJdJlNigeXo6yAZDGVivRTQUjYlHoXS5/LqgWHfLd5VTX4tULU8RGC8mVcCQ70XROP0L51OMBjk0KFDeWs3W232pqkqVIIjmVTVwYOiy1ssMC1bP3RTFUwYSoqoyCRD/1OfgnvuEffgeIzEvTddnzOVmkCCgDuA3+tHb8xsennwuYO0vd1G88pmJp2lTcVDPBL202AA9t8l/q5aHktg5AmqX8yLL4p3hwPuvz+7MdDhgDffFH+fe2769QtlrCxi5JDvNj+68ShHXj7CxCUTmXHxjLzso1Dh9/gJeANUV0FNoxFn6/DmN4GACNTk05utpERMDYsERn4Q9AcZOD5AIBBAmaCg6FJkIRRRcCjeI8cusm07Z5cTv8uPTq9LKKWnEhiu7syUBsYTFCLjWlgWO0dEt1vbljaUgEL17GpMpaakhH42iVqv3/o6Hds7WHnDSiafPXlYx1pELAplvCwSGAUCnU7H9OnTU65jKjPhsXuwVA8NOtrtkYqImprIpHnjxsy9KVL5X6gYC2aj2UKSRBXGK68IGalcCIx0bVcoUMvZk2aFmWvFq/st0NugbC6Uhs5t8U9FkMeWoATdZ4fKJcKYNtHn8SiZDl1vwOCBnM4ja8gysk3mR782iyAh4FVMHGYqEAkS/u53wt/F4YBXGUrEhB8mDy1HXp7E8Le6Gn79a5z7u3ll10NYygyYLToaDFYUFA55zkb2uwk4XPjtLhqvPpd9j4c8ViZNEtJZqh9KlFk8waA4AJU48fng4AlY2igIhU3Pw7ZtsQb0H/yg8CjZtQvuvjv2OJub4dvfFn9/8YuCgIkmR667Tuz7mWdEeVI0qbJwISyqFU/F8ZUooRIuWYa7v3WYz10rhSggPV6MDFJCIHTryUY2JRqF0ufy6oGRDwKj+jQ4+CfhyRBwaxq0VflHnw96eyN2PdHIpd3e/O2bGEuMnHLVKRisqbNdQZAYuZhmqgRGIl1ciMhHzZs3TILA2w9bvy7GzJV/DQ8+mXj4xHfhaOT73puu7XSyDoPNgG/Qh3fAi746s+ll955ujm44Sllz6oqu4SChhFQgKpqnT6C9O7Af+ndB7UpRcaYRZFkMye99Lzz8MDz7bHYExquvij7W3AxTp6Zfv1DGyiJGBkJ2QUdb23SOHcuP7MJA6wDHXz2O0WYcdwSGajiq00v84jY9V101dJ1sZOFG4plKNfLu3NXFY3e8gq3OxoU/08BgowgAPAMiYUKWZXr29WAqNxUNgMcQivfIsYts2071vyhtLE1YQaDG14oSUkOhJk9bqi3IxuFNKqLbbfMfNmM/aufcH5xL7fx6TQh91fMwVTJbEbmhUMbLIoFRIFAzU6dOnZqU1aqcUYmrxxXW6IuGWn1hs4kM0UWLoLRUEBvbtwu/inRQCYwzUyTQn6y69iqBsX17UvuIpMik7QoFKSswouEORXwsUUxH2czYdaLTrC31sOBGUIZWByVESWjwGzyY2foaIZMgYaostaweJiUJRZIJykb8BiGpIiHhNpWDqRwvXtw+N4H5izhyv4j2GqY1w3uSEEAmU8T3Qz2Yl18Xv7mhQqRoq8sDAUF+qKVU8+YJskKtFvH5YrXSLr88VqLL54vIi/k64OhDoOhhwvvEZ5NCmdLd3fD005HvKIpgUEMs4MX7f8MLFw6w8dXI7/pzbuAAM3jyE3/nkueehw3GiLfJ6afD+98vtBP++tdY0sRiCeulBJ9/nvbDh5nQ3IzOZBLfnztXyI11dwsT++jv2mzipc6KNDIfzZuElKKAfY/4W0sCw9ok+rSrDXq2QO0Zmm3aZBL9p6dHBIwTERh+r5+D+w8yY86MjMZKv8fPgX8LknP+h7LVbQptw+2nc2cnkixRvyg50+TqFRlX5srUBEZW8lGKArtugZKp0PBeYdytt4ErlLbrs4vqqRDUDP1Euq8PPJDZLvN1783kPmewCAIjG9mosEm7BibriRAIROQ1YwgMX0i+UG8VPj/x2Hen6IOGEpiQQalDlrjoIkFgPP108qrWRFDlo849N7NhbCzNT4oYHoYru5Ap1Cq18RgcUIPVplITV1wp8cgjQ7NF1flkJr/5SDxTqQSGywW+bldepT/GG1Sfp6A/iKJTCNqD+N3+rDy7ihhdFO+RYxfZtl3fkT4Ayiclll1W42vFCoxY+F1+vA6vGOeCCl6HN7w8F0S3m5oUYCo1aUboj+c5Sr5RKONl8Q5bIAgGg3R2djJ58uSkF4SslympS5ApSOQBXdXQl2VBRPz730JGKh2B0dEhvJJBxBCT4WTVtVerLnIx8s6k7QoBbrcILkIGBIYr9LRkSRLwa38ZDt4Ls/8nsRdCOomhkpAGi+MoBLwgj5zxSETGI8DGjQc588xpnHOOPKpBwu5u8Z6VB0bQJwJrvv7YNpAkEdCP1oGzWsUrGVJpkZxzDlgfFNn6Z30lNmo2axb88pfib5U4ida4+drXmOrxMMntY9NrPrpP+Jj4UAMHNsMu02IuWVsliA+VWGlqEt/T6YTugc8XqTgJpVsEAnDwoU1079iFf0IJjXU+dCiiYqSyEt54A/7xj9hzWLZMSI319Ah9MJXYUKtGvvtdsf2HHhKsRHQlyllnwfTpcOSIiGKHvutRjEzsr6KfuUyoDcLe/bGkicEAFRXiXNTqmUyJE/cJEdzW6SN9RQtIkjCRPv64kJHSkMAAESDu6RH9Y9682M/eeegdtv1lG4Z5Bqb9aFpGY6U6+dQZdMim3DJ+Dr94mLd++xZ1C+pSEhiZVmBkRWC42qDjZeh6FRo/IJbp9MJXyNMjKtaMsQ9Sspx4Yj7a995M7nMlE0uwTbBlZfIbJjCSSHcNF11dke4XM76qBIZhqIwAIIhD+x5RCZUHAuM97xHvb74pKpYqKzP73osvivdM5KNg7MxPihgetJBdyBTjOTigBluMpWLO+oEPxP7mn/60qOTNtOplJMb1MIHhldEDAW8CHcIiskK035arx0XAH0Bfric4EMTn9OHp81DWVJa3+1oR2qF4jxy7yLbtKqdWMu3CadSekjg+EZaQ6nXl3edhLCB6nPMOCALD7/EzeGIQ2SAjyVJSX8FUUNtt0qRJMfdUrQj98BxlYPzNUfKNQhkviwTGGEAyhjN6uVqBURs1Jp99tiAwNmwQWVmp8Npr4n3evNQP0vnUtR9NqEbeuRAYYwXqgG82i7hqSqgVGOYET02uDnjtw+C1Q9u/wFQDSKCLkncxVQsfhWQkhmoC7bOD48jQ6o48Q5Zh9WoFm62bZcumhh828/EwmU3/rc5GqUQ2wpwvZfGFHGEKtWHADX6HyEhOhETESUhXSAZWhJrYPgleuQZuf3I6X/r1dBLe/2pqEnqbRDJMvxZe1tSocNuvgqyZHyIHzjkHTj01lhhRn+BtNlFipS6P9i8BQfKYTGK5xyPeXaFMnNZWET0MVZt4e/28h3kcNM6lwuqFb/5i6HnccosohbvzTlHepRImBoOoNDnzTCHP9cQTcf4k7dCMqFR66j/i+KKJkSVLxLG2tgpyJ/qz0lJxnsGgeMlyLHFSfRq0PgWKL3E7DgP19bBzZ2Ijb1X+yT+YW3a+lGPVTN0CcQ127e4i4A0kLX129+aBwOjbLt7L5saStMbqCIGhSvSlQSHfe+PHOPX/TLKz8k1gqNdibW2ct5dfrcBIQWDwj0gllMZobhZFY7t2CRmpRFI08RgcFIp9MHYkOgsRt99+O3/4wx+QJAmPx8Py5cv5yU9+QmNjIwC7du3ic5/7HP39/UiSxHe+8x3WaFnCoDFGwkchGuNZnkENiBhLxHiuJp+o8Hqz+41HYlwPExg+mVKKBIYWUP22evb38Py3ngcdNPx3A/IemWOvHKP5zGZW/M+KnOQsiyiiiPxg4tKJTFya/AHeVG5iwUcWYK2xoiQakMcZon0FDz57kK5dXbRvF9IDSz6xhKYzmpL6CmaCgCdA0C+UO0ylpqxiMEIuc2i1OozvJIvxgiKBUcCIZj6TSTKozGe0gbeKs88W7y+/nNhUNRqZ+F9AZmajuerajybUoNTx49llQ44lRMtHpYwFBjwiuAaJKzD8A2AoB59TVAG4OoAAWBpEsDvgAk+3ICeSERiSBKUzwd0OgcLRmtTyYTKb/ptTBcZIQTZGyCZPZ3ICI0NcdhmUl4uChpdfzjwQlzTDtFVi7YfkSIZpqmoTszl1473//ck/O+MM8Qph1xsKv30gQMMEkExG+P73h8pwqcdx7rlikImW8FL1bAwGMeConzkcENgrCIzS2YJd9nojvih+vzB2t1rhP/+B11+PPc73vlf8yHv2iMFYkiLkRl0dfO0G4b3wuz/Ck7dESBOjUZz/hAmCbDl4MJYYaWqCmTNFRcz+uGoToxEmTKC+Hgx4aW+VEbRV1E8fIgZyITCMZblXaJU2lGKuMOPuc9O9t5u6+XUJ11MrMCyVQ7WrPR7Yu1f8nR2BsU28V8QZK5lrYGCfIDAyRKp7r4qRvvdmM8Ylw0gRGEN8avyD4j2R/wVEpNsGD2nuF6PioosEgfGf/2RGYGzcKLr/lCniVURueP/7389nPvMZzGYzfr+fm2++mUsvvZQtW7bgdru57LLLuPvuu1m9ejUnTpxg9erVzJgxg4XZGqSNEEbam85UPn6DA2G5i9B41RU3hO/end32osf1eGj1TBUmMDxFAkNL2GpttG5qxWgzUjOvhtLppUyeP5n2Le107+nGXK79PaOIIorIHyRJylmu9mSF6itYNV3oAm/981Z2PbILR6cjvCxXqAkBOr2osk8XgwGRY/jOO/DRjyaXyywSGCc/igRGgUCn09HU1BRTjhPNfCaDynyqElLRFRjLlomO3tkpgi+zZyfff6YEBqT3ESjgRLWkKC8Xsv5Hj4qBMZtsp0RtV4jI2P/C0y2C1pIhubyGzgglU8CpXgB6QV7oQ09KwQxuGgtuBGn0frNE7aYlQZdN/82pAiPgDR30CMhvmWtDBEaX0PMfBiwW4S1+993w5z9nFlAZ6QzTTNDeIRFAL/wvdLqIi3UizJ0rXokwbZp4RcPdAf3vgrUZfhhnkhr9I3zwg3DppbGkiVpeNXEi/Nd/xX5mNAq9f51VEBWDgxHiZGBAVGyAmD2++Wbsd1etEgRGezv8+texx2Q2w223UV8P3+KHrLzvBBwMVY3o9fCpT2Eur6a2Zy/1LXvR/ao9Uo0yaxZccAE4naISJYpQkXZ2gxIUk9H9+wWTYDRGvltZKS4ov1/8Lnr9EHZWkiTqFtRxdMNR2ne0JyQwFEVh0qpJuPvcQ8w3AwFhx+L3i0BQxobtigK9oQqMykWxn5lCTGUWBAYkv/cC/Oxn+b33ZjJHURSFg88cZLB9kPkfnI9sklNmZymKkncCQ/WpGdJuPrt4TySBCIJkMlWL++HAfqjQ/qH2oovE/eTpp9MnmUDE/yKbIPRYmZ+MJKZGuZ/r9Xpuvvlmbr/9dlpbW9m0aRNLlixh9erVANTX1/OVr3yFe+65h1tvvXWUjjg1RtqbTu2r3kEvwUAQnTx+rq2GZQ2c/Z2zwxUY6rOXwSBuk7t3Z9aXo6GO61dfLbahQqtnKpXAcHrF5CjoC6IoSs5VjUVE0HuwF4D6xfVUNlVSN7EOa50VZ4eTljdbmHTWpFE+wiLSoXiPHLvIpu18Th+ODgeljaXIhjGWZVtAqF9Uz65HdtG+rT3n+4jabn6HSHwylYkq+1SEvgqPB66/fujyaLnM0yeL+3ORwNAehTJeFgmMAoF6QcRDZT7TIVEFhskk/CxeeklkOicjMLzeiCxBJgQGpDYbHatYsEAQGDt25EZgFDrUh9e0JXrWBjjrkYjERjIYysHiB9cJkE0R8iJTjCJ5AcnbLVmQcMIE+O1vs3uYzKT/Op0iqR2yrMBofw723gF1q2De19KvPxyYamDgALg7Ndncxz8uCIxHHoHf/CbygJ0MI51hmgnUzG7NDbwBzHXilQjRk0XVnDwRKipiKkaG4IMfFEHcRAHcSy4Rr2ioxElTE/z0p5GKEK83THzU18NfuBImOjj9mijyo6YGs9uE32DDrdShq6mJrSgBMSvduTOGNDHt7QDpfSJg9ve/i6qQaPz3f4ub3MaNcP/9YpnqXzJvHnzmM+D1MvudR7Dtb8f0x5eh+xTx+Uc/KqpYNm5Eam9neZMBphrgjQ2CVGlu5p9/6ePXXz3CsQ4jM9DjdRhZNsnCjb+pE+OA0xkhaeIn8Y4j4veVzaLaLBo5Ehgw9N57990isL1/f9abygqZzlFeuuklPHYPiz++mMqpqUsZ/S4/SlBcV6qmvNZI2k9rzhQEYTIJKYCy2dD5qvDByAOBcfbZYp527JgIfCbjOFVEG3hnirEyPxlNOJ1OJEmiurqaZ599NkxeqFi9ejW33XZb0u97PB48nsiDst0uyDG/348/NL7pdDp0Oh3BYJCgShRHLQ8EAjGSFcmWy7KMJEnh7QLU1UnEV7wlQl1dAL9fbEuv16MoCoEozyoRQJCHHGP8cp1ZR1AJotPp8Dl8GEoMmp+TuhyIOcZUy4dzTvHHmGy5qdLEhKViMPH7/XR06AAdixYpbN4M/f0SLS1+GhuzO6cPfED4sPl8Ej/7WYClS+GssxRMpuGfk9UqAxIOl2g3AK/Li8FsOGnbaaTOaelnlzLzAzMx28yYK80EAgEmrZrEzkd2cuDZA0w6a9KYOycVJ1M7pTunpqYmgsFgzPbH+jmdjO2U6NgnTpyY8tjV5a1bWnnlx69QPbOai391cdJzGmgfoP9YP5YqC+WTyovtFFru9/iR9BKVsyqR9BKOLgcDrQOUNpTmdE719fWc2HoCRVHCc4hgMMgHPgAPPijxoQ/pgMhzVVOTwle/GuTrX9fh8QwlTdTEgS9+UWHbqzbmrJ2DbYItfN7jpZ3yfU4gEnvC80ENzykb2bYigVEgCAQC7N27l1mzZoUvwGyQqAIDxAOySmAkkJQHYMsWETuqrhZxm0yRzGx0rGLBAnjySaGekg2G23YjhYwrMCAkO5MkMzUa1mbQmZLLcGQCdcAa4WywVO0WHST8+McFsfX73wvDRq2hko8Gg/CtzhjefvEupzDn1gpqMN2jDYGxcqXwxT5wQEhDffSjqdcf6QzTTJA0s3ssIOiHrd+Agb2w4o/Jpd6iofZPWU5qolNfDztYyIQAcHbsZ6Z+N32lTRx3VLBk3UcwmAyxK1RWCjP1KBz763Z48F1BYHzsC+JGFe1dot7w5s6FT3xCkCHq56oOoKJgXT4f93Y3vp4gwSDo3O6I78mRI0LHJ1rea80a1r/VzE8+to9P8oeYYzraNpm1a78pJMue+UqkakWV0/rGN0S0/KG74LV9UNoEm34tPlu+HFasgAEzvOqHiiOw7/GId8lZZ4ltbQtJT6nSXgaDYJ5NJnC7kQMBzlkp9tfQIPHCC6JK5Oc/F5vJBzK9z5VMLMFj9zDYNpiWwDBYDVz92NV4B73oTfmZjiaVkDLXiFcqlM0JERj58cGwWkWyxLPPChmpVASG3Q5vvy3+zmbeNVbmJ6OFd999l6997WvcdNNNmEwmWltbufDCC2PWaW5u5mA8eRqFH//4x9x8881Dlm/ZsgVbiGCura1l+vTpHDp0iM7OyH20qamJpqYm9u7dS39/f3j5tGnTqKur45133sGl+jABc+bMoaKigi1btoQfRs1maGw8jdZWCUUZOo+SJIXaWi9m8xY2bRIPisuXL6e/v5/dUZpHFouFRYsW0dXVFXO+5eXlzJ07l9bWVo6HMgkmf2Uy9c31mMpMHDhwQPNzAli4cCFGo5FNmzbFnM+yZcvwer1sj5qsa3FOubTT4cOnAKWUltqZONFES4uZxx/fw7p1jVmdU3n5MtxuPUZjkIULX8BqtbF9u16Tc/L5ZgOVtLYPMs3sQWfQsenNTUyeMXnctFO+z2np0qVs3boVt9uNp8qDNFlCmin64lg9p5OxnRKdk9lsxmQyUVlZyeHDh0+KczoZ2ynROSmKgt/vZ+XKlbS1taU8pxMvnKC/r5+qUiF7lOycnv/d87Q820Ld6jqa3t9UbCdg8fzFPPLBRxh0D7LgpgX4yn0MHhikfVs7QVsw63M6duwYDocDY8DIpA9NYtLUSTHnZDYbgaXodPCjH7VQWtrPokV2tm0rw+OZRzIoChw/LvHgP1tYutSDBw8ul2vctNNInNPBgwc5fPgwNpstTP5qdU7xxEsqSErRpQa73U55eTn9/f2UlWUQtNUYgQC8+GKAjRsPcuaZ0zjnHDnrSoYPfAD++U+4665YouKZZ+A974HJkyHqvhyDX/0K/vd/hfz544/nfBpjHg88AOvWieDqxo2Zf8/v97Np0yaWLVuGXl+4nODHPgZ/+YtInv7acBL2Bw7AK1eBoSJx1YXfAb4+OOvh9Aa1278rpHKW/FxIUo0gMm23K66Axx4Tqjlf+IL2x7FlCyxdKuKTKsmUEfbfBcf/CZM/CFPTMADDRctT0PYfmHAuNF+uySa/9z246SY4/3wRwEuFF1/MLOv4hRdGjlS9/npRPfLNb8IPfzjMjbk7I5I2vVuFhFTlAjCHyqUMZZmRDNnsb9s3RV+e8mGYcE7s5znuT73fzJ8vKtkAHJ0OPHYPSlDhn5/9JwP9A1z+u8uxVYuxI5XEUMAbwN3nRqfXDZF2ygaKovDYxx7D3efm/B+fP0RGyu/2E/AFMJYYkSSJQEB4DLQd92PBhREvBnwY8BFA5oTUQFOjwqH1W5CDUZUmXq+ISFutsP5m2PIEVKyAskXi81NPFQTGgQOCcYiX/vrOd8QBfeUrQt4rGl//upAae/jhmA6jyHr+9x+rubX1g/zpx2183HVnrD+JzQaf/axY+Z//FB4r0Ybxp54q5M9aW8Ur+rPycvFZMIi/u5vN77zD0tNOQ2+xRAigOLz6i1c58uIRFn18EfPWJn/QGCmsWyfu7b/4hZjnZAV3p/BpKp2RFw8MgFtugRtugIsvhn/9K/l6Tz0F73ufuAQOHMh8+/men4z2/DlX3HDDDfzlL3+hvb2dT33qU/z+979Hp9NxwQUX8PWvfz2GxAgGg+GMtERyCYkqMJqbm+nu7g7/JvnOyPvHP+Swj8pQEkPhb38LcsUVke0UswxzO6ejrx3F6/BSe0ottlobP/qRjptu0vGpTwVpbYWnntLx298GuPZaXVbn9PjjMldeKbFkSZDf/vYNli5diizLmpyTeoyf/rTCHXdEtnMyt9NInxPAW2+9FW63k+GcTsZ2SnROgUCALVu2sHTp0hhZlLF8TidjOyU69kAgwObNm1m+fHn4+JOd02u/eI2jG46y+OOLOeWqU5Ke085Hd7Llj1uYdNYkzvjqGcV2Atzdbh7/1ONIssTah9ey65Fd7PjrDiatnMRZ/++srM/J5/OxefNmli5disFgGHJOL70kccEFMjNmwO7dkWN/8EGJj340fYD0L38JcPXVSspzOhnbaSTOyev18vbbb4fvdVqek91up6KiIqPnicKNto4TrF+vStXIgJCZiDaiyRTJKjDOOEMkzB45Il6TJw/9bjb+FyczFiwQ7++8k72G7VhAxhUY+34P3h5ovhLKsijJyQVBtzBIHTw44gRGplDlspMRgMNFTv4XAN4+8Z5Jpcxw0fhe8dIQH/uYIDCef15IqDQ3J1931SpobBQal4mQjbm6VlArMIYtIeXuhFfXCa19EJJsfofQ3zdWiGWmalh5vzYkhrq/gf3g6QH7LrDE6crluD81y13Nend0Oli/bj3ObicAgycG8fl9/POT/wxr0Fqrray5f01CEkM2ytjqspSmSwBJkjj9y6djrbFS1jy0vxx95Shv3PYGDcsbWH3j6ijJMj2DJChpUODYcYkNjqXJCbM1N8Hl3wQlMDT4PX063Hhj8gP+4Q9jyQ2vN+KxcuaZwsQ9VDEi+XwsrmiAH8NdfzHzsZvmI/mjvhudDdHaKi7c6O1Oniy2vXmzIDiisXw5fOpT0NuL7pvfZHJnJ7raWkFeyLJg8HQ6kTlx/DgYDEzd2YP5YCf+LRKsnScMuN58M9a7ZMIEYdIVDAr9ymjSxGgUhlR6vfBlUZTIZ7Kc9Y05aT/t3Ah+J1QuTn6dm2u1JQ4T4KKLBIHx0ktCStCchCfJRT6qiOT4+c9/zs9//nO6u7v57ne/y3//93/z5z//GZPJhFvVdAzB5XJhMpmSaj2bTCZMpqEeLnq9fghpFF2GH41k1THJlsdv98ork/vjLF4scdVVQ7cjSVJCUivZMWa7fLjnlMvyfJ/T3n/spfPdTs78+pmUTywPz99qa3WUlQmicd8+OTxMZXrs77wj3ufPjwQQ1HWGe05qZa/TmXg7J2M7jcQ5vfS9l5BkiUUfXYStwTak3XI99vHUnwrtnLQ410I7p5OxneKJpkTL49cfOD6ATtJRMaUi5bGX1Jagk3S4e90x+x/P7eSxe5CQsFZaMRgMNCxt4N3736V9ezsoibeT6tjVILYaAI9fX423zJgRe+yZqqE2Ncm4Ogfw9HuomFqB3jR0LpbsXGHstlOq5VqeU6J7nRbnlI2fSpHAGEWsXy8MZ+JrYKKNaDIlMRJ5YICYuC5dKmIEGzYMJTAUJVJtMN4JjNmzRRzFbheSQYnInrGMjAmM3s3gbIWG96VeL+DKbnkilEyHvndh8ABwXubfG0FMmSLeDx3Kz/a7Q3HrrPwvAHyhUj1DhZaHM2KYMgVWrxaBu/vug//3/5KvK8tifHr44aGfZWuurhU088Dw2QV5oTOBbBF9T6cHUx3oraI/ebrFeloEU9X9GavE30FfiAQLTVSGsT+VwOjqEvFxj92Ds9spJo8WPaZyEwP2AaxlVpGB4fLj7HbisXsy8noaDiYuTW7+4+4TAUvVnFYzyTKdATAk/zyZfJ7ZnDyS3dAwZBD/wGKw3Aqv7qzk9earklufqJUYiXDJJcJMXSU2/H5xQwQoLSV43XW0b9tG9cyZ6BRFrKdObufMgaoq8PnQO1rw7XTg7gllpA8OihuqKtHl9YqnkmXLOPHGYZQv/QhLpYWKyeWRY/nFL8Tk5f/+L1bTUZKEd8t554kSn7/9LUJuGAyC5bzmGrHuX/4COh2L9hgoxcgph/UweI7Y7v79sPG34D0Oc66FuqViAK6ujhjaR1ex6HR5y2iYP19U37W1wSuviCZIhBdfFO9FAkNbVFdXc9ttt1FRUcHtt99OU1MTR48ejVnn2LFjY8JHRJW+VCu6p0yZxsc/LrN1q1DJS+exkg32PrmX9u3tTLtgGo3LG7XbcIFDNQVV7xXRz16qfF+USkPGUCsWFyzQXhRBtclyODTf9LiFz+WjbXMbSkBh6aeWJlzHftzOoRcOMe2CaZROzJO2YxFFFJEWQX+QgeMDAFRMrki5rqVaVHs7u5z5PqwxA0+/uO8Zy4RXXdWMKhZ8eAF1C5J4NWaInv09+Af9lE8qj0lWUz39pseJeKxaJUiMlpahsVOITWb85yeew9Xj4qJbL6JqetWwjrOIwkORwBglBAIiUypRB1Sz/7/0JfEwkklQLlkFBggfDJXA+MhHYj87ckQ8OOv1IiFyPMNgEHGYHTvEK1MCQ6fTMW3atIRMZSEhIwJDCQr5Ghiala3CUCYytD3dEPQkXsdUnVllQMk08T6YXF86X8i03dQKjHwRGDlXYKiSQ8by1OtpCY39Sj7+cUFg/PnPwjog2WbfeUfIeIH4nVTSB8Rk5dZbs6tY0wKae2DIFkFcoIjAt7maMKmQrJ8NB4ZyIQEX8AhPDGOUX0GO+6uuFvcSvx86OkAVfdJb9BhtRhRFodRQitFoDGda+D3+pNvb/tftBH1BZr53piaVGMmgEhjmCkEaTEzOdcQg6XpBX4i8SIEtXxfE7ZKfRcbBHFFZCVdfDffeC7/7XWrv9qSQZfFKRJwYjegWLqS+oUEYsMePmWdHDE+kpV0cfPcZrO6QN8/SpeKVAH3tHrbVrGXSyomccd2ySGWINfTdSy8VviDR1SjTQr9VRQUsXhz7mSV0xSmKmNj4fEzo9lGDl6b9PnCfLgiMl1+Gf24RFRivPgrGF4U+0wc+APv2iRJYFb5+MNvhm5+FpsvgBz8QXizR/iTr1okb6xtviEhxNPkxfTosXCiInK1bI8tDFSnSrFm85z3wxJ+72PCYxAWnRX03VHHS3y8KZCB7ibyxMj8ZTXg8HrxeL4FAgJUrV/Lkk09y3XXXhT9/6aWXWDlGMnxkGc49V2LBgnJqaiQefVTcO2+9Vfh4ZYpAQDwztLWJcW7VqthnkZ59PRx/9TjVM6vHFYHhHfQCYCwRgZzoZy812WV4BIakeX+NJjCe+9ZzDLYNcva3z6ZyWmqPoiKSo2NHB0pAoWRiCSUTSggGg0PabfMfNtP2dhuSTmLhhxeO4tEWkQrFe+TYRaZtN9A2QNAfRG/WY61N7RtprRGfu3vcKIqSVVb4yQp3f+gZqVw8H+hkHfOvnp/z9tR2O/DXAxx+/vAQyVmVwJgxI/Z7siym52vXinhBdAw1PpnRWGbE1eMKJx0UoQ0KZbwsEhijhIhERWIoipBV2bAh/QOrzweqb0qiLO6zzxYJjS+/PPQzVT5qyZJIzGA8Y8EC8SCxfbuInWQCnU5HXd3wWOh8w+mMXCMpg3OeLhHM1OkFCZEI5lohL6MG0BMhUw39khC9PnhwxHW7Mm23fEtI5VyBMZISUooCm74g9OBPuwtM2mQzrF0rfEX27BEqMytWDF0nEBAqNj6fIHQfeUQo13z5y+JaPnQot8qLdAGadNBMQioaap+SrYTJi3zCWCkkq1xtIbmq4fU/nU78Hi0tokJlakXs55IkYdAb8Hv8GMxpAvzAwacP4upxMfnsyZoQGEdePkLLmy3M/sBsqmdFxjdXr6gaM1eKyXk2WT4JseN74O2GmZ+HiiST/KBPkEfuzmETGACf+5wgMP72N+FrlTUhmgaZjpcl9UKvxNntJOANIBuTdyrPgJegTo+xtjKxMfzkyckzCZqbk+vOSRJ87Wu4XPCNb4tFp1wOl1aCDPDf/w1T/wXuQVh4IxjqIuTH5Mkie0StGOl4C47dD+0vCQJj2TJxQ40mTtRKFadTMHfRn8myIDC6u0VVSDSsVvjVr7joIpj259uZ+0A7+KI+v+46WLiQnb9+jh8Fn6a00kDjH0Lkx/z5wrgsDcbC/GQk4fV66ejoCFdU9PX18ZnPfIa1a9dSVVXF2rVrufHGG3nppZdYvXo1J06c4JZbbuG+++4b5SPPHNFt/uUvCwLj//4PfvSjzMaFiLRtZFm8tK1agTCeggOKouAdEARGogqMOXPE30eOgMsVGVLSweUSvCnAwoXa99doAsPV7cLZ6cTn8qX+UhEpcWKrKMGtXywyWBKNs1POnULb220cfvEwC9YtKAZCCxTFe+TYRaZt139UBEDKJ5en7YeWSgtIomrD0+8JJzaNZ6gVGFr9Fmq77RrcBYCx1BjzuerzFl+BAWIOkkguMz6ZcTzOUUYChTJeFgmMUYJmEhVEJtA6ncjEjMeZZ4r33bvFs3X0dVf0v4iF6oOhZkNlgkAgwDvvvMP8+fOTar2NNtTryGqFlL44rtCK5gkgpQigaqUNbm0Smcp+J7hPJK/6yAMybTc1q663V5BA5RoXPORcgVF9mvAqMWocqUwESYKAUwRcPZ2aERilpWKycd99ogojEYHx61+LxOayMvjtb0Xi89VXB/jyl2VOnFBwu6XwA3qmyCRAkwpud4QQ1JTA8IQuBmOCgTwfME8Adxf4BwUhpsF+6+uTExiuXhcduzuwlFuom5d6AqQoSnjiGT+5zRXHXz/O0Q1HKWsqiyEw4isworN84pFWsizoA/tOCHhTk4umahjYF/E+GSaWLxeJCFu2iL6UtWF1GmQ6XprKTFx828WU1JekJC9gqByLlli/XpCjKi6/PKqPX6GA5AaLHmqbY6vYbLZYrR33JHj9KXAcFG168cXJd3ruuck1niZPhjvvFOVJKjkSMtS78EL4Jp/A3OPinA/6qK0IkR8hgubFg5N4lbN476k+mB2S98rwRjQW5icjic7OTi677DIcDgdmsxmdTse6dev44he/CIDNZuPxxx/n85//PIODgwSDQW6++WZWJLo5FSii23zVKpmlS0UFz+9/D9/8ZurvZiptOx6DAwFPgKBfmFomqsCoqRHPYL29gpBYmGHS/e7dwg6oqgrq6gJs26Ztf40mMHQGMa8PeIeaUBeROdo2i2el+iWCwEg0zjad3oTerMdxwkHX7i5q5+bXT6mI3FC8R45dZNp2FZMrWPixhRnNNXV6HeYKM+5eN84uZ5HAIFKBEf37KUGF468fp31HO0s+sSTsa5gJ1HZTiRFTadR2leQVGCpUucznnhNTckUR8cxopc/xOEcZCRTKeFmslxslDFuiIgrRAdBEFT3V1SJZD4TGcjTyRmC4O2HgQPKXu1PjHWoD9YEjGwJDURRcLhdKolTdAkG0fFTK5IMwgaGVLk4a6PRgmyL+HjgwMvsMIdN2KymJVEfkQ0Yq5wqM2dfDgptGTkLKFHr40rjvfuxj4v3BB4UySzQOH4ZvfUv8/bOfCYl7gJoahcpKH4oisWtXdvtTAzTxFXBqgGb9+vTbUKsvjMbEieM5IegF34DooKZsL4YcoTNB6TQona4ZaRJv5B0Ng9WAElTwDfoI+FIHUPxufzhYpFWAW9Vrbd/RHrPc3RtLYEAky6cqjqtrakrjT2XfIwLdxnKwpnCmVwlglbQaJiRJVGGAiJNrfTvKdLyUJInKaZUYrOkrbPJFYKh9PD4BRO3jj68fjCzUlyTfkLsTvHaQ9OK9/YXhzWMkSVRr2Gxi4Aix1jU1ULNsCruZy1PHF8Kpp8Lpp4czUh7eOpMneD8Vn1gjtMI+8pEY2a5UGAvzk5FEY2Mjb7/9Nrt372br1q1s3ryZr371qxgMket10aJFbNy4kW3btrFjxw7WrVs3ikecPaLbXJJEFQaIykWvN/n30knbgihOCgTGZ3DAMyDOVWfQIZtkFCW2AkOSIlUY2chIReSjALTvr9EEht4k8haLBEbucHY5GWgZAAkmLBQZLInGWb1JT9NKEVE7/MLh0TjUIjJA8R45dpFp25U1lXHKVacw46IkEfE4LPzIQlZ8aUVauanxgrKmMhpOa6ByetRzogSb7tzEvif20bU7u+cYtd3Ue2p0klpXl7Chk6SIAkYiyDK85z2RKo29e2M/H49zlJFAoYyXRQJjlKBKVCQLJkuSSL5LKlERBTUDKFUAVH3WjZaRGhyEbdvE35oSGO5OeHUdvHJV8ter6wqSxFArMPbsSf2gN9aQsYG3OxTYG8FKCKqWQPVyMKQIJI0y8ikjlXMFxkjDpG3AVcV55wliorcXnngislxRhOew0ynGr09/OvZ706YJg7V33818X9kEaFIhWj5KM1WAgAskWWTt67SpOMgIhgpNq3hSERg6ow7ZIqOghKsekkGddMpGORx0GS4mLBDBhu7d3TEEinoslspYzY81a4Q3C4hKxhdeECRmyiqdvpDpdMWi1BeH+ptr2J/WrRNVTfv2wfPPa7bZvEHrChvIrI9//6YBFAC9FXRJMojUeczGD0L3G9DzNrx1bd7mMe95j3j/z39il/f2CusMyN7/oogiQPjeq0bxf/tb8vWykbYdj8GBsHxUqQlJknA4IkkXqv/g8AkM7RFNYKhVcUUCI3e0bRHMePWsaoy21PeuqeeKh4ejG46GEzKKKKKIwsb090xn2vnTwp4P4x3TL5zO6u+sDo9nIJKVVAK3fVt7sq+mRPQ9VYVafdHUlNiSLx5qwXR8MuN4nKOMJxQJjFGCKlEBQ2McaSUq4qAGQBMZeKtQiZANGyLL3nxTlC1PmhRbdjVs+OxCFkNnEsGx+JfOJD5P5aEwSmhqEsoMfn9uRnyFiowJjKAPZCNYRqgCA2DqR2HBjVC5eOT2mSVUGamCqcAI+kSW90hCrQrwaEs8yjJ89KPi7z//ObL8L3+Bp58GkwnuvntoddnUqcK3IBsCI5sATSrkxf9CZ4SyWWCqA78j8gq4NNxJFAKu2P34HcKwePAQKLk/aCciMPwuP16HF5/Dh2ySCfqDONod+F3JDbzzkZ1f2liKucJMwBuge6/oeIqiMPnsyTStbMJSNVS0vKNDvK9YIQLIae/JYQIjjX5IuD9pR2CUlET60p13arbZrNG5q5O3fvcWe5/cm3K9fLRxJn3c3jMoxt1U1RfR8xhjtagWlKS8zWMuuki8P/OMmJepePllccyzZ2deuVtEEdEwGoWdCgh/nGSJc9lI247H4IBtgo2zbzybZZ9fBkSSx8zmiIdgkcA4+WEsMVIzr4aGZekeqESFhqXKgnfQS+vbrSNwdEUUUUQ0Ar4Ax149hr3FPupZ4ycbJiwSD8EntiXIWEsDRVHwDA5NYlL9L5LJR8VDJTB27oxdPh7nKOMJRQJjFKFKVKiyKCrSSlTEIZMKDJXA2Lo1ot2ed/8L2QJ6m3jo1+nF33qbWF6gkKTsfTBkWWbOnDkFrZ2ZMYEx41Nw1iPQ8L68H9NoI5t2Uysw8kFg5FSB0f0WbLgStn1L+wNKBo0lb6Lx8Y+L96eegkcfFcFXVb/+u9+FWbNi15dlmTPOEP4C2RAYWnkPaUpgGMqEH0LQI4KhASf4+iKvoEd8rpVZe8z++mJf9t3CV8XvAH1pTpuPJjBMZSas1Vb8Hj/uPjeefg9Gk5GgP4ir14XP5cNabU0YwA5n55dpl50vSVJYRqpjR0d42bLPLWPV/1sV1jWPhkrE1GfC6QbcQkIKoHLkCQwQVUsgjHszvd4zQTbjpf24nf1P7aflzZaU6/kGhZGslgRGJud8vKeJTZ7vwazPp19ZtoC5WshIBTx5m8eccYaonunqEj4mKl54Qbwns9ZIh7EwPylCWyRq889+VgTaN28eKiWrIhtpWzXg4HOMHzNoo81I4/JGmlaIjK/o5DE18Wz2bPG+Z0/m21WfNebPz09/VQmMwUHQGYoExnDRfEYzF/70QuZfPT+8LFm7STqJyasnY7AZwlKVRRQWivfIsYtkbefodNBzoIeeAz0ce/UYz3/7eZ747BPhZY5OR8rtuvvdtG1po+Odjnwe/piB3+NPSP7ULxIPRj17e/A5M58LyLLMjEkzILTJRBUYiQy8EyFZBUbNnBpOufoUJq+anPFxFZEehTJeFk28RxmqEc2vfgU33CB8Hg8cyKzyQkUmFRiNjTBtGhw8KIiLSy4ZIQPvoBf63hF/V5+Wxx1phwULxAPe9u3w4Q+nX1+SJCo0E8LPD9SgTkYPqJIkgjUjCUUBb28oMKS9oWsiZNNu+ZSQyqkCwxdiIUeSDMyTBwaIrMUZM8TEJZq4NRjEuBUPSZJYvlykPL7zTub70cp7SA1qa0JgmGvh1NtAMiaXHDKURQgkLfa38v7EmeMDB2D3z0GRoPs1aLos682rgf62NrDV2lhz/5qYDBhFUXjh2y/g6HCw7HPLmHLuFGy1Q13Y8+WPULegjqMbjtK+oz0mAJEMWREY/bsg6Be/cTofIXOd8B7RWK5v4UJxT3/1VbjnnoiHzHCRzXhZOlGQX4NtgynXu+zey/A6vBgs6f0yMkUmfdzpsWFuWAJV6dcFIokYxkrEE5dWunERGAxCTu8f/xAyUqeeKpa/+KJ4z5XAGAvzkyK0RaI2r6kRflN33SWeNxLJ065aJaxZ+vqSbVckWK1aBTqpjCsfvDIjr5uTFYmSx6IrMILBxL6E0ejpiSQYzZ+fn/6qEhjBIBgrrZQ2laI3Fx//tUSqdjvlQ6ew8KMLszK5LWLkULxHjl0kajtHp4P169bj7BYyw95BL452B3qznkc++AgA1mora+5fk/DZA6Btcxuv//J16hbWcf4Pz8/rOYwFPPqRR1GCCu/73fuw1UV+M1udjZKJJQy2DdLxbgeNyxtTbCUCSZKoqq3ijC+fgXfQG64MhPQG3vGYN0+8xxMY1TOrqZ5Z6NrcYw+FMl4WKzAKALIMl1wipDS6u5W0E954RJvIpYLqg7Fhg5jIvvaa+D+vBEbAmfjvAka2FRh+v5+33noLvz+5HMpoI+MKjNHCtm/Cax+H3m0jtsts2i1fElJutyjrhywrMLwhAsMwQgbeAOYJUDINbJM03/T69ZFJSzR8PuFZG2+s7ff78Xg2A3D0qDD8ygRaeQ+pFRgZBbXTIeCG7TfBnl+CqUoEteNfWpEXKsy1iffT8B6Y9T+CRDx4L/TvTL+tOMRLSNlqbVRNr6JqehVlk8s42HuQKedNwWgzYj9uT/oAMemsSXzgng9w2he0Jb4nLJgAEgT9QRRFwe/24xnwJC0tz4rAMJbDxAuh7pz05ijmWjj1Vpj39WwOPyNce614v/12+OtfRRA8na9LOmQzXpZMFNJMjnZHSt1vSSdhKjWh02s3FVX7eNJ9ZuEvFoEObFNDY1+oXRXtM5jjfTC6uyM+ZatX57bNsTA/yRaDg4P85je/4dprr+WGG27gqaeeGu1DKigka/MvfUm8P/aYSGaKx6uvgj2JIlq8tK2kkzDajEiamUAVPrr3dnPohUP0He4DEiePTZsGer3w7mpJXYAGRBIwJk+GsrL89Fdb1C125odO5dLfXRqjZR6P6OzlRK902csnM+wt9rDxbDRStZvP6aP/aH/x9yxQnIz3yJMZ0eNT555OXnz0RTr3dIaX9R3qw9ntRG/SY64wI+kldHodpnIT5gozepMeZ7czpbSQtUYkyLm68yThO4bg9/jxu/0EvIGEVeqqjFQ2Phh+v5/N2zbTfHYzsz8wO+YzVUIq0woMNWngxInkyRdFaIdCGS+LKRgFguZm8T44KNHfL7KgMoWaBZSqAgMEgfGnPwlN5V27REe3WkXGZt4QrdPvs4NszePOtEG2BAZAYLjRoTwjIwJj4ADsuR3K58DMa0fkuHB3hjLBJSFb0/WakLdRoWXmeQJk2m7RElKKop1xs1p9IcvCeyVj+ENRhpEkMEqmwLLbNN+sarqbCl/6kqhUi65MKynxMnGiQlubxM6dwqMgHVTvobVrh36WjfeQphJSnRsFiaEEhLb+aKPxUiEl1fYMbPs2zP9OYvmqJH0zlYk3iD4349wZlE4sDUtxJIJskJOSG8NBaWMpVz5wZdh88+grR3njtjeYuGwi59x0zpD1UxIY4fErCqr83sCBvI9fyaAP2TV0dMBHPiKWNTWJaz9TacpEyHS8tFRZkI0yAW8AR6cjXJExElD7+JVXDv1M7eN//OUu5I7jgrQrSVDilQimuAwRx2Ehw2ZP7fORzTWg+mCogeSXXhL/z5s3vLGm0OcnyWCz2ejo6MAWFYHt6+tj2bJlOBwOzjrrLNra2rjnnntYsWIFjz/+OHp98bEGErf53Llw8cXw738LcvPWWyOfHTsm7ovBIJx5Jhw5Eusl09Qk1h/O+DHWceTlI+z5xx7mXjmXxf+1OGEFhsEgMkd37xYv9dkuGRL5X2jdX/V64YPi9YqEmVTJMvHZy4mQLnv5ZMam322ifXs7K7+6kslnx0qTJGq3+N8z4A3EZBvD+P49CwVj9R453hDfnxRFwel0stu6O0ymGywGgv4g+go9BosBxa+g0+swV5jD836/J3XwVSUwnF1OFEUZV0R9PDz9gujR6XXoLUPnV/WL6jnw7wPYW7Lzg0vW57KtwCgrEyozLS0itnnGGWK5ElQYaBvAY/dQM6dmXLeh1iiE8bI40y8QWK1QVubDbjdw7Fh2BEamFRhqxuFbb8Fzz4m/TztNTLjzhmAUw+2zp5fVKACoDxLHj0NvL1RWju7xaIGMCAxXCwweBP0IyRK5O+HVdcII1dsn3u274VCUk7OpWsjdjEIQMBqTQ88pDocgHbKSe0qBaP+LrO6tagWGcQQJjDwhG2Ptc86J/WzePEFgvPtuZgQGiADML38JX/5y7PJsAjSaEhgnng1t7HztmLHhQJJg8tWCzPTZoetVME8cemxJ+qYa6Hc4hOZ2SQKf5IqpFdTM1KgTZQlJksIPMQDuPqFLbS43D1nX54v00SEERvT4lQyZjF+KAiggaVOFsH49rFs31Ki3pUUEKLPx18oVkiRRMrGE/iP9DLQOJCQw+o/1s+P+HZQ1lbHww9pmUZx9tpBuCcYVf6h9/MIFL8OeJ2DyBzMnMKIRcILfKYjH1z4iiGRjOQmlpbK4h02fLl4HDoiqmeHKR411uFyuIQ9K3//+96mpqWHLli2UlorrqqWlhQsvvJBbbrmFb3zjG6NxqGMGX/6yIDD+8Ae44AJRvVhVBf/v/wnCc9EiUQFkNsPDD8M114gA+MGD4j0a2/+6nf4j/cy/Zj6VU0+CiXIaeAdFQpaahZpMvnfOnAiBceGFqbeZbwNvFTZbhMBIBY/dE85eThSs8rv84ezl8RZw93v8dL7bCQpUTs/seld/T9ko03+0n4AnQM3cmjCJMZ5/zyKKyBbx45OiKPh0PsxlZiRJwu/y4+oTVRMDJwZw97jDPgvZSJVaqwWBEfAE8Dl9Mc8M4w3u/tAzUoU5IQnQsKyB9//h/ZRMSPCwlwKeHg+tm1opbyynvFnEMvr7I/fVTCswQCRnxBMYAW+AJz/3JABXPXxVUTbxJENRQqqAMGGCmBwfPZrd9zKtwJg+XQRhvF744Q/FMrWj5wUBlzCCNdeBJINsFln2gcIuySsvh0khlZxs9PULFQMDEYmdlPrgrpBRxkiRTD67CP7pTMJfQRe6uRgqxEtnEp8n0uofYZjNkd9OSxmpnPwvQBguw8hWYKhQFFCSy8Jki+EYa6val9n20/ix8vTTRbtmGtjVzAPD1S48giQJJpw3zI1piKBXBGQlPSCDviTSL9P0zZKSiGRFsiqMTLDvqX1suXcLPQd6ct9IGqhm4iAm5/HoCPn3yXKCrNXo8ctQAToLSCbRJzMdv/b9Dl65Ctr+o8n5qNVMidSw1GVf+tLw5aQygSojNXgisQ/G4IlBjr1yjNZNrZrve/16QV4sXixMsO+/X7yH+7g/dEPMxKg+4BLzluiXogg5Kdkk+oq3F9zdoDNn3E+SIVpGSjXwjiduxwsSPSw///zzfPOb3wyTFwCNjY1873vf47777hvJwxuTuPBCQeQ5HPD+9wuy8+KLhXF8aamQl7LZxJh3xRXiO36/CCzEo31bO8dfO560j59siPdlSlSBAdkZeUcbeOcT6j350NP7ePK6J9nxQOoSc71Fj9FmxGA1oDeLv402Y0JSY7yg891Ogv4g1horpQ3ZVRUarAYMNgM6vQ6/y1/8PYsoYhhQxyedXkfAE8DR4aD3QC+SLjJnkGUZFNAZdFhrrRhLMychZKMcXt/ZNTbkz/MFtQLDWJb499Ob9VmTFwD2XXY2/GAD2+/bHl6mykfV1Yn5SKZIZOQtm+QwUZxKLqyIsYkigVEgkGWZmTPFpPjYsey+m2kFxqOPRh5C1MDM3XcP1ZcfNgxlIusw6IGAA3QGsNQLEsPXJ5abqhPLkhQI1IeJe+9Nrx8uyzILFy4UN8sChBr4LS1Nc0NQCQyNTWXTQraI60HSCxkd2RQy885vJUi27RYtI6UVoiswsoJvFCSkAHbfJgKu7S9otslcjLXVtluwQExW3303u32quvKqdmZ3d3rZqGho5oHR/rx4r1gE5tGpSEgKnRFKZ0LlIjBWiD6pvtL0zWQyUtF9zu/xs/8/+3n91tcT+k8c2XCE3et3M9CaocFJFvAMeHjma8+w/sPrcXaKhxNz5VACI7rSJqk3lWwRv0lgEBwHwdudxfglQcADnq7cTiQO2VQzZYtsx0u16iLZw1++TNoBHnhAvF9zjQj+q+/hQ/eFrilDihti9DzG1zf0hQKls4W0lGQQ6zkOis/01pzvYaqM1AMPRIjZs87KejNhFPr8JBUSjQsnTpxgthohjsKKFSs4oD79jnOkavNHH00+RgwMwObNkf9NpsjcJFECgRrgGS/BgWwqMEBUYKSCokT6uFqBka/+qhIYjl4v9qP28H0vHezH7LRtbsPr8KZf+STHia1iQlO/pH4IuZpJu6myNI4OR1oJmyJGDmP5Hjme4XP76NjRga/Dh6vLhd/tx+uMjFPmSjMTFk6gfkk9lVMrs5YQslSL+dt4JzCiKzDSIZmXYDxkWaahpgFJkmKeAbKVj1KRiMCI3rZ6DkUMH4UyXhap/wLC5MkiQpINgaEomVVgrF8v5CPix5bu7jzISphrhWRCqqzDUdIGzwTr10cCPPfeK17p9MONxsItL1Tlo9IGit2haONIExgQqdAJuEWG6wgF5rNpt6lThTb54cPa7T/nCoyKRaJqxVyn3cFkAknbgCtETHdbWhJnjkuS+DzedNdoNHLKKeLvbAmMrVvF+4c+BDffLOQxvF6hE50ObneECB5WBYaiQHtIy6/+gmFsKI/QW0XfzBL19SKTJlEFRrjPKbD5rs0EvAHmXD6HiikVMevlM8BtLDEy0DqAz+mj5U3htJpocp6Vgbd6v8skq1+F6qugUX8aTjVTJshmvJy3dh6nfOiUpKX33gHxoKl1+7a2RrwjPvShJCupBEaqtspkHuPphrc+B5Ym8HSGKjE6BHmRo5+NWi3Z2xtZtnz58PxLCnl+kgqSJA0JOjQ3N+Pz+RKur0vKMo4/JGrzdH5TkjTUb2riRDFPaW0dWiWg9t3xQmBkWoGRKYFx7JjwutHrI1UbkJ/+qhIY3oCMDiGvkQnU6pq+w33UnTLC880CQ9sWceOsX5J4QpCu3SxVFgZaBwi4A3Tt7qJmToElrYxjjNV75HiGp98Disi0t1aLCgudToejS+jk6fS6YckGWWus9B/ux9VT2Koh+YZ630sks6vC0eng7d+/jaPTwSW3XZLRdoMuoeQQbQyerYG3CpXA2LkzdrmxzIizyxl+3ihCGxTCeFmc7RcIAoEAkiTSorKRkBoYEDrdkDwIOiqyEuZaME+A7reEt0LpdJHt6OsTfxcwebF2bSSIoELVD09UrRIIBNi0aVNBmNokghqsSul/ASMvIRUPfagE0ad9xnUiZNtuU6aI94KowJjxKVhwI1gbtTuYTKAGXN2dmm1SNd2FoTYLyYy11babPVu0XUsL9PVlvk+1AuOSS8SDfSAgSIxMoFavGY3ZeRUNQf9OISGlt0DN6cPY0Agg4BZB9mBmk8BkFRjRfU5v1jNhsWCAjr8+NCXYaw8FuEu1JzAkSaJugQjGKAFxExwegRGMSCPqsyilNoU6vkYERi7VTJki2/HSVGZKqRucL4Lq4YfFvGblyoh30RD4M6jAADFPKZ2e/KW2n84gqpUq5kPlkpzJi/Xr4WMfG7o81fwjHQp9fpIKiqLwvve9j/POOy/8On78OLsTRIa3bNnCFPUmPc6RrM1zqdBS542JSM/xRmAkq8BIJiHV0jL0WSIaqnzUnDmR5Il89VeVwPAExEQqUwJDRbbrn2xw9bjoP9wPkjCtjUcm7abT6aidU4tslgl4BIkx3n/XQsBYvkeOZ3gHvSgoYIXSplIsFRYkOfIQ6Xf58Tq8Q15+V2bVT7PeN4sVX1pB3fzxTdza6mw0rmikamZV0nVMpSZaN7XSd7AvI0nJQCDAgV0HUBRFkwoMVU768GFwRfFN6vPjeJmjjAQKZbwsVmAUEOrqRAfLpgJDzQCy2cCSRK1gOCa5w4LzGBz+qwgClEyHNz8HshFWPiDeM4W7c0SqOdIRPYmy08YCMjLwDnjBE9KaH40KDABTFejkgjWmzoeElFqBkTWBMVowhfqZhhUYIDKLH3lE9L/osSqdsXZ5uVjn+HFRhXHmmen3deKEICF0OiHbMGuW0P/esyeSOZnu+yCqL4bluV0+FxbeLH5LWfsgvaZwHBLEYsnUyDWQAskIjHg0nd5E65utHH/jOPOvjqT3KoqS1woMR6cDa401RhbDY/eE/TZMZSZstbbMCYyAW9wkdHohvZUpNO5P6aqZAJqbh1YzjQbC7asxQfXgg+L96qtTrJSJhFQukK05f/VknX8MB/fdd1/CaoulS5cOWRYMBvnBD34wEoc1ZpFLhZZKdqrzyGiMJwJDUZQhVWPJqt8rK4WGd0eHmFcsW5Z4myNl4A1RBIZfxkJmhEQwEPE6C/qCGcuDnIw4sU1MBiqnVQ5rTiIbZWrn1tK1qwu/289A20DKzOYiiigiMVRCWW+NDWXqdDpM5SZ8Tl9SqTZrtTVtP25Yli7rc3xg0pmTmHTmpJTr6M16qmdV07WrixPbTjCjPj0D4Xf6kZASVmBkS2DU1kJVFfT0iHvu4sVi+Xiao4w3FAmMAkJ9vRiMsyEwMvG/yLesRFJESxJZGsBYKSQWBvZARYYzdncnvLpOSDUkg6laSD0Mk8QYNaInz8iIwPDZwdoE/sHsMoi1hGp8WqBQCQwtJaQy9a+JQdAf8QoZaah9zKNdBYaKNWtEcG7DBjEWTZwoAq3pgnXz52dHYKjVF7NmgdUqSIstW4Tcw2WXpf9+tC/CsCDpoGpoIK4goS8RQV+/Q1MCo3F5I0jQu78XZ5czrBHtc/pQgiJYojWB4eh0sH7degbaBrAfE8S4wWbgX9f/K2wAaK22sub+NZw4YYs5n6TwhzRysw1gR1dgqFHqYUCtZlq7VmwqUbzplltGLgC++Y+bsR+zc9r1p2Gtjv1t8kFQHToEr78uyMmrrkqykhIU1zFkJ/eVZ5ys84/hYN26dRmve/nll+fvQE4S5FKhVazACEGBVd9ehXfAi6nMhN8fkXlLNH+bMydzAiPfBt4QRWD4MqvA8Lv8KAEF2wQbAy0DGEoMeOwegr5gyu+drKhfXM9p/3MaelNuYZP4rO/yyeUMtA5gqbaM29+0iCJyhc/pQ1GUcAW1z+FDkiT8Lj86g46LfnlR2MMiEdQkpSK0Q/3ierp2ddG+rZ0ZF6VnIAKOAHr0CSswspWQkiQhI7Vxo/DBKBIYJz+KBEYBIboCIxhMYRoahWQarNHIp6xESkRLEkkSVC6E9pegd3vmBIbPLsgLnSmxIWbAJT732YdNYIwa0ZNnZERgmGvgtN9pEkTLGgFXdstHCao6xeHDmffPdMipAsO+B7Z+A0qmwLJfD/8gsoEpfwQGiMBqtsG5U06Bf/87cx8M1f9i0SLxrso97NmT2fc1IzDGAtQ+KMmg+AUBbapN2zfVe0k6AsNcYaZmTg1du7o4/sZxZr1vFhCZbOrNemSjttF2j92Ds9uJsdSI3qIn6AtS1lwWljvyu/w4u5147J4wgZGyrQMu8PaI30eSI8HxTMYvY6jjB7yCPNagIiBZNZNOJ8YtVQJtJND6VisDLQMMtAwMJTAGtCcwHnpIvJ97bhrSaeH3hIyUVmR9dFs7Dor2LJ0BwcReDYlwss4/iigc5OI3lUkFhpoJmwscnY6UwYVCCTRJOkkQ7iF0huYBkiQyP+MxZw68/HJqH4x4A+98QiUw3GkIDFOZCWu1FWe3E7/Hj06vo3yyqIpW2zmT7OWxjkTXZeW0SgB6DvRkfF3G/57xnwV9QazVVgLeAO072jFYDUm3Uwj9oIgiRhPR/clWa0MJKjidTty4w35Z1morFVMrht1ffC4fXbu78Lv8NK9s1uLwxyQC3gA6gy6lCbqj04G50ozX4eXoxqPM2T8nZv1E45ff6UePPlyB4XKJuQlkX4EBsQSGiomnTsRYamTCgvHwwD6+MCwCY+/evTz44IMcPXqUYHBoBoHZbOaOO+4Yzi7GDWRZ5uKLFyJJCj6fREdHZsahagZ3KgPvXE1yhw1XnCl0xSJBYPRtAz6c3bZkizDGDHqE3nS0fUtQG2Y1V6JHlmWWLVuGXKC6DhkRGCpGkrwwlIkMZE93pA0VBYIhORa9VXxuKMvL7rNtt+ZmEQT0eERgNqPfMw1yqsDwhRyk5VEoO1c9MPwuEajVj84DVXTbZWvkrVZg5EpgREtI5YydPxe/ZdNlQjqt0BDfN4P+UOXPgAjWS7qUfTNZBUaiPtd0epMgMF4fSmAYy/JnFGawGLBUWnD1uAj6gzF+DWqgIaWEVPRv5OuPVEb5+iLrpBu/ZCNULhJ9OYtgdzokqmbauROuuw5uugk+/GEhc5INcrnPlUwsEQRG6wATFsZ2mPN/dD7eQW/OGa2JkJF8lKSDqiXa7DDRPcw3IK4FT5do1wzvYflKNCn0+YmWaGtrY6LmmThjD8naPFWFVjK/qVQVGBOXTmTtQ2vRW3Lrw2o1nLPbmXQdtRqu0IK36tytslKYcMcjnZG3zxcJtEQTGPnqryqB4QoYsdYkJyBstTbW3L9mTJBK+UIu12Wydsvk9wx4A/z9mr/Tf7Sf0obShEkbhdoPTgaMp3vkWEd8f1IUhWAgiE6OBNi1Gp+cnU5evPFFjCXGcU1gPHHtE3j6PFzw0wuomjH0eVUdLx1dDvoO9YECD13+UMw4lmi8PO9L5+Hp81DWKObHqg9leXnipIB0UI28owmMxuWNMYkHRQwfhTJe5vzk+MQTT7Bu3TouvfRSpk+fjj7BDM5kOrkzNLSGonhpaLDQ0iKqMDIhMDKpwMjloUUTqBJSqil0xULxPrBXaIZnG4D19cHAPqExXjJNc/mH4RA9Xq8XSzITklGGSmAU3HO9uVZIf0X7m3S/BfvvAmuzMKnWyN8kGbJpN4NBkBhHjogqDC0IjJwqMNTfyzAKXiGySRjV6m0Q8IwagQGRtlMJDDWbMR2GS2CoFRiZjM8J4WqHjpfFgNJ4aY4byTMS9c0tN4C3D+beAGWzUvbNVBJS8X2ucUUj2/68DUmSUBQFSZKoml7FB+75AH53ZmZ7ucJcYUZRFAyWxFmPKQmM6N/IvhcGD0LVMlHNpiKT8WtRfnT746uZzjoL7rhDEH3f+x786lfZbzPb+1zpxFLaaGOgbaiTrSRJmvpf7Nol+rbBkNwzR3Mk6ie7fwX9O2Hax6B2Vcb3sHwmmhTy/CQd/v73v3PXXXfR2dnJ4sWL+epXv8o81bExCna7naamplE3FiwUJGvzbP2m1HljIgJDNsjIhtwfHNRqOL1Jn5AEia6GG+3AraPDQce7HZRMKKF2Xm3a5LF0BMbevYLEKC2FyZNjP8tHf1UJDIetjst+l1or01Zrw1Zro+9wH36Pn9KGUkylJrwObwzRf7Ii/rr09HsI+AKYykzIRjnpdZms3dTfMxk6d3cy0DIACji7nFROq0RvjvSHQuoHJyvG8j1yvMFWa8NSZUEn61AUBZfLhcViSVkhkAtUCSrvoBe/x69pss1YgtfuJeANYLAlfk5Sx0uD2SCqMOxeJEnCXCFifMnGrwmnTYhpt2gD71yaMhGBUUR+UAjjZc4iKF/84he59957uf/++/n+97/PTTfdNOT1jW98Q8tjPakRCATYvn07TU3i/6NHM/teJhUYEHloaYwjIpuaxPK8PPCrElJqBYZlApjrIBiA/gzTpeOhKCJw2r8LHEcA7bRDVaIHkg+eiYgete0K8cFZUSIPnikD7jt/Dm9/GXq3jchxhWGuhdLpkVfDxSIo7u0Rn+WRvMil3VQZKa2MvHOqwPD2iffRIDAAFv8Y5n97VCsHottOjWW1t0cIoWRwuSJEhaqROUsk/dPdnf776n5gGBUY7S+I94qFeb2+h434vlm9LFQF5xb/pzh2NeDf3i5ki1Qk6nNljWWs+esazv3eueGJrE6vw1Zro7w5v9e4tcZK9cxqLJWJJ2JpTbzV36jxEph9HdSuiP3NCqh99foIafGb32RO2KnIZbwsmSgkmgbbBrPbWQ5Qqy8uuihN9pbrBLQ9I6QstUB8P6lcKPqJomR1DaSafwwn0aSQ5yfp8Pe//51rrrmGqVOn8slPfhKA5cuX89nPfhaXK1aiTVGUcW0yHI10bb5mjUjCeOEFuP9+8X7oUOLnAHXe2NqamFjTAnqLPhwY9zl9GGwGjDZjzpUd+UDnrk5e/+XrbP+rGDfSJY+piRH79kGiZoj2v4ju7/nqr2ECw5H5d3b+fSfPfPUZ9v97P//64r/4+9V/x9mVvCrhZIN6XXoGPTjaHfg9/qTX5XDaTTbIlDaUigChAv1H+5F0EkabseD6wcmIsXyPHI9QFIXHPv4Y//nf/zDYOZi3tjNYDWEi0dVdWJLWIwW/xx9OJDOXp0461lv02GptmCvNGEuNKcevRH1ONfDO1v9ChUpg7N0L/lDuW8AXwN5ip/dgb24bLWIICmW8zJnA6Ojo4KyzztLyWIoAmpvFE0KmRt6ZVGCoyOahZdgIuCOBVktU9KcylPacS/DAUAFVp0YCAu52kfmq+mAMHEj+cmem2Z+M6NHpRIBkxDI7NcLAQOSBJWUFxuABGNgPjLD/RTyMFWALpaP17RjVQ0kE1chbCwLD6xXtAxlUYLg7I9fy4AEh3xRwZ319n4woKYkQS+lkpN59VwQTamoi/cFmI0wcZxLUzZrAiG47+344vl60X9mcsdV2pSGmZ2Bv2lXr6sS73w89Pek3rWqgFhJczkj/zLnaJlMoiqYSUslw4YVw6aWiXb761bzvjtKJokoyvgLD5/Txyk9f4a3fvaVJ0FlRMpSPArDvgj23w9GHh73fhLCEIr6ulqy/OiqJJgWMn/70p/zyl7/kzjvv5LrrruOee+5h165dHDp0iBUrVnA0LtNH6wzMkxlqhdY114j3ZMSYOvZ5PNDXN/Tzt+54iw0/2hD2tBkOunZ10Xe4j4C78AKJ3gHh/6BWjaVLHps8GUwm8bsdOTL085E08IbcCAyVrCiZUIKkE32rc+cYma9oBEVR8NpF25vL8ifbKskSldMrMdgMBH1B7C329F8qoohxCPtxO55+D/1H+zGV50/pRZIkLDUiuWk8EbfR8PSL+7pOr8uISC2ZUELt3Nohnnfx8Dl99O/sp2df5AExugIjF0yaBFarqGxUyRD7MTtPfu5JXrr5pdw2WkTBImda/5xzzuHBBx/ki1/8opbHM+7RHJLZy5TAyLQCQ0UuJrk5QWcUptDu9liZmYkXC5mNTE284yHJYJsKxipwHAK/ExzH4bWPppazMVULqYcMsiGj9cOPHxel9j092pg2jzRU+ajy8sgDzBAoQdFOEEs2jRYqF4rqmr7tUHvmaB9NDKKNvIcLNdtfp4OKihQrujvh1XWCpAPRVr5BcByGIw+IZVlc35pAUYRpsS5xSelI45RTRJu8+y6cfXby9aLlo6JjXXPmiL6+ezesXJl6X1l5YMS3XcAFzlahw7/rloiXxEi2Xa6oPVOQiyqRkQIGgyCJurpEBVimFUbufjdGm5GWN1vo2t3FxFMnUr9odMYk9d5qsQiZj6To3Sp8D8rnibbMFq3/ggN/EHJDc76Uw5Fmh1tuEab3TzwBzzwjSI18IboCQ5UHA3D3uTn2yjH0Fj3Lr10+7P1s3Sqyrsxm+MAH0qzsC1WDGDQy8I6HJcQ+uBK4HmeARP4lq1blQeJzDGDXrl2siWNtJk2axNNPP80PfvADTjvtNNavX8/KdIN2ETnDbBY+D729Yj4Z751z9JWjeAe8LPzIwmFJwgW8AZSgAhLojIU32VYJGmOpINvTJY/Jsqju3LFDzCumTYv9fCQNvCHKxLvXxdNf3YCkk7jwZ6kHf0eHYDustVZq59bSu7+Xrt1dTD57csrvnUzwu/woQQVJltBb81sJoZN1lE8qp2tXFz5H/hMaiihiLKJ7j3ieqppZhU7O773CWm1l4PhASj+ckxnufjcApnJT1gkiCgruXnfC+7n9uJ0D9xzA+bKTK/50BRAhHXIlMHQ68Sy/ebOQkZo9m7DXk8fuiXkGKWLsI+eef8cdd3Dvvfdy7bXX8sILL3DgwAGOHj0a8zqRSAC7iKSQZZmmJpGNmK2EVFYSNCMBSQfWJlExEY2yWVC7EgzZ+Fcowk/DZxeZy34HSHqwTYuYYwb9oDOJKo34l84UMlrNPKNFJXo+8hH47GfFsj/+MdX6hRldyMjA29Md+v30EZPm0USFWqWTfzmrbNtNywoMlcCorEwTnPLZRRup17dkEG1lLM/5+h4WWv8Nr1wFe349MvtLgui2y9TIO97/QkU2PhhZeWDEt10wEOpntYKEHem2Gw4sE6F6ubjuMkAqI+9E2PDjDTz60Udp395O69ut7H50N127uoZzxCnhd/nxOrxDXn6XqD1W76319Wn0WFuegJ0/g85XczsQ2QwBrzB9HgHMni3MvAH+938jpdaZINvxsmRCiQhI6nXhDGaIBAOTGclmiwdCPO6ll6YhmyDKQyi9sXZOsKoERpvo7zkg0+z4zLdXmPOTdCgvL6c7ia7ft7/9bX7zm99w6aWX8re//W2Ej6zwoWWbpzLyjg4QDAfeQTE+GCwGdAWYLZRtBQak9sFQKzASERj56K8qgeF0KHTv6aZ7b2q9TCWohGVTbLU2auaKZ4PxVoHhdYSuS5sBKU2FuhbtpvpxBTwBggHtJJKLSI2xeo8cj+jaIwbf6lkiYSifbWetEZUE41VCSq3AyKXSxX7MTs++HuHvEwfvgHeID55agZGrhBQM9cFQEw6C/mDePRXHEwphvMw5naCiooKlS5dy11138fvf/z7hOhaLBUc29arjGHq9nuXLl4crL7KVkMq0AmPMwVAmqjkGDorMc9skYmSOjJUi6zXgBdkiqjD8A6FqjKiHoGDuD1ef+AT8+Mfwn/+IdlGrZFSobVeIyIjAUL1KzHWCeBptVIREgZ0tIribS1ZzBsil3bQkMLImH9Xr21QlzLQNFZGqo2Fc31lDbxU+NJ7Re5CNbztVhiGdkbdKYKj+FyoyJTDcbujvF39n5YEhW0BvAf+gIF+tDaPTdiOI+nrRHtEERqo+Zyo1gQLH3zgeDoZpFeCO2U+ZCWu1FWe3E78n8YTWWm2lxyH2nZaoGgwNBiVTczyg0AAwQgQGwI03wl/+Itrnrrtg3rz02f65jJc6vY4rH7hyiPGrlu0bDMJDD4m/r7kmgy/4QxUY+jxVYJhqhZG7qRYCDtDliSjJEIU8P0mHM888k6effpoFSdLU165dS1NTE5dffjn/9V//NbIHV8DQus0nThTJAa0JiopMZSYGWgaGTWB4BiMVDoWYKZltBQZE5hXxBMbAQGQOGX9p56u/qgTGoEsGGygBhWAgmDSD2dXrQgkoSDoJS5WF2rniIbPvUB9+tz/GZPpkhkpgpDMv16rddHodsklGkiUCvkDeM8yLGNv3yPEItQKjZnZN3ttONfIerxJSagVGOv+LRJCN4kHC1eXCN+gj6I8QsgFXgIqKivB2fb6I1GKuFRgwlMDQm/TIRpmAN4DH7gkTxEXkjkIZL3OegXzmM59hcHCQXbt2MX369IJgY8YyFEWhv7+f5uZyQBr7FRjtL4C7A6pPGxrYcbZAxwYh4dB4aertmGth4ntAtkH9+TA5gcC1pxve+pz4O+gRvhjGcigZxigYhRkzRBbkiy/Cn/4E3/lO7Odq25WXlxfcQ5f6wJnS/8IdijCaU600gtDbRNsN7BM+GBPOyctucmk3VULq2DGRuawfxjOcmlia1v8iHqpEyWghHHAdPQIjvu0yqcBQlOFXYHR0iHejMY3sV8IDCIrAps8B+mwq0AoIgwdFpYFlohiPUyBRBUaqPte4opED/zlAyxst2OpEtCUfBIat1saa+9ekDLiZykz83yPiGFISGL5BcZ8DbQgMRUlT7qENqqrg5pvh+uvFK9povalJmEnH+y3kep9LFPjRksB4/XVRsVpaCpdcksEX/KFssHz1QUmC0+/Nz7ZzQCHPT9Lh+uuv54+pSl+B008/nY0bN/Le9753hI6q8KF1myeqwHB0OvDYPQT9QbwOL127u7BNiOiUmspM2GpTyLpGwe/y4+pxEfQHGWgZwO/xU9ZYFq6GKwRoWYGhzlMmThw6/8tXf40hMEIIeAPoLIkD5GrAzlJtQdJJWGusWGusOLucdO/tZsLCbDI4xib8Lj/uXjdBvyB6VDIj0XWpRbup262cUYmEhBJQYqpCtYLad5Mhm757MmAs3yPHG/xuP32H+wConl2d97ZrXtlMaUMpldMq0698EsJSaaFxRSNVM6rSrhs/ThlLjJQ2ltJ3uA/voJdXb3mVM758BrJJpmtPF85+kUTWc6CHI4fBFDChWGyp41VpEE1gRI9zXoeXjnc6wpWeMP7GuUTI5V5QKONlzuG3f/3rX7z99ttMH06tTxFhBAIBdu/eTXPzMkDPiRPC5NeYIunD54uY6hVcBUb7C9CzRcikxAd2HEfg8F9FNUU6AsNnh753RFB7yrr0QaKAG5QAeHvFu6QNsfbJTwoC45574FvfivXDUNtu2bJl6IcT0c4D1AfOjCowCsH/QsX0T4is9VyDghkgl3ZraBD6/j4ftLQIo8ZcUbDkYzqYQoONp3vEAq7xiG+7OXPEYXR1CZJBNZGOxpEjonrCYIgEFlSoBMaBA6mJKTUYX1eXw2lLerCOce1o+1448hBULsqJwEjV5+oX1aM363F1u8Ll2vkgMECQGOkmrupxpyQwHKE0WsuE1B5MqWAMRbACHiGPmC9vhjiofSQYp1LR0gJr1w41jdbyPqclgaGad19+ufArSQtfiMDISsZy7KKQ5yfpcNZZZ3HWWWelXW/69Om8+eabvPzyyyNwVIUPrdtcDSioCTGOTgfr163H2e3E0eHAO+Cl71Af5t9EsjSt1VbW3L8m5TirVsM5OsU2EAq6eAe9uPvc4e3k6z6QDXKpwFDnGfGJEakMvPPVX1UCY8AZS2Aky0h1dgoCw1obMWOtmVvD0Q1H6dzVeVITGOHrssshAl4KBHyB8DUJQ6/L4bRbplWhWvSD6L6bDJn03ZMJY/keOd7Qs78HFEGsWqut+P3+vLZd1fQqqqanD96frJi4dCITl6ZmFNKNX7Y6G84uJ7vX72bv43spmViCu8/NYNcg/Yf6OfTcIex2uBIr705egyTlPu6oBMbhnQ7+vm49rm4n9uN2Ap4AT133FAZr5H433sa5eOR6LyiU8TLnPVssFrxeb/oVi8gKtbVgMoHHIx4W1IzvRIg2AY431ht1uELRH0uCgU818HYcBW8fGCuSb6f9BeHPUDojs2C2oTwk1eICX18kODRMXHklfOELwij4+efhggs02WzekZGElN4qdLutTSNyTBmhIsGTXQFAlgVpsX+/uBaGQ2DkVIGhhCYH0ihOso1VInof9Aui0DT6kzurVZhkHjggshsTERhq9cW8eUOJ4eZmEfx0uYS0w8yZifeTlf/FyYiyENMzsC8teZXMAyMZ3P1uyiaXcWJz5AuuXhc9B3qAkc+WyYjAGDwo3kumpVgpDWSjkEr02UUVxggQGIEAfOUriT9Tm/VLXxJm0sMtrm3f3s6uR3dR3lzOkk8sAbQjMPx+UO0PMpKPggiBMRJVUKNE8I5HlJeX8/73v3+0D+OkRHwFhsfuwdntRG/SYyw14nf5kU0y5gpBYPhdfpzdTjx2T8oxW62Ga93Uyqs/j3gIVc2q4swbzgQKJ0ty8X8txtnpDAezMqnAUBMjOjqgp0dUvsHIG3hDhMBwOCV0Bh1BX5CAN7lHT+W0SpZ+ZmnMGN24ohHZKGeUjTuWEV2lGfAE6D/aL6oiosZyLa/LZFWh0VJqWu0vuu/qLUOfIzLtu0UUMRqQTTLNZzUXBKldhEAmVe2tm1p58nNPEvAGcHY7hdeVrMNYYsRcYaaz148VJ9ObPEDu486MGSIBMeD00N/mxFqqx2A1oAQUDBZD1nOUkxlj/V6QcxTss5/9LN/+9rd58MEHMRiKmmJaQacTEg4HDghZhFQEhpoBVF0dWxEw6ggGwBOS1TAniP4YSkXAZ/Ag9G2HurMTb0dRoO0Z8Xf9hZnv31AhCAxvn2YEhsUC69bB734nzLxPKgJj0lXiVURGmDJFEBiHDsHq1blvJ6cKDE8nOI4JvxLblNx3PhzoZOFL4u4SAdcCIDBAZDMeOCCCA+eeO/TzrVvFe7z/BYjxc9YsQXLs2ZOewMjK/0JFwCGqiiikwTpLWCcJ/xW/E5zHwdacdNVsCAw1E6T3cC/Ojkg2yL+u/xc6vfi9RjpbRj3ulG2t+l/YhkFggJCRUgmMkinD21YG2LABjh9P/rmiCJm8DRuEfOJw4HP5aNvUFpO9GjYGzPEhNBAQx/bMM6JPVlZmcU+e8WnxO5dqIzGZEL1bYe8doqpx4ffyt58iihgBqBUY8Sbeeoseo8+IW+8WwYgoubhkmeTxsNXasNZYqZ5VjXfQi6ffgyRJBZf1OmFB5EagKJlVYJSUQGOjqGrbswfOOEMsT2XgnS+ECQyH0CZPR2CUNpQyu2F2zLIpq6cwZfWUPB5l4SC6SrN2Xv4lDqL35+5z8+LNL+LsdHLFX67Ii0yH3qJP6uuRad8tooiRRvXMas76evqqTK2gBBVObD2Bs9vJ1HOnhp9HxgsC3gA6gy7tGJRJVXtpYymubhflzeW4+9xIsoTRZsRoMyL8tf3DSgoFobAwYwa07xaelWV1ekomlhCoCmCuNGO0Zj9HOdkxVu8FORMYfr+fjRs3MmXKFE4//XTKy8uHrGM2m7njjjuGdYDjBZIkYbFYkCSJ5mYRiEtn5F2wEjSeTkFi6AzJDZgrFwoCozcFgTGwT8hNyUaYkEGkOCBkR9CZRaa6uwvME4SslAb45CcFgbF+vcieVzPno9uu0JARgVGo6N0K7S9B1alQp/2EJdd208rIO+sKjECIlFP84gna74gsH2mYakIERicwa8R3n6jtTjkF/vGP5D4YyfwvVMyeLdbZvRsuTaJslzOB4esHx2FAjq0uGo22Gw50sgj89r0LA3uzJjCS9Tk1E8RSacHdExmvLdVi3dHIBMmuAmOYUncVC4TfU64yVFkiPhCZyXq5jpelE0Wlw2DbYDij9LTrT2PJJ5Yg6bK/Z65fD1/8YiwB4/XCP/851LcjIcrnZr3PrKEzC2lGxZf/faVBIc9P0mHRokX4fJn/hmazmc2bN+fxiMYGtG5zdf6YyMTbNsGGrc6GJOe+r8bljTQub6TvcB//uv5fePqGZwiebzgcokoe0j9/zZkjCIzduwWBoSipCYx89ddoAsNcYUY2yihBRdN9jGdo2W7GUiP9R/oJ+oIMnhgM30PzAa/DS/+xfiomVcTIq4wnjOV75HhH3ttOgpe//zJBf5D6RfVhf77xgme/8Sx9h/s4+9tnp5WSSgfZKFM9uxpTqQmdXodf8oeTmFyhR+HhEhggVBZUAgPAVjO+2izfKJTxMmcCQ5Ikrr322tQbL2oJZgxZllkUiqxNmiSWpSMwMskAGhWoptCW+uTyCRUL4dhjogIjGQb2gaSDmjNTB3YMZYIo8XQLE28QTwmKRwQR9FbxuaEsp9NRsXSpyN7euhX++lf4n/8Ry6PbLp9Qs07b2kRG3KpVqeU9FCUDE+9ClrjoexdOPAtBb14IjFzbTSUwDh8e3v4zJiCjr29vn5BuUvxCIk2FBtd3VihfEJJrGx0d+URtl87IOxMCA1IbeWeUlR8Nte0cR0S7yfrYdoORb7vhonSW6Jv2PSl9MBIRGOn6nLHESNmkMiSdhKnUhKkkkqE/0pkgGREYp3xLkBjl84a3sxmfGt73s0SmJnnR6+U6XpbUC0ksn8OHd8CLqcyEJEkYS1IYfCXB+vXCn0OJi7s5HIl9O0YN1kbx7u4SCRSyOfX6ecRIzU/ygR07djBz5kw+9rGP0dSUXuLSbB6937mQoHWbR1dgxPc9nYbl35ZqYWLjHfQK4+QCyXb1e/wc23gMY6mRhmUNdHaKObPZHCEGkmHOHHjuuYiRd3u7mPtJkgi0xCNf/VU9Tp8PLvr1paQTTmjb0obBaqBiSgV6U+RZXgkq9B3uQ2fQUd48NHHxZMIrP30FS6WFeVfNw1KZ2mBJy3bTyTrKJ5XTe6CXvsN9eSUwOt8VgYTeQ73UnZJAe3UcYCzfI8cTfC4f7j43JfUl4eBpvttOkiQs1RYc7Q6c3c5xR2C4+90EfUHNyE01aclcbsZcHpmvOVUCY9Lw9zF3LrxAhMAoIjP0HOjB7/ZT1lyGuSz5XLpQxsucGYbvf//7Wh7HuEcwGKSrq4uamhqam8Wk/ejR1N/JRIN1VKCaQieSj1JRfoogJ1xt4O4QkjjxaHwf1JwBwTQZeOZaWHm/kOBQcegv0PEyTDgHpnxYBAjNw/uhJElUYVx/vZCRuv76kBVAVNtp+TAXjURZp01NcNttyQM2fX2RATxpwGpgP+y4EcrmwoIbtTzk4aNyERx5UJBceSBacm03VdZtxCow1Ovb0wmbrhe/xdJfxprQanB9Z4VpHxu5fSVAoraLJjDiLxe7HQ6GkuWHQ2Bk7YGhtt2Be6DtP5HxKBoj3XbDheqDYU/xQxH5jXp6RLaqyZRZnytvGv2giKJkSGBYJojXGMOqVeL+0dIyNCCporlZrKci1/FSNspYqi24ul0MtA0MSzbqi19MfryQgW9HwCPmBfoSqDk9f+S9oVS8fANijjPcCp1hYCTmJ/nCc889x913382Pf/xjzjrrLD7xiU9w+eWXY4w3MSoiBlq3uTp/dLmgv3/Ym4tBwBdA0klhPWxJJ6EEFdz9bqzV1vQbGAG4ely8/qvX0Zv1XPXwVTHJJ+mGkHgjb7X6YsYMIU0bj3z112iixeGAiorU67/681fxDni55NeXUDElsvKOB3bw7oPvMvX8qZz+pdM1O75Cg2fAw7FXRBbh/GvSe/Jp3W4VUyvCBEbzGckrXYcDJepmarCNz+oLGNv3yPGEts1tbPzJRuoW1HH+j0Ty1Ei0nbXGiqPdgat7jFXMa4Cw3Gu5tp4jiqLg9XpDczkJtwtMaFOBoRp5RxMYAX8Ad48bc6UZ2TBMY7+TFNYaKz6HL+3vUyjj5bBKJA4dOsSjjz7KgQMHcLvdMTdDKEpIZYNgMMjBgwepqqoKExhjtgJDJTAsKSI/eiuUzgTnMXC1JiYwIHN9fXNtbBBw8tVCqqXmjEg2pAb48Ifhq1+F7dvh7bdh2bLYtstHZ06WddrSkjrrVJX/qKxM/KAEgLsNfIMROaJCQtlsIR/m7Uurt58Lcm03rSWk/j975x3mRnXu/8/MqGt7dVn33jHYMZhiwBACKYAxoSW5KSQ3HdJvekhPbhrJJckv5Ca5KYQWQwotgVBMx7j33tZeb9/Vqmtmfn8cjaTdVRnV1dr7fR492h2NZs7onXPmnPf7vt/XVP91NArddiWaTVR3Vn4nH+VIZrs5c4TjsqdH3PuJsmlbo4leLS2pCaNsCIysJKQcjRDxiCyyhnOhckYWXy5DVEZ/qGCHyCqRk08j6uqEHmk4LIqYTppU/LGyUOjtFbJEkGO9k1yg6yKDsATR+ooiyO+1a4UDLhkpcNttg4mAfGxXMb4Cf5efgZMDNMxp4OU7X0axKiy6ZdGgKKx0KEjdjmAX7PkpWJzQeH9W15A1XBOhbzf4WkecwBgNfS4ZLrnkEi655BL6+vq49957+fGPf8yHPvQhbrrpJt73vvexdOnSkW5iWaLQNnc6hcO7t1c8W5uH8Ee+Lh/edi/OOicVzRVZHfvwM4fZ+KuNzHjTDM5+39mMXzYeWZGhjNSNjOKktipx4dkEjxkEhpGBkamAd7H6q80mxnNVzUxgRIIRQh7xAHQ1DiaRGuaICWvn7s6Cta0c0b2vG4CKCRXYKzM77wptN4M06j3Um/exUiHsFcGBkkWiZkpN0c5T7hjNz8gzCV17xKK5qiWesV4K2xmZgb5OX4Y9Ty9EghHUoKiTZHaebhY6Oj6fD6vNSjAgYagZms0OTweDwAgmEBjd+7oJeUJUa9WxrPAxDMbQrJhUKJfxMucz/+Uvf2Hu3Lk88cQTVFRUMHnyZKZMmTLsNYbsYVZCqmwzMKa+A5b/HCZenX6/BV8Q0cm1Zw3/LNidXxuq58LktQUlL0CQAQZZ8L//W9BDJ0W6qFNj2+23i/2GwlT9C78h91WAp0ahIVuhKppj37tlZNuSAIPAaG2NOzlzgdF/TdfA8OwX7xVl4gDXdVHMuUzgcIioRhguI5VJPgpEEW8Qzvbe3uT75ERg6HpCrYQ8iz2XA+z18IZfwMo/pSQvQDjGsynkXU4w7FxTI+6rpGh7Co7cD94MqZJm0LcLnl8Lr38i/2OZxJo1gvyeOOQRaY/6au68M/475AtDAsNz0oOu6xx66hD7H9uflQZ7LnU7hiHiEe+lkL5zRn9Yf2vxz3Wao7q6mv/8z//kpZde4vnnn6eiooK3vOUtLF26lJ/+9Kd0GdEAYygaDMdCYh2MiD9CyBsi5AkR6AngbfcS8oaI+M3L/XXs7CASiMSi/lZ9eRUXfuFCXA3lkX0BQtIKiMneZRM8ZgRGHDggyPyRKOAN4nlsZGFs/d1G/vXZf3Fqa/IB3nDUWZyWYdIh9XPEhNXT6iHQd/pqdHTtFWNK/SyzE/TConZaLQC9h3uLcvyIP8JAxwBaRMPisBD2hbPuu2MYQynRuUcsmo0xqFQwMgHPNALDyL6QLTIWZ2FKAhhzhrA3jOpXCXvD9HeFsBDBbhMqy/nCeOZGVPB7xPmsLitaRMNzyjM2zg1BbB435FXuv1HOt8oXv/hFfvCDH/Cxj32skO1JCb/fz3e/+13+8Y9/oGkagUCAu+66i0svvRSAkydPcuutt3L8+HE0TeMjH/kIH/zgB0vStkJjUjTQPJOEVNlmYCg2c9HyqbIrfK3w2oegdiks/IooHFtGuPVW+POf4Z574Ic/FJFN+SBdbYt8ok7NERgm5L5GErWLRTHv3q0wMUVl5RKjqUlEJPr9oo8aTvNsEInEneSm+285OcG9R2HjJ0Fxwso/jHRrYliwQGRQ7NgBl18e326GwKiqEn3lxAlxjBUrhu+TdQ0MEJkKYY8Yx9ynAakvSeDKrEcPgsA4dmz0ERim5KPanoTe7YLQcecp3GqtAjUksqxKWJdozRohuZT4/Fm4EM4/H/buhWuvhaefjpMauaJyQmVsAREaCMWiq81EthrIpW7HMEQGxLulBBFYRvCEb4zAKCTmzZvHd7/7Xb797W/z+OOP87vf/Y4vfelLXHHFFXzgAx/g8sSBfwwFw4QJsGuXGCfOXWzHVe/C1+UjEoyg6zpaRCPQG8Df7UeSJVz1LlNycZ27hFOqYV65LWTiMDIwjOvJJnhs4kRBHHi9gsQYKQIDRDv6+6NO8bZO/N3JJVF8HcJR52pwDSvUaa+0UzWpiv5j/XTu7qRlhbm5wGhD174ogTF7ZAiM6ilCSnPg5ACRQASLozAORHtVvO/6O/1oEQ1JkmL9FjDdd0cC3g5vrD8mg73KjrvxzKpTcCZAi2j07O8B4llgpYJBpvu6ziwCwyCo7dX2vAs2J447xpwh7AsT0AL4uiSsgFxZmHHH7YbGFju+4y68fT4kNYIkSWL90RfC2+FFsSplPc6VAok2CQ4E0UIaik1BtsZzG8r5N8r5iXjo0CHWrl1byLakRCQS4corr+SSSy7hxRdfxG63o+s6akLY+XXXXcdHPvIRbrnlFjweD5dffjmTJ0/mqquuKkkb84UkSVRXVyNJUozA6OkRk95UReLKNgMjFyQ6bdr+Ff1fyY+8UIPQ+SJ4DhS0SOrFF4so/EOHRATrLbfEbZct0tW2uPpq+OtfzR0nWdSpKQIjseB6OaJmsXjv3Qa6JuqmFAiJfS6774k6GLt2iXsgFwKjOyHBqLbW5JcazhXkYLKMpVLDViP6lxoUNWrk0urnprLdggWiT+WSgQEicuPECSH3MJTACATi+t+ma2BAnHhyTS757zTSGJqBkanPpYr4KHUkSEYCQ9dhIKohVwhC0R5dkKkBUH1CbqxEUJTh5Pff/y7u/5degg98QGQbPvusxKZNk/B6JS6+OE2diSSYe+1c5l03D0mS6G8VtaqsLmtWRXoz1e2QJPF5Yt2OYTDqZFmr0uxUILinChnEEc5uzPU5V+6QZZmrrrqKq666ipdffpkbbriBhx56iEikvKPGSoFi2DwxA8Pd6GbNPWtijkRd13nqv57C3+1nxW0raFrYZMqRGOwP4mkVWVENc+NOKV3X0VW9bIp455OBIctiXrFxI+zcGZ+bpCIwitlfY4W8NQUroIaSpG4jnMRAyoK1DfMaBIGx6/QkMHRdp3uvmKSbJTAKbTdHtYOGeQ04ahyEvKGCERhG3/V1+Hj89sfRIhoAilXhyruuRJKksiUBvB1e1t28Lq0j2VXvYs09a7Jq/+n6jDyd0HukFzWkYnVbqZwYz6Athe1iBMYZmoFRiPoXQ+cMqqpy+NBhpk6byo9+qHD/r+Ad1xRu3Jm+0M1fjq9hxbuCXH+T2Pbyj1+mY2cHc66ew+y3zC7bca5USLTJgScOsPPBnUx4wwTOef85sX2S/UblMl7m/ERsamrixIkTjC+EYFkG/OEPf6C6upqvfvWrsW2SJGGxiOZv3boVVVW55RZRHLWyspKvf/3r/PznPx81BIaiKMyLCrdVV4to4P5+Eb1qaKgORVlmYIQ9cPC34JwAk67LHEl67GE48QhMWgMTrhSa6m1Pic/G5RlJp4Vg90+E43viWwrmpJdleO974ctfFo6dd70rbrtkSJVhka62xXXXiShvsxIeybqhQWCk7aKxeiVlKCEFUDFT1Eux1UOoR0Q7FwiJfS5bTJsmCIzDh3M7t6F4UVsLFrOjcMMK8SoHWCoFmaKGhK58iQmwVLZbGK21aOhMg+h/RtSjGQLj6aeT18FobxfvNlvmApiD4J4C098t7uPTBcEuUZg81ANnfTvlbkMJjFR2GxqdkwyljATJSGAEO0TdINkCrgLU5lHs8aLPwc6SEhjJMHs23H8/XHkl/P73gkjv61MAkVVgkOzJai8lg6zEHZFD9eTNIrFux1AY04yf/CQDsRKOZmBYS5CBUb9cvEYY+TznyhmBQIAHHniAX//612zdupW3v/3tfOADHxjpZpUFimFzIxDGCJZxN7oHLWynXjyVA08cwHvKS93V5mrXGZIglS2VsWysbX/exs77dzL7bbNZ+p7yqHGSTwYGiDXcxo3w6KMic9fhgBkplECL2V8NAiNCegIjloHRmHzO0jivkYP/PEjHro5iNHPE4ev0EegNICkStdPNRRgVw26Xf7842WRG311771pObjzJK3e+AkB1S3XBiJJiINgfxNflw2K3JJW0ifgjIpq4P5iVY/J0fUaeTjDqX9TPrh/kOC2F7epn13PuJ86lYvyZVTvB6rYyccXEgtWMGDpnaJwtHqD7u6EHmFHArMR58+Dxx93s63RTF33Wzr12Ln1H+uja00Xtx2tH3AFfDjBssk/dh81to3lRM3Uz0s/fymW8zPlJdcMNN/D1r3+dv/zlLzEioVi49957ue2221J+/uSTT7Jq1apB2y688ELWrl2LruvDbtJgMEgwGE9B7O8XUXmRSCQWvSXLMrIso2kamqbF9jW2q6o6qGh5qu2KoiBJ0rCoMCW6yjaySDRNo62tjYkTJyLLMpMmwY4dEocOqcycqWOxWIZlnXR2KoBEfb1GJBJvoyRJKIqSsu1FvSbfcfQT/wR7PdqEa5JeqwGLxYKu+tF9J9G7NqM3XY7U/SpKqBfdWoVavVRo7eR6TdZKtKr50LsNvf1F9Ilvy9tOBv7jPxS++lV47jmJ3/42zMBAH3PnVnPppVZkOW6nhx6S+OQnZY4fj9+DLS06P/iBxqc/LaPrwwdQo1mnTkFNjY6qwsAASfeVJD0adSoNu6YTJ8T90dysEokMvlYANeRHDoiVmGZpQIl+N6mdhtx7qexR8HtPB33Z3THnrxLtz2btlO7eU1WV1tZWxo0bhyzLWV3T5MkyIHPoEDn1p1OndMBCfb0eTT4qzDWV0k6ytR5JPYEU7EC1NebVn7K9JkmSaG1tpbm5OVZESpIkFiwQx9m5UyccVpEk2LtXxu+Xcbl0pk5VjSEl6TXNnCkBCnv2MMx+bW2iPzU1DW5PxmuyNsKEaD2gSGRk+1Oe415su25Fbn8OdB3N14nF1ZD0msaNE/u3topnlPGca2kRUZtG2+21dq754zWEB8JomjaoNoIkS8iyjMVtwV5rJxiM8PzzEm1tEhMnyqxcqSLLhb33TpwQ/bu5WScSSWKn/v1ioHa2oGkg6WoB+lMtUrgfKdiJ6mgpaX9K1vZLL5V497sV/vd/oa9PB+LPn9ZWWLtW5777NK69Vk95Tcm2+7p9aLoWc1hmc03XXgv33adx880ykcjgZ+oPf6jxtrfpRCJp+k2oDxnQlAq0hPOOeH8q4lgeiUQ4ceJE7DlX6GsqdcbDxo0bufvuu7n33ntZsGAB73vf+7jhhhtwuU4jgjhPaJrGiRMnmDBhQsGKLBqBMKlqzIw/ezwHnjjAyY0mi9Ug6l+AcIgbUKxKTI6qXJBPBgbENbkfeki8z5+fmmgthu0MxAgMzVgDZMjASOEENuS+uvd1o0W0ssmUKRT8XX6c9U7s1XYUm7lUw2LarVhw1jmZtnoar/38NbSwRrA/WNYEhgGL04LNnTwAIlUATDqMRtudaUhV/6IUtnM1uJh26bSiHLuc0TivkcYvxZ/N6eTOs0Wi3fbvF3ZLRerngvnR8qm7dsW3tZzbwmvW1+g/1k/v4d5YnaExEMuErWrJnJ1eLuOl6SfVhz70oUFO/2AwyCOPPMKsWbNYsWJF0sWDw+Hg5z//ed6N3LJlC06nk+uuu459+/bR0NDAZz/7Wd70pjcBcOLEiWEFw51OJw6Hg/b2dpqHCJZ/5zvf4Y477hh2nk2bNuGOzvAaGxuZMWMGhw4doqMjHmXS0tJCS0sLe/fupc/QEwGmT59OU1MT27dvx++P64rOnTuXmpoaNm3aNGgxunjxYmw2Gxs2bABEympvby/Nzc2oqkplZQioYf36wzQ0dLN8+XL6+vrYvXt3dH/o6HgDICHL3WzYsD927OrqaubNm8eJEyc4nqBNVIprsntb6e/rxWer41j02pYtW0YoFGLr1q2xfRVFYfny5Xgs01H7elE9z7B/4GKmDPyJ8VbwuJazc+PmvK+pNdhCRd96fDse5tjJCXnbycCyZctYvFhi82aF977XCjREzwvf/raPWbO28cwztXz+87MZiuPH4cYbZRIdQqnwla/sJhiU+fznZyNJ+hASQzi/77ijH0WpZsuWwdd07NgywEIweIgNGzqHXdOmDS8w3t+Monk4smUvy5YvT2mnxHsPRP9asmQJnZ2dHDx4MLZ9JO+9VHZKdU29vb3s2LGD1tZWJEnK6poslvHAFA4dIqdreuUVBzAHh2OAvj414zXZIqeQtQBB63jOWb6yLOzU0h2m0RLCHuxk+5Hi2SnZNS1YsICDBw9y/PjxGEFdXV3NrFnzsFh0+vslHn10K83NIV59dSowjlmzgmzatDntNclyDTCXPXsYdu8dOrQQqKCiwseGDdsKfk2jqz/tZOqADXvkFCdf/wfzL3x30msaN06kvOze3cuGDXvRdR2v18uECRNoa2tLek0HDhygo2v4Ne3atYu//k7mxz+eSnt7PBOjuVnl9tsPcfHFPXleU9xOO3fOBBqoqQmwYcOWYXbytG1B6+ulL6TQtmFDQew0sSdEkxLCHuwq6rhn9t6rqKjmiSfmIQpWDH5WGYqPH/1ohAkTNqEome+9h//rYfoO9lE5q5K+3j7qo9l02V6T272TSGQZoPOFLxzm8sunsWhRH/v27cb4SqprmhLez3igdyDC3oTjF7U/6REWL1qIzVExYmPE/v37Y8+5Qo8RmzZtotjo6+vjT3/6E7/+9a85duwY73znO3nhhReYb6xSxzAImqZx/PjxGGlVCCQr4p2I5iXNSLKEp9WDt92bUn4oEZ27h9e/cNQ6gLh8RTlgxuUzqJ9dT/VkUZcglwwMiEuHpqt/UQzbGTAIjFAGAmPGG2dQO712ELGUiMoJlSx+1+IRK3BdbDTMbeCa311D2B82/Z1i2U3XdYL9QRzVjoIdMxGSJOGodoisk76AqX5bTkgWpJotitnnxlAYTF89HWedkwnLBmtij9muNEgnd242EzsRht2am8fFCIxc5LhTwUgQSCQwbG4bE5ZPoPXlVrr3dY8RGAkwCIzKCZUZ9iyfPifpiaFWafCtb30r60grh8PB5z73uZwalgir1cpFF13EXXfdxdy5c9m6dStvectb+P3vf8/FF1/MrbfeyooVK3j/+98/6HuTJ0/m2WefZdq0wcxpsgyMSZMm0dXVRVWVYJ9KHZGnqiobN25k2bJlWCwWPvABnV//WuZLX9L46le1YRF5/f1QXy/4J49Hw+Eok6jdw39GP3wPevNq9NkfS3qtBiyRHvRgF/rrt4MaRJv1UZT9P0dCR1t0B5p7Jjga87sm70mkV98PkoS24v+Q7TUFiZz861+VqJTF4ImTmEfp/PnPIsNCDPbJJlfDnULJ8Ic/qNx4o540k6OqSufXv9a47jop6TXNmqVw+LDEc89FOO+8zNdULpH9KbdrYRSLDUlWChLhGg6H2bBhA2effXbM/mavad06iRtuUDj3XHjhheyv6de/hg9+UOGqqzT+8Q8p470n7f8l0snH0VuuQZ75vpTXVEo7SXt/itz+b6Rp70Jtua6kkci6rvPaa6/FbJd4TQsW6OzcKfGPf6hccYXOl7+s8N3vSrz//To//3n8OMmu6dAhmD3bgt0O/f2DI/t/+1uFD3xA4qqrNP761/hvk/aaIl7Uzg2iToJzwojYKd32fOwk7f0p0ql/o09aizLz3Umv6a9/VbjuOjj3XJ3169XYc2758uWx9pu9pgcfVHn72+VohpqUcB5xPUY2QCHuvTe/Weaf/5T5zW903vnOJHba9g3ofAV9+vvQJ761MP1p38+RT/0TacpNqJNvHPHI/meflbjssszhVU8+qbJqlZ7x3vvnp/9Jx+4O6mbW0b2/m2mXTGPlp1ZmfU1/+YvG9deLfr55s5pdf/IdQQmcQHNMQHPFg16K1Z+kvT9Fan8WafZHYPzlI/LMDYVCvP7667GxstBjRE9PD/X19fT19cXmz4XC+vXr+dWvfsXDDz/Meeedx6233sq1116L1VretYT6+/uprq4uym9iBpFIhA0bNsTWE4XA+vVw0UXCybBvX/J9nrnjGRSrwuJ3LqZ6UnXGY+5at4tTW09x9vvPpmqi+J1ObDjBs3c8S+2MWt70kzcVpO2Fxpw5sHcvPPMMDEn+T4qtWwfLV37wg/A//5M8erUYtjNw7bXw8MPww1teZ3zfXuatncdZ/3FWQc9xpqIYdhtoG+Dx2x8HHa6797qCSZ74On28fOfLTFg2gblXz+Wx2x6j92Avq762ignnpCuaOLLoPtDNA9c/gKPGgdVppWtfFyFPiOazmlEsCiFviEBvgOsfuD6jDEoiitnnxlBclMp27dvb8Zz0MH7p+FhNjNMdalhFtsg89JCUVO7cGI4efDB7EsOw2+TJy5g40YIkCXlFe4FUgru7oT7Kr3s8UBFVwfKc9GB1WYtGCI9GhLwh/nLjXwBYe99arK708+ti9rls5s6mz/zFL34x74blClmW+exnP8vcaBjL4sWL+cQnPsFvfvMbLr74Yux2O4HA8HRjv9+P0+kctt1ut2NP0kssFsswYxiLuqFQks0802xPZeTE7YY0iiRJTJkiRobWVhmLJS6RYuzf2yu+43JBRYWQuxiKVG0v6jUF2sQkyz1xmLj/oP0DHfDizUjBLiT/SYj4kF97P+gqKA7kjbch2+th5T0xEiOna3KPh8ppMHAYuW8TjFud/TUN2a6qcPvtSXeLOdfe/W6FJLdkAsxNRFtaFCwWuP568YBYv148LO66C8aNk1i7Vok9RBKvSdfjkXKTJ1uS1lkwc62x1ibce4nI1h4533s7vw9dr8Dib0L1vKzanmq70dcURRn0uZm2G6mOhw7ldk1G/21slGP2S9t232ExW6ieHZs1jLidnM2ABMGOvPpTpu3JrikSiSS1HcDChRI7d8KuXQpvfnO8gPdZZyX/bRLbPn26mEAFg3D8uML0hPrMRg2McePiY3LGa/LswbLnh+CaCG/4ZdprghL2JzNtz7S9Zh60Pw3eA0DyazJqSJw6Ff/MWIRnc02qCp/4hJK0eLOuS0gSfOpTCmvWYK4/Zdhu1B4aPz6FnfzHo/1x1qDnXF52qp0PqgdcE0ven5K10bjfM6G9XRn0fEnV9sqJlXTt6WLyysms/ubqmExYttf00kvi+BdeOPieMtWfqmZA1QySz5iK0J+sLkCDwElI0cZB+yegkGNEsrGy2GNEIbBq1SpmzZrFl770pVgg0kOGDk8SOBwO3va2txWtPWcyEjMwjAysobj4qxdndcx5a+Yxb81gLWWjYGg5SUgNRbYZGEbhbgO//CX84x+5R6/mingGhgWLw4Ikj2mAD0UsoKAM9NFdDS7UoIoW0fB1+AqWHXFy00lObT5FxB9h7tVzY7VdyinrKRMkWUINqeiaTsgTwlk73NczhjEUCpt+u4nuvd1c+MULzxgCY/2319O26RQ/fHEFuj512OfGPOD22+Hqq3OTk9ofFY+ZNKlw5AVAXR00NYl1zO7dsGyZ2F45PnOGwZkGzwmRfeGodWQkL8oJo4JqbmpqYvbswVI8M2fO5J///Ccg0vyPHj066HO/38/AwABNTU0la2c+kGWZxsbG2AJ0UrQu6LFjyfc3VA7MTqBLhkC0+mmmotDhflEIVraDvQG0E6IwsK0WZAUkq/g83D+IwMgJ9efCwGHhAI8SGPlg/frBaXTJkJ68yAxJIlrbIr5NUeDii+Gcc+DXvxYRYEMjuwx0d0NIyPamLkSrRUQR2tEAXRMFo3u3QnVhigcN7XPZwEjqOnUKfD5BJGYDYwFsRAikhaaC95D4u6KAIpH5onKmKCpeMT3zvgVGOtstWCDeDadBnMDIfFxFgVmzRBHwPXsYRGAYhZ2HKBKmx0BU6qWc7FYoVEWFvT17U3q0Eot463rufS7TmKvr4lm5fr0YI/NFxiLey+4C3/HMz7lsMO4y8SoTjDd5aWb3MxYOA20DMS35XPDCC+L9ggtyPkTp4BRFz/FlmDAUEfk850YaM2bMIBQK8ctf/jLzzgiZrTECozg2N/q5zyciGouVWGI4IoN9wYLIwxQCx148hmyVaVrYhGS1xqSgzNTAWLcObrll+HZRR2h49Gox+2uMwJi7hOu/kmThAEQCEU5sOIGr0UXDnNQXqIZVTmw4Qc/BHhbdvKgs7FQI9B/v56nPP0XToiYu+Jz5h0wx7CZbZKomVdF7qJeeQz0FIzDaNokJzrilYoJjRCMbxerLHWF/mNBACKvLSsQfIdgXzIvAGM3PyDMB7dvbiQQjNMxtGFb7pFS2czW46N7bja/TV9TzlBOCfUE62jVOdqT2E+W69jLs9vzzhZePMjBvniAwdu2KExiJCPvDWJ2jx2FfLMTkoyaaI3fKZbwcFaP18uXL2bZt26Bt+/btY2b0jl+5ciXPPvvsoM+fe+65mEzFaIAsy8yYMSPW3smTxfYhvEwMhgPUbBG5ksEf9fw4Unl+hkBxCoJCsoAWEpHKjvFie6HQcK54j3iH58DlgFRFDHPF0Hm/8f9PfpKc0a6shKuuEn/ff3/yYxrZF/X1aVjtHd+CF26GjhezbXLpUbNYvPduTb9fFhja57JBbW18AX/kSPbn7uoS76b6r79VkDeKIyZDVBZoWAELvwQTriz5qdPZLpHA6OyM94V0utOJMApuJsjPA/Go/JRO7WTwiOwEKk9DAsM1BRQbRHwpHbQG2eP3C6dXrn3O7JhbiLFZVeMBAiltLVugYiooBQwZKjNceKEg0VP5pSRJBFokkuzpUDFe5HB7TnpybpPPB6+/Lv4+//wcDtC+XjzvIt6c25AVXFECw99amvMlQT7PuZHGvn37OHTokOnXzp07R7rJZYFi2Nztjs95UtXBMOA56cno6Onc04m/xz9suxENrkU0wl7zNQiKiVd++grPff05/N3+GHkhSSLKMx1UVeiGJ88cFO+33y72M1DM/moQGN40w1//8X5e+N4LPP/t59MfTIcXv/8iO+7dgbe9RONpCdC1t4tgX5BAT3ZRaMWyW83UGgB6D/cW5Hi6rnNqi5jMjjtLTHAa5jbQsrKFinEVBTlHsTHQNkDPgR783X6RndLlI+QNEfFnX8AbRvcz8kzAzgd38uzXnuXw04eHfVYq27nqRZSir+vMITACfQGCAQiSWW4p27WXYbeDBwtfwNuAUQdj6LTQ1+Xjyf96kr+//+9oEW34F88wNC1qYuVnVjLvWnPBweUyXo6K0frDH/4wX/jCF2iLhkXu2rWLn/70p3zkIx8B4KKLLiIcDvOnP/0JAI/Hw1e/+lU+9rGPjVibs4WmaRw4cCCmYZyYgZFs8luWGRhqEMK94u9sIlMVF1gqwF4vIu0LjYrpcN7/wZJvpfbGZAGzEaeNjZmdPw88ABMnDv6spSWzpuANN4j3++9Pfn8YC8wJ6fzd/pMQ9oBlFKRD1kajxfp2CWd+ATC0z2UDSYKpU8Xfhw5lf+6sMjAGok7wimkFuX9PB6SznUFg7NwJRn3ZGTME8WcGBoGxZ8/g7QaBkVsGRumzVIoOWYHKOeLaUjiE3e74797WlnufK3Q2QDp0dICmgSyPwPNV18WYXAZQFCFxAtmT7MlgZGB0bO/g1btepe9oX4ZvDMerr0IkIp6ZU6Zk3n8Y9v0CdnwHgt05fDkHuFrEu/9kceY2JpDPc64ccPfdd7No0SJqamo466yz+N3vfjfSTSp7FMvmxnwynbPi9btf5x8f+Af7Hk1RKAPhRH3+28/z8LsepmNXx6DPFJvCuKXjaFnZgqaO/D2rqXEixVZhi629amuHqeQOQzaZg7HzFbG/miEwvB3iQ1dj+nWBYlOonSGKoHbs7Ei772hC114RXVQ/O7sC5cWyW820GqBwBEbPwR6C/UEsDkssw2b2W2Zz4ecvpOXcloKco1iwV9lx1jkJ9gbRIhqOakeM6PR1+ogEI7jqXTES1CxG+zPydIau6/E+OWd4nyy27bwdXroPdKOGVULeEF17uug+0B17GePl6YhgXxC7AwJk7k/Zrr0Mu+3bJxxYxcrAgMGFvAEcNQ76j/cT7AvStqWt8CceZXDVu5hy0RQmvmFi5p0pn/FyVBAYl112GbfffjsXXXQRc+fO5R3veAe//OUvYzUxJEni4Ycf5ve//z2LFi1ixYoVvP3tb+f6668f4Zabh6ZpdHR0xG6IFmPd6ycW8ZOIsszAUOxwwQOw/C6wZhPJIUH1fHBPBSkHEb2Mh5fAbr6gVyaYjUz9+c/j/w/9HITzZ+1aOHwYnn4a7rlHvB86lFkX981vBqdT6AcaTtpEGAvMlASGrkMg6pF1FFAGpVhwThA21MLQvzvz/iYwtM9lC0NGKhcCI6sMjFgUfxGe8PnCcLhqauZ9C4h0tps5E2w2sUj/29/EtmQya6lQMAIj4hWOSzg9JaRAkMLL7oTquSl3SZSRyrXPFTobIB0MOzc2pnDOH/4z7P4J9O9J8mEeUEPw/FqRFRcpjyivNWsEmT6UZG9qyr5wn5GBAXDg8QM5yVUkykdlzeXqGkQGxN/WEung2htAtgq5xoDJoiIFRr7PuZHEAw88wOc+9zne9a53cd999/GhD32IT33qU/z5z38e6aaVNYpl88Q6GKlQN1PMtU9uTM1y+Dp8+Lv9SIpE7fTaYZ9f8vVLuPDzF5ZFoc3QQDxgxlZhy6r+RS6Zg8XsrwaBET7axtNffZotv98ybB9fh3j2ZCIwABrmiQls5+7OwjVyhNG9Tyy4cyEwimG3WAbGod6CHM+Qj2pa3IScpJZbOcPd6Obc285l3NJxTH/jdG565CbmXTeP8eeM5/zPns/1D1zPmnvW4G7MTmprND8jT3cMnBwg5AkhW2Vqpw1/VhTTdt4OL+tuXscD1z/AK3e+wsnXT7LpN5t44PoHYq91N687LUmMSDCCGlSpr4P6CY6Cr70Mux2IujeKkYExf754H0pgyIrM5AuEzM2RZ3OQ0DjDUS7j5SgRwIdbb72VW2+9NeXnU6ZM4Yknnihhi4oLhyNegObo0eGR2kYUUFkRGCBIDPfkkW5FakS8Qp5Kyn3iZkSmrl0rBu/EDIhEcsJw/tx22+AorJaW+OfG8bLVba+oECTGgw+KLIyzzx78edIMjECHqCkCIgI11CeiqEN9wrFjrcq/3kixEOwUeuLeY9D2T7AMmaCabXvib6BGsIePw0AtKEbRVfO/gUFgHD5s7hISkV0GRhlH8b/yXgh0wvL/AXcuIdGFh8UCc+eK+jD33Se2mal/YSDKiw8jMLKugTEQZbYcTaVzmJYaJrzI48bBvn3x3y8XJI65qZqQTTZAOmSsf9H5krCtIU1YKCg2kG2CyAh2gqU8nqNr1ogCfc88o/KhD/nZt6+CT34yO/LC2+El2B9EsSv4u4VsjL/HT/cB4SyyV9lNOR2ej6qa5CQflSghaSmRVIYkC/Lde0TISDmz0Z8bw49//GN+8YtfcIORcgpMmDCBz372s9x0000j2LIzE2YyMMYvFSxHz4EeAr0BHDXDSQgj66J2Ri0We3kvQ0MeQWBY3VZkRc4qeKyUmYNmYBAYwX4/bRvb0LXh6duxDAwThWob5zWy5+E9dO46PQgMNazGiIK6WYULessHBoHhOeEhEozk3V9ObhKd15CPMqDrOpFApOw14U9uOonNbWPO2+ZQP7OeKaum4Ovw4e3wUjejPGw2hsKhc48YW2pn1JaccAv2B/F1+bDYLchWGX+XH0mWYs+0iD+Cr8tHsD+YNWlW7gj2iQAjxSrzw59aSBcPns/ayyAwipmBceCAqAlrSyifMmXVFPY9so/jLx1HDakotiIET48C6LrO3n/spXJCJeOWjBtVpHZ5zxzPcEyaJAiMY8dg6dLBn2UTBTSGKHZ+Tziflnwn70LQZskJw/mzfr1Y9I0fL5jqQjjabrghTmB85zuDfYnDCIxAB7x4syiMDqD6wXdCRIe+eKPYZq+HlfeUH4lhtH3gsIhi7dsBh+8ZvI+Ztg/5DRRdZ5bPh+JxxX+8LH6DfCSkssrAmPZOUSi52mQRh1LCUgl0CodrmRAYIGSktm6NE725ZGCcPAn9/UL3OxCAvqjijekaGDHprzIkngoNLQzIghAdgsQMjHxgjLnvex/09sa319bC3Xdn51BPh7QEhhYB3zHxdzHsam+A8EC0P5UHgQHiebVqlc6VV3ayb18FTz8Nn/2sue8aUWy+Lh/oQsIC4LGPPoakiHHXVe/KGDmpqvBitFxTTgW8jewLxSFqmJQK9cvE2Fgq0uQ0wu7du7l4SHTHFVdcwbXXXovf78fpLGCttDFkhJkMDEeNg5rpNfQe7OXkppNMu2TasH0Mh3fjvNTzLF3X0VV9xBfUQY9w5Ngqhfcjm+AxI3OwtTW51Kskic8LkTloBgaB4Q+J8U8NDc+cNTIwzDjkGuaKH6H3cC9hXxirq7yd35nQe6gXLaIJQr1ABbPzhaPGwaTzJ+FudqOFNUyouaSErutYnVYUm8L4s+OsWefuTp783JO4x7l56/97awFaXRwE+gKxzC5jXJlwzgR6D/XStLBpJJs2hiKha49YLBtyZyMBi9OCbJHFs0gXZLaEmLtGgrnVXSl3BPpEDSBHjYOrr5N48EG45RaxFk7EF7+Y+9rL41Ho6hK/YzEyMCZMEBLGHo8IojPkpUE8u1xNLnztPlpfbY1lZJxpCPQE2PirjUiyxNv/8vaRbk5WGD1Uy2kOWZZpaWkZVBTFKOR97Njw/csyA+P432HPz6B3u/nvqH4RGTn0pQ4v7pc/ZCF10/VKQY62Zo2Ivn/qKY2f/ayLp57Skso/GRkWN90k3gtBXoAo5O1yCQf6hg2DPxtGYIT7heNetoO1RrzLFiH1Zfwf7IpnJ5QTjLZbq4WMlKNJtNl4mW370N/AVoPF2QC2LI8TRa4SUqoal4UzlYFRPQ9argZnNsUXSgR7dAAKlFYDOdl4mYh5Q/jJhQvNH7u6Op5lsXeveG+Pqr/YbFBTY/JATRfBgs/DxPJdEBYE274Bz79dkGxJkEhgZLJbJqxZA+96l/jb4BzXrCkceQEZCAzfMUFiWNxgLwLRa/Qng2guI8iyzFveIhzGzz0nIprMIDGKzVZliy0EHXUOHDUOLHZLLIotHXbsEIRiZSUsyoXLNcZ1a1UOX84D098N8z8DVXNKe94o8u1zI4m+vr5hJIXNZsPpdNLZeXpEfRcDxbK5QWBkkkYynKOpZKSMDAzDAT4Um/9vM/evuZ8d9+/IraEFhJGBYa8UnuNsgsdyqSNUzP5qEBi+gDh2UgKj07yElLPOibvZDXq8dsRohnENdbPrkLLUKCyW3SRJ4oL/uoCl71mKrcKW+QsZjnXRly7iuj9fR+WEeFaw1W1F1/RY1HW54ujzR9FVnbpZdVS1iOd408ImLv7axcy4PHcP6Gh+Rp7uSFf/AgpjO1WFZ56BP/9ZvKtJFJEVm0Lt9NqspeVGKxSbwsRzJ9J8llgMr1kTVyf4zGeIZWQ88oioGZgtZFkmHBZOzuZmoSpSaEhS6joYkiQx5SIRdHnkuTNXRqr/uFgXuZvdpoNFymW8HButywTJbgijkPfRo8P3L8sMjO4NcPKfcd33dLBWiWh3LVr4e+hLC4rPC+lsaDhPvHe+XLBDKgpceqnMRz9az6WXygUjJ8zA5YK3Rn2j998/+DODwBiWmq44o/JLEkgWEUFvcYvt5Q5rFVQvFLVSLO74K9u2R38DyVKBw12HZKnI6ThGBka2ElK9vfFoPFMERjnDyFQJlg+BsW5d3GlgYNUqsd0sjCyM3dFyK0ZdhKamLLT3bbXQuBJqF5s/8WiEJAmnfgkIDIj3t8suE++vvprzoZIiLYERk3OblkMRBhOIERjlVxRVlmVWr26moQF8PnglyzgAi9OCYlOQLTKKQ8FeYcfmtmFxmsuGMOSjzj03c/HcpDAyMM6wTIhyWWzkimSORKt1dEd6FxvFsrkZCSkQUdGAkCkaknoQ9odjMj2N85MvYGSLjBbRCPQGkn5eSuSTgQGp6wi1tCSvI1QKAsMbLEwGBsTrYHTvT1KscZTBXmWncWFjTtH8o2mcVWzKoHHVKHod9obRIuVbB8Lf7Ue2yEy9eGpBjzuabHcmQQ2psYzdVBkY+dpu3Tqxlr/kErj5ZvE+derw9aIkSbgaRIF4I/vidEbNlBou+uJFnHtbXCrX8CvdfLOo8VpZKWqwPvhg9seXZZmBAUGOFEM+ykAqAgNg6qqpAJx47QQhr8mIrNMMnhMeAConmpe5LpfxckxCqkygqip79+5l9uzZKFEvuEFgjJoMDIO4MKPz7GgUUj3pot0LXZOh7myRdeBrBd9xcLUU5LDJbFcq3HCD0Pm//374/vfjPrWkNTASodhFRsPQWhKjBXo4WvA99wFUR2dgwEtFhTunCYmRgdHdHZcaMgODfKyqgox+mO7XRR+pXgSOcursURhR6CXOwEjV59atE3UShso1tLaK7WYLD8+ZI6LMjToYWde/OJNQORs6X0lZ1NogUdvaCjNWHoxyCDfdBP/6F2zfLlKEKwtUZiQ9gRFNtyqWLJgtymgGyy+63LDdJZfM4YEHZP797+ylT4z6F3okiZ5KBhgERk7yUQBhMVEfkXo0uiayakZAnnEk5yf5Qtd1li9fPmyh1N/fz6WXXjqMyLBYLGzdurWUTSxLFMvmZiSkQGRWWJwWgv1Beg70xAp7Q1QSRAdXkwtnXfKgEWet2G7IWIwkmhY0ce4nz405eXMJHstGSraY/TVGYATEcZMRGMs+vAxvu5eK8ZmJXm+Hl5ZzW5h8/mScDc5YTSMDZmsblQumXDQlFpWbLYo9zgY9Qbzt+dV5CPQFcFQPr0ljr7SDBOjiPEb/KzcseecS5l4zF1kZvu7zdfnwnPDQvCj7SfpofkaezpCtMm/6yZvoPtCdMiMsH9tlWi/+6X9ybfnph1AorkQwcaLwPX7qU/C1r8GXvyyecdkEFqmqyosvdgFNRZGPMmAQGDt3Dv+seko1M940g8b5jSjWM7PfGxkYVRPNB4uXy3g5RmCUCXRdp6+vb1C0UjoJqbLLwNBUCEZHN4fJinSOxtIu6C0uqFkM3RtFFsbkJBVhc0Ay25UKb3qTSL07elRExJ57rkjnM5xwKQkMe2NxJFBKBc9+obtfMY3sSAw9qtcPuqahh3rRIwqSJfsJe2WlyKDo6hIyUmbrLGRV/+L43wWJMetDMPGqrNtYdBj3UIkdrsn6nKqKmjTJuqGuC3Lv9tuFIyHTM9fIwDAIDCMDw3T9C18rdLwAVXNP/wyMqtni3UQGRr5jpa7HCYwLLhDPyKNHhYTeJZfkdMhhMJeBUSQCI5bRVH5yHIbtLrlE54EH4Kmn4Ktfze4YzjonnlYPzvrsx9sXXhDvORMY1Qtg/udKT9pHvPDiO0SW0oV/EcXaS4iRnJ/ki3vuuYdg0LysicMx3Dl3JqJYNjebgSFbZM56z1k465wxqRcDNdNqOO9T56WN9LZXC7KgHDIwKsZVUDEu7szPNXjMkJLNhGL216EEhhYaboOWFeYCuwbVNkoBM7WNThcU0269h3t57GOPYauwseaeNVnLW4EIHnj4Px6memo1V/zoikEOO0mWsFfaCX+TvdUAAQAASURBVPYHCfaVL4EBcSm3RPQc7OHx2x4Xv8+f1iDJ2f0+o/kZeTpDkiRqptbECtknQ662M7Ne/OY34WMJfJimafi7/YS9YaqnVGd1vtEGLaIhKVJsrDGe+TZb/Nn3yU/C//yPkFr+3e/g1lvNH1/XdfbvF3+XIgPjtdeERFhi8IAkSbzhI28o3slHAXLJwCiX8XKMwChjpJKQCofjRUzLJgMj2ClIDNkqpJ/KEYEOcE2G9vVw8gmoHVIZvdAZHyWA0wlvexvcc4/Iwjj3XOEgDwsfvXmH62iCFgQ1CFoI+neL+hWKI7PTr3e7cCxLFqhbiuQ7giN0Eimkg2VSTk2ZOlX83ocPmycwDPIxo3yUrsNA9AlfWcQQhXxQRpI369fD8eOpP9d1QQavX5/ZkWBofQ4lMExnYPRuhUN/gLpzTm8CI9AhMqFUH3gOQvfmwRHu1irGjRNjar5FvEFEAPl8YnExZYoY744ehZdfLjyBkdTWWlCcvGJ4YdqCwDUJGs4dsXoJZnDppWLS+vLL4PXGnWJmYHVYGX/2+FjxbrM4elS8FAVWrMjqq3GUOmDCgOISzydtAPwnoGJqfscLdJQ2c3UEceONN450E8aQACMDY2Agc9bbrCtnJd3uqHZklIAxosRHisDwdnhT1uTxtYILO42j0ClvjNUeX/5Rk4m1jZLJAEb8kVhto9FAYIQGQiCBzV1agtkMKidWIskSoYEQ/i4/robM9UmGom2LmNjIFjlptLGtyiYIjAy1qEYCuq6LrKDm5FlB1VOqsbqshAZCdB/opn5W4XwQ6cYCEFlMii11fyp0FlKm9oy2rKdElPLazKwXT56EATdYHaJQt67pdO/rBj0qKVi+amt549X/eZUjzx7hrPeexZy3zqG1VWyfMCGu9FFZKYp4f+ITIhPjlluET8osWlvFc76YGRiG//TAASF9BUK+8c47RdbI6dyfzKC/NfsMjHLBGIFRxjAIjNZWwRYbUcNGBLcsQ23tyLRtGAJRetbRXBxt8HwR6IAXbwb/KfAegZ7XoevVqAxRFPZ6IWs1yhb/N9wQJzB+8IN4en9TUzqJIh1Gq46jbIeaReA9KhznwQ4x23jtg8n313UI9UDEI7IvZCdE/NH6KifzKlw+bRq8/np2hbxNZ2CEeiDUJ/qTe2quTSwunOOgYQU4U6X6lA6ZokKz2c/IwNi3T2Q0ZU1gFDtSvxxgjKnBLvAeE4TiCzcMjnC31zNhxj1AI+3tyYvjZQMj+2LSJBEJdO65Ytx7uXBljdJnYJz9Q1ADIBfJyVE1GxZ+sTjHLhCmTxfk0ZEjYhH4pjdl9/1k8g+ZYGRfLF2aHWFSFpAkcE6E8B7wt+ZHYCT2uVQYpfOYMZQ/KiqE08LjEc/RQsn2DYWjVjg2RqKo8NDMgog/gq7pKHZRv2fpPpiOi0plDTC6BiNj7Gz3V3Lj324cFsnfd7SPvqN9VE+ppnqSuQhji9OCGlLxdflw1jpx1ced65FgpGBtLzb2P76fLf+3hdlvm8057z9npJszCIpVobKlkv6j/fQe7s2NwNgkJjbjzkoe1eaoduA57ilLAqNzdydPfvZJxi0dx8V3XDzsvpUVmaZFTbS+0sqpLacKRmBkyjLSwhqeEx4qJ1amLIBbyCyk0znrKdm1+Tp9KDYFW4UNSZYKem1m1oFB7OgOF5GgLzaWyRaZiD+C95QXe5UdV70rJi94OiHYH0SLaFgcwk1sEBhDazl98IPwox+J4MCf/1zISpnF8ePiOV+sDIx16+DjHx++3ZAIu/d/vQTvWcfAqQFCAyFki4ytYvDabrT2JzPQIhreNi+QXQZGuWCMwCgTyLLM9OnTB2n9jh8vSAtVFU4VY+AwIrjr6jJLoZQM/qjXx0z9i5FAuF8s+i1u0UbZISL3DQJD9YvPw/1ZL/yT2a6UeOMbRT2F1lZ46SVRjwFSyEepQoOc3q2ADNVzRUaCsb2cMbSNjiawOGDgCGgB8OwDexO4JxGTlYr4IHBMvGsRsDjBFdW4lRQUGQj3Cbtr2RdxMupgZFPI23QGxsAB8e5qETVLyhH2elj4pZKfNtV4aQZm9ps6VZB/fr+YmGVdA8MTtd3pTGAYY6psB1sNhLrFeGqtEZ9Hx9T66n4kqRFVhe7u/MZKg8CYHv1Zz43Wl3v55Xjadz4IBOLZjSmz15QzU6bG6HOKIrN6NfzmN0JGyiyBEfEnd2al2p6IvOWjAHq3iToYlbNLX0/INVHUiPG15necxD6nJAl1SzKPGen5yRhKj2LafPx4QWCcOAGzZ6fft3t/N8dfPk7zkmaaFzUz0DbAsReP0bSwifrZqSdARgZG2BvOGOFcaAzNLOjp7iHkCVE9uRp7jYNgJIILH1X2IMUgMIppO4PACAQlNG34+vH4y8fZ+oetTLts2qDirZkQ9oUJdAdEodv67J3r5YCuvYIUzoUcUFV47jmZvXvn4fPJrFpV+LV5zdSaGIExYVl2AUO6rnNqi4jCGb80+QS4aVETjhoHjprym98cfuYwIIjNVPJZzUuaaX2llbYtbcxfOz+r46fqc5myjAI9AcL+MJIiJf3dCp2FdLplPSVi6LVpEY2+I32EfWGqJlbFSNKh15breGlmHejDzZJvrGHZojipt/cfe9nz1z2MO3scyz+0/LSN0DeyHw1yJhWB4XCI7Iv3vQ++8x14//vN1QMNBGQ6O8U9XAwCw4xE2B1fDPKxZh9qSCXYF8Tqtg6SvBzN/ckMJFni8h9cLmR9U9QjS4ZyWVOMrWjKBLIs09TUNOiGUJT4YJEoI2VosJZN/QsQ0eIATpNexJGC4hQODPdkEYFvcYtXMmeASSSzXSnhcAhdfxDRyEYGxqAHtLVKOJu1IIS6RCaCFhQOnXCv+NteH81KKDMktj3cO/ila8I55GgAJLGPxS3qnYQ6wXdYEBeyXTh0KmcBEQj3Iqk+FFlG0iIQaMvpNzDq1KxfD888Yy7C3HQGRswJXkSByFGKZH3uwgtFamgqJ7Ykich9M4WHLZb4pGrPnixrYGgR8B4Wf5er9FchoTij8jxNYKsdNqZalPizqr09v7FyKIGxdKkgmtrbsyMRU8Gws80GNTX5Hy8n6LpwQKvZE6rFRGKfW71abHvqqczfM6LUIsEIgd7AsFckGMkYxWYU8D7//Dwu4OhfYMd3oHdzHgfJEc7oRM6fJ4FhQHHG+1niK8k8ZqTnJ2MoPYppc2NeaSaC9dC/D7Hjvh0cee4IAG2b29j8281s/r/Nab9ndVtpWtREy8oW1HCeaXs5wuK0YHOLyF8jMlOx2wjqwulSV6Ts92LaLjF7zZckiNvbIaIxs3XWGFGrYW8457aNNAwCI9vo/XXrRMDL6tUyH/pQNatXy0ydKrYXErXTxA3Xc6gn6+/2He3D3+1HsSk0zEu+8Fh8y2Iu+K8LaFrYlFc7Cw0tonF0vXCATLsktXTnuCVict65szPrMSNTnzPGgqEvxSFYKosj+efJSIZCIFV7inW+UsK4Nl3TxbhbacNeZU95bbmOlxdemJ7EMNaLq9/qpm5GXew184qZ2Nw2Bk4MUDu99rR0bAME+gSBYRBzqQgMgHe9S8gud3XBf/+38IX8+c+pfSKqCg88IOzldkN1EcqJmJYIGwB3kxvZKqMGVXHPnUb9KR0kWaJ+Vj1TL56aVV2lcllTnN7WGUVQVZXt27ezcOHCQVXdJ00S5MWxY3DeeWKbEcFdNvUvAKbeBJOujRVIPpOQynalxA03wB/+AA88IDJzYEgGhqNRyEqE+4WzfsuXhTb3sjvj+5SrdnZi21Mh2AUvvUs4UA1eVg2Ip5S9QbwiHjj7x7EaLaqm0v7qjxkn7UYadxlMuTGr32DdOrjjDvH3xo1Cgz9RWzEVss7AKPcofl2HyICIvreUJvouWZ9TFPHbr10rJp+JkRfGs/knPzEfGTdnDuzaBbt3Zykh5TsuxkGLCxxlmpFWaNjqxSsFxo0TJENrqwrkPlYaBIahmepwCBLj1VdFFsa01OtbU0gkqobN53b9CAKnYOotxa1rsvETgrxc8g2oPat458kSiX3u0kuF7TZvFouWdGOZu9HNmnvW5Kwz29cHW7eKv/MiMCID4t0yAqnSruiqL98MjBxQDvOTMZQWxbS5Ma80AmXSYfzZ49n7972cfP0kuq7TsUtEXzXOTz/HkiSJ1d9enW9TCwKj2LhskQlFOWVJAmeRpjrFtJ3TGX2u6TrPf/cFXHaVlZ9ZidUptGZ9HYLVcDVmd3FWl/h+JBBBU7WcZAJHEv5uP/4uP0hQN7PO9PfWrRPzzaFRvoZEyYMPpl8LZAOjkHHv4d6sv2vIRzUtakpa/6KcceL1E4Q8IZx1TpoXp56AV02qwlHrINAToHN3J82LzKZLm+tzkWCE7gPdsfEARIR24v9qRKX3YC91s+pyKrSeDXR0eg/1ggS1U8tFS7xwMOaLmWrS5DpeKgrMmpWciE+3XqybVYfFaSHkCdFzsIe6GebHi9GEUL942GXKwAAR8PeNb8D118O3viWKnxsY6hNZt05kRhjkgtcrCOBMfpNsYVZSOhIRz3Z7pT2WBVQ1oQwDecsI5bKmGF2zjNMYuq7j9/uHVXU3IrzLPgMDhLSGdQScA7lADYjaCQWQTkplu1Li8ssFi33ypCAxIImElKNRRITb6kS0pmu8+N94lSN5YcBoe6qXvV44jBMjUN1TRTHciulxqTB7few7uns63daz0BW3IHWy+A2MhYtBRhgwFi7poq9MZ2AYdRTKPYp/94/hhZuh7cmSnTJVn1uzRiwah06yWlqyX0wadTASMzBMERiJxFM51gMaARiZK21t5DVWDs3AgLiM1Esv5dHAKNLWv+jbAX078z9JJhgSXIGO4p8rCyT2uXHjYMEC4bh5+unM33U3Do5iG/pKF8X20kviPDNmmJeJSwqDAB+JOYqRgeE7njynPRdoIejZCJ69aXcrh/nJGEqLYto8mwyMpoVNyFYZX4cPT6uHzl1iwtQwt5yir1JDR0cLDycwLJbiPdqLaTtJApdLVMBrffkYJ147MUjCz9ueWwaGYlViMl9h3+gLYuvaJybl1VOqY5rvmZBJogTg9tvzr/tlwCAwPMc9qKHsDtq2OX39CwO6rpdd3ZLDTx8GYMqqKUhy6k4nSVKM4DDksszCTJ/zdfoID4RRA2r8NcQOoYEQgd6AKAhfZEQCEXwdPnztPtTIyGSpFQueNg/eU2IsMuQEUyHX8fK11+C558TfTUOSjtKtF2VFjmUpZXufjRZEghEiATEOGL9/OgID4s/DVGTuunVxv8nQzAgzfpNsYXatYIkO984G4TvytnnR1NO4OnsCjjx3hL3/2IvnhCer75XLmmKMwChzGIW8jx2LbyvLDIzRBn8rDByKS1+NcthscO214u8dO8T7wECKyXPMkXOas8yyHazpcxN9tig5MHBYFMw2gXwXLqYzMM76Hiz8clT2qgwR6BCR4loIIl7h4PUciL9GyAG7Zo2QE3r6aVHc/umnRZH1bKM75s4V71u3xusimCMwjALeZU48FRx6tI7M8MmfQQicynO+fyDKDSUjMApRyHsQgWHc354Don7CwCFxn6MX5/42zocW7U87y6I/pUI2MlL5oCD1L2BkMzCc46HpApj4FtAL5GyIDAi5uhxqN41hDLnCCIwxQ2BYHBYaF4igkINPHWTg5ABI5gkMXdcHRTiXGrqqo2tiUifbBhMYoxVCRkpCV8RFGE5YXddzysCI+COEvCFkq4wW0fB3+Ql5Q6ZqG5ULcpGPMiNRcuyY2K8QcNY7mXPNHJZ9aFnWjqMZb5zBtNXTGH9Oaq/esRePcd819/Hs15/Nt6kFQ8gbovVV4TmdesnUjPvPumoWKz+zktlvzVCcJwcE+0RGQOXEShrnN9I4v5GaqTWDso0Mp7vh/C0mwgNxolALnT4O14FTA/QfFT6KigkVOOoKX5NF1+GTnxR/v+tdIpvwYx8T/69cmXm92LykOdbW0xHGvS5b5ZiMUjoCQ1WFzyMZjKHqtttEQe1SEb5mJKXHj4eKCvG/q96FxSFqrwy0nZ52HYp9j+3j9f/3Op17OjPvXIYYxdOwMwPJCAwjA6NsCIywB3Z+D5wTYNaHRkfUscUtZIci3pFuScHQ0jL4/x//WGRjDEvNO1MIDBNQ5Qr0WR+Fmtmmf49sFi4XXzz8c9MZGI6G0hebNYtAB7x4s+hDoT4IdooitUcfiO9jrxfSXyOQ2aMoyX/7bGBkYLz6qni3WqHWTKb29PfAuMvLt/B6sdC3U4ynVbPjWQRRxDMwcn82BALxSXQyAmPTJlF03Zl7OaMYgTFzUsL9DSJTz3cCZAu8/B6xrZD396D+1APBbvDsg0P/F99nBPtTMqxeDT/9afEJjILUv9C1OIFhrci7TVlDscP8zxX2mMbcxTIC1zOGMxZGZKMZCSlvh5fKCZUce+EYW/5vCyAcgIaDIJ103Ou/ep19j+5j8TsXM/+67IryFgqGlr6kSMjy6URggCYpQCRGYIR94Zjj1Uwha6O2ka/LRyQYiZFN/m4/slU4dTPVNioXxAiMNIXlh8KsREliP1FVsS44eVL0owsvNC9pKkkSZ7/vbNPtS8SklZOYtHJS2n0sTgu6pqeVeiw1jr1wDC2sUT2lOpaBkg6ZpOlyha7rItxXEuSexSYGgHAgDAlTWqvTSrAvWBLyLuSNBy6oYRUr1qKfs9jQNT3mPK9qqaJyQnGCTdatE/NKp1NIHimK8JH87GdiDZCpT05fPZ2pF0/NmB0yaiFBy3nCoSRJEroe93kkIzDM+ETSfW7sk85vki3MSEp/6UvQ/2tjm0TVpCq693UzcHIAd9PpWdskEQMnxDysauLo9AWO4mnY6QVFUZg7d+4wPbFkElJGBHfZSEj5T0DPFpHVIH14pFuTHjHJKAX0CIR6hSMgDympVLYrJdatEw/ioUiqxXo6Exip7Jhku2E3ubo6K9LN7MIl1X6mMzDKGeF+4WyV7WCrEQXVJSXuuFb94vNwf1EcrqXocwaBYTgtmptN3iayBSqmFqtZ5YdY39LFmBr2gGQd1OcMAqO9XcrZbkaR7srKwX1n6lSRAt7eLkiMlStzugogTmBMGpdwfytOEekuW0RGl7Wm8Pd3Yn+yVov/ZUvJ+pMZDO1zq1aBLMO+fWLhMSm9fyQnhMPwyivi77wyMCLe+ApmJDIwCg3VL4guPQKSJU5mpHnOjdW/OHNQTJubzcDwdnhZd/M6PCc99B+L1y/r3tfNA9eLQAdXvYs196xJSmLIFhld1Qn0BgrW9mwQ8Yt6DlpEQ1EUQt4QgQGwECkqgVHs/hojMBDHNySDjOwLe5Udiz3zBQ6tbdS+vZ3X7nqN5rOaWfafy2LHGg1FbqddMg1XgyuWLWQGZiVKvv99kc17+PBg7XcwVzOvVDCcsYb2fTlgyqopWJwWZEUuak2JTH1ODahUT6pG14SkXCgcim0HkXFhEApaRCPQH8DpdRaNyIj4IwR6A7HstLA/jGyRR1XWUzJIskTlhErUsIq92j6IpEl1bdmOl8EgfC4aS/KZz8QDPxcsEO8HD4q6DO40w5atIn1djtEOd6ObC79wYez/7m7xu0ESaXLM+0TMoJDHMiSlk427P/kJXLwEHvh1/N6SbTKKQ8FitxD2hWPykacjwr4w/m6xXqicmN2aqFzWFGMERplAkiRqamqGbR8VGRj+qNennAvWWqtEBGuwC7SgcGboqnBMBTtAtorPc3Dqp7JdqZBJ0kiSRGre1VdHIwscjVB3TvkXh84GQ+2bDEPsm6vdzC5cku2naWIyABn677GHhTOq6aJ4AdhyhOIUL8kC6CKzyUAqOxQApehzdXXCRgbhZEo+6kzC0D6nhcV4Gu4DKZpWH+1ziRkYudotsf5F4lpWkkQWxt/+JmSkCkFgxIIDFKe4pwPt4h631cTv8WLc34pTPIuk6NSsRP3JDIb2uepqWLZMZCg99RS8+92FP6eRVVNfH5d0ywlG9oXiEMTQSEDXRaaaHhGSUrnA6HOBTtHPdB30sCCQDRToOTeG0Yti2txsBoZRENNWacPqssYi/V0NLhw1DiL+CL4uH8H+YFInt6NWOFSNiNxSITGzQFM1nHVOkCDQGyDiASugVBQvs6DY/dVwzKlRBWnDSeNqdHHhly7MSv7G3eiO2a5mSg2z3zwb2VL+ytTeDu+gTIPqKdVUT6lGDal0H+g2RbwYEiWZIou3bIFzzkn+WbbFvtWQSs+hHoL9QSYuT74uGHptR58/SnVLNVVTqpAkKeW1GfdzsD+IrutFL0I9FEPbbcCIwvd2eE2RYZ6THo6uP4qt0sasK83J76bqc0OzjIZCi2hYndYY0aqGVLSIRsgTihGvhcxCSmyPs96JrdKGYlUGEb3lmvWUyr6aqtG1u4u6WXWxa5MkKSlxnezash0v77pLSNGOGycIDAONjWKNd+qUkOF+wxvMHW8k+kqpYWS+NzSAI0nSSV616Yp4LBDj6tVXi/e//Q1uvBH++EfhC/N2DO/fzjonkiTF6tiUa3/KF0bdC3u1HZs7O0KuXNYUYwRGmSASibBp0yaWLl2KJSG8x8jAaG8XEhoORzlmYEQp03ImMByNQn4jHI8EY/s3wXsEZv4n1C8Ti/4coltT2a5UyFrSqOki8TqdkMy+QzHEvoPs1vU89GyGqe/IKNtkLFxaW5OTRpIkPr/wwuGf9fXFNR7TZmCcfBx8rUKOp5wJDAAl+vDTw4j6B8VfwJaqz82ZkyWB0b0J2p+FumVC8/50xtA+d/whaH0Umi+BqTeLbdE+FycwdF57bUNOdktWwNtAIoGRD2IExtAhQI1GuCvmtcFzhhztT2r5SDlA8j63erUgMP797+IQGIZ81MqVeSpTWquEhNNI1oto/Qfs/xU0roQFn8/tGEaf8x2Dfb8Q0mNLfzD4x0n3nBvN2jdjMI1i2tyIwPR4RJ01Q0M6FaxOK02LRDHvsC+MxWFBsQyO/k8GIyK81BkYQzMLEvGFL8D998PtNxcvs6DY/dUgMCLRDAyDWLK5bbSsaEn1tYwYDcQFxDODfF2+lPukywwyoCjwgx8Ih9hQGMPxL34BzzwD996b/BhJA8zSoPdIL//69L+wV9m59o/XDnOcDr02XdXpPdwLCJJGtsgpr81w0mkRjbAvnLVTKx8UyiYAPQd62PqHrVRPqTZNYKTqc8ZY4DnhSRl1r4bUWAH7QF+Af336XyDBVf9zFYpNKWgWUrqxyUA5Zj2lsq+u63hPeQl7w9TOrGXNH9fEfstkSHZt2YyXnZ3w9a+Lv7/1reHPrkWLBIGxfXtmAqPnYA+v3/06kiyx+lur0+88yqBFNCRFio0vmQp4m/GJGN/NxW+SLxQFrrpKrBF7euLj7GjtT4WAQWBUtWQftF0ua4qx1UwZQU1SvaauTuj0+f3CST1zZhlmYASiXp9cowpLBUfjYIKi4Q0iIlL1QmV+BXeT2a5UyFfS6LTBUPuaQMxurX+H/r1QsxjGXZr2O+m0FQ385CfJFyNG/YuKCrCnIvXVgJBlg9FRCFqyiqhxLQz+UyUbB0rR52bNihcR1nVBPqVdZPZugbanhBP6dCcwYHCfq5oHp54VEe5DxtM4gZG73TIRGFA4AqO+AUhca0lWEb1vyaPAhlkodkEKuoqgyZQnhtpu9Wr4zndEBobhjCkkDAIj7wLeFvfI90dX1PPryxCymwlGn1txt6jtIWV2HI7k/GQMI4Ni2byyUjjBvV4xp5xlwkdoSBLZK8xHMtqrxb4jISGVmFmQiFMh6AGaphb3/MXsrzECQx9MYBQS5RyVbGQGWewWLE4LIV8ISZKwOCxIkpQxMygRRuCYLIvsagOGRMmaNSIIJhWBAdlpv1dPrgZJXEOgN4CzdvB8ZOi1BfoCyBYZxanganClvTbFpmBxWIgEIgT7gyUlMIa2W0en73AfVpcVV70LNaSatknToiYA+o70EegN4KgxV6cgVZ/TVZ2nv/w09bPrufz7lyPJqe9rXddxN7kJe8NYXVZqptSYOnc2SDU2lTOG2hdEvYveI72oQRVJkQgPhFFsCnUz6rI+frrxMrHuzAMPiCDCJUvgP/5j+L4LF8KTT8K2bZnPaXVb6djegaRIRAIRLI7Tx5265Q9b2Pu3vcxbO4/FtyzOSGCYqTdx553iPd0+qfwmhcDixeJ969bB21P1p75jfWz5vy3Mv37+qOtvZtB/XAQe5lpnphzWFKMjZOIMhiQNlpHS9SJmYAQ6wHMg9SvQkfx7RgaGs4wzMJKhcrZ4798zsu3IE1lLGmkjP/CUHWqWiPferen3i8LQVhz6QK+sTJ8Obqr+xcAh0dHtdUKyZjTAPRXsDeA8fXSW1q2Dhx6K///YY6Lewrp1ab7kOSDeTyd5NrOwRSuch7qHfWQQGL29EsFgbo6NdATGsmXCiXDsWDxaKFvoehIJKQNVcwS5KZeiaJ8sxiNb+RfJWblSELEnTsCeAj9GdT1OHuZNYJQDnNHoZv9JQTwUAibIizGModAw5pLFDIoxnLOllpBKxKmtp2h9tTWmFV12wWM5wCAwlDdexo1/vZGJbxCT2NbXWjn6/NG0UfCZ0PpaK499/DFe/kmekQQlgMVpwea24e/w03tQOFJtblvMwZoJnZ3wjW+Iv3/1K3jySZU77tjHk0+qHDoUXwMUMsDMYrfEHE5GZkXS/aLXpgZVZIuMu95t6tpsVYK0GKk+Z7RbQiLsDePv8mN1W03bBETmVs30GkD033zRtrkNdEREehryAoS0yvTLpjP32rlFdWhv/t1mXvnpK+x/Yj+PfOQRnvrCU0U7VyFh2NfitNDf2k/EF0GxKdTOrMXqLnwR8nXrxJrtkkvg5pvj67lrrknuKF+0SLxv35752BXNFbib3eiqTvv29kI1uSwQ7AuK2k/RbJhMBAak9om0tMR9Imb2KRYWLRL+1JMn48/xdNj90G5aX2ll8283oyeLUh3l6G8VBEYuGRjlgrHVzyiAISN17JhI2zaKyhZ0Eh3ogBdvhuevT/168ebkJIaRgeEo8wyMoag7BxZ/DRZ+caRbkheM9L1UAU8GCRZLzdvwEXj+hlFP3BQUtVF6vndL8pSKJFizRhTme/pp+HC0dv20aekfwkYGRtq+O2A4wUdB9oUBW23UaX96PFLWrRORIn19g7cbmsVJSQxdH522KxRs0cipUM+wj2pqwBYN6Ovuzm2hko7AqKwU0VMQL/qcLTwekekI5eCgShjM9fIlnJ1OOP988fdTBV5D798vpDPt9tQa4qbhPQodL4r3kYKjUWQnaWGR+ZkP1PIptDqGMw+GjFSmOhj5IDEDY6QcCNvv285z33gu5ggtO/neHGAQGL6gMsghu/PBnbzwvRfo3JX72CTJEr2Heuna05VvM0sCXdcJ+8IAWWccfP3r8Wjud78bVq3SeeMbu1i1Sh/kHM2nZl4y1EyrAdITGADhQJhAj8heMvpSJkw4ZwKTzp804tHkBmHoqHUgK9mvKcYtEREzbZvb8m7LyU2CWRq/1JyBzr71bJa+dykVzRm09fLAkeeOcPBfB4kEIvQf7afvaF/mL5UReg/3EuoPISkS9XPqs8rMMwtjDZdMXvvrX0++hjPWEGYyMACal4iAvbYt+d9n5QRDUsmQcTRDYMBgn8g994j3RDI3cZ9UhG+xUFEBM6LL8i1bMu+/6OZFKDaFjh0dnHitiBOdEcKyDy7jsu9fxuQLJo90U3LG6eFtOg2gKAqLFy9OWtXdyMA4ejQ+gXa5xKtgCPeLYqyyHaw1w1+yXXw+tMaAFok7WEZbBoatRpAYORTuTkQ625UCRvoeDCcxkqbmhfsh4iuNnnsZY5DdquYJ51KgM07ImTqGSPv+2tfEb711a/oIcHMZGFFP7Whwgqt+iHiHvwoZZZwExexzqgq33ZacxzK23X57vJZJDMEuCHtEVLR7SsHbVfZwNIn6F00XD/tIkuJZGHV187O2m67HCYwZKbpFvjJSRvZFZSW4DGWGVPe36s/tJJkw9Hz+E9C9MasxqVhI1edWR+V/C01gGPJRy5enkdszi47nYcd3RB2KkYIkx+X1fDmmCYF4dr9wA2z4mJAbzICRnp+MofQots1LkYHhqHbQuKCRlvNaiiJzZAaGA9hZJx4IpcjAKLbtDALD6x283dchMi9cjbmvC+pmiiAGT6snRgyUM0KeELqmI1tlFIf533vPHlHfAuCHPxTrgFR2yzrALANqp4lM195DvSn3ifgjdO7qRItoWFwW7JXmHqDLP7ycC/7rAmqm1phrTJEQ8giC3qz801Bk61hOZTtd0zm1RZCX45aWh3/D3+0XfVUiVrPGiJgfDdAiGv4uMX+un11v+t5MhWS2S7eGM5BsDbdggXg/dcpclL5BlBn3yOkCQ7bRID7NEhgQ94ncdJN4T/YYUxS49FKZT396IpdeKhdNNmooUslIJYOrwcXstwmVls2/24yunV5ZGPZKO43zGnE3ZS+PVS5rijECo4xgsyWPAEmUkDIcoEWbQCtOoRc99KWk0P6WLbDyj3DBfWApXsRBuSOV7UoF06l5ugaRAfG3NTftu9MJMbspdqiaK/7uMUHPD0Fjo3C0ATz+eOr9TGVgjAYZImsV2OtBC0K4d/DLs084XiNesBTvHitWn1u/PnnUjoFEzeJBMLIv3JPjhc3PJNjrYN4nYdo7kn5sOL26urLPwOjoEA4XSYIpKbihfAmMU9E1yLhxDL6/A20iM6t/V/we14Li8zzJ7xhS9adQjyA1/CfBUlW48+WIZH3OIDCefjoJqZcHCiofVS7PPFdURiqfOhie/SJwJOIVdVlMYKTnJ2MoPYpp82wyMCL+CCFvaNgr4k9dwBtEUejLvnsZF/zXBbEaGqVGYiR4JCIKgELxMzCKaTuDwAjt2M8L33+B1ldb0dS4UzEfvW9HtQNXgyBAeg4Oz8QsNwT64446CfPSlp/9LEQi8Na3xp9/kNxu6QLMDGSj/W6QCz2HUvy+uij2rYU1rG4rDXMbMkoflRN0XSfkEwRGrnU4mhY0ISkSvnYfA20Dpr6TzHZd+7pEPQu3NUbOZYKu6wT6AkW7/7v2ikVk9eRq3M1uZItw4xljVbkj2B8EnayItUwYartc13BudzzD24yMlEGU9R7qJdBX+lpNxYJxLdlmYGSLUs9Ll0SVws1kYADMXzsfW4WN/mP9HHzqYPEaNgpRDmuKMQKjTKCqKhs2bEhaGCVRQspghYuewqx6hUMu4jG3v8VV+AqepYD3GBz6Axx7KPO+KZDOdqWEmfQ9IgPxsIQzmHCCJHbLsg7GUFx5pXh/7LHU+2TMwNBUCEQ9qXkWli8qHI2w8h644IHhrxW/hoY3gHsadOWo55MBxexzOWsWn8nyUSZgZGC88sqRrO1mZF9MmhSXohoKg8DYsAHCOQR/GhkY48Yx+P5e8i0xNoy/cvB9vvKeeAHzfJGqP13yBEy+HmqXQv1yUWdmhJCqz51zDlRVQW8vbNpUiPPAM8/Ao4+K/w275oVwdB4z0s88Z3QF6M8jJd2zV7xXzTG1e7nMT8ZQOhTb5mYyMOxVdlz1LiLBCIHewLBXJBjBVe/CXlV4+ZBCQA2phL3iQeKsddLdHZ8612VfZ9b8eYtsu1gR7/Yujq4/Su+RXgI9AXRNR1KknKPeDdTOFBkC3fuH18IqNxi1HhxV5q/53/+Gv/0NLBb47/+Ob09nt1QBZrIsCnxnI59iEBie457kUfcSVE+pxlHjoGFuA4ol22xXfcQyngCRuaMJAjObrJhEWBwW6ufUY3FYYlrv6ZDKdoYEVfOSZtNSVv3H+3noHQ/x5H89WRTpu849YhFZP6ceSZJw1Il7N5/aNaWEs85J06KmghU4T2a7fOrOZFMHw1HtoHpqNQDt206fOhihfkEg5pKBYRYjMS/NlsCwuW3Mf/t8ALb9aduIjouFRO/hXjb+eiNH1h/J6fvlsqYYWaHDMZhCMgmpomt0+9uEJEqwS0iD2Io4Yx9J+E/AkfvBPQkmXTvSrckbRvpeShgSYBa3yJ4ZQxy1i+HwnyCcm57olVfCHXfAv/4lHKjWJIHmGTMwZAXOv0dIjNjLXGjZ0ZjcgVs5A7QQ7P8VHPxfqJwJ1fNK374ckbNmcahHkLhnMoGhRSDUKwhty2ApCoPA6OrKPnLjQJQbSlb/wsCcOVBdLXSpt22Ds8/O7hyDCAyI39/eI2K8dE8qLqmYqj8t/hps+Dj074a2J2H85cVrQw6wWMQz529/EzJSy5blfqx160Tqf2L03Ic/LEiNvDRyDQJjpDMwas8S7zWLcz+GUbuqcnbezRnDGHKBkYGRzlHkbnSz5p41MT3tZLBX2TNG/Ou6jq7qsUjjUsHfIyKaZauM1W2lM1o+p65OjHmjFQaBEYwI57AaUvF2CD0pZ70z72j9upl1tL7cWvYERmggFIsGl60yIa9w2qXLDFJV+NSnxN8f/KCYc5jFmjVw9dUi6vvYMSFh092dfdaiq9HF2R84e5gDOFGyS0KicmIlkcDga8mU9bTnb3vY+OuNTLt0GufeXojIgewQ8UcI+UJC+sppiRGImdqdDOd/5nwcNY68xo22TWJSOO4s8/JRFeMqQBJtDvQEYvJzhYJRX6ZhjlhEuupd+Np9sQyqckaiHSVZMtXnckE+dWcWLoS//tV8HYyWc1twN7qxVY58RHohEAlGYuOGvcpOIBD3OxY6A6PUMAiMnTtFLWEzSQSz3zybvX/fi6/Dx/7H9zPnbVkM+mWKzt2d7PnrHsafM54pF45euetRPA07c5AoIVWyDAwtYdERaBe1AWxR+YpAR9wR3voPERHYfImIEgUhc1GoyNRiw4hi9B0X2tKW07wuhGG3EZYiKUtUzhZyaLbqnL6+bJkgJjo74aWX4KKLhu+TNAMjsT8lwqiFMZr6k4GJbxGSOyefhC1fhIVfSe48LMNrMzSLW1uTa6hKkvj8wuUd4Emw2/g3Res/6EIGrAyvrejY9lXo2QrzPgXNFw/6KE5gZC8hla6AtwFZhhUr4J//FDJSeRMYBiIjHL3vahGyXAd+Cwd+LZ6zjhGvMj4Iq1fHCYzPfS63YxhFF4f2uZMnxfZBUojZImbDESQwAh3iHmqMamIZUoEGzIwXuh7PwBgjMMYwQjAcP5kkpNyN7rwkiV752SsceuoQZ996NrPfUtr7PVE+SpKkktS/KAUMAiMQjhIYQTVW/yIXPeyhMKR2ypXAMDKDDJklxaYQGggN2icxM0hVBelw8qSI3N28WQRKfPWr2Z87McDs4EFRO++uu4RevBl4O7wE+4M0zhfPid4jvYBwtG/+3WaW/ecy4dDu8hEJJncKp8t6sjgtoFNyORzDJr4un6hLEp0EGFr8kL7dQ2H8TqmuwwxxCjDjihk4652mC3gDKFaFinEVDJwcoP94f0EJDF3T6d4n+lX9HLGIdNaL45ezhFSifXO5L7PFWWcJ53QolPzz2BouSd2ZbDIwABbfkkcwShlCC2u0rGwh5AlhdVk5dEhst9uLm3lYCkyZIrLF+/tFHSPD1umg2BTmXTuPvuN91M2qo/vA8Oea2fGkXOA5IdZDlRNHt4z8GIExCmAQGP398UjUok+iLW5AEpkXgVNCesjfBj2bYf8vRWYGCG3uiA9OPhF3itvrCyuvUUzYakSGSaBdaPfXLhnpFhUXYwRGasiWnMkLEIuTK66AP/1JyJ8kIzCGZWAEOuDFm+P9KRlGU38yIEkw5SbY8zNxz3W+CI7xw2XmyvDaDM3itWtFcxMdqkbzf/7jDpRXTkO75QubkI8gNFz/1yAGuruLQ2CAkBsyCIwPfzi7c6QkMMJG/YQRlB9quQY6XhTR93v/BxZ9tawkGw0d8Oefh2Aw+6Lb6You6rq41NtvFxGsOdWNG+kMjEKN88EuCHaLguCVMwvfzjGMwQTMZGAUArJFRlf1QY7MUmFoAW8j+KTowWNFxjACIxzPwCiEE6ZuZh0VEyqom1mHrutIZfScgnhmkK/TR8/BHtSQSvPi5kH7GA6pZBmBANdck/8a/AMfgG9+U9R62rxZOF3TwdvhZd3N64ZJBYUGQnhPCft17+vmhodvQLGlfkimc7YZDuR0WVPFwNBsLV3XY+RSYtvM3J+pfqdEuOpdrLlnTcbjTbtkGtMumWbyKuKoaqmKERhD76184O/x46x3EugJUD2pOnau6qnVWBzl685zN7q59k/X8q/P/At3s5sFaxfEiBcDhXICe73wtrfFyYtUa7hUdWcWLhTv27fH555nEmwVNi78fJzZSZSPGu2/hSSJQt7PPy/IaDMEhrfDyys/fQVfl4+Nd29Muo/Z8aRc0H9c+AGrJo5uP2D5jnhnGBRFYdmyZUmruldUQG2tKCBnaEwPmkSniuA2kE0ksBpl8W0NYKRXVbjAexy0gHBQBbtAtovC3v62qOO3TkQXqn7xebh/9DjuqmZHCYy9OREY6WxXdrC4oe6ceDHRMxhp7aZrwkmUJa68UhAYjz0G3/3u8M+HZWCE+wf3J19Up8DeLAqLj8b+ZEALCUIo4hPSQrJ9cIZTHtdW7D5naBYPXby2tIiJ71su74fnE+w2FKPZbvnAkBpMQ2AEArVZO6GzITAgt0LemTMwRjBaRZJh7u3wyn+CHhFyUnKS/OciZv2k63Pz50NTE7S3C6fM6tUius2snbMpuphWIjEVjCLeI2XDxHFesoq5lOIEKfoDmR0vPFH5KPdU8XwwgVE1PxlDQVBsmxsZGH194POBq0iJy0Y9hpEokFo/u57zPn1ezDFYqgyMYtvOIDD8oXgGxpSLplDVUhUr2mogMftg/HhzY7qj2sFb/99bi9H0gsHIDGqcl3qsTZURCPD73wsnaWJGYLZ2Gz8errsO7rtPZGHcfXf6/YP9QXxdPix2C7JNJtQfwtflI+wLI1tkbJU2LC4Lik2hbkZuodIxAqOvtAQG5J+tZSDxdwr5Qvg6fLjqXbgaxSAV8UfwdfkI9gdj5yt0n6tqqeLEaydM1d/IBq56F2/55VuIBCIxqbfFtyweFVkAof4Qge4A4YEwTQubsLqyD2RKhkTb+f2iX65fLyLtv/hF+NnPkq/hUmXzzp4t5J89HiHbPsWkwo633YsaVke9U3goilXAe6TmpUuWxAmMd7wj8/6J44nFaRHZYRpIiuh/ycaTcke+GRjlsqYYIzDKCKFQCKczebrhpEmCwDCKzxQ8gttaJfYLdg2WjzJgqwLbtLiMhmyFQBuiJ1sEsWE4VJJ9v5xROQfan49rS+eAdLYrK9QsEq8xAEnsFuqDXd8Xxd3P+13WJMYVVwiWf+tW8eAf+tBPWQNDcQrnfsQryBP3NFCii8nR1p8SIdugYpoYXyxJHu55XFux+1yiZvGwBXzUpy3s5hZZaqFuUbfEKLQ8mu2WK2IZGMPTbA1ioK1NB7IL5TFLYLzhDeJ93z7R1wZJtWVA6gyMMqmfINuFI/7Eo+KVDEXO+knV5x56SCz4QBAY3/ymWCjeeac52ad8ii6awqwPCyJqBIugA2K88B4RdqycNTgT0sx4YasTcp3OCVmddtTMT8ZQMBTT5lVV4HSC3y/65IwilQaKERgjkIHhanAxddXU2P+lzMAopu2GERghNanzOFn2QTZj+mhGuoxAA8kyArO120c+IgiMP/0Jvv99EaiYCYbMk+GEkhUZd5MbZ6OTYG9+c06DwBoJAgMoaMaOxWlBjajoqk4kFMHmjgd8JJMxGmq7A/86QP3seqonV2fdpqoW8Vw3Ip0LjXLOtkiFQ08LLaKJKyYWjLxQVXjuOThyRGXCBIUf/xj+/W8R9Pv443DeeaJmTTYkrNUKc+eKGhjbtpkjMHY9tIvNv9nM5Ismc/5nzi/ItY0UtIiGpEixe94gMFqKEPM6EvNSow7G1q3Zfc8gL3oP9WJ1W6mbHieJU8milSO0iMZAmwjoyodsK4c1RWmroo0hJVRVZevWrSmruk+eLN4D0Xl8zAGaGNlnrRn+ku3xyL50cDQKx8cFD4j3lfeKvxNf598jHCQgil8Hu+I5dnJhHkgjgqqotm7/3vQz1hTIZLsxlCeS2s1aKbTJQz3DNcpNoKEh7kR97LHBn+l6ihoYsQYFopkfiuno2lGBVORFHihVnzM0i2+6SbynnPhGPMLRrYVT7HCGwISEVFsbRCLm7RYIxCfRmQiM+noRQQXwyiumTxFrF0Dz0Ix/W63IWLNlwYYUA+F+cZ/l+6zPEan6nBGp6h8iwdzaKravW5f52PkUXTSFpgtgwpVgKQMnvkFMq/70+yVD9TyY90mYeqPpr4zNT848FNvmkhSXkcpUByMfGA7VkSAwhsKYuxU7A6PYtjMIDF8wLiE1FMaYPjQrLpsxXddHRvrLDI4+f5TX736dzj2dST/PJiPQQC52u+ACIWni98Nvf2v6a1hcllgMSMX4CqqnViNlGRSSDEYGRiQQQQ2V/nmx++Hd/PU9f2XXQ7sKcrxESax01zPUdr5OH6/+9FUe+9hjsULi2aBYBIaeg3+iHKCpGkeePQLA1EumFuSY69bB1Klw6aUS73mPnSuukHj8cVH74pFHBHkBWazhEpBtHYz6WWJtcGrLqVFrIwN7/raH+669j9d+8RpQvAyMkZqXLo4mKxnB4NlAkiQi/gj+Ln+sAP1ow8CpAXRVR7Erw2TczKJc1hRjBMYogVEHw8CwKCAjEtgSjeS2uMUrmbxJKjgaoXIGeA/D5s9A69/F/8YrMarT3hyPSFWcZBtRW1aomCEi7bVQXC7kdMUof7gWHZIMNVERzN4sKfoorrxSvA8lMDweiESJ+pQEBoz+/pQOqhcG9gPaSLeksDBsZymSlsZogSEhFRyegWE4fkIhmUcflTA79zlyRAxbFRXmnEe5yEhpGpw6Jf4eloEx81Z4wy+EE7wcoDhFwIC/VTjBc3nWFwiZaleAiFTNZOsLLxQRXqkCHSVJzIGSFV0cdTDsdLrPNcZwWsMgE4tZB8PIwBiJiPATr5+g9dXWmHyVISE12mtgVEST6Pdr07nu3us4/zPns+fvezj6/FHUkFqQMb1zTycP3vAgT37+yYK3vxA4/Mxh9v5tL6e2nkr6edEzAqOQJJGFAfDzn4t5iBnIskzdrDpqZ9RSNamqIOQFgNVtjUmjlLoOBkDX3i58nT50tTDrVKvLiq3SBhpZyTm1bRbRLPWz67FVJJHqzICqSVXMfttsFly/oGAO7bAvzF9u/AtPfeGpQWSMv8fPIx9+hIf/4+GydZ63bW4j2BfEXmXPqiB6KqQiWEHUvuhMzkuahlEHY9s2c/vXz6lHsSkE+4L0HenL7+QjjEBfQDi4rYLpKRaBMVJYuFCMu6dOxdd8ZmFz23A2OEGH3sO9Zdvf0iEmHzWhsuzqU2WLMQJjlGAogZHckaMLp2uvSdo4FQLtIhJccaTeR7FD1TyonD36i0kqdlhxN5z/59O/uPX2b8Lzb4f250a6JeWLmmiOYY4ExlVXifcnn4RwQvCOIR/ldKbQjNajO4/mbKa00KB/n3Bue4+NdGMKC+10t51JpMjAWLcO5s2L/3/NNQpTp5qL5EyUjzIz38qFwOjqijtkmprMf2/EEOqGUC94jzKSZGAukarJoChCmiQZMhVdzIhQX7wAejnAHu0j4X4RNGEW4X4hP6WfZuTvGEYlSpGBYa8WEdRGQe1SYsv/beG5bzxH935BxpcqA6PYMDIwPD4Fm9uGFtHY+KuNvPC9F9AiWkHG9IrmCiL+CJ5WD2F/eWWlahGN9m3tACmdqUXPCEzALbdAdTUcOABPPGH+e84aJ656V8HICxARxhNXTGTSBZMy71wEdO0Vi6T62YXJdpWQYsWufZ0+0/fiyU2CmRp31tBoFnOwV9o55/3nMPNNMwvmJOza10XYF2bg1MCg4uY2t43+Y/34u/05ZYuUAoefPgzA5IsmI1vycztmkneTJHNBM+mQbQaGYlVoXCCY7bYtbbmfuAxgBAsYz97TjcBwu2HWLPF3LlkY1ZOqkRSJsDeMr8NX2MaVABOWTeDaP1zLys+sHOmm5I0xAqOMkK4giiEhZSBpFJAaBDUkojL1PEbvoJjc4RiqpZEEthqQ0xAdowWOJnPesRQY6WI2phHph4g/eQHYMxBJ7VYbzTHs2yGKT2eJc84R/bO/H158Mb49rXwUxM8ljT59U3OQRT0MEDUjwvlFqpRVn9MN253hBIa9Xmj0T3hTbIWRrxyFQWCY1Vk3CIxXXjEf0WhE4jQ0CA3csoejWYzhegTCAyU77dA+V8hI1TVr4MEHhb5+IlpaxPacddcH9sOO78Den+d4gAJDdohACV2HYBahgh0vwWsfhe3fyPqUZTVWjqEkKLbNS5GB4ax10riwkfHLxqOppSXu/N3+WBugdEW8obi2i0lI+cQQ5O3wAiL63uqyFmRMd9Q4cDW4QIeeg8PlJEcShiPYVmmjdkbyohO5ZgTmYje3G97zHvH3XXdl/fWC48LPX8gFn7tA2K+ECPQF8LX7QIK6mbkVIU8GW4UNR50DdOg/ljoLw7Cdruuc2iwmhOOW5kZgFANdewS50zBn8ACk2BSRZUJ8zConhP1hjr8kJv/TLpmW9/EKFTSTDkYGxq5dg4MQ06F5ifCXndqSZVh/mcHIvDLkG4tJYIzUvNSQkcq2DgYIsipRIq7U85J8IUkSjhpHjNjNFeWwpjhdPWWjDhaLheXLl6f8PDEDQ5JSFPsySAvFLnT0c0XAIDDShKKm0m/ORdd5lCOT7coKhj66ZYQL0pYBUtrNNRls1SJy17MXqudndVxZFsW8//hHePRRWLVKbE9ZwBtEv4l4hUNSV8XfxvbRjsRrkCxCZijYDgOHwZVdMVoDZdPnDLLYyMDQgiKq+nSwWy6wuIRGfxSZ5CiMaKmhxTATYbaAt4FFi0SWU38/7N4N801035QFvHUNXnkfWCpgyXfAWmGuEUWHJCQcg12iIHQJMgeT9blCR6oaJMaf/yw0iz/wgcxFFzOiXIqwQ3xcsFaILBr/SbBWmxsvPNEMEnd2ToCyGSvHUDKUwuZGBkYxCQyry8pl37mseCdIAU3VYo4cZ50gMJIW8Q50pK85ZK0aLL1rAiltV6BzGQRGpd7P8z/chfe4IBhcjcJhXagxvXZGLb5OH937u2laUD5pjW2bxMO+eUlzyuh4IyNw7drhn6XKCMynz334w+J4jz4q5jvp5joRf/KgqlTbRwuM7IuqlqqCFHlO/D2cdU58HT58Xb6kklCJtus52EOwP4jFYRlGFmSDsC9M37E+FJtC7TQT1dkzwKjXUj9neAScs85JyBPC1+WjenJ+jsmCQ4dF71hEx84O6mblT0yVQt5tyhQhtTcwAPv2ZV5DeDu8OGudhLwhjr98nM69nchKPD7cXmXH3VjYOpDFglG3yF5tR9fjGZaFJjBGcl66ZIlYZ2STgZE4nljdViSrqIfRd7QvFuSQCd4Ob1ppvtFyn5TLmmKMwCgT6LpOX18f1dXVSSdViYNHZap1uCFBk28EdyDKINuTTDqtVSLKNtglnHXJYK8ffVJMES/svUvU/zjnZyCb95hksl1ZwVgAjTb7FAEp7SZJULMY2tdDz9asCQwQdTD++EdRB+N73xPbkmZgJPYn1Rd1hgcg3BvfZzT2J0g9VigOMUZpAZGFkYN2/4j3ucRri3hE9owkD84qGa12KyCyiZa6+OLk+2RLYFgssHw5PPeckJHKi8CIeCHQCXSml1QcCVgq4vdfCZCszxmRqq2tyUkqSRKfZ1O7wrD3ddelvieyQjkQGEPHQh1BjqleMd9S7JnHC88+8V41J6tTj/hYWYZ49NFH+eEPf0hHRweapnHBBRfwox/9CFdU23HXrl188IMfpK+vD0mS+PKXv8yanFOASo9S2NxwYBdTQmqkEOwTfVSSpZgjZ1gGRqADXrxZ9OlUsNfDynuyIjGS2q6A5zLkSx0EOPCvg9ij/lwj4r5QY3rdzDpaX2ml50B5ZWAY9Q0yafGvWQOf+Qx8//uDt7e0CLJh6HCQT5+bNUsEPT3xBPziF/Df/z18H3uVHVe9C1+Xj0gwOVnhqnfFClfnA13X0SJaTAe/FCiUfFSq38lR48DisKDr+rDfKdF2hnxU06KmvOSO9j26jy3/t4Upq6aw8tP5ybXoup4yAwPAWe+k70gf/q7yC5yyuqzMu3Ye866dl3lnEyiFvJskiSyMl18WMlLp1hDeDi/rbl6Hr8sn6iKoOvddcx8WR9wP56p3seaeNaPCOW3UfHJUO+jsFDVFoDByeYkYyXnpkqhKuBkCI+V4UuXA4/EQ8UdwznBmHHcT75NUKMV98vJPXsZeZWf+2vmD2qyqYh1+8qSwdbrAsXJZU4wRGGUCVVXZvXs3y5Ytw2IZbJZ16+DjH4//398PU6eKCJE1lxsH8ItCsnoE0HKP4NbC8QKsziQSUo5GMUkucNTRiENxQffrEPGB72hc6sYE0tmurKCpcbmRM9yxChnsVr9c9B1XS07HvuIKMQnatk04cFtaUmRgDO1Pui4cW4kE2mjsT5B+rAh2w/avC9m7E/+AmR/I6tAj3ucSr817WJCfigsW3xHfZ7TaLV9oEVEDQ3Fy8qS5jIV00VIHDoh3swQGCBkpg8B473sz75+awIiOlxYnyGUythvPdEkRz/tgLzgH4oXki3XaJH0uMVJVkgY7vHKtXWHY26xkWEbEbDiC2TPJxsK+7eCYAPZoVGK68UINiPoXAJWzsjr1iI+VZYiKigp+//vfM3HiRCKRCP/xH//BV77yFX7wgx8QCAS4+uqrufvuu1m1ahVtbW2sWrWKmTNnstjQHihzlMLmpZCQMqDrOrqq562fbhaGFIujxoEkSXi9EIgOr7EMjHC/IBRke/IgDNUvPg/3ZzUPSGq7Ap5LlkWGYsSvoEaAKIFhOE3SZR+AGOPNjOmGDJBRQ6QcEPKGYo5gM/UNjHnBddeJVzrHTr597iMfEQTGr38Nq1dDT8/g87kb3ay5Z03RI3g3/24zu9btYv7181nyziV5HSsbdO8T90m+Ufq5/E6JtjPqo+QrH1U1KS4zky+87V6CfUFki0zt9OHZHK56QT6mc46eLihG0EwyLFok1g/btsHb3556v2B/EF+XD4vdQvWkamSLjK3ChqSICXDEH8HX5SPYHxwVBEaoXzAW9mo7B6LyUU1NYCuw6vhIzksNAmPXLkHQpLu2dONJz8EeaqbV4Kh2ZLRt4n1icQ6/3lLcJ5FAhENPHQJgwdsXxLavWyeUEhKDDVtaoj7mJHE75bKmGFvNlDkM/fChA7WhH/63B6p4S200si/cJ5xHwR7Q9sRrWGQTCRyIhhkpjtQyQ47G088xJ0nCMdCzRRT7zILAGDUwHDmSNLLOnNGA5kvEK0fU18OKFWIC9Nhj8P73p6mBcTr2JwOprq1yBiz6Kmy7A3ytYtwqFwexWRjXVjkDxq2OayKd6dj1faHXP+uDjB//ZlNfSRXdo+vZZ2BAvA7Gk08KOaJMESUpCQwjer8cJPeGRfFHyU49Ip7bim1Esn4M2adkE+Bkkarp0N8fHycLR2CUiQ2HjoWVWVygZ5+wt6MhTniMIWdcdNFFsb8tFguf+cxneNe73gXAP//5T5YuXcqqqPbjuHHj+NSnPsVvfvMbfvKTn4xEc3OC48gRpO5uoYXhdIpXYyM0N4viQOGwWLnn+MwqRRFvgBd/8CJH1x9l+UeXM+PyQg0K6eHviRIYdSLrzsi+sNvjEkwxKE6wpHA4pMpSzxUFOpfbDWG/MqjQrSEhBfEx/b3vhb4hpcrq60WATiYYBEb/8X7C/jBW58gXl/Ke8uKsc6LYFdxN6Z1E4TD8/e/i749/HBKGjKLgqqtE9+zoEBncBhIdSe5Gd9GdoIpNAZ20BEAxUDezjqAnmJdsk4FMv5PnhActoiUlRC/4/AV07OigZmpNXm0wdPI9rR50Xc8rUtkg3Wqm1Qwq4G3AWS9IzXLLwDj24jEiwQiTzps0KCMhHxgE63XXDf8s16CZZDDqYJgt5G1xWlLWjUmVMVVu0DWd8eeMJ9AXwFHt4PjrYvvpUsDbwKRJUFMDvb2CxFiSgadNNZ7Uzch+Lm5xWrC5kzMmxb5P+lsFmWqvtsek9DL5mPOqP1hkjDKP0ZkFM/rhH/5EI1fuugdF64dj6+DEY2IHV4twEEJ2kcCSBM2rAOnMc8ZVzREEhmcv8KaRbk3hEat/4c5KImsMueHKKwcTGGlrYJyJqF8GS74FNYtOj7HmdLiGQsAWjRALdecdLdXRAV6v2G/KFPNN6I4GfR46BDffLP5OF1GSOgPDcH6XAeGbLIp/952CmJ5yA1TOHLGsnzVrRB2Tf/8b3vhGse3ll+NOTrMwsi+amtJIZWaLcpCQygQtDHIaB19/tP5FZXbyUWMwh+7ubhwO4ax+8sknY+SFgVWrVnHnnXem/H4wGCQYjDv8+vtFH41EIkQiYlEqyzKyLKNpGpoWL/xobFdVFT1hkEy1XVEUJEmKHTdxO4joOFVVsR8/jn7kCHpQkJ26rqNfcAH6LbdAayuWb38bXZLQHQ5wu9ErK+Ezn0FRFLRHH0X3+8HlQnc6kVwulDlz0Cor0QYGIBKhscYJOOntBa9Xw24vzjVJioSqqkLCIfp54rUmwmIREjGJ2yVJEtc05HdPtV2WZfzdfnRdx15tJxKJRJ8PFhobQdOibVcjKLoO6EiRAXTvUXTXpNizQoq+ImoEEq4rVdsTtydeg3FNhg3RdaRwL1LgFHrFdHTJKrbpOpqmokDKe8zY7nYrdHUqRCI6IKHrOo46x6B7dc0amYcf1vjDH2TWrNF473t1PvxhmaNHJb75TY1vfENL2vaYLSosTFk1BXezm1AghGSNz48KZads+1Pt9FrefPebCfYHB91Lye69Z59V6OmRaGzUWbFCjZkwlf2AvK7poYckOjpkxF0TR2urHnUkSVx9deHGiFTbLRUWNF3D3+vP+5oS25jJTvNvnM/8G+cjy3KsLYW6pkTs+PMOdj64k6XvX8rMN80ctI+maciKTOPixtj2XK/JUe8AGUL+EN4OLxVNFTlfk+yQaV7aTP2s+mH2AKicUEn1lGpsVbbYsUrRnzLde1vv2Urv4V7O+eA5zHnznKzslG6MuOYajQsvhPXrBxNQLS06P/yhxtvephOJ5HdNQjZKYds2PepvS24n3Xi26jqarsXaCQz6TI2osW2lHveyGSPO/9z5MXscPaoCChMmaOi6lLWd0l1T4nOulHMjA4sWKaxfL7F5s86CBfldk+pX2X7vduZcNwd7pX1QG4391Ygavx/Q0cIakiU+1hvXoarqoPYXatyzWCyi6LiuUTG+gkgkgqZJ3HabEj334OeOuOd1brsN3vxmFUUZfE2Jv08h7ZS4XyaMERgjiYSibJKmUq10InkPxpzLL79axfHjqZ0RMf3w1xq5+OJGaFwJwQ7o2yW0lSumZ+9Uc46HeZ/O+ZJGNSpni3fDWWASkiThdDrLX19akoXTWC5wLuAoRUq7JRZLDHaDOiAKexsw6SS86ir46ldFFHgolCYDw8C2rwtZmFkfOnOibGujkhzGb54qi8GIKk8zXp6xkk3lBlv03g31pJUYMpAuWsrIvmhpEdGvZrBunSAMhyJdREnGDIxycX4PjeJfdmfJiLNMzzlFgcsvF9FNx47BkSO5ExgFy76A8rNhInwnYP//E9mzZ/84tS09e8V71eysTzFq5icjiF/+8pexDIwTJ05w+eWXD/p80qRJHDQGoyT4zne+wx133DFs+6ZNm3BHQ/YbGxuZMWMGhw4dosMI6QdaWlpoaWlh79699CWEu0+fPp2mpia2b9+O3x+PrJ07dy41NTVs2rRp0CJ18eLF2Gw2NmzYgK7reBYtonflSpYvW0bI42HHhg2gKKgbNmANhznn1lsZaG+nde9e5EAAIhH827ezZMkSvNu20b9zJ3IggBwMYrPZqP3KVzhRU8PAffdRu349AL+QJzCguTj83Sl43rwc2eul4Z//pGbCBOpaWmjt6GBA0+g/6yyQZWa6XDTU17PrwAG8uo4ezQBJe01VNvp6+9izdQ/eqUIad9myZYRCIbZu3RrbV1EUli9fTl9fH7t3745tdzqdLFmyhM7OzkE2rK6uZt68eZw4cYLjCaljjY2NjDtrHFNvmcpAeIANGzbw0ks1wFwaGojZyR4+ziyfD6vkxh44TCTkRw/04bXPBaDCIWMFdu7YiU+J14FItFMijGvasmULHo+HjRs3xoplejweZJ8PVZbR5DAVwV1YLRZUz2E8eiOy5kfRfLQdOsTMs2YnvabEe0+WF6Oi4PeHqK21M/kdk2kNt9KxoWPQvff66wHAxfLl+6iv7+Hb317EO97h5oc/hLPP3s6kSYG01/SG299QVDvl0p927Nhhqj89+OA5gJVzz+1g06Z4e1Lde2effTaKorBx48bYWGv2mlQVPvrRpcT0vBKg6xKSpHP77TB79l4GBgozRiQi8Zp6TvbQ19uHtk846UbKToW8JgOKolBVU0U4GOa5u56jq7ILxa7gcDhwOp10d3dz+PDhgl2TT/YR7ApydPtR5l86P69rcl3lYsGyBfj9/uH96ZLl1C6tZffu3bHvjLSdXnjkBY5sPoKkSHQ6O5nsn5yVndKNER0dnezaVQPY+NjHDlFd7efii+cwc+ZJTp48jnGKfK4pHA4Dyzh4EE6c6GPixOR20lQNn89HWA6jhBXUgIpVs2KrtBFQA6h+lbAvzI6dO7h4zsWjqj9t2NACtGC1dqCq9QUdy48dOxZ7zjU1NZV8jBg3bgowntdfjzBnzut5XdOJP57gyKtH2L97P5PXTk5qJ99xHz6fD6zgOe4hGAjinByfl1sRAUz79u5D7o4Tc4Ua95YvX86p/afo6+3DolrYsGED27c3cPz4TIaSFwZ0XeL4cfjf/93L2Wf3x67p6NGjMdtJklRQOyULDEgFSc+G7jhN0d/fT3V1NX19fVRVlUh+wURRtpM99Sz+4D10etI75e65B266KfqPFoH11wlpiXN/KyQHxmAOoR548V3CiXD+fUL3fAxnFhL7ZWQA/KeEnJorIYfSZLFETRPSNe3t8PTT8I1viAjlP/0pHhUeg67Bc9cID+/K38ej2M8EBDrghRsFcSjJYE8yZlkrReHbdAWLcyiYWRAcuV/Uz5lwJTRfXNpzlyNO/gv2/FSQpdEswGQamwDf/jZ8/vOpD3XPPXDLLbBqFTzzTOZTq6qoD5WqcLiR8XHo0GDSZOFC2LFDkI2rVyd84fjfYf+voOkCmP+5zA0YAxdfDM8+C3/8o7BdNvjud8X98I53wB/+UKAG9WwB/wmoWTx4HC8HhD3w0n+IDIxzfiyyaJKhd5t4NayEiqklbWImjMj8uYB44okn+NjHPsa2bduw2+1cdtllfO5znxtEYmiaFou+S0YEJcvAmDRpEl1dXbHfZCSiDNNtNx1lqGlIwSCK04kmy2inTgndKL+fd10forctwDfvbmLxu8+Cnh6kP/wBKRBADgREtkYohPZjQc7J3/se8tGjaNFMAiQJ7b3vRVmxAmnDBtSnnwaHQ1SadjqRp01j96laNt39GvNmhVn0/hXgdKJEZbFUp3MQ6VesyP4//lHiPe9RuPxyeOyxqD0GDqC8eCPYapD6dmJYSK9bLo6v+pDCvUTO+zNUxBnZnOzUvx+evx7dWgOyDal3MxKgW6vRK2dDxIsU7kU7/z6U6tkZr+m88xS2bgjx3eV/YVyzxNq/rEWSpUH767pMZaWO3y+xc2eEWbNAlhXe/GaJxx+HK67Q+PvfNSSpiPdelnZK3D603+iqjsUqsgsy9SdNg+nTFVpbJf76V5Wrrhq8f6Gv6dlnJS67LHNW/JNPqqxaVdwx4tTWUzzzlWeoaqnirb98a0nsNHBqAHu1HYvdUvRxT0bmkQ89Qv/JfhbetJAFNyyItf357z2Po87BnKvn4Kxz5n3vPf/t52l9tZVl/7mMOW+dM/JjeZrthX4+vf6/r7P7od1MXDGRCz5/QUGvaetWjSVLZJxOnY4OFYejONfU0qJw6pTEK6/ovOENye3UfaCbB65/AEeNA6vbSu+hXvydfirGVVA5qZKwN0ygN8B1915H45zGsrPToHFP1bBYLSKrQFV5//tlfvc7ma99TeUrX4lnRpm1Uzlck7F9aNt/8xuJ//xPhcsu03nssfyuqWtXF0/+15MgwWXfv4y6WXXDrql7fzf3vvVegn1BMYeUoWFOA9YKQVyEvWGCfUHW3LtmUJ2bQo4RL/7gRQ49c4jF71zMvOvmcd99Mu94R+a6Yn/4g8qNN+olsVN/fz81NTWm1hNjGRgjhSFF2XR0QuEwNqsVCQlUPxWWLqqc/RkJjEH64bJF1L7wnxSL9mwJjLBHFKM9EyWGbLXC+RnoEJrTRnR4BmiaRmdnJw0NDbH01zGUP5LaLbFf2t3ib10V+umSknWxxCuuEI64Rx/NkIER8cbD00daq73UCPeLsSrijRIYjWCtjn+u+iEQ/fGsVSnHy1wKZhYE3kPQt1NkwI0BbDXiPRgv3mlIDD37rMbevR7+8pcqnnxSylgANtv6F+vXpyYvICFrcb1wtBtImYGhOIQco1FPqlyhBgBZ1MEoEsw+56ZPFwTGoUPZn6MoGRi1S8SrHGGthIbzoP05aPtXagKjZpF45YCx+UlqHDt2jA984AP85S9/wR5N8bLb7QSMis1R+P1+7HZ7yiwWu90e+34iLBbLsCKHxqJuKIyFp9ntqYonWiyWQTY32pxsf0mSkm4f1sZolUsZkMePjy06js6AF9rgPdVwtgUh4v/JT8aPA6DryMbv9oEPgMeD7PdD9CXPmCFICJcLpaFBbO/tjRXXcEwYj00N0rT+r1gCrw6+1p/+VKTm3X23GPijclcWp1Ow3rNni9S7vXuRXS5koxZIdbXQqYu2Tc7wGxiShA0NCfZQLFHyRFybcWcMvUcsigWSHD+V/WRZHtZfJUlI+grHhyxkqiIDSHok9hmShBJdt6W6x4ztFRWgoqCq0bZrEhbb4Pbs3w9+v4TDAbNnW2KE/09/Kgj/J56QeeQRmWuuSX9N/h4//cf6aV48+Blq+t7LcfvQfrP/qf1s+b8tzH7LbBbdPHwsTWzLq6+K26aiAt74RiWZ+Ya1Pd04m6nt7e3Dj58M7e3J25LLGJFqu7vOjSzJhAfCQGns9Oqdr9K1u4sLvnABLStaCn5NQ7H4XYt58fsvsvfhvcx5yxxslTaO7TvG0fVHkSSJhW9fOOh7ud57NZNrOPnaSTwnPDlfU9ATRAtrOOucafcvdX9Kt13XdY6tP4YsycxYPWNQu7KxU6preuop0b6LLpJwOgePl4W8poUL4dQp2LFD4g1vSN1G4yVLMo5qB/5OP4H+ANVSdewzxaKkvaaRsBMMvqaDzx7k1Z+9yqSVkzj/s+fH1meTJyuxOIFC3XvAsPGyFHMjA0uXivctWyQUxTIs+Tmba2pa2MSE5RM48uwRXvrBS1zw+QsGzQNCAyFe/9Xr+Np9osh7jY3a6bXIioyv3YdiU2K1bRRFSXreQox7nhMeZEmmdkotFovFdG2Tlpbhz53u7u5hz7pC2CmbTPExAmOkES3Kpus6voFerA53zIAVFUHGj4NDHSb1w4PdYmLrnBAnMEw64WPY/g0RCb3wS1C/PL9rG42ong/WVlEY1SQ0TePgwYPU1dWVt4NgrMjwIKS1m1Es0VIRdQ5qYImywVkUS7zqKkFgPPYY9ERVBJLWwIjVJ3GOvmLWhYDiBMc4CHWDv02QRgYSteFlm9hH1wkGZayOifEHXqELZpqFYTtrzcicv9yQICGVCEWBiy7ScLl20dKynCefVLjvPvjRj5L6d4DsCYxMhEiy/UKheH2a5qE8xfjLxaucsetH0PEczPtsUUk0s8+5adPEexrFnZQoCoFR7hh/uSAwTj0L099XcBJq1MxPSgyv18s111zDN7/5TZYtWxbb3tLSwtGjRwfte+zYMVpaWkrdxJxRKpsbwVNpx93EOWdjo3glw4IF4jUEjk0nCVvdbH/De5n4zYsEweHzifcoscK8eaIqp0GMDAyIKswgmNQHHmBQxep58+D228Xgf9ttg7I+cLk4ftZboMJNc/9+rAEP1a85WYmTpboTOieLSVwoBD4VnL74esFaJQIxQARV5IC0tjOO6RgHnt0QHhDny/JcbrcgMCqvvYz5k09watspJi4f7M0witfOnz84W3HWLPj0p0X25O23iyAdZ4pk9UBfgIff9TBIcP391xeskG8uaNvURsgTSqWWMQgPPSTer7pK3BpmkE+fGxSEWID98oG9WhCxwf5g3sWnzUBTNXr296BrOpUTShO8NfmCyexat4ue/T3suG8HS967hG3/2oaOTu30WhzVJo2eAS3ntuCsd9IwN3cljMPPHGbjrzYy9dKpnPeJ85Luo+s6j9/2ON52L1f9z1Upi0mXCu3b2vF3+bG6rUxYlqWOqAn861/i/fLLi/usW7gQnnoKtm3LvG/EH60fZJHRIhqh/hCBvgBaWMvwzfJBsD+Iruqx4vatrWJ7MYp4j/S8dOFCEQvQ0SEC2PIZW70dXg4/fZhTW05x8vWTHHn2CPYqMY6GfWG87WJOoOs6jnoHVROr0FWd/rZ+Bk4MYK2wUjWh+NnL/i4xT6icKMbZXGtUjrTtDJyBnrLyhBRsxxbpBc0FirjxJQm+/GW48u3D9cONOcUg/fANHxUZFEa0od+kNycRgVNCzuZMdcbN/dTp6+Q/+FtR5H3y9TDl7SPdmtEBa5UgMMIDOfWJN75RPCS3bxfvkCIDI6bTPvokOAoG5zjQQlHproSxy1Yf75NaJPaZI6IBEzC1Ii0mQlHNR1t1+v3OFBjyZ+Fe8SyRhk9wVq/Wqa8X8mqJhZ+HwnCCm3Vo5+IIMKIfLRaoG42lZyxO0FTo31UWWUAG2ZRPBsbMFIkIWUPXofMlQURXLyjPzNKaJeBogkA7dL4AzZcM/rx7o5CYqp5fnnU8RiFUVeXGG2/kyiuv5J3vfOegz1auXMkjjzzCRz7ykdi2Z599lpUrR75vlRuMGjfRZImiwFHtAEnCO0CK6A/gggtSH+CCC+D880UhbYP8MCZjsix07gziI0qMbPy/bXh7wrzlgm6sPceYus3HuwiybD+w/SaRvrfjCPy2C7QASKooX9ASgat6xbjzRBe4q6DvOajZJwiS5cuFt7+jQxAsBmkSrQWSEtYqIZEZ7BKBGrougjz0iKh7KFvF5ybmj94OLzV6kFrAc6Cdzf/eTO2M2liEt73KjrvRHSMwFi4cfowvfEEE5hw5ImT/kpR/AYTtnPVO/F1+eg720Dg/OXnl7fAS7E8dgGK0KVfoms6pLacAGL808yTBIDCuvTbnU2aFXB1JxYC90k7T4ibsVXbUkIrFXlw3Uf+xfnEep4WqltKsfyRJYu7Vc3n268+y88Gd1M2ro/OVTjSvRlVLFd0HuvO+5wAa5jbkRV4AdO0R0TWV41M/+yVJIuQJEfaG8XX5Sk5gDO2/Ox7YQcgbYtzZ4+g71leQ39JAMCgyfAEuu6wgh0yJRdFELWMsTAZ7lR1XvQtfl49IMFpAXZFQgyqekx7sleJzw6Fdzgj2CRsabS0mgTHScDpFgubu3bB1a34ERrA/SLA/iLvJja/TR8gTonJiJZIi4ev0CflrCepm1YEe/50lSUKLaAR7g4SqQlSMqyjofTK0X1745QsJ9AYI+8OxMe7OO92sXTv8u0l9zGWGMQKjXBBowxoZAG18jMAAEd3y4IPD9cNbWsSNFStGqmvC8QdC6zkyEJfyMAstHJf9cDTleiWjG6creQEiUlwNJHUojiEFjL6YY3R/XR2sWAEvvSR0dQF27RKFbgc9FGIZGGcwgYEkitQG2gdnQMlWiEQjDGVFEB2BdtDD4n62jGy0EeFe8X4mk0+JsNXAuEtFJoYWSRpRbrXC298Ov/iFqHORicAwm4GRiyPglPBr0Nwc92uNKlTNhdZHoW/XSLcEyD0DIxgU8l5QwAyMiBd2fEf8fdFDBTpogSFJMO5yOPwnUT9mKIFx9EFR/2LOx8s/G2iU4LbbbsPpdPKNb3xj2Gdr167lK1/5Cs8++yyrVq2ira2NH/zgB/zxj38cgZaWN0xlYOQJZ72TxoWNOOucuUeES5J46FitkKirbLUO8wrruo7/7/cDIL/33dDo5n/2wF93aPzqHX7OOS+6bF60Ar78e/B0QSAIPj9UVcD5bxBkyZbfQxjYfgB82wRBsnix8JysWwcbN8ZPKsvCW/7GN8L+/Yy/5x6k114TGkZOp8haOf8eGDgEr/4LaueAcy9UNsD4s6CyTgRQZJDP9HZ4WXfzOiZv9PF2wPvXABHVT9eeLk6+Lozoqnex5p41bN8uHI7JCAy3W2ROXn89fO97ggM6cULcB+PHi5/UmN/WzayjtauV7v3dSQkMo02+Ll/KdhttytUJ2n2gm9BACKvbKpxIabBrF+zZIzilq67K6XRZQ1Hgzjth7VqTwYpFhGyRWf2t1Zl3LBC69goHfd2suqJnexjwdnhZ/631dOzqQA2o/ON9/6D/VD8yMgNtA2z/8/a877lCwfh96ucki3yLw1nvxNfpi0VZlwrJ+u9A2wBhb5iBkwPs/fvegv6WL78seOamJkEwaEVMcDDGvnQZGO5GN2vuWTPIUbzrL7vY//h+Ws5rYel7lxaUwCkmAr1COtNebcfvj6tGnI4EBojH8e7dsGWL8LXmi4oJFUSCESL+CIHeADWTa6ifU0//sX4sDgtv+cVbcNYPTld86Ucv0bmrkzlXz2HRzYsKdp9k81x98EE3N98s1mAGhvmYyxBjBEa5QLIhyzKSFh72kaEfvn598gkiIAgLY9YzaU1uEfaBDvGu2MeccWpIzBwT5WtSQJIkqqurSzb5yhkxqZuxSHEwaTc56nzVQjmfZ8oUQWAYuOoq8XC4886Eh4NRnPpMj7CVLEICLxERb5zAkCzgmgxhD7IcRlJ9I0tg6HpC9kzNyLWjnCDJMPcTyT9K6HM33ywIjHXrxPtQGYpgME7amyUw0jkCQPw/1BGQsv4FwPZviqzEmf8JNUk8OeWAqvnifeCAeG4VqQ6G2eecYavjx4XKis1kcw4dEvapqEitMmMagQ7xvAu0i/FDsYP3SPxza1Xpa+Wkw7jVcOQeQVT4T4Iz6hnWNVGPCwS5mwNGzfykROjp6eGuu+5izpw5LDWEkBG/0+OPP05zczN/+9vf+PCHP8zAwACapnHHHXewYsWKEWx1diiVzUtBYDiqHVz2nSKH2iYg5AmhRYRXzFkrHkqdnaAjUzPRDUZ8WW0trLg0/kVdF7KJkgKV1fD5bw0+cOLDaO1aET6cmPkxdSoAktWKrbERSdNEpobfLxqwejV0vAD/726w1kUlfo8Ar8NXvwoTGuHhh4W3zeUSLIPLBWedJTw1vb2oz7yM89humiQXfpw4/H1ILjcWhwVHjYOIP4Kvy0ewP5iWwAC47jpxCU8+CUuWQGLZmMT5bd3MOlpfEQRGMgT7g/i6fFjsFizO4S6JxDbl6txp2yQe8k2LmpCV9FEKRvbF6tWDua5MyLfPrVmTPFixqQl+/vPydiTlg5iDfnZ6B30hYdxzVROrsLqtqCGVgY4BFItCRXMFalDN+54z0Hu4l97DvTQtbMo6MyLQF2DgpAhMzfT7GI7RdA7LYiBZ/3XUOFDDKrJVRvUX7reEuHzUZZcJ3lfXi/esMxQN29rEEJwqAdDd6B50bTOumMHR9UfxHPdQO7121My9DBLGUeOIZV+4XKJkVKFRDvPSJUvg/vsFgVEISJJEzdQafJ2+WMaU1WGlckIlgd4AznondTMGE+jzr5vPyz9+mY6dHQXNnMrmuXrVVfF79wc/gHPOSeJjHnKdI207GCMwygaSYsWiKClrLyjK4KKjwxBzTlfkrqEfjGppOJpO70yETNj1I+hYD/M+Y0qSQ1EU5s2bV4KG5YnYPXKGk1NRmLJbngTGunVw773Dt7e2ijXsgw9GFyZqUPTbM53AMAnJ4saSg/ZzwREZEE5GGOtXJpDY51auhMmT4ehReOQRhqWxHj4cd2inWjgkQypHAAiH29CoyrQEhu84+FrNn3wk4GgCe53InvTsg5rhOvKFgNnnXHOzIKP8fmFbs3JQifUv8pp+BDrgxZuF3IoaEPaTLfD89fF97PWw8p7yITEcjdByDbgmxSXYALxHxTUoDvFZDhg185MSoba2Fj1ZelYClixZwgsvvFCiFhUepbJ5KSSkSg1/t5hT2CptMS3wjmhsV1Ji1XdCkKMnHoGeLSJ4bNo7h++XOKjV16fQEgVlxgzGf/nLyRvXsxk+NB8m3Ai1lwjiw+eLH2vixPg2n08UQ48SIxw/jm3dvczvPMiUoMIiZCwRidcsb0KxKyw79hBaKEwwJGP9RRuX7mqgg7UsXNgMO3aISatRBN3pRGpu5q1vbeCpJzXUQASwYsh5Js5vV8wQ41kqAsOAxWnB5rahazqaqqFY494TQ5olV5zcJBi2YspHFaLPJQYrfv7zItr83e8eGfJC13R0La6HXyx07YsSGLNKR2AYsFfZsbltDLQNYLVZsVfbsVfaCcmhvO85A6/94jU6d3Zy3qfPY+qqqVl91yB3KlsqsbnTR4K46oXz0xi/Sg2j/w5FiML9ljC4/gUU91lXUSEyig8dEjJSaX1wCWic14hiU4gEIvi7/CNek8QsAn3RDIwqOwcS5KOK4Q4sh3npkqja/tathTumvVKMIWbRcl4Lyl0KAycG6N7XXXAi1+iXA20DBAeCuBpcOGsE2Wn0y1dfFQGDzc3wyU9mtnc52A7GCIyRR9QBp2samhpGDnuQLBXZO+aSOacNx5pZyaCAQWAMrWR6hkGxC+kTz15TBIamaZw4cYIJEyaUd5HMMQJjENLazeh/ugb2RlCsWRdLVFXhRE0Go5767beLBYsy8SqYcOXg4tVnGlL9tonbjfFSUsR4GepFstWNHJER8QlnqB45M4uvp4IWFtGosn1QbZChfe6mm4QExZ//PJzASJSPynYCPTRrsbISbr1V/P3978NXvhLfNy2BYcgyWiqya0ApIUlCRqrjRVEHo0gEhtnnnCSJRd/OncKGuRAYeSHcL8gL2S6ioWULKM54hpTqF5+H+8uHwACY8d7h2xKzL3KUfhw185MxFAylsnkpMjAM6JqOrusZI+jzhb9HzCWMmhAgom8hBZHe9Soc+N/4/4FTeZ0/pe20MPRtB4cC0y8ERwP07xT12exRh8ny5eKVDAsX4vva93l555/p6pE5NqAzVRYPP4vdQlv1QvAOoHu8tKt1RDQJd6VCSwvw0B547jmRZhEl/9TVl/Pf/72WaRzis3wfFQUfIrOjS6/np9Lt3H477Pnm87ScfBW13Yr6hB2lplIUUa+qAo8HqacbixYC3U4kEKFzTydaWKN5cTOKLX/NpEggQtdu4QgetzTZQz6OY8dgwwbxDHvb27I7T6H6nBGs+PGPCwLjr38VdUZKiRd/+CJHnj3C8g8vZ+abClWQajgiwQh9h0UNuVJmYAyFbJVRKpRYAfNCompiFZ07O+k/3p/1dw0Co2FO5ggeIwOj1BJSidARY4NUpLqEPT2if0KcwCj2s27RouwJDMWmcMVPrqByQmXRn1eFhCEh5ah20BqdjxdLPqoc5qUGgbF7t3Dg2wvf/TPC6rTScl4LR549wqGnDxVlHFQjKt4OLxF/JCm58swz4v3ii82ttcvBdjBGYIwchhZl00Loakgsqi3RibPJomzAcOf0tjtEtM6iO6B2sbljGBPvM7X+hYHK2cDj0L/H1O6apnH8+HHGjRtX3g6C2D0yFuUPKew2tF9CXJLFqHVgsl+uXz88AjwRui4WTOvXRydGkiQkks40JPvNh8LRIAphRTzDx8ss7VJQOJvhvN8lL7hwJmPfL4Se/7R3DpIzHNrnbr5ZEBiPPCKCRWtq4ofItv7FUAzNWvzJT+Cmm+Db34abb4471lMSGIPkwcp8zKyeLwiMvp1FO0U2z7np0wWBkU0h74IX8Fac0QK7UQLDkiBfkGNNo5LDmINU5iYfBaNofjKGgqFUNjcyMLq7hW/b4SjOedZ/ez3HXz7Oyk+vZMpFU4pzkiiMCGZHrbgYVRXXBykIjGCU3XA2g/8U+NvyOn9K2/XvFpm6tmpwTxVZH1u/Jsa1povMeR4sFsKKg4DdQSc2XBVOJlT6qRhfQbuygJAzRMASoHXK9fyCOs5fHD3smjXipevC0+Pz8eLLVo4fhwqa+A3vxYkfJ35c+FBRYvPb46+dojHcit7jI/TbDpzVNvj0pwWB8c9/4nzw75x3/CDSSQm/V+eYMpV9jsVoHe3MDW0lqCr4/RLWf7ng4Li459KYILhc8WLoFsuw30ENq8y7bh69R3qpGJc+EOHhh8X7+eeLaNRsUOg+d9VVokTL7t3iNXdu3oc0DcWqiGKzaQqrFwK6pnPWe8+i/3j/MG34UkJSJMLB8CDSslComiTWJLkQGJ27xdiSqf4FxDMwSi0hlYiwN0zHzg5sFTYa5xU+OOTf/xY1L+bNizvWi/2sW7gQ/va39HUwkqF60uiT6m5a2IS33YuzzhnzXRSTwBjpeenEiUIJsqdHrFcSFEVLiqmXTOXYC8dQg4UPYNXCGj0He4j4I8gWOSaLmYhEAsPUMcvAdjBGYIwcHI1CwiDqVNZOrce79WdUTlyBNP9TYp9sdJpDIophUBFgLQL+E+YJDPc0MRE2NLXPVFTNEe+e/SIC/3Qoeq1FRAYBjGVgpMOQfpkUJvul2ajEUkQvljXM/uYQ20cNB9m9ZT1zz7oIi8UW32ekIqrPZMm9ZDBkcELpZSMWLYL588Xk8aGH4D3viX+WL4ExFDfcAP/7v0Kz+6MfhcceE2ZLSWCo/ngWo6XMCYyaRdB0AdSePdItAXIr5F2wDIxEGDXFRkt2VMQLbf+GUBdMfzd48icwxjCGYqG2VkQtBoNiHDXUigoNSZFAj0eIFhOBHnEOw5nZ3R2PT0iq+hQSUdJULxAERiA/AiMlejaL99ql4sHlimp7RLwi29Gevjh1IgxVoJC1gsZ5w536e/eK92H1LyRJsFQOB8ejS84BKnmV1PVhNqz8OMvffBWyVUY6Zzy4LXFx7VWrCLjHs/2lh4n0DmC1hPFaqlFsCpKuoUsytrAXW8iHZctGOOaKExi//S20tw8+2Uc+Iup9PPccvPACuFzYnU4WO52wepZofyAAmzfH5bAM8qOujoceEvO4bOWjioHqalGH4/HHBbHyX/9VunMbmQiGpEyxYHVamXt1CZmZFLBX26mYWoFsLfxav6pFrF08rZ6svzvryllUTayieVFmNs3d7KZ6ajWVE0ZurqqGVBFoVqR4rqHyUaXAokXiffv23I+h6/qI1wswg/M+cV7s79YECanTFZIksjCeeUbUwciXwIj4k0ulpdpuYNxZ47jm99dkJT1lBmpYpftAN7qqo9gU6ufUD8tqDAbjNVrNEhjlglGyqjtN4WiMOdz0QDea7BQ9qjKHFbxzPIy7DCqi3h6jEK4/C3HapgvE60yHq0VkwUT8QoO6YupItyh/aGGoXxbN8CljOZRyQEK/BIS0mv+E6FNZZCcZ8gqm9tv/K3GeydfHCbT/z955h7lxlmv/N+ple7F37V33uJc4vTmNhEAIhDgJkITeDhBKDnCA0NshnEb9Qj2cEAgJEHDoSSDdwYQ0xy3uXrf12tubukbz/fFo1FZdo12trfu6dM3uaDSa0TvvO+889/Pc96mE1N8823YA4TDjzlGoPU2y7qqoLMQIjKGsmymKVEN89rNw773lJTAUBe68Ux5IHn5Y9LlvvDELgaHLR5ltZTPGNgw1C2D5J6f6KGLQ26yQCox9+2RpKIGhk/bm6aFBTGBA7gUmM7S/Km48fireE6qoeCiKzF8OHhQfjHIRGI4GqYYod0AVoPOCTtwz3DHdct3/orExw1QjkEBgHH9Mksl03xojMbg5eiCny9Jklec+7zHwHimIwDBFYxhqhoTPjARGAgqZ3y66NENZXUsLx/39HBhuwmRuwdHooOm0JmZaTCgo7GMWQU8Q/7CfhR+/EWeiAerHPpbs9+HziakWSGN1dMTfGxgQHUmQdNu77ko+DkVh4Kvf56mn4Da+yTv39cFXEsiNq66SG9PBg7B3b3y90ynf1dYmLFfYOK1/gNe/XgiMBx6YZAKjTgJp5a7AOBVQNztOYBQayO68oJPOC/Lzvmpd1srV370694ZlhBqUAcUI6bd0mAoCQx8Dt2+PSz/niy0/38KhJw9x7kfOzYuEqiScCgQGxAmMUnww7HV2XM0uvAPejH4vrmZXbFxNhclsMpy8GDk0wlj3GIqiYKux0bykGYt94gRm6xbh9GfOhCXT7DGjGvmpEJjqlzK26AvUL1xT3A4aVydXWhRDYFQhBqChUbC3gH8XnHgctIvj76fJ8jaZTLS2tla2PIPFCau+MNVHUVHIu932/0TkWU77F5h9Td77X7dOnqG6u9MrDCmKvL9uHbB5G4wfLGj/pzIqps91/wV6n4AZl8LsqX14qCjYooGGlAqMdO12001CYDz2mFQj6YERowkMgMWLJRDw5S+L/8wVV8S/p6dHAjp6cmhMPqrSqy8mCYX0Ob3N8q3AUNU42WEogeFsF8K+0iXAdJid4kE2fgCObBBSynNEiMDgUFFVZhUzVlYxaZjMNm9rk7ju/fdDMCjzGbPB8asYgTEJFRi1s2qTsph1/4u0Bt4Ql5ByzwVrjXhS+E/I/0UgbduFPTAeZXh1AgPA1RknMBrzf3ZU1DBWQAtC0JPwNdFM0XwIjILmt1lgr7WjKApWt5W6jjrUgJokpZExe7WhIVlzMhGrVsGqVfhH/PTv6mfmqplYXVZ5r70d/t//E8JDf/n9/OnPCqoKQ4vOoeGq/jgp4vXGT7CrC/70J4n46Fi7Ft73Pkzj4yy7807M994Lbnec4Pj4x0UL6pFHRCczsepj0SLRJfP5JBXW6QSbLRYlvfZaeP/7xWS1u3vygokxAmOkvATG4b8fpn5OPXUddVOSoa5fW5qmYVJNhDwhFEXJmTFdCNwz3ZgsJtSgirfPi3uGO/eHpiHCvjCB0QCRcAQNjaAnGFtvBA4ckJfFApdcEl9f7nvd4sXSfUdHRRJP50jzgbfPi+eEh+Obj1c8gaFFNFCI9cNyExiVMi9dHQ2bbtlS/D7crW7W37s+K+Frr7Pjbs3d98ePj+NocGBxlBae3/fwPqm8cJmpn1tPJBwhGA7G3tf75TP/lP8vuSR/cq5S2m5aEBj33HMPH/7wh5mTMHLY7XY2bdqE2Wymp6eHd7/73Rw9epRIJMKtt97K+973vik84sJhstUyZ6WBtHKhBEZElVJoe8vJIZlUDPx9sOlmyagKDErAYGxvcsDA3ixyNwnrTCYTCw2NulQxGci73exR4WP9QTVPmM3w7W+LObGiJD/k6TeKb30r+rCvyydVg6V5wWQysXCmAru/IQHK06ZovPcegZGdIuFTRRwZKjDS9bkFC+C888Sw8te/FuN7TSsPgQFw++3wi1+IZNGcOTAeLbT48IfF4Pvb3xa5b7SIVOPp51Lp0DS534dGxBPDYBRyn9MlpPKtwOjuluCn1Qqd+SUc5geza/pUX+jzj/Eu+Xt4G7hTfow0849cqM5PTj1MVptv2BB/8P/Wt+TV0ZEwhhoER/3kERip0Csw0vpfaJE4SW9rFvIxNC4+GCUQGBPazuKG8+8WTxx7go6VqxP4p8xD8oCeKTo66sVJGFMQ/MMp2zS42LdZAtgrVmTeV77zW/+gB/+In6H9QwwfHGbuurmY7XGGq2ZWDa0rWgl6gskBIE2CaopZyZq9mgpPnye2nyObjvDSXS/RuKCRi26/KPYbuFvdcrOpi8vpPhCtcJj/5gvh2gw7v+wyeUUiQmL4fBAN4phcLuo/8pFkYsTrjZftHDsmZYb6+nBYSk5bWoShuPfe+A/rcsHy5bS9851cdG6I5c/8hF2fdjH7GmecGFm3TsiOnh7Zl06KOByxYyoWen8rZwVGYCzA37/+dwDW37ve8AzkbEiXMa2gEAjEz7eQay4bTGYTNe01jB4ZZfToaFYCI/Ha7Xu5D7PNTP2c+lhFQ75B0MmULEr8LQNjQmCoQTVprDbit9SrL84/P15MBeW/19lskpm+fbv4YBRCYLSd3sbBxw9y/KXjrHlrfgRz4jWQDvleA4V+V+/2Xp797rM0L2nm/I+ez+ghcGFn9uzyEG6VMi/Vjby3bCm8wiYR7lZ3ye3yj2/+g4OPHeTcj5zLgiuyP/jmuk5W3rySPX/cg9lmJjgeTLuNq9nFpuelXxYiH1UpbTctCIxwOMzVV1/NPffck/b966+/nltvvZVbbrmFsbExrrzySubMmcPVV0+fjNhIJEJXVxfz588vjtUKjkjJsjl6k4gRGMfz65WBXvjne2WifOF9p6ame2hUyAuTXaSCTGbJeLQ2yPuqT94PjSYFEEpuu8lAKSPzSYq8261IAgPkIf43v5GgbKKhd0eHPNxJoFSrGqwXiEgkwtGDe+nsfwLF3jx1BEYoKgRd9ZVJRiKBkTD2ZOpzN98sBMZ990lf6e8XYkFRjJclcTik6uOrX42TFzq6uyUg85vfwPr1p8E53zf2y8uJwRdg25dEG/2cHxi++0LuczqBMTg40Zw9HXT/i/nzDczeVn2FrZ9q6PMPW3PU0ywCig0sUQImw/wjF6bF/KQKQzEZbb5hg4yVqdn3yWNo/vtTVdi4MV6Fl1jJoVdglDsjHODgEwexOC3MXD0Tq9OavQIjOCzJX4oCtgaYcQk0rC5IajQVGdvO1ggt5yVv7I5G0zyH89q3nim66fEAd7wR5syAz9+fvM3L++14H3czc2aWqpMocs1vr1rnYcPNG/AOeBk+OIymatTOriXkC2F1WrE4LLiaXbz2x69Nkp3p29nHCz98gdq2Wi781IV5B+08ffHvA/Cc8BAcDzJ0YIieF8VsztXsYv2965P25/GIrCSIbFNOmExx8/AoIlYrXa2tmfvcW9+a/H8oFH8mW71aDFYSJbEaZQ513TUhup4J0rVpGOYlECMXXCCf/fWvxUQsETffLCm1O3aI2Zczgfhob49Hq154QSZEOvnhckFNTcwDo5z9bXCvEH81s2omlbyAiRnTkUiEo0eO0tHZEWs7IwPFa962BsWk0HRaZpm31Gt39OgoakDF3ebG5hYJ03TXbiKe+upT9G7r5aLbL6Lt9FRN1PIg8bd8+o6nGTowxFnvP4v2M+Iac0b8lpnkoybjXrdqlRAY27fDa16T/+f0NhjcN0hgLJDzOk+9BtIh1zWQL1K/KzAWwNvrpXd7L0c2HWVdD5yJiybHesB4EqNS5qUrVsg8Y2BA5h6zZk3ZocTk5roe78pKYGS6TsL+cKxyw9Xs4g2/fUN2OTebnfculrYthMColLabFgRGNmzduhVVVbnlllsAqK2t5ctf/jLf+973ph2BETz0Z7SICzpeI9IHhWD7l2F0D6z6PDSfLQ+4Jot4HwT6ck+o/SdkaW+qBrrNTiFyHGlK/iITJ3SRSIS+vj7mzp1buQGCnodFCmnGOljy4ak+mopA3u2mZ7zpWscFYv16KQXP9HBOJCAm61ANhOeJSCRCz6iDDhSUwKAEEmwNk38gMQKjfvK/u5KhS0ipQVC9Mp6Suc+94Q0i6fTPf0owWw8adXSISayRUFX46U/Tv6dzLbfdJn3WaCmUskL3SfB2S5Db4LGkkPtcTY0Ev/r6pAojlzmeof4X1joZsz2HJEPa7Jpo4m1vrtyx1uKW+VqgHzxd0HQmKNELMc38IxemxfykCkNR7jZX1XilXCqKGUM3bEgfBNcrOfSAqm+ovOSjpmk8+/+eRQ2oXPOja7A6rdkrMCwuWPEpkRs0WaCzdPfngtrOFa3QyrMCAyTg2LrYzRBgD0JTypi79ylZZpOPSoQ+v33/++HHPxbj6YcflnYf3B/AO+DFYrfgqHcQGA3gH/ajBlTC3jD1c+vxDngx28w0Jfhb2GvtvPjDF/H0eqhpq4kFcHMhMBr/PrPTzNixMUwWE+6ZEgQO+8KSKT4aSAoAPvywFFTMnx+XFCkUBfc5qzX+d2NjjLBIxWvf6OK0z34Yy0G4/rY0m73lLTA2lix5pZet2u3Q1CTrBwdl6fFItCoUIvLDH3G8Rz7ickFbO5i+fgfOJierzduoP3Ycvr4zTnCce678QL29Qo4keoHU1saNxPJImBvYI89Tzac1Z90uE7IRnvkgMWM6HA6zb2AfDfMbsJTBU6/j3I6c2yRdu3YzWkTDZDFRM0PMxTNdu4kIB8KEvKGsAfByQP8tI6EINreN9rXtSf25VKiqSMxCegKj3PMbfSzctq2wzzmbnNTPrWfk0AgntpxgzkXZyzcSrwGLc+J1mM81kC9Sv0sNqZgsJmw1NkxuByHCuPDS4AhQLgKjEualDodU2Lz8slRhTCWBMe/SeWz9+VZ6t/Xi7ffGfLhSke460eXK3G1uHHWOtPfVVDz1lNz3ZsyApUvzP85KabtpT2A88sgjXJIoiAesW7eOG264IWMZXSAQSCoVHB2V7OdwOEw4asJlMpkwmUxEIhEikUhsW329qqpoCbP3TOvNZrNoKqaYe5mjd1o16qKmqir13k0oR8fQGlaiWpNTXywWC5qmxbYH0aozm81yfAHJdI0oThRVlfWNZ6GhoIUCYAlnPyd/LxoaEVsrWspvUOw55Vqf65wSjzHTekPbCXmA0TQt9mSmXz/6OkXTUNUw5uj7qqrG9qWqauWdU7SdlMAQStiHpqqYEo59WraTgdde4nllPCdrE2ZA8/eiJnxvoed08cXJ6/VdmUMjKEBEsRCJmEELl3ROJ2M7pZ4TQESxodnbifiPERnejdJ81qSfkykwhAJgrU+6Noo5p5OrnUyY2l6JyVZHRFWJEI59Tv/+xGNvbobLLzfzyCMK994bYf58DTAzf75GJKIZek5PPBHh6NHMT7qaJlq3GzfCJZdMo3ZSnJics8F7FGX4ZWg519BrTz8mfZtc5zR/vpm+PoX9+yOsXZv9nPbuNQEm5s+PoGlK/JwynWu2c7I0opz7c8zbP482tpfIvLehtZwfPyeTmYi5hoilMWa6WhH9KTqv0DQNk6MFLVrxJ/vXUNDkPhGJEEnYfz7XXuLvY/Q5pa6v4uTHxo3JZEMqEsfQXJl9+VRyXLXOxYxVM8quHx/2hWP+C85GJ6oKL70k73m9KR5JIFXvrReW9Zjw9cCeOyUxrSNF28jVCcs+KssCqqzd0Z/R45n43vbtssyXwAD5TV77WiEw+vsnBpItTguORgchbwhN1TBZTTTMbcDqtqaVBXPPcFMzq4bxY+P0bu/NKwCc+n3yQAdmuxl3qzs2b0w0WdWD4N/4hvx/7bWVl7+3aJG0xfbt8Oc/w5vfnLJBU5O8Mn14UXoD9Q1/sPCJv3ybwW4vTnw48TF/hpcPXFnHdTdaWPGZ6+DQoTgxMj4uOo8gnfP++5Nd4Ds7xcwM4EMfkotAJzdcLnjnO2Wi99xz0N1N5C97aR0YZ5ZihePHhfwIhSSi5nTGpbfSHXsOwrMcKJUwyRcWp0XIC7MJs80cqz4DMhoE63A1S8DTNzj5VaaaptGyvAVPr8fwcfqFF2BoCOrr4ayzDN11XlgVVQjWx8ZC0HZ6GyOHRujZ3JOTwNBhcVoykra5roFCoX+Xb9AnBIbbhmqyEQacljAWa85dTHusXh0nMF796qk7DvcMN60rWunb0cfBJw+y/PrsUsAWpwWr28ro0VE8vR5MFhNmixmz05zXdfLEE7K89NLKu+/lg2lPYBw7doy5c5O1Rp1OJw6Hg97eXmbOnJhFf8cdd/ClL31pwvrNmzfjjs7sWltbWbhwIV1dXfTpKThAR0cHHR0d7Nmzh5GRkdj6BQsWMGPGDLZv347PF795LF26lIaGBjZv3pz08Lp69WpsNhvPP/88IIN/c9CK5oLg+HFe2v98bFuz2czZZ5/NyMgIu3btSjrPNWvW0N/fj7n3ECYtwIGXu3A1m1m2bBnHGt/G0aNHYUc30J39nEInGB/30B/0ccL3vCHnpOOss84iGAyydevWgs7pQIIDaH19vZzTsWNyTlEY2k5m8Hq9hE0mIqYQAHVuO6bQACOBGkyaD3PEy95t21h1/sLYOWmaxvDwMFu2bOGcc86prHOKtlPT0DaavMMMhgdpn+eb3u1k0LU3OjrK8PAwL774IoqiZDynJleYxYB/pJstzz0XG+mNOqflnTbqgMHRIPteeKGkczoZ2yndOa1YsYJgMMhRr426wDB9W/+GOrtu0s9pYW8XTW4FzeSuttOEc7qYNUvX0N/by4EDInGgaRqeaNQk9ZyuvHIBjzwyg7vvDvOKV5wAOqmt7ePYsaCh5/T3v3cBp5EL1mO/IrjpUboCSxl2XRA9p8pup7YRF/W+YepGXibgXm3otaff5w4dOsRpp52W85zq6xcBLWzf7uGGG2qzntPzzy8AmrFaD+PzzSj92nNYWRMcIBixsv14E6H+oYRzWsyxo0c5ejS+/0roT/bQUU7zeomYzdQ1tqPaZ+MLhAiPipm8hSC1dhgaGmLvzng1YK5r79ChQ0n3OaPPafPmzVRxaqGnx5jt8q3k6Opy84qvvaLg4ywUeoWHxWnhD3+xJAVJ77tPAphZg6SaBqFhkU6sMci8aXAzDG2RarJUAsNsh5mXFbzLRAIjlffQs4wLITAg7pexa5fwwqnxZ1tNNCCnQNPCJpxNzpjRbzq0nd7GvmP7OL75eMEEBkBgWJIUdZPwVKQLgv/iFxKYLlcQvFi8/vUSPH3ggTQERhHYsAFuuFFB0xyAA92p7EAfPPpG+I0Z1q8/G84+O/0O1q6FO++Uhtb9PnTyXNPgjW9M9gLx+cRMAODIEbTnnsP13Hbmen3MfPYgLHHANdeIe/x3viPbWa1CZMyaBf/6r7Lu5z/nuefgvu86WYWLRTh5gTMZow7f0QE+fL0f690uXvsGp1SfGBSVK5UwCQfCHHvuGJ4+D8uuW5Zze52AsNYUFj12Njvl8wOTT2AoisJFn7yoLPvW5aMuvzwrr1U26GPhzp3px7ZsaFvbxu7f7+b45uMFeZOoITXmXeBsdBZ6yAVDJ+5NVhOeKKdsOwXICxAfjF/+sjQjb6Mw77J5QmA8fpBl65dlvV40NEYOjeA5Ic/UdZ111LTXEPKE8vquJ5+UZSHyUZUERdPSTR0rC3fffTef+9znmDNnDgMDAyxatIhPf/rTnH/++bz73e/m3HPP5T3veU/SZ+bMmcOTTz7JfF2MOQHpKjA6OzsZGBigLmrqNdkZeZFIhPEX/4NG/z9h7k2onW9M2j5r5mQ4ABuvl/2cdw+KrbbwbNDd30Q78bhkK3ZcZ8g55VpfcZnI4wfQNt6AZm2ISp5oKMNbIBJCq1kAJgdKaBj1gl9irl8cO6dIJMLx48dpa2vDZrNV1jnpFRi7v4nS+yTa/LdhmnvD9G4ng649VVXp7u6mra0Nk8mU+Zw0FfOmG9G0COo5P41JFRl2TmMvo2z7IhFHO5Ezvl3SOZ2M7ZTunBRFkbYL/wPzwZ+htVwAyz85ueekaZj+fj0KGpx3F6qloaRzOhnbKfXY9bGyo6Mj9r+OsTET7e0mAgFYtEhj3z6Ft79d5Uc/UrBajTunxx6LcMUVuVPn9vzx2yyqeYTI3DejdV6f8ZyyrZ/sdlJOPIqy57soDcvh9P/I3k7+PgiPxqsSIhEiWmLlWT1md1vs2PW2mzVrFhaLJec5fe5zJr7+dRPvf7/G976nZD2ns88289JLCg88oHLttaakc8p0rmnPST/28X2YX/o3NIsb9dyfxwIZldJOadeP78e86U1o1gZM1ho0SNq3onpQQiNELvgVEXd8XpvrnMLhMMeOHYvd54w+p6GhIZqbmxkZGYnNn091jI6OUl9fP2W/SSQS4dixY8yaNQuTyfgS/yeeEC/jXHj88ewPx0btxyic2HaCxz79GP3BWj7yt2smECt6LCHm7zGyS8iK2oUi++Y9Bs/+ixALF91fVAB1Qttt/yr0/xPmvwXmvqH0kwRGRuK+RD6fyGjomDVLiKdnnhHFoPyPW1SEvF4hMZYsgcH9g9x/4/04GhxY3VY8JzzY3LYYmRH0BPEP+7nx/hsnSF0cfeYoG/99IzWzanjtD1+b1zHo32dvsDN8YJiwL0zDggbcLe6k76t7943c8sGm3O1bAMrZ5158Ec48UwoZ+vslrl8sVFV8xTJVUCmKBOe7usCkRCsCzcaej6fPwx/e+QcUE9z489dhtluEcBgbgwMH4lJYPp+sv+IKACLf+X/85+fGCI76cCGVI3dwO910cDO/4BKewu2Gm24Gk0mBq66C666TipFf/jLZC6ShAV71Ktnvtm309vczY+5cTG63vF9TAyZTxgqxQq6VoCfIb9/0WwBu+NUNWF0TI8P6tWt1WRncNwgaNC9txlHniO0jU1/Rsfcve3n++88z+7zZXPyZi/NsjcrHpZdKsPV73xOpukSU+14n3yHVH+Pjkqm/LDcHFUPYH+bhjz3MzFUzWfvOtVk9CRLHSzWgMrhvEFuNjdblosiSzzWQL1LH5uObjxMJRWhZ1kLPgJ1d24O01fv55Aulf1c6TEa75YsHH4Srr4a5c+GOO8pbYZULwfEgD7zlASLhCK/6zqtonD9RWnBw/yD333A/QU9QfIoUaJjbEKt8yuc6CQRkCPT7C7+my9l2hcydp0UFxg033MB1111HXV0dmqbxl7/8hde97nVs2rQJu92O3z+xBNXn8+HMcJe32+3Y0whrWyyWCfqH+kNdKswZruxM6zPpKiaub2pbCAefhdBw2u0VRUm73qR65G6qmDA56mN3VpPJhElRIDyeZA6c9pz8J1BQMLvaJ9DLpZxTrvUZzynD717o+kLbSQ+Oym+oiA+G9yiK75hkVCkKFrMl9hvrxz5nzpykfVTSOVksFoh45ZgdjROOPe32KajIcypxvdlsTmq3zMdogQXvQLHWYrG5JvSPks+pcTWs+y2mSAiTeeJxnurtlOmcOjs7YWhQrmdvV2y2MWnnFPaKZ1BoFGz1WFJ19os4p5OqnSIh8A9gUiyY7PFJVGKfSzz2xkZJ7HvmGdi3T8aon/7UzCOP6JluxpzTpZea6eiQZ9p06Rv6A/zCOWMog2C21+fd56e8nRpXygmM7QMtjCVN/beiKFjCQ/DsW5N8fUzRVwz2ZrjgXkyO1tixp7ZdtnPSlSu6upSs52Q2W9ALP5YsMccCAyVde+N7ZX3dUizWib/BlLdTuvXReYWecaUQl8sjtiZ6jAWMHRaLJe19rtznVMXUwWQyxUjicmDdOvIaQ9ety76fQis5tEh5Aqo6/EN+NA0e+4cjP3+PY3+BE4/DgrfDnOvFe1BRQA2IP1YRvlxJbRdRYThaEtGYwUjI2w2DL4rJ94z8MqDdCQovHk+cwNCNTAGWZ1evSHPc8pnnnxd7hCVLkt9XUKiZWZP3/masmoFiUhg/Nl6wLE0kECHsC4MyMXtZ0+ArXzHOv0VHOfvc2rUwZw4cPizZ6K97XfH7ylf+7e63PopzrJdLvnAJs840VhheN/Cun9eAuS6hXWtrJR06A55a/UFuH51wxAD8mdewiQtwebwsPdPHmcu8cUF7k0ki0D6fGHPprF2UwDD97Ge0jabs+GMfQ124mAfe+yBf0jbhxYUPJ15cbGcl/9AuwI2He9//Itd2iKH7s9tdHBtx07ykJSkAanPbcDQ68A/5Ge0ezer74en1gAa2OluMvEiHdJJWU1mBoXso5FthkC88Hti0Sf6O8lhJKPe9Tr5DxrZnn4Uf/EA4sXwD3BaHhdfcWYDzN3Kf070NQv4QGhoK5dP4CXvDREIRFJOCrcaGv1vWp5k6G4bJaLd80R0930OH4Oab5e9yS9Jlgq3GxqxzZnF001EOPXkoLYGhaRqeXg9hfxiT1UTj/MaMfhmZ8OyzxflfQOW03bR48nAnzLYUReE1r3kN1157LQ8++CAdHR0cPnw4aXufz8f4+DgzZuQwrq4gqKpK94lxOtFQgkO5P5CIUPTGa61LzvgZ2QVbPwP2VjjnB9n34e+VZS6z71MBasLN3xKdcIfHxejcPHFCoaoqe/bsYfHixRkf/qcc+jViqc2+3SmEgtrNAHPGrFAUMOdnVFhFQtvNn4tZMUm/jITANIk1rxYXnP/TgrSnTyl0/QyO/E76zsJ3Atn73IYNQl6kIlEL3YjJpNksE9MbbpBmSwxk6M34rW+BKTIu/1in0ZjpnCXzgNAojB+IG3unIjQq5IXJDuY0iR6qT94PjUpQjsLvc3rxa4IqVVr098PoqPz2aQpmi8PYblnWFTgzrwSoGYIPmdbn2t10mJ9UYSjK3ebZxlAd3/pW7gBPe3t+39feDo9/4XGObz7Ous+sK0pSKB/4Bn0MDELvWOYU9yR/j8YoAWyPOnybrEL8+vvBd7woAiOp7cb3SKKEtVaqPNJh5GXY9yNoPD1vAsNiEUWfYFAChM3ReOqOHbKcN09iyYVixQohMLZvL/1ebXPbaF7STP/Ofo6/dJyFr8xw/mlgtpuZuWYmIW9oAtk1Pi6WC5lQiH9LIsrZ5xRFZKS+8x2RkSqFwMiXNPR4FZwakuFrMGaunsnFn7s4qdIvH6Q/dpm0jdDACA0A7KmDMxMLENrb4d3vzrhf9fOfZ//27SycNQtzICAEx+zZbNwImwc68XI6LrxJL4AW+rii9xe89AGNnbukL/lwcgPfoqMDHlr376zoGAGXi5VHjjLaF8L7fCfNp10kclkHDsQ8Qsz9QRzBYbwBG2gRGme5Mz5bZJK0+s9PTh2BseuBXWy/bztLrl3C6W8/3bD9PvWUWKPMnZvezmUy5jcbNsTHxu98R17lCnCrAZX+nf3Uzq4FBbSwRiQUwWwt49zNBK4ZLtAkxqrnhJeTwKiUeemGDfDe905cb/RzZyFYdt0y5lw4h9nnzk77vqIoWFwWwoEwTYuaipIYK8X/olLabloQGOmgqmKYfMEFF/Bv//ZvSe899dRTnH322VNellQINE1j1GeW+1VgsLAPJxIYibA3gRqUwLsWASXD76Fp0PYKmXA72wo/+JMF1jp5+AgMQCRh0mZxyzr/CcmCSvmdNU1jZGSk4MnYpCLTNXIKY1q0WxVpEWs7y2ki1TCV5E+VvEgPW7TqIoGQz9TndC30dCglKzIT1q+XiWm6h8BvfSs6YX1OvAemFemrKEIWWWrF2DUXzE4h4kJjUaIm4VqOJAcuCh0vF0Ql4A8elBL8TNOx/ftlOXt2spRJSRiNEhj104jAyDT/SIS9ueB7ePU+d4rA3xeb52lqGH//DrR2k1T2gFw3UTLSCGQaQ81muPfe/B76C6nkePopE2ikNX02Cr5BHwE/+MkdEOjpAVz98o89IaPa0SYEhv9EUeNPUn8deklWNq7J/PzmjlZXeY8U9D1ud5zA0FGMgXci9M/pwT4dYV96Q9FM63UsuHIBM1fPpGVpS0HHEfaFsTgtmG3mJJ+NsC9MOPtXxpBvoF9HucfZ666TwOkf/1i4Dn8i8iUNG9occBQCo6UTGJ4+z4T96NUCg/sHsdfZcbfmrrAphPAsBJrTyaCisGD27KQftqcHdrCSHaTvEIeYx/v5Ps4XfDFiw4roz3d3w/vuu4T/uH2YC5Z7Me/XiAwcZSyqV8/Bg1JO4/VCJIJ9NMCcMQjNughncJSz9t0DKITNYqocjFh4ofmVPPww3HPrXzif8VhFiA8nu48u5Z0fcvPNVzqYO8uE5vGgOJ2ZJ14Gw9PnIRKOZJVIKga6/8WVV6Z/1Cp3v8skIVZogDsSjtC/q5+WpS2YLJnbxDfkQw2qBEYDKCYFNaDiG/Jhc9tyjpfFQB8r9f4X9AQJesBCuKwERtZ2S5jLpEUkCKYsz/x5znXy9eAy6rkzXzibnZisJkaPpv8NfAM+7LV2nA3OCfc4yH1fhWQCo1BUyjPFtCAwuru7mTlzZqxU/be//S0PPfQQX/va12hrayMUCvGLX/yCW265hbGxMb7whS/wsY99bIqPunCETbWgAqECKzCsddB2hZQQJ8LeKhlBkZAMCM6JhuZANPXxLUUd80kFRytccO/EgTMSgJc+IyXhC99t6EPgpKF2EVjrJ14jVeSH4BB4DoHZDXW5DYALwtE/wPBWmHk5tF5g7L5PBVQrVyoTevZpMDchn6+sQaFZkdmwfr1MTFPL8GMT1bBegZG/7EVFoK1As9vxLgj0g7M9P9IjT3R0yG8ZDMKxY/J/OugExsL8E2yzIzQmyRiKArUGj9XlRKb5RyIMDkJXcZLA3webbo5Jwpk1jdO8XsxjrnjUJyoJZzSJoY+hhw7Jw/7wMHkHifVKjuuvn/heYjWc2QyOBmE3y5ERrmPBlQs4NN7Ewc25Sev2Nk3GTUghMGYC24XAKBVDm2WZST4KwBUdWAMDEPZE/ftyw+2GoSFjCQzdyFsnMOx1dlzNLrwDXsKB9BeFq9mFvW6ipDPAwisLuynY6+w4m534BnwZv8/Z7CJwMP33JaLQIHi5cdFFUikzMABPP138PChf0nDpGjv7joJ/pDTC0NPnYcPNG/AOeDNu42p2sf7e9VlJDE1LX6GbiHyl6/JFfteAgg8XPlwMJKzVNPi7chFvuge6vgJB+y72DW+mwxeVennlK+WlaRAMoh7p58R7/ow6ouKPWHm55lzMkSCWSAiLFsKkqDib3Xz+q3Yu4RgdHMWJDyc+7AT4Pu9nq3I6T7/o4r1zN6B89M/yPQ6HGKi89a3S2e+6K1b1EfMDueIKGWQPHZKobqJXiM2WV5KWt0/a19VamJRNLiQSGJMNowLcmqbxx3/5I95eL1f85xW0Lkt/D/ac8BAJRWISUkFPkEg4gm/ARyQk/mbZxstCkG1sjnjBCrhajPmugpAyl5mASAh8x8A5GzLINuc715mK585cyDReahENb78XV7MLR4MDe62dwFggY0JHtuskEIjLsk1XA2+YJgTGQw89xH/913/FfCuWLFnCY489Rnv07vK73/2O9773vXz9619HVVXe/e53c+ONN07lIReFgLWNyKr/wuQs8AGjZh4sTZO+qihSUeE5Eu3wGQiMKuJwtKYf9Ba9C/b+EHoehM710y9ouvyTU30E0xsnnoD9/wczLobl/5Zz84IwtkdMGutXGLvfUw2TLeXUuxG6/wjNZ8Oc6Xe/KTvSVGBkQqFa6EbBbM4wgdM0CYTD9KrAKBTBoXgQLtAHrtmkOGEUDYtFyv4PHBBD0EkjMKy1cOG9QszkGcyrGGSaf1RRRTakSMJpmoZqMqFZ60STPI0knFFIHEMPH4bPfx7+53/gppvyux3Pm5d+fVI1HHECo5wVGPWd9bz21npc/wVDuapCzh+HZ6KZj7aUCgwAfxadonygaXLvMdtEHioTLG6ptg8MgvdoZtnAFOjKzOUgMHbvFuLa3epm/b3rs2bx55t9nw/crW5Ou/o0Rg6NsOjVi2haNNHA1OK2899nu/GV6N8y2bBY4LWvhZ/+VGSkig085UsaOkMSbymVMAyMBvAOeLHYLVicFtSgZJRb3VbsNXbCvjDeAS+B0QDuVndabwdFgX/9V6lASTzWTPKfRmVL5yJ7ciExALq4Qyonx7rHkjdSFLDbOXEwwKvuegMRNZJxf5FtdvZd52YfyXJYJtTY9/25/xyev3AO56zyxc3QdY04VZWKjOFhySrxRbfR2YFf/nKi5ufb3gYXXCDacA89JMSHTn7MnSsXoqpi3fo8DaNh6rxz4IhNtmlsLKkCpKdHxiRFgVcUmJdjBIwKcCuKQvPiZry9Xnpe7ElLYGiaxp4/7aFtbRsd53Ww5m1r2PGrHRx45AALrljAijfK4GrUeKmPzcNdw/iH/dTPq4/J7Z1+OowH4NHvGDc2541c8rbBIZnPmMxgbZj4fgFznal67swGfbw028wEx4P4R/w0zGtg+OAwYV8YT78HxaxwzQ+uiVWxpUO266QU/4tKwrQgMN71rnfxrne9K+P7c+fO5eGHH57EIzIeJpOJ+QuXYqpvMbbkzzkrTmCQIYsnOASRsLCWmcqUT3W0XwXHHoSWC4DkCYbJZGLBggXTSrKsigLbTdc4DmbICigFeqC0Ku+VN5LaznMIdn9HZpNnfmPyDsLbDSM7Dc1aP6mgExgJkoiZ+ly5pAGKhhYW8j80FvdBmk4YegmGt8t9K9MkPhIE3wm55yuKzAGCw/F2S0Ex97kFC+R5+MCBzAEhncBIp29cNKy10LjawB1OX1TnJ6cQzE6wuFHQcNRYUaw2YrJwmWTJDMT73w933AEvvghPPplfkPWzn5XlTTeJgeZrXyv/b9ki8S8d9noJqJaTwIBkf49UJAVJw9G5oLUuOaFJTxQrsgIj1l/NZlj1ufy8vVxzogTGkaIJDE0rncDo7BTvjLEx2LtXCA13q7ukIFjIG+LEthOYbWba12afAETUCD0v9BAYCeBqcdG0MP29LK/2LTAIPhnj7HXXCYHxu9/JMRabr3PmmXJ+qpq8ftYsIQnWr4d9D0UrngyQkAKwOC3Y3DY8Pg++fh9qQKV2piSH6Bng6bwdZs8WA/N//EP+/5//EdIzdTu3G+6+uzi9+kxtl4vsyZfU6OmBs5bGCQwtoqGY4o3Xu72Xf377n9jr7bzuf1+HxZE+NDfwbPr9R4hfrMM0st/WyNlrtYmG2nV18IEPZD7Q970vauQRJT58vrgWaF2dmJTppMfICESTijWPh7aXHmamGqH+/t3wl+h4+M1vCpHxwx9KFktiZcdll8lA090tJVv6epcLtbaBjftn8etfg5kwa9ZaYhxMKsrZ74wMcLef0c6Rp49w/KXjrL5l4tz0xJYT9G7rxea2ce6Hz8XV4qL9jHaO/uMoalDNOJaVAnerm67Huth2zzbmXDyHC//tQsbH4Ui0+HzhKsO/Moac7Rady0xAOOrvYnJkTlDKc65Tcc+dCbC4LIwdGyMSjDB0YAgtrGFxWKibU4caUHE2O4u+Jkrxv4DKeaaYFgTGqQCTyVS86XjYA4oFzGnKhZyzZOk7lvnz3X+CQ7+G2VfDae8v7hhOdpiscNZ30xI8JbXdZKBqMpwWBbWbTmD4+4w/kKo/ScFIajtLLYzukWtcDaQfB8uB0IgsrfWT833TDbpcXdgjXkxmW8Y+V4gW+qTAZIWzvzdJX1YGHPwFjOyS+3/b5em3USxgqwPFDGYX+HqkCiMLgVHofU435e7qyrzNvn2yNKwCo4okVPz8pArDoaBgt5plbjGJ96eWFknY/cEP4BvfyE1gbNwIDz4o2eVf+YqMAW1tYrK8bx+cfXZ821gFRomSNtmw/6/7sdfZufaadn7zG3NMdUVHUlXIYIqBt46aRdD5eqhZUNQxTOivucgLAHenkNaew3l/TyqBceyYSEqZzcVnZSqKkBbPPCMxyRUGFBV3Pd7FCz94gRmrZuQkME5sOUFgJIC93k7bmsx+juvXw5e/DJ/7XPL61KqfQjAZ4+yVV0o8+PBh2LwZzjijuP189atCXlx+ufwG11wj18Ff/gKro7FVXX7EKAIDQEOLVXTYapJVDB5+GG75YHqvge5uuS5//nMhOiEuXffnP8N//7fEvnXys1Bka7v16+V316WMdHR0iC/4F76Qe//t7eCe4WbdZ9ZR11GXZDWmaRpbfr5F9nl+R0byQt9PLpzJCwz98AB769aw+DWLc38gEfX18kqHxYvllQYhk4Pnlr0NcyTIvK9dFZX58UmjgFyo7e1xUsTni8ckurvlJuDzgabR1QW/ePY0PjfyccyEuZNbMW2zsOdaJ4tXR0mOD35QCJWNGzEdP86MREmsefPku/x+GB+PEyNFBFuNDHC3nS7j0eCeQYKeIDZ3/PrXNI0tP5Nr4LTXnIarRWS42s9o56LbL6J+bvnu4Se2CNE+Y4Vc/93dsr6mRn7iciGv8TISgLF98kxfk5DhFAnB2H7wRQ/W7IjKxRbWxvlUWM2YMTXVeAoKrhaXEJ5hDbPNTPOSZrSIhhpQc+8gC0rxv4DKeaaopmRVCFRVZcuWLajHHoH9d8H4wfw/vOd7sPEG0dJPRT4Ehr9XlvapvyArGhmqU2Jtl5rOUikY2Q4bb4Qtn53qI6koFNRusQqMweJqibNBJzBOZqkag5HUdrZG8VvQNPAcnLyD0AkMW5XASAuLOx58CQ0DmfucnukGE7nWckgDnPSoWybL0Z2Zt1FMcs93zpK2Mjul2iTskTLsFBRzn9OT91JVCRJhqIRURIWtX4SunwuZWUXlz0+qMByaphLo34Y2ujsuETdJ+Nd/lTH7j38UKaFM0DT49Kfl73e/O97/9eD5zpShy1EfJTCGykNgqEGVZ7/7LBv/fSNqUGX9ejj/fHnvve+Fxx8XIjYW3K5ZCCs+BfNuSt6RuxMWvgtmXlbccej91VtAsoxeBerNoneSglQCQ6++WLw4llhdFPTqDX1/pUInLfp39hP2ZzdXOfTUIQA6L+zMapQLEBKvZS67TEznJ7RvgZiMcdbphFe9Sv5+4IHi9rF/v9gggJCGl14av08nZpK7Z7qZuWYmzUsypL4XCA2N0SOj+AZkbpGoz65pcizZHq2amuANb4j/r0vXfe1r0NoKfX3w178Wd2y52k5Psvja15Kvlc98RgKgmfIDFUWqktatA8Wk0HFeB3UddUmVET0v9tD/cj8mq4mVb8pe+qQHXDNBUaCxUaOhJhz7nScD3n4vmsmMuakBy5zZQiIsWxb/Yc4+G173OnjTm1Df+g6eWP4B7tu6gieeAPXMc6RS4/vf53eXfZuz/nYH3xx5R2zfd/EO7g3dwL/+4XKe8Z8u5Ti2aPC/p4fIli303n8/kd/+VsqTdAOeHTukgT76USkN/PCH4w8ZAD/+sZTs3H+/MHdPPCEEC8jF1N3NuhWDLJrtQyH9hZnYvrngbnVTO7sWLaJxYmtydd5w1zBD+4ewOCwsv3F5/DMz3HRe0End7PIwCeFAmP6dMj+YuUYqB3UCY/bssnxlDHmNl7qvU3A4eb2mgRaSOb4agOCIvApEtudOHQMDQpzKMctlct99siz3lNrV4kIxKZjtZlqWtmB1lu6qboT/RaU8U1QrMCoEmqbh8/lQTjwOI1vBPVe8LfJBLIM7TQC0ZgG0ng91yye+p0MvdXZUPTLywtBWOLJBvBAs7ljbaUYHto1CaBRUv7DWVcRQULvZGuMyK6FhY83Qw7qEVJXAyBdJbacoEkwYfEGyMvKUUCgZ1QqM7FAUmH2NZPpHiYxsfW79evjNbyZKA5SSFXnKom4Z8IBInKXC2y1kqb1ZHhD0cmt7tPIiSjZhb06qCivmPqdXYGQiMMbH4UR0+mEIgeE9JOPA6C6Y92YDdjj9UfHzkyoMh4aCig0IwfgBcMzIL5PfACxeLJnQf/iDxKV+8IP02z34oJgROxxxGSkQAuOJJ2DXruTtXa0uZqyeQe2s8syTdGkqk9WE1S2/lR64fMtbxEQ5CbYGaL3Q8OPQNI3QeDfKPz8DtfPhjG+J3nc2NJ8La+cVJGeZicAoVj5KR6qRd6moaa/BNcOFt9dL745eZp05K+12alDlyKYjAMy7dF7O/T76qCxvuSWe1V8KJmucve46kVq65x5YvjzuE5FvcseXvyyBt1e9SqwNQIKw27aJnr+OpoVNXP7VDNWbBULTNMaOjMWqOern1McISZB5wPEcljF9fem9BqxWacNvfUvi0a95TXHHl6nt9u8XssJqhQ99SDLTE6HLkRXjyZGYeb/4msW4mrMbYOfjX3Lzu1wou8hqmm40FLNC54WdmG3ZL8J0EmEdHXJO116r8KF/czBI/LpQsfBPzpPvUGDbr6Dr6wm/5xveQGT9eg48/zxNZ52VnJG9eLGw6XrVh9crVRogDeX3w+CgDIBer7xWrpRt/vAHePZZzMBfl8NfuxX+wOv4C1cznwOsZwNeXPg1J/9yowvz481ihA7SkazWZLkstxtMJtrWtjHWPcbxl47TeX58rG5c0MjV37ua4a7hpH5RbvTv7CcSjuBqccXuqzqBkY0oMwJ5jZfh6M3JnkKimqzgnidxGP/xqOfFSFFxmWzPnZ2dIl33jneIbN8LL6S/dsv1bGqxW5i5ZiaKWTFMrskI/4tKeaaoEhgVBs3WIH8EB7Nul4RYIC0NS1u3GFZ8Ovvn9QoMR7UCIyc0Dfb9QHxFjvwO5t8y1UeUG1WPhdJhssjNMTAI/n7jCIxIOK7pWG2f4lEbJTDG90/ed8aI4yqBkREL31nQ5uvXx6UBEk0cJ73yYvAFOPBTqF8Bp71vkr/cANRHKzC8h+UhQNeK9RyGF/8Vms+Bc+/KrhVrrSvZ8FfP7MwkIaUTG01N0NBQ0lcJRqJRz7rFVdnEKk5hKAQss3DaxuQB33dMqqsm6YHzox+VGNDdd0tmdWvKMBKJxKsvPvjB5GxP/aE6lcCom13HK/69fE6uvkGZhzkaHSiKgt8PhyShn9NOK3BnwRH53e2tcWK4ALgDe0VmxuzMTV6AfEeB31NuAsOoCgxFUWg7vY0Dfz3A8c3HMxIYx54/RtgXxtXqomVpS9ptdIyOiswVxOOO0wWRqAXjwYPiGQP5B9J27xbiA4TI0KEHK7MZFhcLTdPwnPCgBlRMVhMN8xtwtyRr14ezF9bEkMlr4G1vE6Lg978XGbRGgx7PIF7VccEFE8kLyBwATfQT0TFyZISjzxzF2eRkwSsWcOTvRxg+MIzFaWH5DVmSTBOwbFn69Xqiz9pGJ8/sYlIrMOo767noU6kMbzI2bBCiJ51E2A03SLFEyYbZloSQZm1t5gitoggblfoFOtavF301n4/5Xi9LL/Zx151z4DiEsDJIE058nLeon3PrvLC9Pj6Q/OhHEAwm7/szn4E5c5g38hL23Q9iOVEDoTOF3Fi7Fs46izp3hDq1C547EfMCwe2mt1+hf1c/M1fNoHlx9nGtUBzfIqzhzDUzY1VBk1WBkRfCUTMOe8oEQlFENsriFslb3bS7SGR67lQU+NKXZKz8/e8nfk6/dn/zm/KRGGarsQ+/pfpfVBKqBEalQdefDg7l/5mYBE0RAdBIOE6WVCswckNRJLNzxx3Q/XvoeC0o2bMmphxVjwVjYG8VAiPQDxT6RJsBYY9kE2jh6WkWXCnQ9abHs2jVGA29ZLUqIWUodGmAKUVgQGQcp6usoq0BnO3iazG6C5rOlCq8l78ufiShcXDNnjiDDQ7DicekgqNE8gLiBMaxY8mSyDoMN/Ae1QmMIlOLqqhiOkOXftM0TJoPzdaEokXAd1T69sGfwaovZpRDNQoXXyxGwS+8AN//Pnz+88nv33+/mHTX1sKnPpX8XiYCo9zQCQxnkwxSBw5ITKu2VrIVJ6D/GdAiQnKnzgF2fxsGnoPFt8KsVxV8LO7gHrADjWsL/mze31EmAkP//L59kunpMCChuH1tOwf+eoCezZndcg8+eRCAuZfMnWhenIKnnpIqhIULYe7c0o9vsrBhA7z97RPX5xtI+9KXhAB57WuT/WWyERgRNYJiUnL+ppmgKAoWu4WwP0zd7DqsTitBTzzAG/aFk+LO2ZDJa2DNGli1SpLff/1r+Jd/KepQ00L3vrjyyszbJAZAr79eEvvvvVfGwUQM7R9i68+20rqylXmXzmPrPVsBWHrd0iRJrWy4805ZXnutxL6/+EUhNbZtk7nz8S0yfk1mBUYuqKoQPOn4c33dV7+a377yNdYuGInXd2NjEgt2yVnw99v1AHcn/f3v5MMfBstBuOIt8XkuAF//erLXh88Xu4HUX7QaX88oDe0ONKuCNjKG92AvNWchpci//GWcoQRoauLAzGvperSLK8yPwYLa5MqON71JmIZt24TRTHyvrU1YtHA4PvlO6WjHX4oTGDoqhsAIjUbnMwqgxasxIv74MuwRDz/FDCa7EB5qcRKTmZ47P/95+H//T/p0KnQBiNtuk/5oZKJd2Jee1c20Pl/oBMYll5S0m4pAlcCoEJjNZpYuXYppLFpHmW8FhqbFA9SZAmmaJoSIyQbWlCBpoE/eN9uqAe580XI+ONok23vvDzB3rGd5px2z92D8JmhA9qphqBIYaaH3OXO+d52O18tNs9ZAt1lbPaz7rWRCT3c6fBIxoe1qom3iOSikrCnLrc3flz1bI5++q4+ZJmu1AiMbIiEh/RQzOFoK73NThVjV2jSUddOvb8cMMcA78QRYG+DAXVKh4GiFZR9PP94cug+6/wIzLo5XcURRTNs1NUkAcGxMsplTE+IMN/CuEhgTMG36XBXFw1qXJAmnAG6bihIeAbNV3guNQN8zsOubMpfJtq8S566KAh/7mGSI33knfOIT8UB2KBQ3T/74x6E5RR1CHyP27ZNtrSnKV1pEQ9M0TGZjSRjfUDKBsXevrF+cqZir6+dS0bbmK2A7Pfk9PRlMl+fNhYQ5iTmi0mbej6IGJaFtbH9+bTLwnBh5N58DjWtyfmUigRGJxCWfSiUw2tok9jc0JBn/a3IfSk7MXDMTFBg9PIpv0Bdro0TMOmsW/mE/cy/OzUjo8lFGVl+Ue5zNFQTOFUjbsUPio5BcfQGZCYw/ve9PjB0b49XffTUNcxvSHpenz5PV6FsNqjQuamT8+DiapsWk2hIxY66LZo+d4ePpz09R5BgzeQ0oilRhfPzjUvVVKIGRqe3CYXjsMfk7G4Eh+5AA6HnniaXCjh3JBIanz0NEjRD0BOnf2U/fy324Z7gZPz7OzFUz8fR5cLe6M+4fpHLo7rvl7w99SPrZF78oOv36oesyVJNZgRHyhrA4LRlJro0bjavuSSWxJmt+kxrg/vOfxXj+y18W640Y3O744JoAT5+HwMxFuD+4iBAwBBx84iDbvr+NhfueZu271uL+3vekekOXvFJVap+R+8KJzrNofeXc+Hs+X/zmeOSI6DH6fGJyAGLu86Y3CSPxta/JOl3aqrGRwIc+xtD+ITp6nmX2Hj8MvwQuF3UvOWlgFbNnN8LwcFx6y+kU7xGD4hQZ202fy3gOybO82Z78vK6FpTIxosblbp1ikB5To0mRvy0FGzemJy9ih5NPZVABsNfZcTW78A54CQfSkxWuZlfehGcijPC/gMp5pqgSGBUCRVFoaGiAYHQ2n2pakwmRQNzbIFOH3flf0LsRFr1XKgYSEZOPmlkNoOaLQD8Mb5VgydCLKAd/QZ2S0pHtzXDBvZVBYlQJjLSI9bl8MSN7iWwJByLlkFXkjQlt55gpnkGONsnKyETm+vtg080S6MmEfPquosC5/ztpkhzTFod/Cwd/Ae1XwpIPF97npgp66fJ0q4pKvL5Do/L/6E7Y+wO51yuIH1YkmP7zbVcKgdH/DyFxEgicYtpOUSQ7bcsWyWhOJTAMNfAOjUrFCUDtYgN2eHJg2vS5KoqHo1XuWdG5nkKah7vRXTIWH/29LDPBoLnrDTfAJz8pD/f33CNG3SABuL17oaVFJMpT0dkpsRKfT6TnFid05Uduf4S+HX1c9uXLaDu9raTjS0WsAqMxmcDIKB+lzyFsaUyO9YCKP4ewP0yYkyhqAJv3qFTJvPQpGUTzaZOBZ+HYQzKXLJDA6OqS39tuL30sVhSRkXr6aanqMILAsNfaaVrUxODeQXq396YlKRZeuZCFV+Z38I88IksjCYxyj7O5gsC5Amlf/KJsc/31cPrpye9lrMCIJj8HRtITFJ4+Dxtu3jAh2z8SiuAb9OFqdeFudfPq77w6qz+Cvc5OzUY3N9ww8b18vCRAyNJPfEI06/fsSR43ciFT2z33HIyMCFFw5pn57Wv1aiEwtmyJr9N/J0+fh+GuYQAeeMsDKCYFLaLxwFsfwNXsYv2967OSGD/7mfiFLFsmCkfDsit6e6Ufu93gbJbxK+wLE/KFDDH+zYUnvvQEg3sGuejTFzH77Imp+/lWTTQ1CfFZCIk1VfObr3xFCIyf/1yqCLP5CaTrJ1pEY+TwCJqqMXJohK5Hu+Ltb7fHNFXrjooxTbd1HitfdVX6L7j6anmBsNE+X7zjtLbCBz6Q7AUCWF1WrviPK1Dv3Iet/xgc3gc+H2fs9jGTGUJgPPmkXMw6TCYxg7rlFonq//SncXJDX77mNfLde/dKQyZWhTidsg+ytJs+l9n3Yzj+CMy8HOalmBRFgpKQnQkGJhDne+0aVRnkbnWz/t71WUlhe509J9mZDrr/RWtrZim6fFApzxRVAqNCEA6H2bx5M2sX1Eqj5FuBoQenzTYpoUoHXT/On6aH2Zthzg1xjewqciM0ClpIfrNIAC3sY0yto6amFpOiSNmbHkCqEhgVi1ifW7sWS741zFVUBCa0naLAWd/N/cHQqPRNk12yOFJRaN+tkr7ZoetyRyURp02f0yswphuBkXh9O2fHJbDG9kZ9fJol6SHT9V2zEGrmw3iXVG4kJDwU23bz58vDfDofDEMJjNHdsnR3Tqw0PYUxbfpcFaXB0Rrr02nbvHah9O9NNyfc/zQkShmFgXNXq1Uyxj/+cfif/xEi88gRuP12ef/Tn5bqrFSYTLBkCbz0kshIJQYiFZMCGmmzuEuFf0j2qWf379kj69MSGKo/wWA0jS65XoHhy6MCI2VOoqm9hCNgcTag2BrzbxPXHFl6Duf+TpIJDF0+avlyY2QwVq4UAsMoI2+AM//lTAl0t5U2th8/LuerKJKkbBTKPc6WEkh76SWRl1IUITJSkYnAcNQ7GDs6ljGYFhgN4B3wYrFbsDjlnMP+MMM9w6hBFd+wD8WkYLaZaVqY3aNF95J4+9ulYjPx2L71rdwa8+3tcNVV8OCDElT+yleyb5+ITG2ny0e94hX59wudsNu6Nb5O/52sTitWlxU1qGJxWLC6hFwI+8J4B7wERgMZA5ORiEjZgPgGKYoQK/X1QrIcOiT91+q00rKsBVuNDTWoTgqB4e3zEglHMmaFZ5L+SsVHPiLXZyGG6FM1vzn7bHjd68Tr6Utfgvvuy7xtYj8xO8wERgOMHhkVeTW3BVerK2P713VI3Gb0yCiapuWWcjOZkitAXK60LLIJaF3WCv/vc0nrP/xLjWNEJaROu0QG80Q5rERDq7o6WTcyIktVhWuukffuvVe0YxPxvveJ7tnGjUT+9je6BweZtXAh5tpa0ZG99FIpE3hmB/iawL9KqqnHnHIRZTt3LSLPObWnGSqRme+1m+92+cDd6i6KoMgFo/wvKuWZovo0U0FQVRWs0YBDIE8CQzFLdqsWyXxFOqOmZ95jE99zdcCCtxV+sFXIb+c5BOFRIkqjEBp6G2QzR51suDokozjVCKkK6XP5IuyRG2QkBM1n594+Hww8Bz0PQ8OaidVRVWRFQW2XCrNT+qsWAsVCUhCnkvrudIdudp/g6VRSu00WwtNYQgri1zfAyHaZ0FsbxfdCL7NOB0WRKox9P4Ljf4PZ1yTNK4ppO10fWDfsToThFRgWN9QuMWBnJxemRZ+rwlCkbXNTNJhldsq9z3tUqpUSsxkNvP+9+93w2c8KEfGKBP9tszn7A/+yZXEC43Wvi693NEilqn/EeAJjybVLaFvbFpPKyVqBoVdfWJzySoWjgAoMHfqYrfrRMMt8XR/D82kTd6csvUfy+rpEAmPbNvm7VPkoHbqRt5EERsuS9Aa2/hE/RzYdYc6Fc/KS1dDlo9aunShfVirKOc7mGyCbOVM/lrgpre6b8MY3pm9jncAYGRHyQCcW9d8zWzYwgMVpwea2EfQEGTk0ghbRsNXaqO+sT/K7yIX16+VYH3sM3vUuePObJeM+X/LgbW8TAuNnP5OgsqmAOGa6ttMNvHPJRyVi9WpZbtsmpEPiMVicFiwuC1pEI+wPJwUpM8nF6Hj0UZFkq62Ft7wlvn7evHiCyPKoD/iV/1nAAZeIiBqJyVVlCrquWyfXWHd39uqKz3xGrs9UQ/RcJNZUzW++/GUhMH71q/ixZ4PFKaRV/8v9mCxyYTTMbcDitKAG0p9DbXstikkh7A/jH/Knlc8zCuEwHD8h8/3Zs5EqkEyZ9k1N8bLKdPjYxyZ6gcyfL++1tqItW0bg5ZdFlmp0VF4gA9CvfpXgBfI3eX3/+3KhfPObQoy43ULOuFzSQYf+U3xA7G+GlkXxio/GRvEC0TQ5QT3hMU/ke+1mkrerBOj3Al1CMNWbp7h9Tv0zRZXAqDQ4ZsIZ35DAjy5smQ32Zljy4ezbuKIEhi8NgVFF8bDUyEOGtRbNW8H60ouy3GSqyB+ew7Dlc6ItbxSB4TkE/f+cfpnelQrdEygfY+2wR+R1bI1xD418MfSSyHDUr6wSwNmQhsCYFtAlpKYrgZEI91wZu2oXiIF3Lsy8VPwyxrvE56m2NIftTARGMChZg2CQiXfbK6TcvEgTvyqqOHWgyZig+mFsjxhRU0JKXgY8+qhIFqRCVUXyxWZLH5TKZOQdIzDKUIHRMLchSec/0QNjAgL9skxXfQHxCozQGIS9YHHlfRyaYwbhYASLta6wFtErMHw9kmRjyp55na4CY9WqQr4wM3QCQ99vOXF442Fe+OELdD3WxSv/65U5ty+H/8VkIFcgTccdd0gw+4tfnFhRcf756T9TWxvP5D96NC4vYq8XAiMfwjCiRujf3Y8W1rDWWGle3JwxKJsJqiqyTQAf/nCcDMgXr3udnMfhw2LUXorO++goPPOM/F0IgbF4sYxr4+MST00yeAbMFjMhQoz3jFPXWYeSZy/Xqy/e/vbkyrVsFa6TAf+QHy2iYbKYcDSml0I2m+Hb3xb5slSkVlckGqL39AhxVwiJNZlYs0akEn/zG/jCF+C3v839GUVRsLqshLwi/e5sdsb+TgeTxYS7zc34sXFGj44aQmB4B7xs/+V22te203lBZ2z9iRPCG5jNcSK0aNTUyCsdli5FW7SI/uefZ95ZZyWbi7e0wPe+JwZYiQSIzgReeKFopnm98ZeiQM0C6N0JW34D1s74ILl6Ndx6q9zoPvYx+S6d3HC5RMfS4YDHHxdZrES5q4ULMbe28t3/9PHum734cOLDgUYyM5pL3m4qsWHDRELwq18VTidXVVulo0pgVBpMFqjLJLpaJPQKDP+JiQa3411gqRWpDwPLrk4Z1MxH0zTwDU/1kVRRbugPq8GB/MjFfFCV9zIO/l544TZ5eL/o17nbZ2yvVK4FBgonMHzHxRC5auCdHbYECSktkn3bSoLFLeTLydAvLbXRACVAHgSGtRZazofep6QKo0QCQ0+6Sn3APnRIHpZcLjF+NQSKkj4juooqqkiAItIMI9slwB4cio/VBkE3Hc6GTKbDGQmM+vIRGInweCRQDDkqMNL5X4CMQda6qA/RCZHlyxe2JgJWEw5Tgb5otmgVeNgjyWru7GbWiQSGHtwwqgJD309XV1yb3wgce+EY+x/eT9vaNk57tTTMwScPAjDnojk5P69p5fG/mAzoQeAbbkgvsaNpEjh/5JH4OabittuEBEkXuOroSENg6BUYGTwwEuEb9KGFNSwOCy1LWzCZTAUTGLt2SQK22x0nwQqB0wlveAP8+MfitVMKgfHEEzKGLVoUn8PkA4tFjn3zZiEWUgmM2tm1RMIR6jryJy+6uuCPf5S/b701+b1M8ysQUslkLm9cx9MnUnrOZmdWeaP162VcTx3T01VXpBpmVzK++EUhLjZskDZfuzb3Z+rn1TN6eJS6OXW5JaEQGSmdwJi5ulRmAY6/dJz9D+1naP9QEoGh3/Pa2qY4IO87JvHI+raJFSDnnJP+M91rYfUzcPEqWPPvIkXlSzCyt9mkrCvRC8Tnk/UgJ797d5wwCYel1Km1ldfPeZE1V/6Mv2+S+5kfBy+znLvt/8K990RY3/N9uCvBC8TlEq8Qh0OqRYLBZJ+QSZJd2rBB7hephHdvb5x4m84kRpXAqBCYzWZWr15duKt7OGrWY7JnDtjZmsQjQw1KkE+vyADY/mXw98MZ/wN1VePLYqAoCnW1dVU5/GmGgvucrVH6WESF0HA8u7wUxAiMkyDTexKRtu10fX81KBMg10QzuRhUf7KRsaaKHF++CA7L8mQIcJcT1vr403VoBLO1obj73GRjxaen+gimFu1XipF3wsy32DlKYgVGIu+ry0ctWGAAF2wUoXwSoui5ZRXTFnm1uckmUkfeo+DrNpzAKMV0OJHASOzaegVGPgHVQhBRI+x/eD+ORgezz5nNvn0S9GtqktcExCowsmgQzbkx6jvUUNCxxJ8nChzPFEXkYkd3g+dI3gTG8LDEbcA4AqO1VV59fbBzJ5x1ljH7HT06ytF/HCXsD3Paq0/D0+thYNcAKDB3XfbzBamqOXJEYlYXXmjMMemYjHFW94nIJLGzeDGccYYkL2dCJtKwo0MkvxL3m6+EFIC3X8yBXa0uTIVoNyVAr3g455ziA6hve5sQGL/5jVQt5EOepWu7YuSjdKxZI8HsrVvhuuuS37O5bbQuL0zO+fvfl3Hwla8Uf6BEpCMwdj6wk+33bmf+FfM5618M6nwZ4O2Lt3s27Nwp47nJJApBoZAx1RVTPb9ZsQJuukksHz7/+TjRlA32GntB18DqN69m9ZtXUzfbmOfNE1vEm2nmmmQyRCcwZmd5dDYKWdvt0C/Fg2/B28SjNx80ni7L0Z0SB3A45KXDZstMfoDo1SUiFIpPPFauZP63b2POuI+tz3g5vNvHfXfWEgjAmatDonLV359MjJx3nnz2gQeSDXEAbrxRGPSXX4bf/z65IqStLT7oPPusGLonkh+NjXlp4+kJJOmq9fQ5VaZ7QS5MdZ/TUSUwKgg2nQns3SjZwa0XQl0OPecjv4FDv4aOazNLBSmKVGGMH4wG9qIERiQczyRylM7qnrrQMIX6YbRHstoqCYFBePa9Emw/50fVIE8KYn0uH5gs8jsGBoX0M4TA0M2CqwRGoZjQdiYzuOfLA/z4/uwERqBPlopJstMLrT4L68RTQ2GfO9VgMguJERyWLF9rQ2F9roqpQcMaOP/uCcRqMW03b54sx8akQlvXHTfU/6LnITj8G5j1KgkcVpGEap879ZBXmztmiuRQ2Cfjs8m466QU0+HTTpOp6tCQBMFnRK0BY5I2BldgBEYCPP/950GBN/3uTdn9LwBmXCzzi0wVGACdry/8QPy9YHGhmPOXnEqCe47Mf/KQC9YDuzt3SrCjri7uhWAEVq4UVY4dO4wjMNrXtrOZzfTt6EMNqhx6SjQIZ6yakZe0ii4fdeGFEg8yGpMxzmaT2HniiezkRTbSMJ2Rd11HHTNPn0nDvIasx6RFNCKhCCjgai7+h9UJjHPPLXoXXHCBzCn275fYYWpcMhNS20438H5lblWyCdClr1LjlsXA64X//V/5+4MfnPi+TmAcPBhfZ7aaCfvD+AZ9Ez9gMPQKjFymwz/5iSyvuUayv43EVM9vvvAF8Rf4059E/aix0Vjpq8b5BsQaotA0LUZgtK1JLn2eTAIDsrTbaJRRL0QZwdku8t7+XhjZAU1nlnZw1gQJxvp6qK/HDKw9G9YC334ZeBzu/a2d22+/NcNOgFtuEW27RC+QOdFqQZdLBl6d9BgclMoRkPJ0vdMk4qtfleyAX/xCbq6J5MZ55wmD3dfH9rtfYu5RJ6248OFkjFq6kUHehEpEM2e8F+SDqe5zUCUwKgaqqvL8889z1llnYenfBL1Pi2RNLgJDz+DOpaE/4xJoXJus2RrokxmN2VbNJC4GqkwONE0jOHoUuykAngNgnzHFB5aA0CioATnWKnmRhKQ+l29Jn71VCIxAP2CA1FvMLLja/wpBxrarXSiTn7H9EmRIB524VRRwLxZpo7A3uuM8J/zBqBFyPl4bpzraXyUVLpaa4vpcFYUj03Wc7/WtKBPIi2LbzuEQvdVjx6QKoywExuhueXBRjc3MPhlQ7XOnHnK2eeI4YGsQmSPPQXB1Tty2SORrOpxuO6dTiM+uLsna1QmMmpk1zFg9I8mrwgjogT5noxPFpGT3vwBwtsnLSITG5PkBGFPmUFvXIFUY+Y7ZAPNugflvy2s+qRMYuhfnypXGPiKsWBEnMIxCXWcdziYnvkEffS/3xeSj5l0yL6/Pl1M+ajLH2UwSO6WQhukIjFlnzmLWmbMmbpwCNaDSsLAB1a+ihuQFEPZlN6ZOhU5g6MnLxUBR4K1vlaDy3XfnR2Cktt2hQ7Bnj/zOl11W+DHoBMaWLcnrM/0e2X6n++4TInfePLj66onv6wkiiRUYzmYh83Rz7XKitr2Wzgs7aVmWwQ8Iicnefbf8nc33uRhUwvxm8WK45BIZ7xIlvjo6RPbt0jXyfzHtbzTGusfwDfowWU0TqkAmk8DI2G6hUUmqgMJUYRRFYpw9D8Pg5tIJjBx485ulvX/+c/jUp7LcO7MZoc+bF+/AqTCZ4M47Jxqh6/tavlxu4olVH/rN/MQJHI/8ibcQT/TooZ0v8UUAvsFHMaPiw0nzd13wTxe84x3xiVYOVEKfgyqBUZkoxPg0Xw39dGVY/l5ZOmZWg9uFwFonpeOBAYgEUDSNiOKAiEcy800OqXKphKB01WPBWOiSAbqEQKmoto+xcEf1asb3p3/fWidSGcFBCaprAQilBD3tzbnbIzQc318V2TH/lvjf4cmbqBeN4Ahs+Yy07Zp/n173xpR7U1rkc30nwnNEtNXNxV/rCxYIgdHVBWefLev27ZOlIQbeo1Fh5UqrgKyiikpCuvHBZBUSPzQGoRGpLDDgvpbLdFhR5P1169J/funSOIFx5jJPTMZm7TtFZHxw/2BsW3udPWcGcDb4hiTQp5vQ5qzAyAeqHzyHRKqyIYs7tt4m4wclucJsx6x5UULE7z35jtnZJK1SkCqtY5R8lI5yGHl7+73UddYxcmSEl376En07+jBZTNS01TC4fzDrdaCq8Nhj8vcrXmHcMVUSSiENO6Pc5ZEj+X+fvc6Oq9mFd8BLOCBzu7A/eY7nanbFpKiyYWwsTnaVUoEBIl3/hS9Ixc2RI/Fzyxd69cU550jydaHQCYz9+8XMO93vlIp0v5Omxc27b701fTa/Hv8cHpZXQ0O8CmYyCIzOCzqTfBTS4fe/F4WdWbPg1a8u+yFNOjZskOqnVHR3S7XJL39SXPvr0CIau/+wm9HuUc541xlYHMWHbo9vOQ5Ay7IWzLbkC0onMIqqxPP3xeMZ6WCtA0cesll69YVrtjx3FILG04XAGNpc2OeKwPXXwwc+IBWML72Un/dJwbBYpDSyLs29f+3azF+6ciU9n/o27384ggM/TnyYifsR3cdNOPHhwsvVq7zQnuAFMo1QJTAqEQURGNFM4GIeOHQCo5IqBqYDHK1wwb2xwVpVw+zeto3VrmcxDz0vg+iK2/MbrMuNaoDcWNijbWoUgaFGGfKqB4YxqI2mc48fSK+L72iFdfdLv9A08PfA0BaROWq9QLbJZ6IVqkpInbQIjUrgyVo7vcgLmHBvSot8HyQA9v8EjvwO5r4BOm8q+rDmz4enn5YKjNiujarACI2BN/rklatitYoqTmVkGh/6n5GKAve8wsaHLMhlOgyi259JYmPpUnjwQdj9oocN92/AO+DN+F2uZhfr711fNIkRq8CIyhDt2SPrMxIYxx6S36npTDBnCM6O7oYtnxVfinO+n/nL9TbZ9Q3o/yeRtlexd2gBq1atwmKOPqIb1CaJKDeBoe/PqAoMT5+HDTdvYPjgMJ5eDz0vSJau1W3ld2//HZD9Oti8WQK89fVwZnmTc6cMpZCG6SowdETUCIpJmeDL4m51c82PriHsD08IhurIl1x87jk55nnzRAa+FMyfDxdfDE89BV/5ilRRFCLpU4p8FIjCS3u7VLps3w7nnedm/b3rs3qJpPudNm2S4KjDAe98Z/rP1dTE/Wa6uiSmGavAGPShRTQU09TOY3/8Y1m+852T5l88acjHa+DjX3Cz/dn1hD2Ftb8OxaSw49c7CI4FOe3Vp9G4oHhJqUzyUVBCBYa/DzbdHJekTwd7s9znct3HRqM332KSkRrXwNw3QVM52IRk1NeLMtT998M995SJwCgB69bB7A4TR4+68JEs6/dPzovdC5Z+Dpim9ngn2VBykkA30wsOZt8O8g9QR01U8fdBXXRW7peBDEeVwCgYjtb4QBwOE7AOoZ12Nmz+kPiXBIeqBMbJiBnroGYe1BiRNgycd5dkQhqoPX1Kwz1XfBdCYyKRl25sS+y7vm7J2GhYCQvekv/3KFaR3qtKSOWGGozeyxSw5p8hOmWIybpNU1Ix8fouFfpDxPFHYPYbit5NopE3iLyr/nfJBEZixtZ0bbMqqpgspBsfakvthOmRy3R4/frMn9WNvA/sDLB4zIvFbsHilEdWLRotUhSFsC+Md8BLYDRQNIHhH5JEkrwqMCIh2HOn/H3hLzITGLqvoP9E+mSKRNhbwCuVblrblQTGA6L/XUy07+C9MLYPFr1HdMEzYLIqMA4fhtHR9EmkhSAwGsA74MXR6IgRTs1LmlEUBbPdnPM60OWjLrvs5Aui6iiFNExHYETCER546wMEx4Jc/8vrsbknPqd0PdrFgb8d4PR3nM6iVxX/XGSE/0UiVq4UAuPHP44H0HVJn2zjjqrGr5ViDLx1rF4tBMaWLSKJ5W515z0+qapo099+u/x/003Q1JR5+/nzkwkMR4MDFMnc94/4cTbm9ocpFv5hP/Z6+wRyS0dXV/z3zETCTGds3Jie9NOh+868uMvNpZcWXyVY11FH/85+Ro+OlkRg6IkAqQbeED+PggmM0KiQFyY7mNNca6pP3g+N5kFgRKupawuQj9JhrU2u+C8z3vxmITDuvRf+8z/z9zvR+3eqh5GRMJulEu2OOya+l08CyXRAgc6lVZQLZrOZs846S1zdyyEhFRyCTW+BzR+TMmVIkJCqEhilINZ2tXNg5uWy8uA9U3tQOmIeKVUCIxVJfS5f1C2BtiuExDACigJmR+Em0qc4MradyQptV0LndUy4vakBCTyMH4yv0x/w8zC9TMKZ34CLfiOm4VVkR8/D8M/3wIH/K67PTTZCUQLDUg2GiySbAp4jmI//mbOWNmL2HhSPmbH9khCRB3SjSV2nuacH/H6ZPOt+dkVDJzCq1RdpMS36XBWGoqQ2D46kTyctEuvXi8Hs44/Lg/7jj8s4kC2ICAkERpTotDgt2Nw2hg8O079TKmBtbluM1CgFiRUYo6PQG300Sktg6M9lJmv2e4S9ReZ1kVDuZznPQfndzXbMjStK668Dz8krcZ6TBg5H8v/LlhX3dZmgG9kCvPxyfp9RVZFiue8+WarqxG1sNTbs9XYcjQ6sLivOJmde10E5/S+gcsZZnTRMDUJ2dMj6TP1OJzCGhsAjvsyYLCYx54a01QMRNcKhJw8R8oZiWf/Fwgj/Cx0bNsD30xQ96ZI+GzYkr09su82bxUu3tlYkpIrFmqjvQaFG3hs2SBXKZZfFf5O//GXiMSci1cjbZDbFSItyykiF/WEeeMsD/Hr9rydIh+nQfYivvDJ+nEZiqvtdKb4zhaCuQ2I4o91ZqqvzwFX/cxWv+7/X0bQomRHTNAM8MMxOkX1SvVHfQ3dUfnbi2JC23TRNEoBhWsznX/UqIRaPH4/LE+ZCYv+++WZZzpuXvX8XA69X7qMgVVqJyHUvyIWp7nM6qlGzCkIwGJQ/YhUYOSa9mpY/gWFrlECppsUrL1ovFG+M+hXFH3QVQELbzb1JMsAHN4sMyVQjXK3AyIZYu1Ux7ZCx7RbfCgvfCY4UU7njfxPphx3/Hg/Q6ARGYDAu55UvFGX6SQxNBezJ97OK73PhcVlaarJvd7LD3wfPvBWGX4LBF+D5D6L8/Y3w9zfA0zfKa9PNeZEYqRUYunzU3LlgtZZ4nFX/i5yo+D5XheEoqs33/wSeeTsMbzP0WHTT4ZtukmU+z706gXH0qFRs6VDMCmgwemSUiBpJ/+ECkWjirVdfzJyZoWpAlw+1N2e//5ssQmIA+I9nP4Chl2TZsApM1tL6q27E7s1sZrBhAyxJiQ+dfbbxQZRCZKQKCey0Lm+ldVkrVmd+Nw+fTyQMobz+F5UyzhZDGtbVxQNdeiATwFYnVReBkYkExvGXjksGfp2d9rV5GnCkgaYZR2DkkvQBuO22ieSY3na6fNTll5c2N9F9MAohMDZsEIIlNaO/tzc98aIjnZH3zDUzmXX2LBkvywRPnzBdZrs5rS9DOAx33SV/v+c9ZTuMKe13pfjOFILa2UKWjx4tjcAAqQYymZPDv6OjceKyJBPv8DiMd0WJiOz35wntpkVgyUckLumeW9z3R8LQ9w/Y/3+GJmKkg80Gb3yj/H1PHjnLmfp3JmK1FHz1q3IP6OyU7ys0gSQXKuFeVyUwKgSqqrJ161ZUVU2owBiJV0ukQyQk2eAt5+YOUCvKxGzjlnNhwdug3uDUm1MMSW3nnAkL3wtnfKP4AdhI2FuhfqmYileRhKR2yxcRVR42e/5W+s3Rcxi2fxUO3F3afk5BFNx2kTAceUD+7rguHniw1oI1+tTmyxFkqKI4JEgiFtXnJht6BcapLkekl4XbWyUYFwni8YNmqRfvF5M9XhaeA3rm3eHD8lBrqIF37SKRwKmrzmPSYVr0uSoMRdFtHgnL69Avy3NgBaC1VbL4NSCQEDut76hHMSsEx4L07+wnEi6dxFh1yyou/OSFtK1ty+1/ESMwWjJskABnVGdcTxrLhOGo03Xj6aX3V3d2AmMygyj5GnkXekyZ5GoyYdMmuYZmz55I3BiFShtnCyUNFSVudp3YDo56KdVJV4Fx8ImDAMy5eA4mS/HhpIMHRQLJZitdSz5fSZ+NG+PrEtvur3+VdaXIR0EygZHPo2KxxAtMrHAFOP+j53PJ5y+hcX7xckO54O0TOSJXqyvt+w8+CMeOQUsLXHtteY5hqvud7juTaUjS+1U635lCEKvAKIHA0DJciKoKD0Qfj93uidV5BUFP/NLUrDL4advNZIbW8yUuaSq2slKDXf8jz/ueg0XuI3+8+c2y3LAhTgClQyn9u1Ds3An//d/y93e+I34dhSaQZMNU9zkdVQKjEmGtkwD4+T8FJcuVZrbBkg/Dys/m19md0SB2oXIpVRSG2VfHfUamGp3Xwdr/EqKrCmOw9fOw+zsQGi5tP/4T0P/PePZdFcYhNB4PCgD0PS2Sebb6iX3BoRO7edb4ju2DFz8Oe75nzLGe7EiURCxzRowhmO4eGEbDGiUsFDMmLZC1LDwTZs2S4EQ4LMEFwwy8QR52zvyWcbJ+VVRxqqLzenmWGN4Gwwa5LxcJRYlXYfgTiiNtNTZalrVgspoIeUMM7huMydwUi8b5jcy5aA617bXZ/S8gblRqz8PPyRElMHw5CIwVt8PpX4fWEqNcAK6oJp/n8IS3JjOIAnECI1sFxmQcU6J8VLVoNjN0GakjCdyXvU48XvwjyRXKYX+Yo/8QpmDepfNK+l69+mLtWrBnsJTJF6VI+ng88Pe/y9+lEhhLlkgFx+goHMpDjKEY4kVHOgJjMqBXYGTy9tC9R972Npn/nYzQfWcg89hihNeATmCMdY9lJCKyQQ2p/O6tv+PJLz9J0BPPntcr397xDvnf4ylC0igSEsJChytawpHN1LtcMFmlkhEmJbZy/vnS/8bH4Q9/yLxdKf27EGgafOADEArBNdeUjzisBFQJjEqEokgAPFeJcqFIJDDCHnlA8fcbt/8qJiIwOD2CdlXkB5M5nlFeat+pZnqXB6pfpG1euh2Cw9GZwW/lvdmvE+I3EXplmj/PJx9/n+jueyb5aWG6Qicw1KBoo1Y6FIscs7Vhqo+kchA13rNERor6uMmU/JBtKIFRRRVVGANHS5zgr4AqDN2XIZCi7mhzCYlhtptRAyqjx0YZOzZmyHfqBMbiTB6iegWGLR8CQzfyzlHdabJAw4q43GIp0CWkfEcnPHtMVhBFRz4SUvke0/PPFX8cOoFRTvmokwHpjLzt9cIopEpIHfnHEdSASs2sGpoX59EXssBIA+9SJH02blQIhUTaMiOBmSdsNli+XP7OR0aqFOIl0QMjNdxgRIVaJnj7M1dgdHfDn/8sf7/73WU7hIpAJt+ZurrSvAYSUTOzBsWsoAbUonxN+nf14x/2M7hvEKtLtNEMqcZTA7Dnu+DtBi2qGKOT+6FRITfyxbGHRH5dLVGeqPF0WQ5tLm0/eUBR4lUY2WSkJssr5Z57xD/K6YTvfvfkJuxLdz+rwjAUbIiiBoGISDnkc5XqMkLeY5JFvOWz4OqAc9K4XVVRENK23YGfwdEHYPnt0FKCG1gVZUNRJkT2FsksCPQBJcxy9UzvqllwUcjYdmaHkLXebhjfLzP68YOyftbVE7ePEbt5zhxC0SCutb7gYz4lYbaDxQVhLwSHptz4Kyfm3SSvKuKwNaHV2vB7VYpNpJs/H3bvFh8MwwgMX48EElNJySqSUPF9rgrDUXSbz7hU5Bf6/i5SmTULkt+31sUIzXIjsQIj7Jsop9swt4GBvQOoQRWT2YSnz5NW6kaHvc4+IVs47A9z4NEDOJucdJ7fWUAFRh4SUk1nSqVabWFaeUW3nb9Pqk8jAUlS6382yQts8FgdkLvtSg2i6NADuMeOiTl0Yxo1m3y/qzdqtZTuOsi2fmgIXnhB/i43gTHdx9m0BEa0AiO1X3U9Jgk88y+bX7CkVyqMNPDWJX26uzPnDqaT9DGbzfztb3IeV15pTOBv9WrYskUIjNe9Lvu2pRAvc+bI8Xq9IsU1YwYc2XSEZ775DC3LWrjsy5cVfvB5ICYh1TKRwLjrLvEuWrcuPo6XC5XQ79avl2z3jRvFPPlHP5IKNCPICwCTxcSrvvUq3DPcMQIiG1Lvhfv/up+gJ0jrilaGDgxhcdv5yEfcGSvfFEUq3669FsyhvvRSsapPyIuhHVKBERqVBDCQ5+3wOPi6M/oJJrWb6oe93xcfjPPuAnMe99dMaIzq0I3skDhpmZ8PbrkFvvIVePhh8auZMWPiNpPhlTI0BB/7mPz9uc/FvXHKgUroc1UCo0JgsVg4++yz4yv6n4GRl6HpLGhcnf5DvU+KlE3LuSIjlQuJFRj+XvlbzxCqomhMaDsdoRHxMdlzp2Ttp86IJuNBcNObpaTu9P+aaGp8iiNju+WCvQXYXXp5pD4hqBqsF4yMbeePTrSsDRDeA32bZBIT9kDrBTLhsqZMptpfCa0XxvWqcyFGYFTbLW/YGiHsxaKOFtfnqphaKGZMtnoaSngOSDTyLprA8Kc8SG39vBDJS/4V6hZPanB1uqDo+1wV0xZFt7m/D168DUZ3SYXoP94ar1DUYW+GC+6dlH62dCkEsDMadhEOeAkHJgapa2bWYKuz4Wh0sOHmDXgHMlf5uZpdrL93fRKJMX5inBd+8AK2Whud53fm9sCYdxPMuBhq5uc+gdqF8sqGHXeArQE6bwRHS2ltt+lmmZd6j4iXyT/fIYGkKC61NdNSey/9Y9nbrlTDWR11dRIsPnJEqjAuuqj472qfZ+dEswvvQPrrAKR99YC7jscfl4Dc8uUiZVgunAzjbDoCo2FeAzNPn0lNe/K8+ez3n03X410ly0f5/bA5mihtBIGhS/rccIM8cqcL0J5/frKkj952uozOK19Z+nFA3Adjy5bc2+rES6ZqJEWR99N5Kdjtcm13d0uF64wZYHVZCfvDWcfDUpFJQioSgZ/8RP4ud/VFJfU73XdmwQIhMJ59ViTE6gx6VGyY15DXdp4+z4R74Wj3KKpfZeTwCPv/uh+/ycXg0fVAevkvvfLtmSf6uFC5eWK8Q1MlgUgNSBJ17aIoiTEs7ytWuQf5T4DLKvOGhGfmCe02tk/IC3tz6bEqV6dUMwYGYfTleEVGmbBkCZx9Njz3HPzqV/ChD03cZt06aG6GgQxho2z9OxtUVUiznh4hzvr6pHJVJzLKgUrpc1UCo0KgaRojIyPU19dLNsPA89DzsGTvZCIw9EBaBnZzAlyd0HGt6NPFCIw0VGEVBWFC24E8TBx5QErYBl+AgX9MbKdyPwiqQSFQACz565WfKkjbbvlAz7wL9JV2ADECo1qBUSgy9jn9AT44LMvRXVIBEBoV6YcTj03sc45W8slKjKFagVE42q4E1Ytma2JkeLjwPlfFlEMDwuEQFouVYlpOlzl44QXJFII4qZEXEvs3yMPOeFTGzf8p8QubxODqdEHR97kqpi2KbvPQqPQvx0zJiIyEwVwT99hTffJ+aHTSCAwvbn4ZXs+XfhXAlEH02F5nJzAawDvgJRKOYLaaY9I3OsI+CeYFRgNJATf/kOhTOZucDAzEx6ZFmYom3HPlZQSCI5JkATBXqv5KbjuTHeqWT/RPVH00ugZYMn+UgW2taQO7xQZRsmHlSgmEbd+ensDIN3D7ite68V+4vuAKm0T/i3LiZBhn0xEYC16xgAWvmHijrp1Vy+pbMsQmCsBLL4le+4wZxmUM65I+H/lI8rk0Nkr//vWv4brr4E1vkvWaprFr1yg7dtSjKHD55cYcx5o1ssxHQkonXq6/fuJ7+uWUzUth/vw4gXHuueBslmf+YuSG8kX72nbstXbq58izkB5M/etfRc6qrk6IpHKiEvvdnDlCgO/dK3I+uapvjIZ+L7TYLVicFjRVQ1M1TBYTNW01aKqG77gXOwG8GQgMHYMnRqExel/Rfe+0MIwfEMLCZJW5wFl3JvtChcdh87+Jx+Tyj4sfVMKcYUK7jUUzB+qWlP4DKIpUYRx/VCSpykxggMhIPfecSDilIzCeeAJGsijwalrhXikbNkwc40DMusvpOVMpfa7qgVEhUFWVXbt2xV3dY8ang5k/VGgGt7UWFr0bZr1aWFGoEhgGYELbgbRNaFiC3SYLhDxxM1Rrg9wM9AfBckHft8kM5oklnqc60rZbPogRGKVWYFQlpIpFxj6nP8Dr/Q7APR8aVovxsBF9TicFbQ2l7edUwpzrYf5bUO1txfW5ycTWL8BLnxIJsiokaBn2oAUGCQ3uEv+XsEfWFwCdrHjqKVm2t4M7+7NTMhL7t7VBHpxMFunXtubJuadOQxR9n6ti2qLkNrfWS0VT85lgq5c+ZnHHAxiThPnzxQh30O/GY2uiaWH6lx60VoMq4z3jjHaPEvKEsLltsZfFmT5fzzco45ij0RGTj5o9G1xGTZnH9kPvU/F5QyKGo6nZNfNj84mS287slGdCvc0S2k5RRFoCJhaE5xMkLQa5jLzNZvjCF7LvQz8md6s74zWQeB0k4tFHZVlu+aiTYZztjNqnZPMkMRqJ8lFGxsLWr5cg+uOPw733yrKvL56Z/Pa3x79bVVXuvVcS0s48UzKljYBegbF3r8g75cJ110FLmuTzjo7cXgqpRt7OJhmrQ55QxoqlUrH8huVc9KmLaFzQGDODvuwyuOMOeV9V4aGHyvLVMVRqv9MJU51ANQIjR0Z4/gfPs/mu/LwdLE4LNreNSCSCyWzCWmPF2ejE4rRgyTN9vVUPEZqdch8xWcFzSLwtzC6oXSxJgvbmeMVh7UJoXAMX/AIuuFtMtVMSHia02+huWdZmMp8qEDpp4Zuc57g3vlHuUc8+S6yKU8fzz8PrXw/hsIxzOlGciJUrC5Mby+RfAnI/LciEvUBUSp+rEhiVCt3MLTiUeZtSJGgCVQmpSYFzlpRwa2HJaJvMB8Fw9Pqw1J7cTj6TDaMqMHRDY1s1k99QmJ0ymVIskiFidkj1U7Y+1/1nkXrTK9OyoSohdXJjdBcMZ3EdPVVgrZN+FAlAaBglNIxN7Ze5Q3BI1qeUhWeDTmD4orxH0f4X+oOUpkYN1xumJLhaRRUnNWzNIgMxhbBY4lJOu3bl3t5sNeNodIAGwweHGe8dz/kZncBwNjlzG3iHxuHoH0TiN1/s+ia8/F8wvm/ie4MvynISMkR1XHVVesPZfIKkxSAfI289kJyaNVpfX9oxHT4swSSzGS65pLh9nErQA2v9/fH7tA4tIiU7I4dH2Pi1jXQ/a0xg0EgD71Tokj433SRLsxn+4z/gta+FQECCil1d8OSTCn/4g0RqjazUmTlTKks0TSqQcmHrVvntHQ548ME48dLVlbsPJBp5g0hIWRwSpS5nFQZkDqZ6vQWYQZ9kKAeBEfKE2PvnvRx68lBBnwuOiil2orxeTQ20tWUODSmKEJoTlYKiHzDboX5pkkThBBRSpakTGEZUYAA0nwPn/jg/eX0DMHNmXHru3/9d5JyeeAJ27oRXvxrGx6Wy64knkonVX/5SxqXt24XoyAeqKpUXmTx+QPxLKozTMxxVCalKRawCw2ACI+wB79F4gKZagVFeKGYpofMekd99MgMteoZ/NdBqLOqWwtLb4p4yxWL1l0U/UqnyyIZDSbi1RfxgzpHqffxvkinZdHbuMVExiylYVUIqf0RCkh0fDk71kWRHJCxm41CVdnO0ihxTdJ6hhkOMPvEuGmpsKKs+JzIqBXhO6A/YOko28A6L/nLeEppVVFHFtMPSpfDyy0JgXHVVjo0VqO2oxeqySiXG4VEc9Q4s9syPur6hOIGR0//Cdwz2/Vg0ulvyFOx3tknGqu9E8npNg6GX5O+mM/LbV77QVPB0SZVc/UpIEf1LNJzt6ZFquHXrjK280KFXYGQK4G7bBv/3f/L3o49KlupPfwp33y3Z8MWSF6oKd94pfy9ZIgG7KrKjoUEqj7xekSNatAg8vR4e/PCDoMENv7qBrse7OPqPo2gRjdnnzM65z1ww0sA7H5jNEji88EIhDJYsgVDIDMhz8k9+IkFbo4i8NWvgb3+T7zrnnOzb/uEPsrzqKnjVqwr7ntQKDEVRcDY7GesewzvgpXaWsfPZsD9MyBfCWuvgIx9R8jODnnrf30nDZZfJue/cKX0plTAuBrWzpQ19Az757Z35JRhY3VZsNTYc9XGyQa/Gu+WDE7dPqsZLDU+YrFGSQQGTDSKe3Aeg+uVepMc1U+Hvl+dDxSReGkbA4pLXJGLJEiEef/YzeYFc86oq97Lf/U78akAIVR1//jP8/OdCrt5/f+7v2bgxe5Wc7l+ycWPy95xsqEbOKgSKouB0OuN6YuWQkAI4sgFe/Hj8/2oFRsmY0HapcM4UtlqLTNSlLSeqJtFZkbPdMsHRAm2vgPplpR+E2S4TgioKQl5t55wVJTLyuM3pRqX+ntzbrv4SXPQb0disIj/0PQ3/fA+m/T8ors9NFsIJk/FcpNepAEdrrCRcqTuNYM1KFKtbHkhqFxaUYVVfLxrUOkymEjOEdAkrS7WdsqHo+1wV0xaGtXloGEZ2gOegEYdVFJYulWU+FRgACgp1nXXY6mxoEY3hQ8NoZE5VjFVgNMYrMDISGIF+WdoK0JhxtMnSfzx5vfdoVBLPKp4V+vEb0XaKSSSrwr6MUn/pstPLgWXRaXJfn7xS8YlPSMDlxhvFI+PSS+FTn5L3/v73iZUA+UCXs/nP/5T/X35Z/i9nJvjJMM7qfiMQD5BZXVZCnhAhbwg1qHLoCcn+nn95Hib2OXD8OBw6JN87mZ6wNTXwgQ/I36FQ8nv9/cZWDegyUvn4YPz+97IsxjNB9w/RCQxI8MEYNL4Co+fFHn731t/xs7c+mncwtRyo1H7X2AhnnSV/6zJ2pcJea495O411j+X9OVezi9blrVKdmICrroKvfW3i9jmr8Ux2IS/yQc9fYdOboevnSauT2k33v3DPy17RUSyylSoYhA0bxMMmFfozzq23Qm0GDvETn5Dlb39LbA6SDT15hCkK2a5QVEqfqxIYFQKz2cyaNWsw67NImy4hNZy58xUSoPb3SYaxhgRpwh6YcbEwn2P75f0qisKEtpsAE9SvgLrTkgd9NSiZWWP7079KbZMqgZEVudutikpFXm3n6pDMxnwqnhxRAsN3LL8DUJSqLFshiBLyptBwZfe5cFRyxOIW76AqYjCbzbSddgkKishsFYgNG6SMWsddd5USVNIgEq3mMdmzb3qKo3qfO/VgWJtrWvR5IbcUU7lQKIEBQmI0zGsABQLDgZhRdzokmnjnJjCivmf2QgiMaJKYP6UCYyiqY16/Qio6ozCm7ZR49mk4jwzZMsLtjssHpspI/fWvopFvtcZ180EyWWfPFpmfp58u7Psyydl0d5dXzuZkGWcnEBhuK4pZ5rpHNh3B2+/F6rYy66wSK9CBf/5TlitXZg7ulQOqCl/9avr39HCLURIsOoGxZUv27Y4ehRdekMeK17ym8O/RKzAOHYofd+vyVmadPStJOsgoePpkXPGRn6JEuYKpldzvrrxSlkbKSNV1SDxn9GjhXm8KE59ZdVL5ssvykCwLj0vyUpaEgAlwzhKlib6/S8wriqR2az4XzvoOLHpvYSeUC4EB2P5VeP7WspIYuSSdFEV8KTKNJytXSp/XNPjv/879fe3t+R1XvtsVikrpc1UCo0IQiUTo7e0lEonICr0CIxLO/PDQfBa0nBvX5M8Efx9suhmevhFevgMGXxDzuEP3wd/fIOs33VwlMYrEhLZLB8WSbNYcHICx3fDPd8vvn+5VaptYa0Wj0DWn+H2cxMir3TJheAf0/E0IwGKgBmDbV2DXt6SPV1EQSmq7dNArMHxlmmWf6ogS8lpg0Nh2Mxox2b2q3kQqIpEIg+EZks1cIIGhB5VSsx2LDypp4Jwtcm/VCrasMHysrKLiUXKbq7544FsLy7gYHs+YzV9OFEpghH1hgp4gmqrhbHYSCUfw9HoI+9LPs8549xlc+KkLaV7aktsDI6gTGDmeuRLhjFZg+FIqMBSLVGc3JVdyGtZ2ilXaLjAo/09B2+lIJyOlqvBv/yZ/33prsqSgohSnIZ8tkGR0YDoVJ8s4m0pgKIoSC4Dv+r10wjkXzcFsKz14VU7/i2woRIKlVCRWYGSLof7pT7I87zzR0y8UHR3iGRQKxcmC1bes5pLPX0L7WuMjmd4+kVpt7sxPpqdcwdRK7neJY5hR8fMYgdGdm8AIeUOMHB7BP+on6AnGXvq9UNOk0gLgQx/KUY2n+sSnYuhF8YrUE6Fz3VfqV0ildtgLA8/GVie1m8kMNfOhYUU+P0H+sNSKz5QnKuFeJhgxnnzyk7K8+26pTMuGdevEvyQTdP+Sdeuy76dYVEqfq3pgVAgikQgHDhygqakJk8kEJguc9W2wNmTWeM6XrQyNRkuV7bIv/wlAiWbmm2QACgzIdoWY7lQBpGm7RGQa3H0nRFIqMCAPMq5ZJPGJRrTJjIvlVUVaZG23XDhwl9zMV9wuklKFIjQqN3OTBZZ8pPDPn+Ioqs9lm2jlS2D4jsPO/5asyuX/lv8Bn+qIEvJaaJyu/buL63OTgVgFxinuf5EGkUiEfb0mztJA8fWIRIkttw9MrqBSwRrJej/WPWj0QOsUBugqGSXd56qYlii6za11Ul0QGIBIQNZpEXn5+6RSwN48qVW9S6KensePw/Cw6PSng73OjqvZhXfASzggARqzzYyrxYXVbSUcCONqdk3IRm5a1ETToiaOH4exMZG20ysGJkCXkCqEwIhVYByPD3gAs6+WVyQ5mm5c26mSHBMahlD0GXKS207HihXwxz8mV2D87GcS1G1ogM+m8Vm94goJ5hRCYBiqDe7vi1exp0OK/9PJMs52dsry6FHJsg+MBtA0jaAnyIktUkXUuLCRwf2D2OvsuFuLl3CcbP8LHZMpwbJsmRALw8Pym+q/byp0/4ti5KNA5k5z5sCBA7B7swdXIJBxW30MDIxm3yZb2+oVGKdf4KajQ5JR0s3xdFmycgZTK7XfnX8+OJ1yHb38cpzILQW6D0a2Cgz9Xjh8aBjPCQ8mq4n6OclzdVezi5f32Tl8WCTVMnqu6PcVfx+oHim+iAQglJAQkO2+oigw41I4fD+ceBxmXARMUruFRuTZfmQnHPsLtF2R/H4BHn7ZYMR4ctFFcr384x8iRZVYkZiKSEQqG9Mhyb+kTAUSldLnqgRGJaMm0yy6SJidIo2RaHCr60dHMt/IqigC6R4EE2FrksFVMcvSE4SahWBOyGaotknlwtEqBEagyAqMcILBelWKyBjk6nOQeaKlG7L7T8hDvynDrTE4KO2e7cG2iomw1MhvqoYwR/LXbp10aKqQLZnM5k5xREwukWaL+CQLKw8Cw7CgUin9u4oqqsgNRytccG/y/e3l/4CxfbDwXWJcbdBDf76oq4NZs+DYMdi9O3O2trvVzfp71xcdlNOrL+bMiRttTkApElJhrxDk1hRy3CipwtS28/fDltvlvnvmd6RSbZLbTsfKlbLUKzA8njhp8dnPQnOan1PPXt68WXwJWvLgjAwLTOuqBXp7p4O9WX7vkyzpT6/A6NnnYcPNG/AOeBk7NhbL2jZZTDz2mccACYKuv3d9USRGOAzPPSd/TzaBMZkSLHa7VJFt3y6EXToCY3w87pNQLIEBIiN1/ICH52/fwD6bN7Zei2gopvhzpr02SmCMZR4rc7WtXoFR0+bi29+WStpUTEYwtZLhcAhx89e/ChFrBIGhV2D4hzPLIur3wqfveJrjLx3ntFefxtL1S5O2sdfZ+fzXpW2vuUaIlvQnEb2vDG2BHV+Te8gZ/5O8Ta77yszLhMAYekHuT4lzdO8ROPZ7aFgNbZdn3keh0Mfwsf0yjo/uhH0/TN7GoDHciPFEUaQK4/Wvh+9/H26/XeY+6fC1r8H+/UJi1NUl3886OqS/ZfQvOYlQJTCmKyJhKQ822YsPgHqPQp0BRsRVTES6B8FEeA7BC7eJYZGvR8z2RnZA7ZJqAGY6QDdxLJbA0K+Laqa3ccjV5yDzRMvWKNmlahACffGKjFQER+L7qSJ/KIr8xr5eLGoFExgt58qrioyIrPkPTPb8iVfDgkqJ/dvfK/Mfe3OyB8YUBeiqqOKkgaM1uQ81rZU5qhaC2oWZP1dGLFsmBMauXdnlZtyt7owBN/+In71/2cvKN66MBfP8I34ObzyMe6abvXtnA1n8LyCBwCigAsNsh8W3yv1PH6v8/dH/DY7qJbZdzQKRrwqNSaLUFLUdxAN3O3YIYf2Nb0h7zpsHH/xg+s+0tQnxsX07PPYYvOENub/HsMB0ompBOg81vUJ+vCs+31TD2ENHYbwRzNHQyjS8H+kERu/RAF6bF4vdgs1tIxISuRDXTBeOBgdhXxjvgJfAaKAoAmPHDiGy6uriRu+ThXXrmNSqgdWr5TresiW9v8Vf/wrBoMiolfJbzJsHLxLAP+DFMs+ChsbQ/iFMFhMtS2XMCvvCePqlesJR58DinBgGzKdtdQLD3epm/bkiRfSmNyXLhJ5KwdRMuOKKOIHxEQPEFtrWtLH+3vUxEioTLHYLQweGsLltrHjTCuo7k5ONEuWjbrwxx5c6WoGIJDw3rCr8XuLulM+M7Yfep6XyMAplZAccf1TGUyMJDH0MtzVIknBEjVZtR59bDFSeMWo8ee1rpf/v3Ak//GFcYjERzz8PX/mK/P2//yttt3GjPD+1t8t3nCpkYZXAqBAoikJ9fX2yq/vA8+JVUb8KWs5J/sDIy7DlM1AzD876bmFf5polDySuDLWMVRSEtG0HEx8EU2GySGZyw0oY3y/BUf9x44Kjmz8hwdhl/wb1y43Z50mEjO2WD/QH2KIJDL0Co0pgFIOi+1zmHcKZ3xZiypLFlC4UJTBsDYV/x6kOWxOKv5cGV6S4PlfFlCLW56w1BSVNGJrtqPfv7j/KQ8/8t8DcPCJbpzBKus9VMS1haJu7o+6w4wdL31eRWLpUMpQLMfJORESN8NeP/xXPcQ/2OjuLXyMmF6NHRnnhhy9QM6uGvU1CYGT0vwCZSwf6wD23sAOYlaLPseNr4OuGFZ+GxjVJbxnWdooiCWrBwSmv5l66VA5naAi+/nXJIAX5O2O1CxL8275dgn/5EBh6IClTxV/BgWldtSAdwuPw4kdBlUCuWdNYGghg3pSQVDgNqzR0AuN4DzAXLE4LjkYHilnB3eqWv6Pnp0u1FQPdwPucc0S2bTJhNhOrGlCU5KBjOaoG1qwRg+StW9O/r8tHXXttaQX5upF3ICjtZraaURQFLaKJ9I8CGhpqUMVsM2NxCjkV8odAk8/oRs/Z2lYNqrEKAFera8Kxf/ObcPrpkxNMrfT5jV5J9sQTQu5Y87RsU9X0gWmzzZyX/8zhpw+jqRoNCxomkBcAzz4Lhw9LFv+rX53HAXkOyrJmfn4nkIqZl0UJjMdh9tXxdhvbJO/XLSluv7lgbQBzL0RCyEWeIMlv0H3RqPHEZBLS4p3vlD704Q8n3x99PnjLW+TaeOMbhTCEPOQQDUal9LnKEow7hWE2m1m2bFmyq/vwNjjyOxhOc9fTsz7M+RkoJcHZAY1nZPbWqKIgpG27QqBYwDVXCA1zdla9IAT6JNNLqfKU6VBSu5VMYET7bzWTvyiU3OfSwdWRnbyAOIFRbbfCMfMylLlvZN7S84trN3+fTIAzvfx9xh9zFTFM6HOalpczoR5UyjTXLcpwzi9a3DF5lioyoixjZRUVDUPbvGa+ZPIXUnVgMAo18k6FyWxi2XWS2rz1Z1vxDYpfjr50NjnZs0e2zVqBUXcatF6QOaidD0JjML5PJKVcHRPeNrTtVn4WzvymJElNIf7yl3jw5tOfBr8fbDbxBsiGQo28zWYJ/KRD0YFp1Rc3b9cRGomuH5QqDWsDiq0RZ20biq1RgmYmezzDdxpBJzAGBsX6BqC2vZaWJS04m5yGBa2mysBbx/r1koE+e3by+o4OWW9k1UCikXcqwuG4gXcp8lEQJzCC0bisyWqShHMN+l7uo29HH4N7BvEc9yR9buTwCL3bevH0Jq/PhEg4wpLXL2HOxXOw1dgA8d4IBkWK6EMfymIGbTAqfX6zZo3I342PC2mQDzZskGqayy6Dm2+W5bx5sj5fdD3eBcD8y9MTDvffL8us8lGJGJf9FU1gzLgYFrwVln0CiLebaTyq3VguAgPiz+vB4bJ9hVHjyS23yD56euCee5Lf+/SnZQ7U3g533mnMcReDSulz1chmhSASiXDs2DFmzZoVN0WxN8kyODjxA/qkKA8N6rRQKnOwn45I23aFwuyAxrXEytuMQDVInhUltZtRFRhVCamiYEifKwYxAqPIcfdUxuzXxNvNFSms3SZLk/rA3aKV2nFdVUoqBUl9bve3YOhFWP2VnA80Zcl29PfK0jGjkFM4JTFlY2UVUwZD27xuCZz7Y2MOrEjoBMbOncXvY9GrFnHgkQMM7h3kxf99kQs/cSG+oSiB0eiMeWBkJTCKhb8fRneBxSWBb00TWY00XhqGtl0FZCVv2CBjfyrXHQyK/EW24M4llwjJ0dUlAdKM5uoJmBnltFPvNUXJ2ag+8B4Wb6xaS3Tep4HniLynKGDzgK0RzVKDX7XicLpjWexTXflSDJqbRbsfPwRDkE9ssxhMlYF3Itavl8qBJ5+M8PLLQyxf3sgll5gMD7zrBMbu3ZJFnRgw/sc/YGAAGhvhwgtL+57ECgyQTOmatpoYUQugmBUiOjMVhRpQAfD2eqmZkTux1eqycsa7zkha9/LLsly6dHIlbCp9fmMywSteAb/6lRCxudo403jZ3S3rf/MbOL3+AIc3HmbOujkseMXEQXGsZ4yBXQOgwNx1E6sFE+Wj8qlsQ9PiBIZ7Xh4fSANbI8yJa1VFIhF6juxllveIjJe12UofS4StAdRA7gTFEqGPJ6VIOtls8K//Ch//OPznf0qfPnECjh+X+xfAT36S3jdqslApfa7yevspikgkwtGjR4lEEm4supFocGjiB2Ia+gUEp1UfhD0TX6ov92eryIi0bZcvktrEa1ybqAHR84cqgZEBJbVbjMAYiKcpFQI1mulSlZAqCiW1XSaMH4Q934Oun2feRvfAqEpIFYWi2y1Rk9raMPFlVLbjeBcM7xB5iCqSkNR2wSHpC6P5pUQbmu0YUePEcbUCIyfKMlZWUdE42dpcJzD270/WWC8EiknhnA+eQzgYZt9D+9j9+9307+on6AmihiL07xmkkUE6mzNkIXuOwNE/wFAGHZhsGH5JzNCP/g4GN8u6xrVpNy1L26lBGTcnGaoquu/ZCvVuu022S4eaGjj/fPk73yqMX/xClm99Kzz+uEj3PP64kCCFkRd+qeyMhMHsjlfdaCpYa0AxAZrMFXw9MLYX88gWuU6mMXSZLSi+r+XC8HCcjJyqCgwdZjNcfHGEs87ay8UXR8oSfG9vl4BjJBIP9OvQ5aNe85rcFUm5oBMYoVD8sbS+s562NW2xV8vSFmrbk587W5a1gAIhb4iQr7hG37FDlssnWa16Otzr8q0kyzZe6utuuw1Gjo7R80IPA7vTJ3T17+pHMSm0rW3D2TQxaP/cc3DoUAHyUWiw5EMw941pqwaLQSQSYeDgM3Jezvbik7Hzga1ZZNRt5a8iNZul+uimm4qvQnrve8Hlgj17hPy6+Wb46EflvSuvzLfNyodK6XPVCoxKhk2vwEhDYIQLyK631kmmT2Agc0aIvbka6J5MZGsT1S8VMs624ttEz/A3WdKb0FVRGmyNsPQ2aUNNK7xwZuG7YN5bgMqddJ1yCI3CsQfBNVu09dNBUcTsuzpWFo5ICHzHsYVPFL+PbJrURmQ76sRFVV4xO+qWwNBLMLobZuU3mzYiOwkQOQ8tIvc2PcmjiiqqKC80TcZws23Sv3r2bAm2eDxCYuiERqGw1doYPTLKWPcYv3/X7zHbzIQ8Ifr3DfO6gEQWX/yMiwX3rZ9oXjuyA/b9GJrPgcbVhX2xTrT6joO3W/7OQGAYjm1fhqHNsPqr0LBicr4zio0bM/tRgFxSR47Idpl0vK+4Qt7/298ksJMNwSD8+tfy91veUoI2+MjL4Dsmz2HWRskO1lULFItkIZudYmxvdssyNCaRY6ON2acAHR0wsE9+z3JAl9JZuBBap489SNFQFJESeuwxkZE688z4ezqBUap8FEj1kd0GBMEfAHue+XFmixlHgwP/kB9vvzdt0DsRvkEfKOBoiPuh6MTMiskdYqYFdALjmWdgbAxqM7RLvuPlwUF5/hw9mj5ha/5l82lf205wPH0H1sfIvOWjFBO0XiivUtG3CXr+Ch034gwdBjNQV8bqi2mIv/0NvN707z3yiFTpGClxN11RJTAqGbEKjDQSUnomcD6BNEerSGtky0611k0ro7Fpj0xt0vVz6H1KTP8WvKP4NkkkuCqgjPykg8kMba8obR9TEAioIgucURdh/wnJVkz3ILrs47LMQ/u/ihQMvYRp65do97iB10z10aRHOEr8ViujsqMuGkHMswJDh56dVBJi/hczqve2KqqYDBx/BPb/BJrPlcSNSYaiCGnxwguiAV0sgREYDcRMULWIhhpQMVlMaDYHPhzUOsL4B70ERgMTCQxdvtBRYBanvw/C0UrrsX2yzmQROamx/eV/9lJMUkUwtnfSCYyentK3u+IK+MIXJPirqtkJ7wcfFKPw9vYc9xl/X+bn4aEtsPs7MsezuCTjWPUnb6NXyCtWeU63uNE0DV/kGBZHezyfKRKWuWS274P4c3yFPKN3dMAWhI8J+9KbOWdanw90A++prr6YTKxeLdfwli3xdbt3S5a11QpXXVX6d8SqZw6AZyScNjid2G6Jf9vcNrx9XjwnPFic2UODW3+xlQN/PcCqW1ax8k3irzNVFRjTAfPmCVm3fz88+aQQB+mQ73g5qtVhA8a6xzJu42hw4GhwTFifKB91440T3i4v/H3Q/SfofwZF03AEj4HNK0oy5boXJqqZaKqMsbaGiWN6hUCvwsmG226TZLAKtX2ZNFQJjAqByWSitbU1WU9MJzDCPuls5oTBqFB/A0drlaAoE9K2XT5I1ybtr4TBFyQDKI0+bt6ISYxVA3GZUHS7VTHlKEvb2VvAZJUs00A/OLPI01QDp4XD1oiiQI0tZEy7RYKAIm1mFKreNBmR1Od0wz1vt/xmk0n46P4X9qr/RT6o3udOPRje5mY3hMbBc9CY/RWBRAKjFCgmhfo59ahBFf+wHzWoElRshLBhcwFkCMzqsnW2AublMe+mfpEn1BMfLE7Y9Gb5O8W7yfC2q10M/f8UAmOS0d5e+nbnnCMZy4OD8NJLydnrqdDlo266KUtwJ5uflhoE31HALB6U1obMpIKtIT5fQKaEFkdDgsl1RKSltn8JvEeyy1Jaa0EjnkCRDkZ4fOWJjg4IYCdgdhEOeAkH0vcJV7MLe5097/2qqmSZ62bE55xjxNGWjsm4R6Yz8tarLy67DOoMKupun2fHe8BFYNyLfzh9u7lbhJwNjAVibatpGlpEI+QNERgOUNdRl7FtvX2SHu5qdQHSrvq4PNkVGNNlfnPFFUJgPPJIZgIj3/GyY3kdvY9LJUzQE8TmjidDhnwhrM7Mz0S6fJTLVYAUUf8/hXSvW5q5Aj4X9HHXcwR8PZiGNjPD2o6CFfZ+D/b9wNgxLp3KieegEMquWVJBV4HKM0ZULZYbldLnqgRGhcBkMrFw4cLklWYnmO3iZxAcimcIAzSsFELDOWtyD7SKCUjbdsWi+WzRVw0MwPA2aFxT3H4UC9QvBUeed8RTECW323iXZNS558QDevli93clG2DezVUj2iJgaJ/ToSgi2+Y5Av6e7ARGFflDzz4MDqOEvdSa/TC+P6ohTXFZN16ZBONsA9ccY45T0+JBBmtVQioVSX3OVCtSa95ukZFqPmvyDqRmoUi82cuvZ3syoCxjZRUVDcPbvGaeLD2HM1cnlhl61UWpBAaAvd6OzW0T/4uAStcxCfg4XVk+pBMYhSQWxbybHGB2SSDFUgv2RgmOq764d1MCgWFo29VGXcmngMBYt06C4d3d6YtW9Yzxdesy78NikQDvH/4gwb9MBMboKPzxj/L3LbdkOahEP61UeV0roHqlnc6/D5xZ5iWBAXjufbEMXwVw2xAvQxCVBC0MYwdgbKc8v6d7HlN9YvIOMhdKJ/mb5jopJzo6wIubPavW87HvZJbmtNfZJ1YqZcCGDZJZnBic+9rXoLNz6uVQJuMeuSb6KL91a1R1WDFWPkpH51I3P31sPbddE+B9H0u/jU5MBEaT23bLz7Zw9B9HWX7jcpa+fmnGtvX0iU+Q/n5XF/j9Yv6u+3BMFqbL/OaKK+CHP8zug7FunXilDKS3toiNl5deaeWP9znxDfoY6x6jebHckyLhCH98zx+pn1PPBR+/IK0U2P33y/K1rxUSIy903S3Pxau/CE1ZGORs0MddW6M8C2oh7HYnWKPeF0aPcelUTnSFkxkXwfy3VaTyjBFVi+VGpfS5KoFRIYhEInR1dTF//vw4q6UocMb/yIQ3Vet57hsn/yCrSIu0bVcsTFZovQiOPQQnHiuewGhYCWv/q7RjOclRcrudeAyO/A46ryucwOj7u0gKzJnsGs6TA4b2uUQ42mWi5j0Gjacnvxf2wNbPy4Rr5WfjAfgqMiMx21HT0MYPEIlEMG28HsUUnX4UmnUTHo8TDYnlwaVC9SZkyFYJjFRM6HN1S6MExq5JJjDmxQOqVeRE2cbKKioWhre5o00SplS/eAO4O0vfZ4EwksDQYXPbwA2e/fK/O5seeDAaVSqGODWVltdSAACmu0lEQVQ75Z4SUiWQnbiPFO8mw9uudpEsfT1SRTOJ5LzZDN/+NtxwgzzOJpIYeqHCt76VWwrjiiviBMYnP5l+mw0bJIi6dCmszcdeJMlPSyNmZOeeB6EhIS9qswRpUjJ8NU0jGAxis9mkCkNRoGGN3KtGX5bKQS0C7vkTq0b1IFu5Pb7yRGe0ex/qc9O0sMiM6wRs2CDXQCqJ1dcn63/zm6klMSbjHrl8OZhMEpzu6RHZqE2b5D0jCYz584V86hp205QjxphKUJz74XO54OMXYK/NXFWjadqECgzd/2Lp0smXtZku85vLL5chYceOuAdcKk6cgECGbp46XtbOrsU36GO0ezRGYPS82ENgJMDo0VHs9RPbUNPiBEbe8lFqELxR1tFtADtldoJzJprvOCHfMFZnO4o+9ho9xqWqnMy5QRRORvdI4mm+1fv5SAAaRIQYUbVYblRKn6vc3n6KIRKJ0NfXN9HV3T1XSlmrkiUVi4xtVyxmXi7Lvk0Vq9N3MqDkdtMfQvXMvLy/OCzBcKi48sXpAsP7nI6YD0aa9IbgsEx8RrZXyYt8kZjtaGsEkwNVM6GZ3ZKBarLHs27ygeqToHlwSLIbg2PSl4wgMsK+mJ61obJUJwkm9LmGlVC/HOyVlcFURTLKNlZWUbEwvM0VRZ5FYMpkpBIJDKMtqMajfHj2CowSCAyQanrIGaQxvO2stVKpCDC+z5h9FoD16yVAPXt28vqOjvwD17oJ7saN4Mtwq9flo9785gIfl/3Ho9UpiRdVHjvQM3wvuh8uuh/1gl/ycvOXUC/4ZWwd6+6HFZ+NVu2YZA45si3lmSEi85ngsJBM3iPymsJnv44OWWaTMskXuqZ7uj6rr7vtNtluqjAZ90iHA5ZE89y2bIE//xkiESHbOg3kg+fNk2VXV+GfdTW7spIXAMFxqVrTt4ep9b+YLvObpqZ49dijj058X1Vl7BofFxIq13hZ11GH1W1N8jHpelwafe7FczGZJz6jPv98EfJR3sPSUa1pEqmLhX4PDQ6ll/IrF+pXyFgc9sDgi/l9Rk/Ce/rGzK9NN8t2BkCvWsx0D1MUGS+yVS2WG5XS56pRmOkITZNgS9VI9uRE3VJ54FD90P/MVB9NFZlQLIGhZ48rSvF6klWUB7okX3Bo4nsx36H6yTuekwV6ZqHFBYpZJEgs7vRyCemgZztGApIdGQnLS/VKZmwkULqeqaMFLvgZXHhf8fs4ldB2Baz9D5hlgPtkIRjaKlVSkSmMeFRRxamGmmj25RQRGIsWSQbzyIhkqhoFTQNvVPXHlel2FPbFpYGK9aazt4qc01RI302hjBRI0O3gQXj8cbj3Xll2deWfdb90KcyaJdnJf//7xPePHYsHBW++uYAD00JCFgSH08/5csERrdKoXQg1CwlYO0TiUF/naJV5vq1B2sDijiYwJfhhaJp4aYRHIdArJIavJ0o2Tc0zvk5gHD8OwWBp+ypE0/1kR6KMVDnkoyAu4XTwYGn78Q+nJ9D06gt7vR2zTcot9AqMyfa/mG7Qidh0MlJf/7qMi243PPSQEA3f/ra819gIBw4kj5dnvOcMrr/vek67Wsb2oCfIsWePATD/8vSVEr/+tSyvuaYA+Sj9fl8z37hEanO+X24wFAVmRCP/vU/m95nEJDxrw8RXoUl4OaBXLeqHm3r4kF/V4qmAKoFR6RjaAvv+V3TbdAT64Ok3wN/fWCUxTkYoCsy8TP7OlyVOxa5vwTPvgBOPG3ZYVaSgWAIjZhRcU83krzTMvAwu+iUs+/jE94LDsqwSGMUjWtmgRDIYpWaCnu14/t3QsFp0WFvOleWar0u2o1Hmb9Vqx8pFRIWtn4PnPgChkak+miqqOHXgnifL8SJSew1Aor56qTJSYV+YoCdI0BNkbCCIWQtiJ4g5k4G3ySpyvis/I1JaxcDijlYh5m96bBiazhTdb6P8ooqA2SymozfdJMtCAjCKAldeKX+nC/798pfyKHzBBQVq8IdG5YMWF9iaCvhgETA7pGLRPS95Dqkokt1srQP7DKkCVixCmE1mdnICWlrAZpOfplSt9emg6T5Z0I28n30WHn5Y/i4XgXH8eOZqpWwI+8P87RN/4/fv+D3+kYkkRsz/YkY8+U4nMKaiAmM6IZHASAzdbdoEX/iC/H3nnbB4sYyP//Ivct8bGhID8ESYrWaRqoviyKYjqEGVus46GuY3TPjuouSjAMYPytII+ajE46lfRcDajlbucTcVMy6R5cA/C6tyiyXhpbzyTcIrAEZULZ4KqEbPKgQmk4mOjo6JemJje+Do75MD2Yl6mdVgy5QjY9uVgrZXwul3wNJ/Le7zgf64MVwVaVFyu8UIjAHRts0XsUz+2uK+t4ry9DkASxYNYj1gWiUwioe1Tqpcirn2Ha0QCUr2Tu0i8QqyuAEtnu1YRdmQsc+FPRAYnJyDCEbHWpPFuHL2kxxlGyurqFiUpc1rF0PTWgnCThGWLZNlsQSGvc6Oq9lFOBDGP+zHP+xntM+PEz/1Dj9qICwyKnUpJIPJAnWLoeW80k4gD5TneeIVsPyTQvpPU2TLXtblo7Kad6dD0Liq2vzaTQHHjJR7l0n+tzYIeeHqjEuZBoyRJSkUJlM8eFaqjNR00HSfrHukTmD87ndS9dXSEl9nFBoboS5aiFxMFYbFYUENqkTCEQ4/fXjC++4Zbpa8fglz1gkZGonAzp3y3lRUYEyn+c2FFwoh0d0Nu3fLuuFhqRpTVVm+9a3x7e12OC96y3nqqQm7S8LBJw4CMO+yeUnEhqrCE0/Av/+7VHU4nXD11QUctCeasGCw75xicWBxz0o61klBzUJwzYJISPz7CoHqn7TEqVKrFsuJSulzVRPvCoF+QUyAPtFJLG+NBUCr+vmVgIxtVwocLfIqFtVrJCdKbjdbo1RQaBHJzrfnmUmgt42lSmAUi7L0uVzQ281WJTCKheJsx1ZKwspINNWrfrn0v4HnwHvIkGOjdyMc+zM0nSVmb1UkIW2fO/p72P8TkZNa8uHyH4S/V5aOGdXkjTwxJWNlFVOKsrR53Wmw+svG7rNALF4sy9//XmSF1q0rLJPf3epm/b3rCYzGfSh+/jP49Zfhiovg8z8QkiPV3LZkZPJoSrO+2l/T4xWvkOWLL4oRcnNUyWvXLllnscAb3lDADlVflBAPS4WN7ktXpJ9WznbL5xrQ/7bWgKMN7I3GeXwViI4OCZiVSmDomu6Z9qMo8v5UarpPVp/r7palLh3f3y8VE9/+tnGBSUWRfW7ZIu2nk76FYN5l8xjaP8TBxw+y+DWLk95rnN9I47viBNzBg1LpYbcXWP1kEKbTeOlwCInx6KMiGfW2t8H3vifEwoIF8P3vT5zWXnyxEBBPPgnveU/ye0//x9MMHRji3A+dS+82mRvPu3Re7P0NG8R/JrXvPfRQntebpsUrLvUKzFIRHcsUwGkhLs04WWOcosDSj6YhkvOAp0sUNNxzZHwuM/SqxUpDpfS5yqcsU7Br1y7sdjtf+tKXYut6enp4zWtew5o1a1i1ahU/+MEPpvAIi4OqquzcuRM11ckqRmAkZDhWtdgrChnbzigUKrcCENZliqoERiaU3G6KKU5aFCIjpevfVsmlolHWPnfoV7D1CzC6O3l9aFiW1XG3aGhojI2PoxWr7awTGHXLoHEtdF4HzecYc3C+YzC8A3zHjdnfSYa0fc7RLg85hWYyFQt/VPzePmNyvu8kQNnnJ1VUHE7GNt+wAe66S/5+6CG47DIxrN2wobD9uFvdNC1sir0ODDcxRBNzT5f/05IXQ1uErB0t0EMiybtpeOIrjXdT2dpO08DbPW0rs9vbJcNb0+Cxx+Lr9eqLq66SjPac0NskPCbBM02VZ6wsbZIPMrZbPteAo0UqumPbjIpPWGi0pGMqBbqxdKkEhtkMH/hA+vcqRdN9MsbLDRvgfe+buL67G264ofBxLBtKMfIGMYFGgYHdA4wdG8u6rW7gvWSJkIiTjel0r9uwQYy0Ae6+Gy6/XCSBTCa477545UwiLokqHj35ZFx2ytPnYXD/IL07ehncK8sVb1hB54WdBEYDePo8bNgg11Vq//X5Crze1nxVlEBKlR9MGQe14BC+seNowaHJH+PqlhROXgSHhLxQTKd89Xel9LlpV4HxkY98hMsvv5xQKBRbd/3113Prrbdyyy23MDY2xpVXXsmcOXO4uqA6qamFpmmMjIygpXpa6Ppw1QqMikXGtjMC+/8Pjj8Ka/49/xI+Tatmi+cBQ9rttA+AyQauAtjo9itFhzFSojveKYyy9rmRnSLZ13KBTHQSYbZXCYxiEM2u0TSNSGAEzTQipcMmW/77iKgiqQhRLelOaDSw/l73pqlKu6VF2j6n9w/PEQiNS+ZoOZFYgVFFXijrWFlFRaKsbR4ak7lLsWbWRUAPxqSejh78K0UXem+UkzjttCwb9f0djj0Ic98klSj5Qvduymbwaa1Lkj8sW9vt/R4ce0jOYX6hWkuVgSuvlIDpI4+IjrumFSEfpbfJkQfg0C+hfplk5CYipU3yQcZ2y/cagPTbRFTwHobGNZMqk6kn2JZKYHg8ceLR5RLppMTv+Na3pl4Wpdz3SFWVTPh0u9c0IXJuuw2uvdYYIqdUI29no5O2tW0cf/E4B584yKqbV8XeGzkygq3GhqPBgaIoU27gPV3mN5nuYSAVOUePwjlpcrHOOw+sVnn/4EGYUeNhw80b8A54GT8xTmg8xKO3P4qjQbyZdvx6B84mF195eT2alrmSMK/rTVGgZoG8SkXKOKiqYXZt28aqVauwmKOh6CLG3ZIRCYtEZDZoajyxzd4i5LcSOGVjsJXS56YVgfHb3/6WmTNnsmDBAsJhyUrfunUrqqpyS3T2Ultby5e//GW+973vTSsCIyN0AiM0Gu9oVQLj1IH/uLT3iceh5h35fSYSADUaHK/KFJUXzWcX9zmzTV5VVB5cs2DwBfCnuAoueq+8KnyiXFHQs24CAxAJoGgaNrUfZXxUrn9XZ/5ZNyYznHcXjOwqjDDMF7GqtTIH4U8m2OpFr9vXI+RS0xnl/T69AsMxs7zfU0UVVUzE4d/Agbuh/ZWw5EOT8pXlDv7pBMbixVk20its7fmk+KfA0VoZ/ky6CetYgVUkFYQrrpCAt+6D8Y9/SJa5212gGbKjFWoXQMMKmHmZeGiVE/leA6nbBEfgpU8KcX/Oj8pzbBmgExhHjpS2n09+UvpYRwe89BJs2yaG3e3thUvATVds3JidCNI0+Z03bjRGMkYnMIqtwACYf9n8GIGx8qaVMa+CJz7/BN5+L6/8n1fSvLg5VoFRNfDOjGz3MMh+D3O54Oyzxej7qafgtRcF8A54sdgtOBocqH4Vk8UUIzDCvjC9h7wM9ASA9ASG0ddb3kgcB8NhAtYh8aSYitKd8YOw/8fyY5z+tezbBgYkacPiilac74lKiJunRN6vCsG0ITC8Xi+f//zn+dvf/saPfhS/kT/yyCNcotdYRbFu3TpuuOEGNE1LaxATCAQIBOIaqKOjQgiEw+EYMWIymTCZTEQiESK6YGHCelVVk9inTOvNZjOKosT2m7geiJXg6J/TX7HSHMWFCQWTpqEFh1EtDSj+QRRNA3MNJphwjIqiYDabMx77ZJ1TrvUWiyX5XLMceyWfk74vVVWNP6fmdZh6N8Hxx2DeWzCZLbnPyT+ISdNQzFYUs6PaTlnOKfG8TpZzOhnbKfWcgLzPteBzss+UcdXTTSTh+GPnlOexV9tJwexoJXLePUSCw7HP7d72PKvDdwMR1FVfBfcCsDRiikRyn5PigoYzQFVlvepBHT2AZp8RmxgXe06RwAiKpqGZXGjh8KnVTnmck35M+jb6eqXmNBTvMRh+GVPTGeU9J/8JGbetzWjRz1XbKfc5Je7L6HNKXV/FSQxd99lzcNK+spzBv1AoHuTLWoERGJBlKb50U43aRbIc2xNnfqYZLr5YYl0HDshLr7647johMQpC2xXyquSEFGudBMq83XDwF7D0I5P21UZUYDzyCNx5p/z9f/8nviWVqOlebvT05N6mkO1ywQgCo+O8DiwOC+M94wzsHqBlaQuRcATvgJTQuFpdAFNegTEdUOo97OKLhcB48kl47UWyzuK04FAc+Pp9BEYCmBeYMVsLm5PlvN56/gpqAFrOPfmqni01MLRV/vb3Z763e4/Jb6BFRH1BC0UlB0fjstKTLO9XhWDaEBhf+9rXuOWWW5g1a1bS+mPHjjF37tykdU6nE4fDQW9vLzNnTszUu+OOO5I8NHRs3rwZd3QW1NraysKFC+nq6qKvry+2TUdHBx0dHezZs4eRkbgb/YIFC5gxYwbbt2/H54szckuXLqWhoYHNmzcnPYyuXr0am83G87ogHhAKhdA0DZ/Px9atW2PrF42ptNSaGBs4xMtH9tHgVXEFOgicUDnt/7d35mFyVOX+/1bvs89kMlknZA8kkJUESAgEFEEURAP4Y/FeN9xAwA3R60UFQRC9XAFFFOSCIAIiIoIoIhj2JSSQhYSQPZPJMpPMPr33+f3xdnX3zHTPdM/0Uqf7+3mefmqmurr6nP7WOVXnvOd932lAa2srtm/fHju+pqYGs2fPRnNzM5oSes1C1AkAFi9ejEAg0KdOdrsdS5YsQUdHBzZvjsfPLisrw/z587WrUzAYxDvvvIPjjjsuq3VqPWhgemcAdrUDgff/hbFHnTFknZyhVozvrcWo+tHwGAZ1SlGnrq4uBINBrF27dvh1mliF/e8+jbZOH7o889Oq056X/wfwNqO97AT4XFPYnoZRp7lz56K+vj6mXTbrNGt0OUYBOLx3A7b2xMtJnYZZp24D27fHQyA6KufAsC+B78Bb2L3hZbRV2AHsGl6dWn6L7m1/x/6Kj6Kt4uQR1am9aSvKgu3Yu3UPvPvWlp5OadQpGAxi9+7dmDFjRqxOtb02jO1sh735LVRP+1RO6xQa/WHsaa1A5/YeBHevLs32lGGddu/e3ec+l+06JfbBxBrYbDZMmzYNNluW0xxWRmfGenbJgN7IfRrFXE7+7dghq2PLy4F+Q8u+mB4YrtyHzcqpdjaHhADzHQTK9PNiq6qSkCovvQTcdhtw//2y/8ILR3DSLBlycqKbYQDTPgOs+RZw4F/ApI8DFZOH+lRWGKkBo70d+Gw0aMCll0r4L6uSszYXZfz47B43FCPNgQEADo8Dcz81F2V1ZaidWgsA8B72AgqxFf+RCLBpkxxfKA+MXGuXDUZ6D1uxQpJ+r1oF4Hvx/Q5PfAo30B1AWV2Z7E9zZnfI623vX8VToWxc1g0YBdfNM1o88No3Ai0vSj7F/kTCwO6HJN9iwwnA1E9LZIZ3vi/hpI+9XfroQoS+KiAF1y6KoQodxCoNtm3bhrPOOgtr166Fx+PBD3/4Q4RCIVx//fW45JJLcPzxx+MLX/hCn88cccQRWLVqFaaapugEknlgTJo0CYcOHUJ1NIuOpVbk9e6Fw1MN5ahGOBI/N1cZlkadjK2/grHvH8DYU2Gb882iqNNQZbd8nXwtsIW7YWtfC/X+r6AqpiEy+9tSdsMGm7sWYeeopHWKrLkK6NiEyOyrgNEnWqdOKEKdhlMn/37Y3vwKlM2F8NKH5AFFKdjX/xcMZxVCM67oE+tfizpZTad9T0JtvQuq+hhE5l0/dJ0MA5ENN0CVHwE18RzAUSH7dz8EtfNBRMZ8EGrW5SOqk3rjy0DvXkTm/gionUud0q1T9zbY1n4ThrMCxokPIRxd8a91nYYoO+sUL3tbWxvq6+vR0dERe34udTo7O1FTU1N8v4mKAC+dLyFKj/u1hFvMMf/+tyTsHornn89sdXc4DNx8M/Bf/wVMmwZs2ZIinE04ALx4rvx94oN650h66+tA11ZgztXAmOWFLs2wuOAC4OGH++5rbARuvTWDXAre/RKeWZcwrhtvBFpekZC1c7+fl6/ct0+MejYb4PdnHuXl058Gfvc7YMYMCR2VsYdMEREOi1Fh797kDj+GIdfwjh3ZCanV3S3GPkAMSTVZStvX8m4Lnr36WVSOr8TZvzkbO3ZI3+lySa6TQkQC0oGR3sM6O4G6OsmVsfHFw3jxij/CU+uBs8yJA+sOADZg7NyxMAwDgZ4AvG0+3Lb/fGw+MGr411skKPf6SBg44Z7inKBvfhrYcofk+Fh868D3lQIOvS65kub9SO79kRDw0nnR3+X/9PbKtCCZPDtr0d1ceeWVuP766+HxeAa853a74fP5Buz3er0oKytLej632w232z1gv8PhgKNfD2wO6vpjT9HqU+3vf97++8PhMDZs2IBjjjkGdru97/HVsuLCAOBIYvBKVcZM92e7TunsNwwj6X6d6pSoHZCDOo3/ELD/GeDQa0DYB7t9YDvIdp36Uww69ScSiWDjxo2xNpd22X0twBv/KWEFwj4YvXthtK+DrT1hJaq7HvZlDya96dvCPYBhwOau6/PER53Sr1P//jKdsqddJ9s4wLDBiATgiHQB7lFAqAfolNXQjjnlgH1gOanT0HWK6TZjCey4G0bXJtiUt8+kUNI69TbDduh1oG0NMPXCeNK1iskwYMDuaxowesq0TobdBdidsHni7bJUdUq2v/99LnZ89Qy5R1XPAlQIdrtTmzqlu18nnfqjlEp6n8t1nUjhGOz+OCIMG1A+WfIo9OzIiwHjpJNksmWoyb+TTkr/nI89JjHJzdXl27fLBGPSSfDAYdnaXXnJj5Qz7QAJI9W1VfTT0IDx2GMDjRfAMJK5b/op0L0DOOZ7wKhjs1K2nOo29T+B1teAQ28C7RuA2mOye/4kjB0rj0GhELB/f9wjIxXhsITA2bcP2LpVjBc2G3DffdY3XuRUO8gk8a23yjUaXQ8Vw3QA+vnPs5cPpLISGD0aaG2VxM/z52fnvD0tPQAGho868sjCGS9yrV02GOk9rLoaWLQIWL0aeOPNhM/ZDIyZOyZ6jrgnmWEA11wDXPzV5N8FpHG99TbJJL2jYni5n4bAEro1nAhs/TXQvV3C9JVP7Pu+YQCjTwDqj4//cDaHhNLs3Qt495akAcMS2gGwrs9VlL///e/o7e3Fueeem/T9xsZG7N69u88+r9eL7u5ujBmjT8w2M3RUWg4xIa+143aWGBlpNxxco+Qm4j8kSRS7tvV9+VqGPgcZwLB1C3aKFjY34BodnUw1JD6is1b2+w/Jcak+DzBm4gjIaZuzOcRd1lkVj3EZiIZacZTps2rPgsR0c48BKqfIit7Dq4f8HDqjI6WqmYAtYYLcDKfQs3vk98TFtwMnPZa3EA26kbLN2ewSm3vCmX21yTb+Q8DhtyRhOEmbnD+fEMuRU80rp8i2e2f2z50Ec/IPSB3tJ5PJv8cek4nE/qFxzEnwxx7r94HEBN55yBuRU+2qopnKNUzkHY4mwk2G+VN97Wty3KAEu6X+kaAY47JETnUrnwhM+LD8vf3evIz/bTZgYnQ+b6gwUo89JgbAU08FLroI+H7USeScc4Bly3JazKyQj3vkypViYJvYb460sTEDw1sGZCMPBgD42n3Y+MhGrL1nLXpbovkvRosBw0zgXcj8Fzo83wx2D0vXoHCyRMjFm2/03W+z22CzD5zKPeMM4DvfGXietK83M89V5dSc3PcsoZuzWsJDAcDBVfH9SsmiRZP+9S+PWnN7R5AgSGMsoR00MGDs2LEDTU1NWLBgQex155134u6778bixYuxbNkyrFq1qs9nXnjhBSxZsiTpajQt6XgX2Ho3sO+f0rBevgB4cSXgP1zokpFc42sBXr1YVt50bwPe/Ym49SW+XrmorxFj1yPAq5+RLckd9jLAVS2TdoYdsLnE0GRP7vkFQNpvsEv+pgHDuiy+XcJFVE6T/4NRA4YzS77YBKg/QbZd24Y+tiNqwKjpF2jXM14MTmGfxPUeKYahZXLTkqDtbWDdD8XlmxBSGCrMPBgjnBnLgFSTf4BMkqY7+WdOgicbd6ecBK+aASz6HyAaolBrao8BjjgfmJTl2dI8kEki3EFpXycHV0zSa/Xs5AsAu0fGGaHuvHyl6XWxZ0/qY1IZBAHg8ceTGARLmJUrxSPi+eeBBx+U7Y4d2TdeANkzYHgPe7Hu/nXY8uQWtO2QXHYVDeJSY3pgFCr/hU6M1IC1YoVs34gaMELeEAI9gQGvkDce6nPnTtmee+4wrrfu6IVTMSWNgzVmTNQydPCF+ENA66vA65cA+/+V/DMlbsCwCpb3/f7KV76Cr3zlK332/TAhB4ZSCsFgEL///e9x8cUXo6urCz/4wQ/wzW9+s0AlzgHdO4GmvwCjjwcalsmqVRXJizszKTDman/PmOQT42FvfLW/Ga4ocEj2RYL5LWtJYsiAIuwHIgH5ezDCvdJ2Ab1jKRc7/cO00YCRfSacCYz7AFCWRubCVAYMmx0onyQP2z27tExMWhSoiHjB9O6ODwiyjWmgynIyQUJIBtQeDUz8CFCT+zA2iaxcKcYKM0zNzp2Sv+KZZ4CDB4F0HO7TnQR/7d8tOPG4BA9awy7P36axXdeknWXjgWn/OfRxvpbUHsRAvP7pHpcFspbMvS0a6rVuwUiKk39cdcDcHwA2j9wLky3YyPJ1OVQi78EMgiZf+5q0W4tG98k7dntmuXoGkGaby0YibwBwVjrhqnGhu7kbXc1daFzWCHeNG4e3HcaOt4ByuHH00XmOEZb4G4RDcAebgO66eGhfc3HgUL9TOsdksT31v4eNHy9ho9JpGyedJGur3t3uhuPkcoS6ehHyh5IeW15fjpDNjb/8Rf7/9reB447LsLCmB0axGzAqpstCxVHHSnjFSADY9DMg0C6LpmrnDbwGxp4K1M61/m+Tx/tzIbC8ASMZTqczFu/NMAw8/vjj+OIXv4ibbroJ4XAYl1xyCc4///wClzIz7HY7jjrqqOTxxNyjZBtoj1+Mdg9DmViEQbXL2peUyer+ZET8ff9niKK0yJpupgEj3Du0UTGx/eYy3EqRk5c2l4gZQoptakT00c0xKr0PBTok3igAVB818P2KyWLA6N0FINOn9Cg9e4D3fwmUTQCOvGJ45yhyBm1zwQ5g9eUywhp1bOp71UjwHZCth0aqTMh7X6kR99xzD77yla/gvffewxRzxgfApk2b8OUvfxkdHR0wDAPXXHMNVuZimWyOyKnmldOAmV8Z+rgckDj5p5Ss8H7jDUnG/bOfDf35dCbBR1e1YNqBi4CXDqU+yF0PpMhxNlIK3l59LeLV7R+i/sfeBrx1xdDHZel3Gp/GOoe0josZMBaOqDz9ybluvhZgzdfz9nsDQxswMvGKGdGkfY4peJtLl3Tb5rIHMXWqXAPmKvzh0NPSgz9f/Gcc3noY3sNetLzbgqoJVXj3j+9CKeDod4HJKMe0cSsB5MmI0e83sAOYGw7D/mqCds4qQAEIdaU+TzrH5KCfH64Bq64OmDsXWLeuAhX/uRKnn+JPeay72o3Hn6lAby8wfTqwZMkwCtqzS7ZmyMgsY4k252sB3vi8XEstLwLv/VxCRgY6ZH4m0Abs+/vAa6BisvVDDWfQV2R6fVtCO2hqwPje977X5//JkyfjH//4R4FKkx0Mw0BtbW3yN111sg0c5uS0BRlUu6yiRH/fAcBR1Xe1cWIn1bNL4vcFu/RfMZZDsqabs0Z+60AH4B5iGWAsfBS9L0ZCzttczy5g2/+Jkfjo/4p7YLhy+J0lQErdVEQSxCajc5NsKyYlbzfjPgjUHD2yxJaBw0D7xnj7JANIqZ25ysfukfvQ/uf6espk695DD4xhkb/nE7245pprsHr1atTV1SEUiq9k9Pl8OOecc3DXXXdhxYoV2L9/P1asWIEZM2Zg3rx5BSxx+pSC5oYBXHstcOaZwB13AN/6FjBu3OCfSWcSvLqsE5WOaI4ze5lMYqgQ4KgG7O7kXs9ZJOfahXqAzs3yd7IE1ok53gbz+vbuS++4LP1OWUnm7t0HeA9I2MnauSMuU9/vz7FuibrYXIC/JXovjD435eC6nDRJtqmMFFnziikw2vSX6bbNYGfMgDESDwx/px+9h3pRProc/k4/IsEInOVO2F12+LyAPxJCBXoxYZQfeTNg9PsNDACOxLWAYS/gi+Ytclan/p3SOSaH/fxwWLECWLcOeOWdClzw+cF/7wcflO3FFw8zKu6SX8k42AwZmWUs0eb6t6ewFwj1yv2hcipgOCx3DaRNBn1FpnWzhHbQIAdGqRAKhfDmm2/2GUjFiBkw2hJWAjOUiVUYVLtsoiJA7x7xxOndI+5tvhYgHADWfCOeE2P3HyXZ6YbrUufJINnTzT0aqJ4FVE0f+tjqWcBJfwIW/e/IvrPEyX2bs0kbantHRssqLBO07HdHxADd/IeBDdcDb3wpdRyCYKes6K9JkSmwboEkuDTjkg4HGhaHJGmbM1f5vHQ+0PqKtJm3rhg8R9NwoQfGsMjb84lGRCIRjB8/Hk8++SQ8nr7hAp955hksXLgQK6JBp8eNG4dvfvObuOeeewpR1GGRc83DPqBzi3iuFZAzzgBOOAHweoGf/GTo4086KXkeDRPDACaMByorEfd6DnaK8dQwhs5xlgVyrt2h1ZJLaOcfBj/OrL+jAjAAQMkig8T6h/3R+7aSYxzl8c9k+XfKRiJcHI56X1QfNTBM6AjJWz9rL5Pxn/+QPLfk6PcGhvbAyJpXTIHR7h5ptk27R15JroHEHBgjzbXrqnLBU+uBzWGDv9MPV4UL3ogLITjg9vQzIOSL6G8QsZejvSeIiIoAUFLZiF9esb7JHv+NHBXieTHgGOS0/8oGZiLvfml/B9DSAphrui+6aJhf5igHambnLNKLpdqcvUw8Lrq3y7XiGQt4xg1+DbS+Dux8EPDuz185h0PifTzxNYLr2yraaemBUayE+2SOS8A0YERCgDcaSoMeGJYipXbZxLDLJJ7/EOBtjiaubZbrItgBlE8E7OUA9gG2iFw3plVZVytyjhmRbmFv/G/DCYS8A/cnw+5i+LcskNM25xkro+JQr7SbKRfKa6QjAdJXN2c10L5efueuLUD1kQM/MP50YNyHhm5XI8FMism8UoMyoM0lrvJx1cs9CQCctdEPZOneEwmLazdAD4xhkJfnE42w2Wy49NJLk7737LPPxowXJitWrMCt5uxpEvx+P/z+eEiHzk7xlA6FQrFBns1mg81mQyQSQSQS6VMWm82GcDgMlXB/SbXfbrfDMIwBg0fTnT8cDiMcDiMUCiEcDvfZn4jD4YBSqs9+wzBgt9sHlLH/fmPngzD2PAZMOBO2Iy/LS51S7f/BDwyceaYdv/qVwlVXGRg/PnWdlIpgwQIb9u61QWaxjITjpDz/9b0IDANQUFAqAiMS7dNsThiQ/VAK4XAICIVyUidTu5Hq1P93j0QiiJRPhU0poGs7EArA5nD1LXs4BLtSUlcVATrfi4dZcVbDKJ8UK4/d2yxGDSM6leAeDWWu2I2eA0oh3O83GG6dPvaxCB5+2MA3vmFDU1Ncu4kTFW65JYKPfUwhEhnk2hu1COGpn4Vy1QHRMo1Ep/4k6jZinfrvB6CUkjp5xsHo3gZ4m2G46qDsHiilYESvS7tSWalTY6Po2tSkEAqFB5R96dIIGhvtUQPHwGXehqGiXjFGQfuIRJJde2afGYlE+px/WDrlsk7RzyqlgEgYRsd6AAaMunlQMPpcA42NYQB29PQA+/eH0NCQeZ3CoXh53TVu+Lv96D7QjeojqtHVJXp7PArhUDhWh1zqZBgG7EhoB9FtJKJgdG6CAsRbLhyQDwTaYBgOKGcNVNWs+Hm6t8II+6AMOSax/zIqp0X7/uy3p5H25cuWAYAD69cDra0R1NYmv/YeeggIh+1YtEhh5kwFIL/tKZ06Jd7nrNCeDDNkFgCj/Agoc3/0GjCiz1JmGW27HgU6N0G5x8NeNs46fURsv1y3ZjuxGYbUKdQrRs9o3QxgWPenxHtdNuuUeNxQ0IChAzanrAwNdsXj0tGAUaIYsuLfXS8TQz275GYdbAe8Nkk4ZACAQ64Zm1s+1j9PBhk+zur475/qd3XXs43qjN0l7czXImEHXFHPi2H54pKU2BzAqMXAwReA1teSGzCA6OrX8tTn6doqeTBGHRvPGZUJ5gSNgx4Yw8JeJi/fAUmCl5gDIxv3nsAh8UC0OQDXMPQlJE2am5vxoQ99qM++SZMmYfv27Sk/c+ONN+Laa68dsH/t2rWoqJC20NDQgOnTp2PHjh1oaYl7JDU2NqKxsRFbtmxBR0dHbP+0adMwZswYbNiwAV5v3Hh71FFHoba2FmvXru0zeJ03bx5cLhdWr14NpRTa29uxZs0aLFmyBIFAAOvWrYsda7fbsWTJEnR0dGDz5s2x/WVlZZg/fz5aW1v71LempgazZ89Gc3MzmpqaUOX1Y0JHOwy8jdojkZc6JbJ48eJYnWpqgHnz5mDdumrceCPwox+lrtM997ThqafkHlNbG0Z7e3wI3NAQwNe/vhNz5uwFdgNerw+BYA/KA14ABsJBA2UOoKfXCwR68f769fA727Jep3feeSemncPhGJFO8bolXHsHD2JGpx921Q7fjjcxfuaJfXRyB5twTDgEJwDfoc2wh9oA2KAMBxwOO+wAent7sXPLe5gZBhQMOF1OGCqIQE8rekOy4M4W8aLao+Dz+/DOu/G6jvTamzQJeOghYOfOSbDZJiIS2YspU/bAbgdWrx7q2huHDe3TRaddq0d87SXWaeHChQiFQlizZk0sR+eIdOrfnmoAn9+PgK8TEaMMnrALLsMHe9cWdBmToMI+2CNyXU6dOzYrdWpslOD5e/cqvP76atjtA+v0H//RgBtvnIaBKCgFfOc7B2C3j8O2bYXrIxLrlOzac7tlnHzo0CHs2hWfyByWTrms01F1MCIRdHd1QqEX5YEeGAbgDPsQUg70dnXGrgFbtQ8TJsxHczPwt79twtFH92Rcp96mXoSC0UnGKsAoM2Avs6OjvQOdHTK+dThCWL9+PcrbynOuU1lZGeZPq0QoHEKvrxMRWxBKRSdx7W6EI2GEQ2HYlAwZlLLD6XAjGFLo6WiPnacKDjhsDkTCYYQiBmAYMFQA9shB2MvGorMnDIS6s96estGXT5++CNu2ufCnPx3A/PnxazXx2rv77okAqrF8+S60tpZnfO25Dz6J3VvWoKNsCfzOiTmp0549e2L3uTFjxligPVXDE+5C0DEaVTYnQqHggPaUWKexHRHUetvRu/1NTBx/qnX6iKhO761fj5m9vQjbbFD2EGprahH2tiLSsQVBRz1CRhWchg/lQMb3p127dsW0Mwwjq3XKZLGVoTIxdxQpnZ2dqKmpQUdHB6qrCzPpGAqFsHr1aixevBgORxK70puXirv2xI+Ixbh2HjDxrPwXlAxgSO1GQtc2CcMxWBw77z7xwKicIkloO98Tr4zaowHYovkw2oHlf0wvzFGJMCLdzLjvJhE/sPcp+e1nXyWTqMlWHB94XkJ/1R8PNCwbUflLmZy2OZN3vge0rQNmfwMYe2puvqPESKrbwReAd38qIaCO+1XfDwyWGyORNd+StjfnamDM8swLtu0eYM+fgUmfAKZ/LvPPlwBJtTPvT85aMTAdfks0q50nq3yyde8J9QCH3gCC3UDj2dmoTsmQ677SCs/PI2HKlCl49tlnMWPGDADAaaedhquvvrqPESMSicRWpBlJjNjJPDAmTZqEQ4cOxX6TfHtgrFmzBosWLYLL5YrtT2REq0F7dsO25goYDg+M5X9ERKmCrq5+/nkDp59uh8sFbN2qMH78wDpt3RrB4sUGOjoMfP3rEdx0k8Irr9ixd28E48YpLF+uYLcDtp4dsL3y/6CcNdLv9OwSz7ya2TBgQIW6gUA7wsseAiqnZ71OgUAgpp3dbs++B0YkAtv67wPt64BZl8E28cy+Ze/eBvsrF8CIhKD8BwEYUFUzAWcNDMOAEeqBCrQhPP9m2N/5NpSzFoajDGhbIyuia+dLjoZQD4xQB3DiIwiXT+lT11zUqf/+fK/sB4A333wzplvW69SzA+rF86CctdEwOCEYne/CCPuhHJVQ5Y0wgp0IL3sI9ppZWamTYTjgdiuEwwZ27QphwoSBZf/a12z45S9tcLkUAoF439jYKF4x555rWEqnVB4Ya9euxaJFi2CzxZ83LXft9e4EXv4klKMGcFTA6HxX2lnVdChXPVSwG0ZQ+iajagZWrLDj5ZeBBx8M4/zzVcZ1atvWhj9d8CeU1ZXBWe4U77MoL79koKcjiNlTvPj8389F3fS64dUp05X9vTulHRg2GFCIuMaio7MLtbW1YrQI9sDwHZCFnJ6xMByVUY+K+LmNcA8M30HZ7x4rv2XPdiDsh1E5BcpeJueJ/pbZak/Z6Pcuu8yO3/zGwNe/rnDzzfHzmPpt2xbGjBl2GIbCjh1hTJo0jGvvrSuhurYjMue7MleRgzoFg8HYfc7pdFqiPcXOb3or9GtPfbxQmx6HseNeoOFE2I75rnX6CHN/xxbYX7lAnmMMG2yOCijffqBntxzojPYhJz2KSMXUjO5PgUAAb731Vuxel806dXZ2ora2Nq3xBD0wLILdbse8efNSZ3U/5hqZwHZWpzehQ/LGkNqNhHRW+5dPBIJV0bAdNqB6dvbLUYSMSDdPQ18DhYoAG24Qo0bEnzpcSscmSXLrHkMDxgjIaZszKZsgBozeZmB9dHXtzK8whM0ISKrbqGNlZX1vE9C7V/ozkz1/Avb9A2j8+OAG+4ojxIDRswvAMAwYDCE1JEO3uWiM+GAXEO7JboxxRwWNiMMkL31lEeF2u+Hz+frs83q9cLvdSY0X5mfMVbyJOByOAUYjc1DXn1T6pNqfyhjlcDhgt9sxf/58uFyuWJmTHW8YRtL9qcoY2181GbA7JQeC7wBsZeNyXqfB9p92miQ4XbUKuPFGA3fc0fd4vx+44AIbOjqApUuBn/zEBqcTOOUUYEAqyGg9DBhxzzxXDcwQOQZkxa7D7gASypWtOrlcrgHaDVunVPtrjgQ61gPd2waW3e4Awr2A/xAMww5UHAHDVdvnPIZhwGG3y8plw5DwtvYyINQLI9wL2N1xb9UUZc96nfoxQI/W14BQD+yjjgX61QcY/rVnopQaoNtwy55qvxH7vQ0JW1s1C+jYBCPUDcPbDDir5LocpM1nun/8eANNTcD+/Q4ccUTfMjY323DXXfL/X/9qwOWShN3jx0vYqEQN8tHvpbu//7Vn3iOdTmfSPn7E194Q+9Mue0J/EMvJE+oBwl4YCfsddgdgt2PqVODll4Hdu+3of6p06mR32Pv0QUa0D1QAurql5ywrM2B32AeUNRc6xfYDMLz7ARWCDQZqqsdGy5fw28SOlD67r6yJ+6PHV06FeS8Y8FtmsT2NtN9bsQL4zW+AF19Mfp5HHpFr7NRTDUyePIz7UyQE9O6BYRiwVc9A/wsnW3VKdp8reHtKfAsD21OfMlZNls/4moesa0H6veh1a4S6ZGwdDY8Gwybj5MAhuc+rSMZldzqdSe912ahTqmfspJ9P+0iSc8yVUkkps3gWrBJnUO1GgqcBWPZg39X+/fEfAt78MpLFICWDkzXdDBtQvwTY/y/g0GtA3bzkxyXEEiYjI2dtzsTsc73N4jUTCQEzaTweKQN0c1QANcfIb3zodaB8Zfy9jncB7wExEA5GxWTZ9u4eXqHM8ERM4j0oQ7a5solAmRo83BfJOznvK4uIxsZG7N7dtx/Zs2cPGs2MtpqQU81tdqD8CEl62bMTKBuXu+9KA8MArr1WDBJ33w185zvoM9n6rW8Bb70F1NcDDz8MONNJOhv2Av7DElPdcMpEobk/x+S8vVbNlG3X+8nfjwQAFZZwfY7KeN2BgfU3/zeccg8Ne4GQKy+/U0bs+bM8T8y6DJjw4Zx8RV762f6/a/kECZ8Z7Ez/vtvfg7w/zurYIqxJkySJd1MTcNxxfQ+74QYgEJDkwh/60AgjrKZbpgzKngla3SPDvdI+YUj/FOgAnHUDro3J0cfif/4TOP544KSTYvOwGRHy9l0p7fcBtjDgQghJ7Pa5J9Qlv4HNBTgqYUS8kIQW6PsbpOqDMj1mKHJ0TSbDTOS9Zg3Q2Qn0X6T+4IOyHXbybm80r6qjLOeL9SzT5oZzDZRHnwe9zZIb04rhpZWSvMkq2n5DPYC9QuY2unfJYrP3bgNmfVXu3clIce1aQTsaMCxCOBxOz80/7JNwQlZsLCVK2toNl/6r/UlWyLpuo5eKAaP1dWD6F5K3UfMhhxOlIyLnbQ4APONlcj3ilwc6gIanEZJSt9EniAGj9TVgUtSAoZR4LAFAzZzBT2waMBISsWXEUV8DjrxyeJ8tEdJqc7lqH+3rZVKtckY8Hw1Ji7z0lUXEsmXL8NRTT+Gyyy6L7Vu1ahWWLdPHYzIvmldOFQNG9w7pvwvMihXABz4APPcc8KMfARdfLKvBN28GfvELOeb++2VCdlBMr2fv/rjXcyQABIPxY3KY4ywv2pkGjJ6dkkPPnjAZ4awGqmZIyGJ7mYQA7I+7XiZBEr3DnZXygop/xiq54EK9QNd78nfdwpx8Rc51G8wb310vHo+eMUP/3r4W4JWL5DypcNfLwjlPA0y7bUIodADAzp3Ab38rf193XRaMF+mU6djbgLeuSLvs6aLNPdK8Brp3Av6Dknw6EpJxZb8299hjwB13yK5//UtejY3ArbcCKxPWCIXDwIsvJnrOxI0c7mo3yuvL0XuoFyF/3IjR2QmUAfB4gIrR5XBX59GKYXNJe1YRMa4GO9Hb24uK8vL4RegZLW4ioa7UkStSHaMiEqbd7pZxRZbb00hpbASmTQO2bwdeeQX4cIItdt06YMMGwOUCzj13mF/Qs1O2FVNyOs9oiTY3knymnrFRg30A8LdYLzKDs1rWNId94iFpcybcyw25Fv2HgJ0PSqQDz9jkeie5di2hHWjA0IfO94GD/waanpDVT/NvAmqOKnSpiJXIxkoCMnzqFshA0HdQHgIqpw48Jmh6YNCAYXlGnyAv7z4xStk9fQf6JHvUHw8cXCVGQHM1S+/u6IoRD1CRpC0lYhowvM0DJ2TShYsChk+u7z27Hormo/kmMPaU7JyTkCScd955+P73v49Vq1ZhxYoV2L9/P372s5/hgQceKHTRrEXFFNmaEx4W4NprxYBx993ySuQTnwDOPDONkyR6PUcCgG+/eJskksUVtQXBPRo46utA5TSZ2ADkvgmk5/Vt1j/d4wpN+3ogEhajS9nYQpdmeGTyew/2DBTslImrwfIq+g/JcYMYMG64QWx6H/ygGA9HRLpl8u7LqOxFh3kNvPdzoOVVyfd28CV5b8kv5HdxVuOxvzXgvPPQJ+8DAOzdC5x3HvDoo2LEeOwx4Mor+2qbaOSoaKjAygdXwt/Zd3L3t3cDj9wEnHkqsPI+NyoaKpA3Wl8Vj213LTD3OoSVgffXr8fcuXMlbA4Qn3Aeqq0kO2bfP4Ddj0YNZrcOfR1l2J6ywYoVYsBYtaqvAcP0vvjoR4Ha2mGevHuHbJPNXxQbI7mHGTYJM92zG+jdY0EDRpUs+HLWAZMvAMZ9cOAxLS8Bb10pxjpX3cD3Ld6f0oChC759YrwA5EHMkccbBrE2I7Eik+xhd8vqrtbX5ZXsAcAMIeWgFpbHnNAOdsg2SdxkkiU8o4GFN/fd1/GubKuPFKP9YDhr5YEt2AV4m2RihuSe/vceFRGjk4rEPSWyce/xHZCt1QYJRHtcLhecCTGFKioq8MQTT+DSSy9Fd3c3IpEIrr32Whx//PEFLKUFGbVI7pFVRxa6JDEOHkz93uOPy4Rd4urjlCR6PdcUWU45M9xJxWQJQ9O9XWY6t90N+FuBo68WD410JiySeYdHwgAiccOIFWh7W7ajcuN9kTfS8cbf+RDQ9Bgw++rkz6zmSnF7Wep5hIRxZDIDxrZtwP/9n/x93XXpFT0t0ixT2scVI+7REtPeUSHeyjVHi2GucjpgdyMcFqNEf+MFEF8b9LWvAZEI8MlPDm3kqGioGGCg2HQAaAMw63igIp/zmoEOYM+jMtaeeZks4g2F4He2Sf37rwZPtw9LpPzzEsrWfxg4/CZQfk56ZcvjNblihbS/F15I+IoI8Ic/yN/DDh8F9PXAKAVGEuHkyK9J2D5PYUNoJmXP4+J9UTUDmP651CGiyhslJ6uG/SkNGLrgGtX3f05GExOdVkIVO/XHi/Hi0GvAlAsGvh8LIcX2qw2mAcPJ0DV5xTRgDBU+CpBRWcVkoH2DhJHK1IDxzvdkAHLklfSOyoT+9x7/IeDt78jD8uJfyiqlkd57ImGZWAPkQZuQLLJly5YB++bPn4+XX365AKXRiIoj5GURzIm7wfja14BzzhleHPiiIFW4k0CbTNgZBtCxEVjxxPD67Pd/JWFUZ10GjD01O2XOBm1rZVu7oKDFyDk9e4G135KFHAf+LTmp+nuW2sviIVEjfiDULR4bngYJSdQP04CxZ098349+JO3twx8GchZZL9QjiWYBIOyX1cDNf5NrN9QLlE8E7CWYa6t3j7RVuwuoniOe/wm8+OJAb5lElBItL7xwaCNHqr7y3eij+Zw0Hs2zyu5HgJAXqJoOjDk5N99hdwNTLgbeux3Y9TAw7rTMFgwHO4BIUAxNOcLMg/Hmm0BvL1BebiZrl5wYH/3oCE4eaJNtqRgwRkL1zEKXIDmBDgkLBQBT/yO18QKQkGwxojkzyiZCh5y6NGBYBLvdjsWLF6fM1t7HvccwJPYfsQRDapcPmCcjY3KiW/1x0lYrp8nEW+LK8XAg7qbPSdIRkbc2t/0+cScGaHTKAkPqFuwCDr0JNCzLzIABAJMvBCZHxG02E8IBCU8EAEeV6szW0KTULvHeUzEFcFVL3+eqE8+akRI4HO1LHYB71NDHkz5Y4vmE5JVS1DzdibsXX5Rk34PSsQl4/w5g9InJF6LkkJxqlxjuxOaKGi5aZSLb5pA42BH/8ENGGA6ZbO563zoGDF8L0LtXjOl183L2NZZocxGf3HfDPvGuCXX1DX8W9gKBdtnvawEQib+nwvHEtAn098B47z3JJQNIyLacEfZKHhpAktBGQsDBl6X8tu5o3o/sGDAsoV26mMa4mqOThgnbty+904RCqd8brK9UKm7AOPro9L4ra0z4qDwPjj8jZpjLiXZjPwg0PS55gHb/CZj2n+l9zncgnofPUZ4zA9uUKZLLac8e4LXXJPeTGT5q5UqgLEkkq7Q59ufSxnI8x6hVm9MN337JR1XeCDQsT/9zPbvluh3CeGEV7WwF/XbSh0AgkPrNRAOGzTl0SA2SVwbVjliWrOvmqgGW3gccecXANmp3ASf9CVh6b/JYmSQj8tLmAu3xvxlCKisMqtuabwKb/xc4tBqoXyyGwHRDlNTNk9VozgwfvM2wboaN7XIIhmxzNnt85ZkZ9mmkmOdxN4hGJGP4fFJ65EVz7z5Z6d35Xu6/awjSnbhL67i2tyVRbu+uEZRo+ORcO3sZ4CiTvlWFxfBQ3jjyUBhVs2TbOdCrqWB0vS/b6lk5D71siX7W5pRwOjanLAjxNcuq/T7PNwagAjIJbK7ADfcmPZ2Z9H7vXglTc911sj3rLOC443JYD0dZNGfJePG8dNUCY1fI1j1GcqOZhH0j/jpLaJcOh6MGDDMZfcgr+6K5MMaPz95XJesrm5qAri6J1jQjw7VCI6Z8AjDn6gFeJ1nXzmYHpn5a/t77l8ETdJv4WuLGC0BWwecIw4h7YaxaBQQCwCOPyP8jCh9l4qodfNV+ltCmzaUi1APsekQ8D61E9ZHAkl8Dc76TWW7H8kYZZ6WBFbTjaNAihMNhrFu3DuFwuO8bvhagaxvQ2yyNJdQjq2O6tsnL11KYApMYKbUjliZnug12w7C7ZOUQEwaPiJy3ObPfhSF9bu1cYPyZ7HdHyKC6+VokDFSoB9j3d2Dc6WII9Dbn9jcPRg0Yziq2y0FIu815oklS/YMEpM8EX/Q8zH8xLPh8UnrkTfPmp4FN/yNGjAKT7sRdWse1vyPb2vnDLs9wyZt2hjOep8JRCZRPGvk5q6MGjO7t8TBFhaZhGbDsAWDWV3P6NZbqZx0V8RAw/jaZMzCfcwAxZFROB0YdGw+3mcIIMG6cPBYFg8D3vx9f6Z3V3BfJsFfINVk+KWrEqAfGny7bsvGyul2FxFO3YyOggsP+KktpNxiRINCxQf42DRi9TcC67wNb7wQAnHSSeM2kepQ1DKAhTeeqZH3lxo2ynTkTcKXIE591Iql1yZl29cdJ/qNwANj1UOrjlJKQXr5m+d8R9boYLKR3FlixQrarVgHPPAMcPgyMHSveGDqgTZsbFAPYcT+w928ybrUSdhdQNjazzxjpLYy3inYMIWVl+scr7doWf++l82XrrpcY1AwfRIh1UEpWfpVPkpVERB8S+91QN+A9IAOkfX+PH8N+N7uYv3lvkxjr294G9j7VdxQ21G+uFHBwlayCOuL8+EBiKELdsmVYxuxgGjC82fLAoAGDEEtSOVW2PTsKWw7EJ+727k0e290w5P2TThriRGFf3KOkLv8GjLxSOVUm2rIV89ozTjwgg92SDLYq30u0U+CqkVcp4W4AVEQm1pyVksPNNCoZdnmFvHKMCkUXSHYPMGT89a/SdpQCbrhB9nk8wI4dwMJs50QPe+UZ0OYAXKPjq8DD3oHHmUQCEvase2ffSBXFiFLAzC8DHZtlsQ8geYgMQ1b8B9phd9Xi1lslEbepm4n5OP3LXwLf+EbqvhIQz5tkfWVBwkdtuBZw1gLTPpO/MKKGAUz7LLDvGeCI/zfEwRFpR54x0s8E2mVBVKhn4LWbJUwPjFdeiRsTP/nJEeZ32n4f0L0DmPTxAV4uJAmOcrke/Yel36pOM1pArujaJs9iYz+Qmbd6qms0R9dutqABw8okxiu1l8Vv5ja3dOZhr7w/3HilhJDcsO6/Ja7+nKviycbaNwL7n5GQOBM/UtjykdQk9rsud9SAbEifC7DfzQXmb+6sBeyHZUBtc8aNCun85oYBbL9XjjNXT6WDacBgXprsYBowshVCasxJspKICbwJsQ6+FgA2maRp2wB0bu1rcHZW5/X+aLdjyIm7n/88jQmejo0y0esZM/KQSlbHWRt/rskGhgFUzZSQNl3vp2/A8LUMvmI5k2spm+fSHU+/FbiRkExsOWskZFTEHz/O5pQExIAsFnFW47HHpD31n+T2+2X/o49KzP0R46yW7/QdBPwHAAUxsJgeQmaZTE8M/6F42Z2Vordvv4TDKhub3Xx1Vrqe7C5JKj3utIR9bvldepvFaOhagJUrRZsrr+ybF6ixUfrAlSulH0zWV5p84hNZTuCdzu8IDDymY6OEx7I5JPdFPvOg1cyWkKjBjnjb6I8Kypje2wTYndKu7C65Ps1rNNqessmGDYDNJp5Rb74p+x5+WHKWpN0m+2tyYJVcQzVzAEdVafWVw6W8MX0DRi7vc0oB790ixs3Da8XYN9S5zH43sT/tTw6u3WxBA4aFSJkQxV4mLqGjjpWHD8OB2GqZVBcdySuFTmZDhkfOdKs6UgwYra/FDRg9u4D9z8lkLA0YIybnbc5eJnF2DQdkRBWRhzqA/e4IGFQ3e5msogu0SXsZtSj6+yO937xisjyM9exK34BhhlZw0IAxFGm1uWwbMMonyosMGz6flB451dz0mPO1yoo/pYAXV/aNm10AL8V0Ju6GpC0aPqpufsFCCmrdXqtmyQRK5xZgwplDH98/0kAy0r2W+p8r0C4Tis6aeP6LHF6XWuhmcwKLbpHfIRXOaoSdDbjyyuST20pJ0/ja14Bzzhnhqm9AtFj2ILDnzxKup3IacPR3B5Qpdlyqibsxy4FZlw9L26TaZfPazCUVk6MGjF2xlfMrV4o2L74ouSzGjxePCrOaqfrKmhqgowO4917x0pg8ue9XmSGkMvLASOd3dFbJMMvMSWfS2wSE/eLZsOZrSX/rnLW7/uU2L3ylxKDhrAE8o4HFt8VzySQjy4aAxx4Dzj9/YNtsacnAsDhAEyXeF0oBG34k/USOr20t+suhKG+UuZ7evYMfl8v7HACEeiUnmWEA/hZZLDvUuZL1p/1Jce1aQTsaMCyCw+HAkiVLBj/I5s5PYUhGpKUdsRw51W308cDuPwKHV0vcUpszfpPgROmIyVubSzMmJEmPtHQzDRhA3HiRLhWTgcNrgN7d6X/GbJ8MITUoabe52nnAvB8CZRNyXiYyNHw+KT1yrrnpMWf3SL8Z9sqqU0d0pV4mXopZXuE81MTdkJgGjALkvwDy1F5zGTKi9hjJgZFuOI3+kQaSlSnda6n/ufyHo/f33EctsFQ/O5S+7nqgavqgp3jx330ntvujFLBnj7SzU04ZTiH74WkAuraIoanxnNTl8zQM1G7WFcDaq6TtRjJPLptSu3SuTe9+oH19PKRTf7I1eR3sAg48J7kvKo7o+175ZACv9k0iDenzBtMmWV+5dClw6qnAq68Cn/408NxzstIfEM2H5YGRzu/oa5W/ndXxY4LtgArL5zzjkrbdnLY7s9ywA8FDkoujcqqML4Id4iluGGK8SLxewz7g0BtAz25g6qeyWqRwGNkxLPbXJOyPhpaziddJ2JfTSAOW6i9HQlmjbL2DdJZAbu9zUOK9ZnNI6EBHZfrnStafDoFVtKMBwyIopdDR0YGamhoYTCSqFdROT3KqW9WseGzE9vWyktxcWWJRdzydyGubqz5KVvHR8DRi0tLNPVoGLeaKyUwwB5H9BnKDMvEjslJURTL/vhIi7TbnHpU9N38VAfb9Q8K51C4AbDQoZgqfT0qPvGluL5PnmUhQZk8S++x0POZytMJ5qIm7lCglE1ShTqBu3jBOMHJyql0+QkbULRhe/HQz0kAyMvV4tZdJ7rmITxZBeMaKsW0450oTS/Szw9W38z3g4AsSimjiWQBkQjsd0j1uSLz7pRyGIZ4UmVBzlIQNPfQGsPMBYM7VGX18SO1SXZuRgDxrrr5cQjklI1ur2NveAbbeLc+4S37R9z3zubd7Z8anTdZX3n8/MH++JIi+5RbgW9+S/Xv3Ap2d8plZszL+qsHbuGnEjh0TAbq3Svstb5RJ2WD7gI/lpd3ZywBvNF9Mz07JHWNzRsOaJhk3hP3Auz+VvyeeBbhqs1aUF1/MsmHR/L0jfvmtHZXRxVxGTiMNWKK/zAblUQNG7xAGDJOs3+cqgEBr1FDvkeeXsL8ktMsgywfJJeFwGJs3by54VneSOdROT3Kqm2HIAzUAHHpdtmaoGsbaHzF5bXPO6uKPhZ0n0tbNM3Z4HhHDMWAA0VVUnBwfjILc5/yHgS13ABuuL1g4F93h80npkVfNHeWyHc6AOXEloZmPIfFlc8dXEuYDwwCO+hpw/D0FSwicU+3MkBHL/5j6VehwONki2CWzeXZ33HiRQyzRzw5X3969QNMTQOursV3jx6f3lekeNyQHX5Bt7fzhtb2p/yHt1ww5lAFpaRfqBjo29H22jIRlFb7dlfv+q22tbJMZByunyLZ3d+qs3BkwfbqE3AOA730PWLdO/ja9L2bOBFyDREwaEm8z0P4O0P62vDo3Ab5+lrCOd0VHmwsoSz3+yku7szniZQh5xUuh6sjUhl5XjYRBA4C2t7NalJwZFkO9sjXv5znGEv1lNjANGL4DmS2CC3ZKfhczB+OwicTDV5WNzzxqwTCwinb0wCCEkFxQfzzQ/HfJgzHjywwhRUiuKZ8kg9hAh7xcNYUuUWly6E2gaxvQcCJQMWn45zHzaLgbZNBICLEWjmpZ9ecePfxzZHNVYjYoZmPpMEJGZIxSMnGrgjKpUgjM5+1S83gejr5mjqmEVcQnnSS5Y/buTT4nbhjy/kknjaCsiRxcJduxK4b3+copwIKbgOrZ2W+/oU6ZJFSRfuO3iKx8DvYCrlGAPcnkbzb6L6XiE+GjFg583zMeOOrKaCip7PD5zwN//SvwxBPAxRcDr70G/OUv8t6YMRLKKPMw+Eo8GHwH++0OiTEoETN8b3kjLLHW2jM+6tkUBqpnynUQ6kl9/KiFEkqv7W1g7ClZK0ZuDIsRwB8N4cVQupnhHg0s+WXUeJDBddq9TfqOrq3D81g08bUkGPrGDv88GkIDhg7kMl4pISQ31M6TlV/+w3KTYggpvWC/m39G+pvbo7FyvftkpZwrjTAgW++SifIjzk8/bjcZnL1PSi4Sd/3IDBj+6EDXMyY75SKEZBdHxfDC/SUjcEgmhconASiAEaFnd9wITobP3r/KfXXM8szC+QSiOSucVdHJ4hFMpgU7ZMvn7aEpixow/Icl9r3dA7sduPVWSQps5i02MZvHz3+ehQTeABAOiOEh2AWMXjr889RkkpghTYJdkpPNsEs/l2gcCvvkOvUflPBGNqcYMiqOQFb7L29zPMZ9TZLs2TY7MO607H0fROO77hLDxYYNwLhxQHd0sfgLLwBTpsj1MWSy6ET8h6QehgGUHwE4o+075B0YRrBiqmzz4D2VFoYdqJ0b/SeNieq6hcDuP4nnjJmcIgvkxLCowtLnhrqzF/61VDCMgTlphiLik/scIOHvRoKjQrRz18MShr48Ulq1tTCGYaCsrKxvPDEznmXELzfH/q+If+TxSsmISaodsTw5183uAmZ+GVhwo7iTBmnAyBY51Y79bs5IqVs2f/PZVwEn/DZhsDEE7RuA1tez4Mpb3GTU5kyDQ/+VdpniPdD3fCRj+HxSehRO84isph0WSry2vPvjk8/5xNcKvHkZ8Oqn45MLBaAo2mvFFNl2bsnsc+bCg/YNskJ1uKhQPCRKnp7TtNbNWRn3VvU2x3avXAk8+igwcWLfwxsbZX9Gk9eDYXcBR34VWHpvdoyhYT9w4Pm0wyml1O7Ac9FnGCXPnzVz+npZ2JxiPHBUyOrrSFAWw3izlRgkiul9UTMnrxP6Y8aIJwYQN16Y7N0rxq3HHsvghO5Rkg+icno0L01F9FUmv2Uidk9adc1vu7Mh7WnT6tlyXfsPA717slYC07AIDLSJDNuwaDhFk9q5yNe0sNb95UgxjXWuGslbMRIclXKtufM3RrKKdvTAsAh2ux3z58/vu9OMZzlY/ERndXHEK9WYpNoRy5MX3cZ9MP734l/IJClzYIyYnGrHfjdnpNQtm7959czMCmV6RjG026Bk1OY8UVdmMwTUcIl5YJSWa3Q24fNJ6ZE3zRM940LdEoLGUS4h3zLGiCcgDnVLDPl80v6ObD1jBk6m5ZGiaK9VM2Q2zXcw/VCOwY6+hqtQSDwyMokrHvtsj1yHKiKr+xGQ/Tn0ntVet7IJolVvUzx+P8RIcc45khR43z4JTXPSSVnyvOhPNsJERkLA6q+KIdRZDYw6dsiPJNVu5x/kBQDOGvHsNY1iJiocTTI9Sfou30ExwHmboyuis8Rg+S9MfK2SxNzmAMafnpWvDYcloXcyTKeCr31Nro+U10OoN25ICvvj3j6J4ZcS22WGXth5aXfD8Qy3u8Rb5vBa0S/TVfqDYBoWr7yyb0LvxkYxXmRkWCxQpAHt+8tEOjYB+56RUHxHnDf4sWGv9E0qJIZPsx0MZ9FGiWtHA4ZFiEQiaG1txejRo2GzJdzE8xGvlIyIlNoRS5N33ewuwE73zGyQc+3Y7+aEQXUr1G8e84yiAWMwMmpzMQPG/pF9qY8eGCOFzyelR841Nz3m/IfiMd7DfhmMh3okeW35hMxXvzuroklvu4Gy7Bd7UNqiBoy6wg7Mi6K9Osoldn3PHqBrC1C/JPWx5rXU+b5MPjsqAEQkrIz3AOCqTt/7MvG6NBNBB9v7HpMj71ntdSubKBNxvc0D3rLbgVNOydH3evfJwpWqWdkJs2OGWeraBmy+DTjmmoHnNfWPLpiJRCJoa2tDXV1dXLtAuxgnKo4Qg2b/6wgAIgE5xnAAMOS5J9Am5/UfEsPHSFEK6Nwsf9clyX9h0rsLeP9XErIzSwaMF1/sO0GerGh79gCv/bsFJx6XZPFRoA3Y/L+S8NpVLyECU+UE8YwGFGRBUapjkrTdnLa7ZPe5NMoUo26hGDB6B/kRh8mIDYvOark+u96XvjKZ0T6HkQa07y8T8bcC+58FamanNmCY11LvXnlGMuPy+VtlnBQJyvWS7n0uEozf55IZfktAOxowLEIkEsH27dsxatQo/RtziUHt9CQvuvlagLZ1QOvLEtOzYVnf97mSf1iwzelJXnQLeYGmx2UV3FHfSD4o9rXIIDMSirvz+g72DTvBdtmHjLTLVggp8/N5dI8uNthXlh451zyVx9zW3wCH3gRq5wPH/Fd6fagKS19dNh6ATVYmBjrECyPsy37Zk5ZBxT0wagtvwCiK9lo1SwwYnUMYMDwNwNLfA6svlXArM74khuumx2XF+azL0r8fF9B7VnvdzETe/hHeszOl6QnJmdX4MWDGF0Z+Pl8L0PQXCbt0+C3g0KsDc6k4q+KT5QAMpeDu7YVRXh5/XnTVi/GjZnbq66lnF/DW16Ihy6Irqd1j4pPD2VgJbRjA8b8FOjZKmJ9UmAm8e/fK5GYWvMj2pREJa3RVC6YduAh4qV8Oi0hQ+vVICHA8D5z8+OBeKf2MSimP6dd2c9ruRtqfjDsNGHNydr1xEhiRYdHTADSeI+2vchpw9HcHHpPDcZD2/WUiZt85mKHKvJa2/gbY/y9g9PHA9EvES3DDj+Szjeek93vbHEDFZBmvzrosuWdWCWhHAwYhhOQCXwvwykVA13axsgNiLU9MkuWul5saJ0sJyQ42J7D7YSASBqb+58C2ZbZL/yGZPOveKfkWX/sMYokX2S5HhmecbM2krMMdTB/1dXG3rpicvbIRQkZOMo+5I68A3rwU6N4q/etQ/aezWowU3v1isPA0yIBehaSftrvyk2+qt0kmz+0uoHqESTWJUDVLJmq63h/6WP9B8eDxNACTPiETw/v/CXj3Sj4NWwbxihzRHAW8d2fG+DOAcR/Kb263SBhoeVH+HrUoO+cMdko4Fvdoef4I9kTDFkWf7cJeCbcESF3tZVBKIWwY8l7ZWLkWA4dkYnIwz2BntRyfbIW+6bGRjf7L7h7693HXx0PS9O7pEwZsuIwfP/Qx1WWdqHQcEq87e9RtLuyNe/LYPVI2dz1QNYgBxsRq7XYknuFW9ur2tQIHX5BrZtal6WlDkmOGRQt2Sf+Tqr17GmRR3egTJOyc+ZsfeQWw/lrg4CpgykVDX2+7HgFgSJ8waWXWEsTrBg0YhBCSC0w3Yldd/GE27I3Hdg57o+ESOq330EaIrtgc8kDZs1smQvq3LbNd2twADDnecADOaMgJtsuR46yWQXfYLxOR5ROGd56aOfIihFif8omS92vfP4EdvwPm3zD44Nq3X1YOlzcCc74jk26b/wfo2AxM/RQwZkV+vOFM7wsz8SoZOVXRXFRdW+IB81Nx4HnZNpwov3/VdEksHewWA0hNBkallleA924DxiwH5lw9/PKXGoWYbG1fJ95Wzursez6VTQDCveIBEDgMlCfkIPAfAhCJ5mZQACJwhw8CgaDYOTzjU4cMSmSoFfrBruTPoLnAMIDKKUD7RvnOLBgwTjpJ8irs3Zs8H7phABPGA5WVEOOFo0K8Wnp2AVCia/kkMU6XOkP1gflm10OyuKj2mMFDk5GhsbvF69x3UBZDDDZmcZT1zY0KSJ6e2mOA9g3Azt8DR30t9ed9B4F9f5e/p/6nta6pPKO5307xYBgGampqCp7VnWQOtdOTvOnmrE6Ikwp5yHNUxFerkIxhm9OTvOlmrtjv2ZX6GHuZrA6zuWTLdjkoGWlnGMDcHwJL7mD+igLDvrL0KKjmky8So3D7+rhhIBmRILDllzL4P+I8YPyHZOJ69FLphw2b/J+PyT+L5L8Aiqi9Vk6RkBgzvjR0Im4Vkmtm7Knyv2GLT2ibCYzTxbzmzFWxeaJodMsnB1fJdsxy0T+bGHYxYgDi4ZVI4LCEKet+H+h4F0bnZjjgk8+Y3qPp4mmQfqr/y10PbLoZ2P5bye8yHEI9EqJq2/+Jt8pQpPPcmwF2O3DrrfJ3/8va/P+//zvhPRWSkHEqLAax6tnxcW+OsHy769kDrPsB8M5/FbokcXqbxcMNAKb+R0EmwS2vW6aUN8p2OPlODAOY9ln5+8BzEhUgFTsfFKNs3QKgbl7m35UFrKIdDRgWwW63Y/bs2bCnnYGHWAVqpyd51c1VK9scP8yVCmxzepI33dIdyDkqgFGLZfULGZSMtas9RhJKDndiomsb0Pz34Q/+CQD2laVIQTX3jAYmfFT+Prwm9XF7HpN47a4aWUloMvFjwIkPAtM+k9Ni9mHSJ4DJnwTqj8vfd6agaNqrzQnMuAQYu2LoEFBHfR1Y+jugJuE+PPEsYM5VwMSPpv+dSknOOSDvxqii0G33o8D66/Jzzw0HgNZX5e8xK3LzHZ4xMvYyw4qZmAvKbG7A7oZhd8PhqYVRM3tgvozh4qqVEC9KAe/fMbQRLxnt6+U56NDr6YVRq5gi28EmQDNk5Urg0UeBif3sgY2Nsv+MMxJ2BrvEeGH3ANVHikEox1i+3Tkq5D7YsVF+Hyuw8/dyPdYvLpiHs+V1y5ShDBihHmDNt4A9f05ujKyeJYZcpYAd9yU/R88eMXAAYngqEFbRjgYMixCJRNDU1IRIZBg3OVJQqJ2e5FW3yunyMF09K/ffVQKwzelJ3nQbzIChFBDolPBGMfgoNBR5b3OH3pQV2vuezs/3FSnsK0uPgmt+xPnAgpuA6Z9L/r53H7DrYfl7+hdkksfEVZP/cDY1c2RCwAK5dgquXaFwVvVdCVx7jCTAzSR/QO8eINBWkFwmRaFb+3q57/bsyP13HV4tSWg9o2Wlfk6wSS6Wmn4LVNz14p1RfRRQOx+qdh68rqlQ2fa+nX6J9G1dW4G9T2X+edP7KFmS3mSY/VdvdjwwTFauBHbuBJ5/HnjwQdnu2CH7++CqE8Nh5TTk65na8u3OPQqoOCJqXB3EIzFfdO+Q3BdAQSfBLa9bpsTyYKQIJ9fyCtD5nni+GCnaxpT/EENloAMIeQe+v+sPch01LC3oXJJVtOOo3SJY5YIgmUPt9CSvuhl2WR2TrdU9JQ7bnJ7kRTdfizzkhXqAzk2ymrBrm7xa3wA2Xg/4WwBvEyT+MUmHjLXr2SOTlM1/H94X+g/K1s0QVCOBfWXpUXDNXTVA7dGp3996l4SQqlsgk9QkRsG1yybhgMT1PvDv5O+HegeG9xkJ5gRh9RzxAMkjRaFbeXQSrndv7r/r8GrZNpxc8DjuSgE+nzdpnocR4aoFpn1a/t55fzT3Rga0vS3bdHMUVE4HFtwIHHtbZt+TBnY7cMopwIUXyjbl4mubO6/jXC3analfpuHwcoFnHDDlYmD8GVnJkzJctNAtE8Z9ADjp0dT5K8w8T2NPTd3flU8AFv4MWPQ/kiujP9M+J6E2p3wqK0UeLlbRjvFMCCGEEKI/vhbglYsAf6usNAKAF1fKZEagEwgcgmRpBOCsif9Nsk/vHmDHAxJKYMKHM/+874Bsy8Zmt1yEkPzgawF6dkvM+cTJkrGnSPs+4pPJB/MtLwPNf5PklpP6L/PNQpkSV0ke+Leskq0+Uibf8pE0vFTwHQDe/q54RDScNDAMzoF/A+//Chh/OnDk5QM/790vq4XtZUDj2UN/n5n/wgK5TLTEXEXszbIBo3+bA4DxZwKVMwDXKHk/220unGQFc//95t9KwRbxAiGn9EepPjscxn8Y2P8vCW327k8kJ0wyTE8j83fyH5LFN4Yt6sWxbei+ye4uXDjUdH7vUmXUQqDpL2KQGiqZd7K2kki696fBzlO/JDPPNjI0dk/q93wt4t0GAGNOGfw8VTMG127CR5mnMQoNGIQQkkv4YEdIfgh2ysDP5pGwATanrPTtbQJC0fizhk0GAI4K8dLoD9tldvBEDQ+mISJTfPTAIERbfC3Aqo+JF5zhAMonDZy46doCLHtw4IRMoEMm/AxHdg0YMQN3dCW0UhIuR6lovh6XhJdJViaSOeWNspI05BWDduWUvu8f/Hf0uEnJP9+zE9hxv3gGDGXAiITjk0S1NGAMi1x4YPRvc8nIZptzVsv5/IeAiD/5MZ7R4nwb6gIifhhKwR7phRGMxPsod312JnkNA5h8gcS1P/Q60PTEwJwcgIRQM8sEyLOsr0UmRl/9z3iZrNY3mb93zy4g2A04Kwd6YGTrt9SVmqMlF5zvoIRPLJ+Q/LhstZV8tzkyOAdXybb2mKF/b1M704jhrJH9ic9O1A4ADRiWwWazoaGhATYbo3rpBrXTk5zrls6DdKk/2A0Ttjk9yZtu9jIxUIR7gO7tkljQ5gLcDQCUPBSGe9kuMyBj7TxRw0OgXfKN2N3pf5mKSJgvIG4IIcOCfWXpYQnNg51iDDacACLSph01MpEDyHv+Q3Jc/4F4TTR/Qed7Q69YzbRM/kPR5L1lQKhbwnvaXGIoHaxMecIS2mULwwCqZooxqmtLXwOGdx/QsUmOSRVGrHauLDjo3SuTf57BjNkKOPIKSZZbNT2btUiLotDN9MDw7ReDUDqJo4eif5vrT7bbnKdBJveGWsVulg2AikRwqGkPyhsnwTD1y6YnlqsOsJcDjip5numf3DrsBXyt8e+1lwHBDukr3aMBZ236v1Pn+0DLCxIqaOJHs1P+wTB/7823AK2vAxM/AjR+ou8xOfRq06Ld2T2S56V9vYSRSmXAyFZbSXaeULf0uWXj5frjfS777H4UOPwWcMR54j0KyPOLmXh77KlDn8PUzrdfFt85qoBgu/TFngkylqV2AGjAsAw2mw3Tp+f/oYuMHGqnJznXLd0H6RK3og8Htjk9ybtu9nKZODfsQMVUefgLtgOLbhEjRSrYLgeQsXaOSllpGOqVyaeKFKtsk+E/HJ9AcY/KvLAkBvvK0sMymhs2mTDxHQACrYD/gEzglR8h76cyIJdPlrBDoR7xnsuk70gH08AdaBMvD/eoeCLxVGXKE5bRLltUzRIDRucWCRVlYubFqFuQuo93VEhor45NEn4l8fP9sTmAhhPlVQCKQjf3aGl34YDkoCobn71zm21OBYH2jYC7buh+YLh4GtJ7foseYwMwuWZmdsvQH3e9GCLMfqY/5jjR/J0cFbLwwzMms77J2wTseVxyEOXDgAHIddO7V8o57vS8GhC1aXejj5dxyGDjDhPzGkhGJm0l8Tw9O4FIQD7vqud9Lhf07JacT3UL4waMnh2SD9DmzOze5B4jz03+g7L4I2KTMZWpYQGxinZFZPrSm0gkgm3bthU8KQrJHGqnJ3nRzdMgD3OpXpwkHRZsc3qSf90MmQCpnt03Rqm7nu0yQzLWzjCGH0bKPN7dIJOgZNiwryw9LKW5u16MyJGQrEaMhIb+jM0uE98A0Lk5d2UzJw0t5G1nKe2yQVV0Yrjr/fg+pfomNR2MugWytUIC3EEoCt0MAyibIM9K/sO5+Q7/YZmEC3bDKjnI8q6dCklItd7d8vI2izE10CZ/Q4kRt25h5gmxK6bItnsnsp+VPAW9TVJ2uwuoPio/3xlFm3bXeA4w/0fA6BPy/92BNlkMYNiza5QcAdrolgnljbLtbUrYaQPGLAcalqc2SiXDXS/9sIr+Pp6xYgSxAFbRjiNDixCJRNDS0lLwC4JkDrXTE+qmL9ROTwqim2GNhz7dGZZ2ZsgPM5/FYPhaJFFl1zb5f+alwMSz4vt8LZkXmrCvLEGspbkRD01jc6TvTWFOhOXKgKGCEmIQsJwBwzraZQHTENWzU1aUAxJOyrtPDFujlw7++bqFsm17J/WEbDgA7HxIPDXyNWnbj6LRbcFNwPJHZAV/1lEJua3SWImeJ/KqXcQPdG2V69+7X17+FiDYJfkv/C2QZBjDpLxRFn2EegbPgZBN2t6WbfUcMWLkkaJpd4lEQgCyVR8Vn1AvG2uZ8VBR6mYaMLwJOYQqpwBzrgaO+nqGJzPi57OQ4QmwjnYMIUUIIYQQQrKLZ5xsffsHP45JBwkpXtyjZWsvS38CpfpI2Xa+l5sy+Q/JZLejUnJgkNzgrpcQUf7Dko+qZnY8qenopX09I5NRNVNCEQa7gO5tQNWMgcd0bgJ2/h7Y9zRwwr1Zr0JJkckq4Uzxt0ocf5sjmo+sBDHsYjBN/J3Dfsl5YRiAoxoj8kyxOSUZe88eoHeXJCzPNe3vyLZufu6/S3f8h8XzLzEfUCKRIODdKkndq2YCMMRjxxjmdG1im/NYZxK8KEn0wOifu2s4ebxco4CqaYDNM3z9ixj+IoQQQggpHsLezPaT3ND4MWD8GUMkX0X+E30SQvKLO8OJtOojAWeltHcVyX4ouVDU+8JCK8GLEsMAZn1VJm0rp8m+qZ8WD5t0JtRsDknm3faOrFZPZsAwV4DXzs9ewneSZSKSJwGQMFX9E1mXCoZD6p9IqCfev3nGYsShtSomiwGjZ1c8Fn+uiIQl7j8g7Y+k5uBLwLs/ESPuwpsHvu/bF129bwBhp1wX3r3iYVZ7zDC+kG0ur5SNl/tPqFfCdnVukpBu5ROHf05XHgyQmkIDhkWw2WxobGwseFZ3kjnUTk+om75QOz3JuW7OapmQ8h9KnejMXW+pkCG6MCzthjJc9MdMOmiGUXDWxeO+Fjhxna6wryw9LKX5cI3JrjrxuMrFhHTYK95hzhrxvjCNGRYwcFtKu2xRv6Tv/3Y3MObk9D8/6zLAUSXGjGS0mSvAFwyreNmgaHTztQJbfw2Ee4H5N2TvvL3Nck6bW+7xpdjm0ukLs7H4pnwygJckD0auCXaIN0FvU16Td5to1e4SvQpDPX29cLq3A+/+VAxCjjLRUEWAQEc0Z0pT8sU9g+E7yDaXT2xOea7w7hNvwc0/B8I+YPGtceN9ulh4EZ5VtKMBwyKYFwTRD2qnJ9RNX6idnuRcN0+DTHqZyVmT4azmKv5hkNc2522WsAo1ZZZJXKcr7CtLD0tong1jcraNF8nK1L9sBTZwW0K7bOJrGfn92FWX+r1QD9C9Vf4uYAibotHN7gZaX5O/w76hQ3wNhbNaQqF07wRUWP7ufz0Ue5tLpy/0jJbUF6GukS++McMT+fOQN8w9SvKmRELZ95JLA23andkPOisB7wGg+el4fp/u7cCWXwIhL+Cqkf4u3C3vOSrkuundDZQfIX3lUNdA4vXmrgdglF6byzemvvZyGbPs/qP8/p4xQCQi76cz7tRgEZ5VtKMBwyKEw2Fs2bIFs2bNgt1ONy+doHZ6Qt30hdrpSV508zTQQJEDhqWdUvIg79sPzPhiepMhYZ8YLwxDBgNkRLCvLD0soXk2jcn9V6uOpExLfw8EWmVV6kjKlCMsoV22SMxtFOyS1cBhn/zGzlrp4zPNbRQJ9fXEaF8v95nyiQUNB1Y0ujmrRJ9gpywkyHTlcH88DcCJfxADRsuLwMSPDTRMFnubS7cvBLLTX9YtAJbeN7jhL9uk8o7KMVq0u8R+0N8qXhVrviFhFcNeCY1nOIDJ/w84/jdi6DOJBIF118hnJ30CmHzB0NeABgu5tNAtXRL1NXNftL4qoaTcdcDLn0z/Pkft0oYGDIuglEJHRweUUoUuCskQaqcn1E1fqJ2eUDd9GZZ2hgE0PS6TVxM/ljpxYSKBNtk6qxmzNwuwzZUeltF8pMZk737gne/JJM+y32fHI8N/EFj/Q2DcB4GZXxn5+bKMZbTLBom5jUL7E7xeAtFVxhnkNjr4giTqHrUEmHFJfH8sfFRh4+8XlW7lE4GOTgn7NFIDBhDvB0YvGfrYApAX7dLtC7MxMWn3jNxzJh0iIWnDzqrcf1cKtGh3if2gu0EM8pFQ1IjrBnAQsNmBWZcmf0aedRmw6X+AlpeAaZ8e+vsCHYCj0tILubTQLV365/BTITE82RyAZyKASGY5/Cy+CM8q2hVR8DFCCCGEEGIZzDwYvoPpHR80DRh5XD1ICLEe7tFAsF0MoN7m7JzzwL/FwysSzM75yNDYywBXrawyNhwSJ9xRkWFMd5tMqJsJu00635MtEwhnDzPJtHfvyM/l3TfycxBr0rEBeOViYP11hS6JHtjLJEm7zSleFjaHhA6rngV4xqf2ChyzQgyJoV5g1yNDf8+2u4E3vgAcejO75SeDY+bwC3tl8ZWzVkKsZZq7hKQFDRiEEEIIIST7eMbK1ndg6GND3fGkhWZy3VCPJRLXEULyjM0BVEYTw3ZuHvn5wgFZxQoAY08d+flI+iSuCHePzvzzdfPFA6dnF+A/HN+/8KfAop/F48mTkVMejW/eO0IDRvd24I0vARtvlOTEJH+0vAqs/xHQ9NfcfUfbOxIyx1GZu+8oNgy7hEZ1lEvOC0AmuAfzLjQMYNpn5O+2NeK9kYru7WKkD3RIvhmSZyJAz275083fP5fQgGERbDYbpk2bVvCs7iRzqJ2eUDd9oXZ6Qt30ZdjapWPAMBPXBQ7LymjDDoR7oquv2yX0SIET1+kK21zpUVSaVx8lW3Ol/Ug4vFoMop7RQM0xIz9fDigq7RJx1UuSX/coMU5nirMKqJwhfyd6YdjsQPWRgKOwq1yLSrdseWBs+z+Z4LY5RSeLUlTamfgPAofeADrW5+47LBC+TUvt3KMljJ4jgxxvoxYCR38XOPa2wfONbL9XtmNXAFXTR1TMXKKlbmlhE11tzuEZ6jXAKtoxB4ZFsNlsGDNmTKGLQYYBtdMT6qYv1E5PqJu+DFu7dAwYZuK67fcC+/4u+TImntX3mAInrtMVtrnSo6g0jxkwsuCBceB52Y45JTv5NHJAUWmXiN0jyYWNEUx61C0Aut4XA8a4D2SpYNmhqHQrnwjYXcMzNJm0vS0vmwOY8h/ZKllOKCrtTComy7ZnV27OH+oBurfK3wUM36aldp6x4h1hc2b2uYZlg7/f9jZweC3bXKGpmRNN5m1do+1IsIp2xWb60pZwOIx33nkH4TDdLHWD2ukJddMXaqcn1E1fhq1duiGkPA3AnKuAkx4Dpn9WVo8lvmi8GBZsc6VHUWlefaRse3YCYd/wzxPsEg8MwNLho4pKu/4YDoxo2qFugWzb1soE0dvfBd77hYRLKTBFpVv5EcDyR4EFNw7v80qJ9wUATPgIUDY2e2XLAUWlnUnFFNl690nOn2zTvl50Lp8oHm0FQlvtMjVeJBIJAa2v9d2nVNz7gm2uwNiK1ngBWEc7GjAsglIKXq+34FndSeZQOz2hbvpC7fSEuunLsLWLGTDSTOLtqmGoqCzCNld6FJXm7nqZIFMK6Nwy/PO0vCQTP5XTgIojsle+LFNU2pmEvfF8RomvTHMb1cwG7G4g0A60vgq0bwAO/Ktvfo0CUVS6GcbIPJQOviCx+B3lwOT/l71y5Yii0s7EVSvPUkrFY/JnEwuEjwI00y4b/WAkBLx1JbDhhr6h9FpeBLq2SSg9trnCkK37nMWxinbahJC67bbbcPfdd8MwDPj9fixZsgQ33XQTJk6cCADYtGkTvvzlL6OjowOGYeCaa67BypUrC1xqQgghhJASpWw8sOSOoT0owgEJW0EIIYmMORUI98qE3HAZtQSY9mkxiJD8YOY28h+SPEbJyCS3UaA9nrvkwL9lcqj6SKC3Kf599NTLL74WINgZ/z8SBLb8QrQZ9yFZ/T+CxeZkGJiaOKuB3mYx9iWGbjPbW6Ju/RmqLbVHDRgFDB+lDdnsB20OMcJ3bAI2/xw4+r/F2Nj6Gttcocj2fY6khTYGjLPPPhtf/OIX4fF4EAqFcO211+Kss87C2rVr4fP5cM455+Cuu+7CihUrsH//fqxYsQIzZszAvHnzCl10QgghhJDSw+YAKiYNfkwkDLz+OaB8EjD7Kkn0SgghADDtP0d+Ds9o4IjzRn4ekj5mbqORTJSa+FqAVy6SSSIA8O6XCbueHcCh12Wfu16+j0aMkbPvn8DeJ4D6E4CpFyc/pr8mgCQn9u4DoIDAYaD5r9QknyRq4m+V8Grd2/oabp1VgAIQ6kp9nsHaklLApJXiAVA7N9s1KD6y3Q/ufTKa7+ItoPUVwFEp74X9wPZ72ObyTTb1JWmjjQFj6tSpsb8dDgeuvfZa3HbbbWhubsbq1auxcOFCrFixAgAwbtw4fPOb38Q999yDn//85wUqcWbY7XYcddRRsNuLN25asULt9IS66Qu10xPqpi851a5zkwy0VQRwjmCVNRkA21zpQc31pei08zRkZ+Im2CmTsjY3YC8DeveKcdw9VkIVhb3yfrCzIBNFRadb2Ad074yHgExGf01M3KPjeRcKqEm6FJV2iZq4RgEhr4RYc9bK+2Ev4GuVv53VfXUzGaotGQYw7jR5FRhttMtmPxjsANwNQOAQEOwGyiYCMMTrosD9YLpoo1u6ZEtfDbCKdtoYMPrT29sLwzBQX1+PZ599Nma8MFmxYgVuvfXWpJ/1+/3w++NuPp2dYjULhUIIhUIAJMu6zWZDJBJBJBKJHWvuD4fDfeJ/pdpvt9thGEbsvIn7AfRJglJZKVZUpdSA5CgOh2PAfsMwYLfbB5Qx1f5C1Gmw/cVUp8rKSoTD4aKqk0mx1gmI61YsdSpGnVLVqbq6Oq266lSnYtSpfxmrqqpgGEZR1akYdUq2v7KyEkqpQfVLWqfDbyLS8hoidQuA0ScOqJNx8GUYSkHVHQubYQOSPANRp+HVSSnV5z6X7Tr1308Kj2EYqK2tLXQxsks4ICuJyxtlBXG6KCUhbermA6OXjix5ah4oSu2ySeKEq+GIJhCO5mtIFb4jDxSdbuWNsvU2D32svQxwVPTd56gSD5kCapIuRacdIJq468WrFRAdIkHpD01NlIK4YkCMgIlpcTXQDShS7dKhfIIYKyIB8XQqT8jrpIF2JatbEWAV7bQ0YGzcuBHf/va38YMf/AButxvNzc340Ic+1OeYSZMmYfv27Uk/f+ONN+Laa68dsH/t2rWoqJCbcENDA6ZPn44dO3agpaUldkxjYyMaGxuxZcsWdHR0xPZPmzYNY8aMwYYNG+D1xhO2HHXUUaitrcXatWv7DEbnzZsHl8uF1atXAxCjRUdHB0499VSEw2GsW7cudqzdbseSJUvQ0dGBzZs3x/aXlZVh/vz5aG1t7VPXmpoazJ49G83NzWhqaortz3edTBYvXoxAIFC0dTK1GzVqFI477riiqFMx6tS/TocPH8abb76JmpoaGIZRFHUqRp2S1enoo4/Gq6++CofDASOabFD3OhWjTv3rpJRCb29vLNRjMdQJKD6dktXJvM9Nnz4dM2fOzKxO3ZvR9f5jOORpxsFqd986rVmDyQeegjPcjr1GNaZN8VKnLNZp27Zt2LZtW+w+l+06rV27FsRahEIhrF27FgsXLowt1tCedf8tcb9nfxMYe0r6n+t6H9j3DHBwFVB/HKweHLwotcs2PTtka9gQM14UmKLTrVzyi8K7TzwjE3MoJCMSjK4Mr4dVNEmXotMuRoIO/hbJh6FCYgw2AATaxAgIALXHAPbyoU+pFLDvH0DNbJk0H0my9yxQvNoNhU2MGN07JZyee3R6+lmE0tVNf6yinaEKnUY8A6666ircf//9OHDgAC655BL8+te/hs1mw2mnnYarr766jxEjEonEVqUZ/TrYZB4YkyZNwqFDh1BdLUlW8r0iLxwOY82aNVi8eDEcDgdXGWpUJ1O7RYsWwe12F0WdEikWnfrXKRgMYvXq1Vi0aFHsvLrXqRh1SlYnpRTefPPNmHbFUKdi1Kl/2c2+csmSJbHy616nwfYXU51M7Y499li4XK7M6rT/Gagtv0CkbjHU0f/dt04dW2Fb8zXA5kLkhN/B7qrIW536l70YdOpfxkAggLfeeivWV2a7Tm1tbaivr0dHR0fs+bnU6ezsRE1NTcF+k1AohNWrV8fGE0XB1ruBpr8AE88CZn4p/c+9/2uJGT72FDF+WJyi1C4bdG0DXjpfQuEYNqBnp6wuN+O/h3qAYDuw/I9A1fS8F6/odFMR4MXzxDBx/F1A2biBxyRq4m8BfAcltE1lNOR3gTVJl6LSLlGTRK8Y337RJxKUlfuAeGmYHmlVM+PeTYPp1tsMvPElCd924h8kPFUBKSrt0qGPvuVA9w7pDyumyPtscyTH5FK7TJ6dtbpqfvrTn+KnP/0pDh06hB/+8If47Gc/i/vuuw9utxs+n6/PsV6vF263e4DxAgDcbjfcbveA/Q6HY4AY5qCuP+bgLd39qURO3G8YRuyV7PhU+1OVMdP9uajTUPuLpU7mJIL5dzHUKZFirZOpW+L7utepGHXqvz8UCiXVbrCyW71Og5WxmOpk3pOLqU7D3a9bnQzDiP2dUZ08Y2HAgD3QAvT7bkf7W7KKb9RC2NyVea9TOvt106n//mR9Za7rREhWqT5Stp2bBz8ukUgIOPiC/J2J1waxNvYyoHp2oUtR3Bg2oGwC0LML8O5NbsAwCfvFgAGUTBx47fCMk1eoB/AdiO4bOzD0lwoCvXsG7jdpf0e21UcV3HhBDKByWqELQUhBGMIn0JrU19fj1ltvxZ///Gd0dHSgsbERu3fv7nPMnj170NjYWKASEkIIIYSQWCJQ34Fo3OUEWl+X7egT8lsmQog+mAaMnh3xBMFD0bZWkpm6aoHaBbkqGSHFiRlGqnfv4Mf59sl93VUX94ghetK1TXIqePdKSLD+tEUNGLXz81suQghJQNulU36/H4FAAOFwGMuWLcNTTz2Fyy67LPb+qlWrsGzZsgKWMDPsdjvmzZuXcvUbsS7UTk+om75QOz2hbvoyIu3MVZlhf3RCsUb+VwqYcCbQWgvUL8laWUkctrnSo+g097UAgU4JdxJoB/Y/B1TPir/vrJY+xtci/YvJ7kdlxfHopRJyyDzOwhSddtkm7M1sf54oSt0qJgO9TYDNlfqYsC+aS8EOuEZJe4u9V1hN0qUotUunnSQ7xj1a+thwAHj3ZuCYa+KeFkoBLS+Lxq46MXYUuE8tSu3SwaL9YLqUrG5FgFW008KAEQgEcPDgwZhHRXt7O774xS/ivPPOw6hRo3Deeefh+9//PlatWhVLDvqzn/0MDzzwQIFLnhku1yAPCcTSUDs9oW76Qu30hLrpy7C1szkB9yjAf1jiMJsGDMMAxn9IXiRnsM2VHkWjua8FeOUiwH9IkpWGeoDVl4lXhYm7Hjj2NuCtK+Q4QGL49+yUSTffAWDPo3Lcsgctb8QoGu2yibNa9PMfAiIpPHDc9XJcgSg63aZcJK9UOKqAUK+0NUclEPHJK5ECa5IuRaNdOu3EMxpQAEJdyY8pGxf1wtgPPPchMUzZXbIApbdJwou9/R15frNAn1o02qWDBv1gupSUbkWGFbTTwoDR0tKCc845Bz09PfB4PLDZbLjoootw5ZVXAgAqKirwxBNP4NJLL0V3dzcikQiuvfZaHH/88QUuefqEw2EmtNEUaqcn1E1fqJ2eUDd9GbF2nrFiwPC3ApiZ9fKR5LDNlR5FpXmwUyZrbG6ZmIn4AQOSxBSQFaf+Q4B3X/w4e5lMtjlrABWSvsc8LthpaQNGUWmXTTwNMlGa6GHTnwKuBi9J3Xp2AFWzAJsBzLtBVuX3RwOvp6LSLt12Agx+jAoD638kYfjCAaBqBmAEJXm3s1q0tkCfWlTapYPF+8F0KTndigiraKfFVTNx4kS89dZbgx4zf/58vPzyy3kqESGEEEIISYs5V0tiSDMcQagH2P8vyX3hGVPYshFCrI29LN5/OKr6JplNXIlqHueoEK8vRBBL95hqxSrRA0+D5SfmihIzb5Vh9N3vqgNGzQdq5gL1i/NfLpKcdNvJUMfMuQrY/4wYgf0HJJSY4ZAwU2b/yz41/7AfJEQPAwYhhBBCCNEUd33f/w+/BWy9C2j+G3DcnYUpEyFEH+xl8gIk7n6oSzwtwr3A3ifFuyvUC9jdQNl4wHAiZrwghGTOO9cAnZuBhT8FKqf0fa96FrDgZlmtT4oPR4X0o6EeoHKqhAINdsX7YEIIKRA0YBBCCCGEkPzR+rpsRy8tbDkIIfoR6pYY7SoEREJAy6tAoAOw9URXCTcAdmehS0mI3oS9kqi7t2mgAQMQrwyDU0lFi2EDyidJaD5AwvIRQkiB4V3HItjtdixevLjgWd1J5lA7PaFu+kLt9IS66cuItfO1ALsfkcnGWZcBh1fL/np9cpXpCNtc6VESmjurZfI07Bevi3ErAN8+wFEuHhg2PYe3JaFdEVK0upU3Ap3vAd7m+L6mv4ix8Ihz+4Zy05Si1a4EoHZ6Qt30xSra0bfWQgQCgUIXgQwTaqcn1E1fqJ2eUDd9GZF2KgI0/x04uApoXyeTjq5aoPrIrJWPJIdtrvQoes2dNUBZI+AZJ7kuxp0uW8842W/o631R9NoVKUWpW9kE2Xr3yjbYCex8ENj9R+DQ6sKVK8sUpXYlArXTE+qmL1bQjgYMixAOh7Fu3TqEw4wlqRvUTk+om75QOz2hbvoyYu3c9bJiOhIE9v1d9tUfPzAxKMkqbHOlR1FqHvZKLPb+r7B3eMdZlKLUrgQoWt3KJ8q2N2rA2PWILD6onAaMOblw5coiRatdtrBwn0rt9IS66YtVtNPTx5YQQgghhFgfX4us3LS5Af8hYN+zsr98ItC1TcLBeBoKW0ZCiPVwVovx038IiPiTH+Oul2Sz6RznrM5dWQkpJnwtEvIx1COJvFtej4eBHPdBwN/K+3Yxk27fyz6VEJJnaMAghBBCCCHZx9cCvHKRDIK9zUAoumrPsAHrrxMPDHc9sOxBToYQQvriaZC+IdiZ+hjTAJrucYSQwTHv275WoHu77Dv8cfG+cJQB63/I+3axwz6VEGJRaMCwEIVOiEKGD7XTE+qmL9ROT6ibvgxLu2CnGC9sbsBRJSGk3GMkhr2jXMIQ+A/JcRwI5wS2udKjqDT3NKTXN6R7nMUpKu1KiKLSzbxv2z2Aq05W4EcCgM0BVEyVY4rovl1U2mUTDfpUaqcn1E1frKCdoZRShS5Eoens7ERNTQ06OjpQXU1XOEIIIYSQEdO1DXjpfMBZCwQ7gN4mGRCbkyChHiDYDiz/I1A1vZAlJcOAz88D4W9CCNGaxPu2owLo2gIE2sXronI679uEEEKySibPzkzibRGUUmhvbwftSfpB7fSEuukLtdMT6qYvWdHO5pJtJJSdQpEhYZsrPai5vlA7PSl63SqmyMKD8sZClyTrFL12RQy10xPqpi9W0Y4GDIsQDoexefPmgmd1J5lD7fSEuukLtdMT6qYvWdHOPQqoXwxUzcxewcigsM2VHtRcX6idnhS9bjaXeE3a3IUuSdYpeu2KGGqnJ9RNX6yiHXNgEEIIIYSQHMM1M4QQQgghhBBCMoejSUIIIYQQQgghhBBCCCGEWA56YFgEwzBQVlYGwzAKXRSSIdROT6ibvlA7PaFu+jJi7cLezPaTrMA2V3pQc32hdnpStLqVwH27aLUrAaidnlA3fbGKdoYqdBYOC5BJ1nNCCCGEEJIGvhbglYsA/6HUx7jrgWUPSpJQohV8fh4IfxNCiNbwvk0IISSPZPLsTA8MixCJRNDa2orRo0fDZmNkL52gdnpC3fSF2ukJddOXYWvnaZBJjmBn6mOc1ZwEyRFsc6UHNdcXaqcnRadbCd23i067EoLa6Ql10xeraMerxiJEIhFs374dkUik0EUhGULt9IS66Qu10xPqpi8j0s7TAFRNT/0qgkkQq8I2V3pQc32hdnpSlLqVyH27KLUrEaidnlA3fbGKdjRgEEIIIYQQQgghhBBCCCHEctCAQQghhBBCCCGEEEIIIYQQy0EDhkUwDAM1NTUFz+pOMofa6Ql10xdqpyfUTV+onZ5Qt9KDmusLtdMT6qYv1E5fqJ2eUDd9sYp2hlJKFbQEFiCTrOeEEEIIIYSUOnx+Hgh/E0IIIYQQQtIjk2dnemBYhEgkgqampoInRSGZQ+30hLrpC7XTE+qmL9ROT6hb6UHN9YXa6Ql10xdqpy/UTk+om75YRTsaMCyCVS4IkjnUTk+om75QOz2hbvpC7fSEupUe1FxfqJ2eUDd9oXb6Qu30hLrpi1W0owGDEEIIIYQQQqLcddddmDt3LubPn48zzzwTe/fuLXSRCCGEEEIIKVlowCCEEEIIIYQQAP/4xz/wm9/8Bi+99BLeeecdfPazn8XKlSsLXSxCCCGEEEJKFhowLILNZkNDQwNsNkqiG9ROT6ibvlA7PaFu+kLt9IS6DY9f//rXuO6661BTUwMA+OQnPwm73Y633367sAVLA2quL9ROT6ibvlA7faF2ekLd9MUq2hlKKVXQEliATLKeE0IIIYQQUuoU6/NzTU0NmpubUVFREdv33e9+F6NGjcJVV13V51i/3w+/3x/7v7OzE5MmTcKhQ4div4nNZoPNZkMkEukTO9jcHw6HkTgcS7XfbrfDMAyEQqE+ZbDb7QCAcDic1n6HwwGlVJ/9hmHAbrcPKGOq/awT68Q6sU6sE+vEOrFOrBPrNNI6dXZ2ora2Nq3xhGPQd0neiEQi2LFjB6ZOnVpwqxbJDGqnJ9RNX6idnlA3faF2ekLdMqe7uxsOh6OP8QIAJk2ahPXr1w84/sYbb8S11147YP/atWtj52hoaMD06dOxY8cOtLS0xI5pbGxEY2MjtmzZgo6Ojtj+adOmYcyYMdiwYQO8Xm9s/1FHHYXa2lqsXbu2z2B03rx5cLlcWL16NQCgt7cX5eXlWLx4MQKBANatWxc71m63Y8mSJejo6MDmzZtj+8vKyjB//ny0trZi+/btsf01NTWYPXs2mpub0dTUFNuf7zqZFHudTO2KqU7FqFNinY499li8++676O7uLpo6FaNOyerk8XhQVVWFiooK7Ny5syjqVIw6papTJBLBCSecUFR1Kkad+tfJvM8VU52KUaf+ddq+fTt2796N8vLyrNepv+FlMOiBAWusIAuFQli9ejUWL14Mh4N2JZ2gdnpC3fSF2ukJddMXaqcnudbNCs/P2aapqQnHH3/8gKTd99xzD1atWoX77ruvz36reWCEw2GsWbMGixYtgsvliu1PRJcVecnKrvsqw8HqFAgEYtrZ7faiqFMx6pRsouXNN9+M6VYMdSpGnZLVKRwOY+3atVi0aFEfI7/OdSpGnZKV3bzXLVmyJFZ+3es02P5iqVMwGIzd55xOZ1HUqRh1Slb2QCCAt956K3avowcGIYQQQgghhBQIt9sNn883YL/X60VZWVnS491u94D9DodjgNHIHNT1xxx4prs/lTHK3G8OQg3DSHm8YRhJ96cqY6b7s12ndPbrXidTM9N4Aehfp2LUqf/+UCg0QLfhlt0qdQKKTydg8Dplo65Wq1Mx6tTf0JRsf6rjhyq7Feo03P261CnxPmceo3udilGnVPuT3euyUSezLacDDRhAzDLU2dlZsDKEQiH09PSgs7OTqxs1g9rpCXXTF2qnJ9RNX6idnuRaN/O5uZicuUePHg2v14vu7m5UVlbG9u/ZsweNjY1Dfr7QYwq2VX2hdnpC3fSF2ukLtdMT6qYvudQuk/EErxoAXV1dACS+LSGEEEIIISQ9urq6UFNTU+hiZAXDMHD88cfjhRdewEc+8pHY/lWrVuGGG24Y8vMcUxBCCCGEEJIZ6YwnaMAAMGHCBOzZswdVVVUZua9kEzNm7p49e4omjnCpQO30hLrpC7XTE+qmL9ROT3Ktm1IKXV1dmDBhQtbPXUiuuOIKfP/738fy5ctRXV2NRx55BD09PTjllFOG/GyhxxRsq/pC7fSEuukLtdMXaqcn1E1fcqldJuMJGjAg8bzScQvPB9XV1WzMmkLt9IS66Qu10xPqpi/UTk9yqVuxeF4k8olPfAJ79uzB0qVLYbPZMG7cOPzlL39JGhO4P1YZU7Ct6gu10xPqpi/UTl+onZ5QN33JlXbpjidowCCEEEIIIYSQKFdccQWuuOKKQheDEEIIIYQQAmDopUSEEEIIIYQQQgghhBBCCCF5hgYMi+B2u/GDH/wAbre70EUhGULt9IS66Qu10xPqpi/UTk+oW+lBzfWF2ukJddMXaqcv1E5PqJu+WEU7QymlCloCQgghhBBCCCGEEEIIIYSQftADgxBCCCGEEEIIIYQQQgghloMGDEIIIYQQQgghhBBCCCGEWA4aMAghhBBCCCGEEEIIIYQQYjlowCCEEEIIIYQQQgghhBBCiOWgAYMQQgghhBBCCCGEEEIIIZaDBgxCCCGEEEIIIYQQQgghhFgOGjCKAKVUoYtASEnBNqcn1E1fqJ2eUDdC9IHtlZD8wjanL9ROT6ibvlA7AtCAoTV//etfAQCGYbBBa8TDDz+Mjo6OQheDDAO2OT2hbvpC7fSEuukLn1FKD7ZXPWFb1Re2OX2hdnpC3fSF2ulJrp5RaMDQlNWrV+Occ87BRRddBIANWhe2b9+OCy+8EF/96ldx+PDhQheHZADbnJ5QN32hdnpC3fSFzyilB9urnrCt6gvbnL5QOz2hbvpC7fQkl88oNGBoisPhwAUXXIDVq1fjwx/+MAA2aB0Ih8M47bTTsHPnTnzmM59BW1tboYtE0oRtTk+om75QOz2hbvrCZ5TSg+1VT9hW9YVtTl+onZ5QN32hdnqSy2cUGjA0ZdOmTZg3bx62bNmCpqYmnHXWWQDYoK1OU1MTTjnlFLz44osIhUL4/Oc/z0GHJrDN6Ql10xdqpyfUTV/4jFJ6sL3qCduqvrDN6Qu10xPqpi/UTk9y+YxiKCqvFUopGIYBAHjjjTdw3HHHAQCOOeYYTJ48GU899dSA40jhSKbDa6+9hhNOOAEA8JGPfAQejwe//e1vUVdXV4gikiFgm9MT6qYv1E5PqJt+8BmldGF71Qu2Vf1hm9MXaqcn1E1fqJ1e5PMZhQYMTejs7ERFRQUAwG63Jz2GDdp67N+/H7W1tQiFQqisrEx6DAcd1oRtTk+om75QOz2hbvrCZ5TSg+1VT9hW9YVtTl+onZ5QN32hdnqSz2cUGjA04Mc//jE2bNiA7u5uLFiwACtXrsSCBQtijTUUCsHhcABgg7YSN9xwA1577TU4HA64XC5cfvnlWL58eez9RN046LAWbHN6Qt30hdrpCXXTFz6jlB5sr3rCtqovbHP6Qu30hLrpC7XTk7w/oyhiaX72s5+pk08+WR04cED95S9/Ubfffruqra1VTz75ZJ/jgsFg7O958+apFStW5LmkJJHbb79dnXjiiaqtrU2tW7dO3XvvvWrixInq3nvvVe3t7bHjEnU766yz1Omnn666uroKUWQShW1OT6ibvlA7PaFu+sJnlNKD7VVP2Fb1hW1OX6idnlA3faF2elKIZxQaMCzORRddpFavXq2UUiocDiullHrggQfUpEmT1MMPP6yUUioSiSil+l4Yp5xyitqzZ0+eS0tMvvSlL6mnn35aKRXX5a9//atavny5+uUvf9mnwYZCodjfF154oWpqaspvYUkf2Ob0hLrpC7XTE+qmL3xGKT3YXvWEbVVf2Ob0hdrpCXXTF2qnJ4V4RnFkw22EZJ9IJIJgMIj9+/ejq6sLgLhGKaVw8cUXw+1244orrkB1dTU+/OEPIxKJwOFwxFx0nn/++QLXoDRRUfc1v9+P3t7ePvvPOussuN1uXHPNNaiursanPvUpRCIR2O32mG4PPvhgAUtf2rDN6Ql10xdqpyfUTV/4jFJ6sL3qCduqvrDN6Qu10xPqpi/UTk8K+owyLLMHyRu33HKLOu+881RLS4tSSiySpvXx3nvvVaNGjVLr1q0rZBFJEu6++261aNGimEU40eL4+OOPq/r6evXqq68WqnhkENjm9IS66Qu10xPqpi98Rik92F71hG1VX9jm9IXa6Ql10xdqpyeFeEaxZcEAQ3KAiuZW/8QnPoEJEybgz3/+M7q7u2Gz2RCJRKCUwqc//Wlcfvnl+Oc//9nnM6TwfO5zn8Npp52GO+64A62trbDb7QiHw1BK4ZxzzsF3v/td/N///R+CwSB1swhsc3pC3fSF2ukJddMfPqOUDmyvesO2qh9sc/pC7fSEuukLtdObQjyj0IBhUQzDAABMmTIFCxYswOuvv44nn3wS3d3dMfcbAKirq8PmzZv7fIYUFhV1qTr99NPR1dWFu+66K9agA4EAAGDGjBnw+/1wOp3UzSKwzekJddMXaqcn1E1v+IxSWrC96gvbqp6wzekLtdMT6qYv1E5fCvWMQgOGRUm0UH32s5/FkiVL8Mwzz+Dee+9Fa2srnE4nAKC6uhputzvWuElhMRsyAHzgAx/ARz7yEezZswc33XQT9u7dC7fbDQDo6upCMBiEz+ejFdkisM3pCXXTF2qnJ9RNX/iMUnqwveoJ26q+sM3pC7XTE+qmL9ROTwr5jMIk3hYkHA7DbrcDAG699VZ0d3fje9/7HlwuF1avXo1PfvKT+PznP4/m5mbcf//9ePDBB+FwUMpCYyalAYCf/vSnaG5uxv/+7//C6XTi73//O04++WRceumlaG9vx2OPPYaHH34YHo+nwKUmANucriTePKmbPpgPMNROPyKRCGw2WftC3axNS0sLGhoaYv8n9pd8RikN+GyjJxxP6AvbnL5wTKEfHE/oDccUemC18YShuFyjoNxzzz3YvXs3bDYbPvjBD+LEE0+MvXfHHXfggQcewAMPPIBp06YBAPbu3YvHHnsMGzZsgMPhwFe/+lXMnj27UMUvWe6//374/X44HA6cfPLJMX0A0e33v/89fve732H69Omx/Q888AB27tyJQCCAiy++GEceeWQhil7y/PGPfwQgg4yzzz4bFRUVsffY5qxLZ2cnqqurk75H3azNCy+8gLKyMiilcNxxxwGIP/xQO+vyzDPPoLOzE6FQCOeee25sFRTANmd1brjhBjz//PO4/vrrccIJJ/R5j88oxQnHE3rC8YS+cDyhLxxT6AnHE/rCMYWeWHI8Mfz832Sk3HjjjWrZsmXqwQcfVJ/61KfUl770JdXV1aWUUuq1115TH/3oR9WuXbuUUkoFAoFCFpUkcMMNN6glS5aoe+65R1144YXq29/+trrxxhuVUkrt2LFDXXDBBTHdgsFgIYtK+nHDDTeopUuXqp///OfqhBNOUN/61rfUL37xC6WUUhs3blRnnHEG25wFue2229SHP/xhtWHDhgHvvf766+wrLcz111+vjj/+eHXBBReoSZMmqdtvvz32Hu9z1sXU7Stf+YqaNGmSuuqqq2LvUTfr86UvfUktXbpUfepTn1LPPfdcbP/69evVpz71KT6jFBkcT+gJxxP6wvGEvnBMoSccT+gLxxT6YsXxBA0YBeKRRx5RK1asUJ2dnUoppfbs2aMmTJig3njjDaWUUpFIRLW3tyullAqFQrHPRSKR/BeWxPj73/+uli9fHtOtu7tbPffcc2rlypXquuuuU0rFNUrUjRSeV199VS1dulR1d3crpZRqbW1Vv//979XFF18cu5GamrHNWYsbb7xRjR8/Xl1++eXq3Xff7fMe+0rrcsstt6iTTz5Z+f1+pZQMDMePHx97AKJ21uT6669Xp5xySmwCdP/+/WrGjBnq6aefVkqJPocPH1ZKUTerYWpw6623qvPOO0/ddddd6oILLlCrVq2KvW8+v/AZpTjgeEJPOJ7QF44n9IZjCv3geEJfOKbQEyuPJ5jEu0C0tLTg3HPPRVVVFQBgwoQJmDBhApxOJzo7OwEANTU1Az6XreztZHi0trZi/vz5qKqqQiAQQEVFBU488UTU1tbinnvuwXXXXRfTyIyBSqxBd3c3xo4di4qKCoTDYdTX1+Pcc8/F5Zdfjq1bt+Kqq66KaWbGYwTY5gpNOBxGW1sbfvzjH6O7uxu33HILNm3aFHvfMAzU1NRAKdWnzVG3wnLgwAE0NTXhzjvvhMvlgs/nw3HHHYdLLrkEu3btAkDtrMjGjRvx7rvv4oEHHkBlZSV6e3sxduxYnHnmmX1iDdfV1QEAdbMYpgYrV65EbW0tli9fjjlz5uCOO+7A888/D8MwUFVVNaDNEX3heEJPOJ7QF44n9IVjCv3geEJfOKbQFyuPJ2jAyDNmY923bx+amprQ3t4OAPjLX/6CLVu24Je//CXmzp2LCy64AN/97ncB8MHVSpg3TvPvQCAAl8uFpUuX4sorr0RnZydWrVpV4FKSRMw2d9RRR8HlcmHHjh2w2+1QSsHtdmPx4sX45je/iX379uGuu+4CwJumlbDb7Vi4cCFOO+003HbbbUkHHAA1sxp1dXW48MILY7FMzeRdY8aMwd13341IJBI7ltpZh8mTJ+P666/HxIkTEQqFUF5eDgDYtm0bnnnmGXzyk5/E7bffHpsYJdYjEonAbrdj48aNKCsrw+c+9znMmzcPv/71r7F582bs2bMHe/fuLXQxyQjheEJvOJ7QD44n9IdjCv3geEJfOKbQG6uOJ2jAyBOJVkYA+PjHP46nnnoK559/PhYuXIjzzjsPSimccsop+PGPf4wLL7wQ77//Pv76178Wstglj+qX437u3Ll4+umnYw+mLpcLLS0teOihhzB9+nS43W688MILhSgqSYHZ5srLy+FwOPDEE0/E9psd86JFi7B8+XKsX7++kEUlKbjgggswYcIEVFZW4q677kJPT0/SAQexDi6XC/PmzYPb7QYQ70vPOOMM1NbW9lmVSKxDZWUljjjiCACAw+EAANx8881YtWoVTjjhBHzsYx/Dfffdhx//+MeFLCZJgVIKNpsN48ePxxlnnIG3334bEydOxMc//nEsX74cl19+ORYtWhSb7Cb6wfGEnnA8oT8cTxQHHFPoBccT+sIxhb5YeTzhyPs3lij79u3DhAkTEIlEoJTCscceiyeffBIdHR3YvXs3/va3v2HZsmW4+OKLAQCBQACPPfYYmpqaClzy0qa/bkcddRTuv/9+nH/++XjxxRcxatQorF69GitXrsTZZ5+NmTNn4stf/jIuvfRSjBo1iisBCsi///1vdHR0YMqUKZg6dSpGjRqFyy+/HOeddx5qamrwmc98BjabDUoplJWV4eyzz8aNN96Ic889FytWrCh08UuWRN2mT5+OysrK2E00FAqhsrISv/nNb/DFL34Rt9xyC77xjW9g9uzZhS42QV/tpk2bFnMtNQwj1hdOmDABHR0d2LdvH8aOHQubzYa3334bU6dOTRrmhOSeVG3OMAzs2LEDW7duxfr16zF16lQAwLHHHoszzjgDn//85zFjxgze5wpIMu3C4TDsdjvcbjcefvhhnHPOOZgzZw42bNiATZs2Ye7cuYUuNhkBHE/oCccT+sLxhL5wTKEnHE/oC8cUeqLTeIIGjDxw33334corr8STTz6J5cuXIxwOIxKJYMqUKQCAWbNm4aabbsKHPvQhABKf0eVyYcaMGSgrKytgyUubZLqFQiF84AMfwKpVq/Daa6+hoqIC55xzDk499VQopeD3+zFx4kTU19cXuvglzQ033IAnn3wStbW1Mev/9773PZxwwgm4//77ccYZZyAUCuGSSy6J3SgnTpyIs88+G7W1tYUtfAnTX7fJkyfjmmuuiT2IOhwORCKRPgOOW2+9FVdccQXmzJlT4NKXNkNpBwChUAh+vx+9vb0IBoOw2Wy4//778dOf/hTPPvtsAUtfugyl29SpU3HrrbeirKwMgUAANpsNtbW1WLp0KRobGznQKCBDaffZz34WV199NQDg5Zdfxk9+8hP88Ic/REdHB37xi1/glltuibnzEz3geEJPOJ7QF44n9IVjCj3heEJfOKbQE93GE/S5ygPd3d045phj8B//8R949tlnB8Sg9fv98Hq96OnpASDxGR966CH8+c9/xkknnVSIIhMk180wDITDYRxzzDG45JJLcOGFF+LUU08FIC7E69evh9vths/nG+AuTvLDbbfdhn/+85949tln8fTTT+OrX/0q2trasH37dgDAqaeein/84x/4wQ9+gJtvvhmvv/46AODhhx/Giy++GEskRfJLMt0OHz6MrVu3Aoi7DNtsNoTD4Zjrd3NzM+677z4Eg8FCFr+kyUS7UaNGYdy4cRg9ejQeffRR/PKXv8SDDz6IMWPGFLIKJclQuoVCIQBAWVkZlFJwuVxwOBx4/vnnEQqFEA6HC1n8kiadNmcYBnbv3o2bb74Z3/jGN3D99dfjkksuwcqVK3HDDTfQeKEhHE/oCccTesLxhL5wTKEnHE/oC8cUeqLleEKRnBGJRJRSSn3mM59Rv/vd79QjjzyiJk2apP75z3/G3g+Hw0oppR5//HFVVVWlPv/5z6srrrhCLVy4UG3cuLFgZS9lhtLN1Kw/v/vd79SSJUvUhg0b8lZW0peWlhb1rW99S23ZsqXP/osvvlh94QtfUErF9Vu9erX63Oc+p04++WT1kY98RM2fP5/aFYh0dOuPqWNPT4/au3dvzstIkjMc7S688EJ13nnnqWOPPZZtrkAMRzellPr973+vFi9eTN0KSDramc8xt956q5o+fbp68skn815Okj04ntATjif0heMJfeGYQk84ntAXjin0RNfxBA0YeeCdd95Ra9asUUopdeeddw54eA2FQkoppd544w111113qccff1zt3LmzYOUlwmC6mY3ZZOPGjWrlypXsgC3AunXrVGtrq1IqrtPq1avVhRdeqJRSKhQKxR5UvV6vamtrU1u3blUtLS2FKTBRSg2tW/82p1TqwT/JL+lqFw6HVTAYVKeffroaP378gAcmkl8ybXO33367mjFjBu9zFmAo7cy+sbm5WW3fvr3PcURfOJ7QE44n9ITjCX3hmEJPOJ7QF44p9ETH8YShFP1Sc43P54PH44n9f9ddd+FHP/oR7rnnHpx22mmxhG79XcFJYRlKNxV1qQKAzs5OhEIhjBo1qlDFJYPw7rvv4uyzz8Zrr72Guro6OBxM/6MDg+kWiURgszEKolUZTLs333wTFRUVjDFsQQbT7Z133kF5eTlmzpxZwBKSVCRqV1tbC6fTGXvPTMRH9IbjCT3heKJ44HhCXzim0BOOJ/SFYwo9sfp4gnfdLHPPPffg4MGDKCsrw7HHHovly5fD4/EgFArFGu0XvvAFAMDnPve5PoMOUjhGopthGKiuri5k8UuaZNoB6KPd9OnTMWnSJHg8nti+f/7zn1i4cCFGjx5dsLKXMtRNX4ar3TPPPIN58+ZhyZIlBSt7KTOSNjdv3jzMnz+/YGUvdTLVzhxssL/UF44n9ITjCX3hc6m+UDs94XhCXzim0JNiGE/Q1JxFfvKTn+C3v/0tpk2bho0bN+K3v/0tPvOZzwAAHA5HLHkNIA+v11xzDb74xS/i6aefptW/gIxUN3PVFMk/6WrndrsBAPv37wcAPPDAA7j66qvR3d1dkHKXOtRNX0ai3Xe+8x34fL6ClLvUGWmb83q9BSk3YX9ZinA8oSccT+gL+1l9oXZ6wvGEvnBMoSdF01cWMn5VMfHee++p5cuXqwMHDiillPL5fKqpqUmdcsopavny5bHjgsFgn8/94he/UIsWLVI9PT0FjydWilA3fUlXO5/Pp8LhsFqwYIHatGmT+uMf/6gWL17MpJYFgrrpC7XTE+qmL9Su9OBzqZ5QN31hP6sv1E5PqJu+UDs9KSbduEwnS0QiEVRUVGDMmDEAxHI1ceJEPP/884hEIjjllFMAiHUr0b37sssuw/PPP4/y8nKuvCkA1E1f0tXO7XbDZrNh8eLFuPHGG/Hzn/8c9957L+NlFgjqpi/UTk+om75Qu9KDz6V6Qt30hf2svlA7PaFu+kLt9KSYdKMBI0sceeSRCIfDePjhh2P7TDecl19+GTabLRbztL97N+OdFg7qpi+ZaAdIx/3444/jt7/9LY4++ui8l5cI1E1fqJ2eUDd9oXalB59L9YS66Qv7WX2hdnpC3fSF2ulJMelGA8YIeOONN7Bx40a8/fbbMAwDF1xwAd566y28//77APrGErvzzjvR09ODd999t5BFJqBuOjMc7davXw8AuOKKK7BhwwYceeSRBSt/qULd9IXa6Ql10xdqV3rwuVRPqJu+sJ/VF2qnJ9RNX6idnhSrbo5CF0BXrr/+ejz99NOora1Fe3s7vvSlL+H888/HV77yFfzpT3/Cpz/9aYwfPx4OhwNKKYwfPx579+7F+vXrLeWCU2pQN30ZrnYbN27E3LlzMX/+/EJXoSShbvpC7fSEuukLtSs9+FyqJ9RNX9jP6gu10xPqpi/UTk+KWTcaMIbB//zP/+Cf//wn/v3vf6Onpwdr1qzBd77zHSxfvhzf/va3cdVVVyEcDuMTn/gE5syZA8MwUFVVhaVLl8Jutxe6+CULddOXkWjncLCbKxTUTV+onZ5QN32hdqUHn0v1hLrpC/tZfaF2ekLd9IXa6Umx68YQUhmyZ88ebN++Hffeey+cTicqKytx3HHHYcGCBXj77bcxf/583Hjjjdi4cSPuvPNO3HHHHfD7/bjvvvvwxBNPYOHChYWuQklC3fSF2ukJddMXaqcn1E1fqF3pQc31hLrpC7XTF2qnJ9RNX6idnpSCboZSShW6EDrh8/nw+uuvY9myZXA4HDAMAwDw05/+FP/617/wt7/9DTabDdu3b8eLL76I3/zmN2hoaEBLSwt+85vfWC4JSqlA3fSF2ukJddMXaqcn1E1fqF3pQc31hLrpC7XTF2qnJ9RNX6idnpSCbjRgDINwOBxzAVZKwTAMvPLKK7jhhhvw1FNPIRKJwGaLO7d4vV6EQiFUVVUVqsgE1E1nqJ2eUDd9oXZ6Qt30hdqVHtRcT6ibvlA7faF2ekLd9IXa6Umx68YQUsMgMX6padWaPXs2AoEAenp6Yu+98847AICysjJtLohihrrpC7XTE+qmL9ROT6ibvlC70oOa6wl10xdqpy/UTk+om75QOz0pdt1owMgC4XAYXq8Xzc3N6OjogM1mw/3334+LLroIra2thS4eSQF10xdqpyfUTV+onZ5QN32hdqUHNdcT6qYv1E5fqJ2eUDd9oXZ6Umy60YCRJUaNGoUxY8ZgwoQJePTRR3HHHXfgkUcewejRowtdNDII1E1fqJ2eUDd9oXZ6Qt30hdqVHtRcT6ibvlA7faF2ekLd9IXa6Ukx6cYcGFnkwgsvRFVVFdasWYP77rtPiyQohLrpDLXTE+qmL9ROT6ibvlC70oOa6wl10xdqpy/UTk+om75QOz0pFt0chS5AMaCUQiAQwLvvvov9+/fjpZdewsyZMwtdLDIE1E1fqJ2eUDd9oXZ6Qt30hdqVHtRcT6ibvlA7faF2ekLd9IXa6Umx6UYPjCzyyiuvoKamRltrVqlC3fSF2ukJddMXaqcn1E1fqF3pQc31hLrpC7XTF2qnJ9RNX6idnhSLbjRgEEIIIYQQQgghhBBCCCHEcjCJNyGEEEIIIYQQQgghhBBCLAcNGIQQQgghhBBCCCGEEEIIsRw0YBBCCCGEEEIIIYQQQgghxHLQgEEIIYQQQgghhBBCCCGEEMtBAwYhhBBCCCGEEEIIIYQQQiwHDRiEEEIIIYQQQgghhBBCCLEcNGAQQgjJC06nEzt37hz0mAceeACnn376sL/jjDPOwP333z/szxNCCCGEEEKsC8cUhBBSetCAQQghJC+EQiGEQqFBj/H5fAgEAn327d+/H+Xl5fB4PKisrMR1112Hj370o/B4PCgrK8O8efOglAIA+P1++P3+nNWBEEIIIYQQUjg4piCEkNLDUegCEEIIIYMxbtw49Pb2AgC+//3vY+fOnXjqqacAAFu3bsW8efNgGEYhi0gIIYQQQgixMBxTEEKIvtADgxBCiKVYtWoVDMOAYRh49tln+7y3Zs0aLFiwIPb/e++9h/nz5+e5hIQQQgghhBArwzEFIYQUDzRgEEIIsRQnn3wygsEggsEgTjvttNj+3t5evPjiizjjjDNi+5577jmceOKJhSgmIYQQQgghxKJwTEEIIcUDQ0gRQgjJCzabDW1tbejq6kIgEEAgEMD+/fuxbds2bNu2DcceeywAwDAMOBwDb08PPfQQenp6UFlZCQCIRCJ47LHH8Ic//CGv9SCEEEIIIYQUBo4pCCGk9KAHBiGEkLxw5pln4vjjj0d1dTXGjBmDOXPm4IILLsCdd96JnTt3oqysLOVng8Egfvazn+EDH/gAvvzlL0Mphb/97W/YuXMnysvL+xz7hS98AYZhYOzYsbmuEiGEEEIIISSPcExBCCGlBz0wCCGE5IUnn3wSkUgEkUgk6WooANi8eXPS5Hm33nor6urq8PTTT+O0007D1Vdfjeeeew5nnXUWrrzySjz33HOxz/3qV7/CZz7zGdjt9pzWhxBCCCGEEJJfOKYghJDSgx4YhBBC8obNZks50ACAc889F3fffXeffa+++ip+/OMf45e//CXsdjsef/xxPPXUU/B4PHjiiSfgcrnw3//937HjHQ4HPB4PnE5nzupBCCGEEEIIKQwcUxBCSGlBAwYhhJC8ceGFF8LhcKR8NTQ04M4774wdH4lEcNFFF+HOO+/EggULAAAHDx7Evn378Itf/AKGYeCBBx7An/70J9x1110FqhUhhBBCCCEkX3BMQQghpQUNGIQQQvLGH/7wB4RCoZSvO+64Ay+//HLseJvNhldffRWf/OQnAQBtbW34+Mc/jh//+MexwUdDQwNefvllfOITnyhElQghhBBCCCF5hGMKQggpLZgDgxBCiGWIRCID9o0bNy7297/+9a9Y0r1E6uvrc142QgghhBBCiPXhmIIQQooLemAQQgjJGzfddBNcLldKd+8rr7wSJ510UsrPn3feebj99tvzWGJCCCGEEEKIleCYghBCSgt6YBBCCMkbGzZswFVXXYUbbrih0EUhhBBCCCGEaAjHFIQQUlrQA4MQQkjeOOaYY3DzzTfDMIyUL5vNhn379g3r/G63G263O8ulJoQQQgghhFgFjikIIaS0MJRSqtCFIIQQQgghhBBCCCGEEEIISYQeGIQQQgghhBBCCCGEEEIIsRw0YBBCCCGEEEIIIYQQQgghxHLQgEEIIYQQQgghhBBCCCGEEMtBAwYhhBBCCCGEEEIIIYQQQiwHDRiEEEIIIYQQQgghhBBCCLEcNGAQQgghhBBCCCGEEEIIIcRy0IBBCCGEEEIIIYQQQgghhBDLQQMGIYQQQgghhBBCCCGEEEIsBw0YhBBCCCGEEEIIIYQQQgixHP8fJmUp2hVU0KIAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 월별로 시간의 흐름에 따라 PM10, PM25 변수의 변화 추이 시각화\n",
-    "\n",
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "# 월별 평균 PM10, PM25 계산 함수\n",
-    "def get_monthly_trend(df, variable):\n",
-    "    # 'year', 'month' 컬럼을 기준으로 월별 평균, Q1, Q3 계산\n",
-    "    monthly = df.groupby(['year', 'month'])[variable].agg(['mean', \n",
-    "                                                           lambda x: x.quantile(0.25), \n",
-    "                                                           lambda x: x.quantile(0.75)]).reset_index()\n",
-    "    monthly.columns = ['year', 'month', 'mean', 'q1', 'q3']\n",
-    "    # 날짜 컬럼 생성 (월의 첫날로)\n",
-    "    monthly['date'] = pd.to_datetime(monthly['year'].astype(str) + '-' + monthly['month'].astype(str).str.zfill(2) + '-01')\n",
-    "    monthly = monthly.sort_values('date')\n",
-    "    return monthly\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
-    "variables_to_analyze = ['hm', 'PM25']\n",
-    "\n",
-    "for i, var in enumerate(variables_to_analyze):\n",
-    "    if var in daejeon.columns:\n",
-    "        trend = get_monthly_trend(daejeon, var)\n",
-    "        ax = axes[i]\n",
-    "        # 평균값\n",
-    "        ax.plot(trend['date'], trend['mean'], 'o-', color='blue', label='평균값')\n",
-    "        # Q1, Q3\n",
-    "        ax.plot(trend['date'], trend['q1'], 's--', color='orange', alpha=0.7, label='Q1')\n",
-    "        ax.plot(trend['date'], trend['q3'], 's--', color='purple', alpha=0.7, label='Q3')\n",
-    "        # 선형 트렌드선\n",
-    "        if len(trend) > 1:\n",
-    "            z = np.polyfit(range(len(trend)), trend['mean'], 1)\n",
-    "            p = np.poly1d(z)\n",
-    "            ax.plot(trend['date'], p(range(len(trend))), \"r--\", linewidth=1, alpha=0.6)\n",
-    "            slope = z[0]\n",
-    "            ax.text(trend['date'].iloc[0], trend['mean'].max(), \n",
-    "                    f'월별 변화율: {slope:.4f}/month', fontsize=10, color='darkred')\n",
-    "        ax.set_title(f'{var} 월별 변화  추이', fontsize=14)\n",
-    "        ax.set_xlabel('날짜', fontsize=12)\n",
-    "        ax.set_ylabel(var, fontsize=12)\n",
-    "        ax.grid(True, linestyle='--', alpha=0.7)\n",
-    "        ax.legend()\n",
-    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
-    "        plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n",
-    "    else:\n",
-    "        axes[i].text(0.5, 0.5, f'{var} 컬럼 없음', ha='center', va='center', fontsize=12)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e149d5dc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Analysis_code/find_reason/make_trend_plot.ipynb b/Analysis_code/find_reason/make_trend_plot.ipynb
deleted file mode 100644
index 3a4866f251838a774d005359bb6bf62588022f8f..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/make_trend_plot.ipynb
+++ /dev/null
@@ -1,176 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import numpy as np\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False\n",
-    "\n",
-    "seoul = pd.read_feather(\"../../data/data_for_modeling/df_seoul.feather\")\n",
-    "busan = pd.read_feather(\"../../data/data_for_modeling/df_busan.feather\")\n",
-    "incheon = pd.read_feather(\"../../data/data_for_modeling/df_incheon.feather\")\n",
-    "daejeon = pd.read_feather(\"../../data/data_for_modeling/df_daejeon.feather\")\n",
-    "daegu = pd.read_feather(\"../../data/data_for_modeling/df_daegu.feather\")\n",
-    "gwangju = pd.read_feather(\"../../data/data_for_modeling/df_gwangju.feather\")\n",
-    "\n",
-    "feature = ['hm','PM10','PM25','multi_class','year','month','hour']\n",
-    "seoul = seoul[feature]\n",
-    "busan = busan[feature]\n",
-    "incheon = incheon[feature]\n",
-    "daejeon = daejeon[feature]\n",
-    "daegu = daegu[feature]\n",
-    "gwangju = gwangju[feature]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x1500 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualization of PM25 variable trends over time by month\n",
-    "\n",
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "# Function to calculate monthly PM25 averages\n",
-    "def get_monthly_trend(df, variable):\n",
-    "    # Calculate monthly mean, Q1, Q3 based on 'year' and 'month' columns\n",
-    "    monthly = df.groupby(['year', 'month'])[variable].agg(['mean', \n",
-    "                                                           lambda x: x.quantile(0.25), \n",
-    "                                                           lambda x: x.quantile(0.75)]).reset_index()\n",
-    "    monthly.columns = ['year', 'month', 'mean', 'q1', 'q3']\n",
-    "    # Create date column (first day of month)\n",
-    "    monthly['date'] = pd.to_datetime(monthly['year'].astype(str) + '-' + monthly['month'].astype(str).str.zfill(2) + '-01')\n",
-    "    monthly = monthly.sort_values('date')\n",
-    "    return monthly\n",
-    "\n",
-    "# List of city data\n",
-    "cities = [seoul, busan, incheon, daejeon, daegu, gwangju]\n",
-    "city_names = ['Seoul', 'Busan', 'Incheon', 'Daejeon', 'Daegu', 'Gwangju']\n",
-    "\n",
-    "# Create 3x2 subplots\n",
-    "fig, axes = plt.subplots(3, 2, figsize=(13, 15))\n",
-    "axes = axes.flatten()\n",
-    "\n",
-    "for idx, (city, name) in enumerate(zip(cities, city_names)):\n",
-    "    trend = get_monthly_trend(city, 'PM25')\n",
-    "    ax = axes[idx]\n",
-    "    \n",
-    "    # Mean values\n",
-    "    ax.plot(trend['date'], trend['mean'], 'o-', color='blue', label='Mean')\n",
-    "    # Q1, Q3\n",
-    "    ax.plot(trend['date'], trend['q1'], 's--', color='orange', alpha=0.7, label='Lower 25% (Q1)')\n",
-    "    ax.plot(trend['date'], trend['q3'], 's--', color='purple', alpha=0.7, label='Upper 25% (Q3)')\n",
-    "    \n",
-    "    # Linear trend line\n",
-    "    if len(trend) > 1:\n",
-    "        z = np.polyfit(range(len(trend)), trend['mean'], 1)\n",
-    "        p = np.poly1d(z)\n",
-    "        ax.plot(trend['date'], p(range(len(trend))), \"r--\", linewidth=1, alpha=0.6)\n",
-    "        slope = z[0]\n",
-    "        # ax.text(trend['date'].iloc[0], trend['mean'].max(), \n",
-    "        #         f'Monthly change rate: {slope:.4f}/month', fontsize=10, color='darkred')\n",
-    "    \n",
-    "    ax.set_title(f'PM2.5 Trend in {name}', fontsize=14)\n",
-    "    ax.set_xlabel('Date', fontsize=12)\n",
-    "    ax.set_ylabel('PM2.5', fontsize=12)\n",
-    "    ax.grid(True, linestyle='--', alpha=0.7)\n",
-    "    ax.legend()\n",
-    "    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
-    "    plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Analysis_code/find_reason/wasserstein_distance.ipynb b/Analysis_code/find_reason/wasserstein_distance.ipynb
deleted file mode 100644
index cd92bde0d757dfec9665606fd8b36c5ae25bbedf..0000000000000000000000000000000000000000
--- a/Analysis_code/find_reason/wasserstein_distance.ipynb
+++ /dev/null
@@ -1,541 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 분석에 필요한 라이브러리 임포트\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from scipy import stats\n",
-    "from scipy.spatial import distance\n",
-    "from scipy.stats import wasserstein_distance, entropy, ks_2samp\n",
-    "from sklearn.manifold import TSNE\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "from sklearn.ensemble import RandomForestClassifier # Added\n",
-    "from sklearn.model_selection import train_test_split # Added\n",
-    "from sklearn.metrics import roc_auc_score # Added\n",
-    "from statsmodels.distributions.empirical_distribution import ECDF # Added\n",
-    "import ot\n",
-    "\n",
-    "\n",
-    "# 한글 폰트 설정\n",
-    "plt.rcParams['font.family'] = 'NanumGothic'\n",
-    "plt.rcParams['axes.unicode_minus'] = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "seoul = pd.read_feather(\"../../data/data_for_modeling/df_seoul.feather\")\n",
-    "seoul= seoul[['datetime','hm','PM10','PM25','year','month','hour','multi_class']]\n",
-    "\n",
-    "busan = pd.read_feather(\"../../data/data_for_modeling/df_busan.feather\")\n",
-    "busan= busan[['datetime','hm','PM10','PM25','year','month','hour','multi_class']]\n",
-    "\n",
-    "incheon = pd.read_feather(\"../../data/data_for_modeling/df_incheon.feather\")\n",
-    "incheon= incheon[['datetime','hm','PM10','PM25','year','month','hour','multi_class']]\n",
-    "\n",
-    "daegu = pd.read_feather(\"../../data/data_for_modeling/df_daegu.feather\")\n",
-    "daegu= daegu[['datetime','hm','PM10','PM25','year','month','hour','multi_class']]\n",
-    "\n",
-    "daejeon = pd.read_feather(\"../../data/data_for_modeling/df_daejeon.feather\")\n",
-    "daejeon= daejeon[['datetime','hm','PM10','PM25','year','month','hour','multi_class']]\n",
-    "\n",
-    "gwangju = pd.read_feather(\"../../data/data_for_modeling/df_gwangju.feather\")\n",
-    "gwangju= gwangju[['datetime','hm','PM10','PM25','year','month','hour','multi_class']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.5920662  0.92351786]\n",
-      "[0.60414398 0.9190468 ]\n",
-      "[0.60250035 0.9391276 ]\n",
-      "[0.60112832 0.92493121]\n",
-      "[0.58469137 0.90476229]\n",
-      "[0.617718   0.93503164]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.decomposition import PCA\n",
-    "\n",
-    "# 특성 선택 (예: PM10, PM25, hm 등)\n",
-    "features = ['PM10','PM25', 'hm']\n",
-    "# 스케일링\n",
-    "scaler = StandardScaler()\n",
-    "scaled_features = scaler.fit_transform(seoul[features])\n",
-    "pca = PCA(n_components=2)\n",
-    "pca.fit(scaled_features)\n",
-    "print(pca.explained_variance_ratio_.cumsum())\n",
-    "seoul_pca = pca.transform(scaled_features)\n",
-    "seoul.drop(columns=['PM25', 'hm'], inplace=True)\n",
-    "seoul[['pca_x', 'pca_y']] = seoul_pca\n",
-    "\n",
-    "\n",
-    "scaled_features = scaler.fit_transform(busan[features])\n",
-    "pca = PCA(n_components=2)\n",
-    "pca.fit(scaled_features)\n",
-    "print(pca.explained_variance_ratio_.cumsum())\n",
-    "busan_pca = pca.transform(scaled_features)\n",
-    "busan.drop(columns=['PM25', 'hm'], inplace=True)\n",
-    "busan[['pca_x', 'pca_y']] = busan_pca\n",
-    "\n",
-    "scaled_features = scaler.fit_transform(incheon[features])   \n",
-    "pca = PCA(n_components=2)\n",
-    "pca.fit(scaled_features)\n",
-    "print(pca.explained_variance_ratio_.cumsum())\n",
-    "incheon_pca = pca.transform(scaled_features)\n",
-    "incheon.drop(columns=['PM25', 'hm'], inplace=True)\n",
-    "incheon[['pca_x', 'pca_y']] = incheon_pca\n",
-    "\n",
-    "scaled_features = scaler.fit_transform(daegu[features])\n",
-    "pca = PCA(n_components=2)\n",
-    "pca.fit(scaled_features)\n",
-    "print(pca.explained_variance_ratio_.cumsum())\n",
-    "daegu_pca = pca.transform(scaled_features)\n",
-    "daegu.drop(columns=['PM25', 'hm'], inplace=True)\n",
-    "daegu[['pca_x', 'pca_y']] = daegu_pca\n",
-    "\n",
-    "scaled_features = scaler.fit_transform(daejeon[features])\n",
-    "pca = PCA(n_components=2)\n",
-    "pca.fit(scaled_features)\n",
-    "print(pca.explained_variance_ratio_.cumsum())\n",
-    "daejeon_pca = pca.transform(scaled_features)\n",
-    "daejeon.drop(columns=['PM25', 'hm'], inplace=True)\n",
-    "daejeon[['pca_x', 'pca_y']] = daejeon_pca\n",
-    "\n",
-    "scaled_features = scaler.fit_transform(gwangju[features])\n",
-    "pca = PCA(n_components=2)\n",
-    "pca.fit(scaled_features)\n",
-    "print(pca.explained_variance_ratio_.cumsum())\n",
-    "gwangju_pca = pca.transform(scaled_features)\n",
-    "gwangju.drop(columns=['PM25', 'hm'], inplace=True)\n",
-    "gwangju[['pca_x', 'pca_y']] = gwangju_pca\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "seoul_2018 = seoul[seoul['year'] == 2018]\n",
-    "seoul_2019 = seoul[seoul['year'] == 2019]\n",
-    "seoul_2020 = seoul[seoul['year'] == 2020]\n",
-    "seoul_2021 = seoul[seoul['year'] == 2021]\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "\n",
-    "\n",
-    "busan_2018 = busan[busan['year'] == 2018]\n",
-    "busan_2019 = busan[busan['year'] == 2019]\n",
-    "busan_2020 = busan[busan['year'] == 2020]\n",
-    "busan_2021 = busan[busan['year'] == 2021]\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "\n",
-    "\n",
-    "incheon_2018 = incheon[incheon['year'] == 2018]\n",
-    "incheon_2019 = incheon[incheon['year'] == 2019]\n",
-    "incheon_2020 = incheon[incheon['year'] == 2020]\n",
-    "incheon_2021 = incheon[incheon['year'] == 2021]\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "\n",
-    "\n",
-    "daegu_2018 = daegu[daegu['year'] == 2018]\n",
-    "daegu_2019 = daegu[daegu['year'] == 2019]\n",
-    "daegu_2020 = daegu[daegu['year'] == 2020]\n",
-    "daegu_2021 = daegu[daegu['year'] == 2021]\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "\n",
-    "\n",
-    "daejeon_2018 = daejeon[daejeon['year'] == 2018]\n",
-    "daejeon_2019 = daejeon[daejeon['year'] == 2019]\n",
-    "daejeon_2020 = daejeon[daejeon['year'] == 2020]\n",
-    "daejeon_2021 = daejeon[daejeon['year'] == 2021]\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "\n",
-    "\n",
-    "gwangju_2018 = gwangju[gwangju['year'] == 2018]\n",
-    "gwangju_2019 = gwangju[gwangju['year'] == 2019]\n",
-    "gwangju_2020 = gwangju[gwangju['year'] == 2020]\n",
-    "gwangju_2021 = gwangju[gwangju['year'] == 2021]\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          2018      2019      2020      2021\n",
-      "2018       0.0  0.130217  0.063132  1.081307\n",
-      "2019  0.130217       0.0  0.059051  0.830648\n",
-      "2020  0.063132  0.059051       0.0  0.039927\n",
-      "2021  1.081307  0.830648  0.039927       0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 연도별 데이터 준비\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "data_dict = {\n",
-    "    2018: seoul_2018[['pca_x', 'pca_y']].values,\n",
-    "    2019: seoul_2019[['pca_x', 'pca_y']].values,\n",
-    "    2020: seoul_2020[['pca_x', 'pca_y']].values,\n",
-    "    2021: seoul_2021[['pca_x', 'pca_y']].values\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# 결과를 저장할 데이터프레임 생성\n",
-    "result_df = pd.DataFrame(index=years, columns=years)\n",
-    "\n",
-    "for i, year1 in enumerate(years):\n",
-    "    for j, year2 in enumerate(years):\n",
-    "        if year1 == year2:\n",
-    "            result_df.iloc[i, j] = 0.0\n",
-    "        if j < i:\n",
-    "            # 이미 계산된 값 사용\n",
-    "            result_df.iloc[i, j] = result_df.iloc[j, i]\n",
-    "        else:\n",
-    "            X = data_dict[year1]\n",
-    "            Y = data_dict[year2]\n",
-    "            a = np.ones(len(X)) / len(X)\n",
-    "            b = np.ones(len(Y)) / len(Y)\n",
-    "            W = ot.emd2(a, b, ot.dist(X, Y))\n",
-    "            result_df.iloc[i, j] = W\n",
-    "            result_df.iloc[j, i] = W  # 대칭 위치에 동일 값 저장\n",
-    "\n",
-    "# 결과 출력\n",
-    "print(result_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          2018      2019      2020      2021\n",
-      "2018       0.0  0.116261   0.10445  1.424479\n",
-      "2019  0.116261       0.0   0.09933  1.164067\n",
-      "2020   0.10445   0.09933       0.0  1.075336\n",
-      "2021  1.424479  1.164067  1.075336       0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 연도별 데이터 준비\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "data_dict = {\n",
-    "    2018: busan_2018[['pca_x', 'pca_y']].values,\n",
-    "    2019: busan_2019[['pca_x', 'pca_y']].values,\n",
-    "    2020: busan_2020[['pca_x', 'pca_y']].values,\n",
-    "    2021: busan_2021[['pca_x', 'pca_y']].values\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# 결과를 저장할 데이터프레임 생성\n",
-    "result_df = pd.DataFrame(index=years, columns=years)\n",
-    "\n",
-    "for i, year1 in enumerate(years):\n",
-    "    for j, year2 in enumerate(years):\n",
-    "        if year1 == year2:\n",
-    "            result_df.iloc[i, j] = 0.0\n",
-    "        if j < i:\n",
-    "            # 이미 계산된 값 사용\n",
-    "            result_df.iloc[i, j] = result_df.iloc[j, i]\n",
-    "        else:\n",
-    "            X = data_dict[year1]\n",
-    "            Y = data_dict[year2]\n",
-    "            a = np.ones(len(X)) / len(X)\n",
-    "            b = np.ones(len(Y)) / len(Y)\n",
-    "            W = ot.emd2(a, b, ot.dist(X, Y))\n",
-    "            result_df.iloc[i, j] = W\n",
-    "            result_df.iloc[j, i] = W  # 대칭 위치에 동일 값 저장\n",
-    "\n",
-    "# 결과 출력\n",
-    "print(result_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          2018      2019      2020      2021\n",
-      "2018       0.0  0.080291  0.074071  0.449094\n",
-      "2019  0.080291       0.0  0.060171  0.384189\n",
-      "2020  0.074071  0.060171       0.0   0.04047\n",
-      "2021  0.449094  0.384189   0.04047       0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 연도별 데이터 준비\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "data_dict = {\n",
-    "    2018: incheon_2018[['pca_x', 'pca_y']].values,\n",
-    "    2019: incheon_2019[['pca_x', 'pca_y']].values,\n",
-    "    2020: incheon_2020[['pca_x', 'pca_y']].values,\n",
-    "    2021: incheon_2021[['pca_x', 'pca_y']].values\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# 결과를 저장할 데이터프레임 생성\n",
-    "result_df = pd.DataFrame(index=years, columns=years)\n",
-    "\n",
-    "for i, year1 in enumerate(years):\n",
-    "    for j, year2 in enumerate(years):\n",
-    "        if year1 == year2:\n",
-    "            result_df.iloc[i, j] = 0.0\n",
-    "        if j < i:\n",
-    "            # 이미 계산된 값 사용\n",
-    "            result_df.iloc[i, j] = result_df.iloc[j, i]\n",
-    "        else:\n",
-    "            X = data_dict[year1]\n",
-    "            Y = data_dict[year2]\n",
-    "            a = np.ones(len(X)) / len(X)\n",
-    "            b = np.ones(len(Y)) / len(Y)\n",
-    "            W = ot.emd2(a, b, ot.dist(X, Y))\n",
-    "            result_df.iloc[i, j] = W\n",
-    "            result_df.iloc[j, i] = W  # 대칭 위치에 동일 값 저장\n",
-    "\n",
-    "# 결과 출력\n",
-    "print(result_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          2018      2019      2020      2021\n",
-      "2018       0.0  0.127512  0.112157  0.731476\n",
-      "2019  0.127512       0.0  0.094651  0.647071\n",
-      "2020  0.112157  0.094651       0.0  0.041217\n",
-      "2021  0.731476  0.647071  0.041217       0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 연도별 데이터 준비\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "data_dict = {\n",
-    "    2018: daegu_2018[['pca_x', 'pca_y']].values,\n",
-    "    2019: daegu_2019[['pca_x', 'pca_y']].values,\n",
-    "    2020: daegu_2020[['pca_x', 'pca_y']].values,\n",
-    "    2021: daegu_2021[['pca_x', 'pca_y']].values\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# 결과를 저장할 데이터프레임 생성\n",
-    "result_df = pd.DataFrame(index=years, columns=years)\n",
-    "\n",
-    "for i, year1 in enumerate(years):\n",
-    "    for j, year2 in enumerate(years):\n",
-    "        if year1 == year2:\n",
-    "            result_df.iloc[i, j] = 0.0\n",
-    "        if j < i:\n",
-    "            # 이미 계산된 값 사용\n",
-    "            result_df.iloc[i, j] = result_df.iloc[j, i]\n",
-    "        else:\n",
-    "            X = data_dict[year1]\n",
-    "            Y = data_dict[year2]\n",
-    "            a = np.ones(len(X)) / len(X)\n",
-    "            b = np.ones(len(Y)) / len(Y)\n",
-    "            W = ot.emd2(a, b, ot.dist(X, Y))\n",
-    "            result_df.iloc[i, j] = W\n",
-    "            result_df.iloc[j, i] = W  # 대칭 위치에 동일 값 저장\n",
-    "\n",
-    "# 결과 출력\n",
-    "print(result_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          2018      2019      2020      2021\n",
-      "2018       0.0  0.273013  0.053969  0.877338\n",
-      "2019  0.273013       0.0  0.137817  0.780071\n",
-      "2020  0.053969  0.137817       0.0  0.042294\n",
-      "2021  0.877338  0.780071  0.042294       0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 연도별 데이터 준비\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "data_dict = {\n",
-    "    2018: daejeon_2018[['pca_x', 'pca_y']].values,\n",
-    "    2019: daejeon_2019[['pca_x', 'pca_y']].values,\n",
-    "    2020: daejeon_2020[['pca_x', 'pca_y']].values,\n",
-    "    2021: daejeon_2021[['pca_x', 'pca_y']].values\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# 결과를 저장할 데이터프레임 생성\n",
-    "result_df = pd.DataFrame(index=years, columns=years)\n",
-    "\n",
-    "for i, year1 in enumerate(years):\n",
-    "    for j, year2 in enumerate(years):\n",
-    "        if year1 == year2:\n",
-    "            result_df.iloc[i, j] = 0.0\n",
-    "        if j < i:\n",
-    "            # 이미 계산된 값 사용\n",
-    "            result_df.iloc[i, j] = result_df.iloc[j, i]\n",
-    "        else:\n",
-    "            X = data_dict[year1]\n",
-    "            Y = data_dict[year2]\n",
-    "            a = np.ones(len(X)) / len(X)\n",
-    "            b = np.ones(len(Y)) / len(Y)\n",
-    "            W = ot.emd2(a, b, ot.dist(X, Y))\n",
-    "            result_df.iloc[i, j] = W\n",
-    "            result_df.iloc[j, i] = W  # 대칭 위치에 동일 값 저장\n",
-    "\n",
-    "# 결과 출력\n",
-    "print(result_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          2018      2019      2020      2021\n",
-      "2018       0.0  0.105633   0.08202   1.00155\n",
-      "2019  0.105633       0.0  0.069322  0.892938\n",
-      "2020   0.08202  0.069322       0.0  0.480667\n",
-      "2021   1.00155  0.892938  0.480667       0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 연도별 데이터 준비\n",
-    "years = [2018, 2019, 2020, 2021]\n",
-    "data_dict = {\n",
-    "    2018: gwangju_2018[['pca_x', 'pca_y']].values,\n",
-    "    2019: gwangju_2019[['pca_x', 'pca_y']].values,\n",
-    "    2020: gwangju_2020[['pca_x', 'pca_y']].values,\n",
-    "    2021: gwangju_2021[['pca_x', 'pca_y']].values\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# 결과를 저장할 데이터프레임 생성\n",
-    "result_df = pd.DataFrame(index=years, columns=years)\n",
-    "\n",
-    "for i, year1 in enumerate(years):\n",
-    "    for j, year2 in enumerate(years):\n",
-    "        if year1 == year2:\n",
-    "            result_df.iloc[i, j] = 0.0\n",
-    "        if j < i:\n",
-    "            # 이미 계산된 값 사용\n",
-    "            result_df.iloc[i, j] = result_df.iloc[j, i]\n",
-    "        else:\n",
-    "            X = data_dict[year1]\n",
-    "            Y = data_dict[year2]\n",
-    "            a = np.ones(len(X)) / len(X)\n",
-    "            b = np.ones(len(Y)) / len(Y)\n",
-    "            W = ot.emd2(a, b, ot.dist(X, Y))\n",
-    "            result_df.iloc[i, j] = W\n",
-    "            result_df.iloc[j, i] = W  # 대칭 위치에 동일 값 저장\n",
-    "\n",
-    "# 결과 출력\n",
-    "print(result_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Analysis_code/ft_transformer.py b/Analysis_code/ft_transformer.py
deleted file mode 100644
index 217edcf2434d14c2fdaa1705608cf16a1b4cb324..0000000000000000000000000000000000000000
--- a/Analysis_code/ft_transformer.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import torch
-import torch.nn as nn
-
-# FT-Transformer Implementation
-class FTTransformer(nn.Module):
-    def __init__(self, num_features, cat_cardinalities, num_classes, d_token=192, n_blocks=6, attention_dropout=0.2, ffn_dropout=0.2):
-        super(FTTransformer, self).__init__()
-
-        self.num_classes = num_classes  # 클래스 개수 저장
-
-        # Embedding layers for categorical features
-        self.cat_embeddings = nn.ModuleList([
-            nn.Embedding(num_categories, d_token) for num_categories in cat_cardinalities
-        ])
-
-        # Linear layer for numerical features
-        self.num_linear = nn.Linear(num_features, d_token)
-
-        # Transformer blocks
-        self.transformer_blocks = nn.ModuleList([
-            nn.TransformerEncoderLayer(
-                d_model=d_token,
-                nhead=8,
-                dim_feedforward=4 * d_token,
-                dropout=attention_dropout,
-                activation='gelu'
-            ) for _ in range(n_blocks)
-        ])
-
-        self.ffn_dropout = nn.Dropout(ffn_dropout)
-        if num_classes == 2:
-            self.output_layer = nn.Linear(d_token, 1)  # Binary classification
-        elif num_classes > 2:
-            self.output_layer = nn.Linear(d_token, num_classes)  # Multi classification
-
-    def forward(self, x_num, x_cat):
-        # Numerical feature embedding
-        x_num = self.num_linear(x_num)
-
-        # Categorical feature embedding
-        x_cat = [embed(x_cat[:, i]) for i, embed in enumerate(self.cat_embeddings)]
-        x_cat = torch.stack(x_cat, dim=1)
-
-        # Combine numerical and categorical embeddings
-        x = x_num.unsqueeze(1) + x_cat
-
-        # Pass through transformer blocks
-        for block in self.transformer_blocks:
-            x = block(x)
-
-        # Pooling and output
-        x = x.mean(dim=1)
-        x = self.ffn_dropout(x)
-        x = self.output_layer(x)
-
-        return x
diff --git a/Analysis_code/make_oversample_data/gan_sample_10000_1.py b/Analysis_code/make_oversample_data/gan_sample_10000_1.py
deleted file mode 100644
index 9dc2a31c7b2909f5d30ebce3c3bfbdf5b5669ae7..0000000000000000000000000000000000000000
--- a/Analysis_code/make_oversample_data/gan_sample_10000_1.py
+++ /dev/null
@@ -1,181 +0,0 @@
-import pandas as pd
-import numpy as np
-
-from imblearn.over_sampling import SMOTENC
-import optuna
-from ctgan import CTGAN
-import torch
-import warnings
-
-# 지역별 데이터 파일 경로
-regions = ['incheon', 'seoul','busan', 'daegu', 'daejeon', 'gwangju']
-file_paths = [f'../../data/data_for_modeling/{region}_train.csv' for region in regions]
-output_paths = [f'../../data/data_oversampled/ctgan10000/ctgan10000_1_{region}.csv' for region in regions]
-
-# GPU 사용 설정
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-# 경고 무시
-warnings.filterwarnings("ignore", category=UserWarning, module="optuna.distributions")
-
-# 지역별 처리
-for file_path, output_path in zip(file_paths, output_paths):
-    # 데이터 로드
-    data = pd.read_csv(file_path, index_col=0)
-    data= data.loc[data['year'].isin([2018,2019]),:]
-    data['cloudcover'] = data['cloudcover'].astype('int')
-    data['lm_cloudcover'] = data['lm_cloudcover'].astype('int')
-    X = data.drop(columns=['multi_class', 'binary_class'])
-    y = data['multi_class']
-
-    # 불필요한 열 제거
-    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)
-
-    # SMOTENC에서 사용할 범주형 변수 열 번호 설정
-    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']
-
-    # sampling_strategy 설정
-    count_class_0 = (y == 0).sum()
-    count_class_1 = (y == 1).sum()
-    count_class_2 = (y == 2).sum()
-    sampling_strategy = {
-        0: 500 if count_class_0 <= 500 else 1000,
-        1: int(np.ceil(count_class_1 / 100) * 100),  # 백의 자리로 올림
-        2: count_class_2
-    }
-
-    # SMOTENC 적용
-    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)
-    X_resampled, y_resampled = smotenc.fit_resample(X, y)
-
-    # Resampled 데이터 생성
-    lerp_data = X_resampled.copy()
-    lerp_data['multi_class'] = y_resampled
-
-    # CTGAN에서 사용할 범주형 변수 열 이름 설정
-    categorical_features = [
-        col for col, dtype in zip(lerp_data.columns, lerp_data.dtypes) if dtype != 'float64'
-    ]
-
-    # Optuna 목적 함수 정의
-    def objective(trial):
-        # 하이퍼파라미터 탐색 범위 설정
-        embedding_dim = trial.suggest_int("embedding_dim", 64, 128)
-        generator_dim = trial.suggest_categorical("generator_dim", [(64, 64), (128, 128)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(64, 64), (128, 128)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [64, 128, 256])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 3)
-
-        # CTGAN 모델 생성
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        # 범주 0 데이터 필터링
-        data_0 = lerp_data[lerp_data['multi_class'] == 0]
-
-        # 모델 학습
-        ctgan.fit(data_0, discrete_columns=categorical_features)
-
-        # 샘플 생성
-        generated_data = ctgan.sample(len(data_0) * 2)
-
-        # 평가: 샘플의 연속형 변수 분포 비교
-        real_visi = data_0['visi']
-        generated_visi = generated_data['visi']
-
-        # 분포 간 차이(MSE) 계산
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    # Optuna로 최적화 수행
-    study = optuna.create_study(direction="maximize")
-    study.optimize(objective, n_trials=50)
-
-    # 최적 하이퍼파라미터 출력
-    best_params = study.best_params
-
-    # 최적 하이퍼파라미터로 CTGAN 학습 및 샘플 생성
-    ctgan = CTGAN(
-        embedding_dim=best_params["embedding_dim"],
-        generator_dim=best_params["generator_dim"],
-        discriminator_dim=best_params["discriminator_dim"],
-        batch_size=best_params["batch_size"],
-        discriminator_steps=best_params["discriminator_steps"],
-        pac=best_params["pac"]
-    )
-
-    # 범주 0 데이터로 최종 학습
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 0], discrete_columns=categorical_features)
-    generated_0 = ctgan.sample(10000)
-
-    # 범주 1 데이터 최적화 및 생성
-    def objective_class1(trial):
-        embedding_dim = trial.suggest_int("embedding_dim", 128, 512)
-        generator_dim = trial.suggest_categorical("generator_dim", [(128, 128), (256, 256)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(128, 128), (256, 256)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [256, 512, 1024])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 5)
-
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        data_1 = lerp_data[lerp_data['multi_class'] == 1]
-        ctgan.fit(data_1, discrete_columns=categorical_features)
-        generated_data = ctgan.sample(len(data_1) * 2)
-
-        real_visi = data_1['visi']
-        generated_visi = generated_data['visi']
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    study_class1 = optuna.create_study(direction="maximize")
-    study_class1.optimize(objective_class1, n_trials=30)
-
-    best_params_class1 = study_class1.best_params
-    ctgan = CTGAN(
-        embedding_dim=best_params_class1["embedding_dim"],
-        generator_dim=best_params_class1["generator_dim"],
-        discriminator_dim=best_params_class1["discriminator_dim"],
-        batch_size=best_params_class1["batch_size"],
-        discriminator_steps=best_params_class1["discriminator_steps"],
-        pac=best_params_class1["pac"]
-    )
-
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 1], discrete_columns=categorical_features)
-    generated_1 = ctgan.sample(10000 - int(np.ceil(count_class_1 / 100) * 100))
-
-    # 데이터 병합 및 저장
-    well_generated0 = generated_0[(generated_0['visi'] >= 0) & (generated_0['visi'] < 100)]
-    well_generated1 = generated_1[(generated_1['visi'] >= 100) & (generated_1['visi'] < 500)]
-    smote_gan_data = pd.concat([lerp_data, well_generated0, well_generated1], axis=0)
-    # 제거변수 복구
-    smote_gan_data['binary_class'] = smote_gan_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)
-    smote_gan_data['hour_sin'] = np.sin(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['hour_cos'] = np.cos(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['month_sin'] = np.sin(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['month_cos'] = np.cos(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['ground_temp - temp_C'] = smote_gan_data['groundtemp'] - smote_gan_data['temp_C']
-
-    filtered_data = smote_gan_data[smote_gan_data['multi_class'] != 2]
-    original_class2 = data[data['multi_class'] == 2]
-    final_data = pd.concat([filtered_data, original_class2], axis=0)
-    final_data.reset_index(drop=True, inplace=True)
-
-    # 결과 저장
-    final_data.to_csv(output_path, index = False)
-    print(len(final_data[final_data['multi_class']==0]),'|',len(final_data[final_data['multi_class']==1]),'|',len(final_data[final_data['multi_class']==2]))
\ No newline at end of file
diff --git a/Analysis_code/make_oversample_data/gan_sample_10000_2.py b/Analysis_code/make_oversample_data/gan_sample_10000_2.py
deleted file mode 100644
index 424c6f5fec77d74644d9f4d25f7127e8e80aeac7..0000000000000000000000000000000000000000
--- a/Analysis_code/make_oversample_data/gan_sample_10000_2.py
+++ /dev/null
@@ -1,182 +0,0 @@
-import pandas as pd
-import numpy as np
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-from imblearn.over_sampling import SMOTENC
-import optuna
-from ctgan import CTGAN
-import torch
-import warnings
-
-# 지역별 데이터 파일 경로
-regions = ['incheon', 'seoul','busan', 'daegu', 'daejeon', 'gwangju']
-file_paths = [f'../../data/data_for_modeling/{region}_train.csv' for region in regions]
-output_paths = [f'../../data/data_oversampled/ctgan10000/ctgan10000_2_{region}.csv' for region in regions]
-
-# GPU 사용 설정
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-# 경고 무시
-warnings.filterwarnings("ignore", category=UserWarning, module="optuna.distributions")
-
-# 지역별 처리
-for file_path, output_path in zip(file_paths, output_paths):
-    # 데이터 로드
-    data = pd.read_csv(file_path, index_col=0)
-    data= data.loc[data['year'].isin([2018,2020]),:]
-    data['cloudcover'] = data['cloudcover'].astype('int')
-    data['lm_cloudcover'] = data['lm_cloudcover'].astype('int')
-    X = data.drop(columns=['multi_class', 'binary_class'])
-    y = data['multi_class']
-
-    # 불필요한 열 제거
-    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)
-
-    # SMOTENC에서 사용할 범주형 변수 열 번호 설정
-    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']
-
-    # sampling_strategy 설정
-    count_class_0 = (y == 0).sum()
-    count_class_1 = (y == 1).sum()
-    count_class_2 = (y == 2).sum()
-    sampling_strategy = {
-        0: 500 if count_class_0 <= 500 else 1000,
-        1: int(np.ceil(count_class_1 / 100) * 100),  # 백의 자리로 올림
-        2: count_class_2
-    }
-
-    # SMOTENC 적용
-    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)
-    X_resampled, y_resampled = smotenc.fit_resample(X, y)
-
-    # Resampled 데이터 생성
-    lerp_data = X_resampled.copy()
-    lerp_data['multi_class'] = y_resampled
-
-    # CTGAN에서 사용할 범주형 변수 열 이름 설정
-    categorical_features = [
-        col for col, dtype in zip(lerp_data.columns, lerp_data.dtypes) if dtype != 'float64'
-    ]
-
-    # Optuna 목적 함수 정의
-    def objective(trial):
-        # 하이퍼파라미터 탐색 범위 설정
-        embedding_dim = trial.suggest_int("embedding_dim", 64, 128)
-        generator_dim = trial.suggest_categorical("generator_dim", [(64, 64), (128, 128)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(64, 64), (128, 128)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [64, 128, 256])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 3)
-
-        # CTGAN 모델 생성
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        # 범주 0 데이터 필터링
-        data_0 = lerp_data[lerp_data['multi_class'] == 0]
-
-        # 모델 학습
-        ctgan.fit(data_0, discrete_columns=categorical_features)
-
-        # 샘플 생성
-        generated_data = ctgan.sample(len(data_0) * 2)
-
-        # 평가: 샘플의 연속형 변수 분포 비교
-        real_visi = data_0['visi']
-        generated_visi = generated_data['visi']
-
-        # 분포 간 차이(MSE) 계산
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    # Optuna로 최적화 수행
-    study = optuna.create_study(direction="maximize")
-    study.optimize(objective, n_trials=50)
-
-    # 최적 하이퍼파라미터 출력
-    best_params = study.best_params
-
-    # 최적 하이퍼파라미터로 CTGAN 학습 및 샘플 생성
-    ctgan = CTGAN(
-        embedding_dim=best_params["embedding_dim"],
-        generator_dim=best_params["generator_dim"],
-        discriminator_dim=best_params["discriminator_dim"],
-        batch_size=best_params["batch_size"],
-        discriminator_steps=best_params["discriminator_steps"],
-        pac=best_params["pac"]
-    )
-
-    # 범주 0 데이터로 최종 학습
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 0], discrete_columns=categorical_features)
-    generated_0 = ctgan.sample(10000)
-
-    # 범주 1 데이터 최적화 및 생성
-    def objective_class1(trial):
-        embedding_dim = trial.suggest_int("embedding_dim", 128, 512)
-        generator_dim = trial.suggest_categorical("generator_dim", [(128, 128), (256, 256)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(128, 128), (256, 256)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [256, 512, 1024])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 5)
-
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        data_1 = lerp_data[lerp_data['multi_class'] == 1]
-        ctgan.fit(data_1, discrete_columns=categorical_features)
-        generated_data = ctgan.sample(len(data_1) * 2)
-
-        real_visi = data_1['visi']
-        generated_visi = generated_data['visi']
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    study_class1 = optuna.create_study(direction="maximize")
-    study_class1.optimize(objective_class1, n_trials=30)
-
-    best_params_class1 = study_class1.best_params
-    ctgan = CTGAN(
-        embedding_dim=best_params_class1["embedding_dim"],
-        generator_dim=best_params_class1["generator_dim"],
-        discriminator_dim=best_params_class1["discriminator_dim"],
-        batch_size=best_params_class1["batch_size"],
-        discriminator_steps=best_params_class1["discriminator_steps"],
-        pac=best_params_class1["pac"]
-    )
-
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 1], discrete_columns=categorical_features)
-    generated_1 = ctgan.sample(10000 - int(np.ceil(count_class_1 / 100) * 100))
-
-    # 데이터 병합 및 저장
-    well_generated0 = generated_0[(generated_0['visi'] >= 0) & (generated_0['visi'] < 100)]
-    well_generated1 = generated_1[(generated_1['visi'] >= 100) & (generated_1['visi'] < 500)]
-    smote_gan_data = pd.concat([lerp_data, well_generated0, well_generated1], axis=0)
-    # 제거변수 복구
-    smote_gan_data['binary_class'] = smote_gan_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)
-    smote_gan_data['hour_sin'] = np.sin(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['hour_cos'] = np.cos(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['month_sin'] = np.sin(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['month_cos'] = np.cos(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['ground_temp - temp_C'] = smote_gan_data['groundtemp'] - smote_gan_data['temp_C']
-
-    filtered_data = smote_gan_data[smote_gan_data['multi_class'] != 2]
-    original_class2 = data[data['multi_class'] == 2]
-    final_data = pd.concat([filtered_data, original_class2], axis=0)
-    final_data.reset_index(drop=True, inplace=True)
-
-    # 결과 저장
-    final_data.to_csv(output_path, index = False)
-    print(len(final_data[final_data['multi_class']==0]),'|',len(final_data[final_data['multi_class']==1]),'|',len(final_data[final_data['multi_class']==2]))
\ No newline at end of file
diff --git a/Analysis_code/make_oversample_data/gan_sample_10000_3.py b/Analysis_code/make_oversample_data/gan_sample_10000_3.py
deleted file mode 100644
index 8232e7434bc321e254698e183dd2cc98ea4ff933..0000000000000000000000000000000000000000
--- a/Analysis_code/make_oversample_data/gan_sample_10000_3.py
+++ /dev/null
@@ -1,182 +0,0 @@
-import pandas as pd
-import numpy as np
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-from imblearn.over_sampling import SMOTENC
-import optuna
-from ctgan import CTGAN
-import torch
-import warnings
-
-# 지역별 데이터 파일 경로
-regions = ['incheon', 'seoul','busan', 'daegu', 'daejeon', 'gwangju']
-file_paths = [f'../../data/data_for_modeling/{region}_train.csv' for region in regions]
-output_paths = [f'../../data/data_oversampled/ctgan10000/ctgan10000_3_{region}.csv' for region in regions]
-
-# GPU 사용 설정
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-# 경고 무시
-warnings.filterwarnings("ignore", category=UserWarning, module="optuna.distributions")
-
-# 지역별 처리
-for file_path, output_path in zip(file_paths, output_paths):
-    # 데이터 로드
-    data = pd.read_csv(file_path, index_col=0)
-    data= data.loc[data['year'].isin([2019,2020]),:]
-    data['cloudcover'] = data['cloudcover'].astype('int')
-    data['lm_cloudcover'] = data['lm_cloudcover'].astype('int')
-    X = data.drop(columns=['multi_class', 'binary_class'])
-    y = data['multi_class']
-
-    # 불필요한 열 제거
-    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)
-
-    # SMOTENC에서 사용할 범주형 변수 열 번호 설정
-    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']
-
-    # sampling_strategy 설정
-    count_class_0 = (y == 0).sum()
-    count_class_1 = (y == 1).sum()
-    count_class_2 = (y == 2).sum()
-    sampling_strategy = {
-        0: 500 if count_class_0 <= 500 else 1000,
-        1: int(np.ceil(count_class_1 / 100) * 100),  # 백의 자리로 올림
-        2: count_class_2
-    }
-
-    # SMOTENC 적용
-    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)
-    X_resampled, y_resampled = smotenc.fit_resample(X, y)
-
-    # Resampled 데이터 생성
-    lerp_data = X_resampled.copy()
-    lerp_data['multi_class'] = y_resampled
-
-    # CTGAN에서 사용할 범주형 변수 열 이름 설정
-    categorical_features = [
-        col for col, dtype in zip(lerp_data.columns, lerp_data.dtypes) if dtype != 'float64'
-    ]
-
-    # Optuna 목적 함수 정의
-    def objective(trial):
-        # 하이퍼파라미터 탐색 범위 설정
-        embedding_dim = trial.suggest_int("embedding_dim", 64, 128)
-        generator_dim = trial.suggest_categorical("generator_dim", [(64, 64), (128, 128)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(64, 64), (128, 128)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [64, 128, 256])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 3)
-
-        # CTGAN 모델 생성
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        # 범주 0 데이터 필터링
-        data_0 = lerp_data[lerp_data['multi_class'] == 0]
-
-        # 모델 학습
-        ctgan.fit(data_0, discrete_columns=categorical_features)
-
-        # 샘플 생성
-        generated_data = ctgan.sample(len(data_0) * 2)
-
-        # 평가: 샘플의 연속형 변수 분포 비교
-        real_visi = data_0['visi']
-        generated_visi = generated_data['visi']
-
-        # 분포 간 차이(MSE) 계산
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    # Optuna로 최적화 수행
-    study = optuna.create_study(direction="maximize")
-    study.optimize(objective, n_trials=50)
-
-    # 최적 하이퍼파라미터 출력
-    best_params = study.best_params
-
-    # 최적 하이퍼파라미터로 CTGAN 학습 및 샘플 생성
-    ctgan = CTGAN(
-        embedding_dim=best_params["embedding_dim"],
-        generator_dim=best_params["generator_dim"],
-        discriminator_dim=best_params["discriminator_dim"],
-        batch_size=best_params["batch_size"],
-        discriminator_steps=best_params["discriminator_steps"],
-        pac=best_params["pac"]
-    )
-
-    # 범주 0 데이터로 최종 학습
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 0], discrete_columns=categorical_features)
-    generated_0 = ctgan.sample(10000)
-
-    # 범주 1 데이터 최적화 및 생성
-    def objective_class1(trial):
-        embedding_dim = trial.suggest_int("embedding_dim", 128, 512)
-        generator_dim = trial.suggest_categorical("generator_dim", [(128, 128), (256, 256)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(128, 128), (256, 256)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [256, 512, 1024])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 5)
-
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        data_1 = lerp_data[lerp_data['multi_class'] == 1]
-        ctgan.fit(data_1, discrete_columns=categorical_features)
-        generated_data = ctgan.sample(len(data_1) * 2)
-
-        real_visi = data_1['visi']
-        generated_visi = generated_data['visi']
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    study_class1 = optuna.create_study(direction="maximize")
-    study_class1.optimize(objective_class1, n_trials=30)
-
-    best_params_class1 = study_class1.best_params
-    ctgan = CTGAN(
-        embedding_dim=best_params_class1["embedding_dim"],
-        generator_dim=best_params_class1["generator_dim"],
-        discriminator_dim=best_params_class1["discriminator_dim"],
-        batch_size=best_params_class1["batch_size"],
-        discriminator_steps=best_params_class1["discriminator_steps"],
-        pac=best_params_class1["pac"]
-    )
-
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 1], discrete_columns=categorical_features)
-    generated_1 = ctgan.sample(10000 - int(np.ceil(count_class_1 / 100) * 100))
-
-    # 데이터 병합 및 저장
-    well_generated0 = generated_0[(generated_0['visi'] >= 0) & (generated_0['visi'] < 100)]
-    well_generated1 = generated_1[(generated_1['visi'] >= 100) & (generated_1['visi'] < 500)]
-    smote_gan_data = pd.concat([lerp_data, well_generated0, well_generated1], axis=0)
-    # 제거변수 복구
-    smote_gan_data['binary_class'] = smote_gan_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)
-    smote_gan_data['hour_sin'] = np.sin(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['hour_cos'] = np.cos(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['month_sin'] = np.sin(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['month_cos'] = np.cos(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['ground_temp - temp_C'] = smote_gan_data['groundtemp'] - smote_gan_data['temp_C']
-
-    filtered_data = smote_gan_data[smote_gan_data['multi_class'] != 2]
-    original_class2 = data[data['multi_class'] == 2]
-    final_data = pd.concat([filtered_data, original_class2], axis=0)
-    final_data.reset_index(drop=True, inplace=True)
-
-    # 결과 저장
-    final_data.to_csv(output_path, index = False)
-    print(len(final_data[final_data['multi_class']==0]),'|',len(final_data[final_data['multi_class']==1]),'|',len(final_data[final_data['multi_class']==2]))
\ No newline at end of file
diff --git a/Analysis_code/make_oversample_data/gan_sample_20000_1.py b/Analysis_code/make_oversample_data/gan_sample_20000_1.py
deleted file mode 100644
index 217e6cd62db7f9ad65a761887b3cedce14ac0281..0000000000000000000000000000000000000000
--- a/Analysis_code/make_oversample_data/gan_sample_20000_1.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import pandas as pd
-import numpy as np
-# import os
-# os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-from imblearn.over_sampling import SMOTENC
-import optuna
-from ctgan import CTGAN
-import torch
-import warnings
-
-# 지역별 데이터 파일 경로
-# regions = ['busan', 'daegu', 'daejeon', 'gwangju', 'incheon', 'seoul']
-regions = ['incheon', 'seoul','busan', 'daegu', 'daejeon', 'gwangju']
-file_paths = [f'../../data/data_for_modeling/{region}_train.csv' for region in regions]
-output_paths = [f'../../data/data_oversampled/ctgan20000/ctgan20000_1_{region}.csv' for region in regions]
-
-# GPU 사용 설정
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-# 경고 무시
-warnings.filterwarnings("ignore", category=UserWarning, module="optuna.distributions")
-
-# 지역별 처리
-for file_path, output_path in zip(file_paths, output_paths):
-    # 데이터 로드
-    data = pd.read_csv(file_path, index_col=0)
-    data= data.loc[data['year'].isin([2018,2019]),:]
-    data['cloudcover'] = data['cloudcover'].astype('int')
-    data['lm_cloudcover'] = data['lm_cloudcover'].astype('int')
-    X = data.drop(columns=['multi_class', 'binary_class'])
-    y = data['multi_class']
-
-    # 불필요한 열 제거
-    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)
-
-    # SMOTENC에서 사용할 범주형 변수 열 번호 설정
-    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']
-
-    # sampling_strategy 설정
-    count_class_0 = (y == 0).sum()
-    count_class_1 = (y == 1).sum()
-    count_class_2 = (y == 2).sum()
-    sampling_strategy = {
-        0: 500 if count_class_0 <= 500 else 1000,
-        1: int(np.ceil(count_class_1 / 100) * 100),  # 백의 자리로 올림
-        2: count_class_2
-    }
-
-    # SMOTENC 적용
-    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)
-    X_resampled, y_resampled = smotenc.fit_resample(X, y)
-
-    # Resampled 데이터 생성
-    lerp_data = X_resampled.copy()
-    lerp_data['multi_class'] = y_resampled
-
-    # CTGAN에서 사용할 범주형 변수 열 이름 설정
-    categorical_features = [
-        col for col, dtype in zip(lerp_data.columns, lerp_data.dtypes) if dtype != 'float64'
-    ]
-
-    # Optuna 목적 함수 정의
-    def objective(trial):
-        # 하이퍼파라미터 탐색 범위 설정
-        embedding_dim = trial.suggest_int("embedding_dim", 64, 128)
-        generator_dim = trial.suggest_categorical("generator_dim", [(64, 64), (128, 128)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(64, 64), (128, 128)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [64, 128, 256])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 3)
-
-        # CTGAN 모델 생성
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        # 범주 0 데이터 필터링
-        data_0 = lerp_data[lerp_data['multi_class'] == 0]
-
-        # 모델 학습
-        ctgan.fit(data_0, discrete_columns=categorical_features)
-
-        # 샘플 생성
-        generated_data = ctgan.sample(len(data_0) * 2)
-
-        # 평가: 샘플의 연속형 변수 분포 비교
-        real_visi = data_0['visi']
-        generated_visi = generated_data['visi']
-
-        # 분포 간 차이(MSE) 계산
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    # Optuna로 최적화 수행
-    study = optuna.create_study(direction="maximize")
-    study.optimize(objective, n_trials=50)
-
-    # 최적 하이퍼파라미터 출력
-    best_params = study.best_params
-
-    # 최적 하이퍼파라미터로 CTGAN 학습 및 샘플 생성
-    ctgan = CTGAN(
-        embedding_dim=best_params["embedding_dim"],
-        generator_dim=best_params["generator_dim"],
-        discriminator_dim=best_params["discriminator_dim"],
-        batch_size=best_params["batch_size"],
-        discriminator_steps=best_params["discriminator_steps"],
-        pac=best_params["pac"]
-    )
-
-    # 범주 0 데이터로 최종 학습
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 0], discrete_columns=categorical_features)
-    generated_0 = ctgan.sample(20000)
-
-    # 범주 1 데이터 최적화 및 생성
-    def objective_class1(trial):
-        embedding_dim = trial.suggest_int("embedding_dim", 128, 512)
-        generator_dim = trial.suggest_categorical("generator_dim", [(128, 128), (256, 256)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(128, 128), (256, 256)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [256, 512, 1024])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 5)
-
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        data_1 = lerp_data[lerp_data['multi_class'] == 1]
-        ctgan.fit(data_1, discrete_columns=categorical_features)
-        generated_data = ctgan.sample(len(data_1) * 2)
-
-        real_visi = data_1['visi']
-        generated_visi = generated_data['visi']
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    study_class1 = optuna.create_study(direction="maximize")
-    study_class1.optimize(objective_class1, n_trials=30)
-
-    best_params_class1 = study_class1.best_params
-    ctgan = CTGAN(
-        embedding_dim=best_params_class1["embedding_dim"],
-        generator_dim=best_params_class1["generator_dim"],
-        discriminator_dim=best_params_class1["discriminator_dim"],
-        batch_size=best_params_class1["batch_size"],
-        discriminator_steps=best_params_class1["discriminator_steps"],
-        pac=best_params_class1["pac"]
-    )
-
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 1], discrete_columns=categorical_features)
-    generated_1 = ctgan.sample(20000 - int(np.ceil(count_class_1 / 100) * 100))
-
-    # 데이터 병합 및 저장
-    well_generated0 = generated_0[(generated_0['visi'] >= 0) & (generated_0['visi'] < 100)]
-    well_generated1 = generated_1[(generated_1['visi'] >= 100) & (generated_1['visi'] < 500)]
-    smote_gan_data = pd.concat([lerp_data, well_generated0, well_generated1], axis=0)
-    # 제거변수 복구
-    smote_gan_data['binary_class'] = smote_gan_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)
-    smote_gan_data['hour_sin'] = np.sin(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['hour_cos'] = np.cos(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['month_sin'] = np.sin(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['month_cos'] = np.cos(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['ground_temp - temp_C'] = smote_gan_data['groundtemp'] - smote_gan_data['temp_C']
-
-    filtered_data = smote_gan_data[smote_gan_data['multi_class'] != 2]
-    original_class2 = data[data['multi_class'] == 2]
-    final_data = pd.concat([filtered_data, original_class2], axis=0)
-    final_data.reset_index(drop=True, inplace=True)
-
-    # 결과 저장
-    final_data.to_csv(output_path, index = False)
-    print(len(final_data[final_data['multi_class']==0]),'|',len(final_data[final_data['multi_class']==1]),'|',len(final_data[final_data['multi_class']==2]))
\ No newline at end of file
diff --git a/Analysis_code/make_oversample_data/gan_sample_20000_2.py b/Analysis_code/make_oversample_data/gan_sample_20000_2.py
deleted file mode 100644
index 6492981291780b4b9e04eea8d5e338e3e14c98cc..0000000000000000000000000000000000000000
--- a/Analysis_code/make_oversample_data/gan_sample_20000_2.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import pandas as pd
-import numpy as np
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-from imblearn.over_sampling import SMOTENC
-import optuna
-from ctgan import CTGAN
-import torch
-import warnings
-
-# 지역별 데이터 파일 경로
-# regions = ['busan', 'daegu', 'daejeon', 'gwangju', 'incheon', 'seoul']
-regions = ['incheon', 'seoul','busan', 'daegu', 'daejeon', 'gwangju']
-file_paths = [f'../../data/data_for_modeling/{region}_train.csv' for region in regions]
-output_paths = [f'../../data/data_oversampled/ctgan20000/ctgan20000_2_{region}.csv' for region in regions]
-
-# GPU 사용 설정
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-# 경고 무시
-warnings.filterwarnings("ignore", category=UserWarning, module="optuna.distributions")
-
-# 지역별 처리
-for file_path, output_path in zip(file_paths, output_paths):
-    # 데이터 로드
-    data = pd.read_csv(file_path, index_col=0)
-    data= data.loc[data['year'].isin([2018,2020]),:]
-    data['cloudcover'] = data['cloudcover'].astype('int')
-    data['lm_cloudcover'] = data['lm_cloudcover'].astype('int')
-    X = data.drop(columns=['multi_class', 'binary_class'])
-    y = data['multi_class']
-
-    # 불필요한 열 제거
-    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)
-
-    # SMOTENC에서 사용할 범주형 변수 열 번호 설정
-    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']
-
-    # sampling_strategy 설정
-    count_class_0 = (y == 0).sum()
-    count_class_1 = (y == 1).sum()
-    count_class_2 = (y == 2).sum()
-    sampling_strategy = {
-        0: 500 if count_class_0 <= 500 else 1000,
-        1: int(np.ceil(count_class_1 / 100) * 100),  # 백의 자리로 올림
-        2: count_class_2
-    }
-
-    # SMOTENC 적용
-    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)
-    X_resampled, y_resampled = smotenc.fit_resample(X, y)
-
-    # Resampled 데이터 생성
-    lerp_data = X_resampled.copy()
-    lerp_data['multi_class'] = y_resampled
-
-    # CTGAN에서 사용할 범주형 변수 열 이름 설정
-    categorical_features = [
-        col for col, dtype in zip(lerp_data.columns, lerp_data.dtypes) if dtype != 'float64'
-    ]
-
-    # Optuna 목적 함수 정의
-    def objective(trial):
-        # 하이퍼파라미터 탐색 범위 설정
-        embedding_dim = trial.suggest_int("embedding_dim", 64, 128)
-        generator_dim = trial.suggest_categorical("generator_dim", [(64, 64), (128, 128)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(64, 64), (128, 128)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [64, 128, 256])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 3)
-
-        # CTGAN 모델 생성
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        # 범주 0 데이터 필터링
-        data_0 = lerp_data[lerp_data['multi_class'] == 0]
-
-        # 모델 학습
-        ctgan.fit(data_0, discrete_columns=categorical_features)
-
-        # 샘플 생성
-        generated_data = ctgan.sample(len(data_0) * 2)
-
-        # 평가: 샘플의 연속형 변수 분포 비교
-        real_visi = data_0['visi']
-        generated_visi = generated_data['visi']
-
-        # 분포 간 차이(MSE) 계산
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    # Optuna로 최적화 수행
-    study = optuna.create_study(direction="maximize")
-    study.optimize(objective, n_trials=50)
-
-    # 최적 하이퍼파라미터 출력
-    best_params = study.best_params
-
-    # 최적 하이퍼파라미터로 CTGAN 학습 및 샘플 생성
-    ctgan = CTGAN(
-        embedding_dim=best_params["embedding_dim"],
-        generator_dim=best_params["generator_dim"],
-        discriminator_dim=best_params["discriminator_dim"],
-        batch_size=best_params["batch_size"],
-        discriminator_steps=best_params["discriminator_steps"],
-        pac=best_params["pac"]
-    )
-
-    # 범주 0 데이터로 최종 학습
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 0], discrete_columns=categorical_features)
-    generated_0 = ctgan.sample(20000)
-
-    # 범주 1 데이터 최적화 및 생성
-    def objective_class1(trial):
-        embedding_dim = trial.suggest_int("embedding_dim", 128, 512)
-        generator_dim = trial.suggest_categorical("generator_dim", [(128, 128), (256, 256)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(128, 128), (256, 256)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [256, 512, 1024])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 5)
-
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        data_1 = lerp_data[lerp_data['multi_class'] == 1]
-        ctgan.fit(data_1, discrete_columns=categorical_features)
-        generated_data = ctgan.sample(len(data_1) * 2)
-
-        real_visi = data_1['visi']
-        generated_visi = generated_data['visi']
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    study_class1 = optuna.create_study(direction="maximize")
-    study_class1.optimize(objective_class1, n_trials=30)
-
-    best_params_class1 = study_class1.best_params
-    ctgan = CTGAN(
-        embedding_dim=best_params_class1["embedding_dim"],
-        generator_dim=best_params_class1["generator_dim"],
-        discriminator_dim=best_params_class1["discriminator_dim"],
-        batch_size=best_params_class1["batch_size"],
-        discriminator_steps=best_params_class1["discriminator_steps"],
-        pac=best_params_class1["pac"]
-    )
-
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 1], discrete_columns=categorical_features)
-    generated_1 = ctgan.sample(20000 - int(np.ceil(count_class_1 / 100) * 100))
-
-    # 데이터 병합 및 저장
-    well_generated0 = generated_0[(generated_0['visi'] >= 0) & (generated_0['visi'] < 100)]
-    well_generated1 = generated_1[(generated_1['visi'] >= 100) & (generated_1['visi'] < 500)]
-    smote_gan_data = pd.concat([lerp_data, well_generated0, well_generated1], axis=0)
-    # 제거변수 복구
-    smote_gan_data['binary_class'] = smote_gan_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)
-    smote_gan_data['hour_sin'] = np.sin(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['hour_cos'] = np.cos(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['month_sin'] = np.sin(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['month_cos'] = np.cos(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['ground_temp - temp_C'] = smote_gan_data['groundtemp'] - smote_gan_data['temp_C']
-
-    filtered_data = smote_gan_data[smote_gan_data['multi_class'] != 2]
-    original_class2 = data[data['multi_class'] == 2]
-    final_data = pd.concat([filtered_data, original_class2], axis=0)
-    final_data.reset_index(drop=True, inplace=True)
-
-    # 결과 저장
-    final_data.to_csv(output_path, index = False)
-    print(len(final_data[final_data['multi_class']==0]),'|',len(final_data[final_data['multi_class']==1]),'|',len(final_data[final_data['multi_class']==2]))
\ No newline at end of file
diff --git a/Analysis_code/make_oversample_data/gan_sample_20000_3.py b/Analysis_code/make_oversample_data/gan_sample_20000_3.py
deleted file mode 100644
index 94b1d7230ac20db2105b7abf28ae7c71b39810b4..0000000000000000000000000000000000000000
--- a/Analysis_code/make_oversample_data/gan_sample_20000_3.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import pandas as pd
-import numpy as np
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-from imblearn.over_sampling import SMOTENC
-import optuna
-from ctgan import CTGAN
-import torch
-import warnings
-
-# 지역별 데이터 파일 경로
-# regions = ['busan', 'daegu', 'daejeon', 'gwangju', 'incheon', 'seoul']
-regions = ['incheon', 'seoul','busan', 'daegu', 'daejeon', 'gwangju']
-file_paths = [f'../../data/data_for_modeling/{region}_train.csv' for region in regions]
-output_paths = [f'../../data/data_oversampled/ctgan20000/ctgan20000_3_{region}.csv' for region in regions]
-
-# GPU 사용 설정
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-# 경고 무시
-warnings.filterwarnings("ignore", category=UserWarning, module="optuna.distributions")
-
-# 지역별 처리
-for file_path, output_path in zip(file_paths, output_paths):
-    # 데이터 로드
-    data = pd.read_csv(file_path, index_col=0)
-    data= data.loc[data['year'].isin([2019,2020]),:]
-    data['cloudcover'] = data['cloudcover'].astype('int')
-    data['lm_cloudcover'] = data['lm_cloudcover'].astype('int')
-    X = data.drop(columns=['multi_class', 'binary_class'])
-    y = data['multi_class']
-
-    # 불필요한 열 제거
-    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)
-
-    # SMOTENC에서 사용할 범주형 변수 열 번호 설정
-    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']
-
-    # sampling_strategy 설정
-    count_class_0 = (y == 0).sum()
-    count_class_1 = (y == 1).sum()
-    count_class_2 = (y == 2).sum()
-    sampling_strategy = {
-        0: 500 if count_class_0 <= 500 else 1000,
-        1: int(np.ceil(count_class_1 / 100) * 100),  # 백의 자리로 올림
-        2: count_class_2
-    }
-
-    # SMOTENC 적용
-    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)
-    X_resampled, y_resampled = smotenc.fit_resample(X, y)
-
-    # Resampled 데이터 생성
-    lerp_data = X_resampled.copy()
-    lerp_data['multi_class'] = y_resampled
-
-    # CTGAN에서 사용할 범주형 변수 열 이름 설정
-    categorical_features = [
-        col for col, dtype in zip(lerp_data.columns, lerp_data.dtypes) if dtype != 'float64'
-    ]
-
-    # Optuna 목적 함수 정의
-    def objective(trial):
-        # 하이퍼파라미터 탐색 범위 설정
-        embedding_dim = trial.suggest_int("embedding_dim", 64, 128)
-        generator_dim = trial.suggest_categorical("generator_dim", [(64, 64), (128, 128)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(64, 64), (128, 128)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [64, 128, 256])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 3)
-
-        # CTGAN 모델 생성
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        # 범주 0 데이터 필터링
-        data_0 = lerp_data[lerp_data['multi_class'] == 0]
-
-        # 모델 학습
-        ctgan.fit(data_0, discrete_columns=categorical_features)
-
-        # 샘플 생성
-        generated_data = ctgan.sample(len(data_0) * 2)
-
-        # 평가: 샘플의 연속형 변수 분포 비교
-        real_visi = data_0['visi']
-        generated_visi = generated_data['visi']
-
-        # 분포 간 차이(MSE) 계산
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    # Optuna로 최적화 수행
-    study = optuna.create_study(direction="maximize")
-    study.optimize(objective, n_trials=50)
-
-    # 최적 하이퍼파라미터 출력
-    best_params = study.best_params
-
-    # 최적 하이퍼파라미터로 CTGAN 학습 및 샘플 생성
-    ctgan = CTGAN(
-        embedding_dim=best_params["embedding_dim"],
-        generator_dim=best_params["generator_dim"],
-        discriminator_dim=best_params["discriminator_dim"],
-        batch_size=best_params["batch_size"],
-        discriminator_steps=best_params["discriminator_steps"],
-        pac=best_params["pac"]
-    )
-
-    # 범주 0 데이터로 최종 학습
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 0], discrete_columns=categorical_features)
-    generated_0 = ctgan.sample(20000)
-
-    # 범주 1 데이터 최적화 및 생성
-    def objective_class1(trial):
-        embedding_dim = trial.suggest_int("embedding_dim", 128, 512)
-        generator_dim = trial.suggest_categorical("generator_dim", [(128, 128), (256, 256)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(128, 128), (256, 256)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [256, 512, 1024])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 5)
-
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        data_1 = lerp_data[lerp_data['multi_class'] == 1]
-        ctgan.fit(data_1, discrete_columns=categorical_features)
-        generated_data = ctgan.sample(len(data_1) * 2)
-
-        real_visi = data_1['visi']
-        generated_visi = generated_data['visi']
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    study_class1 = optuna.create_study(direction="maximize")
-    study_class1.optimize(objective_class1, n_trials=30)
-
-    best_params_class1 = study_class1.best_params
-    ctgan = CTGAN(
-        embedding_dim=best_params_class1["embedding_dim"],
-        generator_dim=best_params_class1["generator_dim"],
-        discriminator_dim=best_params_class1["discriminator_dim"],
-        batch_size=best_params_class1["batch_size"],
-        discriminator_steps=best_params_class1["discriminator_steps"],
-        pac=best_params_class1["pac"]
-    )
-
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 1], discrete_columns=categorical_features)
-    generated_1 = ctgan.sample(20000 - int(np.ceil(count_class_1 / 100) * 100))
-
-    # 데이터 병합 및 저장
-    well_generated0 = generated_0[(generated_0['visi'] >= 0) & (generated_0['visi'] < 100)]
-    well_generated1 = generated_1[(generated_1['visi'] >= 100) & (generated_1['visi'] < 500)]
-    smote_gan_data = pd.concat([lerp_data, well_generated0, well_generated1], axis=0)
-    # 제거변수 복구
-    smote_gan_data['binary_class'] = smote_gan_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)
-    smote_gan_data['hour_sin'] = np.sin(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['hour_cos'] = np.cos(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['month_sin'] = np.sin(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['month_cos'] = np.cos(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['ground_temp - temp_C'] = smote_gan_data['groundtemp'] - smote_gan_data['temp_C']
-
-    filtered_data = smote_gan_data[smote_gan_data['multi_class'] != 2]
-    original_class2 = data[data['multi_class'] == 2]
-    final_data = pd.concat([filtered_data, original_class2], axis=0)
-    final_data.reset_index(drop=True, inplace=True)
-
-    # 결과 저장
-    final_data.to_csv(output_path, index = False)
-    print(len(final_data[final_data['multi_class']==0]),'|',len(final_data[final_data['multi_class']==1]),'|',len(final_data[final_data['multi_class']==2]))
\ No newline at end of file
diff --git a/Analysis_code/make_oversample_data/gan_sample_7000_1.py b/Analysis_code/make_oversample_data/gan_sample_7000_1.py
deleted file mode 100644
index 7d06856b83033460ebcd100e20735b0112846b09..0000000000000000000000000000000000000000
--- a/Analysis_code/make_oversample_data/gan_sample_7000_1.py
+++ /dev/null
@@ -1,180 +0,0 @@
-import pandas as pd
-import numpy as np
-from imblearn.over_sampling import SMOTENC
-import optuna
-from ctgan import CTGAN
-import torch
-import warnings
-
-# 지역별 데이터 파일 경로
-regions = ['incheon', 'seoul','busan', 'daegu', 'daejeon', 'gwangju']
-file_paths = [f'../../data/data_for_modeling/{region}_train.csv' for region in regions]
-output_paths = [f'../../data/data_oversampled/ctgan7000/ctgan7000_1_{region}.csv' for region in regions]
-
-# GPU 사용 설정
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-# 경고 무시
-warnings.filterwarnings("ignore", category=UserWarning, module="optuna.distributions")
-
-# 지역별 처리
-for file_path, output_path in zip(file_paths, output_paths):
-    # 데이터 로드
-    data = pd.read_csv(file_path, index_col=0)
-    data= data.loc[data['year'].isin([2018,2019]),:]
-    data['cloudcover'] = data['cloudcover'].astype('int')
-    data['lm_cloudcover'] = data['lm_cloudcover'].astype('int')
-    X = data.drop(columns=['multi_class', 'binary_class'])
-    y = data['multi_class']
-
-    # 불필요한 열 제거
-    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)
-
-    # SMOTENC에서 사용할 범주형 변수 열 번호 설정
-    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']
-
-    # sampling_strategy 설정
-    count_class_0 = (y == 0).sum()
-    count_class_1 = (y == 1).sum()
-    count_class_2 = (y == 2).sum()
-    sampling_strategy = {
-        0: 500 if count_class_0 <= 500 else 1000,
-        1: int(np.ceil(count_class_1 / 100) * 100),  # 백의 자리로 올림
-        2: count_class_2
-    }
-
-    # SMOTENC 적용
-    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)
-    X_resampled, y_resampled = smotenc.fit_resample(X, y)
-
-    # Resampled 데이터 생성
-    lerp_data = X_resampled.copy()
-    lerp_data['multi_class'] = y_resampled
-
-    # CTGAN에서 사용할 범주형 변수 열 이름 설정
-    categorical_features = [
-        col for col, dtype in zip(lerp_data.columns, lerp_data.dtypes) if dtype != 'float64'
-    ]
-
-    # Optuna 목적 함수 정의
-    def objective(trial):
-        # 하이퍼파라미터 탐색 범위 설정
-        embedding_dim = trial.suggest_int("embedding_dim", 64, 128)
-        generator_dim = trial.suggest_categorical("generator_dim", [(64, 64), (128, 128)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(64, 64), (128, 128)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [64, 128, 256])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 3)
-
-        # CTGAN 모델 생성
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        # 범주 0 데이터 필터링
-        data_0 = lerp_data[lerp_data['multi_class'] == 0]
-
-        # 모델 학습
-        ctgan.fit(data_0, discrete_columns=categorical_features)
-
-        # 샘플 생성
-        generated_data = ctgan.sample(len(data_0) * 2)
-
-        # 평가: 샘플의 연속형 변수 분포 비교
-        real_visi = data_0['visi']
-        generated_visi = generated_data['visi']
-
-        # 분포 간 차이(MSE) 계산
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    # Optuna로 최적화 수행
-    study = optuna.create_study(direction="maximize")
-    study.optimize(objective, n_trials=50)
-
-    # 최적 하이퍼파라미터 출력
-    best_params = study.best_params
-
-    # 최적 하이퍼파라미터로 CTGAN 학습 및 샘플 생성
-    ctgan = CTGAN(
-        embedding_dim=best_params["embedding_dim"],
-        generator_dim=best_params["generator_dim"],
-        discriminator_dim=best_params["discriminator_dim"],
-        batch_size=best_params["batch_size"],
-        discriminator_steps=best_params["discriminator_steps"],
-        pac=best_params["pac"]
-    )
-
-    # 범주 0 데이터로 최종 학습
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 0], discrete_columns=categorical_features)
-    generated_0 = ctgan.sample(7000)
-
-    # 범주 1 데이터 최적화 및 생성
-    def objective_class1(trial):
-        embedding_dim = trial.suggest_int("embedding_dim", 128, 512)
-        generator_dim = trial.suggest_categorical("generator_dim", [(128, 128), (256, 256)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(128, 128), (256, 256)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [256, 512, 1024])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 5)
-
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        data_1 = lerp_data[lerp_data['multi_class'] == 1]
-        ctgan.fit(data_1, discrete_columns=categorical_features)
-        generated_data = ctgan.sample(len(data_1) * 2)
-
-        real_visi = data_1['visi']
-        generated_visi = generated_data['visi']
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    study_class1 = optuna.create_study(direction="maximize")
-    study_class1.optimize(objective_class1, n_trials=30)
-
-    best_params_class1 = study_class1.best_params
-    ctgan = CTGAN(
-        embedding_dim=best_params_class1["embedding_dim"],
-        generator_dim=best_params_class1["generator_dim"],
-        discriminator_dim=best_params_class1["discriminator_dim"],
-        batch_size=best_params_class1["batch_size"],
-        discriminator_steps=best_params_class1["discriminator_steps"],
-        pac=best_params_class1["pac"]
-    )
-
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 1], discrete_columns=categorical_features)
-    generated_1 = ctgan.sample(7000 - int(np.ceil(count_class_1 / 100) * 100))
-
-    # 데이터 병합 및 저장
-    well_generated0 = generated_0[(generated_0['visi'] >= 0) & (generated_0['visi'] < 100)]
-    well_generated1 = generated_1[(generated_1['visi'] >= 100) & (generated_1['visi'] < 500)]
-    smote_gan_data = pd.concat([lerp_data, well_generated0, well_generated1], axis=0)
-    # 제거변수 복구
-    smote_gan_data['binary_class'] = smote_gan_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)
-    smote_gan_data['hour_sin'] = np.sin(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['hour_cos'] = np.cos(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['month_sin'] = np.sin(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['month_cos'] = np.cos(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['ground_temp - temp_C'] = smote_gan_data['groundtemp'] - smote_gan_data['temp_C']
-
-    filtered_data = smote_gan_data[smote_gan_data['multi_class'] != 2]
-    original_class2 = data[data['multi_class'] == 2]
-    final_data = pd.concat([filtered_data, original_class2], axis=0)
-    final_data.reset_index(drop=True, inplace=True)
-
-    # 결과 저장
-    final_data.to_csv(output_path, index = False)
-    print(len(final_data[final_data['multi_class']==0]),'|',len(final_data[final_data['multi_class']==1]),'|',len(final_data[final_data['multi_class']==2]))
\ No newline at end of file
diff --git a/Analysis_code/make_oversample_data/gan_sample_7000_2.py b/Analysis_code/make_oversample_data/gan_sample_7000_2.py
deleted file mode 100644
index 0aa6d20604a2d191f53eeaf2c56d7db7bff9ba86..0000000000000000000000000000000000000000
--- a/Analysis_code/make_oversample_data/gan_sample_7000_2.py
+++ /dev/null
@@ -1,182 +0,0 @@
-import pandas as pd
-import numpy as np
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-from imblearn.over_sampling import SMOTENC
-import optuna
-from ctgan import CTGAN
-import torch
-import warnings
-
-# 지역별 데이터 파일 경로
-regions = ['incheon', 'seoul','busan', 'daegu', 'daejeon', 'gwangju']
-file_paths = [f'../../data/data_for_modeling/{region}_train.csv' for region in regions]
-output_paths = [f'../../data/data_oversampled/ctgan7000/ctgan7000_2_{region}.csv' for region in regions]
-
-# GPU 사용 설정
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-# 경고 무시
-warnings.filterwarnings("ignore", category=UserWarning, module="optuna.distributions")
-
-# 지역별 처리
-for file_path, output_path in zip(file_paths, output_paths):
-    # 데이터 로드
-    data = pd.read_csv(file_path, index_col=0)
-    data= data.loc[data['year'].isin([2018,2020]),:]
-    data['cloudcover'] = data['cloudcover'].astype('int')
-    data['lm_cloudcover'] = data['lm_cloudcover'].astype('int')
-    X = data.drop(columns=['multi_class', 'binary_class'])
-    y = data['multi_class']
-
-    # 불필요한 열 제거
-    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)
-
-    # SMOTENC에서 사용할 범주형 변수 열 번호 설정
-    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']
-
-    # sampling_strategy 설정
-    count_class_0 = (y == 0).sum()
-    count_class_1 = (y == 1).sum()
-    count_class_2 = (y == 2).sum()
-    sampling_strategy = {
-        0: 500 if count_class_0 <= 500 else 1000,
-        1: int(np.ceil(count_class_1 / 100) * 100),  # 백의 자리로 올림
-        2: count_class_2
-    }
-
-    # SMOTENC 적용
-    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)
-    X_resampled, y_resampled = smotenc.fit_resample(X, y)
-
-    # Resampled 데이터 생성
-    lerp_data = X_resampled.copy()
-    lerp_data['multi_class'] = y_resampled
-
-    # CTGAN에서 사용할 범주형 변수 열 이름 설정
-    categorical_features = [
-        col for col, dtype in zip(lerp_data.columns, lerp_data.dtypes) if dtype != 'float64'
-    ]
-
-    # Optuna 목적 함수 정의
-    def objective(trial):
-        # 하이퍼파라미터 탐색 범위 설정
-        embedding_dim = trial.suggest_int("embedding_dim", 64, 128)
-        generator_dim = trial.suggest_categorical("generator_dim", [(64, 64), (128, 128)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(64, 64), (128, 128)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [64, 128, 256])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 3)
-
-        # CTGAN 모델 생성
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        # 범주 0 데이터 필터링
-        data_0 = lerp_data[lerp_data['multi_class'] == 0]
-
-        # 모델 학습
-        ctgan.fit(data_0, discrete_columns=categorical_features)
-
-        # 샘플 생성
-        generated_data = ctgan.sample(len(data_0) * 2)
-
-        # 평가: 샘플의 연속형 변수 분포 비교
-        real_visi = data_0['visi']
-        generated_visi = generated_data['visi']
-
-        # 분포 간 차이(MSE) 계산
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    # Optuna로 최적화 수행
-    study = optuna.create_study(direction="maximize")
-    study.optimize(objective, n_trials=50)
-
-    # 최적 하이퍼파라미터 출력
-    best_params = study.best_params
-
-    # 최적 하이퍼파라미터로 CTGAN 학습 및 샘플 생성
-    ctgan = CTGAN(
-        embedding_dim=best_params["embedding_dim"],
-        generator_dim=best_params["generator_dim"],
-        discriminator_dim=best_params["discriminator_dim"],
-        batch_size=best_params["batch_size"],
-        discriminator_steps=best_params["discriminator_steps"],
-        pac=best_params["pac"]
-    )
-
-    # 범주 0 데이터로 최종 학습
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 0], discrete_columns=categorical_features)
-    generated_0 = ctgan.sample(7000)
-
-    # 범주 1 데이터 최적화 및 생성
-    def objective_class1(trial):
-        embedding_dim = trial.suggest_int("embedding_dim", 128, 512)
-        generator_dim = trial.suggest_categorical("generator_dim", [(128, 128), (256, 256)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(128, 128), (256, 256)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [256, 512, 1024])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 5)
-
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        data_1 = lerp_data[lerp_data['multi_class'] == 1]
-        ctgan.fit(data_1, discrete_columns=categorical_features)
-        generated_data = ctgan.sample(len(data_1) * 2)
-
-        real_visi = data_1['visi']
-        generated_visi = generated_data['visi']
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    study_class1 = optuna.create_study(direction="maximize")
-    study_class1.optimize(objective_class1, n_trials=30)
-
-    best_params_class1 = study_class1.best_params
-    ctgan = CTGAN(
-        embedding_dim=best_params_class1["embedding_dim"],
-        generator_dim=best_params_class1["generator_dim"],
-        discriminator_dim=best_params_class1["discriminator_dim"],
-        batch_size=best_params_class1["batch_size"],
-        discriminator_steps=best_params_class1["discriminator_steps"],
-        pac=best_params_class1["pac"]
-    )
-
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 1], discrete_columns=categorical_features)
-    generated_1 = ctgan.sample(7000 - int(np.ceil(count_class_1 / 100) * 100))
-
-    # 데이터 병합 및 저장
-    well_generated0 = generated_0[(generated_0['visi'] >= 0) & (generated_0['visi'] < 100)]
-    well_generated1 = generated_1[(generated_1['visi'] >= 100) & (generated_1['visi'] < 500)]
-    smote_gan_data = pd.concat([lerp_data, well_generated0, well_generated1], axis=0)
-    # 제거변수 복구
-    smote_gan_data['binary_class'] = smote_gan_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)
-    smote_gan_data['hour_sin'] = np.sin(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['hour_cos'] = np.cos(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['month_sin'] = np.sin(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['month_cos'] = np.cos(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['ground_temp - temp_C'] = smote_gan_data['groundtemp'] - smote_gan_data['temp_C']
-
-    filtered_data = smote_gan_data[smote_gan_data['multi_class'] != 2]
-    original_class2 = data[data['multi_class'] == 2]
-    final_data = pd.concat([filtered_data, original_class2], axis=0)
-    final_data.reset_index(drop=True, inplace=True)
-
-    # 결과 저장
-    final_data.to_csv(output_path, index = False)
-    print(len(final_data[final_data['multi_class']==0]),'|',len(final_data[final_data['multi_class']==1]),'|',len(final_data[final_data['multi_class']==2]))
\ No newline at end of file
diff --git a/Analysis_code/make_oversample_data/gan_sample_7000_3.py b/Analysis_code/make_oversample_data/gan_sample_7000_3.py
deleted file mode 100644
index 83e32ad955a7171ec6938610ff6b7570f18e0d9e..0000000000000000000000000000000000000000
--- a/Analysis_code/make_oversample_data/gan_sample_7000_3.py
+++ /dev/null
@@ -1,182 +0,0 @@
-import pandas as pd
-import numpy as np
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-from imblearn.over_sampling import SMOTENC
-import optuna
-from ctgan import CTGAN
-import torch
-import warnings
-
-# 지역별 데이터 파일 경로
-regions = ['incheon', 'seoul','busan', 'daegu', 'daejeon', 'gwangju']
-file_paths = [f'../../data/data_for_modeling/{region}_train.csv' for region in regions]
-output_paths = [f'../../data/data_oversampled/ctgan7000/ctgan7000_3_{region}.csv' for region in regions]
-
-# GPU 사용 설정
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-# 경고 무시
-warnings.filterwarnings("ignore", category=UserWarning, module="optuna.distributions")
-
-# 지역별 처리
-for file_path, output_path in zip(file_paths, output_paths):
-    # 데이터 로드
-    data = pd.read_csv(file_path, index_col=0)
-    data= data.loc[data['year'].isin([2019,2020]),:]
-    data['cloudcover'] = data['cloudcover'].astype('int')
-    data['lm_cloudcover'] = data['lm_cloudcover'].astype('int')
-    X = data.drop(columns=['multi_class', 'binary_class'])
-    y = data['multi_class']
-
-    # 불필요한 열 제거
-    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)
-
-    # SMOTENC에서 사용할 범주형 변수 열 번호 설정
-    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']
-
-    # sampling_strategy 설정
-    count_class_0 = (y == 0).sum()
-    count_class_1 = (y == 1).sum()
-    count_class_2 = (y == 2).sum()
-    sampling_strategy = {
-        0: 500 if count_class_0 <= 500 else 1000,
-        1: int(np.ceil(count_class_1 / 100) * 100),  # 백의 자리로 올림
-        2: count_class_2
-    }
-
-    # SMOTENC 적용
-    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)
-    X_resampled, y_resampled = smotenc.fit_resample(X, y)
-
-    # Resampled 데이터 생성
-    lerp_data = X_resampled.copy()
-    lerp_data['multi_class'] = y_resampled
-
-    # CTGAN에서 사용할 범주형 변수 열 이름 설정
-    categorical_features = [
-        col for col, dtype in zip(lerp_data.columns, lerp_data.dtypes) if dtype != 'float64'
-    ]
-
-    # Optuna 목적 함수 정의
-    def objective(trial):
-        # 하이퍼파라미터 탐색 범위 설정
-        embedding_dim = trial.suggest_int("embedding_dim", 64, 128)
-        generator_dim = trial.suggest_categorical("generator_dim", [(64, 64), (128, 128)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(64, 64), (128, 128)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [64, 128, 256])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 3)
-
-        # CTGAN 모델 생성
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        # 범주 0 데이터 필터링
-        data_0 = lerp_data[lerp_data['multi_class'] == 0]
-
-        # 모델 학습
-        ctgan.fit(data_0, discrete_columns=categorical_features)
-
-        # 샘플 생성
-        generated_data = ctgan.sample(len(data_0) * 2)
-
-        # 평가: 샘플의 연속형 변수 분포 비교
-        real_visi = data_0['visi']
-        generated_visi = generated_data['visi']
-
-        # 분포 간 차이(MSE) 계산
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    # Optuna로 최적화 수행
-    study = optuna.create_study(direction="maximize")
-    study.optimize(objective, n_trials=50)
-
-    # 최적 하이퍼파라미터 출력
-    best_params = study.best_params
-
-    # 최적 하이퍼파라미터로 CTGAN 학습 및 샘플 생성
-    ctgan = CTGAN(
-        embedding_dim=best_params["embedding_dim"],
-        generator_dim=best_params["generator_dim"],
-        discriminator_dim=best_params["discriminator_dim"],
-        batch_size=best_params["batch_size"],
-        discriminator_steps=best_params["discriminator_steps"],
-        pac=best_params["pac"]
-    )
-
-    # 범주 0 데이터로 최종 학습
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 0], discrete_columns=categorical_features)
-    generated_0 = ctgan.sample(7000)
-
-    # 범주 1 데이터 최적화 및 생성
-    def objective_class1(trial):
-        embedding_dim = trial.suggest_int("embedding_dim", 128, 512)
-        generator_dim = trial.suggest_categorical("generator_dim", [(128, 128), (256, 256)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(128, 128), (256, 256)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [256, 512, 1024])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 5)
-
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        data_1 = lerp_data[lerp_data['multi_class'] == 1]
-        ctgan.fit(data_1, discrete_columns=categorical_features)
-        generated_data = ctgan.sample(len(data_1) * 2)
-
-        real_visi = data_1['visi']
-        generated_visi = generated_data['visi']
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    study_class1 = optuna.create_study(direction="maximize")
-    study_class1.optimize(objective_class1, n_trials=30)
-
-    best_params_class1 = study_class1.best_params
-    ctgan = CTGAN(
-        embedding_dim=best_params_class1["embedding_dim"],
-        generator_dim=best_params_class1["generator_dim"],
-        discriminator_dim=best_params_class1["discriminator_dim"],
-        batch_size=best_params_class1["batch_size"],
-        discriminator_steps=best_params_class1["discriminator_steps"],
-        pac=best_params_class1["pac"]
-    )
-
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 1], discrete_columns=categorical_features)
-    generated_1 = ctgan.sample(7000 - int(np.ceil(count_class_1 / 100) * 100))
-
-    # 데이터 병합 및 저장
-    well_generated0 = generated_0[(generated_0['visi'] >= 0) & (generated_0['visi'] < 100)]
-    well_generated1 = generated_1[(generated_1['visi'] >= 100) & (generated_1['visi'] < 500)]
-    smote_gan_data = pd.concat([lerp_data, well_generated0, well_generated1], axis=0)
-    # 제거변수 복구
-    smote_gan_data['binary_class'] = smote_gan_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)
-    smote_gan_data['hour_sin'] = np.sin(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['hour_cos'] = np.cos(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['month_sin'] = np.sin(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['month_cos'] = np.cos(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['ground_temp - temp_C'] = smote_gan_data['groundtemp'] - smote_gan_data['temp_C']
-
-    filtered_data = smote_gan_data[smote_gan_data['multi_class'] != 2]
-    original_class2 = data[data['multi_class'] == 2]
-    final_data = pd.concat([filtered_data, original_class2], axis=0)
-    final_data.reset_index(drop=True, inplace=True)
-
-    # 결과 저장
-    final_data.to_csv(output_path, index = False)
-    print(len(final_data[final_data['multi_class']==0]),'|',len(final_data[final_data['multi_class']==1]),'|',len(final_data[final_data['multi_class']==2]))
\ No newline at end of file
diff --git a/Analysis_code/make_oversample_data/oversampling_code.py b/Analysis_code/make_oversample_data/oversampling_code.py
deleted file mode 100644
index 836f48472695b97947d6cfb6ff40f0960ec64c9e..0000000000000000000000000000000000000000
--- a/Analysis_code/make_oversample_data/oversampling_code.py
+++ /dev/null
@@ -1,355 +0,0 @@
-import pandas as pd
-import numpy as np
-from imblearn.over_sampling import SMOTENC
-
-
-# smote와 ctgan을 이용한 oversampling 진행
-
-# 파일 경로와 지역 이름 리스트
-regions = ['busan', 'daegu', 'daejeon', 'incheon', 'seoul','gwangju']
-input_paths = [f'../data/data_for_modeling/{region}_train.csv' for region in regions]
-
-
-
-
-# 반복적으로 각 지역 데이터 처리
-for region, input_path in zip(regions, input_paths):
-    # 데이터 읽기
-    data = pd.read_csv(input_path, index_col=0)
-    data.drop(['Unnamed: 0'], axis=1, inplace=True)
-    print("\n######",region,"#######")
-    print(len(data[data['multi_class']==0]),'|',len(data[data['multi_class']==1]),'|',len(data[data['multi_class']==2]))
-    print(len(data.columns))
-
-
-
-
-
-import pandas as pd
-import numpy as np
-from imblearn.over_sampling import SMOTENC
-
-# 파일 경로와 지역 이름 리스트
-regions = ['busan', 'daegu', 'daejeon', 'incheon', 'seoul','gwangju']
-input_paths = [f'../data/data_for_modeling/{region}_train.csv' for region in regions]
-output_paths = [f'../data/data_oversampled/smote_{region}.csv' for region in regions]
-
-# 반복적으로 각 지역 데이터 처리
-for region, input_path, output_path in zip(regions, input_paths, output_paths):
-    # 데이터 읽기
-    data = pd.read_csv(input_path, index_col=0)
-    data.drop(['Unnamed: 0'], axis=1, inplace=True)
-
-    # X와 y 분리
-    X = data.drop(columns=['multi_class', 'binary_class'])
-    y = data['multi_class']
-
-    # 불필요한 열 제거
-    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)
-
-    # 범주형 변수 식별
-    categorical_features = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']
-
-    # 각 지역의 multi_class 값이 2인 데이터 개수 계산
-    count_class_2 = (y == 2).sum()
-
-    # SMOTENC 적용
-    smotenc = SMOTENC(
-        categorical_features=categorical_features,
-        sampling_strategy={0: 10000, 1: 10000, 2: count_class_2},
-        random_state=42
-    )
-    X_resampled, y_resampled = smotenc.fit_resample(X, y)
-
-    # 추가 변수 생성
-    X_resampled['multi_class'] = y_resampled
-    X_resampled['binary_class'] = X_resampled['multi_class'].apply(lambda x: 0 if x == 2 else 1)
-    X_resampled['hour_sin'] = np.sin(2 * np.pi * X_resampled['hour'] / 24)
-    X_resampled['hour_cos'] = np.cos(2 * np.pi * X_resampled['hour'] / 24)
-    X_resampled['month_sin'] = np.sin(2 * np.pi * X_resampled['month'] / 12)
-    X_resampled['month_cos'] = np.cos(2 * np.pi * X_resampled['month'] / 12)
-    X_resampled['ground_temp - temp_C'] = X_resampled['groundtemp'] - X_resampled['temp_C']
-
-    # 결과 저장
-    X_resampled.to_csv(output_path)
-    print(f"Processed and saved: {region} -> {output_path}")
-
-
-smote_seoul = pd.read_csv('../data/data_oversampled/smote_seoul.csv')
-print(smote_seoul[smote_seoul['multi_class']==0]['visi'].describe())
-print(smote_seoul[smote_seoul['multi_class']==1]['visi'].describe())
-
-
-import pandas as pd
-import numpy as np
-from imblearn.over_sampling import SMOTENC
-
-# 파일 경로와 지역 이름 리스트
-regions = ['busan', 'daegu', 'daejeon', 'incheon', 'seoul','gwangju']
-input_paths = [f'../data/data_oversampled/smote_{region}.csv' for region in regions]
-
-# 반복적으로 각 지역 데이터 처리
-for region, input_path in zip(regions, input_paths):
-    # 데이터 읽기
-    data = pd.read_csv(input_path, index_col=0)
-    data.drop(['Unnamed: 0'], axis=1, inplace=True)
-    print("\n######",region,"#######")
-    print(len(data[data['multi_class']==0]),'|',len(data[data['multi_class']==1]),'|',len(data[data['multi_class']==2]))
-    print(len(data.columns))
-
-
-
-import pandas as pd
-import numpy as np
-from imblearn.over_sampling import SMOTENC
-
-# 파일 경로와 지역 이름 리스트
-regions = ['busan', 'daegu', 'daejeon', 'incheon', 'seoul','gwangju']
-input_paths = [f'../data/data_for_modeling/{region}_train.csv' for region in regions]
-
-# 반복적으로 각 지역 데이터 처리
-for region, input_path in zip(regions, input_paths):
-    # 데이터 읽기
-    data = pd.read_csv(input_path, index_col=0)
-    data.drop(['Unnamed: 0'], axis=1, inplace=True)
-    print("\n######",region,"#######")
-    print(len(data[data['multi_class']==0]),'|',len(data[data['multi_class']==1]),'|',len(data[data['multi_class']==2]))
-    print(len(data.columns))
-
-
-import pandas as pd
-import numpy as np
-from imblearn.over_sampling import SMOTENC
-import optuna
-from ctgan import CTGAN
-import torch
-import warnings
-
-# 지역별 데이터 파일 경로
-regions = ['busan', 'daegu', 'daejeon', 'incheon', 'seoul','gwangju']
-file_paths = [f'../data/data_for_modeling/df_{region}.feather' for region in regions]
-output_paths = [f'../data/data_oversampled/ctgan_{region}.csv' for region in regions]
-
-# GPU 사용 설정
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-# 경고 무시
-warnings.filterwarnings("ignore", category=UserWarning, module="optuna.distributions")
-
-# 지역별 처리
-for file_path, output_path in zip(file_paths, output_paths):
-    # 데이터 로드
-    data = pd.read_feather(file_path)
-    data.drop(['Unnamed: 0'], axis=1, inplace=True)
-    X = data.drop(columns=['multi_class', 'binary_class'])
-    y = data['multi_class']
-
-    # 불필요한 열 제거
-    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)
-
-    # SMOTENC에서 사용할 범주형 변수 열 번호 설정
-    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']
-
-    # sampling_strategy 설정
-    count_class_0 = (y == 0).sum()
-    count_class_1 = (y == 1).sum()
-    count_class_2 = (y == 2).sum()
-    sampling_strategy = {
-        0: 500 if count_class_0 <= 500 else 1000,
-        1: int(np.ceil(count_class_1 / 100) * 100),  # 백의 자리로 올림
-        2: count_class_2
-    }
-
-    # SMOTENC 적용
-    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)
-    X_resampled, y_resampled = smotenc.fit_resample(X, y)
-
-    # Resampled 데이터 생성
-    lerp_data = X_resampled.copy()
-    lerp_data['multi_class'] = y_resampled
-
-    # CTGAN에서 사용할 범주형 변수 열 이름 설정
-    categorical_features = [
-        col for col, dtype in zip(lerp_data.columns, lerp_data.dtypes) if dtype != 'float64'
-    ]
-
-    # Optuna 목적 함수 정의
-    def objective(trial):
-        # 하이퍼파라미터 탐색 범위 설정
-        embedding_dim = trial.suggest_int("embedding_dim", 64, 128)
-        generator_dim = trial.suggest_categorical("generator_dim", [(64, 64), (128, 128)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(64, 64), (128, 128)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [64, 128, 256])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 3)
-
-        # CTGAN 모델 생성
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        # 범주 0 데이터 필터링
-        data_0 = lerp_data[lerp_data['multi_class'] == 0]
-
-        # 모델 학습
-        ctgan.fit(data_0, discrete_columns=categorical_features)
-
-        # 샘플 생성
-        generated_data = ctgan.sample(len(data_0) * 2)
-
-        # 평가: 샘플의 연속형 변수 분포 비교
-        real_visi = data_0['visi']
-        generated_visi = generated_data['visi']
-
-        # 분포 간 차이(MSE) 계산
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    # Optuna로 최적화 수행
-    study = optuna.create_study(direction="maximize")
-    study.optimize(objective, n_trials=50)
-
-    # 최적 하이퍼파라미터 출력
-    best_params = study.best_params
-
-    # 최적 하이퍼파라미터로 CTGAN 학습 및 샘플 생성
-    ctgan = CTGAN(
-        embedding_dim=best_params["embedding_dim"],
-        generator_dim=best_params["generator_dim"],
-        discriminator_dim=best_params["discriminator_dim"],
-        batch_size=best_params["batch_size"],
-        discriminator_steps=best_params["discriminator_steps"],
-        pac=best_params["pac"]
-    )
-
-    # 범주 0 데이터로 최종 학습
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 0], discrete_columns=categorical_features)
-    generated_0 = ctgan.sample(19500 if count_class_0 <= 500 else 19000)
-
-    # 범주 1 데이터 최적화 및 생성
-    def objective_class1(trial):
-        embedding_dim = trial.suggest_int("embedding_dim", 128, 512)
-        generator_dim = trial.suggest_categorical("generator_dim", [(128, 128), (256, 256)])
-        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(128, 128), (256, 256)])
-        pac = trial.suggest_categorical("pac", [4, 8])
-        batch_size = trial.suggest_categorical("batch_size", [256, 512, 1024])
-        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 5)
-
-        ctgan = CTGAN(
-            embedding_dim=embedding_dim,
-            generator_dim=generator_dim,
-            discriminator_dim=discriminator_dim,
-            batch_size=batch_size,
-            discriminator_steps=discriminator_steps,
-            pac=pac
-        )
-
-        data_1 = lerp_data[lerp_data['multi_class'] == 1]
-        ctgan.fit(data_1, discrete_columns=categorical_features)
-        generated_data = ctgan.sample(len(data_1) * 2)
-
-        real_visi = data_1['visi']
-        generated_visi = generated_data['visi']
-        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
-        return -mse
-
-    study_class1 = optuna.create_study(direction="maximize")
-    study_class1.optimize(objective_class1, n_trials=30)
-
-    best_params_class1 = study_class1.best_params
-    ctgan = CTGAN(
-        embedding_dim=best_params_class1["embedding_dim"],
-        generator_dim=best_params_class1["generator_dim"],
-        discriminator_dim=best_params_class1["discriminator_dim"],
-        batch_size=best_params_class1["batch_size"],
-        discriminator_steps=best_params_class1["discriminator_steps"],
-        pac=best_params_class1["pac"]
-    )
-
-    ctgan.fit(lerp_data[lerp_data['multi_class'] == 1], discrete_columns=categorical_features)
-    generated_1 = ctgan.sample(20000 - int(np.ceil(count_class_1 / 100) * 100))
-
-    # 데이터 병합 및 저장
-    well_generated0 = generated_0[(generated_0['visi'] >= 0) & (generated_0['visi'] < 100)]
-    well_generated1 = generated_1[(generated_1['visi'] >= 100) & (generated_1['visi'] < 500)]
-    smote_gan_data = pd.concat([lerp_data, well_generated0, well_generated1], axis=0)
-        # 제거변수 복구
-    smote_gan_data['binary_class'] = smote_gan_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)
-    smote_gan_data['hour_sin'] = np.sin(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['hour_cos'] = np.cos(2 * np.pi * smote_gan_data['hour'] / 24)
-    smote_gan_data['month_sin'] = np.sin(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['month_cos'] = np.cos(2 * np.pi * smote_gan_data['month'] / 12)
-    smote_gan_data['ground_temp - temp_C'] = smote_gan_data['groundtemp'] - smote_gan_data['temp_C']
-
-    # 결과 저장
-    smote_gan_data.to_csv(output_path, index = False)
-    print(f"Processed and saved: {region} -> {output_path}")
-
-
-
-import pandas as pd
-import numpy as np
-from imblearn.over_sampling import SMOTENC
-
-# 파일 경로와 지역 이름 리스트
-regions = ['busan', 'daegu', 'daejeon', 'incheon', 'seoul','gwangju']
-input_paths = [f'../data/data_oversampled/ctgan_{region}.csv' for region in regions]
-
-# 반복적으로 각 지역 데이터 처리
-for region, input_path in zip(regions, input_paths):
-    # 데이터 읽기
-    data = pd.read_csv(input_path)
-    print("\n######",region,"#######")
-    print(len(data[data['multi_class']==0]),'|',len(data[data['multi_class']==1]),'|',len(data[data['multi_class']==2]))
-    print(len(data.columns))
-
-
-
-
-busan_check = pd.read_csv('../data/data_oversampled/ctgan_busan.csv')
-print(busan_check[busan_check['multi_class']==0]['visi'].describe())
-print(busan_check[busan_check['multi_class']==1]['visi'].describe())
-print(busan_check[busan_check['multi_class']==2]['visi'].describe())
-
-
-
-
-import pandas as pd
-import numpy as np
-from imblearn.over_sampling import SMOTENC
-
-# 파일 경로와 지역 이름 리스트
-regions = ['busan', 'daegu', 'daejeon', 'incheon', 'seoul','gwangju']
-origin_paths = [f'../data/data_for_modeling/{region}_train.csv' for region in regions]
-augment_paths = [f'../data/data_oversampled/ctgan_{region}.csv' for region in regions]
-
-# 반복적으로 각 지역 데이터 처리
-for region, origin_path, augment_path in zip(regions, origin_paths, augment_paths):
-    # 데이터 읽기
-    origin = pd.read_csv(origin_path, index_col=0)
-    augment = pd.read_csv(augment_path)
-
-    # 증강된 데이터에서 범주 2 데이터 제거
-    filtered_data = augment[augment['multi_class'] != 2]
-
-    # 원본 데이터에서 범주 2 데이터 추출
-    original_class2 = origin[origin['multi_class'] == 2]
-
-    # 제거된 데이터에 원본 범주 2 데이터를 추가
-    final_data = pd.concat([filtered_data, original_class2], axis=0)
-
-    # 인덱스 재설정
-    final_data.reset_index(drop=True, inplace=True)
-
-    # 결과 저장
-    final_data.to_csv(augment_path, index = False)
-
-    print("\n######",region,"#######")
-    print(len(final_data[final_data['multi_class']==0]),'|',len(final_data[final_data['multi_class']==1]),'|',len(final_data[final_data['multi_class']==2]))
-    print(len(data.columns))
\ No newline at end of file
diff --git a/Analysis_code/make_oversample_data/smote_sample_1.py b/Analysis_code/make_oversample_data/smote_sample_1.py
deleted file mode 100644
index f31b66093d7fcf5e3dd5ba881b5d660e65df1b26..0000000000000000000000000000000000000000
--- a/Analysis_code/make_oversample_data/smote_sample_1.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import pandas as pd
-import numpy as np
-
-from imblearn.over_sampling import SMOTENC
-
-# 지역별 데이터 파일 경로
-regions = ['incheon', 'seoul','busan', 'daegu', 'daejeon', 'gwangju']
-file_paths = [f'../../data/data_for_modeling/{region}_train.csv' for region in regions]
-output_paths = [f'../../data/data_oversampled/smote/smote_1_{region}.csv' for region in regions]
-
-# 지역별 처리
-for file_path, output_path in zip(file_paths, output_paths):
-    # 데이터 로드
-    data = pd.read_csv(file_path, index_col=0)
-    data= data.loc[data['year'].isin([2018,2019]),:]
-    data['cloudcover'] = data['cloudcover'].astype('int')
-    data['lm_cloudcover'] = data['lm_cloudcover'].astype('int')
-    X = data.drop(columns=['multi_class', 'binary_class'])
-    y = data['multi_class']
-
-    # 불필요한 열 제거
-    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)
-
-    # SMOTENC에서 사용할 범주형 변수 열 번호 설정
-    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']
-
-    # sampling_strategy 설정
-    count_class_2 = (y == 2).sum()
-    sampling_strategy = {
-        0: int(np.ceil(count_class_2 / 1000) * 500),
-        1: int(np.ceil(count_class_2 / 1000) * 500),
-        2: count_class_2
-    }
-
-    # SMOTENC 적용
-    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)
-    X_resampled, y_resampled = smotenc.fit_resample(X, y)
-
-    # Resampled 데이터 생성
-    lerp_data = X_resampled.copy()
-    lerp_data['multi_class'] = y_resampled
-
-    # 제거변수 복구
-    lerp_data['binary_class'] = lerp_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)
-    lerp_data['hour_sin'] = np.sin(2 * np.pi * lerp_data['hour'] / 24)
-    lerp_data['hour_cos'] = np.cos(2 * np.pi * lerp_data['hour'] / 24)
-    lerp_data['month_sin'] = np.sin(2 * np.pi * lerp_data['month'] / 12)
-    lerp_data['month_cos'] = np.cos(2 * np.pi * lerp_data['month'] / 12)
-    lerp_data['ground_temp - temp_C'] = lerp_data['groundtemp'] - lerp_data['temp_C']
-
-    # 결과 저장
-    lerp_data.to_csv(output_path, index = False)
-    print(len(lerp_data[lerp_data['multi_class']==0]),'|',len(lerp_data[lerp_data['multi_class']==1]),'|',len(lerp_data[lerp_data['multi_class']==2]))
\ No newline at end of file
diff --git a/Analysis_code/make_oversample_data/smote_sample_2.py b/Analysis_code/make_oversample_data/smote_sample_2.py
deleted file mode 100644
index 7a61d13f1fdedbb6d2decb694ab6112e698abfc5..0000000000000000000000000000000000000000
--- a/Analysis_code/make_oversample_data/smote_sample_2.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import pandas as pd
-import numpy as np
-
-from imblearn.over_sampling import SMOTENC
-
-# 지역별 데이터 파일 경로
-regions = ['incheon', 'seoul','busan', 'daegu', 'daejeon', 'gwangju']
-file_paths = [f'../../data/data_for_modeling/{region}_train.csv' for region in regions]
-output_paths = [f'../../data/data_oversampled/smote/smote_2_{region}.csv' for region in regions]
-
-# 지역별 처리
-for file_path, output_path in zip(file_paths, output_paths):
-    # 데이터 로드
-    data = pd.read_csv(file_path, index_col=0)
-    data= data.loc[data['year'].isin([2018,2020]),:]
-    data['cloudcover'] = data['cloudcover'].astype('int')
-    data['lm_cloudcover'] = data['lm_cloudcover'].astype('int')
-    X = data.drop(columns=['multi_class', 'binary_class'])
-    y = data['multi_class']
-
-    # 불필요한 열 제거
-    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)
-
-    # SMOTENC에서 사용할 범주형 변수 열 번호 설정
-    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']
-
-    # sampling_strategy 설정
-    count_class_2 = (y == 2).sum()
-    sampling_strategy = {
-        0: int(np.ceil(count_class_2 / 1000) * 500),
-        1: int(np.ceil(count_class_2 / 1000) * 500),
-        2: count_class_2
-    }
-
-    # SMOTENC 적용
-    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)
-    X_resampled, y_resampled = smotenc.fit_resample(X, y)
-
-    # Resampled 데이터 생성
-    lerp_data = X_resampled.copy()
-    lerp_data['multi_class'] = y_resampled
-
-    # 제거변수 복구
-    lerp_data['binary_class'] = lerp_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)
-    lerp_data['hour_sin'] = np.sin(2 * np.pi * lerp_data['hour'] / 24)
-    lerp_data['hour_cos'] = np.cos(2 * np.pi * lerp_data['hour'] / 24)
-    lerp_data['month_sin'] = np.sin(2 * np.pi * lerp_data['month'] / 12)
-    lerp_data['month_cos'] = np.cos(2 * np.pi * lerp_data['month'] / 12)
-    lerp_data['ground_temp - temp_C'] = lerp_data['groundtemp'] - lerp_data['temp_C']
-
-    # 결과 저장
-    lerp_data.to_csv(output_path, index = False)
-    print(len(lerp_data[lerp_data['multi_class']==0]),'|',len(lerp_data[lerp_data['multi_class']==1]),'|',len(lerp_data[lerp_data['multi_class']==2]))
\ No newline at end of file
diff --git a/Analysis_code/make_oversample_data/smote_sample_3.py b/Analysis_code/make_oversample_data/smote_sample_3.py
deleted file mode 100644
index 16137ba86e88d4faeee6b45405c8f2de5b46fdcb..0000000000000000000000000000000000000000
--- a/Analysis_code/make_oversample_data/smote_sample_3.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import pandas as pd
-import numpy as np
-
-from imblearn.over_sampling import SMOTENC
-
-# 지역별 데이터 파일 경로
-regions = ['incheon', 'seoul','busan', 'daegu', 'daejeon', 'gwangju']
-file_paths = [f'../../data/data_for_modeling/{region}_train.csv' for region in regions]
-output_paths = [f'../../data/data_oversampled/smote/smote_3_{region}.csv' for region in regions]
-
-# 지역별 처리
-for file_path, output_path in zip(file_paths, output_paths):
-    # 데이터 로드
-    data = pd.read_csv(file_path, index_col=0)
-    data= data.loc[data['year'].isin([2019,2020]),:]
-    data['cloudcover'] = data['cloudcover'].astype('int')
-    data['lm_cloudcover'] = data['lm_cloudcover'].astype('int')
-    X = data.drop(columns=['multi_class', 'binary_class'])
-    y = data['multi_class']
-
-    # 불필요한 열 제거
-    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)
-
-    # SMOTENC에서 사용할 범주형 변수 열 번호 설정
-    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']
-
-    # sampling_strategy 설정
-    count_class_2 = (y == 2).sum()
-    sampling_strategy = {
-        0: int(np.ceil(count_class_2 / 1000) * 500),
-        1: int(np.ceil(count_class_2 / 1000) * 500),
-        2: count_class_2
-    }
-
-    # SMOTENC 적용
-    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)
-    X_resampled, y_resampled = smotenc.fit_resample(X, y)
-
-    # Resampled 데이터 생성
-    lerp_data = X_resampled.copy()
-    lerp_data['multi_class'] = y_resampled
-
-    # 제거변수 복구
-    lerp_data['binary_class'] = lerp_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)
-    lerp_data['hour_sin'] = np.sin(2 * np.pi * lerp_data['hour'] / 24)
-    lerp_data['hour_cos'] = np.cos(2 * np.pi * lerp_data['hour'] / 24)
-    lerp_data['month_sin'] = np.sin(2 * np.pi * lerp_data['month'] / 12)
-    lerp_data['month_cos'] = np.cos(2 * np.pi * lerp_data['month'] / 12)
-    lerp_data['ground_temp - temp_C'] = lerp_data['groundtemp'] - lerp_data['temp_C']
-
-    # 결과 저장
-    lerp_data.to_csv(output_path, index = False)
-    print(len(lerp_data[lerp_data['multi_class']==0]),'|',len(lerp_data[lerp_data['multi_class']==1]),'|',len(lerp_data[lerp_data['multi_class']==2]))
\ No newline at end of file
diff --git a/Analysis_code/make_train_test.ipynb b/Analysis_code/make_train_test.ipynb
deleted file mode 100644
index 5896362ec1c2fba146dc70c630b02cbb5f1890dd..0000000000000000000000000000000000000000
--- a/Analysis_code/make_train_test.ipynb
+++ /dev/null
@@ -1,1099 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from collections import Counter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul = pd.read_feather(\"../data/data_for_modeling/df_seoul.feather\")\n",
-    "df_busan = pd.read_feather(\"../data/data_for_modeling/df_busan.feather\")\n",
-    "df_incheon = pd.read_feather(\"../data/data_for_modeling/df_incheon.feather\")\n",
-    "df_daegu = pd.read_feather(\"../data/data_for_modeling/df_daegu.feather\")\n",
-    "df_daejeon = pd.read_feather(\"../data/data_for_modeling/df_daejeon.feather\")\n",
-    "df_gwangju = pd.read_feather(\"../data/data_for_modeling/df_gwangju.feather\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({2: 48534, 1: 3941, 0: 109})"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Counter(df_seoul['multi_class'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({2: 50069, 1: 2350, 0: 165})"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Counter(df_busan['multi_class'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({2: 44944, 1: 6658, 0: 982})"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Counter(df_incheon['multi_class'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({2: 50919, 1: 1610, 0: 55})"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Counter(df_daegu['multi_class'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({2: 48047, 1: 4227, 0: 310})"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Counter(df_daejeon['multi_class'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({2: 48405, 1: 4015, 0: 164})"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Counter(df_gwangju['multi_class'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(52584, 30)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_seoul.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul = df_seoul.loc[df_seoul['year'].isin([2018, 2019, 2020, 2021]),:].copy()\n",
-    "df_busan = df_busan.loc[df_busan['year'].isin([2018, 2019, 2020, 2021]),:].copy()\n",
-    "df_incheon = df_incheon.loc[df_incheon['year'].isin([2018, 2019, 2020, 2021]),:].copy()\n",
-    "df_daegu = df_daegu.loc[df_daegu['year'].isin([2018, 2019, 2020, 2021]),:].copy()\n",
-    "df_daejeon = df_daejeon.loc[df_daejeon['year'].isin([2018, 2019, 2020, 2021]),:].copy()\n",
-    "df_gwangju = df_gwangju.loc[df_gwangju['year'].isin([2018, 2019, 2020, 2021]),:].copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cols = [col for col in df_seoul.columns if col != \"multi_class\"] + [\"multi_class\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul = df_seoul[cols]\n",
-    "df_busan = df_busan[cols]\n",
-    "df_incheon = df_incheon[cols]\n",
-    "df_daegu = df_daegu[cols]\n",
-    "df_daejeon = df_daejeon[cols]\n",
-    "df_gwangju = df_gwangju[cols]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul_train = df_seoul.loc[df_seoul['year'].isin([2018, 2019, 2020]),:].copy()\n",
-    "df_seoul_test = df_seoul.loc[df_seoul['year'].isin([2021]),:].copy()\n",
-    "\n",
-    "df_busan_train = df_busan.loc[df_busan['year'].isin([2018, 2019, 2020]),:].copy()\n",
-    "df_busan_test = df_busan.loc[df_busan['year'].isin([2021]),:].copy()\n",
-    "\n",
-    "df_incheon_train = df_incheon.loc[df_incheon['year'].isin([2018, 2019, 2020]),:].copy()\n",
-    "df_incheon_test = df_incheon.loc[df_incheon['year'].isin([2021]),:].copy()\n",
-    "\n",
-    "df_daegu_train = df_daegu.loc[df_daegu['year'].isin([2018, 2019, 2020]),:].copy()\n",
-    "df_daegu_test = df_daegu.loc[df_daegu['year'].isin([2021]),:].copy()\n",
-    "\n",
-    "df_daejeon_train = df_daejeon.loc[df_daejeon['year'].isin([2018, 2019, 2020]),:].copy()\n",
-    "df_daejeon_test = df_daejeon.loc[df_daejeon['year'].isin([2021]),:].copy()\n",
-    "\n",
-    "df_gwangju_train = df_gwangju.loc[df_gwangju['year'].isin([2018, 2019, 2020]),:].copy()\n",
-    "df_gwangju_test = df_gwangju.loc[df_gwangju['year'].isin([2021]),:].copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>temp_C</th>\n",
-       "      <th>precip_mm</th>\n",
-       "      <th>wind_speed</th>\n",
-       "      <th>wind_dir</th>\n",
-       "      <th>hm</th>\n",
-       "      <th>vap_pressure</th>\n",
-       "      <th>dewpoint_C</th>\n",
-       "      <th>loc_pressure</th>\n",
-       "      <th>sea_pressure</th>\n",
-       "      <th>solarRad</th>\n",
-       "      <th>...</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>hour</th>\n",
-       "      <th>ground_temp - temp_C</th>\n",
-       "      <th>hour_sin</th>\n",
-       "      <th>hour_cos</th>\n",
-       "      <th>month_sin</th>\n",
-       "      <th>month_cos</th>\n",
-       "      <th>visi</th>\n",
-       "      <th>multi_class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>360</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>-12.6</td>\n",
-       "      <td>1015.8</td>\n",
-       "      <td>1024.6</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-5.4</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.866025</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>360</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>-13.9</td>\n",
-       "      <td>1015.5</td>\n",
-       "      <td>1024.3</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-5.4</td>\n",
-       "      <td>0.258819</td>\n",
-       "      <td>9.659258e-01</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.866025</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>20</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>-13.9</td>\n",
-       "      <td>1015.7</td>\n",
-       "      <td>1024.5</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>-5.4</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>8.660254e-01</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.866025</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>320</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>-12.9</td>\n",
-       "      <td>1015.9</td>\n",
-       "      <td>1024.7</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>-5.0</td>\n",
-       "      <td>0.707107</td>\n",
-       "      <td>7.071068e-01</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.866025</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>340</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>-11.5</td>\n",
-       "      <td>1016.0</td>\n",
-       "      <td>1024.9</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>-4.3</td>\n",
-       "      <td>0.866025</td>\n",
-       "      <td>5.000000e-01</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.866025</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>-0.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>50</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>-11.2</td>\n",
-       "      <td>1016.0</td>\n",
-       "      <td>1024.9</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>0.965926</td>\n",
-       "      <td>2.588190e-01</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.866025</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>-0.5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>20</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>-11.0</td>\n",
-       "      <td>1016.5</td>\n",
-       "      <td>1025.4</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>-4.1</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>6.123234e-17</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.866025</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>-0.8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>340</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>-11.2</td>\n",
-       "      <td>1017.1</td>\n",
-       "      <td>1026.0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>-4.5</td>\n",
-       "      <td>0.965926</td>\n",
-       "      <td>-2.588190e-01</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.866025</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>-0.5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.2</td>\n",
-       "      <td>360</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>-11.5</td>\n",
-       "      <td>1017.4</td>\n",
-       "      <td>1026.3</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>0.866025</td>\n",
-       "      <td>-5.000000e-01</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.866025</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>20</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>2.7</td>\n",
-       "      <td>-10.8</td>\n",
-       "      <td>1018.1</td>\n",
-       "      <td>1026.9</td>\n",
-       "      <td>0.46</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>0.707107</td>\n",
-       "      <td>-7.071068e-01</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.866025</td>\n",
-       "      <td>1953.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>10 rows × 30 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   temp_C  precip_mm  wind_speed wind_dir    hm  vap_pressure  dewpoint_C  \\\n",
-       "0     1.2        0.0         1.6      360  35.0           2.3       -12.6   \n",
-       "1     0.5        0.0         1.3      360  33.0           2.1       -13.9   \n",
-       "2     0.1        0.0         1.5       20  34.0           2.1       -13.9   \n",
-       "3     0.0        0.0         2.1      320  37.0           2.3       -12.9   \n",
-       "4    -0.1        0.0         2.3      340  42.0           2.5       -11.5   \n",
-       "5    -0.1        0.0         2.8       50  43.0           2.6       -11.2   \n",
-       "6    -0.5        0.0         2.1       20  45.0           2.6       -11.0   \n",
-       "7    -0.8        0.0         2.5      340  45.0           2.6       -11.2   \n",
-       "8    -0.5        0.0         1.2      360  43.0           2.5       -11.5   \n",
-       "9     1.7        0.0         2.1       20  39.0           2.7       -10.8   \n",
-       "\n",
-       "   loc_pressure  sea_pressure  solarRad  ...  year month hour  \\\n",
-       "0        1015.8        1024.6      0.00  ...  2018     1    0   \n",
-       "1        1015.5        1024.3      0.00  ...  2018     1    1   \n",
-       "2        1015.7        1024.5      0.00  ...  2018     1    2   \n",
-       "3        1015.9        1024.7      0.00  ...  2018     1    3   \n",
-       "4        1016.0        1024.9      0.00  ...  2018     1    4   \n",
-       "5        1016.0        1024.9      0.00  ...  2018     1    5   \n",
-       "6        1016.5        1025.4      0.00  ...  2018     1    6   \n",
-       "7        1017.1        1026.0      0.00  ...  2018     1    7   \n",
-       "8        1017.4        1026.3      0.03  ...  2018     1    8   \n",
-       "9        1018.1        1026.9      0.46  ...  2018     1    9   \n",
-       "\n",
-       "   ground_temp - temp_C  hour_sin      hour_cos  month_sin  month_cos    visi  \\\n",
-       "0                  -5.4  0.000000  1.000000e+00        0.5   0.866025  2000.0   \n",
-       "1                  -5.4  0.258819  9.659258e-01        0.5   0.866025  2000.0   \n",
-       "2                  -5.4  0.500000  8.660254e-01        0.5   0.866025  2000.0   \n",
-       "3                  -5.0  0.707107  7.071068e-01        0.5   0.866025  2000.0   \n",
-       "4                  -4.3  0.866025  5.000000e-01        0.5   0.866025  2000.0   \n",
-       "5                  -4.0  0.965926  2.588190e-01        0.5   0.866025  2000.0   \n",
-       "6                  -4.1  1.000000  6.123234e-17        0.5   0.866025  2000.0   \n",
-       "7                  -4.5  0.965926 -2.588190e-01        0.5   0.866025  2000.0   \n",
-       "8                  -4.0  0.866025 -5.000000e-01        0.5   0.866025  2000.0   \n",
-       "9                   2.8  0.707107 -7.071068e-01        0.5   0.866025  1953.0   \n",
-       "\n",
-       "   multi_class  \n",
-       "0            2  \n",
-       "1            2  \n",
-       "2            2  \n",
-       "3            2  \n",
-       "4            2  \n",
-       "5            2  \n",
-       "6            2  \n",
-       "7            2  \n",
-       "8            2  \n",
-       "9            2  \n",
-       "\n",
-       "[10 rows x 30 columns]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_busan_train.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>temp_C</th>\n",
-       "      <th>precip_mm</th>\n",
-       "      <th>wind_speed</th>\n",
-       "      <th>wind_dir</th>\n",
-       "      <th>hm</th>\n",
-       "      <th>vap_pressure</th>\n",
-       "      <th>dewpoint_C</th>\n",
-       "      <th>loc_pressure</th>\n",
-       "      <th>sea_pressure</th>\n",
-       "      <th>solarRad</th>\n",
-       "      <th>...</th>\n",
-       "      <th>year</th>\n",
-       "      <th>month</th>\n",
-       "      <th>hour</th>\n",
-       "      <th>ground_temp - temp_C</th>\n",
-       "      <th>hour_sin</th>\n",
-       "      <th>hour_cos</th>\n",
-       "      <th>month_sin</th>\n",
-       "      <th>month_cos</th>\n",
-       "      <th>visi</th>\n",
-       "      <th>multi_class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>26294</th>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>270</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>-12.9</td>\n",
-       "      <td>1013.3</td>\n",
-       "      <td>1022.1</td>\n",
-       "      <td>2.07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>12</td>\n",
-       "      <td>14</td>\n",
-       "      <td>5.8</td>\n",
-       "      <td>-0.500000</td>\n",
-       "      <td>-8.660254e-01</td>\n",
-       "      <td>-2.449294e-16</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26295</th>\n",
-       "      <td>1.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>270</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>-12.6</td>\n",
-       "      <td>1013.2</td>\n",
-       "      <td>1022.0</td>\n",
-       "      <td>1.71</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>12</td>\n",
-       "      <td>15</td>\n",
-       "      <td>5.6</td>\n",
-       "      <td>-0.707107</td>\n",
-       "      <td>-7.071068e-01</td>\n",
-       "      <td>-2.449294e-16</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26296</th>\n",
-       "      <td>1.6</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>290</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>-12.6</td>\n",
-       "      <td>1012.8</td>\n",
-       "      <td>1021.6</td>\n",
-       "      <td>1.14</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>12</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>-0.866025</td>\n",
-       "      <td>-5.000000e-01</td>\n",
-       "      <td>-2.449294e-16</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26297</th>\n",
-       "      <td>1.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.8</td>\n",
-       "      <td>250</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>-11.5</td>\n",
-       "      <td>1012.8</td>\n",
-       "      <td>1021.6</td>\n",
-       "      <td>0.48</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>12</td>\n",
-       "      <td>17</td>\n",
-       "      <td>-0.4</td>\n",
-       "      <td>-0.965926</td>\n",
-       "      <td>-2.588190e-01</td>\n",
-       "      <td>-2.449294e-16</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26298</th>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.8</td>\n",
-       "      <td>270</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>-11.2</td>\n",
-       "      <td>1013.1</td>\n",
-       "      <td>1021.9</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>12</td>\n",
-       "      <td>18</td>\n",
-       "      <td>-0.8</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-1.836970e-16</td>\n",
-       "      <td>-2.449294e-16</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26299</th>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>270</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>-11.1</td>\n",
-       "      <td>1014.0</td>\n",
-       "      <td>1022.8</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>12</td>\n",
-       "      <td>19</td>\n",
-       "      <td>-1.1</td>\n",
-       "      <td>-0.965926</td>\n",
-       "      <td>2.588190e-01</td>\n",
-       "      <td>-2.449294e-16</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26300</th>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>270</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>2.7</td>\n",
-       "      <td>-10.7</td>\n",
-       "      <td>1014.8</td>\n",
-       "      <td>1023.6</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>12</td>\n",
-       "      <td>20</td>\n",
-       "      <td>-0.9</td>\n",
-       "      <td>-0.866025</td>\n",
-       "      <td>5.000000e-01</td>\n",
-       "      <td>-2.449294e-16</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26301</th>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>290</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>2.9</td>\n",
-       "      <td>-9.9</td>\n",
-       "      <td>1014.6</td>\n",
-       "      <td>1023.4</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>12</td>\n",
-       "      <td>21</td>\n",
-       "      <td>-0.8</td>\n",
-       "      <td>-0.707107</td>\n",
-       "      <td>7.071068e-01</td>\n",
-       "      <td>-2.449294e-16</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26302</th>\n",
-       "      <td>-0.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.7</td>\n",
-       "      <td>270</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>-9.6</td>\n",
-       "      <td>1014.8</td>\n",
-       "      <td>1023.6</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>12</td>\n",
-       "      <td>22</td>\n",
-       "      <td>-0.6</td>\n",
-       "      <td>-0.500000</td>\n",
-       "      <td>8.660254e-01</td>\n",
-       "      <td>-2.449294e-16</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26303</th>\n",
-       "      <td>-0.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.8</td>\n",
-       "      <td>250</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>-8.6</td>\n",
-       "      <td>1015.1</td>\n",
-       "      <td>1024.0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>12</td>\n",
-       "      <td>23</td>\n",
-       "      <td>-0.6</td>\n",
-       "      <td>-0.258819</td>\n",
-       "      <td>9.659258e-01</td>\n",
-       "      <td>-2.449294e-16</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>10 rows × 30 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       temp_C  precip_mm  wind_speed wind_dir    hm  vap_pressure  dewpoint_C  \\\n",
-       "26294     0.1        0.0         6.3      270  37.0           2.3       -12.9   \n",
-       "26295     1.2        0.0         5.9      270  35.0           2.3       -12.6   \n",
-       "26296     1.6        0.0         3.6      290  34.0           2.3       -12.6   \n",
-       "26297     1.2        0.0         3.8      250  38.0           2.5       -11.5   \n",
-       "26298     0.9        0.0         3.8      270  40.0           2.6       -11.2   \n",
-       "26299     0.6        0.0         6.2      270  41.0           2.6       -11.1   \n",
-       "26300     0.1        0.0         6.0      270  44.0           2.7       -10.7   \n",
-       "26301    -0.2        0.0         5.0      290  48.0           2.9        -9.9   \n",
-       "26302    -0.7        0.0         2.7      270  51.0           3.0        -9.6   \n",
-       "26303    -0.7        0.0         3.8      250  55.0           3.2        -8.6   \n",
-       "\n",
-       "       loc_pressure  sea_pressure  solarRad  ...  year month hour  \\\n",
-       "26294        1013.3        1022.1      2.07  ...  2020    12   14   \n",
-       "26295        1013.2        1022.0      1.71  ...  2020    12   15   \n",
-       "26296        1012.8        1021.6      1.14  ...  2020    12   16   \n",
-       "26297        1012.8        1021.6      0.48  ...  2020    12   17   \n",
-       "26298        1013.1        1021.9      0.02  ...  2020    12   18   \n",
-       "26299        1014.0        1022.8      0.00  ...  2020    12   19   \n",
-       "26300        1014.8        1023.6      0.00  ...  2020    12   20   \n",
-       "26301        1014.6        1023.4      0.00  ...  2020    12   21   \n",
-       "26302        1014.8        1023.6      0.00  ...  2020    12   22   \n",
-       "26303        1015.1        1024.0      0.00  ...  2020    12   23   \n",
-       "\n",
-       "       ground_temp - temp_C  hour_sin      hour_cos     month_sin  month_cos  \\\n",
-       "26294                   5.8 -0.500000 -8.660254e-01 -2.449294e-16        1.0   \n",
-       "26295                   5.6 -0.707107 -7.071068e-01 -2.449294e-16        1.0   \n",
-       "26296                   1.4 -0.866025 -5.000000e-01 -2.449294e-16        1.0   \n",
-       "26297                  -0.4 -0.965926 -2.588190e-01 -2.449294e-16        1.0   \n",
-       "26298                  -0.8 -1.000000 -1.836970e-16 -2.449294e-16        1.0   \n",
-       "26299                  -1.1 -0.965926  2.588190e-01 -2.449294e-16        1.0   \n",
-       "26300                  -0.9 -0.866025  5.000000e-01 -2.449294e-16        1.0   \n",
-       "26301                  -0.8 -0.707107  7.071068e-01 -2.449294e-16        1.0   \n",
-       "26302                  -0.6 -0.500000  8.660254e-01 -2.449294e-16        1.0   \n",
-       "26303                  -0.6 -0.258819  9.659258e-01 -2.449294e-16        1.0   \n",
-       "\n",
-       "         visi  multi_class  \n",
-       "26294  5000.0            2  \n",
-       "26295  5000.0            2  \n",
-       "26296  5000.0            2  \n",
-       "26297  5000.0            2  \n",
-       "26298  5000.0            2  \n",
-       "26299  5000.0            2  \n",
-       "26300  5000.0            2  \n",
-       "26301  5000.0            2  \n",
-       "26302  5000.0            2  \n",
-       "26303  5000.0            2  \n",
-       "\n",
-       "[10 rows x 30 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_busan_train.tail(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 26304 entries, 0 to 26303\n",
-      "Data columns (total 30 columns):\n",
-      " #   Column                Non-Null Count  Dtype   \n",
-      "---  ------                --------------  -----   \n",
-      " 0   temp_C                26304 non-null  float64 \n",
-      " 1   precip_mm             26304 non-null  float64 \n",
-      " 2   wind_speed            26304 non-null  float64 \n",
-      " 3   wind_dir              26304 non-null  category\n",
-      " 4   hm                    26304 non-null  float64 \n",
-      " 5   vap_pressure          26304 non-null  float64 \n",
-      " 6   dewpoint_C            26304 non-null  float64 \n",
-      " 7   loc_pressure          26304 non-null  float64 \n",
-      " 8   sea_pressure          26304 non-null  float64 \n",
-      " 9   solarRad              26304 non-null  float64 \n",
-      " 10  snow_cm               26304 non-null  float64 \n",
-      " 11  cloudcover            26304 non-null  category\n",
-      " 12  lm_cloudcover         26304 non-null  category\n",
-      " 13  low_cloudbase         26304 non-null  float64 \n",
-      " 14  groundtemp            26304 non-null  float64 \n",
-      " 15  O3                    26304 non-null  float64 \n",
-      " 16  NO2                   26304 non-null  float64 \n",
-      " 17  PM10                  26304 non-null  float64 \n",
-      " 18  PM25                  26304 non-null  float64 \n",
-      " 19  binary_class          26304 non-null  int64   \n",
-      " 20  year                  26304 non-null  int64   \n",
-      " 21  month                 26304 non-null  int64   \n",
-      " 22  hour                  26304 non-null  int64   \n",
-      " 23  ground_temp - temp_C  26304 non-null  float64 \n",
-      " 24  hour_sin              26304 non-null  float64 \n",
-      " 25  hour_cos              26304 non-null  float64 \n",
-      " 26  month_sin             26304 non-null  float64 \n",
-      " 27  month_cos             26304 non-null  float64 \n",
-      " 28  visi                  26304 non-null  float64 \n",
-      " 29  multi_class           26304 non-null  int64   \n",
-      "dtypes: category(3), float64(22), int64(5)\n",
-      "memory usage: 5.7 MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_busan_train.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul_train.to_csv(\"../data/data_for_modeling/seoul_train.csv\")\n",
-    "df_seoul_test.to_csv(\"../data/data_for_modeling/seoul_test.csv\")\n",
-    "\n",
-    "df_busan_train.to_csv(\"../data/data_for_modeling/busan_train.csv\")\n",
-    "df_busan_test.to_csv(\"../data/data_for_modeling/busan_test.csv\")\n",
-    "\n",
-    "df_incheon_train.to_csv(\"../data/data_for_modeling/incheon_train.csv\")\n",
-    "df_incheon_test.to_csv(\"../data/data_for_modeling/incheon_test.csv\")\n",
-    "\n",
-    "df_daegu_train.to_csv(\"../data/data_for_modeling/daegu_train.csv\")\n",
-    "df_daegu_test.to_csv(\"../data/data_for_modeling/daegu_test.csv\")\n",
-    "\n",
-    "df_daejeon_train.to_csv(\"../data/data_for_modeling/daejeon_train.csv\")\n",
-    "df_daejeon_test.to_csv(\"../data/data_for_modeling/daejeon_test.csv\")\n",
-    "\n",
-    "df_gwangju_train.to_csv(\"../data/data_for_modeling/gwangju_train.csv\")\n",
-    "df_gwangju_test.to_csv(\"../data/data_for_modeling/gwangju_test.csv\")\n",
-    "\n",
-    "df_seoul_train = pd.read_csv(\"../data/data_for_modeling/seoul_train.csv\")\n",
-    "df_seoul_test = pd.read_csv(\"../data/data_for_modeling/seoul_test.csv\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Counter({2: 8266, 1: 481, 0: 13})\n",
-      "Counter({2: 23686, 1: 2579, 0: 39})\n",
-      "Counter({2: 8455, 1: 281, 0: 24})\n",
-      "Counter({2: 24694, 1: 1516, 0: 94})\n",
-      "Counter({2: 7373, 1: 1205, 0: 182})\n",
-      "Counter({2: 21893, 1: 3892, 0: 519})\n",
-      "Counter({2: 8631, 1: 128, 0: 1})\n",
-      "Counter({2: 25149, 1: 1107, 0: 48})\n",
-      "Counter({2: 8089, 1: 618, 0: 53})\n",
-      "Counter({2: 23471, 1: 2660, 0: 173})\n",
-      "Counter({2: 8087, 1: 643, 0: 30})\n",
-      "Counter({2: 23798, 1: 2411, 0: 95})\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(Counter(df_seoul_test['multi_class']))\n",
-    "print(Counter(df_seoul_train['multi_class']))\n",
-    "\n",
-    "print(Counter(df_busan_test['multi_class']))\n",
-    "print(Counter(df_busan_train['multi_class']))\n",
-    "\n",
-    "print(Counter(df_incheon_test['multi_class']))\n",
-    "print(Counter(df_incheon_train['multi_class']))\n",
-    "\n",
-    "print(Counter(df_daegu_test['multi_class']))\n",
-    "print(Counter(df_daegu_train['multi_class']))\n",
-    "\n",
-    "print(Counter(df_daejeon_test['multi_class']))\n",
-    "print(Counter(df_daejeon_train['multi_class']))\n",
-    "\n",
-    "print(Counter(df_gwangju_test['multi_class']))\n",
-    "print(Counter(df_gwangju_train['multi_class']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Analysis_code/model_result/best_sample/ensemble_best_sample.csv b/Analysis_code/model_result/best_sample/ensemble_best_sample.csv
deleted file mode 100644
index ba152590b33fe45b19b3705c400c3e805461d1a6..0000000000000000000000000000000000000000
--- a/Analysis_code/model_result/best_sample/ensemble_best_sample.csv
+++ /dev/null
@@ -1,157 +0,0 @@
-model,CSI,MCC,Accuracy,region,data_sample
-deepgbm+ft_transformer,0.6992424754332419,0.8049778134563997,0.9617050719032529,seoul,best
-deepgbm+resnet_like,0.7090721222546449,0.8134996041947694,0.9639575067994112,seoul,best
-deepgbm+XGBoost,0.6692268948642789,0.7828377071996622,0.9579950262411524,seoul,best
-deepgbm+LightGBM,0.6935149238495999,0.801572479004747,0.9620935948299524,seoul,best
-ft_transformer+resnet_like,0.6252731559795573,0.7509360530096033,0.9488043848924154,seoul,best
-ft_transformer+XGBoost,0.6142839547044981,0.7403116223696319,0.9470938235563208,seoul,best
-ft_transformer+LightGBM,0.6085686211212852,0.7359331482423171,0.9466367866856302,seoul,best
-resnet_like+XGBoost,0.6194270544060083,0.7446567256109252,0.9478222130731675,seoul,best
-resnet_like+LightGBM,0.6136847718241535,0.741095701007121,0.9457727208457053,seoul,best
-XGBoost+LightGBM,0.5861106806341969,0.7149062718037028,0.9421973118413721,seoul,best
-deepgbm+ft_transformer+resnet_like,0.6821685259093041,0.7946632359980877,0.9596256373148356,seoul,best
-deepgbm+ft_transformer+XGBoost,0.6730333196442752,0.7867573605932577,0.9584481123836616,seoul,best
-deepgbm+ft_transformer+LightGBM,0.6764764169794865,0.7902017631994056,0.9590548610591277,seoul,best
-deepgbm+resnet_like+XGBoost,0.6784375221650579,0.7915054245115618,0.9591382422170655,seoul,best
-deepgbm+resnet_like+LightGBM,0.6775625748710835,0.7913206958685782,0.9590235671332685,seoul,best
-deepgbm+XGBoost+LightGBM,0.6325378315490732,0.7547274504003308,0.9511999817018905,seoul,best
-ft_transformer+resnet_like+XGBoost,0.6332314687362993,0.7561343704178952,0.9500612362868145,seoul,best
-ft_transformer+resnet_like+LightGBM,0.6306548804840734,0.7545886604473321,0.9496810306826026,seoul,best
-ft_transformer+XGBoost+LightGBM,0.6052879958628536,0.7319675532436248,0.9455772637672482,seoul,best
-resnet_like+XGBoost+LightGBM,0.6103490502864374,0.7369455626993492,0.9460374196338716,seoul,best
-deepgbm+ft_transformer+resnet_like+XGBoost,0.6729967545904317,0.7875845379790564,0.9581108449567916,seoul,best
-deepgbm+ft_transformer+resnet_like+LightGBM,0.6719561859986568,0.7873723840805852,0.9580337017907196,seoul,best
-deepgbm+ft_transformer+XGBoost+LightGBM,0.6531335492225346,0.7716405213564462,0.9549966314843926,seoul,best
-deepgbm+resnet_like+XGBoost+LightGBM,0.6495283344540792,0.769203226760809,0.9539740166845488,seoul,best
-ft_transformer+resnet_like+XGBoost+LightGBM,0.6233313312481723,0.7475113353931637,0.9482768587136429,seoul,best
-deepgbm+ft_transformer+resnet_like+XGBoost+LightGBM,0.6569118107203898,0.7752226196801869,0.955377564854322,seoul,best
-deepgbm+ft_transformer,0.6231286091598959,0.762319461740938,0.9692817368232819,busan,best
-deepgbm+resnet_like,0.693965736363066,0.8126142282014271,0.9774987316083207,busan,best
-deepgbm+XGBoost,0.5949465703841524,0.7372757652859034,0.9695935324500337,busan,best
-deepgbm+LightGBM,0.6134672160989406,0.7540785415035627,0.969397659505452,busan,best
-ft_transformer+resnet_like,0.5993289393140034,0.7415869283802814,0.9687921584283586,busan,best
-ft_transformer+XGBoost,0.5275703861594696,0.6836131837976293,0.9627103242924039,busan,best
-ft_transformer+LightGBM,0.5340117437566735,0.690570910687501,0.9611092397135513,busan,best
-resnet_like+XGBoost,0.5476348496496576,0.6975679320922632,0.9653371426670326,busan,best
-resnet_like+LightGBM,0.5541563454462936,0.7052881357398278,0.9637749415708096,busan,best
-XGBoost+LightGBM,0.4789290487253062,0.6395338303931094,0.9572717310843294,busan,best
-deepgbm+ft_transformer+resnet_like,0.6446887106206897,0.777619884223958,0.9728593291247681,busan,best
-deepgbm+ft_transformer+XGBoost,0.6102029002552042,0.7511249566662123,0.9699326712744633,busan,best
-deepgbm+ft_transformer+LightGBM,0.6162985577163776,0.7570233197218496,0.9692066729878318,busan,best
-deepgbm+resnet_like+XGBoost,0.6261344637257955,0.7612451856135231,0.9726364248821019,busan,best
-deepgbm+resnet_like+LightGBM,0.6357139958459742,0.770523881133966,0.9720613859986192,busan,best
-deepgbm+XGBoost+LightGBM,0.550954042564896,0.7024853673988001,0.9645722608977718,busan,best
-ft_transformer+resnet_like+XGBoost,0.5708235070191732,0.7183051671189992,0.9673502466086118,busan,best
-ft_transformer+resnet_like+LightGBM,0.5747801644059322,0.723481589964096,0.9662444585838926,busan,best
-ft_transformer+XGBoost+LightGBM,0.5188547215134367,0.6758426217880089,0.9612265139606259,busan,best
-resnet_like+XGBoost+LightGBM,0.5283032797443571,0.6828500944597963,0.9625962730077933,busan,best
-deepgbm+ft_transformer+resnet_like+XGBoost,0.62252465197324,0.7595480882291313,0.9716073641573305,busan,best
-deepgbm+ft_transformer+resnet_like+LightGBM,0.6273921955959187,0.7650978772099609,0.9711861957398673,busan,best
-deepgbm+ft_transformer+XGBoost+LightGBM,0.5803171388664561,0.7273427892946641,0.9671579085260872,busan,best
-deepgbm+resnet_like+XGBoost+LightGBM,0.5883015315947713,0.7322310098800981,0.9685279794728481,busan,best
-ft_transformer+resnet_like+XGBoost+LightGBM,0.5527408442450511,0.7040063830266932,0.9648394548826841,busan,best
-deepgbm+ft_transformer+resnet_like+XGBoost+LightGBM,0.5949050119452819,0.7385090163673939,0.9689076652444045,busan,best
-deepgbm+ft_transformer,0.5873884001633557,0.7086005084355499,0.9163964576523526,incheon,best
-deepgbm+resnet_like,0.5938343436639008,0.7129218344958757,0.9139223661119011,incheon,best
-deepgbm+XGBoost,0.5919031180535835,0.7111840783628853,0.9141871688665986,incheon,best
-deepgbm+LightGBM,0.5936054700869063,0.71280494431202,0.9151763064434298,incheon,best
-ft_transformer+resnet_like,0.5967079690105518,0.7167701525416347,0.9161293676339713,incheon,best
-ft_transformer+XGBoost,0.5958609493419124,0.7170391565071776,0.9169653625105005,incheon,best
-ft_transformer+LightGBM,0.5970916463252486,0.7186247751024871,0.9177277490830152,incheon,best
-resnet_like+XGBoost,0.6048691059122082,0.7230658352305586,0.9150990593108267,incheon,best
-resnet_like+LightGBM,0.6006642978391444,0.7204725929547785,0.913995974415916,incheon,best
-XGBoost+LightGBM,0.5923037998052801,0.7130252479770401,0.9127417221847942,incheon,best
-deepgbm+ft_transformer+resnet_like,0.5991788818331828,0.7177110572369007,0.9171559331619964,incheon,best
-deepgbm+ft_transformer+XGBoost,0.5957796816002722,0.7161817507572641,0.9171555172958721,incheon,best
-deepgbm+ft_transformer+LightGBM,0.5956266359998414,0.7160886468480842,0.9173455681147126,incheon,best
-deepgbm+resnet_like+XGBoost,0.606730149793507,0.7239728449518917,0.9167729204614451,incheon,best
-deepgbm+resnet_like+LightGBM,0.6063493089884,0.7235778913430906,0.9167717768296031,incheon,best
-deepgbm+XGBoost+LightGBM,0.6018765631426876,0.7202419676609231,0.9160501451372774,incheon,best
-ft_transformer+resnet_like+XGBoost,0.6044191487414033,0.7234603140558615,0.9175339554690555,incheon,best
-ft_transformer+resnet_like+LightGBM,0.6061367487233379,0.7248883683993704,0.9179906804401528,incheon,best
-ft_transformer+XGBoost+LightGBM,0.6013258447351273,0.721556226775587,0.9174587876670742,incheon,best
-resnet_like+XGBoost+LightGBM,0.6028836532105156,0.7216380012607432,0.915023475642721,incheon,best
-deepgbm+ft_transformer+resnet_like+XGBoost,0.6058688939038325,0.7235044499077539,0.9180666799743826,incheon,best
-deepgbm+ft_transformer+resnet_like+LightGBM,0.6053266964324343,0.7232391120300768,0.9180279004582844,incheon,best
-deepgbm+ft_transformer+XGBoost+LightGBM,0.6030917448851759,0.7220222485555309,0.9181051475908876,incheon,best
-deepgbm+resnet_like+XGBoost+LightGBM,0.6054312862809185,0.7231150429381622,0.9164303507414893,incheon,best
-ft_transformer+resnet_like+XGBoost+LightGBM,0.6046920079336132,0.7233826726564859,0.9171152822483387,incheon,best
-deepgbm+ft_transformer+resnet_like+XGBoost+LightGBM,0.6109420059115421,0.72786127635734,0.9189396869359815,incheon,best
-deepgbm+ft_transformer,0.6446005403603131,0.7684243711794304,0.9801833553742378,daegu,best
-deepgbm+resnet_like,0.6137498386585009,0.7496378336201855,0.9772962048057489,daegu,best
-deepgbm+XGBoost,0.5974990513708366,0.7398559419115336,0.9765007568763463,daegu,best
-deepgbm+LightGBM,0.6115123902876293,0.7497436182930696,0.9776771381756785,daegu,best
-ft_transformer+resnet_like,0.5469112144188149,0.6944690161411611,0.9743305595062838,daegu,best
-ft_transformer+XGBoost,0.5161941376039343,0.6722802482596641,0.9719755096439354,daegu,best
-ft_transformer+LightGBM,0.5073005220098658,0.6635248688295593,0.9720125217290049,daegu,best
-resnet_like+XGBoost,0.5029516970497304,0.6645749569024574,0.9683687027472115,daegu,best
-resnet_like+LightGBM,0.4951131290074195,0.6589965524803283,0.968139976378804,daegu,best
-XGBoost+LightGBM,0.4549973810817563,0.6238297325596118,0.9647562816578087,daegu,best
-deepgbm+ft_transformer+resnet_like,0.6183060943020874,0.7512947555661068,0.978549833237684,daegu,best
-deepgbm+ft_transformer+XGBoost,0.6017176745611856,0.7392748101693137,0.9775999950096065,daegu,best
-deepgbm+ft_transformer+LightGBM,0.6004996462619208,0.7388281525299276,0.9778661493292079,daegu,best
-deepgbm+resnet_like+XGBoost,0.5739664154870325,0.7226992953385828,0.9747144039390839,daegu,best
-deepgbm+resnet_like+LightGBM,0.5760590538958189,0.7240729842389874,0.975132349394083,daegu,best
-deepgbm+XGBoost+LightGBM,0.5273980786589871,0.6866104535247874,0.9714085801498781,daegu,best
-ft_transformer+resnet_like+XGBoost,0.5287822236272242,0.6819314770838893,0.9723570668130517,daegu,best
-ft_transformer+resnet_like+LightGBM,0.520853018041326,0.675931210971238,0.972014705026158,daegu,best
-ft_transformer+XGBoost+LightGBM,0.48437190164347993,0.6467498445011408,0.9690512430238457,daegu,best
-resnet_like+XGBoost+LightGBM,0.4803327637605557,0.6466009408894583,0.9668476723973018,daegu,best
-deepgbm+ft_transformer+resnet_like+XGBoost,0.5787636584539054,0.7225361676622583,0.9760432001730003,daegu,best
-deepgbm+ft_transformer+resnet_like+LightGBM,0.5827514275296815,0.7252407488954487,0.9763842103949898,daegu,best
-deepgbm+ft_transformer+XGBoost+LightGBM,0.5563231124331217,0.7066219754268251,0.9745609493391888,daegu,best
-deepgbm+resnet_like+XGBoost+LightGBM,0.5497033929377105,0.7036672133164642,0.9729662067187331,daegu,best
-ft_transformer+resnet_like+XGBoost+LightGBM,0.5057939271151667,0.6644447331953721,0.9703813908226664,daegu,best
-deepgbm+ft_transformer+resnet_like+XGBoost+LightGBM,0.5538086053234044,0.7036101925279703,0.9742201470502616,daegu,best
-deepgbm+ft_transformer,0.6798477338109503,0.7890627213525137,0.9581849730934616,daejeon,best
-deepgbm+resnet_like,0.665680366173557,0.7799054500657331,0.9548432808510284,daejeon,best
-deepgbm+XGBoost,0.6574065703012586,0.7732790182043394,0.9541580374445858,daejeon,best
-deepgbm+LightGBM,0.6615432493953055,0.7763462848232718,0.9551088113714433,daejeon,best
-ft_transformer+resnet_like,0.5874777029243061,0.7124933721407519,0.9448812078415716,daejeon,best
-ft_transformer+XGBoost,0.5819133878684225,0.7079795420327318,0.9433591378263508,daejeon,best
-ft_transformer+LightGBM,0.5805334443959108,0.7070987268766625,0.9437394473970939,daejeon,best
-resnet_like+XGBoost,0.5621088257073251,0.6925993749297947,0.9389891126248638,daejeon,best
-resnet_like+LightGBM,0.5552708987061287,0.6862528661139199,0.9386090109871829,daejeon,best
-XGBoost+LightGBM,0.5288440675809284,0.6634738950206458,0.9327136928080112,daejeon,best
-deepgbm+ft_transformer+resnet_like,0.6605998838783642,0.7743701608802702,0.9553335870116691,daejeon,best
-deepgbm+ft_transformer+XGBoost,0.6495239084733958,0.7651993867637442,0.9536631567565769,daejeon,best
-deepgbm+ft_transformer+LightGBM,0.6556854096497969,0.7699591127202252,0.9550316682053713,daejeon,best
-deepgbm+resnet_like+XGBoost,0.646377396487214,0.7644362437422076,0.9526370070946761,daejeon,best
-deepgbm+resnet_like+LightGBM,0.6441717942778256,0.7623630196443353,0.9527517861450042,daejeon,best
-deepgbm+XGBoost+LightGBM,0.6104936580055047,0.7355161749128437,0.9470099225657277,daejeon,best
-ft_transformer+resnet_like+XGBoost,0.5926724219238438,0.7169506517948077,0.9453380367792,daejeon,best
-ft_transformer+resnet_like+LightGBM,0.5868504053718506,0.7120674745305696,0.9448050003742795,daejeon,best
-ft_transformer+XGBoost+LightGBM,0.5697749480416849,0.6978540261423789,0.9408133093794446,daejeon,best
-resnet_like+XGBoost+LightGBM,0.5568421888456356,0.6879639430352676,0.9383421289018639,daejeon,best
-deepgbm+ft_transformer+resnet_like+XGBoost,0.6464484806055241,0.7628617034260374,0.953282847185834,daejeon,best
-deepgbm+ft_transformer+resnet_like+LightGBM,0.6432761028976647,0.7603027833589299,0.9530166928662326,daejeon,best
-deepgbm+ft_transformer+XGBoost+LightGBM,0.6268590959555621,0.7470252829879999,0.9501275669336527,daejeon,best
-deepgbm+resnet_like+XGBoost+LightGBM,0.617990326312608,0.7412687534166494,0.9484927971987257,daejeon,best
-ft_transformer+resnet_like+XGBoost+LightGBM,0.5778772266115083,0.7046854387067157,0.9426776372150277,daejeon,best
-deepgbm+ft_transformer+resnet_like+XGBoost+LightGBM,0.6251709503819354,0.7454103687111592,0.9500901389824588,daejeon,best
-deepgbm+ft_transformer,0.6798477338109503,0.7672365450318365,0.9541375560379602,gwangju,best
-deepgbm+resnet_like,0.665680366173557,0.7714595133764687,0.9543727976423164,gwangju,best
-deepgbm+XGBoost,0.6574065703012586,0.7719143895834364,0.9543191075928837,gwangju,best
-deepgbm+LightGBM,0.6615432493953055,0.7728007686314036,0.9544770483485955,gwangju,best
-ft_transformer+resnet_like,0.5874777029243061,0.7627495358829616,0.9528777415974249,gwangju,best
-ft_transformer+XGBoost,0.5819133878684225,0.7549252510472145,0.9515179410587,gwangju,best
-ft_transformer+LightGBM,0.5805334443959108,0.7489469355258954,0.9505456293509993,gwangju,best
-resnet_like+XGBoost,0.5621088257073251,0.742686095459662,0.9492615719369842,gwangju,best
-resnet_like+LightGBM,0.5552708987061287,0.7370427725250878,0.9481963158420041,gwangju,best
-XGBoost+LightGBM,0.5288440675809284,0.7303546927519567,0.9467888046570956,gwangju,best
-deepgbm+ft_transformer+resnet_like,0.6605998838783642,0.7743701608802702,0.9553335870116691,gwangju,best
-deepgbm+ft_transformer+XGBoost,0.6495239084733958,0.7651993867637442,0.9536631567565769,gwangju,best
-deepgbm+ft_transformer+LightGBM,0.6556854096497969,0.7699591127202252,0.9550316682053713,gwangju,best
-deepgbm+resnet_like+XGBoost,0.646377396487214,0.7644362437422076,0.9526370070946761,gwangju,best
-deepgbm+resnet_like+LightGBM,0.6441717942778256,0.7623630196443353,0.9527517861450042,gwangju,best
-deepgbm+XGBoost+LightGBM,0.6104936580055047,0.7355161749128437,0.9470099225657277,gwangju,best
-ft_transformer+resnet_like+XGBoost,0.5926724219238438,0.7169506517948077,0.9453380367792,gwangju,best
-ft_transformer+resnet_like+LightGBM,0.5868504053718506,0.7120674745305696,0.9448050003742795,gwangju,best
-ft_transformer+XGBoost+LightGBM,0.5697749480416849,0.6978540261423789,0.9408133093794446,gwangju,best
-resnet_like+XGBoost+LightGBM,0.5568421888456356,0.6879639430352676,0.9383421289018639,gwangju,best
-deepgbm+ft_transformer+resnet_like+XGBoost,0.6464484806055241,0.7628617034260374,0.953282847185834,gwangju,best
-deepgbm+ft_transformer+resnet_like+LightGBM,0.6432761028976647,0.7603027833589299,0.9530166928662326,gwangju,best
-deepgbm+ft_transformer+XGBoost+LightGBM,0.6268590959555621,0.7470252829879999,0.9501275669336527,gwangju,best
-deepgbm+resnet_like+XGBoost+LightGBM,0.617990326312608,0.7412687534166494,0.9484927971987257,gwangju,best
-ft_transformer+resnet_like+XGBoost+LightGBM,0.5778772266115083,0.7046854387067157,0.9426776372150277,gwangju,best
-deepgbm+ft_transformer+resnet_like+XGBoost+LightGBM,0.6251709503819354,0.7454103687111592,0.9500901389824588,gwangju,best
diff --git a/Analysis_code/model_result/deepgbm_sampled_data_test.csv b/Analysis_code/model_result/deepgbm_sampled_data_test.csv
deleted file mode 100644
index f236f1cf3efa80c74a9c65fbe901efe6434e7dae..0000000000000000000000000000000000000000
--- a/Analysis_code/model_result/deepgbm_sampled_data_test.csv
+++ /dev/null
@@ -1,31 +0,0 @@
-region,model,data_sample,CSI,MCC,Accuracy
-seoul,deepgbm,pure,0.6442509203188513,0.7660970126461858,0.9575257213197927
-busan,deepgbm,pure,0.6818642050717673,0.8049988566475562,0.9749078856534504
-incheon,deepgbm,pure,0.5671286321477068,0.6887072986816571,0.9090567324566875
-daegu,deepgbm,pure,0.5768403304029271,0.7158561147888857,0.9752356921259908
-daejeon,deepgbm,pure,0.6879666308365847,0.797628286801526,0.9573174763580109
-gwangju,deepgbm,pure,0.6169039999207967,0.7404995782864335,0.9513605060259002
-seoul,deepgbm,smote,0.6178386591753761,0.7437507767516012,0.9473890885046287
-busan,deepgbm,smote,0.5771081499427251,0.7328290895553372,0.9590890660478578
-incheon,deepgbm,smote,0.559840116480077,0.6829120896700739,0.9007683126647871
-daegu,deepgbm,smote,0.6425770288404345,0.7686582033950807,0.9787012085069575
-daejeon,deepgbm,smote,0.5569398315371836,0.6906810572279379,0.9299289492726501
-gwangju,deepgbm,smote,0.5526168770566259,0.6910361569617187,0.9341257662333341
-seoul,deepgbm,ctgan20000,0.7095252362606882,0.8058537207489471,0.9619258968152972
-busan,deepgbm,ctgan20000,0.5799669524449778,0.7334430424969706,0.9640024203408438
-incheon,deepgbm,ctgan20000,0.5289538894298621,0.6580659191964314,0.8935004283421081
-daegu,deepgbm,ctgan20000,0.44609227542135127,0.616365605274888,0.9682904159492977
-daejeon,deepgbm,ctgan20000,0.5716353901847903,0.7095774395951602,0.9385713751029269
-gwangju,deepgbm,ctgan20000,0.4571493095973662,0.620850435638757,0.9200951709625637
-seoul,deepgbm,ctgan10000,0.5616336216829804,0.700657840332931,0.9383062604486363
-busan,deepgbm,ctgan10000,0.5879352056252677,0.7351657312694786,0.9672767422711281
-incheon,deepgbm,ctgan10000,0.5290269463089048,0.6582877751557777,0.8928201753291581
-daegu,deepgbm,ctgan10000,0.5557972102197738,0.7032214449405951,0.9747075421480318
-daejeon,deepgbm,ctgan10000,0.5413249801262358,0.6761149432485588,0.9286541116683718
-gwangju,deepgbm,ctgan10000,0.41816087724732665,0.5735307327535618,0.9039835816054095
-seoul,deepgbm,ctgan7000,0.5813133937275518,0.7156872321691327,0.9403897497317663
-busan,deepgbm,ctgan7000,0.597435747844303,0.7454746315945583,0.9670328367891807
-incheon,deepgbm,ctgan7000,0.5123361043389418,0.646967905975133,0.8907633014779882
-daegu,deepgbm,ctgan7000,0.6146700981464019,0.7399778687018386,0.9766398640949504
-daejeon,deepgbm,ctgan7000,0.5581763081057168,0.6896808823936648,0.9308861691244354
-gwangju,deepgbm,ctgan7000,0.5798647223742114,0.7012366964380711,0.9421613394216134
diff --git a/Analysis_code/model_result/ft_transformer_sampled_data_test.csv b/Analysis_code/model_result/ft_transformer_sampled_data_test.csv
deleted file mode 100644
index 45e3edc4e1cd1c03d0b036141aec39ee8a4573d4..0000000000000000000000000000000000000000
--- a/Analysis_code/model_result/ft_transformer_sampled_data_test.csv
+++ /dev/null
@@ -1,31 +0,0 @@
-region,model,data_sample,CSI,MCC,Accuracy
-seoul,ft_transformer,pure,0.5998006360910436,0.7351618125239984,0.9481156066239157
-busan,ft_transformer,pure,0.525891811597548,0.6847515269647338,0.9593201836464805
-incheon,ft_transformer,pure,0.574439584484584,0.6997555499382345,0.9142701341584117
-daegu,ft_transformer,pure,0.5325909397972257,0.6846558598973905,0.9751660345501576
-daejeon,ft_transformer,pure,0.5841417763225328,0.710099560862753,0.9446515457743843
-gwangju,ft_transformer,pure,0.5123621312403577,0.6524953764059983,0.9400000831732248
-seoul,ft_transformer,smote,0.6027209460369666,0.7326561727884195,0.9459808618409561
-busan,ft_transformer,smote,0.49442102850607217,0.6656582087002536,0.9448999218171686
-incheon,ft_transformer,smote,0.5695903712638072,0.6933882645176865,0.9045313812577455
-daegu,ft_transformer,smote,0.5152208156718004,0.680387175933169,0.9707628440252515
-daejeon,ft_transformer,smote,0.5604907640952831,0.6919263799254157,0.9305719822674684
-gwangju,ft_transformer,smote,0.4852746797180565,0.6329231459699102,0.9290042709450974
-seoul,ft_transformer,ctgan20000,0.5803899707808177,0.7130066787294806,0.9414579018722292
-busan,ft_transformer,ctgan20000,0.4639353486936595,0.6467071571334145,0.9446345992298159
-incheon,ft_transformer,ctgan20000,0.5640202503368519,0.68857629970368,0.9072701715863629
-daegu,ft_transformer,ctgan20000,0.3506001798809408,0.5079853139279686,0.9495035598140248
-daejeon,ft_transformer,ctgan20000,0.5239938645095054,0.669429458663152,0.9276992830468016
-gwangju,ft_transformer,ctgan20000,0.4145097694671273,0.5668829126361233,0.9129590122347814
-seoul,ft_transformer,ctgan10000,0.5205630914565772,0.6720401513203828,0.9380401061290349
-busan,ft_transformer,ctgan10000,0.4161261582289995,0.5928041147082687,0.9366729462451447
-incheon,ft_transformer,ctgan10000,0.5623027751442908,0.6844425259764174,0.9051445758581398
-daegu,ft_transformer,ctgan10000,0.4662085962650442,0.6230709704268124,0.9675943600236212
-daejeon,ft_transformer,ctgan10000,0.5229201335739658,0.6589209425719041,0.9264041719689597
-gwangju,ft_transformer,ctgan10000,0.4575447167412667,0.6033826231809732,0.9180866415483528
-seoul,ft_transformer,ctgan7000,0.5679676743815468,0.7057250825135037,0.9415375402350475
-busan,ft_transformer,ctgan7000,0.4934746936691438,0.6650891221099499,0.9561240445475793
-incheon,ft_transformer,ctgan7000,0.5469994030503834,0.6742953373159715,0.9032061198858864
-daegu,ft_transformer,ctgan7000,0.37617290206033815,0.5389485667787934,0.9553074914123645
-daejeon,ft_transformer,ctgan7000,0.5437623015445493,0.6785419094519339,0.9309658074872537
-gwangju,ft_transformer,ctgan7000,0.46978186086856627,0.6149128334271473,0.9244421155941479
diff --git a/Analysis_code/model_result/lightgbm_sampled_data_test.csv b/Analysis_code/model_result/lightgbm_sampled_data_test.csv
deleted file mode 100644
index cf1cf069cc9a88018d0da4b5dda3b20d33136231..0000000000000000000000000000000000000000
--- a/Analysis_code/model_result/lightgbm_sampled_data_test.csv
+++ /dev/null
@@ -1,31 +0,0 @@
-region,model,data_sample,CSI,MCC,Accuracy
-seoul,LightGBM,pure,0.4830701326492519,0.6294046921291281,0.9297619790237127
-busan,LightGBM,pure,0.43506984201427806,0.603839321192473,0.9573520972128652
-incheon,LightGBM,pure,0.5522436617260514,0.6857225827228159,0.9117255533098785
-daegu,LightGBM,pure,0.2974860165425261,0.4862244089903067,0.9538572622701301
-daejeon,LightGBM,pure,0.48570589458995483,0.6313963695110991,0.9337746712578286
-gwangju,LightGBM,pure,0.4841000529921969,0.6363547628117262,0.9430086666500319
-seoul,LightGBM,smote,0.5811441298770988,0.7097862880128495,0.9400710923139624
-busan,LightGBM,smote,0.468977364244475,0.6345551645621824,0.9506557169116118
-incheon,LightGBM,smote,0.5845373158941275,0.7079881355705151,0.9115271851685506
-daegu,LightGBM,smote,0.45130042516888275,0.6228321490115053,0.9638063394632
-daejeon,LightGBM,smote,0.5294747877274647,0.6632804903422373,0.9322567599038517
-gwangju,LightGBM,smote,0.5204263336367001,0.658370785349228,0.9371142841696402
-seoul,LightGBM,ctgan20000,0.5278642102510234,0.6732186063208289,0.9409731059377364
-busan,LightGBM,ctgan20000,0.4608874696247123,0.6276000553143347,0.958718945197162
-incheon,LightGBM,ctgan20000,0.5536461635852592,0.6798438480715302,0.8955189385433041
-daegu,LightGBM,ctgan20000,0.4051438319036602,0.5791827944687878,0.965851153196763
-daejeon,LightGBM,ctgan20000,0.4682791445662134,0.6207261950649575,0.9317582403872545
-gwangju,LightGBM,ctgan20000,0.4817029529408228,0.6339890466307277,0.9420201528723874
-seoul,LightGBM,ctgan10000,0.5442974261785484,0.6853617099956927,0.9422277740349827
-busan,LightGBM,ctgan10000,0.4518917046571256,0.6185787696267423,0.9571202518485249
-incheon,LightGBM,ctgan10000,0.5472322273711279,0.6822157410012658,0.9102826018248206
-daegu,LightGBM,ctgan10000,0.42595146846779247,0.5975633254437179,0.9694990268732688
-daejeon,LightGBM,ctgan10000,0.48065206351943335,0.6295068759845875,0.9330893238848551
-gwangju,LightGBM,ctgan10000,0.48182318531993423,0.6331992271211336,0.9421339962239356
-seoul,LightGBM,ctgan7000,0.5395567725547653,0.6810123059399511,0.9410132370187391
-busan,LightGBM,ctgan7000,0.4584344058363401,0.6253420379252826,0.9581093894253563
-incheon,LightGBM,ctgan7000,0.5498019261194692,0.6849206337149152,0.911005065249395
-daegu,LightGBM,ctgan7000,0.39073560033282706,0.5690411701305705,0.9644477089935207
-daejeon,LightGBM,ctgan7000,0.4739750751092528,0.6246889480882462,0.9325196912609893
-gwangju,LightGBM,ctgan7000,0.48000404186608364,0.6311702463153694,0.9417549342515658
diff --git a/Analysis_code/model_result/resnet_like_sampled_data_test.csv b/Analysis_code/model_result/resnet_like_sampled_data_test.csv
deleted file mode 100644
index 4b30e7603153ae194d032f614bdfd4b67484576b..0000000000000000000000000000000000000000
--- a/Analysis_code/model_result/resnet_like_sampled_data_test.csv
+++ /dev/null
@@ -1,31 +0,0 @@
-region,model,data_sample,CSI,MCC,Accuracy
-seoul,resnet_like,pure,0.5556513128454227,0.6944843892225018,0.939922004308373
-busan,resnet_like,pure,0.5246252526595989,0.6874494677398264,0.9591643378163702
-incheon,resnet_like,pure,0.5799566950805518,0.7030239128662202,0.9153711397227005
-daegu,resnet_like,pure,0.4394531403147615,0.6143074280063717,0.9659717543728323
-daejeon,resnet_like,pure,0.5430465060488938,0.6757025454804458,0.9349603055784282
-gwangju,resnet_like,pure,0.5577057247702664,0.689775566381261,0.9437623200339346
-seoul,resnet_like,smote,0.6137456175954706,0.7422130871955783,0.9452800234548494
-busan,resnet_like,smote,0.5023305919728182,0.6726508273995124,0.9473319069125267
-incheon,resnet_like,smote,0.5860766625532056,0.7078408929922378,0.9089022381914814
-daegu,resnet_like,smote,0.4901581106489206,0.6522246131616541,0.9662389483577446
-daejeon,resnet_like,smote,0.5108150204429799,0.6519519041478157,0.9180820670209847
-gwangju,resnet_like,smote,0.50385899296261,0.6510080124725456,0.9282384534770567
-seoul,resnet_like,ctgan20000,0.5072327540718837,0.6620612708395112,0.9275254510068119
-busan,resnet_like,ctgan20000,0.38986584909236405,0.5796375719673601,0.9392384243664279
-incheon,resnet_like,ctgan20000,0.5432016410802436,0.6676977064251691,0.8980009315401186
-daegu,resnet_like,ctgan20000,0.2688077976760475,0.440646061562418,0.9229636075554558
-daejeon,resnet_like,ctgan20000,0.5006204822135542,0.6492987602101151,0.9202535119894204
-gwangju,resnet_like,ctgan20000,0.444672867325423,0.5955785036774024,0.9136339629546457
-seoul,resnet_like,ctgan10000,0.5304050574661009,0.6815565974252659,0.9339477755321007
-busan,resnet_like,ctgan10000,0.5675115951993178,0.7176500871011381,0.965488621902837
-incheon,resnet_like,ctgan10000,0.5608852333036517,0.6835219927895569,0.9037794953048714
-daegu,resnet_like,ctgan10000,0.3723832547505761,0.5464472897265699,0.9578420914739127
-daejeon,resnet_like,ctgan10000,0.5181167801633592,0.6610880657167911,0.9187646072976188
-gwangju,resnet_like,ctgan10000,0.40351880029410764,0.5649610782208994,0.9066332726168792
-seoul,resnet_like,ctgan7000,0.5731369888329633,0.7091329811132822,0.9410729138075871
-busan,resnet_like,ctgan7000,0.4605071450423024,0.638239831036732,0.9479047624988564
-incheon,resnet_like,ctgan7000,0.5343315289869124,0.6644525961066555,0.8994391005647463
-daegu,resnet_like,ctgan7000,0.44530071901663565,0.6174979682782439,0.9673712478478929
-daejeon,resnet_like,ctgan7000,0.5092618652909245,0.6496900098750791,0.9181710623716013
-gwangju,resnet_like,ctgan7000,0.49270227739086486,0.6375371144090152,0.9272533705949381
diff --git a/Analysis_code/model_result/xgboost_sampled_data_test.csv b/Analysis_code/model_result/xgboost_sampled_data_test.csv
deleted file mode 100644
index b65bec53cf62b5f99bbb717ceed70f2e87cb5c8d..0000000000000000000000000000000000000000
--- a/Analysis_code/model_result/xgboost_sampled_data_test.csv
+++ /dev/null
@@ -1,31 +0,0 @@
-region,model,data_sample,CSI,MCC,Accuracy
-seoul,XGBoost,pure,0.565011886957529,0.7004827765196581,0.9455723773402865
-busan,XGBoost,pure,0.4602444563246328,0.6260853753404706,0.9597446789929386
-incheon,XGBoost,pure,0.5581443524210103,0.6892177198119791,0.9130578844058522
-daegu,XGBoost,pure,0.4166514505499186,0.600956509945712,0.9683242050719033
-daejeon,XGBoost,pure,0.5044504047922097,0.6471930212882061,0.9357524265788357
-gwangju,XGBoost,pure,0.49003643817143744,0.6379812479286601,0.943428795402184
-seoul,XGBoost,smote,0.5833152545530768,0.7120196607674459,0.9418898828089262
-busan,XGBoost,smote,0.46473886869778475,0.6265333263141976,0.9518734768903195
-incheon,XGBoost,smote,0.5891561059817683,0.7103919570983672,0.9131620588700086
-daegu,XGBoost,smote,0.4611201906216012,0.6275336823062336,0.9654021217489666
-daejeon,XGBoost,smote,0.5188768068576874,0.6552123484066442,0.9321021616721145
-gwangju,XGBoost,smote,0.5099103046808192,0.6499937333212472,0.9358984995550234
-seoul,XGBoost,ctgan20000,0.5570923548170014,0.6942604158597989,0.9440149587044938
-busan,XGBoost,ctgan20000,0.46709831119818074,0.6290561623391081,0.9589464239671965
-incheon,XGBoost,ctgan20000,0.5523124093005438,0.6854497320743039,0.9114981785063753
-daegu,XGBoost,ctgan20000,0.4182253863349025,0.5952665980456898,0.9664244246492171
-daejeon,XGBoost,ctgan20000,0.4918910489160739,0.6390630711637499,0.933889866174281
-gwangju,XGBoost,ctgan20000,0.5013450661414979,0.6465339172273498,0.9428181999650672
-seoul,XGBoost,ctgan10000,0.5651854445647394,0.7027118144017459,0.9450405885337393
-busan,XGBoost,ctgan10000,0.46692094075656104,0.6325842231097966,0.958222712944249
-incheon,XGBoost,ctgan10000,0.5578011155842045,0.6901038363927982,0.9120290316141428
-daegu,XGBoost,ctgan10000,0.424541930975018,0.6005603470129561,0.966994369172676
-daejeon,XGBoost,ctgan10000,0.4845439338249185,0.63402545260005,0.9338891384085635
-gwangju,XGBoost,ctgan10000,0.490651260954804,0.6368315107576099,0.9422102036912277
-seoul,XGBoost,ctgan7000,0.5637353305761614,0.7003517728134977,0.9446616305279004
-busan,XGBoost,ctgan7000,0.4616459581003174,0.6279091576031567,0.9586027106153988
-incheon,XGBoost,ctgan7000,0.5600003713221271,0.6911778795112914,0.9122589016143924
-daegu,XGBoost,ctgan7000,0.41141211423242163,0.5889669181000196,0.9661207384118904
-daejeon,XGBoost,ctgan7000,0.4880797047418282,0.6352680209224976,0.9336605160066872
-gwangju,XGBoost,ctgan7000,0.49570507941388103,0.6441170200639816,0.9430463025342881
diff --git a/Analysis_code/model_visualize.ipynb b/Analysis_code/model_visualize.ipynb
deleted file mode 100644
index 2c91b85d8a7fcc18138a8da0142a9fef83dcb59f..0000000000000000000000000000000000000000
--- a/Analysis_code/model_visualize.ipynb
+++ /dev/null
@@ -1,2399 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd \n",
-    "import numpy as np \n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a= pd.read_csv(\"./model_result/deepgbm_sampled_data_test.csv\")\n",
-    "b= pd.read_csv(\"./model_result/ft_transformer_sampled_data_test.csv\")\n",
-    "c= pd.read_csv(\"./model_result/lightgbm_sampled_data_test.csv\")\n",
-    "d= pd.read_csv(\"./model_result/xgboost_sampled_data_test.csv\")\n",
-    "e= pd.read_csv(\"./model_result/resnet_like_sampled_data_test.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "models= pd.concat([a,b,c,d,e], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "region\n",
-       "seoul      25\n",
-       "busan      25\n",
-       "incheon    25\n",
-       "daegu      25\n",
-       "daejeon    25\n",
-       "gwangju    25\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "models['region'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "data_sample\n",
-       "pure          30\n",
-       "smote         30\n",
-       "ctgan20000    30\n",
-       "ctgan10000    30\n",
-       "ctgan7000     30\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "models['data_sample'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "model\n",
-       "deepgbm           30\n",
-       "ft_transformer    30\n",
-       "LightGBM          30\n",
-       "XGBoost           30\n",
-       "resnet_like       30\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "models['model'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "models['model_data'] = models['model'] + '_' + models['data_sample']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>model</th>\n",
-       "      <th>data_sample</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "      <th>model_data</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.644251</td>\n",
-       "      <td>0.766097</td>\n",
-       "      <td>0.957526</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.681864</td>\n",
-       "      <td>0.804999</td>\n",
-       "      <td>0.974908</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.567129</td>\n",
-       "      <td>0.688707</td>\n",
-       "      <td>0.909057</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.576840</td>\n",
-       "      <td>0.715856</td>\n",
-       "      <td>0.975236</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.687967</td>\n",
-       "      <td>0.797628</td>\n",
-       "      <td>0.957317</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.460507</td>\n",
-       "      <td>0.638240</td>\n",
-       "      <td>0.947905</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.534332</td>\n",
-       "      <td>0.664453</td>\n",
-       "      <td>0.899439</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.445301</td>\n",
-       "      <td>0.617498</td>\n",
-       "      <td>0.967371</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.509262</td>\n",
-       "      <td>0.649690</td>\n",
-       "      <td>0.918171</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.492702</td>\n",
-       "      <td>0.637537</td>\n",
-       "      <td>0.927253</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>150 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     region        model data_sample       CSI       MCC  Accuracy  \\\n",
-       "0     seoul      deepgbm        pure  0.644251  0.766097  0.957526   \n",
-       "1     busan      deepgbm        pure  0.681864  0.804999  0.974908   \n",
-       "2   incheon      deepgbm        pure  0.567129  0.688707  0.909057   \n",
-       "3     daegu      deepgbm        pure  0.576840  0.715856  0.975236   \n",
-       "4   daejeon      deepgbm        pure  0.687967  0.797628  0.957317   \n",
-       "..      ...          ...         ...       ...       ...       ...   \n",
-       "25    busan  resnet_like   ctgan7000  0.460507  0.638240  0.947905   \n",
-       "26  incheon  resnet_like   ctgan7000  0.534332  0.664453  0.899439   \n",
-       "27    daegu  resnet_like   ctgan7000  0.445301  0.617498  0.967371   \n",
-       "28  daejeon  resnet_like   ctgan7000  0.509262  0.649690  0.918171   \n",
-       "29  gwangju  resnet_like   ctgan7000  0.492702  0.637537  0.927253   \n",
-       "\n",
-       "               model_data  \n",
-       "0            deepgbm_pure  \n",
-       "1            deepgbm_pure  \n",
-       "2            deepgbm_pure  \n",
-       "3            deepgbm_pure  \n",
-       "4            deepgbm_pure  \n",
-       "..                    ...  \n",
-       "25  resnet_like_ctgan7000  \n",
-       "26  resnet_like_ctgan7000  \n",
-       "27  resnet_like_ctgan7000  \n",
-       "28  resnet_like_ctgan7000  \n",
-       "29  resnet_like_ctgan7000  \n",
-       "\n",
-       "[150 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **단일 모델 성능 비교 by 원데이터**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plt.figure(figsize=(20, 10))\n",
-    "# sns.barplot(x='region', y='CSI', hue='model', data=models.loc[models['data_sample']=='pure',:], ci=None)\n",
-    "# plt.title('Single Model Using Pure', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "# plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
-    "# plt.ylabel('csi', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "# plt.xlabel('', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "# plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **증강데이터에 대한 각 모델별 성능 확인**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def model_filter(model_name, df):\n",
-    "    return df.loc[df['model'].str.contains(model_name), :]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **증강데이터에 따른 XGBoost 모델 성능 비교**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "plt.figure(figsize=(15, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(model_filter(\"XGBoost\", models)))  # 더 많은 색상 지원\n",
-    "\n",
-    "sns.barplot(x='region', y='CSI', hue='model_data', ci=None, data=model_filter(\"XGBoost\", models), palette=palette)\n",
-    "\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=15)\n",
-    "plt.title('XGBoost Models with Different Oversampling Methods', fontsize=20, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('', fontsize=20)  # x축 라벨 제거\n",
-    "\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **증강데이터에 따른 LightGBM 모델 성능 비교**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(15, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(model_filter(\"LightGBM\", models)))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model_data', ci=None, data=model_filter(\"LightGBM\", models), palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=15)\n",
-    "plt.title('LightGBM Models with Different Oversampling Methods', fontsize=20, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **증강데이터에 따른 DeepGBM 모델 성능 비교**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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a+DfQ9OnTFcoUvkaIfe+K6tPChQulurq6xZ6P/v7+0rS0NIWyYp9hUa+Cv8WUfWabN28usj9FfWbZ2dnSoUOHin6PC7/q168vvXr1qtK6Hj58KK1fv77S8q1bt5Zeu3atyH2UKY9rBxEREVFpMeBCVIlKE3DZtWuX6H8U7t27p5Dv7du3Ujc3txL9p6xfv36ibb548UJqZ2dXorqmTJkiWpfYf2zr1KlTbH2tWrUSBITEborVr19f9Q+gCKoGXNLS0qQWFhaCfMpunCn7T17B9x4eHvL8f/31l8I2e3t76bp16wR1lHfA5cCBA1JDQ0P5e319fWlGRoZUKpVKc3JypLVq1ZJvU1NTk544cULlc3nWrFklOpcASJctWyZaV1hYWLFla9asKXojV8zChQtL1K/atWtLHz9+LKhHlYBLVFRUidoqbcBl1apVgrqOHTumkMfV1VWQZ8GCBQp5vv76a0F/0tPTFfIUrqMiAy41atRQ6YZVREREqY6bVFrygItUKpUuWLBAUEZbW1v65s0bhXwfS8Dl9u3bUjMzM5XLSiQS6YYNG0T3vXDeKlWqKFxHCr4KBlwq8ntnbm5ebH2GhobSZ8+eFfm5F/Uq6c3e5cuXC+rQ1dWVvn37ttiyYg9ddO/eXb79yJEjgu2TJ08Wreurr74SfLaFf0vs3LlTabBK2bEUC7qI3egt6sa6VCqVpqSkiAbrinqVNeAi24c7d+4I6hE7v0xNTYutb/r06UUGW4D8IMnNmzdValOV30wSiUT04ZOKDrhUq1at2L5paGiIniMpKSkKvy2UvWxsbMr8HSxuX6OjoxXOeysrK/lv0JSUFKmOjo58m5GRkfTAgQMq92no0KElOh+dnJykr169kpcv74CLlZVVsd9xZZ9Zbm6utFOnTiXqj56envTChQuCurKysqQNGzYstrxYnsK/N8vr2kFERERUWlzDhegTIzaPNJA/vUHB6QVGjx6N2NhYhTwaGhrw8fGBlZUVHj58iIiICIUpQNatWwdvb2/06tVLoVxISAju3LmjkKarq4v27dujRo0auHPnDqKiohSmzpg4cSK8vLzQunXrYvfp/v37MDAwQMeOHWFsbIyTJ08KFmOPiYnBypUrFdZFEZsKpl27dsW2VxZTpkyBoaEhgPwF5Y8cOSKYasLHxwfBwcEq1+nh4YFGjRrhxo0bAPKnCblx4wYaNWokmCN84MCBSqdJK0/6+vro06cPli5dCiB/yreNGzdiyJAh2Lt3r8LUcZ06dUKdOnVUqjcmJgbjxo0TpFtaWsLX1xd5eXmIiIhQmEINAIYOHQoPDw84OTnJ006dOiW6tk3NmjXx+eef4/379zh06JCgLmXOnz+PkSNHCtIbNWqE5s2bIycnB9HR0Xj06JF826NHj9CnTx8cPXpUpTYK2rJliyCtcePGaNasGXR0dJCeno74+Hhcv369TFNdiE0nEhMTI/+uvH37VjBlnCzP8OHDFd4X1KRJExgZGancD0dHRwwdOhRA/jRIBVWpUgX9+vVTSDMxMSmyvidPngDIn9avU6dOyM7Oxq5du/Du3TuFfEuXLkX79u1V7mdZiR3vd+/eIS4uDi1atFC5HtmxOnLkCP7++2+FbUFBQbCwsJC/d3R0RL9+/ZCamorY2FicO3dOIb+vry8cHR3l793c3ADkrxsVFBQkWJDd2NgYPj4+qFq1Km7cuIGzZ8/Kt0mlUgwcOBAtW7aEnZ1dkftQcErA1q1bo2HDhkhLS8OpU6fk6RX9vXv27BkAoH79+vD09MS9e/cE5TIzM7Fp0yaMGDECANC+fXsYGxvj4cOH2Llzp0LeBg0aCM4nV1fXYvtR0NWrVwVpjRo1go6OTrFlXVxcBNOxFfz+tmvXDhYWFnj48KE8bfv27YJF0F+/fo2DBw8qpLVp00bhd8T9+/fx9ddfC6bxsbW1RcuWLaGhoYHTp08r/D7IzMxEcHAwrl+/Dg2Nov+bIftMdXR04Ovri9q1a+PRo0eIiooCABw6dEgwjV3t2rXh5eUFY2NjvHr1Cvfu3UNcXJxK00fWq1cPVlZWMDU1hYmJCd6/f4+HDx/i1KlTyMzMVNiH6dOnY926dcXWKZs+1NPTE3Z2doiMjFSYGhAAfvzxRwD5U8N98cUX0NLSwu7du/HmzRt5npycHPz666+Ca6OY+/fvAwCcnZ3RokULPH36FIcOHVK49kmlUvzvf//D3bt3P8g0pDLPnz8HkP83vUOHDkhJSRFMBfj+/XusXLlSsCbRuHHjRKelbdGiBZydneXf3cTExIrdCQBWVlbo2LGjfDrI5ORkHDp0CP7+/li/fr3CWll9+vSBvr6+SvXu3LlT9DN2dXWFk5MTXr58iaNHjyI1NVW+LS4uDsOGDZNPH+zm5oahQ4ciMzMT69evV6indu3agt+fxV2nk5OTAZTuM5s9e7bgOgIATZs2haurK168eIFDhw4pnOtv3rxBUFAQ4uPjFa55c+bMwV9//SWoy8nJCS1btpSf52J5CivvawcRERFRiVVquIfoP640I1ykUqnoE8MFp/F68OCBVFNTU2F7rVq1pPHx8Qr1XL16VTCtk52dncJIktjYWNGny548eaJQV0REhFRDQ0MhX9u2bQV9F3uSsHr16tK7d+/K87x//17au3dvQT4HBweFusSeylY27UFxT7gWfsJT2QiX4l6DBg2Svn79WulnJ1ZGKpVKly1bJqjn1atXCp+1jo6ONCUlReXpqlQl9qT98ePHpTdu3BB87lKpVOrt7a2QfujQIaVTPhXm6+sryOfn56fwZPfLly+lrVq1EuT76quvFOoKCgoS5HF1dVWYLislJUXpE5OFff7550V+r6RSqfTNmzfSDh06CPIVnoJGlc+o8LEIDg4W/XzevHkjjYiIEEzrVhKFnxpu3769fJvYE69A/ggSmcePHwu2jxw5UtBO4TwFR6mUJp+Msu9jo0aNFKYb3L9/vyCPsbFxyQ5WAaUZ4ZKamira18LTrxU3wqWoPhSejqsgVZ9gl0ql0u3btwvytm3bVpqZmamQb+3atYJ8oaGhgvrE9ltHR0d6+PBhhXzv37+Xj/ip6O8dAOnXX38tff/+vTzfyJEjBXm6dOki2B9l0++Uldg0iIGBgSqVXbx4saCsqampQp5x48YJ8sTFxSnk2bp1qyDPunXrFPJ8++23gjw//PCDwm+EnJwcaWhoqCBf4VFQyq4zdevWFYyqkV3Dp0+frpC3Tp06on9fc3NzpRcuXJCOHTtWev36dcH2iIgI6cOHD5Ue02fPnknr1q2r0FbVqlWleXl5CvmUnV9Tp06V53n+/LlUX19fkEddXV3hCfqdO3cK8jRu3FjQN2Vtjh49WiHfpUuXRNstPEVpRY9wAfKnUCw4ou+XX34pdl8LjxqRvQr/3fz9999F2yzvES6JiYnSgwcPKqR17NhRmpeXJxjx/ddff6l8rWjQoIFCHg0NDenu3bsV8qSmpkqdnJwE50/B38lSqfKpNotSnp/Zq1evREcPF/7MEhISREc+FRy5nJOTI61Ro4Ygz+DBgxW+h8rO88L7XV7XDiIiIqLSUgMRfXKqVKkiSCv4dOa+ffsEC83/9NNPcHBwUEhr3LgxunfvrpB2584dhadvCz/dCwBz585F9erVFdLat28vWGA4OjpapcXjf/rpJ9ja2srfq6urY8mSJdDW1lbIl5CQgLt378rfiy3gbGBgUGx7FWnTpk2CJwBV8fXXXyv0fdOmTfj1118VPtdu3boV+9R/eWrYsCHatGkjf//XX39h1apV8qePgfzFwjt06KBSfRkZGQplgfxFtn/99VeFpxwNDAywbNkyQflDhw7Jz+u8vDzRhVwXLVqkcBxNTEwwZ86cYvuWmZkpeOrd1dUVQ4YMUUjT1dUVPCkOADt27Ci2jcIKf4/T0tIUngIt2Gb79u2xatWqErchU3hkXGxsrPyp9YIjV1q2bCkfQfXkyRP5k+unT58W1KnqQrwVaebMmQrfCX9/f9SuXVshT3p6OtLS0j5Yn8Suz4DiNfpjIXbeLlu2TLAP/fr1g729vULa7t27VVrAeNy4cYJrhLq6OnR1dT/I905HRwfz58+Hurq6PG3gwIGCfB/iqXkZsXNBT09PpbJiT9IXfkK7b9++gjzbtm1TeL99+3ZBvV999ZX8vVQqFfz9r1mzJmbOnAk1tf//74OGhgamTZsmaE/Va+K6desEi3/LruGFz8OsrCzR3xRqampo3rw5Zs6ciUaNGgm2t2/fHrVr10Zqair279+PBQsW4KeffsKIESMwbNgwTJ8+HZqamgpl0tLSBCNVxNSsWRNjx46VvzczM0OzZs0E+YKCguDh4SF/LxvpUpCq52DNmjUxffp0hbSmTZti0KBBgrx//vmnSnWWp4ULF0JXV1f+fsCAAQrfP0C4r1FRUQqjRoD8/Rw/frxC2ldffaV0pHd58/Pzg7W1tfz94cOHsWrVKoURXZ6engqjB4ty8+ZNwWjFwMBABAQEKKRVrVoVY8aMUUjLzc3Fnj17StT/kijNZ3b8+HHB31Y7OzvBtdre3l4+yquggvtz5coV+ahVGWNjY8ydO1dhVLey87yw8rp2EBEREZUWAy5En6CC07TIFJzap/B0MgAwaNAgSCQSwUs2RUFBBaePEaurY8eOonUVvnEmlUoF05qJ+fzzzwVpVatWFZ2mpWAwSGw6o1evXhXbXkV6+fIlxo0bh3nz5pWoXJUqVRSmcpPVU1DB6dQ+lMJtFr4R+s033yjcfCvK5cuXkZubq5DWsGFDWFlZCfI2btxYcOP89evXuHnzJgDg7t27gu+BoaEh3N3dBXX5+PgUO7XNpUuXFKbXA/KnOhI7z1u1aiUoX/A7o6rC099FRkaiWrVqaNGiBfr06YPp06dj7969gumeSqNwcCQzMxPXr18HoBhM6dKlC+rVqyd/LwvGFJ5OTF1dXaXpAiuSkZEROnXqJEivWbOmIE3smllRlLVVkunXPhSx67ujo6PoeX/79m2FfOnp6YKbh2LEbv7LfIjvna+vL6pVq6aQVtnniGxayoLEgq1iXr9+LUgrfG7Vq1dPPm2cTMEAS2ZmpuBGfFBQkEKwOikpST4dm8w///wDdXV1wWdT+FoNqPbZ2NjYKAT1C/P29lZ4/+zZM9jY2OCzzz5DcHAwfvzxR2zevFnhQQwx9+7dQ1BQEMzNzfHFF19g5MiRmD59OhYuXIjFixdj8eLFgmlMAah07W3btq3g70uNGjUE+Xx8fBTea2howNTUVCFN1XOwXbt2ggARkB8gKExs+rqK9Nlnn+Gzzz5TSNPW1kbVqlUV0grvq1g/O3XqJDodWkmmbC0LNTU1fPPNN/L3eXl5+O677xTylOR3mdj19vfffxe93hWe2hco3e8MVZT2MxOb1tfPz0/0N6HY3+qC5cU+/9atWysEgQq2UZzyunYQERERlRYDLkSfmBcvXog+HVvwhpJsDu3SKviUWXnWpYylpaVoesE1CmQK3gAxNzcXbFf2hGjPnj0xdOhQDB06FA0aNCi2T8okJiZCKpVCKpXi5cuXOHv2rOiN52nTppU4+FP4qb2Co5SaNm1aovUfyktQUJDCaKaCfdLW1kb//v1VrkvsXFL22SvbJrsBKPakothNPwDQ0tIS3HBVpW8locp5Xli/fv0En+mbN29w/vx5bNq0CT/99BMCAgJgbm6Odu3aqRS8VEbZOi55eXkKN3FatWql8BS2LBhTeIRLSddvqQiWlpai6xmJrYOhykiM8qLsBk5x52BlqOjz3tDQUDSg+qHaByDafmWfI2ZmZoK0gmuuFKXgWjZF1Vc40HX37l1cvHgRQP6T5YXXOgoJCVF4X9bPJiUlRRBMK6zwTd7CGjZsKF/LSCY3Nxc3btzAzp07MWPGDHz99dews7NDw4YNsXHjRkEd9+7dg5ubG3bt2iUI+BdHlSCY2N8psfNLLF/hYIK0wJoZJW0TKP4304eg7Pte+JgU3lexv+nK9rOo3w3lrX///gqfU8HfQNWrV0dgYKDKdVXG7wxVlPYzK8lvOrH09PR0+fp0Yp+/2PlcVHpB5XHtICIiIioLBlyIPjGFF8uVKemivUUpz1EiFTniRGyfjx8/Lpp3xIgRWLRoERYtWlRux8rAwABubm7Ys2ePYJqXjIwMHDt2rET1yRYGFVMZo1sAQFNTU2lQJTg4WPRG339Rac5zHR0dREdHY9asWYLpmgqSSqWIioqCp6enYKSJqhwcHARP9cfExODGjRvy6Yi0tbXRvHlzhZEEp0+fxqtXr3Dt2jWFsh/DdGKFnw6XKTwNyocmdo3W0dGBk5PTh+9MBSvuvDc2Nq7U9gHx86SyzxFnZ2dB2o0bNwRTKokRe6pcrL7u3bsLpuWUjXIpPJ2YlZVVuX+npVKp6GicglQ5PxYtWoTt27ejZcuWRY6mvHnzJvr27Yu5c+cqpI8ZM6bUN7pVCYCIBVdUDQT/G5XndVlsdAMgfnwrSrVq1ZSOqAkLCxMdaVRRKur39Mf6t7SsynrtICIiIiqLoudYIaKPjtj6IHZ2dgrzTIuN/AgODlY6AqCwglN8mJubC6aN6devn9J1Cgpr0qRJsXkePHiAunXrCtLFnvgteIO/ffv22LBhg8L269ev4+TJk0VOU1LeTExMUL9+fVy6dEkhXZX53wv79ttvcebMGYU0IyMj9OjRoyxdLJP//e9/mDVrluAJ8JIGgcSe8H/w4IHS/GLbZOe22A0CsSe/ASA7O7vYG25i3xlHR0f4+voWWU5G2Y2h4ujo6GDMmDEYM2YMHj58iOvXr+P27duIj4/HiRMn8Ndff8nzZmdnY8qUKYiIiChVW56engrrOJw+fVphNEvz5s2hra2tkBYfH4+DBw8KnlT/GAIuH6Ps7Gz8+uuvgnQPD4+P8oarubk5kpOT5e/V1NQwZMgQlW9oFlx7S0xx9VTW966yFV7vDMgfTbFv3z507dpVabnHjx/j5MmTKtVnbGyMLl264Pfff5en/f777xg7dqxg+s8+ffoIPiuxz8bCwgJBQUFK+1eY2HRQBal6nnXt2hVdu3ZFeno6rl27hvj4eNy5cwcXLlzAiRMnFAIjU6ZMwdChQ6GlpYX379/j4MGDgvp++OEHDBgwAJaWlvKgVI8ePQTr3HyslP3dLO4308dMrJ/K/qYX9buhInz77bf47bffFNLU1NTwv//9r0T1iH2n3NzcVB69rOpv+A+lJL/pxNKNjY3l1wix33TKRv2pOhoQKP21g4iIiKisGHAh+oTMnTsXp06dEqQXHjbv4uIiCEQ0adJEdNHKwnJzcxWeanNxccGJEycU8nh7e4vOL11cXcocPnwYgwcPVkhLT0/H+fPnBXkLBnACAwNRu3ZtwX/Kw8LCEBsb+0EXmC881z2AYqdTEfPVV19h+PDhCtOA9OnTR3Sh5A/FysoKHTt2xIEDB+RpTk5OomsqFKVp06ZQV1dXmNblr7/+QnJysmBKi7i4OMHnqq+vL1+ctm7duqhSpYrCnOKZmZmIjY0VrF0QGRlZ7Gfh7Ows6Nv79++xcOFClW4KlnSqGjEWFhawsLCQr2kklUrRpk0bhem8xOaAV5WXl5fCDcX79+8rvJd9nnZ2dqhevTqePn0KqVQqeOqzPNZvUVNTUwjglcfx+xiMGDFCdEqxwtfoj4WLi4tCwCUvLw9du3ZV6but6vW9KB/D964oYvtXHm3Wq1cPHh4egqn6JkyYAH9/f+jp6YmWGzNmjKB9Q0NDpUGakJAQhYDL/fv3MXr0aIVpkSQSieg6O1ZWVqhWrZpCsDojIwMzZ85UKdBVHudHYcbGxvD09FRYNP2nn35SWED+1atXuHnzJpo0aYIXL17g7du3CnU0atQIs2fPVkjLy8uTT7f2KYiMjEROTo5gdIVYMF6Vh14+BmL9VDZKeOfOnRXcG0UeHh747LPP5OueAflrktSpU6dE9bi4uAjSDAwMsGjRomLLyqayLaiirk+qEtufw4cPIy8vTzCi5NChQ0WWF/v8T506hbdv3wquN6V56KSk1w4iIiKisuKUYkSfgKdPn2LQoEH44YcfBNscHBwwYMAAhbTOnTsLFnGdNm0a9u/fL1p/Xl4eYmNjMXLkSMGTdmLzUw8dOlTp4p05OTk4fvw4/ve//yEgIKCo3ZKbOnUq7t27J3+fm5uLYcOGCeaYt7e3VxgJo6Ojg8mTJwvqu337Nlq2bKl0+rXytm7dOtGn9wqOOlKVtrY2BgwYAG1tbWhra0NHR6fSphMraPDgwfI+aWtrC9abUYWRkZFgIVOpVIqBAwcqTKXz+vVrQQAOADp27Ci/waSmpob27dsL8gwfPlxhGpu0tDSMGTNGpb4VXsQ+ISEB33zzjdJpcR4+fIjly5ejWbNmooHQ4qxevRozZ84ULEYu8+7dO/l0XwXTSktsVErBp+UL3mQv+O/CI7fKY/2WgotzA/nXuNTU1DLVWZnu3buH4OBgLFu2TLCtTZs28Pf3r4ReFU/s+t6nTx/RBcQB4O3btzh48CB69OhRqmtAYZXxvSuJwucpAMGIz9KaOHGiIC0+Ph6dO3cWrNWQnZ2NESNGYPPmzYIyQ4cOVTo1V/v27QVTCa5fv17hvYeHh+gIU4lEgi+//FIh7eXLl+jatavS7+qLFy+wYcMGeHp6iva1pM6dO4fvv/8e586dU7rGztOnTwVpsuuk2JPqjx8/Vnig4f379xgxYgTu3LlT5v5+KP/88w9++uknhbSrV69i+fLlgryyAP7Hrm3btoJRgH/99RcWL16skLZr1y6lU8dWpPL4DdSwYUPUq1dPIe3YsWOYPHmyfC2Twm7fvo25c+eifv36uH//vsI2sevTnTt3FAKqFalt27aoWrWqoP2pU6cqpN2+fRvTpk0TlC/4fwRnZ2eF9QKB/Aevxo4dqxBoUnaeF1bWawcRERFRWXGEC9FH5s6dOxg2bBikUikyMjKQkJCACxcuiD6hX6VKFezZs0cwT3udOnUwYMAArFixQp6WlZWFL774Ag0aNEDjxo1hamqKV69eISkpCXFxcUhLSwMgXDzT3d0dn3/+Of788095WkpKClq2bIlmzZqhfv36MDY2RkZGBu7du4e4uDj5PNMFnyQrytOnT9G4cWN06tQJxsbGOHnypOhNLbGnxPv374+zZ89i7dq1Cunx8fFo27YtbG1t0aJFC1StWhWvX79GfHy86MgZVU2ZMgWGhoYA8gMDN27cEF3MXE9PDz4+PqVqY8aMGZgxY0ap+1gR/Pz8VFpfoDgTJ07EsWPHFP4DffjwYTg4OKB9+/bIy8tDREQEHj9+rFBOQ0NDcIPpu+++EzzpGhsbCwcHB/j5+SE3NxeHDh1Sef7+yZMn49ixYwr/OV+9ejV+//13tGnTBrVq1UJeXh6ePn2Kv/76S+ni6KpKTk7G9OnTMX78eFhYWKBhw4aoXbs29PT0kJaWhhMnTgimznBwcCh1e/Xq1UONGjWULrxbcP0gDw8P7Nq1SzRfeUwnZm9vrxDIycnJQYsWLeDl5SUfzdWlSxfRqZIq2/nz5zFs2DDk5eUhPT0dN27cwLVr10Rv6lhYWCiMMPjYdOvWDTNmzMCNGzfkaffu3YOjoyNatmwJOzs76OvrIy0tDbdv31ZYZ0RsVERpfOjvXUnUrVsXEolE4Xp16dIltGrVCo0bN5bf0B85cmSJF/L28fHBqFGjMG/ePIX0qKgo2NjYoF27dqhTpw5SU1MRFRUleh1r1aoVfv75Z6VtqKuro3fv3kWuTVDU5zh+/Hhs2rRJYZTIgQMHYGFhAU9PT1haWkIikSAlJQU3b95EfHy8/HMMDQ1VWq+qXr58iV9++QW//PILjI2N8dlnn8HGxgZVqlTB27dvcfnyZVy9elWhjJqamjyAZGJiAmtra4XpPVNTU9GwYUP4+fkByF/L6kOeU+Vlzpw5OHr0KFq0aIFnz57h4MGDgpvFtWvXVrr+yMfGxMQEffr0wapVqxTShw0bhh07duCzzz7DvXv3cOTIkUrp3zfffINvvvmmzPVMmTIF3bp1U0ibNGkSVqxYgZYtW6JGjRrIzs7GP//8g7i4uCKnz6patSpMTU0VFpx/8uQJXFxc4ObmJg9ghYaGonHjxmXue2H6+voYPXo0xo8fL9ifffv2wdXVFSkpKTh48CDevHmjkKdOnTro16+f/L2GhgYGDRokCEQvWbIEJ06cgLu7u9LzXExZrx1EREREZcWAC9FH5tGjR4In+sTY2Nhg586daNCggej2+fPn4/Lly4IpiP7+++8SP6G7YcMGuLu7C25KXLp0SfD0e2k4Ojri5s2bgoV8C3Jzc8PAgQNFt61YsQIaGhpYuXKlYNu9e/cURs+UVeGng5WZNGlShS8Y/Slq1aoVZs6cibFjxyqkP3jwQBA0K2jx4sWCRcc9PT0RGhoq+EweP36MdevWyd8bGRkhLy9PYfoxMW5ubpg3bx5GjBihkJ6RkaF0dFh5efjwoUrzkpd1tJOnp6fo96x+/foKc+gXXMelsPIIuHTs2FFw7bhz547CU+YWFhYfZcBF1Wto06ZNsWPHDsFTux8TNTU17Nq1C+7u7go37aRSKWJiYhATE1PhfajM711xDA0N4eHhIRhJc+bMGYW1tnr37l3igAsAzJo1Cy9fvhT87crKyhJde6Qgd3d37Nq1q9hFu4taDFpPT6/INWOsrKywadMmdO3aVSEg9vbtWxw+fLjIdstbeno6Tp06Veyopq+++krhWvbdd99h5MiRCnmePXuGjRs3yt8bGhqiYcOGSkfufmxkv5muXLmCK1euiOaRSCRYuXLlJ7UexcyZM3HgwAHBAxenT59WmH5Ptv+foq5duyImJkawHuPTp0+xe/fuEtfXsWNHbNq0SSHt2rVruHbtmvy9h4dHhQRcgPz1kGJiYgTXq8uXL+Py5cuiZfT09LBz507BiKYffvgB27ZtE/x9Lbw/lpaWJVrHp7TXDiIiIqKy4JRiRJ+YKlWq4Pvvv8elS5fg7OysNJ+uri4iIyMRFhYmmEtZGU1NTcGUT0D+wpixsbHo0qWLyv3U09NTeZ2Hw4cPy9fmEOPs7Iz9+/crnQ9eU1MTv/76K37//XfUr19f5T4C+U9VDh06tNSjUQrT1tbG9OnTMXr06HKp799ozJgxWLduHapUqVJsXmNjY2zdulXp9B2//vprkeelqakpDh06pPJ6PsOHD8e2bdtEF3BV5rPPPkOtWrVUzi+j6oLRsrwjRowo8xO2yoIlhdfscHZ2Fl1HojzWbwHyn1ouzZR7nwIzMzP8/PPPiImJgY2NTWV3p1j29va4ePFikUG2wqpWraryQs+q+JDfu5KaPXu24MZgeVFXV8evv/6K8PBwlQM2+vr6GDt2LKKjo0UX4S6sYcOGaN68uei2wMDAYq/DQUFBOHr0aInWq7C1tYW9vb3K+ZUpyTUSANq1a4dff/1VIW3YsGGCEQUFGRoa4o8//ijT6MEP7auvvsLs2bOV/rbT0NDAihUr0KlTpw/cs7IxMTHB0aNHi1wcPjAwUDAqDIBgpPfHbPHixVi0aFGJ1uZzc3OTj64uaOLEiYJpvT4kdXV17Nq1C0OHDlVpzab69evjzJkzotckHR0dREREFPldbNy4MXbs2FFsO+Vx7SAiIiIqC45wIfoIqampQVNTE3p6ejAxMUHt2rXh4OCAli1bIigoSPQ/XWL09fWxevVqjBkzBuHh4Th16hQSEhKQlpYGqVQKIyMj2NjY4LPPPkPbtm3h5+en9IaXmZkZ9uzZg6tXr2LTpk2IiYlBYmIi0tPToaamBmNjY9StWxeNGzdGu3bt0L59e9H5pcVYWlri4sWLWLJkCbZv3447d+5AKpXCwcEBvXv3xpAhQ4p9ihfIvwkRHByMw4cP4+jRo4iJicGjR4+QkpKC3NxcVKlSBebm5qhXr568n+7u7irVLUYikcg/owYNGqBt27bo2bNniRdS/S8KDQ3Fl19+iXXr1uHo0aO4fv26/Al7MzMzODk5oUOHDggNDS3yhqCWlhb27NmDLVu2YNWqVbh27Rqys7NhaWkJf39//PDDDyUeYdCtWzd07twZv/32GyIiInDp0iW8ePECb968gb6+PmrVqoX69eujVatW6NChAz777LNSHYOJEyeiffv2iI6OxsWLF5GQkIB//vkHr169grq6OoyMjFC3bl20atUKffv2LXU7BakacNHQ0ECLFi0Ec+WXx/otQP6NtfPnz2Pu3Ln4888/kZiYqHTNjo+RmpoatLS0oK+vD1NTU1hYWKBBgwZo3bo1unTpUmE36CuKtbU1Tp06hZMnT2L79u04e/Ys7t+/j4yMDGhqasLExAT29vZo2rQpfHx84O3tXe43OD/U966k3N3d5efqqVOn8M8//5T7PP99+/ZFjx49sHv3bhw5cgTnzp3D06dPkZ6eDgMDA5iamsLJyQnt2rVDt27dSvwUdt++fUUXhQ8JCVGpvLe3N+7cuYOdO3fiwIEDuHDhAp4+fYpXr15BT08P5ubmqF+/Plq0aIH27duXWzCuXbt2uHHjBo4dO4bz58/j77//xoMHD5CRkQGpVAoDAwPUqVMHzZo1Q9euXeXThBWkpqaGrVu3omPHjlizZg2uXr2KnJwc1K5dG35+fhgxYgRsbW3x22+/lUufP5QffvgBbdq0waJFi3D69Gk8f/4cpqamaNu2LX744YcKG9FQ0RwdHfHXX39hzpw52LVrF5KSkqCvr4/PPvsMYWFh6NWrl8LoJJlq1apVQm9Lb+jQoejTpw82bNiAyMhIxMXFISUlBe/evYOBgQEsLCzg6OiI1q1b4/PPP1c61VXdunVx+fJlzJkzB8eOHcPDhw8VpgD8ELS0tLBo0SJ89913WLt2LaKjo3Hnzh2kp6dDR0cH5ubmcHV1RUBAAIKCgooMzFhaWuLq1atYuHAhtm3bhrt370JDQwMODg7o0aMHhgwZIhgBJaY8rh1EREREZSGRFpyYmoiogllbWyM5OVkhjZchIiIiIkXh4eGC9XAmTpyISZMmVU6HPgKBgYGC6bd+//13fPXVV5XUIyIiIiIiRZxSjIiIiIiIiCrVlClTcPjwYbx//16wLTc3F7NnzxYEW3R1dcttWlgiIiIiovLAKcWIiIiIiIioUkVFRcnXJXFxcYGVlRW0tLTw9OlTnD59Gk+ePBGUGTZsWKWuY0JEREREVBgDLkRERERERPRRSEtLw5EjR4rN165du//09GpERERE9HHilGJERERERET0SVBXV8fw4cNx4MABaGlpVXZ3iIiIiIgUcIQLERERERERVaotW7Zg165diI6Oxu3bt/Hs2TOkpqZCW1sbJiYmaNSoETw8PNCnTx/Url27srtLRERERCRKIpVKpZXdCSIiIiIiIiIiIiIiok8ZR7iUQl5eHh4/fowqVapAIpFUdneIiIiIiIiIiKiSSKVSvHz5ErVq1YKaGmfvJyL6L2PApRQeP34MS0vLyu4GERERERERERF9JB48eAALC4vK7gYREVUiBlxKoUqVKgDy/5AaGhpWcm+IiIiIiIiIiKiyZGZmwtLSUn6/iIiI/rsYcCkF2TRihoaGDLgQERERERERERGnnSciInBiSSIiIiIiIiIiIiIiojJiwIWIiIiIiIiIiIiIiKiMGHAhIiIiIiIiIiIiIiIqIwZciIiIiIiIiIiIiIiIyogBFyIiIiIiIiIiIiIiojJiwIWIiIiIiIiIiIiIiKiMGHAhIiIiIiIiIiIiIiIqI43K7gARERERERERERH9e+Xk5CA3N7eyu0FEVCLq6urQ1NQsURkGXIiIiIiIiIiIiKjcZWZm4sWLF3j37l1ld4WIqFS0tbVhZmYGQ0NDlfIz4EJERERERERERETlKjMzE48ePYKBgQHMzMygqakJiURS2d0iIlKJVCpFTk4OMjIy8OjRIwBQKejCgAsRERERERERERGVqxcvXsDAwAAWFhYMtBDRJ0lXVxdVqlTBw4cP8eLFC5UCLmofoF9ERERERERERET0H5GTk4N3797ByMiIwRYi+qRJJBIYGRnh3bt3yMnJKTY/Ay5ERERERERERERUbnJzcwGgxItNExF9jGTXMtm1rSgMuBAREREREREREVG54+gWIvo3KMm1jAEXIiIiIiIiIiIiIiKiMmLAhYiIiIiIiIiIiIiIqIwYcCEiIiIiIiIiIiKqRJMmTYJEIkF4eHhld6XChISEQCKRIDo6urK7QlRhNCq7A0RERERERERERPTf0mz0xsrugkouze1T2V0gok8IR7gQERERERERERERERGVEQMuREREREREREREREREZcSACxEREREREREREdEHsG/fPri7u0NPTw+mpqYICgpCQkKC0vxv3rzBzJkz4ezsDAMDAxgYGMDNzQ0bNmxQWiY1NRXjxo2Do6MjdHV1YWRkBG9vbxw4cECQNykpCRKJBF5eXsjMzMTQoUNhaWkJHR0dNGjQAAsXLkReXp5oO3FxcejcuTOMjY1RpUoVtGnTBkePHkV0dDQkEglCQkKU9vHPP/+Eh4cHDAwMULVqVQQGBuLWrVuCfOHh4ZBIJJg0aRLu3r2Lrl27wszMDIaGhvj8889x8+ZNAMD79+8xY8YMODg4QEdHB3Z2dli2bJnS9lVRcM0ZVftb3Fo81tbWkEgkCmkFj9eTJ08QFhYGCwsLaGhoYNGiRfJ8Dx48wJAhQ1C3bl3o6OjAxMQE/v7+OHPmTJn2k8oXAy5EREREREREREREFezXX39Fly5dcO7cObi4uMDX1xeXLl2Cq6sr7t69K8j/7NkzuLu7Y/z48Xjy5Ak8PT3Rpk0b3Lp1CyEhIfjuu+8EZRISEtCkSRPMmjULb9++RYcOHdC8eXOcO3cOnTt3xrx580T79u7dO3h7e2Pjxo1wdXWFr68vkpOTMWLECPTr10+Q/+zZs3B3d8eBAwdgZWUFf39/ZGVlwc/PD7t27SryOPzxxx/o1KkTsrOz0blzZ9SqVQu7d++Gm5sbrl27JlomMTERrq6uuHHjBnx8fGBtbY3Dhw/Dy8sLT548QXBwMObMmYOGDRvCy8tLHpxYvXp1kX1RRWn6WxrPnz+Hi4sLDh48CHd3d3z++efQ09MDkH+8GzdujGXLlkFTUxOdOnVCo0aNEBERgTZt2mD79u3l1g8qG43K7gARERERERERERHRv1lycjKGDx8OTU1N7N+/Hx06dAAA5OTkIDQ0FJs3bxaUCQ0NRVxcHIYOHYrZs2dDW1sbAPD06VP4+/tj6dKl6NSpE/z8/AAAubm5CA4OxoMHDzBnzhyMHDkSamr5z9vfuXMH7du3x9ixY+Hn54dGjRoptBUbGwsnJyfcvn0bZmZmAIC7d++iTZs22LBhAwICAhAQEAAAyMvLQ0hICN68eYPp06dj/Pjx8nrWrl2LsLCwIo/F8uXLsWrVKgwYMAAAIJVKMW7cOMyePRshISG4cuWKoMzGjRsxduxYzJgxAxKJBFKpFP369UN4eDjatWsHNTU13L59G9WqVQMAREZGwsfHB9OnT5e3U1ql6W9pHDp0CF9++SV+++036OjoyNMzMzMRFBSEzMxMbN68Gb169ZJvu3jxItq3b4+wsDB4e3vL958qD0e4EBEREREREREREVWgdevWISsrCz169JAHWwBAU1MTixcvlo9kkLl69SoOHToEFxcXLFiwQB5sAYDq1atj1apVAIAVK1bI0/fv34/r168jKCgIo0ePlgdbAMDOzg7z589Hbm6u0lEf8+bNkwdbAKBu3br4+eefAQBLly6Vp0dFRSEhIQH29vYYO3asQh39+/dHq1atijwWLVu2VAiCSCQSTJ06FRYWFrh69SpOnz4tKGNra4spU6bIp+OSSCQYPnw4AODmzZtYtGiRQrChXbt2cHZ2RnJyMpKSkorsT3FK09/S0NbWxi+//KIQbAHyz51//vkHw4YNUwi2AEDz5s3x888/49WrV6JBO/rwGHAhIiIiIiIiIiIiqkCnTp0CAHTv3l2wzdTUFO3bt1dIO3LkCAAgICBAIXAiI1vT5fz584IygYGBon1o3bo1ACiUkTExMYGvr68gvUePHgCAM2fOyNdyiYmJAQAEBQWJ9q1bt26i7cuIHQNNTU0EBwcD+P9jVZCXlxc0NTUV0mxtbeVlvby8BGVk2//5558i+1Oc0vS3NJo2bYratWsL0svyudKHx4ALERERERERERERUQV6/PgxAMDKykp0u7W1tcJ72aiMH3/8ERKJRPT16tUrvHjxQlCmV69eovllI0AKlpFR1i8jIyMYGxvj7du3SEtLA/D/AQxLS0vRMnXq1BFNL64t2TGQHauCxAIRBgYGAIAaNWpAXV1d6fZ3794V2Z/ilKa/paHsuMk+11atWol+ri4uLgDEP1f68LiGCxEREREREREREdFHRDaaxMPDA3Xr1i1RGT8/P1SvXl1pvoLThn0qxEbSqLLtYyP7jMQUnkqscJng4GDo6+srLV+/fv2ydY7KBQMuVGnuT/ms0tquM+F6pbVNRERERERERET/LTVr1kR8fDySk5Ph6Ogo2J6cnKzw3sLCAkD+lGIjR45UqQ1ZmbCwMAQFBZWof/fv3xdNz8zMRHp6OnR1dWFsbAwgf18A4MGDB6JllKXLFN7Xwum1atVSpcsfTEn6q6WlBQB49eqVIH9ubi6ePHlS4vYtLCwQHx+PsWPHolmzZiUuTx/WpxP+IyIiIiIiIiIiIvoEydbZ+P333wXbUlNT5et0yMjWU9m9e7fKbZSmjExKSgoiIyMF6du2bQMAuLu7y6ftatWqlbwdqVQqKCO2j8Vtf//+PXbu3Akgf1TPx6Qk/ZUFoxISEgRljh8/jpycnBK3X5bPlT48BlyIiIiIiIiIiIiIKlBoaCi0tbWxZcsWHDt2TJ6ek5OD4cOH4/Xr1wr5W7RoAV9fX8TExGDw4MHIzMwU1Hnt2jUcPnxY/j4oKAiOjo7YsmULpk6dKli7RCqVIiYmRr7ofWGjRo1CSkqK/H1iYiKmTJkCABg8eLA83dvbG/b29oiPj8ecOXMU6ggPDy92EfnTp09j3bp1CmkTJ07E/fv34eTkJA9OfSxK0t82bdoAADZv3ixfewXIP5bff/99qdr/5ptvYG5ujjlz5mDVqlWCacnev3+PiIgI3Lhxo1T1U/n6ZAMu27ZtQ9OmTaGrqwsTExMEBwfj7t27SvNHR0crXWBKIpEgPDz8w3WeiIiIiIiIiIiI/jNsbGwwf/585OTkoEOHDmjbti169OgBBwcH7N27F7169RKU2bx5M5ydnbF8+XJYWVmhbdu26NWrF/z9/VGnTh00adJEIeCioaGBPXv2wMbGBhMmTECdOnXg6+uLXr16oUOHDqhRowY8PDxw4cIFQVtubm5QU1ODnZ0dgoKC8MUXX6BRo0Z49OgRevfujcDAQHleNTU1bNiwAXp6ehg7diyaNGmCnj17okWLFujXr588OCObXquwb7/9FmFhYWjRogV69uyJRo0aYcaMGTA0NPwo79GWpL9169ZFnz59kJaWhiZNmuCLL76Aj48PPvvsMzRq1AhWVlYlbt/Y2Bh79+6FkZERvvnmG1hbW6Njx47o1asX2rVrh2rVqsHPzw937twppz2msvgkAy5r165Fjx49cOXKFdSsWRO5ubnYuXMnWrZsqXQePENDQ7Ro0ULhZW1tLd8uG+5FREREREREREREVN4GDx6M3bt3w8XFBefOnUNERAQaN26M2NhY2NnZCfKbm5vjzJkzWLJkCRwdHXHlyhXs2LEDcXFxsLW1xdy5czFq1CiFMvb29rhy5QqmTZsGCwsLxMbGYteuXUhISICzszOWLVuG3r17C9rS1tZGVFQUevbsidjYWERERMDS0hLz5s0TDYK4u7vjzJkz8Pf3R2JiIvbt2wdNTU0cOnQI7u7uAABTU1PR49C1a1fs27cP6urq2Lt3Lx4+fIguXbrg7NmzcHZ2LsWRrVgl7e/q1asxduxYGBoaIiIiAklJSRg3bhy2bt1a6j64ubnh+vXr+OGHH2BoaIgTJ05gz549SE5OhqenJ8LDw+Hj41OW3aRyIpGKTbT3EcvOzkbt2rXx4sULBAUFYceOHXj8+DHq16+Ply9f4rvvvsOSJUtUqsvf3x8HDx5EvXr18Pfff0MikahULjMzE0ZGRsjIyIChoWFZduc/7f6Uzyqt7ToTrlda20RERERERET078H7REJZWVlITEyEjY0NdHR0Krs7VISkpCTY2NjA09MT0dHR5VLnwIEDsXLlSmzbtg3dunUrlzorQ0hICDZs2IDjx4/Dy8ursrtDlagk17RPboTLhQsX8OLFCwD58xICQK1ateDm5gYACsPoivL333/j0KFDAICRI0cWGWx59+4dMjMzFV5ERERERERERERE/0WpqakKa5TIbN++HWvWrIGxsTH8/f0/fMeIKplGZXegpB48eCD/t7m5ufzf1atXBwDcv39fpXrmzZsHqVQKc3Nz9OnTp8i8M2fOxOTJk0vRWyIiIiIiIiIiIqJ/l4SEBLi7u8PJyQm2trYA8h9wj4+Ph7q6OlauXAl9ff1K7iXRh/fJBVyUKcnMaE+ePMGWLVsAAN999x20tbWLzD9u3DiMGDFC/j4zMxOWlpal6ygRERERERERERHRJ8zW1haDBw9GVFQUjh8/jtevX8PMzAyBgYEYNWqUfB2Xj8WtW7cwa9YslfJ6eHggLCysgntE/1afXMClYKDj2bNngn/XqVOn2Dp++eUXvHv3Dvr6+hg0aFCx+bW1tYsNyhARERERERERERF9SqytrUv0ILuMubk5li5dWgE9qhhPnjzBhg0bVM4fFhaG8PBwhIeHV1yn6F/pk1vDxcXFBaampgCAnTt3AgAeP36M2NhYAICfnx8AoH79+qhfv77gi//69WusWLECABAaGgoTE5MP1XUiIiIiIiIiIiIi+sC8vLwglUpVejHIQmXxyQVctLS0MGPGDAD5ARdbW1s0aNAAL1++hJmZGcaOHQsAiI+PR3x8PF68eKFQfu3atUhLS4O6urrCNGFERERERERERERERESl9ckFXADgf//7HzZv3owmTZrg8ePHkEgkCAwMxJkzZ1CrVi2l5XJzc7Fo0SIAQGBgIGxsbD5Qj4mIiIiIiIiIiIiI6N/sk1vDRaZXr17o1auX0u1icw+qq6vj3r17FdktIiIiIiIiIiIiIiL6D/okR7gQERERERERERERERF9TBhwISIiIiIiIiIiIiIiKiMGXIiIiIiIiIiIiIiIiMrok13DhYiIiIiI6N+m2eiNldb2pbl9Kq1tIiIiIqJ/A45wISIiIiIiIiIiIiIiKiMGXIiIiIiIiIiIiIiIiMqIARciIiIiIiIiIiKiSjRp0iRIJBKEh4dXdlcqTEhICCQSCaKjoyu7K0QVhgEXIiIiIiIiIiIiIqJPmJeXFyQSCZKSkiq7K/9pGpXdASIigAvEEhEREREREf2XHDp3v7K7oJKOLepUdheI6BPCES5ERERERERERERERERlxIALERERERERERER0Qewb98+uLu7Q09PD6ampggKCkJCQoLS/G/evMHMmTPh7OwMAwMDGBgYwM3NDRs2bFBaJjU1FePGjYOjoyN0dXVhZGQEb29vHDhwQJA3KSkJEokEXl5eyMzMxNChQ2FpaQkdHR00aNAACxcuRF5enmg7cXFx6Ny5M4yNjVGlShW0adMGR48eRXR0NCQSCUJCQpT28c8//4SHhwcMDAxQtWpVBAYG4tatW4J84eHhkEgkmDRpEu7evYuuXbvCzMwMhoaG+Pzzz3Hz5k0AwPv37zFjxgw4ODhAR0cHdnZ2WLZsmdL2VZGdnY3ly5fDxcUFpqam0NPTg7W1Nfz9/bFt2zaFvAWn89q+fTtcXFygp6eH2rVr44cffkB2djYA4O7du+jRowfMzc2hp6eHtm3bIi4uTrT99+/f45dffkGzZs3kn72rqytWrFiB3NxceT7ZZ3jixAkAgI2NDSQSifxVkFQqxdatW+Ht7Y2qVavKP+dJkybhzZs3ZTpelI9TihERERERERERERFVsF9//RXffvstJBIJWrdujZo1ayI2Nhaurq7o3LmzIP+zZ8/g6+uLuLg41KhRA56enpBKpThz5gxCQkJw8eJF/PLLLwplEhIS4OPjgwcPHsDa2hodOnTAy5cvERsbi86dO2Pu3LkYNWqUoK13797B29sbd+/ehbe3N7KzsxEZGYkRI0bg2rVrCA8PV8h/9uxZ+Pj44M2bN3BycoKjoyPu3r0LPz8/DB48uMjj8Mcff2DFihVo3rw5OnfujLi4OOzevRtRUVE4ceIEGjduLCiTmJgIV1dXVK9eHT4+Prh58yYOHz6MS5cuIS4uDgMHDkR0dDTatm0LW1tbHD9+HEOGDIGWlhYGDBigwqcj1KtXL+zYsQNVqlRB69atYWhoiEePHuH06dN49eoVunfvLiizePFiLF26FF5eXvDz88OpU6cwd+5cPH36FD/99BNatmwJMzMzeHt74+bNm/I+37x5E9WrV5fXk5ubiy5duuDQoUMwNDSEr68vpFIpoqKiMGjQIBw9ehQ7duyAmpoaDAwM0LdvXxw+fBhPnz5FUFAQDAwMBH3Ly8tD7969sXXrVhgYGKB58+aoWrUqLl68iMmTJ+PPP/9EdHQ0dHV1S3W8KB8DLkREREREREREREQVKDk5GcOHD4empib279+PDh06AABycnIQGhqKzZs3C8qEhoYiLi4OQ4cOxezZs6GtrQ0AePr0Kfz9/bF06VJ06tQJfn5+APJv0gcHB+PBgweYM2cORo4cCTW1/AmO7ty5g/bt22Ps2LHw8/NDo0aNFNqKjY2Fk5MTbt++DTMzMwD5ozHatGmDDRs2ICAgAAEBAQDyb9yHhITgzZs3mD59OsaPHy+vZ+3atQgLCyvyWCxfvhyrVq2SB0KkUinGjRuH2bNnIyQkBFeuXBGU2bhxI8aOHYsZM2ZAIpFAKpWiX79+CA8PR7t27aCmpobbt2+jWrVqAIDIyEj4+Phg+vTppQq4JCYmYseOHbCyssKlS5dgamoq35aVlSXaRwBYs2YNzp49i+bNmwMAnjx5giZNmmDTpk24cOECwsLCFPahb9++2LRpE5YvX47JkyfL61m0aBEOHTqEhg0bIjIyUh6M+eeff9C2bVvs3r0by5cvx5AhQ2BmZobw8HB4eXnh6dOnmDdvHqytrQV9mz9/PrZu3QovLy9s3boVNWrUAJA/kmfQoEFYu3YtJk+ejFmzZpX4eNH/45RiRERERERERERERBVo3bp1yMrKQo8ePeTBFgDQ1NTE4sWLoaenp5D/6tWrOHToEFxcXLBgwQJ5sAUAqlevjlWrVgEAVqxYIU/fv38/rl+/jqCgIIwePVoebAEAOzs7zJ8/H7m5uVi9erVoH+fNmycPtgBA3bp18fPPPwMAli5dKk+PiopCQkIC7O3tMXbsWIU6+vfvj1atWhV5LFq2bKkQBJFIJJg6dSosLCxw9epVnD59WlDG1tYWU6ZMkU+RJZFIMHz4cADAzZs3sWjRInmwBQDatWsHZ2dnJCcnIykpqcj+iHn+/DkAwNnZWSHYAgA6Ojpwd3cXLTds2DB5sAUAatSogZ49e0IqleLdu3eCfZCNNpJNByazZMkSAMCCBQsURr7UrFkTc+fOBZA/mkZV79+/x5w5c6Cvr49t27bJgy0AoKWlhV9++QU1atTAqlWrlE4hR6phwIWIiIiIiIiIiIioAp06dQoARKehMjU1Rfv27RXSjhw5AgAICAhQCJzIyNZ0OX/+vKBMYGCgaB9at24NAAplZExMTODr6ytI79GjBwDgzJkz8hvxMTExAICgoCDRvnXr1k20fRmxY6CpqYng4GAA/3+sCvLy8oKmpqZCmq2trbysl5eXoIxs+z///FNkf8TUr18f+vr6OHjwIObOnYvHjx+rVK7w51iwH0XtQ8E+3r9/H/fv30e1atVE6/P394exsTHu3LmDJ0+eqNSvy5cv48WLF2jZsqVCAEdGV1cXzZo1Q1paGm7fvq1SnSSOARciIiIiIiIiIiKiCiS7YW9lZSW6vfAUULJRGT/++KPCAugFX69evcKLFy8EZXr16iWaXzYCpGAZGWX9MjIygrGxMd6+fYu0tDQA/x8csLS0FC1Tp04d0fTi2pIdA7HgRu3atQVpsnVKatSoAXV1daXb3717V2R/xBgaGmL16tXQ1tbGDz/8gNq1a6NevXoYOHCgPOAkpqh+FrWtYB+LO1ckEol826NHj1TaH9m5cfToUaXn08GDBwGInx+kOq7hQkRERERERERERPQRkY0m8fDwQN26dUtUxs/PT3QUg0zBacM+FWIjaVTZVhY9evSAj48P9u7diyNHjuDEiRNYuXIlVq5ciREjRmD+/PmV1k/ZtGSqkp0bdnZ2xU75VngKNSoZBlyIiIiIiIiIiIiIKlDNmjURHx+P5ORkODo6CrYnJycrvLewsACQP6XYyJEjVWpDViYsLAxBQUEl6t/9+/dF0zMzM5Geng5dXV0YGxsDyN8XAHjw4IFoGWXpMoX3tXB6rVq1VOnyB1GtWjWEhYUhLCwMUqkUERER6NatGxYsWIB+/fqhYcOG5d6mbP+VHaeC28RGzYiRnRv169dHeHh42TpIReKUYkREREREREREREQVSLZ+yu+//y7YlpqaKl9/RUa2nsru3btVbqM0ZWRSUlIQGRkpSN+2bRsAwN3dXT5tl2yExO7duyGVSgVlxPaxuO3v37/Hzp07AeSP6vkYSSQS+Pn5oVOnTgCAv/76q0LaqVOnDurUqYPnz5+LfiYHDx5EWloa7OzsUKNGDXm6lpYWgPxjWZiLiwuMjIxw4sQJpKamVki/KR8DLkREREREREREREQVKDQ0FNra2tiyZQuOHTsmT8/JycHw4cPx+vVrhfwtWrSAr68vYmJiMHjwYGRmZgrqvHbtGg4fPix/HxQUBEdHR2zZsgVTp04VrF0ilUoRExOjdA2SUaNGISUlRf4+MTERU6ZMAQAMHjxYnu7t7Q17e3vEx8djzpw5CnWEh4eLLnpf0OnTp7Fu3TqFtIkTJ+L+/ftwcnKSB6cq05UrV7Br1y5kZ2crpKempuLcuXMAlK9hUx6+++47AMCIESPw/PlzefqTJ08wevRoAMDQoUMVyshGxsTHxwvqk61F8/LlSwQGBuLevXuCPI8ePcKmTZvKbR/+qzilGBEREREREREREVEFsrGxwfz58zFkyBB06NABbdq0QY0aNRAbG4u0tDT06tULW7ZsUSizefNm+Pn5Yfny5fjtt9/QpEkT1KpVCxkZGYiLi8ODBw8wdOhQ+Pn5AQA0NDSwZ88edOjQARMmTMDSpUvh5OQEc3NzvHjxAlevXsWzZ8+wcOFCwToebm5uyM7Ohp2dHby9vZGTk4PIyEi8efMGvXv3RmBgoDyvmpoaNmzYAB8fH4wdOxZbt26Fo6Mj7t69iwsXLmDw4MFYtmyZfMRFYd9++y3CwsKwcuVK1K1bF3Fxcfjrr79gaGj40Ux3lZycjKCgIBgZGaF58+aoUaMG0tPTcfLkSbx8+RKdO3eGu7t7hbU/fPhwREVF4c8//4S9vT28vb0hlUoRGRmJly9fIiAgAIMGDVIo88UXX2DDhg3o2bMn2rdvDyMjIwDAmjVrAABjx47FrVu3sGnTJjRo0ADOzs6wsbFBdnY24uPjcfPmTTg5OeHrr7+usP36L+AIFyIiIiIiIiIiIqIKNnjwYOzevRsuLi44d+4cIiIi0LhxY8TGxsLOzk6Q39zcHGfOnMGSJUvg6OiIK1euYMeOHYiLi4OtrS3mzp2LUaNGKZSxt7fHlStXMG3aNFhYWCA2Nha7du1CQkICnJ2dsWzZMvTu3VvQlra2NqKiotCzZ0/ExsYiIiIClpaWmDdvnmgQxN3dHWfOnIG/vz8SExOxb98+aGpq4tChQ/JAhLLF17t27Yp9+/ZBXV0de/fuxcOHD9GlSxecPXsWzs7OpTiy5c/NzQ3Tpk1Ds2bNEB8fjz/++AMXL16Ek5MT1q1bJ5/+rKKoq6tj3759WLx4MWxtbREREYEjR46gXr16WLZsGXbs2AE1NcVb+4GBgVi4cCEsLCywf/9+rF27FmvXrpVvV1NTw8aNG7F37174+voiMTERO3fuxOnTp6Gjo4PRo0cLRh5RyUmkYhPtUZEyMzNhZGSEjIwMGBoaVnZ3Pln3p3xWaW3XmXC90tomcc1Gb6y0ti/N7VNpbRMREREVxN9ERESfHt4nEsrKykJiYiJsbGygo6NT2d2hIiQlJcHGxgaenp6Ijo4ulzoHDhyIlStXYtu2bejWrVu51ElUmUpyTeMIFyIiIiIiIiIiIiJSWWpqKpKSkgTp27dvx5o1a2BsbAx/f/8P3zGiSsY1XIiIiIiIiIiIiIhIZQkJCXB3d4eTkxNsbW0BAH///Tfi4+Ohrq6OlStXQl9fv5J7SfThMeBCRERERERERERERCqztbXF4MGDERUVhePHj+P169cwMzNDYGAgRo0aVaELypfGrVu3MGvWLJXyenh4ICwsrIJ7RP9WDLgQERERERERERER/QdZW1ujNEt8m5ubY+nSpRXQo4rx5MkTbNiwQeX8DLhQaTHgQkRERERERERERET/Wl5eXqUKLBGVlFpld4CIiIiIiIiIiIiIiOhTx4ALERERERERERERERFRGTHgQkREREREREREREREVEYMuBAREREREREREREREZURAy5ERERERERERERERERlxIALERERERERERERERFRGTHgQkREREREREREREREVEYMuBAREREREREREREREZURAy5ERERERERERERERERlxIALERERERERERERERFRGTHgQkRERERERERERFSJJk2aBIlEgvDw8MruSoUJCQmBRCJBdHR0ZXeFCsjLy8OpU6fwww8/oFmzZqhSpQq0tbVRt25dDBw4EImJiUWWj4mJQceOHWFiYgIDAwO4urpi48aNRZZ5+PAhQkNDUatWLejo6MDBwQETJ05EVlaW0jJv377FhAkT4ODgAB0dHdSqVQv9+vXDo0ePimwrPDwcrq6uMDAwgImJCTp27IgzZ84UWaYsNCqsZiIiIiIiIiIiIiIR96d8VtldUEmdCdcruwv0CUhKSoKNjQ08PT0/uYDSvXv30KZNGwBAjRo14O3tDXV1dZw/fx4rV67Eb7/9hkOHDsHDw0NQdufOnejWrRvy8vLQpk0bmJmZITIyEn379kVcXBzmzZsnKHPnzh24u7vjxYsXaNSoEVq3bo2LFy9iypQpiIyMRGRkJLS1tRXKZGVlwdvbG7GxsahZsya6dOmCpKQkrF+/HgcOHEBsbCxsbW0FbQ0bNgyLFy+Grq4u2rdvj6ysLBw9ehRHjhzBjh07EBAQUD4HsQAGXD4CzUYXHfGrSJfm9qm0tomIiIiIiIiIiIio8kgkEvj6+mLs2LFo27YtJBIJAODdu3cYOHAgwsPD0atXL9y5cweamprycqmpqejXrx9yc3Oxc+dOBAYGAgCePn0KDw8PzJ8/H/7+/vDy8lJoLyQkBC9evMD333+PxYsXAwDev3+Prl27Yvfu3Zg5cyYmTZqkUGbatGmIjY2Fu7s7jhw5AgMDAwDAggULMHLkSPTr108Q6Dp27BgWL14MU1NTnD17Fvb29gCAs2fPwsvLC6GhofDy8oKxsXE5Hcl8DLgQEREREQE4dO5+pbXdsUWdSmubiIiIiIj+u+rWrYsjR44I0rW1tbF8+XLs3r0b9+/fx5kzZ+Dp6SnfvmbNGmRmZqJLly7yYAsAVK9eHXPmzEFgYCDmz5+vEHA5f/48YmJiYG5ujjlz5sjTNTQ0sGLFChw4cABLlizBTz/9BA2N/NBFdnY2li5dCgBYtmyZPNgCACNGjMCGDRtw4sQJXLp0Cc2aNZNvW7BgAQDgp59+kgdbAMDd3R0DBw7EkiVLsHbtWowcObK0h04U13AhIiIiIiIiIiIi+gD27dsHd3d36OnpwdTUFEFBQUhISFCa/82bN5g5cyacnZ1hYGAAAwMDuLm5YcOGDUrLpKamYty4cXB0dISuri6MjIzg7e2NAwcOCPImJSVBIpHAy8sLmZmZGDp0KCwtLaGjo4MGDRpg4cKFyMvLE20nLi4OnTt3hrGxMapUqYI2bdrg6NGjiI6OhkQiQUhIiNI+/vnnn/Dw8ICBgQGqVq2KwMBA3Lp1S5AvPDwcEokEkyZNwt27d9G1a1eYmZnB0NAQn3/+OW7evAkgf4TEjBkz5Ot72NnZYdmyZUrbV5VUKsXWrVvh6+sLU1NT6OjowNraGl27dkVkZCSA/PV3bGxsAAAnTpyARCKRvwofgxMnTsDb2xtVqlRB1apV0bFjR1y8eFFhPwu6c+cOJk2aBHd3d9SoUQNaWlqwsLBAnz59lJ43EokE1tbWyM3NxezZs+Hg4ABtbW1YWlpizJgxePfuncr7r6urCwcHBwDA48ePFbYdPHgQABAcHCwo16lTJ+jo6ODYsWMK67LIynTu3FkwbVj16tXRunVrpKWl4fTp0/L0mJgYZGRkoG7dunB2dha0JWt///798rS3b98iKipKaf/EypQXBlyIiIiIiIiIiIiIKtivv/6KLl264Ny5c3BxcYGvry8uXboEV1dX3L17V5D/2bNncHd3x/jx4/HkyRN4enqiTZs2uHXrFkJCQvDdd98JyiQkJKBJkyaYNWsW3r59iw4dOqB58+Y4d+4cOnfuLLqmBpA/fZS3tzc2btwIV1dX+Pr6Ijk5GSNGjEC/fv0E+c+ePQt3d3ccOHAAVlZW8Pf3R1ZWFvz8/LBr164ij8Mff/yBTp06ITs7G507d0atWrWwe/duuLm54dq1a6JlEhMT4erqihs3bsDHxwfW1tY4fPgwvLy88OTJEwQHB2POnDlo2LAhvLy88ODBAwwZMgSrV68usi9Fyc3NRbdu3dCzZ0+cPHkSjRs3xpdffgkLCwscPHgQv/zyCwCgSZMmCAoKApAfNOjbt6/8VXDdk127dqFdu3Y4fvw4GjVqBD8/P9y/fx8eHh44d+6caB/WrFmDKVOm4PXr13BxccEXX3wBQ0NDbNq0CS4uLoiLi1Pa/549e2LatGmoV68e2rdvj5cvX2LOnDno37+/yscgLy8PycnJAPLXdylI9lk1bdpUUE5LSwuNGjVCVlaWQmCoqDIF0wvuV2nKxMfH4927d6hWrRosLCxUKlNeOKUYERERERERERERUQVKTk7G8OHDoampif3796NDhw4AgJycHISGhmLz5s2CMqGhoYiLi8PQoUMxe/Zs+YiAp0+fwt/fH0uXLkWnTp3g5+cHID9AEBwcjAcPHmDOnDkYOXIk1NTyn7e/c+cO2rdvj7Fjx8LPzw+NGjVSaCs2NhZOTk64ffs2zMzMAAB3795FmzZtsGHDBgQEBMgXGM/Ly0NISAjevHmD6dOnY/z48fJ61q5di7CwsCKPxfLly7Fq1SoMGDAAQP4oknHjxmH27NkICQnBlStXBGU2btyIsWPHYsaMGZBIJJBKpejXrx/Cw8PRrl07qKmp4fbt26hWrRoAIDIyEj4+Ppg+fbq8nZKaOXMm/vjjDzg6OuLAgQPyUSwAkJGRgatXrwIAAgIC0KRJE+zcuRP169dHeHi4oK7MzEwMGDAAubm52LJlC3r27CnfNmHCBEydOlW0DwEBAfjmm28U2gaA9evXo1+/fhg2bJh8JEdBycnJ0NPTw+3bt+WBksTERDRt2hRbtmzB5MmTUbdu3WKPwdatW/Hs2TNUq1YNLVu2VNifjIwMABANaMjSL168iOTkZDg5OQEA7t+/X2wZWf9lKqKMvr4+jI2NkZaWhpcvX6JKlSqi+UqDI1yIiIiIiIiIiIiIKtC6deuQlZWFHj16yIMtAKCpqYnFixdDT09PIf/Vq1dx6NAhuLi4YMGCBQrTL1WvXh2rVq0CAKxYsUKevn//fly/fh1BQUEYPXq0PNgCAHZ2dpg/fz5yc3OVjvqYN2+ePNgC5K/t8fPPPwOAfA0NAIiKikJCQgLs7e0xduxYhTr69++PVq1aFXksWrZsqRAEkUgkmDp1KiwsLHD16lWF6aRkbG1tMWXKFPmC7hKJBMOHDwcA3Lx5E4sWLZIHWwCgXbt2cHZ2RnJyMpKSkorsj5js7GzMnz8fQP5nVzjgYWRkpLCeSXF+//13pKamol27dgrBFiA/4GJlZSVazs3NTdA2kB+Ma9WqFaKjo+WBj8KWLFmiMCrFxsYGvXv3BgCcOnWq2D4/ePAAw4YNAwBMmTJF4Rx89eqV/N+Fz10ZfX19AMDLly8F5Sq7jLJy5YEBFyIiIiIiIiIiIqIKJLvB3b17d8E2U1NTtG/fXiFNtoh5QECAQuBERramy/nz5wVlCi5gXlDr1q0BQKGMjImJCXx9fQXpPXr0AACcOXNGvpZLTEwMACAoKEi0b926dRNtX0bsGGhqasrX1RALBnh5eUFTU1MhzdbWVl624MLshbf/888/RfZHzMWLF5Geno7GjRujRYsWJS5fmOyYffXVV4JtGhoa8inJxLx69Qpbt27FmDFjMGDAAISEhCAkJAT//PMPpFKp6HR0mpqaaNu2rSBdth5Lccfk9evXCAwMxIsXLxAQEICBAwcWmZ/+H6cUIyIiIiIiIiIiIqpAsgXHlY1ksLa2VngvG5Xx448/4scff1Rab8EFyWVlevXqhV69eikt8+LFC0Gasn4ZGRnB2NgY6enpSEtLg6mpqfxmvaWlpWiZOnXqKG27qLZkx6Dw4uwAULt2bUGagYEBgPy1RdTV1ZVuL8ki8TIPHjwAAJWm3VJFaY9ZVFQUunfvjufPnyutW2yEhrJjIps6q6hjkpOTg6+++goXL16Eh4cHfvvtN0Ee2bEFgDdv3sDQ0FCQ5/Xr1wptFiz35s0b0bY/VBll5coDAy5EREREREREREREHxHZaBIPDw+Vb/rLyvj5+aF69epK8xWcNuxTITaSRpVtn7JXr16ha9euSE1NxYQJE9C9e3dYWVlBV1cXEokEPXv2xNatWyGVSgVlS3tM8vLy0LdvX/z5559o0qQJ9u/fD11dXUE+Q0NDGBkZISMjAw8fPoSjo6Mgz8OHDwEoBtjq1KmDK1euyLepWqbgtvIo8/r1a6Snp6Nq1aoMuBARERERERERERF9SmrWrIn4+HgkJyeL3pwuuOA38P+LfQcEBGDkyJEqtSErExYWVuQUVWJki4wXlpmZifT0dOjq6sLY2BhA/r4A/z8KpDBl6TKF97Vweq1atVTpcoWSjUQRm66rNEpzzE6dOoWUlBQEBwdj8uTJgu337t0rl74V9N1332Hr1q1wcHBARESE/DMX07hxY5w8eRKXL18WnNM5OTm4ceMGdHR05NOYycrs3bsXly9fFq1Tlu7k5KRQpuA2VcrUq1cP2traeP78OR49eiQYISVWprz8O8N/RERERERERERERB8J2fopv//+u2BbamqqfP0VGdl6Krt371a5jdKUkUlJSUFkZKQgfdu2bQAAd3d3+RRVrVq1krcjNrpCbB+L2/7+/Xvs3LkTQP6onsrWrFkzGBsb49q1a6Jr3hSmpaUFIH8/xMiOmWwfC8rNzcWuXbsE6WlpaQD+P5BW0J07d5QGIErrp59+wvLly1GnTh0cPXoU5ubmRebv1KkTAGDHjh2CbQcOHEBWVhZ8fHygo6MjKLN//37BtGZPnz7FqVOnULVqVfnxAvKPnZGREe7evYurV68K2pK137lzZ3marq4uvL29AQB//PGHSmXKCwMuRERERERERERERBUoNDQU2tra2LJlC44dOyZPz8nJwfDhw+XrSci0aNECvr6+iImJweDBg5GZmSmo89q1azh8+LD8fVBQEBwdHbFlyxZMnTpVcENbKpUiJiZGvoB7YaNGjUJKSor8fWJiIqZMmQIAGDx4sDzd29sb9vb2iI+Px5w5cxTqCA8PF130vqDTp09j3bp1CmkTJ07E/fv34eTkJA9OVSZtbW0MHz4cANC/f3/BqJyMjAycOHFC/t7MzAyampq4e/cucnNzBfV99dVXMDExwdGjR+VBLJlp06YhMTFRUEY2MmTXrl0Ka7ikp6ejf//+yMnJKf0OFrJw4UJMnz4dNWrUwLFjx4pdhwfIH0llaGiIvXv3KgSMnj17hh9++AEABKOzXF1d0apVKzx79gxjxoyRp79//x6DBg1CTk4Ovv/+e2hqasq3aWlpYciQIQDyz8OC35UFCxYgLi4Onp6eaNasmUJbI0aMAJB/fG/fvi1PP3v2LFauXAljY2P079+/2P0sKU4pRkRERERERERERFSBbGxsMH/+fAwZMgQdOnRAmzZtUKNGDcTGxiItLQ29evXCli1bFMps3rwZfn5+WL58OX777Tc0adIEtWrVQkZGBuLi4vDgwQMMHToUfn5+AAANDQ3s2bMHHTp0wIQJE7B06VI4OTnB3NwcL168wNWrV/Hs2TMsXLhQYQQBALi5uSE7Oxt2dnbw9vZGTk4OIiMj8ebNG/Tu3RuBgYHyvGpqatiwYQN8fHwwduxYbN26FY6Ojrh79y4uXLiAwYMHY9myZfJRH4V9++23CAsLw8qVK1G3bl3ExcXhr7/+gqGhIcLDw8v3wJfB+PHjceXKFezZswcODg5o3bo1zM3N8eDBA1y+fBm+vr7w9PQEkB8U8PPzw/79+9G4cWM0bdoUWlpaaNWqFUJDQ2FkZITVq1eja9eu6NGjB5YsWQJra2tcv34dCQkJ+N///odVq1YpHLPmzZvD19cXR48ehYODA7y8vAAA0dHRMDMzQ5cuXbB3794y7+fVq1flgREbGxtMnz5dNF9YWJjC6CMTExOsW7cOXbt2RXBwMLy8vGBqaopjx44hPT0dI0aMkPe5oPXr18Pd3R2LFy9GVFQUHB0dceHCBdy7dw8tW7bEuHHjBGV++uknHDt2DGfOnIG9vT1at26N5ORknDt3DtWqVRME8ADAx8cHQ4cOxeLFi9GkSRP4+voiOzsbR48ehVQqxfr164ucMq20OMKFiIiIiIiIiIiIqIINHjwYu3fvhouLC86dO4eIiAg0btwYsbGxsLOzE+Q3NzfHmTNnsGTJEjg6OuLKlSvYsWMH4uLiYGtri7lz52LUqFEKZezt7XHlyhVMmzYNFhYWiI2Nxa5du5CQkABnZ2csW7YMvXv3FrSlra2NqKgo9OzZE7GxsYiIiIClpSXmzZsnGgRxd3fHmTNn4O/vj8TEROzbtw+ampo4dOgQ3N3dAQCmpqaix6Fr167Yt28f1NXVsXfvXjx8+BBdunTB2bNn4ezsXIojWzE0NDSwc+dOhIeHw83NDRcvXsSuXbvw8OFD+Pv7Y9iwYQr516xZg6+//hopKSn47bffsHbtWoVRMIGBgTh27Bi8vLwQFxeHgwcPolatWjh16pR8REnhY7Z37178+OOPqFatGv78809cunQJ3bt3R2xsbLkFC9LT0+VTw509exYbNmwQfd25c0dQNigoCCdPnkSHDh1w5coVHDp0CHZ2dggPD8f8+fNF25OdoyEhIXj+/Dl2794NNTU1/Pzzz4iMjIS2tragjI6ODo4fP46ff/4Zenp62LNnD5KTkxESEoLLly/D1tZWtK1FixZh/fr1aNCgAY4ePYqzZ8/Cx8cHJ0+eREBAQOkPWhEkUrGJ9qhImZmZMDIyQkZGBgwNDctcX7PRG8uhV6VzaW6fSmv7/pTPKq3tOhOuV1rbJO6/+j0gIqKPx6Fz4guFfggdWxQ/ZJ/+G/ibiIjo01Pe94n+DbKyspCYmAgbGxuF9Rvo45OUlAQbGxt4enoiOjq6XOocOHAgVq5ciW3btqFbt27lUue/nZ+fHyIiIhAbG4sWLVpUdneokJJc0zjChYiIiIiIiIiIiIhUlpqaiqSkJEH69u3bsWbNGhgbG8Pf3//Dd+wj9ujRIzx9+lQhLS8vDwsXLkRERAQcHBzg6upaSb2j8sI1XIiIiIiIiIiIiIhIZQkJCXB3d4eTk5N8Oqe///4b8fHxUFdXx8qVK6Gvr1/Jvfy4nDp1Cr1794azszOsrKzw7t073LhxA0lJSdDT08OaNWsgkUgqu5tURgy4EBEREREREREREZHKbG1tMXjwYERFReH48eN4/fo1zMzMEBgYiFGjRsnXcflY3Lp1C7NmzVIpr4eHB8LCwsq9D82aNUOfPn1w6tQpxMfHIysrCzVq1MDXX3+NsWPHwtHRsdzbpA+PARciIiIiIiIiIiKi/yBra2uUZolvc3NzLF26tAJ6VDGePHmCDRs2qJy/IgIu9vb2WLduXbnXSx8XBlyIiIiIiIiIiIiI6F/Ly8urVIElopJSq+wOEBERERERERERERERfeo4wuU/7tC5+5XWdqNKa5mIiIiIiIiIiIiIqHwx4EJEREREREREH41mozdWWtuX5vaptLaJiIjo08cpxYiIiIiIiIiIiIiIiMqII1yIiIiIiKhStPqlVaW1HfNdTKW1TURERERE/04c4UJERERERERERERERFRGDLgQERERERERERERERGVEQMuREREREREREREREREZcSACxERERERERERERERURkx4EJERERERERERERUiSZNmgSJRILw8PDK7kqFCQkJgUQiQXR0dGV3hQp58OABli9fjpCQEDRo0ABqamoqf1YxMTHo2LEjTExMYGBgAFdXV2zcuLHIMg8fPkRoaChq1aoFHR0dODg4YOLEicjKylJa5u3bt5gwYQIcHBygo6ODWrVqoV+/fnj06FGRbYWHh8PV1RUGBgYwMTFBx44dcebMmWL3q7QYcCEiIiIiIiIiIiIiKqWkpCRIJBJ4eXlVdldKZefOnRg8eDA2bNiAW7duQSqVqlzO09MThw8fhpOTE/z8/HD79m307dsXo0aNEi1z584dODs7Izw8HKampujSpQtyc3MxZcoU+Pj44N27d4IyWVlZ8Pb2xtSpU/Hq1St06dIFlpaWWL9+PZydnXHv3j3RtoYNG4bQ0FDcuHEDPj4+cHV1xdGjR9GmTRvs2bNH5eNTEhoVUisRERERERERERGREmnH5lR2F1RS1eeHyu4CUYWztbXFsGHD4OLiAhcXFwwZMgRHjhwpskxqair69euH3Nxc7Ny5E4GBgQCAp0+fwsPDA/Pnz4e/v78gCBUSEoIXL17g+++/x+LFiwEA79+/R9euXbF7927MnDkTkyZNUigzbdo0xMbGwt3dHUeOHIGBgQEAYMGCBRg5ciT69esnGI1z7NgxLF68GKampjh79izs7e0BAGfPnoWXlxdCQ0Ph5eUFY2Pj0h00JT7ZES7btm1D06ZNoaurCxMTEwQHB+Pu3bvFlktMTERISAhq1qwJLS0tVK9eHZ06dUJGRsYH6DURERERERERERER0cfjiy++wMKFC9GzZ0/Y29tDIpEUW2bNmjXIzMxEly5d5MEWAKhevTrmzMkPqM6fP1+hzPnz5xETEwNzc3N5HgDQ0NDAihUroKmpiSVLluD9+/fybdnZ2Vi6dCkAYNmyZfJgCwCMGDECTk5OOHHiBC5duqTQ1oIFCwAAP/30kzzYAgDu7u4YOHAg0tPTsXbt2mL3s6Q+yYDL2rVr0aNHD1y5cgU1a9aUR9FatmyJJ0+eKC2XkJAAFxcXbNiwAZmZmWjQoAFMTExw9OhRvHz58gPuAREREREREREREf3X7Nu3D+7u7tDT04OpqSmCgoKQkJCgNP+bN28wc+ZMODs7w8DAAAYGBnBzc8OGDRuUlklNTcW4cePg6OgIXV1dGBkZwdvbGwcOHBDkLTgVVmZmJoYOHQpLS0vo6OigQYMGWLhwIfLy8kTbiYuLQ+fOnWFsbIwqVaqgTZs2OHr0KKKjoyGRSBASEqK0j3/++Sc8PDxgYGCAqlWrIjAwELdu3RLkCw8Ph0QiwaRJk3D37l107doVZmZmMDQ0xOeff46bN28CyB8hMWPGDPn6HnZ2dli2bJnS9lUllUqxdetW+Pr6wtTUFDo6OrC2tkbXrl0RGRkJIH/9HRsbGwDAiRMnIJFI5K/Cx+DEiRPw9vZGlSpVULVqVXTs2BEXL15U2M+C7ty5g0mTJsHd3R01atSAlpYWLCws0KdPH6XnjUQigbW1NXJzczF79mw4ODhAW1sblpaWGDNmjOiUXaVx8OBBAEBwcLBgW6dOnaCjo4Njx44prMsiK9O5c2doa2srlKlevTpat26NtLQ0nD59Wp4eExODjIwM1K1bF87OzoK2ZO3v379fnvb27VtERUUp7Z9YmfLyyQVcsrOzMXbsWABAUFAQ7t27h7///htVqlTBs2fPMGPGDKVlv//+e6SkpKBt27Z49OgRrl27hr///hsZGRmoUaPGh9oFIiIiIiIiIiIi+o/59ddf0aVLF5w7dw4uLi7w9fXFpUuX4OrqKjpzz7Nnz+Du7o7x48fjyZMn8PT0RJs2bXDr1i2EhITgu+++E5RJSEhAkyZNMGvWLLx9+xYdOnRA8+bNce7cOXTu3Bnz5s0T7du7d+/g7e2NjRs3wtXVFb6+vkhOTsaIESPQr18/Qf6zZ8/C3d0dBw4cgJWVFfz9/ZGVlQU/Pz/s2rWryOPwxx9/oFOnTsjOzkbnzp1Rq1Yt7N69G25ubrh27ZpomcTERLi6usrX4rC2tsbhw4fh5eWFJ0+eIDg4GHPmzEHDhg3h5eWFBw8eYMiQIVi9enWRfSlKbm4uunXrhp49e+LkyZNo3LgxvvzyS1hYWODgwYP45ZdfAABNmjRBUFAQgPygQd++feUvDw8PeX27du1Cu3btcPz4cTRq1Ah+fn64f/8+PDw8cO7cOdE+rFmzBlOmTMHr16/h4uKCL774AoaGhti0aRNcXFwQFxentP89e/bEtGnTUK9ePbRv3x4vX77EnDlz0L9//1Ifk4Jkn1XTpk0F27S0tNCoUSNkZWUpBIaKKlMwveB+laZMfHw83r17h2rVqsHCwkKlMuXlk1vD5cKFC3jx4gUAyE/kWrVqwc3NDUePHsXhw4dFy6WlpcnnnatatSqaN2+Op0+fomHDhpg6dSp8fX2Vtvnu3TuFyF9mZmZ57Q4RERERERERERH9yyUnJ2P48OHQ1NTE/v370aFDBwBATk4OQkNDsXnzZkGZ0NBQxMXFYejQoZg9e7Z8RMDTp0/h7++PpUuXolOnTvDz8wOQHyAIDg7GgwcPMGfOHIwcORJqavnP29+5cwft27fH2LFj4efnh0aNGim0FRsbCycnJ9y+fRtmZmYAgLt376JNmzbYsGEDAgICEBAQAADIy8tDSEgI3rx5g+nTp2P8+PHyetauXYuwsLAij8Xy5cuxatUqDBgwAED+KJJx48Zh9uzZCAkJwZUrVwRlNm7ciLFjx2LGjBmQSCSQSqXo168fwsPD0a5dO6ipqeH27duoVq0aACAyMhI+Pj6YPn26vJ2SmjlzJv744w84OjriwIED8lEsAJCRkYGrV68CAAICAtCkSRPs3LkT9evXR3h4uKCuzMxMDBgwALm5udiyZQt69uwp3zZhwgRMnTpVtA8BAQH45ptvFNoGgPXr16Nfv34YNmyYfCRHQcnJydDT08Pt27flAw0SExPRtGlTbNmyBZMnT0bdunVLekgU9ke2RIdYQEOWfvHiRSQnJ8PJyQkAcP/+/WLLyPovUxFl9PX1YWxsjLS0NLx8+RJVqlQRzVcan9wIlwcPHsj/bW5uLv939erVAfz/wSzs9u3bkEqlAPKjiXl5edDR0cG5c+fw+eefK40iAvlfLiMjI/nL0tKyPHaFiIiIiIiIiIiI/gPWrVuHrKws9OjRQx5sAQBNTU0sXrwYenp6CvmvXr2KQ4cOwcXFBQsWLFCYfql69epYtWoVAGDFihXy9P379+P69esICgrC6NGj5cEWALCzs8P8+fORm5urdNTHvHnz5MEWAKhbty5+/vlnAJCvoQEAUVFRSEhIgL29vXwmIpn+/fujVatWRR6Lli1bKgRBJBIJpk6dCgsLC1y9elVhOikZW1tbTJkyRb62iEQiwfDhwwEAN2/exKJFi+TBFgBo164dnJ2dkZycjKSkpCL7IyY7O1u+/si6desEAQ8jIyN4enqqXN/vv/+O1NRUtGvXTiHYAuQHXKysrETLubm5CdoG8oNxrVq1QnR0tNK1yZcsWaIwq5ONjQ169+4NADh16pTKfRfz6tUr+b8Ln7sy+vr6AKCwlIesXGWXUVauPHxyARdlZMEUZQoutOPj44O7d+/izp07MDExQW5ursLFqbBx48YhIyND/ioY9CEiIiIiIiIiIiIqiuwGd/fu3QXbTE1N0b59e4U02Uw9AQEBCoETGdmaLufPnxeUKbiAeUGtW7cGAIUyMiYmJqIzAPXo0QMAcObMGflaLjExMQDyZx8S61u3bt1E25cROwaamprydTXEggFeXl7Q1NRUSLO1tZWX9fLyEpSRbf/nn3+K7I+YixcvIj09HY0bN0aLFi1KXL4w2TH76quvBNs0NDTkMzmJefXqFbZu3YoxY8ZgwIABCAkJQUhICP755x9IpVLR6eg0NTXRtm1bQbqDgwOA0h0TUs0nN6VYwdElz549E/y7Tp06ouVq164t/3fz5s0hkUhgZGQEBwcHxMbGFhnp1NbWFiziQ0REpdfql6KfdqlIMd/FVFrbRERERERE9N/0+PFjAFA6ksHa2lrhvexe5Y8//ogff/xRab0FFySXlenVqxd69eqltIxsuYaClPXLyMgIxsbGSE9PR1paGkxNTeU365XNAqTs/mxxbcmOgexYFVTw3q6MgYEBAKBGjRpQV1dXur00i8TLHrgvy7RbBZX2mEVFRaF79+54/vy50rrFRmgoOyayqbNKc0wKkh1bAHjz5g0MDQ0FeV6/fq3QZsFyb968Ea33Q5VRVq48fHIBFxcXF5iamiIlJQU7d+5Ejx498PjxY8TGxgKAfM7C+vXrAwCGDBmCIUOGwMrKCvb29rh9+zYuXboEqVSKly9fyhftsbe3r5wdIiIiIiIiIiIiIipANprEw8ND5Zv+sjJ+fn7y5RfEFJw27FMhNpJGlW2fslevXqFr165ITU3FhAkT0L17d1hZWUFXVxcSiQQ9e/bE1q1bRWd+quhjYmhoCCMjI2RkZODhw4dwdHQU5Hn48CEAxQBbnTp1cOXKFfk2VcsU3FYeZV6/fo309HRUrVqVARctLS3MmDED33zzDXbu3AlbW1ukpKTg5cuXMDMzk88bGB8fD0AxYjtr1iwEBwfj6NGjsLOzw8uXL5Gamgp9fX2MGDGiUvaHiIiIiIiIiIiI/t1q1qyJ+Ph4JCcni96cLrjgN/D/i30HBARg5MiRKrUhKxMWFlbkFFVilK2LnZmZifT0dOjq6sLY2BhA/r4AULrsQnHLMRTe18LptWrVUqXLFUo2EkVsuq7SKM0xO3XqFFJSUhAcHIzJkycLtt+7d69c+lZajRs3xsmTJ3H58mXBOZ2Tk4MbN25AR0dHPo2ZrMzevXtx+fJl0Tpl6U5OTgplCm5TpUy9evWgra2N58+f49GjR4IRUmJlyssnGf773//+h82bN6NJkyZ4/PgxJBIJAgMDcebMmSK/kIGBgdizZw9cXFzw+PFjqKmpISAgABcvXkSDBg0+4B4QERERERERERHRf4Vs/ZTff/9dsC01NVW+/oqMbD2V3bt3q9xGacrIpKSkIDIyUpC+bds2AIC7u7t8iqpWrVrJ2xEbXSG2j8Vtf//+PXbu3Akgf1RPZWvWrBmMjY1x7do10TVvCtPS0gKguI54QbJjJtvHgnJzc7Fr1y5BelpaGoD/D6QVdOfOHaUBiA+lU6dOAIAdO3YIth04cABZWVnw8fGBjo6OoMz+/fsF05o9ffoUp06dQtWqVeXHC8g/dkZGRrh79y6uXr0qaEvWfufOneVpurq68Pb2BgD88ccfKpUpL59kwAXIn4vwypUryMrKQnp6Onbu3KkwLZhUKoVUKsWkSZMUyn3xxRc4f/483r59iydPnmD37t3y6ceIiIiIiIiIiIiIyltoaCi0tbWxZcsWHDt2TJ6ek5OD4cOHy9eTkGnRogV8fX0RExODwYMHIzMzU1DntWvXcPjwYfn7oKAgODo6YsuWLZg6darghrZUKkVMTIx8AffCRo0ahZSUFPn7xMRETJkyBQAwePBgebq3tzfs7e0RHx+POXPmKNQRHh4uuuh9QadPn8a6desU0iZOnIj79+/DyclJHpyqTNra2hg+fDgAoH///oJRORkZGThx4oT8vZmZGTQ1NXH37l3k5uYK6vvqq69gYmKCo0ePyoNYMtOmTUNiYqKgjGxkyK5duxTWcElPT0f//v2Rk5NT+h0sB2FhYTA0NMTevXsVAkbPnj3DDz/8AACC0Vmurq5o1aoVnj17hjFjxsjT379/j0GDBiEnJwfff/89NDU15du0tLQwZMgQAPnnYcHvyoIFCxAXFwdPT080a9ZMoS3ZjFbTpk3D7du35elnz57FypUrYWxsjP79+5f1MAh8clOKEREREREREREREX1KbGxsMH/+fAwZMgQdOnRAmzZtUKNGDcTGxiItLQ29evXCli1bFMps3rwZfn5+WL58OX777Tc0adIEtWrVQkZGBuLi4vDgwQMMHTpUvqa1hoYG9uzZgw4dOmDChAlYunQpnJycYG5ujhcvXuDq1at49uwZFi5cqDCCAADc3NyQnZ0NOzs7eHt7IycnB5GRkXjz5g169+6NwMBAeV41NTVs2LABPj4+GDt2LLZu3QpHR0fcvXsXFy5cwODBg7Fs2TL5qI/Cvv32W4SFhWHlypWoW7cu4uLi8Ndff8HQ0BDh4eHle+DLYPz48bhy5Qr27NkDBwcHtG7dGubm5njw4AEuX74MX19feHp6AsgPCvj5+WH//v1o3LgxmjZtCi0tLbRq1QqhoaEwMjLC6tWr0bVrV/To0QNLliyBtbU1rl+/joSEBPzvf//DqlWrFI5Z8+bN4evri6NHj8LBwQFeXl4AgOjoaJiZmaFLly7Yu3dvuezrP//8gy+//FL+/tatWwCAQYMGwdDQEED+6JSff/5ZnsfExATr1q1D165dERwcDC8vL5iamuLYsWNIT0/HiBEj5H0uaP369XB3d8fixYsRFRUFR0dHXLhwAffu3UPLli0xbtw4QZmffvoJx44dw5kzZ2Bvb4/WrVsjOTkZ586dQ7Vq1QQBPADw8fHB0KFDsXjxYjRp0gS+vr7Izs7G0aNHIZVKsX79evk0eeXpkx3hQkRERERERERERPSpGDx4MHbv3g0XFxecO3cOERERaNy4MWJjY2FnZyfIb25ujjNnzmDJkiVwdHTElStXsGPHDsTFxcHW1hZz587FqFGjFMrY29vjypUrmDZtGiwsLBAbG4tdu3YhISEBzs7OWLZsGXr37i1oS1tbG1FRUejZsydiY2MREREBS0tLzJs3TzQI4u7ujjNnzsDf3x+JiYnYt28fNDU1cejQIbi7uwMATE1NRY9D165dsW/fPqirq2Pv3r14+PAhunTpgrNnz8LZ2bkUR7ZiaGhoYOfOnQgPD4ebmxsuXryIXbt24eHDh/D398ewYcMU8q9ZswZff/01UlJS8Ntvv2Ht2rUKo2ACAwNx7NgxeHl5IS4uDgcPHkStWrVw6tQp+SLvhY/Z3r178eOPP6JatWr4888/cenSJXTv3h2xsbHlGix49+4dzp07J39lZGQAAP7++295mth6NkFBQTh58iQ6dOiAK1eu4NChQ7Czs0N4eDjmz58v2pbsHA0JCcHz58+xe/duqKmp4eeff0ZkZCS0tbUFZXR0dHD8+HH8/PPP0NPTw549e5CcnIyQkBBcvnwZtra2om0tWrQI69evR4MGDXD06FGcPXsWPj4+OHnyJAICAkp/wIogkYpNtEdFyszMhJGRETIyMuQRvrJoNnpjOfSqdKYGe1Va240iOlVa23UmXK+0tklcZX4PLs3tU2lt/1e1+qVV8ZkqSMx34kOniYgOnRNfKPRD6NiiTqW1XZn490CIv4mI+D2gT0953yf6N8jKykJiYiJsbGwU1m+gj09SUhJsbGzg6emJ6Ojocqlz4MCBWLlyJbZt24Zu3bqVS53/dn5+foiIiEBsbCxatGhR2d2hQkpyTeOUYkRE/1Fpx+YUn4mIiIiIiIiIqJDU1FRkZmbC2tpaIX379u1Ys2YNjI2N4e/vXzmd+0g9evQIGhoaqF69ujwtLy8PixcvRkREBBwcHODq6lqJPaTywIALEREREREREREREaksISEB7u7ucHJykk/n9PfffyM+Ph7q6upYuXIl9PX1K7mXH5dTp06hd+/ecHZ2hpWVFd69e4cbN24gKSkJenp6WLNmDSQSSWV3k8qIARciIiIiIiIiIiIiUpmtrS0GDx6MqKgoHD9+HK9fv4aZmRkCAwMxatQo+TouH4tbt25h1qxZKuX18PBAWFhYufehWbNm6NOnD06dOoX4+HhkZWWhRo0a+PrrrzF27Fg4OjqWe5v04THgQkRERERERERERPQfZG1tjdIs8W1ubo6lS5dWQI8qxpMnT7BhwwaV81dEwMXe3h7r1q0r93rp48KACxERERERERERERH9a3l5eZUqsERUUmqV3QEiIiIiIiIiIiIiIqJPHUe4EH1grX5pVWltx3wXU2ltExEREREREREREf2bMeBCRFSJ7k/5rNLartLy60prm4iIiIiICOBDiURE9O/CKcWIiIiIiIiIiIiIiIjKiCNciOg/79C5+5XWdqNKa5mI6OPUbPTGSmt7arBXpbVNREREREREnz6OcCEiIiIiIiIiIiIiIiojBlyIiIiIiIiIiIiIiIjKiAEXIiIiIiIiIiIiIiKiMmLAhYiIiIiIiIiIiIiIqIw0KrsDRERERERE9N92f8pnldZ2nQnXK61tIiIiIvp34QgXIiIiIiIiIiIioko0adIkSCQShIeHV3ZXKkxISAgkEgmio6MruytUiEQiKfbl7e0tWjYmJgYdO3aEiYkJDAwM4Orqio0bNxbZ3sOHDxEaGopatWpBR0cHDg4OmDhxIrKyspSWefv2LSZMmAAHBwfo6OigVq1a6NevHx49elSmfS9vHOFCREREREREREREH1SrX1pVdhdUEvNdTGV3gT4BSUlJsLGxgaen5ycZUOrbt6/SbQcPHsSLFy/QunVrwbadO3eiW7duyMvLQ5s2bWBmZobIyEj07dsXcXFxmDdvnqDMnTt34O7ujhcvXqBRo0Zo3bo1Ll68iClTpiAyMhKRkZHQ1tZWKJOVlQVvb2/ExsaiZs2a6NKlC5KSkrB+/XocOHAAsbGxsLW1LfuBKAcMuPxfe3ceb0VZ+A/8c2VHFgUEJVBEUURTXFBcUNRQ3NIAV7RcSCvTSlvQci1x+alpYVm5lpaVaO4biooL7uKOigomCoLKmoh4fn/44nw5Xi5yGfRy8f1+vc6Lc+aZZ55nDjNnzrmfeWYAYDmx+c8WfwbIF+mJ//ftOmsbAAAAqDs1jaz64IMPcs011yRJDj744Iqy9957L4cffnjmz5+fESNGZMCAAUmSyZMnZ7vttst5552XPffcM3379q2od+ihh2bq1Kk59thjc+GFFyZJPv744+y33365/vrrc+aZZ+bUU0+tqPOb3/wmY8aMydZbb50777wzLVq0SJKcf/75Of7443P44YcvN0GXS4oBAAAAAAAV/v3vf2fu3Lnp3bt3unXrVlF2ySWXZMaMGdl7773LYUuSdOjQIeecc06S5Lzzzquo8+ijj+bBBx9M+/bty/MkScOGDfPHP/4xjRo1yu9+97t8/PHH5bKPPvoow4cPT5JcdNFF5bAlSY477rhsvPHGue+++/LEE08suxUvQOACAAAAAPAluPHGG7P11lunefPmadu2bQYOHJiXX365xvnnzJmTM888M5tuumlatGiRFi1apHfv3rnyyitrrPPee+/lhBNOSI8ePdKsWbO0bt06O+20U26++eZq877xxhupqqpK3759M2PGjPzoRz9K586d07Rp02ywwQb57W9/m08++WSR7TzzzDPZa6+9ssoqq6Rly5bZfvvtc9ddd+Xee+9NVVVVDj300Br7eNttt2W77bZLixYtsuqqq2bAgAF56aWXqs13xRVXpKqqKqeeemrGjx+f/fbbL+3atUurVq2y22675YUXXkjy6QiJYcOGle/vse666+aiiy6qsf0lVSqV8o9//CP9+vVL27Zt07Rp03Tp0iX77bdf7r777iSf3n9n7bXXTpLcd999Ffc9+ex7cN9992WnnXZKy5Yts+qqq2b33XfP448/XrGeC3v11Vdz6qmnZuutt87qq6+exo0bp1OnTvn2t79d43ZTVVWVLl26ZP78+Tn77LOz3nrrpUmTJuncuXN+8YtfZO7cuUu8/ldddVWS5JBDDqlWdssttyRJBg0aVK1sjz32SNOmTTNy5MiK+7IsqLPXXntVu2xYhw4d0qdPn7z//vt54IEHytMffPDBTJ8+Peuss0423XTTam0taP+mm25a4vX6IglcAAAAAAC+YBdffHH23nvvPPLII+nVq1f69euXJ554IltuuWXGjx9fbf4pU6Zk6623zoknnph33nknO+ywQ7bffvu89NJLOfTQQ3PMMcdUq/Pyyy+nZ8+eOeuss/K///0vu+66a7bYYos88sgj2WuvvRZ5T40kmTt3bnbaaaf89a9/zZZbbpl+/fplwoQJOe6443L44YdXm//hhx/O1ltvnZtvvjlrrbVW9txzz3z44Yfp379/rrvuusW+D//+97+zxx575KOPPspee+2Vjh075vrrr0/v3r0zduzYRdZ5/fXXs+WWW+a5557LN77xjXTp0iW33357+vbtm3feeSeDBg3KOeeckw033DB9+/bNm2++mR/+8If5y1/+sti+LM78+fOz//7756CDDsr999+fTTbZJN/61rfSqVOn3HLLLfn973+fJOnZs2cGDhyY5NPQ4Dvf+U75sd1225WXd91112XnnXfOqFGjstFGG6V///6ZOHFitttuuzzyyCOL7MMll1yS008/PbNnz06vXr3yzW9+M61atcrf/va39OrVK88880yN/T/ooIPym9/8Juuvv3522WWXzJw5M+ecc06OOOKIJVr/iRMnZvTo0WnUqFH233//auUL/q8222yzamWNGzfORhttlA8//LAiGFpcnYWnL7xeS1OnLrmHCwAAAADAF2jChAn5yU9+kkaNGuWmm27KrrvumiSZN29eDjvssPJIgoUddthheeaZZ/KjH/0oZ599dnlEwOTJk7Pnnntm+PDh2WOPPdK/f/8knwYEgwYNyptvvplzzjknxx9/fFZa6dPz7V999dXssssuGTp0aPr375+NNtqooq0xY8Zk4403ziuvvJJ27dolScaPH5/tt98+V155ZfbZZ5/ss88+SZJPPvkkhx56aObMmZMzzjgjJ554Ynk5l156aYYMGbLY9+IPf/hD/vznP+e73/1ukk9HkZxwwgk5++yzc+ihh+app56qVuevf/1rhg4dmmHDhqWqqiqlUimHH354rrjiiuy8885ZaaWV8sorr2S11VZLktx99935xje+kTPOOKPcTm2deeaZ+fe//50ePXrk5ptvLo9iSZLp06fn6aefTpLss88+6dmzZ0aMGJHu3bsv8n4oM2bMyHe/+93Mnz8/V199dQ466KBy2cknn5xf//rXi+zDPvvsk6OOOqqi7SS5/PLLc/jhh+fHP/5x7rnnnmr1JkyYkObNm+eVV17J6quvnuTT0GqzzTbL1VdfndNOOy3rrLPOYtf/6quvTqlUym677Za2bdtWW5/p06cnSTp16rTI+p06dcrjjz+eCRMmZOONN07yaYjzeXUW9H+BpalTlwQufCW9P/Kcz58JAAAAAJaByy67LB9++GG+/e1vl8OWJGnUqFEuvPDCXH/99ZkzZ055+tNPP51bb701vXr1yvnnn18OTpJPR1H8+c9/zmabbZY//vGP5cDlpptuyrPPPpuBAwfmZz/7WUX76667bs4777wMGDAgf/nLX8o3K1/YueeeWw5bkmSdddbJSSedlO9///sZPnx4OXC555578vLLL6dbt24ZOnRoxTKOOOKIXH755XnwwQdrfC+22WabihCkqqoqv/71r3P11Vfn6aefzgMPPFAxMiRJunbtmtNPPz1VVVXlOj/5yU9yxRVX5IUXXsjIkSPLYUuS7Lzzztl0003z1FNP5Y033kiXLl1q7M+ifPTRR+X7j1x22WXVAo/WrVtnhx12WOLl/etf/8p7772XnXfeuSJsST4NXP76178uMjDo3bv3Ipd32GGH5dJLL829996b6dOnp3Xr1tXm+d3vflcOW5Jk7bXXzsEHH5zhw4dn9OjRnxu4LO5yYrNmzSo/b968+SLrr7zyykmSmTNnVqv3RdepSwIXACC3PjKxztrefas166xtAACAL8Po0aOTJAcccEC1srZt22aXXXbJf/7zn/K0O++8M8mnIxwWDlsWWHBPl0cffbRanYVvYL6wPn36JElFnQXatGmTfv36VZt+4IEH5vvf/34eeuihfPLJJ1lppZXKYcrAgQMX2bf9999/sYHLot6DRo0aZdCgQbngggsyevToaoFL375906hRo4ppXbt2Ldft27dvtWV27do1Tz31VN5+++1aBy6PP/54Pvjgg2yyySbZaqutalV3URa8H/vuu2+1soYNG2bgwIE5//zzF1l31qxZuemmm/L000/nvffey7x585Ikb7/9dkqlUsaPH1/tcluNGjXKjjvuWG1Z6623Xrnu4jz55JN54YUXssoqq2Svvfb6/BWkTOACAAAAAPAFmjRpUpJkrbXWWmT5ZwOBN954I0nyy1/+Mr/85S9rXO7CNyRfUGfw4MEZPHhwjXWmTp1abVpN/WrdunVWWWWVfPDBB3n//ffTtm3b8h/rO3fuvMg6a665+JPqPu89WPBeLexrX/tatWktWrRIkqy++upp0KBBjeW1uUn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",
-      "text/plain": [
-       "<Figure size 1500x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(15, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(model_filter(\"deepgbm\", models)))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model_data', ci=None, data=model_filter(\"deepgbm\", models), palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=15)\n",
-    "plt.title('DeepGBM Models with Different Oversampling Methods', fontsize=20, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table= model_filter(\"deepgbm\", models).copy()\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **증강데이터에 따른 Resnet-like 모델 성능 비교**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table= model_filter(\"resnet_like\", models).copy()\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(15, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(model_filter(\"resnet_like\", models)))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model_data', ci=None, data=model_filter(\"resnet_like\", models), palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=15)\n",
-    "plt.title('ResNet-like Models with Different Oversampling Methods', fontsize=20, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **증강데이터에 따른 FT-transformer 모델 성능 비교**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table= model_filter(\"ft_transformer\", models).copy()\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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/fvy42te9up/aPrTesmWLoA4NDQ3xixcvaizr5uYmKDt69GjJ9pMnTwq2BwUFyazr3XffFby2d+7ckcqjyM+x+t6zlGuo+zFZ55ehoWGN9X311VfVBo2AsmDP9evX5WqzVatWNbYpEonEx44dE9TX2IGjut6flr/eFe8tqvqxsrKq93uwpn2NioqSOu8tLCzEJSUlkn6qq6tLtunp6YmPHj0qd59mzZpVq/OxU6dO4ry8PEn5hg4cWVhY1Pger+o1KykpEQ8aNKhW/dHU1BRfuHBBUFdBQYG4Q4cONZaXlafy/WZDXTuIiIiIGhvXOKK3zu3btzF79uxq8yxfvhwaGhqvpkON4MGDBwAAW1tb9OnTB4WFhYKpvMrZ2trCwsIChoaGMDAwQHFxMR48eICYmBip6dByc3Px1VdfYceOHTW2/+TJEwBl61YMGDAAmZmZgil8iouL8f3330utiXDs2DHBVENmZmZwc3ODvr4+8vLycOfOHVy9elXmFF/+/v54+vQp4uLiEB8fL7XN09MT7du3l/zeq1cvAEBJSQlGjBghaFdNTQ3e3t4wMTFBUlISzp8/L7X97Nmz+Pzzz7Fy5coaj0dtXo9yWVlZkjKurq64cuUKEhISpPJUXK+kX79+sLKywokTJyTtldu4cSMmTZoklXbv3j2MGzdOMK2GtbU1evfuDWVlZZw9exa3b9+WbMvNzcXIkSPx559/Qlm5+o+Phw8fAgDU1dXh6ekJMzMzPHz4EJGRkdWWk5eWlhbGjx+PTZs2AQDy8/Px008/Yfr06QgLC0NaWpok76BBg9CqVSu56o2NjcWnn34qSDc3N4enpydKS0tx4sQJPHr0SGr7rFmz4OLigk6dOknSYmJisG3bNkFdJiYmeOedd1BcXIxjx44J6qpKQkICPvnkE0G6vb09HBwcUFRUhKioKMmxB8peh/HjxyM8PFyuNir65ZdfBGmdO3dG9+7doa6ujuzsbNy4cQN//vlnvaZQkTVNTWxsLDw8PACUTQsj6/0SGxuLOXPmSP1eUZcuXaCnpyd3P9q3b49Zs2YBKJteqyIdHR34+/tLpRkYGFRb33//+18AgJGREQYNGoTCwkIcOHAAL1++lMq3adMmeHl5yd3P+pJ1vF++fImrV6+iZ8+ectdTfqxOnjyJv//+W2rbiBEj0LJlS8nv7du3r9P1ubS0FCNGjEBGRoZUfn19ffTv3x9NmzbFtWvXpK7PYrEYU6dORe/evdGmTZtq96HiVJN9+vRBhw4dkJWVhZiYGEl6Y7/vHj9+DACws7ODq6sr7ty5IyiXm5uLn3/+GXPnzgUAeHl5QV9fHw8ePBCstdiuXTvB+eTo6FhjPyq6cuWKIM3e3h7q6uo1lu3Ro4dgmr+K718PDw+0bNlS6nPq119/Faz7mJ+fj99//10qrW/fvlLTXyn6c6y+9yyVNcb9WGZmJgDA1dUVbdq0walTp6SmnASAxYsXAyi77xkyZAhUVVVx8OBBPH/+XJKnqKgI3333neDaKMu9e/cAAF27dkXPnj2Rnp6OY8eOSV37xGIxPvzwQyQnJ9dpGrW6quv9KQB8+umnUvcW5Xr27ImuXbtK3rspKSmNuxMALCwsMHDgQMk0o3fv3sWxY8fg4+OD4OBgqbXkxo8fDy0tLbnqDQ0NlfkaOzo6olOnTnj27BnCw8Px9OlTybarV69i9uzZ+PHHHwGUXb9nzZqF3NxcwZqtZmZmGDlypFRaTdfpu3fvAqjba7Zy5UrBdQQAunXrBkdHR2RkZODYsWNS5/rz588xYsQI3LhxQ+qat2rVKpnTVXfq1Am9e/eWnOc1TWkNNPy1g4iIiKjRKDRsRdTIavutt/KfytMuyPI6jzgCIP7iiy8EU5RVnP7pxIkT4gcPHlTZj8ePH4tbt24tVWfTpk0FdVZ1HDw8PKS+wb5x40ZBns6dO0vV9dVXX0ltb9Wqlcwp4UpKSsQXLlwQL1y4UPznn38Ktsv7jVWxWCz+5ZdfBHl1dXUFU1XI+sa5hoaG+MmTJ1L56vJ6VPWae3t7iwsLCyX7XNVUTcuXL5fU+fDhQ7Gampogz9OnT6Xa/uijjwR5AgICJN9YFYvLRu74+fkJ8lX+Nn9V50Dr1q0F3w6vPAWZPGQd09OnT4uvXbsm+JanWCwW9+vXTyr92LFjVU4lVpmsaf+8vb2lvmn/7NkzsbOzsyDfu+++K1XXiBEjBHkcHR2ljkFmZmaV32Ct7J133hHk2bhxo1Se58+fiwcMGCDIV3lqI3lGHFU+FiNHjpT5+jx//lx84sQJwXSBtVH5W9xeXl6SbVWdXy1atJDkSUtLE2z/5JNPBO1UzlNx1FBd8pWr6j1sb28vzszMlOQ7cuSIII++vn7tDlYFdRlx9PTpU5l9rTytX00jjqrrQ+Vp3iqqzfX5119/FeR1d3cX5+bmSuXbvn27IJ+fn5+gPln7ra6uLj5+/LhUvuLiYsnnV2O/7wCIx40bJy4uLpbk++STTwR5hg4dKtifqqZ1qi9ZI1mHDx8uV9n169cLyhoaGkrl+fTTTwV5rl69KpVn7969gjw7duyQyqPoz7GGumdpqPuxqs6vL7/8UpLnyZMnYi0tLUGeJk2aSI1oCA0NFeSpfM9WXZvz58+XypeYmCiz3V9++UUqX2OPOALqdn9aeRRP+U/lz819+/bJbLOhRxylpKSIf//9d6m0gQMHiktLS8Vt2rSRSv/rr7/kvlZUnlZZWVlZfPDgQak8T58+FXfq1Elw/iQnJ0vlk/e+q6KGfM3y8vJkjuaWNVW1rJFoFUeSFxUViVu0aCHIM23aNKn3YVXneeX9bqhrBxEREVFjY+CI/tXe1sCRu7u73O1lZmaKDx8+LP7mm2/EixcvFs+ZM0c8a9Ys8axZs2TO4135AUpVx6HyQ6CCggLB/OK6urpSeTZs2CC1vVmzZuJ79+7JvS/lavNgUtbD/aqmzenatasgb+WHHnV5Pap6zStPu7FkyRJBHlNTU3FRUZFUPnd3d0G+S5cuSbaXlpYKpkcyMTGRethW7uHDh4K6Bg8eLJWnqnOgqnUYaquqwJFYLBb37dtXKr3yOjdWVlbikpISuR5gZGdnC85RkUgkTk1NFfTpypUrgvq0tLSkAn2y1kg6d+6coK7KD39kXUtycnIE6105OjrKPF6xsbGCumbMmCGVR57AUeWHxx4eHlWu7VVflddr0NXVlZyPy5Ytk6T37t1bagrHW7duicVi2Q/rKgdCxOJXHziS1QdZ019VDuzKqy6Bo6KiIpl9leda9qoDR5WnKgMgc7ossVgsWLtJX19fcE2Ttd9VXe/F4lfzvlNXV5eahk4sLpuer3K+Tp06CdpsrMBR//79BfV+8MEHcpXdtm2boKyysrJUHllrKC1atEgqz7BhwwTX14pB99fhc6yh7lnK1fd+TNb5ZWJiIrhHqPy5CUA8atQoqTxFRUWCdYAq37NV12b5Z2FF8+fPr/G8ehWBo7rcn/72228y91PeKYsbI3BUUlIitrS0lKQpKSmJv/vuO6l85fc58lwr/vrrrxrPi3KyvnT1zTffSOVpyMBRXV4zWV/UaNOmjcxrxLp16wR5PT09JdsTEhIE2/X19QVTvIrFss/zyvvd0NcOIiIiosbCqeqIGtkXX3whNaVDZY6Ojhg7dmyDtjlhwoQa89y5cwfz589HWFgYSkpK5K47IyNDaqoYWTp27IiOHTtKpampqaFp06ZSUw5VnCYIKJturaLHjx/DysoK7dq1g62tLWxtbdGuXTs4OTmhdevWcve5OhcuXBCkDRo0SGbegQMHCqbMunDhQo2vnzyvR2Wmpqbo0KGDVFqLFi0E+dzc3ATT7cjKV/FYp6amSqZHKvfo0SM0adJErr5VnrZPFisrK/Tt21eu+urjo48+wpkzZyS/T58+XWr7lClToKSkJFddly5dErwXOnToAAsLC0Hezp07S6YtKpefn4/r16+jc+fOSE5OFpzfurq6cHJyEtTVv39/KCsro7i4uMq+JSYmCrYnJCRAJBLJtW/yvGaVeXh44MCBA5LfT506BWNjY9jb20vej/b29nB2doaRkVGt66/Izc0Ne/fulfyem5uLP//8E507d8bZs2cl6UOHDsXTp0/xzz//ACibnq5NmzaCaeqaNGmCPn361KtP9aWnpyfzWmJiYiJ13gBl78+mTZu+kn5VPi/L1WZav1el8pR2AKSmtKtOdnY2/v77b8F1tLLqrs+v4n3n6ekJY2NjqTQTExNBvqpet8agq6srSKs4lVN18vPzBWmVzy1bW1v06tULcXFxkrRff/0VX331FYCy9/8ff/whVWbEiBHQ1taW/P46fI411D1LY96Pubu7y3WP0L9/f6nflZWVYWhoKDWVqrznoIeHB1RUVATp3t7eWL16tVSarGkRG1Nd709l9XPQoEEyp9kbOXIkoqOjG6bD1VBSUsKUKVMk0+uWlpZixowZUnk++ugjueuTdb3dt28f9u3bJ1f5utxnyKOur5ms+3tvb2+Z94SDBg0STGNesbys179Pnz4ypzWXdZ5Xpoi/d4iIiIjqgoEjeuu4uroK5t9vTDt27JDMzy3LhAkTGjxwVHGdFVnu3LmDXr16SeZ6rw15Hh7JetAOQLA+grjC/ORA2UP6WbNmSc2vXlJSgmvXruHatWtSedu3b48FCxZg/Pjx8nZdJlnHwNzcXGZeWemVH1zJUtPrIW9bstaXkJVP1oOMimtA1OV1rygzMxPFxcXVrg9R+Y/8xjJixAg0b94c6enpAMrWYSinpqaGiRMnyl1Xbc6F8m2VAwDl50P5uhIVmZmZyaxHVVUVxsbG1a51VN/XrHy9ndrw9/fHTz/9JPUw6fnz50hISJBab0skEsHd3R1fffWVZG2a2qpqnaOOHTtKPYxydnbGrVu3JIGjs2fPYsKECVLBJaD26xs1BnNzc5kBBlnv48prtDSm5ORkmemVgxevg4Y476sLHOnq6lb5edVQ7ddEVvuKPkdkBYIrr51XlcrXxKrqmzBhglTgKDk5GRcvXoSDgwMOHTokWAvM19dX6vfX4XOsIe5ZGvt+rCHvJSrfs9WmTQBS656Vq7x+WWOr6/2prM/02twrNpaJEydi6dKlknUGK94DNW/eHMOHD5e7LkXcZ8ijrq9Zfe/vs7OzUVhYCFVVVZmvv6zzubr0ihTx9w4RERFRXcj3NWwieqPo6+tXu33BggV1/gNRngcHhoaGMtPl+SbwunXr8Ouvv6J3797VjhS5fv06JkyYUOO3+l4HNb0essh6sCPvQ+jGJhaLZX6rvKK67HNdqKioVBkcGjlyZL1Hwvxb5OXl1bqMuro6oqKisGLFCrRt27bKfGKxGJGRkXB1dRWM/JGXjY2NYJRFbGwsrl27JlkYWk1NDQ4ODnB2dpbkOXv2LPLy8pCUlCRVVlYg6lWrz3WwMcn64oS6unqdAtyvu5rO+8a+TsnzvpN1nij6HOnatasg7dq1aygoKKixrKxv+cuqb/To0VBTU5NK+/XXX6X+LWdhYdHg7+mG+hyr7z1LY9+Pvc73EorQkNdlWaNNANnHt7EYGxtj5MiRMrdNmjRJ5sivxlKX+wx5vK6fpfX1b/x7h4iIiP59OOKI6F+ouj9ai4uL8fvvvwvSAwICMHnyZJibm0se5owZMwYhISGN1s+qjBo1CqNGjUJ2djaSkpJw48YN3L59GxcuXEB0dLTUw5IvvvgCs2bNkjnKRh7Gxsa4d++eVNr9+/fRrFkzQd779+8L0mTlq+xVPkSQh6w+t2zZEiNGjJC7jpqO96vc5w8//BArVqwQfCO/NlO0ALJHXMh6zavbVn5sZT3okPVNfAAoLCys8cGhrNesffv28PT0rLZcuaoecNVEXV0dCxYswIIFC/DgwQP8+eefuHXrFm7cuIHo6Gj89ddfkryFhYX44osvcOLEiTq15erqKnW9OXv2LFxcXCS/Ozg4QE1NTSrtxo0b+P333wXTib0OgaPXUWFhIb777jtBuouLy2v54LhZs2ZSI3aVlJQwffp0ua8v1tbW1W6vqR5Fve8Uzd3dXZD2/PlzHD58GKNGjaqyXFpamtTUodXVp6+vj6FDh0pNg7Vv3z4sXLgQ4eHhUnnHjx8veK1ep8+xut6zvAn3Y3VR1eemrFFrb8qXO2T1s6rP9OruGxrDRx99hD179kilKSkp4cMPP6xVPbLeU7169ULPnj3lKl/VqGpFqc09nax0fX19yTVC1j1dVaMw5R2dCbzav3eIiIiI6oKBI6JGlpqaquguSMnIyMCLFy+k0uzt7bFy5UqptNLSUly8ePFVdk1AX18frq6ucHV1laR99tlnknUQgLJvOF6/fh1dunSpUxs9evQQBI6OHTuG7t27C/IeO3ZMZvk3jYWFBYyNjaWCFTk5OVi+fLlcDzpLSkpeq296WlhYYODAgTh69KgkrVOnTlIjU+TRrVs3NGnSRGqNib/++gt3794VTJVy9epVwUMjLS0tyforrVu3ho6OjtSc+7m5uYiLixNM53bq1Klq1zcCyr6xX7lvxcXF+Pbbb+V6uFmbdTOq0rJlS7Rs2RLvvPMOgLJvu/ft21dqmjhZayTIy83NTerB6L1796R+L38927RpI5meUCwWC76F2xDrGykpKUkFIhvi+L0O5s6dK3OqulmzZimgNzXr0aOHVOCotLQUo0aNkuu93RDXqdfhfVcdWfvXEG3a2trCxcVFMAXkkiVL4OPjA01NTZnlFixYIGhfV1e3ymCTr6+vVODo3r17mD9/vtR0WyKRSOY6VK/j51ht71nepPux2jh16hSKiooEo11kfamgrvdur5qsfkZERMjMGxoa2si9kebi4oKOHTvizz//lKQNGjQIrVq1qlU9su5ntbW1sW7duhrLisViwQi4xro+yUvW/hw/fhylpaWCET413d/Lev1jYmLw4sULwfWmLl+eeRV/7xARERHVBaeqI3rLyPqmWlpamtQ888XFxZg7dy5u3779KruG+Ph4zJw5E/Hx8VWu51C+lk1FlddCqA1Z879/8803uHr1qlTa+vXrcfnyZak0DQ0NeHl51bltRRGJRPjPf/4jlfbs2TOMGjUKT58+lVkmIyMDu3btgqurK3bv3v0qulkr06ZNg5qamuTn448/rnUdenp6ggWLxWIxpk6dKjVFU35+PqZNmyYoP3DgQMmDMiUlJZnnxpw5c6SmR8rKysKCBQvk6puHh4dU2s2bNzFlypQqp1t68OABtmzZgu7duyMmJqbGNir78ccfsXz5cty6dUvm9pcvX0qmkauYVleyRglVHL1QMVhQ8f+JiYlSZRpifSNtbW2p39PT06t8b7wJ7ty5g5EjR2Lz5s2CbX379oWPj48CelUzWdfn8ePHS9a4quzFixf4/fffMWbMmDpdAypTxPuuNiqfpwDw999/N0jdS5cuFaTduHEDgwcPFqxlUlhYiLlz58r8bJg1a1aVU755eXkJpqgMDg6W+t3FxUXm4vCvw+dYfe9ZXuf7sfp49OgRPvvsM6m0K1euYMuWLYK85V9EeN25u7sLRmX+9ddfUmvUAMCBAwdw+vTpV9k1AA1zD9ShQwfY2tpKpUVERCAoKEiyhlJlt27dwurVq2FnZyf4Epas69Pt27elAsONyd3dHU2bNhW0/+WXX0ql3bp1C8uWLROUHzZsmOT/Xbt2RfPmzaW2Z2dnY+HChVIBs6rO88oU8fcOERERUV1wxBHRW8bAwACWlpZSI6GePn2KDh06wNvbG0DZ2iJVLaDemJ49e4aNGzdi48aN0NfXR8eOHWFlZQUdHR28ePECly5dwpUrV6TKKCkpyXyoJK/33nsPX331Fa5fvy5Jy8nJgaOjI9555x2YmJggKSkJ586dE5SdMWPGGzPNSmWLFi3Czz//LPVt56NHj6Jly5ZwdXWFubk5RCIRMjMzcf36ddy4cUPyx62fn5+iul0lb29vudbfqMnSpUsREREh9SDg+PHjsLGxgZeXF0pLS3HixAmkpaVJlVNWVhY8KJsxY4bgm8dxcXGwsbGBt7c3SkpKcOzYMbnXtwgKCkJERITUQ4Yff/wR+/btQ9++fWFqaorS0lKkp6fjr7/+qvd7+O7du/jqq6+waNEitGzZEh06dICZmRk0NTWRlZWF6OhowZQsNjY2dW7P1tYWLVq0qHKB7d69e0v+7+LiggMHDsjM1xDT1LVt21YqIFVUVISePXvCzc0NWlpaAIChQ4fKnIJL0RISEjB79myUlpYiOzsb165dQ1JSksyHUy1btpQa8fG6ee+99/D1119LLRZ+584dtG/fHr1790abNm2gpaWFrKws3Lp1S2odHlmjVOriVb/vaqN169YQiURS16vExEQ4Ozujc+fOksDEJ598UuWi8FXp378/5s2bhzVr1kilR0ZGwsrKCh4eHmjVqhWePn2KyMhImdcxZ2dnfP7551W20aRJE3zwwQfVrt1R3euo6M+x+t6zvM73Y/W1atUqhIeHo2fPnnj8+DF+//13wUNvMzOzKtfned0YGBhg/Pjx+OGHH6TSZ8+ejf3796Njx464c+cOTp48qZD+TZkyBVOmTKl3PV988QXee+89qbTAwEBs3boVvXv3RosWLVBYWIhHjx7h6tWr1U7L1rRpUxgaGiIzM1OS9t///hc9evRAr169JIE4Pz8/dO7cud59r0xLSwvz58/HokWLBPtz+PBhODo6IjMzE7///jueP38uladVq1bw9/eX/K6srIyPP/5YEFDfsGEDoqOj4eTkVOV5Losi/t4hIiIiqgsGjojeQjNmzMAnn3wilfb48WP89NNPkt91dXXRoUMHnD9//lV3D0DZN/liYmJq/Mb2u+++W6/gTZMmTRAaGgonJydkZ2dL0l++fIlDhw5VWc7FxUXwrcU3iYWFBX7++WeMGjVK6oHoixcvcPz4cQX2TLGcnZ2xfPlyLFy4UCr9/v372L59e5Xl1q9fj06dOkmlubq6ws/PT/AN+rS0NOzYsUPyu56eHkpLS6WmtZOlV69eWLNmDebOnSuVnpOTgyNHjlRbtr4ePHgg17z9tV1XqjJXV1f8+uuvgnQ7Ozup93nFdY4qa4jA0cCBAwUjmW7fvi31rf+WLVu+loGjv//+W65RJ926dcP+/fsF36J+nSgpKeHAgQNwcnKSevgoFosRGxuL2NjYRu+DIt93NdHV1YWLi4vgc/LcuXNSX3b44IMPah04AoAVK1bg2bNn+P7776XSCwoKZK7NU5GTkxMOHDggmK6ssuoWfdfU1Kx2TaXX6XOsrvcsb8L9WG21b98e169fx+XLlwUjtcuJRCJ8//33b9R6LcuXL8fRo0cFXxw5e/as1LSO5fv/Jho1ahRiY2OxYcMGqfT09HQcPHiw1vUNHDgQP//8s1RaUlISkpKSJL+7uLg0SuAIKFsvLDY2VnC9unTpEi5duiSzjKamJkJDQwUjzAICAhASEiL4fK28P+bm5rVa5+pV/b1DREREVBecqo7oLTR79mzBNwor0tXVxW+//Vav0QN1Ie9C1OU8PDxkLvReW3Z2dkhISEC3bt1qzCsSieDn54cTJ068UQ88ZBkxYgTCw8NrNQ++tbU12rZt24i9UrwFCxZgx44d0NHRqTGvvr4+9u7dW+W0MN999x2GDh1aZXlDQ0McO3YMBgYGcvVtzpw5CAkJkblQc1U6duwIU1NTufOXq837USQSYe7cufX+xnNVQZ/Ka9p07dpV5jorDbG+EVB2jbS0tKx3Pa8jIyMjfP7554iNjYWVlZWiu1Ojtm3b4uLFi9UGCytr2rSp3Au6y+NVvu9qa+XKlYIHnA2lSZMm+O6777Bz5065A09aWlpYuHAhoqKi0KxZsxrzd+jQAQ4ODjK3DR8+vMbrsCI/xxrinuV1vR+rj3fffRcrV64UrCNTTllZGVu3bsWgQYNecc/qx8DAAOHh4TAzM6syz/DhwwWj9ABATU2tMbvWoNavX49169ZJRtfKo1evXtDV1RWkL126VDBd3KvUpEkTHDhwALNmzZJrTTM7OzucO3dO5jVJXV0dJ06cqPa92LlzZ+zfv7/GdhT19w4RERFRbXHEEdFbSElJCXv37sXAgQOxbds2XLlyBUVFRTAzM4O3tzfmzp0La2tr7Nmz55X2y8PDA9euXUNERAQSEhLw999/4/79+8jJyYFYLIa2tjZatWqF7t27Y9SoUZKpXBpC+cPJ33//Hfv370dcXBwePXqE58+fQ19fH5aWlujbty/8/f3RoUOHBmtX0fr164fbt28jNDQUR48exYULF5Ceno68vDxoamqiWbNmsLOzQ8+ePeHl5dWgD2NfZ35+fvjPf/6DHTt2IDw8HH/++adkxIORkRE6deqEAQMGwM/Pr9oHm6qqqjh06BB++eUX/PDDD0hKSkJhYSHMzc3h4+ODgICAWo/4eO+99zB48GDs2bMHJ06cQGJiIjIyMvD8+XNoaWnB1NQUdnZ2cHZ2xoABA9CxY8c6HYOlS5fCy8sLUVFRuHjxIm7evIlHjx4hLy8PTZo0gZ6eHlq3bg1nZ2dMmDChzu1UJG/gSFlZGT179hSsJdEQ6xsBZQ8IExISsHr1avzxxx9ISUmpck2b15GSkhJUVVWhpaUFQ0NDtGzZEu3atUOfPn0wdOjQRgs0NBZLS0vExMTgzJkz+PXXX3H+/Hncu3cPOTk5UFFRgYGBAdq2bYtu3bqhf//+6NevX4M/qH1V77vacnJykpyrMTExePToUYOvgzFhwgSMGTMGBw8exMmTJxEfH4/09HRkZ2dDW1sbhoaG6NSpEzw8PPDee+/V+lvxEyZMwMWLFwXpvr6+cpVX1OdYQ9yzvK73Y/UVEBCAvn37Yt26dTh79iyePHkCQ0NDuLu7IyAgoNFGmDS29u3b46+//sKqVatw4MABpKamQktLCx07dsSkSZPw/vvvS40WK2dsbKyA3tbdrFmzMH78eOzatQunTp3C1atXkZmZiZcvX0JbWxstW7ZE+/bt0adPH7zzzjtVTqHWunVrXLp0CatWrUJERAQePHggNbXkq6Cqqop169ZhxowZ2L59O6KionD79m1kZ2dDXV0dzZo1g6OjI4YNG4YRI0ZUG2AyNzfHlStX8O233yIkJATJyclQVlaGjY0NxowZg+nTpwtGpMmiyL93iIiIiGpDJK44MToRERERERFRDXbu3ClYL2rp0qUIDAxUTIdeA8OHDxdM67Zv3z68++67CuoREREREVHdcKo6IiIiIiIiomp88cUXOH78OIqLiwXbSkpKsHLlSkHQSENDA/37939VXSQiIiIiajCcqo6IiIiIiIioGpGRkZJ1e3r06AELCwuoqqoiPT0dZ8+exX//+19BmdmzZyt0nR8iIiIiorpi4IiIiIiIiIhIDllZWTh58mSN+Tw8PN7qafuIiIiI6M3GqeqIiIiIiIiIGkCTJk0wZ84cHD16FKqqqoruDhERERFRnXDEEREREREREVE1fvnlFxw4cABRUVG4desWHj9+jKdPn0JNTQ0GBgawt7eHi4sLxo8fDzMzM0V3l4iIiIioXkRisVis6E4QERERERERERERERGR4nHEUR2UlpYiLS0NOjo6EIlEiu4OEREREREREREpiFgsxrNnz2BqagolJa4KQUREbz4GjuogLS0N5ubmiu4GERERERERERG9Ju7fv4+WLVsquhtERET1xsBRHejo6AAouyHQ1dVVcG+IiIiIiIiIiEhRcnNzYW5uLnleRERE9KZj4KgOyqen09XVZeCIiIiIiIiIiIi4nAEREf1rcOJVIiIiIiIiIiIiIiIiAsDAEREREREREREREREREf0PA0dEREREREREREREREQEgIEjIiIiIiIiIiIiIiIi+h8GjoiIiIiIiIiIiIiIiAgAA0dERERERERERERERET0PwwcEREREREREREREREREQBAWdEdICIiIiIiIiIion+voqIilJSUKLobRERvrSZNmkBFRUXu/AwcERERERERERERUYPLzc1FRkYGXr58qeiuEBG99dTU1GBkZARdXd0a8zJwRERERERERERERA0qNzcXDx8+hLa2NoyMjKCiogKRSKTobhERvXXEYjGKioqQk5ODhw8fAkCNwSMGjoiIiIiIiIiIiKhBZWRkQFtbGy1btmTAiIhIwTQ0NKCjo4MHDx4gIyOjxsCR0ivqFxEREREREREREb0FioqK8PLlS+jp6TFoRET0mhCJRNDT08PLly9RVFRUbV4GjoiIiIiIiIiIiKjBlJSUAECtFmInIqLGV35dLr9OV4WBIyIiIiIiIiIiImpwHG1ERPR6kfe6zMARERERERERERERERERAWDgiIiIiIiIiIiIiIiIiP6HgSMiIiIiIiIiIiKiV2DDhg3o0KED1NTUIBKJ4ObmpuguvRH27t2L7t27Q1NTEyKRCJaWloruEtG/mrKiO0BERERERERERERvl+7zf1J0F+SSuHp8g9V14MABzJo1C02bNsWQIUOgpaUFOzs7iEQiuLq6Iioqql71BwYGIigoCMHBwfD19W2QPr8OLly4gA8++ADq6urw8vKCvr4+jIyMFN0ton81Bo6IiIiIiIiIiIiIGtmhQ4cAAPv370e/fv0AAKmpqfj0008V2KvX35EjR1BaWoqNGzfC399f0d0heitwqjoiIiIiIiIiIiKiRvbgwQMAgLW1tYJ78mbhcSN69Rg4IiIiIiIiIiIiImokgYGBEIlEOH36NADAysoKIpEIIpEIVlZWAIDo6GhJmkgkqvVUc5aWlggKCgIA+Pn5SdVVPgXezp07IRKJEBgYiJs3b2L06NFo3rw5lJSUJKOhbt++jcDAQDg5OaFFixZQVVVFy5YtMX78eNy8eVNm2+VrDpWUlGDlypWwsbGBmpoazM3NsWDBArx8+VJQ5smTJ1i4cCHat28PbW1t6OnpwcbGBuPHj0dCQoJUf4ODgwEA7u7ukn3auXOnpK7nz5/jyy+/hL29PTQ0NKCnp4e+ffsiJCSkymMlEokgFouxceNGdO7cGZqamujSpQsAwNfXV3LcIiIi0LdvX+jo6KBZs2aYPHkycnJyAACPHz/GlClTYGZmBnV1dTg6OlY73WB8fDzeffddmJiYSI7rpEmTcO/ePUHe8nNm586dSEhIgI+PDwwNDSESiXDlypUq25Cl4ut+48YNjBgxAoaGhtDS0oKzszOOHTsmKBMVFVXteVjxGFVUfi4UFhbiiy++gJ2dHdTU1DBs2DBJnufPn2P58uXo2rUrtLW1oa2tjV69emHXrl212i9qXJyqjoiIiIiIiIiIiKiRdOnSBRMmTMDx48eRnp6OESNGQFtbG4cOHUL//v0RGhqK5s2bw9vbW1LGxcWlVm2MHDkSERERSEpKgrOzM9q0aSPZ1qJFC6m8N27cQI8ePWBoaAh3d3dkZWVBRUUFALBt2zasWrUK9vb26NGjB9TU1HD9+nX8/PPPCAsLQ0xMDDp16iSzD2PHjsWxY8fg5uYGW1tbxMTEYNWqVXj48CF2794tyffs2TP07NkTKSkpMDc3h6enJ5SVlXHv3j2EhITA2toajo6OaNOmDSZMmICzZ88iOTkZAwYMkOxL+f49e/YM7u7uSExMhLGxMXx8fJCfn4/IyEjExMTg/PnzWL9+vcz+Tp06FcHBwXB1dUW7du1QWFgotf3gwYPYvHkznJyc4O3tjbi4OGzbtg23bt3C/v374eTkhJKSEvTp0wepqamIj4+Ht7c3Lly4gI4dO0rVtWXLFsyYMQMA0KNHD/Tp0wc3btzA9u3bcfjwYURHR6Ndu3aCPp45cwYffvghbGxs4OXlhbS0NCgp1W0sSHJyMnr27AkDAwNJXTExMfDx8cH27dvh5+dXp3orKy0txbBhw3DmzBm4urqiU6dOMDQ0BFAWbPP09MTVq1fRokULuLq6QiwW49y5c/D19cXFixexcePGBukH1Q8DR0RERERERERERESNZNiwYRg2bBjc3NyQnp6ONWvWwNLSEkDZGkehoaGws7OTGkVTW2vWrEFgYCCSkpIwadKkakcshYSEYPr06Vi3bh2aNGki6OuUKVMkI6HKBQcHw9/fH7Nnz0ZkZKSgzrt370JTUxO3bt2SBHdSUlLQrVs3/PLLLwgKCkLr1q0BlK3xlJKSgiFDhuDgwYNSgZAnT54gPT0dQFnwzMXFBb6+vkhOTsbChQvh5uYm1e6iRYuQmJgId3d3hIWFQUdHBwDwzz//wNXVFRs2bICnpyd8fHwEfT5w4AAuX76MDh06yDxOmzZtwuHDhzFo0CAAZUGq3r17Izo6Gq6urujduzd27NghCbp9/vnnWLZsGdasWSM1eiYuLg4zZ86EiYkJwsLC0L17d8m27du3Y9KkSfDz80NcXJygD8HBwVi5ciUCAgJk9rE2du/ejfHjx2P79u1QVi4LCxw9ehTDhg3D9OnT4eXlBTMzs3q3c//+faipqeHGjRuC+vz8/HD16lXMmjULK1euhJqaGgAgPT0dPj4+2LRpEwYNGiQVRCXF4FR1RERERERERERERG8JY2NjrFy5UhA0AoBevXoJgkZA2QN/Z2dnREVFSaZqq2zDhg1So5usrKzwwQcfAABiYmIk6U+ePAEA9OvXTzB6xtjYGPb29nLtR35+PrZv3w4lJSVs2bJFEjQCADs7O3z22WcAUOWIowULFlQZNALKRlCVB40AQEdHB5MnTwZQtu7Shg0bJEEjAJg3bx5EIhGio6Ol6lmxYgVKSkrw3XffSQWNAGDixIkYMmQI4uPjcfnyZUEfOnbsiPnz51fZx9rQ1tbGunXrJEEjAPDx8cHIkSPx/PlzyZSADWH58uWCoNGVK1dw7Ngx9OjRA2vXrpUEjQCgefPm+OGHHwAAW7dubbB+UN0xcERERERERERERET0lujfvz80NTWr3J6Xl4e9e/diwYIFmDx5Mnx9feHr64tHjx5BLBYjOTlZUEZFRQXu7u6CdBsbGwDAo0ePJGnlwZPVq1cjJCQEz549q9N+JCYm4sWLF+jWrRvs7OwE28eNGwcAiI2NRWlpqWD7kCFDqq3fy8tLkGZtbQ0AcHBwQNOmTaW26enpwcDAQGpfS0tLcerUKWhqamLAgAEy2+nTpw8ASNZ2qsjHxwcikajafsrLy8tL0GcAGDNmDADp4F59iEQiDB48WJB+8uRJAGWj2mRNt1e+5pGs40Cv3hs7VV1ISAhWrVqFv//+GxoaGujXrx9WrlwpGfJYlZSUFAQFBeHEiRPIzMxE06ZN4eDggD179kBPT+8V9Z6IiIiIiIiIiIjo1WvVqlWV2yIjIzF69GjJqCBZZAV6WrRoIXMEU/kooJcvX0rSPDw8MGfOHKxbtw5jxoyBsrIyunXrBk9PT/j7+0uCMzVJS0sDAMm0f5Xp6+tDT08POTk5yMrKkqyzU6664wBA5rRt2traVW4r356ZmSn5PSMjA3l5eQAAVVXVatvLyMgQpNXUx9qwsLCQmV5+/MqPZ301a9ZMajRRudTUVADA4sWLsXjx4irLFxQUNEg/qH7eyMBR+dyPQNmQx8zMTISGhiImJgZJSUmCBd/K3bx5E71790ZmZiY0NTUli56Fh4fj2bNnDBwRERERERERERHRv5q6urrM9Ly8PIwaNQpPnz7FkiVLMHr0aFhYWEBDQwMikQhjx47F3r17IRaLBWVljSCpztq1azFlyhSEhYUhIiICsbGxSEhIwKpVq7B3716MGDGiTvtWWXWjdao6DuWq2yd597d8pJO2tnaN+yRr2rya+qgoskZwlauqz+VlXFxcahz8QYr3xgWOCgsLsXDhQgDAiBEjsH//fqSlpcHOzg6PHz/G119/jQ0bNsgsO3PmTGRmZsLd3R0HDhyAvr4+AODFixdS81G+TY7F31NY2wN7NlzEnIiIiIiIiIiIiOouJiYGmZmZGDlyJIKCggTb79y506Dt2draIiAgAAEBASgoKMCmTZswf/58fPTRR3IFjkxNTQEAd+/elbk9JycH2dnZ0NDQkDlF26tgZGQEdXV1KCkpITg4uMGmnauLqo5TeXr58QT+f3RU+Wipyu7fv1/r9lu2bAmgbKq6Tz75pNbl6dV649Y4unDhgmTYXvkFxNTUFL169QIAHD9+XGa5rKwsyTyK5dPT6ejooFevXjh79qzUomCVvXz5Erm5uVI/RERERERERERERPVR/oC+uLhY4XVlZWUB+P8H/BXdvn0bly5dqnvnaqCuro558+bBxMQET548wePHj2ss0717d2hoaCAxMRG3bt0SbN+9ezcAwNnZudYjohqKsrIy3NzckJubi1OnTimkD+VOnjyJ7OxsQXpISAiAspFA5UxMTACUzeBV2dOnT+t0Lnh6egIADh48WOuy9Oq9cSOOKkYzmzVrJvl/8+bNAQD37skeQXPr1i3JMMoDBw7AysoK6urqiI+PxzvvvIPY2Fj07NlTZtnly5fLjLI3lO7zf2q0umvy5Ug3hbVNRERERERERET0NjMyMoKKigqSk5NRUlIic50geZWPGLlx40adytvY2AAoe3a6aNEiGBsbAwCys7MxceJEFBUV1blvFR06dAgtWrSQDAQol5iYiPT0dGhra0tmiqqOlpYW/P39sXnzZkybNg0HDx6ElpYWgLKAx7JlywCUzUKlSIsXL8bJkyfh5+eHn3/+GW5ublLb8/LysG/fPowZMwYaGhqN1o+8vDzMnTsXP/zwg2QQxR9//IF9+/ZBQ0MDfn5+krxWVlZo1aoV/vzzT4SFhWHo0KEAgPz8fHz44Yd1GljRs2dPeHp6Ijw8HNOmTcPy5cuhq6srlScpKQmPHj2Ct7d3PfaUGsIbFziqiqy5NSuqGGnv378/Tp48idzcXFhbW+Pp06fYunVrlYGjTz/9FHPnzpX8npubC3Nz84bpOBEREREREREREb2VVFVV4e3tjSNHjqBz587o1q0bVFVV4ezsLPUgXx5eXl5QV1fHt99+i2vXrsHU1BQikQjz58+Hra1tjeUdHBwkD/ZtbGwkAY6oqCgYGRlh6NChCAsLq8tuSomKisL69ethZmaGrl27QldXF2lpaYiJiUFpaSmCgoIko6dqsnz5csTFxSE8PBzW1tZwdXVFfn4+IiMjUVBQgJkzZ2Lw4MH17nN9uLi4YPPmzZg+fTrc3d1hb28PGxsbqKioIDU1FVeuXMHLly8xfPjwRg0cvf/++zhw4ACioqLQs2dPPHr0CGfOnIFYLMaGDRsEI82WLl2KiRMnYsSIEejbty+0tbWRkJAAXV3dOp8Lu3fvhre3N7Zs2YI9e/agS5cuMDU1RU5ODq5evYr79+9j1qxZDBy9Bt64qeoqBmwqDlks/3+rVrLXzTEzM5P838HBASKRCHp6epJIempqapVtqqmpQVdXV+qHiIiIiIiIiIiIqL62bduGcePGITMzE3v27MH27dsRHR1d63pMTU0RFhYmWZpjx44d2L59Ox49eiR3HWFhYVi8eDGMjY3xxx9/IDExEaNHj0ZcXJxco4Dk4evri08++QSmpqZISEhAaGgoUlJSMHDgQEREREh9gb8mOjo6iI6ORlBQEIyMjHD48GHExMTAwcEBe/bswfr16xukz/U1depUXLx4ERMmTMCzZ89w9OhRnDhxAnl5eXj//fdx9OhR6OnpNWof2rRpg/Pnz6NTp044ceIEEhIS0KtXLxw5cgSTJk0S5Pf390dwcDDatWuH2NhYJCQkYPDgwTh//nydz4VmzZrh3Llz2LBhA9q3b4/Lly9j//79uHr1KqytrbF69WrMmzevnntKDUEkrmmozmumsLAQpqamyMzMxIgRI7B//36kpaXBzs4Oz549w4wZM7BhwwbY2dkBAKZPn47p06cDKBtueevWLXh6euLEiRN49uwZrKys8PTpU0yaNAk//vijXH3Izc2Fnp4ecnJyGiSI9LZOVTewp+wgHxERERERERHRm6KhnxP9GxQUFCAlJUWyVAQRKc7OnTvh5+eHpUuXIjAwUNHdIQWT9/r8xo04UlVVxddffw0ACA0NhbW1Ndq1a4dnz57ByMgICxcuBFA2l+eNGzeQkZEhKbtixQqIRCKEh4ejTZs2aNOmDZ4+fQotLa1aRbKJiIiIiIiIiIiIiIj+jd64wBEAfPjhh9i9eze6dOmCtLQ0iEQiDB8+HOfOnZMsAifL8OHDcejQIfTo0QNpaWlQUlLCsGHDcPHiRbRr1+4V7gEREREREREREREREdHrR1nRHair999/H++//36V26uagW/IkCEYMmRIY3WLiIiIiIiIiIiIqF4yMjLkXuvFzs5OMgsT/fv5+vrKlc/IyAhr1qxp3M7Qv9YbGzgiIiIiIiIiIiIi+jfKy8vDrl275Mrr6urKwNFbRN7zwsLCAmvWrIGvr6/cwSaicgwcEREREREREREREb1GLC0tq5xRid5uPC/oVXgj1zgiIiIiIiIiIiIiIiKihsfAEREREREREREREREREQFg4IiIiIiIiIiIiIiIiIj+h4EjIiIiIiIiIiIiIiIiAsDAEREREREREREREREREf0PA0dEREREREREREREREQEgIEjIiIiIiIiIiIiIiIi+h9lRXeAiIiIiIiIynSf/5PC2k5cPV5hbRMRERER0euDI46IiIiIiIiIiIiIiIgIAANHRERERERERERERK/Ehg0b0KFDB6ipqUEkEsHNzU3RXXoj7N27F927d4empiZEIhEsLS0V3SWifzVOVUcKc++Ljgpru9WSPxXWNhERERERERERvX0OHDiAWbNmoWnTphgyZAi0tLRgZ2cHkUgEV1dXREVF1av+wMBABAUFITg4GL6+vg3S59fBhQsX8MEHH0BdXR1eXl7Q19eHkZGRortFjeTfeh6/aRg4IiIiIiIiIiIiolfqWPw9RXdBLgN7tmqwug4dOgQA2L9/P/r16wcASE1NxaefftpgbfwbHTlyBKWlpdi4cSP8/f0V3R2itwKnqiMiIiIiIiIiIiJqZA8ePAAAWFtbK7gnbxYeN6JXj4EjIiIiIiIiIiIiokYSGBgIkUiE06dPAwCsrKwgEokgEolgZWUFAIiOjpakiUSiWk/RZWlpiaCgIACAn5+fVF3lU+Dt3LkTIpEIgYGBuHnzJkaPHo3mzZtDSUlJMhrq9u3bCAwMhJOTE1q0aAFVVVW0bNkS48ePx82bN2W2Xb7mUElJCVauXAkbGxuoqanB3NwcCxYswMuXLwVlnjx5goULF6J9+/bQ1taGnp4ebGxsMH78eCQkJEj1Nzg4GADg7u4u2aedO3dK6nr+/Dm+/PJL2NvbQ0NDA3p6eujbty9CQkKqPFYikQhisRgbN25E586doampiS5dugAAfH19JcctIiICffv2hY6ODpo1a4bJkycjJycHAPD48WNMmTIFZmZmUFdXh6OjY7XTDcbHx+Pdd9+FiYmJ5LhOmjQJ9+4JR9+VnzM7d+5EQkICfHx8YGhoCJFIhCtXrlTZRlWOHTsGT09PmJmZQU1NDaampnBxcZGcM7LaTUxMxDvvvAN9fX0YGBhg1KhRkiBefn4+AgICYGlpCXV1ddjb22P//v01tt+0aVOoq6vD1tYWCxcuRHZ2tlQ+ec7juhxPqj1OVUdERERERERERETUSLp06YIJEybg+PHjSE9Px4gRI6CtrY1Dhw6hf//+CA0NRfPmzeHt7S0p4+LiUqs2Ro4ciYiICCQlJcHZ2Rlt2rSRbGvRooVU3hs3bqBHjx4wNDSEu7s7srKyoKKiAgDYtm0bVq1aBXt7e/To0QNqamq4fv06fv75Z4SFhSEmJgadOnWS2YexY8fi2LFjcHNzg62tLWJiYrBq1So8fPgQu3fvluR79uwZevbsiZSUFJibm8PT0xPKysq4d+8eQkJCYG1tDUdHR7Rp0wYTJkzA2bNnkZycjAEDBkj2pXz/nj17Bnd3dyQmJsLY2Bg+Pj7Iz89HZGQkYmJicP78eaxfv15mf6dOnYrg4GC4urqiXbt2KCwslNp+8OBBbN68GU5OTvD29kZcXBy2bduGW7duYf/+/XByckJJSQn69OmD1NRUxMfHw9vbGxcuXEDHjtJru2/ZsgUzZswAAPTo0QN9+vTBjRs3sH37dhw+fBjR0dFo166doI9nzpzBhx9+CBsbG3h5eSEtLQ1KSrUbC7J582ZMnz4dTZo0gbOzM1xdXZGRkYG///4bgYGBWLp0qaBMfHw8pk6dCnt7ewwYMACXLl3Cb7/9hqSkJCQkJMDT0xN3795F3759kZGRgejoaIwaNQp//PEHBgwYIFXX8uXLsWjRIigrK8PV1RVGRkaIjY3FypUrcfDgQZw5cwbNmzcHIP95XNfjSfJj4IiIiIiIiIiIiIiokQwbNgzDhg2Dm5sb0tPTsWbNGlhaWgIoW+MoNDQUdnZ2UqNoamvNmjUIDAxEUlISJk2aVO2IpZCQEEyfPh3r1q1DkyZNBH2dMmWKZCRUueDgYPj7+2P27NmIjIwU1Hn37l1oamri1q1bkgf8KSkp6NatG3755RcEBQWhdevWAMrWeEpJScGQIUNw8OBBqUDIkydPkJ6eDqAseObi4gJfX18kJydj4cKFcHNzk2p30aJFSExMhLu7O8LCwqCjowMA+Oeff+Dq6ooNGzbA09MTPj4+gj4fOHAAly9fRocOHWQep02bNuHw4cMYNGgQgLIgVe/evREdHQ1XV1f07t0bO3bskATdPv/8cyxbtgxr1qzBrl27JPXExcVh5syZMDExQVhYGLp37y7Ztn37dkyaNAl+fn6Ii4sT9CE4OBgrV65EQECAzD7KY9WqVRCJRIiLi4ODg4MkXSwWIzo6WmaZ7777Dlu3bsXUqVMBAEVFRRg4cCAiIiLQu3dvtGjRAnfu3IGWlpbUfnz99ddSgaMLFy7gs88+g7a2NiIiItCzZ08AwMuXLzFu3Dj89ttvmDZtmmS0kjzncX2OJ8mPgSMiIqLXRPf5Pyms7cTV4xXWNhEREREREb06xsbGWLlypSBoBAC9evWSWcbPzw/bt29HVFQUcnJyoKenJ8izYcMGqVEhVlZW+OCDD7Bp0ybExMRIAkdPnjwBAPTr108wesbY2BjGxsZy7Ud+fj62b98OJSUlbNmyRRI0AgA7Ozt89tlnmDlzJtavXy8zcLRgwYIqg0ZA2Qiq8qARAOjo6GDy5MmYNWsWHjx4gLNnz0qCRgAwb948fPXVV4JgzIoVK1BSUoLvvvtOKsgBABMnTsThw4dx+PBhXL58GV27dpXa3rFjR8yfP1+u41GVJ0+eQF9fXypoBJRNMVg5EFfOxcVFEjQCABUVFcyYMQMRERH4559/cPDgQUnQCCib3m/hwoU4f/48ioqKJMdl06ZNKC0txYwZMyRBIwBQU1PDpk2bcPToURw8eBD379+Hubm5XPtTn+NJ8uMaR0RERERERERERERvif79+0NTU7PK7Xl5edi7dy8WLFiAyZMnw9fXF76+vnj06BHEYjGSk5MFZVRUVODu7i5It7GxAQA8evRIklb+sH/16tUICQnBs2fP6rQfiYmJePHiBbp16wY7OzvB9nHjxgEAYmNjUVpaKtg+ZMiQauv38vISpFlbWwMAHBwc0LRpU6ltenp6MDAwkNrX0tJSnDp1CpqamoIp3Mr16dMHACRrO1Xk4+MDkUhUbT9r0r17d2RlZWHixIn466+/5CpT3b5bWlpKXtdyTZo0gYWFBYqKipCRkSFJj4mJAQC8//77gvqaNWsGLy8vlJaWIjY2Vq5+1fd4kvw44oiIiIiIiIiIiIjoLdGqVasqt0VGRmL06NGSUUGyyAr0tGjRQuYIpvJRQC9fvpSkeXh4YM6cOVi3bh3GjBkDZWVldOvWDZ6envD395cEKGqSlpYGAJJp/yrT19eHnp4ecnJykJWVBUNDQ6nt1R0HADAzMxOkaWtrV7mtfHtmZqbk94yMDOTl5QEAVFVVq22vYsBF3j7KY/PmzRg2bBh27NiBHTt2oHnz5nB1dcXw4cMxcuRIma9bXfcdkH6ta3qNytMfPnwo177U93iS/Bg4IiIiIiIiIiIiInpLqKury0zPy8vDqFGj8PTpUyxZsgSjR4+GhYUFNDQ0IBKJMHbsWOzduxdisVhQtvKUczVZu3YtpkyZgrCwMERERCA2NhYJCQlYtWoV9u7dixEjRtRp3yqrbrROVcehXHX7JO/+lo900tbWrnGfZE2bV1Mf5dGpUydcv34dx48fx7FjxxAVFYV9+/Zh3759cHJyQlRUlCAI0xD7Lo/ajqaq7/Ek+TFwRERERERERERERPSWi4mJQWZmJkaOHImgoCDB9jt37jRoe7a2tggICEBAQAAKCgqwadMmzJ8/Hx999JFcgSNTU1MAwN27d2Vuz8nJQXZ2NjQ0NATTyr0qRkZGUFdXh5KSEoKDg+s97VxdqaurY9iwYRg2bBgA4K+//sLYsWNx/vx5bNu2DR9//HGjtGtqaoqUlBTcvXsX7du3F2xPTU0FUPUopspel+P5NmDgiIiIiHAs/p7C2h7Ys/5D74mIiIiIiN5E5SM9iouLFV5XVlYWAKBly5aCbbdv38alS5fq3rkaqKurY968eVi7di0ePXqEx48fo1mzZtWW6d69OzQ0NJCYmIhbt26hbdu2Utt3794NAHB2dm7QUTK1oaysDDc3Nxw/fhynTp1C//79FdKPyjp06IBp06ZhypQpuHbtWqO106dPH6SkpGDv3r348ssvpbY9efIEJ06cgEgkgrOzsyS9uvP4dT2e/0YMHBG9Ys4bnWvO1EhiZ8i30BwRERERERERETU+IyMjqKioIDk5GSUlJTLXm5FX+QicGzdu1Km8jY0NAODAgQNYtGgRjI2NAQDZ2dmYOHEiioqK6ty3ig4dOoQWLVqgV69eUumJiYlIT0+HtrY29PX1a6xHS0sL/v7+2Lx5M6ZNm4aDBw9CS0sLAHDz5k0sW7YMADBz5swG6XddLV68GCdPnoSfnx9+/vlnuLm5SW3Py8vDvn37MGbMGGhoaDRo28+fP8e2bdswfvx4qWNaWlqK48ePAwDMzc0btM2Kpk2bht27d2PDhg0YOnQoHBwcAACFhYWYMWMGXrx4gREjRkj1oabzWJHH823CwBERERERERERERGRAqiqqsLb2xtHjhxB586d0a1bN6iqqsLZ2Rl+fn61qsvLywvq6ur49ttvce3aNZiamkIkEmH+/PmwtbWtsbyDgwM8PT0RHh4OGxsbyQP5qKgoGBkZYejQoQgLC6vLbkqJiorC+vXrYWZmhq5du0JXVxdpaWmIiYlBaWkpgoKCBGvuVGX58uWIi4tDeHg4rK2t4erqivz8fERGRqKgoAAzZ87E4MGD693n+nBxccHmzZsxffp0uLu7w97eHjY2NlBRUUFqaiquXLmCly9fYvjw4Q0e6CgsLMSsWbMwb948dO/eHZaWligsLMSFCxdw//59WFpa4sMPP2zQNitydHTEl19+icWLF8PJyQlubm4wMjJCbGws7t+/j7Zt22Lz5s1SZWo6jxV5PN8mihmjR0RERERERERERETYtm0bxo0bh8zMTOzZswfbt29HdHR0resxNTVFWFgYevXqhbNnz2LHjh3Yvn07Hj16JHcdYWFhWLx4MYyNjfHHH38gMTERo0ePRlxcnFyjgOTh6+uLTz75BKampkhISEBoaChSUlIwcOBAREREYO7cuXLXpaOjg+joaAQFBcHIyAiHDx9GTEwMHBwcsGfPHqxfv75B+lxfU6dOxcWLFzFhwgQ8e/YMR48exYkTJ5CXl4f3338fR48ehZ6eXoO3q62tjc2bN2Pw4MF48uQJDh8+jMjISDRt2hRBQUFITEyEoaFhg7db0aJFi3D06FG4urriwoULOHDgANTU1BAQEID4+Hg0b95cKr8857GijufbRCQWi8WK7sSbJjc3F3p6esjJyYGurm696+s+/6cG6FXdfDnSTWFt258YpLC2Wy35U2Ftc6o6IqrK2/p5wDWOiIj+nyI/CxJXj1dY20REb7KGfk70b1BQUICUlBRYWVlBXV1d0d0hIqL/kff6zKnqiIiISKHufdFRYW0r8osERERERERERESvI05VR0RERERERERERERERAA44oiIiIjeYlkRqxTWdtP+AQprm4iIiIiIXm8ZGRmYN2+eXHnt7OywcOHCRu4RvS58fX3lymdkZIQ1a9Y0bmfoX4uBIyIiIiIiIiIiIqLXSF5eHnbt2iVXXldXVwaO3iLynhcWFhYMHFGdMXBEREREREREOBZ/T2FtD+zZSmFtExERvY4sLS0hFosV3Q16DfG8oFeBaxwRERERERERERERERERAAaOiIiIiIiIiIiIiIiI6H8YOCIiIiIiIiIiIiIiIiIADBwRERERERERERERERHR/zBwRERERERERERERERERAAYOCIiIiIiIiIiIiIiIqL/YeCIiIiIiIiIiIiIiIiIADBwRERERERERERERERERP/DwBEREREREREREREREREBYOCIiIiIiIiIiIiIiIiI/oeBIyIiIiIiIiIiIqJXYMOGDejQoQPU1NQgEong5uam6C69Efbu3Yvu3btDU1MTIpEIlpaWiu4SvYb++ecfrFy5Eu7u7jAyMoKKigpatGiB4cOHIyYmptqyDx48gJ+fH0xNTaGurg4bGxssXboUBQUFVZZ58eIFlixZAhsbG6irq8PU1BT+/v54+PBhtW3t3LkTjo6O0NbWhoGBAQYOHIhz585VWyY2NhYDBw6EgYEBtLW14ejoiJ9++qnaMvWh3Gg1ExEREREREREREclw74uOiu6CXFot+bPB6jpw4ABmzZqFpk2bYsiQIdDS0oKdnR1EIhFcXV0RFRVVr/oDAwMRFBSE4OBg+Pr6NkifXwcXLlzABx98AHV1dXh5eUFfXx9GRkaK7ta/lqWlJe7evQuxWKzortRa//798fDhQ2hra6NXr14wMDDA9evXcfDgQRw6dAhr167F7NmzBeVu374NJycnZGRkwN7eHn369MHFixfxxRdf4NSpUzh16hTU1NSkyhQUFKBfv36Ii4uDiYkJhg4ditTUVAQHB+Po0aOIi4uDtbW1oK3Zs2dj/fr10NDQgJeXFwoKChAeHo6TJ09i//79GDZsmKBMaGgo3nvvPZSWlqJv374wMjLCqVOnMGHCBFy9ehVr1qxpqEMowcARERERkQI4b3RWWNuxM2IV1jYRERER0dvq0KFDAID9+/ejX79+AIDU1FR8+umnCuzV6+/IkSMoLS3Fxo0b4e/vr+ju0GvMzs4Oy5cvx7vvvgt1dXVJ+vfff4+pU6di3rx58PLyQvv27aXK+fr6IiMjAzNnzsT69esBAMXFxRg1ahQOHjyI5cuXIzAwUKrMsmXLEBcXBycnJ5w8eRLa2toAgLVr1+KTTz6Bv7+/IBgcERGB9evXw9DQEOfPn0fbtm0BAOfPn4ebmxv8/Pzg5uYGfX19SZmnT5/C398fJSUlCA0NxfDhwwEA6enpcHFxwTfffAMfH58GH73IqeqIiIiIiIiIiIiIGtmDBw8AQOYoBKoajxvJKyIiAuPGjZMKGgHAlClT4OXlhZKSEvz2229S2xISEhAbG4tmzZph1apVknRlZWVs3boVKioq2LBhA4qLiyXbCgsLsWnTJgDA5s2bJUEjAJg7dy46deqE6OhoJCYmSrW1du1aAMBnn30mCRoBgJOTE6ZOnYrs7Gxs375dqsy2bduQm5uLoUOHSoJGANC8eXNJf7/55hv5D5KcGDgiIiIiIiIiIiIiaiSBgYEQiUQ4ffo0AMDKygoikQgikQhWVlYAgOjoaEmaSCSq9VRzlpaWCAoKAgD4+flJ1VU+6mHnzp0QiUQIDAzEzZs3MXr0aDRv3hxKSkqS0VC3b99GYGAgnJyc0KJFC6iqqqJly5YYP348bt68KbPt8jWHSkpKsHLlStjY2EBNTQ3m5uZYsGABXr58KSjz5MkTLFy4EO3bt4e2tjb09PRgY2OD8ePHIyEhQaq/wcHBAAB3d3fJPu3cuVNS1/Pnz/Hll1/C3t4eGhoa0NPTQ9++fRESElLlsRKJRBCLxdi4cSM6d+4MTU1NdOnSBUDZ6JPy4xYREYG+fftCR0cHzZo1w+TJk5GTkwMAePz4MaZMmQIzMzOoq6vD0dGx2ukG4+Pj8e6778LExERyXCdNmoR79+4J8pafMzt37kRCQgJ8fHxgaGgIkUiEK1euVNlGdeLj4zF69GiYmZlBTU0NJiYm8PDwwI8//ggAiIqKgkgkwt27dwFA6hyqvKZUamoqxo4dC2NjY2hpacHBwQEhISFITU2VuXZXdnY2Nm7ciAEDBsDCwgJqamowNDSEt7c3wsPDZfbXzc0NIpEIqampOHToEHr16gUtLS0YGBhgzJgxkoCivDp37gwASEtLk0r//fffAQCDBw8WTEfXvHlz9OnTB1lZWTh79qwkPTY2Fjk5OWjdujW6du0qaGvkyJEAykbLlXvx4gUiIyOlttdUpmL/ZJUZNGgQ1NXVERERUe1aTHXBqeqIiIiIiIiIFIDTlhIRvR26dOmCCRMm4Pjx40hPT8eIESOgra2NQ4cOoX///ggNDUXz5s3h7e0tKePi4lKrNkaOHImIiAgkJSXB2dkZbdq0kWxr0aKFVN4bN26gR48eMDQ0hLu7O7KysqCiogKgbHTDqlWrYG9vjx49ekBNTQ3Xr1/Hzz//jLCwMMTExKBTp04y+zB27FgcO3YMbm5usLW1RUxMDFatWoWHDx9i9+7dknzPnj1Dz549kZKSAnNzc3h6ekJZWRn37t1DSEgIrK2t4ejoiDZt2mDChAk4e/YskpOTMWDAAMm+lO/fs2fP4O7ujsTERBgbG8PHxwf5+fmIjIxETEwMzp8/L5l6rLKpU6ciODgYrq6uaNeuHQoLC6W2Hzx4EJs3b4aTkxO8vb0RFxeHbdu24datW9i/fz+cnJxQUlKCPn36IDU1FfHx8fD29saFCxfQsaP0Gl5btmzBjBkzAAA9evRAnz59cOPGDWzfvh2HDx9GdHQ02rVrJ+jjmTNn8OGHH8LGxgZeXl5IS0uDklLtx4KsX78ec+fORWlpKbp3746+ffsiIyMDV69exfz58zF58mS0aNECEyZMwP79+5Gfn48JEyZIyldcU+r27dvo3bs3njx5gjZt2qB///5IS0vD2LFjMXPmTJntx8XFYebMmbC0tIStrS2cnJxw7949nDx5EidPnsS2bduqnIZwy5YtWLt2Lfr06YOBAwciPj4eISEhSExMRFJSEjQ0NOQ6Bnfu3AEgfD8kJSUBALp16yazXLdu3RAZGYmrV69KAmLylAGAq1evStJu3LiBly9fwtjYGC1btpSrTE1tqaqqwt7eHhcvXsTNmzerfG/WBQNHRERERERERERERI1k2LBhGDZsGNzc3JCeno41a9ZIRnCkpqYiNDQUdnZ2UqNoamvNmjUIDAxEUlISJk2aVO2IpZCQEEyfPh3r1q1DkyZNBH2dMmWKZCRUueDgYPj7+2P27NmSURMV3b17F5qamrh165bkwXxKSgq6deuGX375BUFBQWjdujWAsjWeUlJSMGTIEBw8eFAqEPLkyROkp6cDKAueubi4wNfXF8nJyVi4cKFgJMuiRYuQmJgId3d3hIWFQUdHBwDwzz//wNXVFRs2bICnpyd8fHwEfT5w4AAuX76MDh06yDxOmzZtwuHDhzFo0CAAZUGq3r17Izo6Gq6urujduzd27NghCbp9/vnnWLZsGdasWYNdu3ZJ6ikPmpiYmCAsLAzdu3eXbNu+fTsmTZoEPz8/xMXFCfoQHByMlStXIiAgQGYf5XHmzBnMmTMH2traOHjwIDw8PCTbiouLcfLkSQCQnINRUVHIz8+v8nycOnUqnjx5gqlTp2LTpk2Sc+jEiRMYPHiwzDK2trY4f/48evXqJZV++fJl9OvXD3PmzMGoUaOkpnwrt3nzZsTExMDJyQlA2QgzT09PnDt3Dnv37pVr3avk5GQcPXoUADBkyBCpbeUjvmQFcyqml4/EaqwyWlpa0NfXR1ZWFp49ewYdHR3k5uZKRrhV19bFixdx9+7dBg0ccao6IiIiIiIiIiIioreEsbExVq5cKQgaAUCvXr0EQSOgbPo7Z2dnREVFSR5kV7Zhwwap0RxWVlb44IMPAAAxMTGS9CdPngAA+vXrJxg9Y2xsDHt7e7n2Iz8/H9u3b4eSkhK2bNkiCRoBZUGQzz77DACqHHG0YMGCKoNGQNkIqvKgEQDo6Ohg8uTJAMrWXdqwYYMkaAQA8+bNg0gkQnR0tFQ9K1asQElJCb777jupoBEATJw4EUOGDEF8fDwuX74s6EPHjh0xf/78KvsojxUrVkAsFmPx4sVSQSOgbB2fgQMHyl3X7du3cerUKejr62P16tVS59CAAQMwatQomeWsrKwEQSMA6Nq1K6ZNm4bc3FzJVI6VzZkzRxI0AgBNTU3MnTsXQFlQrCbFxcXw9fXFy5cv8d577wleg7y8PEm9smhpaQEoCxw2ZhlZ5crL1LathsARR0RERERERERERERvif79+1f78DovLw9HjhzBlStX8PTpUxQVFQEAHj16BLFYjOTkZMG0WSoqKnB3dxfUZWNjIylbrvzB/erVq9G8eXMMGjRIKugjr8TERLx48QIODg6ws7MTbB83bhxmzpyJ2NhYlJaWCoJUlUeeVObl5SVIs7a2BgA4ODigadOmUtv09PRgYGAgta+lpaU4deoUNDU1MWDAAJnt9OnTB4cPH0ZCQoJgvRwfHx+IRKJq+1md4uJiybpLH374YZ3rKRcbWzbVrbe3t8zRQe+99x5++eUXmWVLSkpw6tQpnDt3Do8ePZKsfXXr1i2pfyuT9TrIOq+qMnPmTJw9exbW1tbYsmVLjfmpDANHRERERERERERERG+JVq1aVbktMjISo0ePlowKkkXWyIYWLVrIHMFUHhAqDxIAgIeHB+bMmYN169ZhzJgxUFZWRrdu3eDp6Ql/f39JcKYmaWlpACCZ9q8yfX196OnpIScnB1lZWTA0NJTaXt1xAAAzMzNBWnmwRNa28u2ZmZmS3zMyMiSjRlRVVattLyMjQ5BWUx9rkpmZiRcvXsDAwEAQ6KqL8kCNubm5zO1V9ffBgwfw8fGRrNcjS1UjZmRN0SbrvJLlq6++wtatW9G8eXOcOHECBgYGgjzlr+nz589l1pGfny/VZmOVkVWuYnDu+fPn0NXVlauthsDAEREREREREREREdFbQl1dXWZ6Xl4eRo0ahadPn2LJkiUYPXo0LCwsoKGhAZFIhLFjx2Lv3r0Qi8WCspVH89Rk7dq1mDJlCsLCwhAREYHY2FgkJCRg1apV2Lt3L0aMGFGnfausutE6VR2HctXtk7z7W1paCqAsAFDTPsmaNq+mPr4pJk2ahKSkJIwYMQIBAQGwtbWFjo4OlJSU8MMPP2DKlCkyzyug9udWue+++w6fffYZ9PT0cPz4cbRp00ZmvlatWuHy5ct48OCBzO3l6RYWFlJlKm5riDL5+fnIzs5G06ZNJUEgXV1dSfDzwYMHaN++vVxtNQQGjoiIiIiIiIiIiIjecjExMcjMzMTIkSMRFBQk2H7nzp0Gbc/W1hYBAQEICAhAQUEBNm3ahPnz5+Ojjz6SK3BkamoKALh7967M7Tk5OcjOzoaGhkaDjLapCyMjI6irq0NJSQnBwcH1mnauru1raGjg6dOnyM7Ohr6+fr3qMzExAQDcv39f5nZZ6fn5+QgPD0fz5s3x66+/CkamNfR5BQAhISGYNm0aNDU18fvvv6NLly5V5u3cuTPCwsJw6dIlmdvL0zt16iRVpuI2ecrY2tpCTU0NT548wcOHDwWj1mSVKW/rzJkzuHTpkiBwVFRUhGvXrkFdXV0yfV9DqVu4joiIiIiIiIiIiIjqpXz6suLiYoXXlZWVBUD21GC3b9+u8iF5Q1BXV8e8efNgYmKCJ0+e4PHjxzWW6d69OzQ0NJCYmChzfZzdu3cDAJydnes8aqW+lJWV4ebmhtzcXJw6deqVt9+kSRO4ubkBAH744Qe5ylR3HvXu3RsAcOLECckUaRXt27dPkJaTk4PS0lKYmJgIgkZFRUU4ePCgXP2S17FjxzB+/HgoKyvj4MGDcHZ2rjb/oEGDAABHjhwRTH2Xnp6OmJgYNG3aVKoeZ2dn6OnpITk5GVeuXBHUuX//fgDA4MGDJWkaGhro168fAOC3336Tq0zF/pVvr+jo0aMoKChA//79G3x0GgNHRERERERERERERApgZGQEFRUVJCcno6SkpF51lY/AuXHjRp3Kl49YOHDggNQaR9nZ2Zg4cSKKiorq1b9yhw4dQlxcnCA9MTER6enp0NbWlmtkjJaWFvz9/VFaWopp06ZJBTJu3ryJZcuWAQBmzpzZIP2uq8WLF0NJSQl+fn6IiooSbM/Ly8OOHTvw4sWLRml/wYIFEIlE+Oqrr3D69GmpbcXFxTh27JhUWnXnUdu2beHh4YGsrCwsWLBAMhUfAISHhyMkJERQplmzZtDT08O1a9cQGxsrSS8pKcGCBQtw8+bNeu1fRbGxsRg5ciTEYjF+/fVXeHl51VjG0dERzs7OePz4MRYsWCBJLy4uxscff4yioiLMnDkTKioqkm2qqqqYPn06AAjOvbVr1+Lq1atwdXVF9+7dpdqaO3cuAGDZsmVSwc7z58/j+++/h76+PiZOnChVZtKkSdDV1UVYWBgOHDggSX/8+DECAgIAAJ988kmN+1lbnKqOiIiIiIiIiIiISAFUVVXh7e2NI0eOoHPnzujWrRtUVVXh7OwMPz+/WtXl5eUFdXV1fPvtt7h27RpMTU0hEokwf/582Nra1ljewcEBnp6eCA8Ph42NjWSkSlRUFIyMjDB06FCEhYXVZTelREVFYf369TAzM0PXrl2hq6uLtLQ0xMTEoLS0FEFBQZJRLzVZvnw54uLiEB4eDmtra7i6uiI/Px+RkZEoKCjAzJkzBSM4XjUXFxds3rwZ06dPh7u7O+zt7WFjYwMVFRWkpqbiypUrePnyJYYPHw4NDY0Gb9/V1RWrVq1CQEAA+vXrBwcHB7Rt2xYZGRlISkrCy5cvkZ2dLck/ZMgQREdHw8PDA+7u7tDS0oKRkRFWrFgBANi6dSucnZ2xefNmnDx5Eg4ODpLX7+OPP8amTZukXj9lZWUEBARg8eLFcHV1Rb9+/WBgYID4+Hikp6dj2rRp2Lx5c4Psq4+PD168eAErKyscOnQIhw4dEuRxcXHBpEmTpNKCg4Ph5OSE9evXIzIyEu3bt8eFCxdw584d9O7dG59++qmgns8++wwRERE4d+4c2rZtiz59+uDu3buIj4+HsbExduzYISjTv39/zJo1C+vXr0eXLl3g6emJwsJChIeHQywWIzg4WBA0NTAwwI4dOzBq1CiMHDkSbm5uMDQ0REREBLKzszF37lzJe7UhMXBEREREREREREREpCDbtm3DvHnzEB4ejj179qCkpATFxcW1DhyZmpoiLCwMX3zxBc6ePYu8vDwAwAcffCBX4AgAwsLC8NVXX2Hfvn34448/0KxZM4wePRrLli1rsFENvr6+UFZWxpkzZ5CQkICcnBy0aNECAwcOxKxZs+Dh4SF3XTo6OoiOjsY333yDX3/9FYcPH4aqqiocHBzw8ccfY8yYMQ3S5/qaOnUqevXqhXXr1iEqKgpHjx6FpqYmzMzM8P7772P48OHQ09NrtPbnzZuHnj174ttvv0VsbCySkpJgZGSEjh07Co7RzJkzkZWVhb179yI0NBRFRUWwsLCQBI7atm2L+Ph4LF68GCdPnsShQ4dgZ2eHXbt2wcrKCps2bYKhoaFUnYsWLULLli2xbt06xMbGQkNDAy4uLvjiiy8adArE8gBYSkoKUlJSqsxXOXDUtm1bXL58GUuWLMHx48dx8OBBtGrVCp9//jkWLVoENTU1QR3q6uo4ffo0li9fjj179uDQoUMwMDCAr68vvvzyS5lTPgLAunXr0KVLF2zatAnh4eFQVVVF//798fnnn0umAqxsxIgROHPmDJYtW4a4uDgUFhaiffv2mD59OiZMmCDn0akdkVgsFjdKzf9iubm50NPTQ05ODnR1detdX/f5PzVAr+rmy5FuCmvb/sQghbXdasmfCmvbeWP182o2ptgZsTVnIiKF4efBq6fTe5zC2vb5u/7f1Kur1/nzQJHvg8TV4xXWNlG5t/WzYGDPVgprW5H4twHRv0NDPyf6NygoKEBKSgqsrKwafN0NInp9rVixAp9++ilWrFghNe0bvT7kvT5zjSMiIiIiIiIiIiIiIqpRQUEBrl+/Lkg/ffo0vv76aygrK2P06NEK6Bk1JE5VR0RERERERERERERENcrOzkaHDh1ga2uLtm3bQl1dHbdu3UJSUhIAYM2aNbCwsFBwL6m+GDgiIiIiIiIiIiIieo1kZGRg3rx5cuW1s7PDwoULG7lH9Lrw9fWVK5+RkRHWrFnT4O3r6elJ1uQ6d+4ccnNzoa+vj3feeQczZszAO++80+Bt0qvHwBERERERERERERHRayQvLw+7du2SK6+rqysDR28Rec8LCwuLRgkcaWhoYPXq1Q1eL71eGDgiIiIiIiIiIiIieo1YWlpCLBYruhv0GuJ5Qa8CA0dE9FroPv8nhbWduHq8wtomIiIiIiIiIiIiep0oKboDRERERERERERERERE9HrgiCMiIiIiIiJSqHtfdFRY262W/KmwtomIiIiIXkcMHBEREREREdFbKytilaK7QERERET0WuFUdURERERERERERERERASAgSMiIiIiIiIiIiIiIiL6H05VR28lTkdBRERERERERERERCTEEUdEREREREREREREREQEgIEjIiIiIiIiIiIiIiIi+h8GjoiIiIiIiIiIiIhegQ0bNqBDhw5QU1ODSCSCm5uborv0Rti7dy+6d+8OTU1NiEQiWFpaKrpL9BrKz8/Hzz//jBkzZqBnz56S91lgYGCNZR88eAA/Pz+YmppCXV0dNjY2WLp0KQoKCqos8+LFCyxZsgQ2NjZQV1eHqakp/P398fDhw2rb2rlzJxwdHaGtrQ0DAwMMHDgQ586dq7ZMbGwsBg4cCAMDA2hra8PR0RE//fRTjftVV1zjiIiIiIiIiIiIiKiRHThwALNmzULTpk0xZMgQaGlpwc7ODiKRCK6uroiKiqpX/YGBgQgKCkJwcDB8fX0bpM+vgwsXLuCDDz6Auro6vLy8oK+vDyMjI0V361/L0tISd+/ehVgsVnRXau3WrVsYP358rcvdvn0bTk5OyMjIgL29Pfr06YOLFy/iiy++wKlTp3Dq1CmoqalJlSkoKEC/fv0QFxcHExMTDB06FKmpqQgODsbRo0cRFxcHa2trQVuzZ8/G+vXroaGhAS8vLxQUFCA8PBwnT57E/v37MWzYMEGZ0NBQvPfeeygtLUXfvn1hZGSEU6dOYcKECbh69SrWrFlT632uCQNHREREREQAjsXfU1jbA3u2UljbRERERIqQFbFK0V2QS9P+AQ1W16FDhwAA+/fvR79+/QAAqamp+PTTTxusjX+jI0eOoLS0FBs3boS/v7+iu0OvMR0dHUycOBE9evRAjx498Pvvv2PJkiU1lvP19UVGRgZmzpyJ9evXAwCKi4sxatQoHDx4EMuXLxeMWlq2bBni4uLg5OSEkydPQltbGwCwdu1afPLJJ/D39xcEgyMiIrB+/XoYGhri/PnzaNu2LQDg/PnzcHNzg5+fH9zc3KCvry8p8/TpU/j7+6OkpAShoaEYPnw4ACA9PR0uLi745ptv4OPj0+CjFzlVHREREREREREREVEje/DgAQDIHIVAVeNxI3m1bt0a27Ztw5QpU9CtWzeoqKjUWCYhIQGxsbFo1qwZVq36/4C2srIytm7dChUVFWzYsAHFxcWSbYWFhdi0aRMAYPPmzZKgEQDMnTsXnTp1QnR0NBITE6XaWrt2LQDgs88+kwSNAMDJyQlTp05FdnY2tm/fLlVm27ZtyM3NxdChQyVBIwBo3ry5pL/ffPNNjftZWwwcERERERERERERETWSwMBAiEQinD59GgBgZWUFkUgEkUgEKysrAEB0dLQkTSQS1XqqOUtLSwQFBQEA/Pz8pOoqH/Wwc+dOyXovN2/exOjRo9G8eXMoKSlJRkPdvn0bgYGBcHJyQosWLaCqqoqWLVti/PjxuHnzpsy2y9ccKikpwcqVK2FjYwM1NTWYm5tjwYIFePnypaDMkydPsHDhQrRv3x7a2trQ09ODjY0Nxo8fj4SEBKn+BgcHAwDc3d0l+7Rz505JXc+fP8eXX34Je3t7aGhoQE9PD3379kVISEiVx0okEkEsFmPjxo3o3LkzNDU10aVLFwBlo0/Kj1tERAT69u0LHR0dNGvWDJMnT0ZOTg4A4PHjx5gyZQrMzMygrq4OR0fHaqcbjI+Px7vvvgsTExPJcZ00aRLu3RPOfFB+zuzcuRMJCQnw8fGBoaEhRCIRrly5UmUb1YmPj8fo0aNhZmYGNTU1mJiYwMPDAz/++CMAICoqCiKRCHfv3gUAqXOo8ppSqampGDt2LIyNjaGlpQUHBweEhIQgNTVV5tpd2dnZ2LhxIwYMGAALCwuoqanB0NAQ3t7eCA8Pl9lfNzc3iEQipKam4tChQ+jVqxe0tLRgYGCAMWPGSAKK9fX7778DAAYPHiyYjq558+bo06cPsrKycPbsWUl6bGwscnJy0Lp1a3Tt2lVQ58iRIwGUjZYr9+LFC0RGRkptr6lMxf7JKjNo0CCoq6sjIiKi2rWY6oJT1RERERERERHRa6P7/MZb6Lkmiatrvy4CEVFNunTpggkTJuD48eNIT0/HiBEjoK2tjUOHDqF///4IDQ1F8+bN4e3tLSnj4uJSqzZGjhyJiIgIJCUlwdnZGW3atJFsa9GihVTeGzduoEePHjA0NIS7uzuysrIkIzO2bduGVatWwd7eHj169ICamhquX7+On3/+GWFhYYiJiUGnTp1k9mHs2LE4duwY3NzcYGtri5iYGKxatQoPHz7E7t27JfmePXuGnj17IiUlBebm5vD09ISysjLu3buHkJAQWFtbw9HREW3atMGECRNw9uxZJCcnY8CAAZJ9Kd+/Z8+ewd3dHYmJiTA2NoaPjw/y8/MRGRmJmJgYnD9/XjL1WGVTp05FcHAwXF1d0a5dOxQWFkptP3jwIDZv3gwnJyd4e3sjLi4O27Ztw61bt7B//344OTmhpKQEffr0QWpqKuLj4+Ht7Y0LFy6gY8eOUnVt2bIFM2bMAAD06NEDffr0wY0bN7B9+3YcPnwY0dHRaNeunaCPZ86cwYcffggbGxt4eXkhLS0NSkq1Hwuyfv16zJ07F6WlpejevTv69u2LjIwMXL16FfPnz8fkyZPRokULTJgwAfv370d+fj4mTJggKV9xTanbt2+jd+/eePLkCdq0aYP+/fsjLS0NY8eOxcyZM2W2HxcXh5kzZ8LS0hK2trZwcnLCvXv3cPLkSZw8eRLbtm2rchrCLVu2YO3atejTpw8GDhyI+Ph4hISEIDExEUlJSdDQ0Kj18agoKSkJANCtWzeZ27t164bIyEhcvXpVEhCTpwwAXL16VZJ248YNvHz5EsbGxmjZsqVcZWpqS1VVFfb29rh48SJu3rxZ5XuzLhg4IiIiIiIiIiIiImokw4YNw7Bhw+Dm5ob09HSsWbNGMoIjNTUVoaGhsLOzkxpFU1tr1qxBYGAgkpKSMGnSpGpHLIWEhGD69OlYt24dmjRpIujrlClTJCOhygUHB8Pf3x+zZ8+WjJqo6O7du9DU1MStW7ckwZ2UlBR069YNv/zyC4KCgtC6dWsAZWs8paSkYMiQITh48KBUIOTJkydIT08HUBY8c3Fxga+vL5KTk7Fw4ULBSJZFixYhMTER7u7uCAsLg46ODgDgn3/+gaurKzZs2ABPT0/4+PgI+nzgwAFcvnwZHTp0kHmcNm3ahMOHD2PQoEEAyoJUvXv3RnR0NFxdXdG7d2/s2LFDEnT7/PPPsWzZMqxZswa7du2S1FMeNDExMUFYWBi6d+8u2bZ9+3ZMmjQJfn5+iIuLE/QhODgYK1euREBA3dfaOnPmDObMmQNtbW0cPHgQHh4ekm3FxcU4efIkAEjOwaioKOTn51d5Pk6dOhVPnjzB1KlTsWnTJsk5dOLECQwePFhmGVtbW5w/fx69evWSSr98+TL69euHOXPmYNSoUVJTvpXbvHkzYmJi4OTkBKBshJmnpyfOnTuHvXv31nvdq/IRX7KCORXTy0diNVYZLS0t6OvrIysrC8+ePYOOjg5yc3MlI9yqa+vixYu4e/dugwaOOFUdERERERERERER0VvC2NgYK1euFASNAKBXr16CoBFQNv2ds7MzoqKiJA+yK9uwYYPU6CYrKyt88MEHAICYmBhJ+pMnTwAA/fr1E4yeMTY2hr29vVz7kZ+fj+3bt0NJSQlbtmyRBI2AsiDIZ599BgBVjjhasGBBlUEjoGwEVXnQCAB0dHQwefJkAGXrLm3YsEFqDZ158+ZBJBIhOjpaqp4VK1agpKQE3333nVTQCAAmTpyIIUOGID4+HpcvXxb0oWPHjpg/f36VfZTHihUrIBaLsXjxYqmgEVC2js/AgQPlruv27ds4deoU9PX1sXr1aqlzaMCAARg1apTMclZWVoKgEQB07doV06ZNQ25urmQqx8rmzJkjCRoBgKamJubOnQugLChWX3l5eZJ6ZdHS0gJQFjhszDKyypWXqW1bDYEjjoiIiIiIiIiIiIjeEv3796/24XVeXh6OHDmCK1eu4OnTpygqKgIAPHr0CGKxGMnJyYJps1RUVODu7i6oy8bGRlK2XHnwZPXq1WjevDkGDRokFfSRV2JiIl68eAEHBwfY2dkJto8bNw4zZ85EbGwsSktLBUGqIUOGVFu/l5eXIM3a2hoA4ODggKZNm0pt09PTg4GBgdS+lpaW4tSpU9DU1MSAAQNkttOnTx8cPnwYCQkJgvVyfHx8IBKJqu1ndYqLiyXrLn344Yd1rqdcbGwsAMDb21vm6KD33nsPv/zyi8yyJSUlOHXqFM6dO4dHjx5J1r66deuW1L+VyXodZJ1X1LAYOCIiIiIiIiIiIiJ6S7Rq1arKbZGRkRg9erRkVJAsskY2tGjRQuYIpvKAUHmQAAA8PDwwZ84crFu3DmPGjIGysjK6desGT09P+Pv7S4IzNUlLSwMAybR/lenr60NPTw85OTnIysqCoaGh1PbqjgMAmJmZCdLKgyWytpVvz8zMlPyekZEhGTWiqqpabXsZGRmCtJr6WJPMzEy8ePECBgYGgkBXXZQHaszNzWVur6q/Dx48gI+Pj2S9HlmqGjEja4o2WedVXZW/ps+fP5e5PT8/X6rNxiojq1zF4Nzz58+hq6srV1sNgYEjIiIiIiIiIiIioreEurq6zPS8vDyMGjUKT58+xZIlSzB69GhYWFhAQ0MDIpEIY8eOxd69eyEWiwVlK4/mqcnatWsxZcoUhIWFISIiArGxsUhISMCqVauwd+9ejBgxok77Vll1o3WqOg7lqtsnefe3tLQUQFkAoKZ9kjVtXk19fFNMmjQJSUlJGDFiBAICAmBrawsdHR0oKSnhhx9+wJQpU2SeV0Dtz63aatWqFS5fvowHDx7I3F6ebmFhIVWm4raGKJOfn4/s7Gw0bdpUEgTS1dWVBD8fPHiA9u3by9VWQ2DgiIiIiIiIiIiIiOgtFxMTg8zMTIwcORJBQUGC7Xfu3GnQ9mxtbREQEICAgAAUFBRg06ZNmD9/Pj766CO5AkempqYAgLt378rcnpOTg+zsbGhoaDTIaJu6MDIygrq6OpSUlBAcHFyvaefq2r6GhgaePn2K7Oxs6Ovr16s+ExMTAMD9+/dlbpeVnp+fj/DwcDRv3hy//vqrYGRaQ59XtdW5c2eEhYXh0qVLMreXp3fq1EmqTMVt8pSxtbWFmpoanjx5gocPHwpGrckqU97WmTNncOnSJUHgqKioCNeuXYO6urpk+r6G0rjhukYUEhKCbt26QUNDAwYGBhg5ciSSk5OrLePr6wuRSCT4kTXcjYiIiIiIiIiIiKgxlU9fVlxcrPC6srKyAMieGuz27dtVPiRvCOrq6pg3bx5MTEzw5MkTPH78uMYy3bt3h4aGBhITE2Wuj7N7924AgLOzc6OPWqmKsrIy3NzckJubi1OnTr3y9ps0aQI3NzcAwA8//CBXmerOo969ewMATpw4IZkiraJ9+/YJ0nJyclBaWgoTExNB0KioqAgHDx6Uq1+NZdCgQQCAI0eOCKa+S09PR0xMDJo2bQpnZ2dJurOzM/T09JCcnIwrV64I6ty/fz8AYPDgwZI0DQ0N9OvXDwDw22+/yVWmYv/Kt1d09OhRFBQUoH///g0+Ou2NDBxt374dY8aMweXLl2FiYoKSkhKEhoaid+/e+O9//1tjeTMzM/Ts2VPyU3kxNyIiIiIiIiIiIqLGZmRkBBUVFSQnJ6OkpKRedZWPwLlx40adypePWDhw4IDUGkfZ2dmYOHEiioqK6tW/cocOHUJcXJwgPTExEenp6dDW1pZrZIyWlhb8/f1RWlqKadOmSQUybt68iWXLlgEAZs6c2SD9rqvFixdDSUkJfn5+iIqKEmzPy8vDjh078OLFi0Zpf8GCBRCJRPjqq69w+vRpqW3FxcU4duyYVFp151Hbtm3h4eGBrKwsLFiwQDIVHwCEh4cjJCREUKZZs2bQ09PDtWvXEBsbK0kvKSnBggULcPPmzXrtX305OjrC2dkZjx8/xoIFCyTpxcXF+Pjjj1FUVISZM2dCRUVFsk1VVRXTp08HAMG5t3btWly9ehWurq7o3r27VFtz584FACxbtkwq2Hn+/Hl8//330NfXx8SJE6XKTJo0Cbq6uggLC8OBAwck6Y8fP0ZAQAAA4JNPPqnvYRB446aqKywsxMKFCwEAI0aMwP79+5GWlgY7Ozs8fvwYX3/9NTZs2FBtHZMmTUJgYOAr6C0RERERERERERGRbKqqqvD29saRI0fQuXNndOvWDaqqqnB2doafn1+t6vLy8oK6ujq+/fZbXLt2DaamphCJRJg/fz5sbW1rLO/g4ABPT0+Eh4fDxsZGMlIlKioKRkZGGDp0KMLCwuqym1KioqKwfv16mJmZoWvXrtDV1UVaWhpiYmJQWlqKoKAgyaiXmixfvhxxcXEIDw+HtbU1XF1dkZ+fj8jISBQUFGDmzJmCERyvmouLCzZv3ozp06fD3d0d9vb2sLGxgYqKClJTU3HlyhW8fPkSw4cPh4aGRoO37+rqilWrViEgIAD9+vWDg4MD2rZti4yMDCQlJeHly5fIzs6W5B8yZAiio6Ph4eEBd3d3aGlpwcjICCtWrAAAbN26Fc7Ozti8eTNOnjwJBwcHyev38ccfY9OmTVKvn7KyMgICArB48WK4urqiX79+MDAwQHx8PNLT0zFt2jRs3ry5wfb3P//5Dx49egQASEtLAwBs27YNx48fB1A23V7lUU7BwcFwcnLC+vXrERkZifbt2+PChQu4c+cOevfujU8//VTQzmeffYaIiAicO3cObdu2RZ8+fXD37l3Ex8fD2NgYO3bsEJTp378/Zs2ahfXr16NLly7w9PREYWEhwsPDIRaLERwcLAiaGhgYYMeOHRg1ahRGjhwJNzc3GBoaIiIiAtnZ2Zg7d67kvdqQ3rgRRxcuXEBGRgYASOa6NDU1Ra9evQBAcgJUZ926dVBTU4O5uTlGjx5d4xR3RERERERERERERI1h27ZtGDduHDIzM7Fnzx5s374d0dHRta7H1NQUYWFh6NWrF86ePYsdO3Zg+/btkofo8ggLC8PixYthbGyMP/74A4mJiRg9ejTi4uLqvT5OOV9fX3zyyScwNTVFQkICQkNDkZKSgoEDByIiIkIyKkMeOjo6iI6ORlBQEIyMjHD48GHExMTAwcEBe/bswfr16xukz/U1depUXLx4ERMmTMCzZ89w9OhRnDhxAnl5eXj//fdx9OhR6OnpNVr78+bNQ3R0NP7zn//g3r172L9/P65du4aOHTvim2++kco7c+ZMfPbZZ9DW1kZoaCi2b98uNZKobdu2iI+Px5gxY/D06VMcOnQIubm52LVrF0aPHg0AMDQ0lKpz0aJF2LVrFzp16oTY2FhERESgc+fOiIuLg4ODQ4Pu6+XLlxEfH4/4+HjJmksPHz6UpF2+fFlQpm3btrh8+TJ8fX3x5MkTHDx4EEpKSvj8889x6tQpqKmpCcqoq6vj9OnT+Pzzz6GpqYlDhw7h7t278PX1xaVLl2BtbS2zf+vWrUNwcDDatWuH8PBwnD9/Hv3798eZM2cwbNgwmWVGjBiBM2fOYMCAAbh8+TKOHTuGNm3aYOfOnYLXr6GIxGKxuFFqbiQhISEYM2YMACAiIgIeHh4AgHHjxmH37t1QU1NDQUGBzLK+vr7Yu3cvrK2tUVhYKFl4q2nTpvjzzz8FC1KVe/nypdT8hrm5uTA3N0dOTg50dXXrvU/d5/9U7zrq6suRbgpr2/7EIIW1rdN7nMLa9vm7/t/MqKvYGbE1Z1IQRb4PElePV1jbRBXx8+DV4+fB6+dtfR8M7NlKYW3T6+VtfQ/ws+DV42eBbPzbgOoiNzcXenp6Dfac6N+goKAAKSkpsLKyavB1N4jo9bVixQp8+umnWLFihdS0b/T6kPf6/MaNOKqKPPGvefPmITMzE3///TeSk5Px3XffAShb+C04OLjKcsuXL4eenp7kx9zcvMH6TURERERERERERET0JigoKMD169cF6adPn8bXX38NZWVlycgjenO9cYGjikGbx48fC/7fqlXV39a0t7eHtra25Pf3339f8v979+5VWe7TTz9FTk6O5Kd8iBsRERERERERERER0dsiOzsbHTp0gJ2dHQYPHox3330XXbp0Qb9+/fDs2TOsWLECFhYWiu4m1ZOyojtQWz169IChoSEyMzMRGhqKMWPGIC0tDXFxcQAAb29vAICdnR0AYPr06Zg+fToAYOnSpZg+fTqMjY0BQGpuRktLyyrbVFNTkzmPIREREREREREREVFDy8jIwLx58+TKa2dnh4ULFzZyj+h14evrK1c+IyMjrFmzpsHb19PTw7x58xAeHo5z584hNzcX+vr6eOeddzBjxgy88847Dd4mvXpvXOBIVVUVX3/9NaZMmYLQ0FBYW1sjMzMTz549g5GRkeQieePGDQBlF9lyX3zxBZYtWwZra2uIxWIkJycDAFq0aIFJkya9+p0hIiIiIiIi+r/27jtOqurgH/9nacvSpQoqIBHBrtgQgoCKYjeixvpYo35jNLGjsSbRqDFGLKka9IlR80RiS9QIiqgoYohibKhYUDEgFpqKCPP7w2V+jMsiS3FZfb9fr3kxc88995y73Dt3dj9zzgWAL5gzZ05uvPHGZVq3f//+gqNvkGU9Lrp06bJKgqOKior84he/WOnbZfVS56aqS5Jjjz02N910UzbffPNMnTo1ZWVl2XffffPYY4+lU6dO1da76KKL0qdPn8yaNStvv/121ltvvRx//PH517/+lfbt23+FewAAAAAAsGRdu3ZNoVBYpsdDDz1U293lK7Ssx8Xrr79e212lDqtzI44WOeSQQ0ruUfRFhUKhyrKzzz47Z5999qrsFlAH3fNE9fc4W9V227b6+7IBAAAAAHzV6mxwBEDd1ffqvrXW9tgTx9Za2wAAAACwuquTU9UBAAAAAACw8gmOAAAAAAAASCI4AgAAAAAAoJLgCAAAAAAAgCSCIwAAAAAAACoJjgAAAAAAAEgiOAIAAAAAAKBSg9ruAAAAAMDq4J4nptRa27tt27nW2gYAWJwRRwAAAAAAX4GrrroqG220UcrLy1NWVpYBAwbUdpfqhFtuuSVbbrllmjRpkrKysnTt2rW2u8RqaMCAASkrK1vqo169JUcib731Vo488sh06tQpjRs3zvrrr5/zzz8/n3zySbXtffzxxznvvPOy/vrrp3HjxunUqVOOOuqovP3220vt5w033JBtttkmzZo1S+vWrbPbbrvlscceW6F9X9mMOAIAAAAAvlJ9r+5b211YJmNPHLvStvW3v/0tP/zhD7PGGmtkr732StOmTdOzZ8+UlZWlf//+eeihh1Zo+xdccEEuvPDCDB8+PEccccRK6fPq4Mknn8yhhx6axo0bZ+edd06rVq3Stm3b2u7W11bXrl3zxhtvpFAo1HZXamzw4MHVhooTJkzIs88+m379+lUpe+WVV7LddttlxowZ2XjjjdOvX7/861//yk9+8pM88MADeeCBB1JeXl5S55NPPskOO+yQcePGpWPHjtl7773z+uuvZ/jw4fn73/+ecePGpVu3blXa+tGPfpRhw4aloqIiO++8cz755JOMHDky999/f2677bbss88+K+NHscIERwAAAAAAq9gdd9yRJLntttuyww47JElef/31nHXWWbXYq9Xf3XffnYULF+bqq6/OUUcdVdvdYTU2dOjQasu23XbbJMlhhx1WpeyII47IjBkzctJJJ2XYsGFJks8++ywHHHBAbr/99vz85z/PBRdcUFLnZz/7WcaNG5ftttsu999/f5o1a5YkueKKK3LqqafmqKOOqhIGjxo1KsOGDUubNm3y+OOPp3v37kmSxx9/PAMGDMiRRx6ZAQMGpFWrVsv5E1h5TFUHAAAAALCKvfXWW0myxFEIVM/PjRX18ssvZ/z48WncuHH233//krLx48dn7Nixad++fS677LLi8gYNGuQ3v/lNGjZsmKuuuiqfffZZsezTTz/NNddckyS59tpri6FRkpxyyinZdNNNM2bMmEyYMKGkrSuuuCJJcs455xRDoyTZbrvtcvzxx+fDDz/M9ddfv/J2fAUIjgAAAAAAVpELLrggZWVlGT16dJJk3XXXLd5vZd11102SjBkzpuQ+LDWdaq5r16658MILkyRHHnlkybYWjXq44YYbUlZWlgsuuCAvvfRSDjzwwHTo0CH16tUrjoZ65ZVXcsEFF2S77bbLmmuumUaNGmXttdfO//zP/+Sll15aYtuL7jm0YMGCXHrppVl//fVTXl6eddZZJ2eeeWbmzZtXpc67776boUOHZsMNN0yzZs3SsmXLrL/++vmf//mfjB8/vqS/w4cPT5IMHDiwuE833HBDcVsfffRRfvrTn2bjjTdORUVFWrZsme233z633nprtT+rsrKyFAqFXH311dlss83SpEmTbL755kk+H32y6Oc2atSobL/99mnevHnat2+f733ve5k5c2aSZPr06TnuuOOy1lprpXHjxtlmm22WOt3gE088kf333z8dO3Ys/lyPOeaYTJkypcq6i46ZG264IePHj88ee+yRNm3apKysLE8//XS1bSzNE088kQMPPDBrrbVWysvL07Fjx+y44475wx/+kCR56KGHUlZWljfeeCNJSo6hL07/9vrrr+fggw9Ou3bt0rRp02y11Va59dZb8/rrry/x3l0ffvhhrr766uyyyy7p0qVLysvL06ZNmwwePDgjR45cYn8X3a/o9ddfzx133JHevXunadOmad26dQ466KBioLgsbrrppiTJnnvumZYtW5aU/eMf/yiWfXE6ug4dOqRfv3754IMP8uijjxaXjx07NjNnzsy3vvWtbLHFFlXa22+//ZJ8PlpukY8//jgPPvhgSfmX1alNpqoDAAAAAFhFNt988xx++OG57777Mm3atAwZMiTNmjXLHXfckZ122ikjRoxIhw4dMnjw4GKdb3/72zVqY7/99suoUaMyceLE9O3bN+utt16xbM011yxZd9KkSdl6663Tpk2bDBw4MB988EEaNmyYJLnuuuty2WWXZeONN87WW2+d8vLyPP/88/nTn/6UO++8M4888kg23XTTJfbh4IMPzj333JMBAwakR48eeeSRR3LZZZfl7bffLv7hPklmz56dbbfdNq+99lrWWWedDBo0KA0aNMiUKVNy6623plu3btlmm22y3nrr5fDDD8+jjz6ayZMnZ5dddinuy6L9mz17dgYOHJgJEyakXbt22WOPPTJ37tw8+OCDeeSRR/L4448Xpx77ouOPPz7Dhw9P//79s8EGG+TTTz8tKb/99ttz7bXXZrvttsvgwYMzbty4XHfddXn55Zdz2223ZbvttsuCBQvSr1+/vP7663niiScyePDgPPnkk9lkk01KtvXrX/86J554YpJk6623Tr9+/TJp0qRcf/31ueuuuzJmzJhssMEGVfr48MMP59hjj83666+fnXfeOVOnTk29ejUfCzJs2LCccsopWbhwYbbccstsv/32mTFjRp555pmcfvrp+d73vpc111wzhx9+eG677bbMnTs3hx9+eLH+4veUeuWVV9KnT5+8++67WW+99bLTTjtl6tSpOfjgg3PSSSctsf1x48blpJNOSteuXdOjR49st912mTJlSu6///7cf//9ue6666qdhvDXv/51rrjiivTr1y+77bZbnnjiidx6662ZMGFCJk6cmIqKii/d/z//+c9JkkMPPbRK2cSJE5MkvXr1WmLdXr165cEHH8wzzzxTDMSWpU6SPPPMM8VlkyZNyrx589KuXbusvfbay1SnNgmOAAAAAABWkX322Sf77LNPBgwYkGnTpuXyyy8vjuB4/fXXM2LEiPTs2bNkFE1NXX755bngggsyceLEHHPMMUsdsXTrrbfmBz/4Qa688srUr1+/Sl+PO+644kioRYYPH56jjjoqP/rRj4qjJhb3xhtvpEmTJnn55ZeL4c5rr72WXr165c9//nMuvPDCfOtb30ry+T2eXnvttey11165/fbbS4KQd999N9OmTUvyeXj27W9/O0cccUQmT56coUOHVhnJcvbZZ2fChAkZOHBg7rzzzjRv3jxJ8uKLL6Z///656qqrMmjQoOyxxx5V+vy3v/0tTz31VDbaaKMl/pyuueaa3HXXXdl9992TfB5S9enTJ2PGjEn//v3Tp0+f/PGPfyyGbueee25+9rOf5fLLL8+NN95Y3M6i0KRjx4658847s+WWWxbLrr/++hxzzDE58sgjM27cuCp9GD58eC699NKcccYZS+zjsnj44Ydz8sknp1mzZrn99tuz4447Fss+++yz3H///UlSPAYfeuihzJ07t9rj8fjjj8+7776b448/Ptdcc03xGPrnP/+ZPffcc4l1evTokccffzy9e/cuWf7UU09lhx12yMknn5wDDjigZMq3Ra699to88sgj2W677ZJ8PsJs0KBBeeyxx3LLLbd86X2vHn/88UyePDlt2rTJrrvuWqV80YivJYU5iy9fNBJrVdVp2rRpWrVqlQ8++CCzZ88uHsu1xVR1AAAAAADfEO3atcull15aJTRKkt69e1cJjZLPp7/r27dvHnrooeJUbV901VVXlYxuWnfddYsjPB555JHi8nfffTdJssMOO1QZPdOuXbtsvPHGy7Qfc+fOzfXXX5969erl17/+dckf2nv27JlzzjknSaodcXTmmWdWGxoln4+gWhQaJUnz5s3zve99L8nn91266qqriqFRkpx22mkpKyvLmDFjSrZzySWXZMGCBfntb39bEholydFHH5299torTzzxRJ566qkqfdhkk01y+umnV9vHZXHJJZekUCjkxz/+cUlolHx+H5/ddtttmbf1yiuv5IEHHkirVq3yi1/8ouQY2mWXXXLAAQcssd66665bJTRKki222CInnHBCZs2aVZzK8YtOPvnkYmiUJE2aNMkpp5yS5PNQ7Mv86U9/SpIceOCBJf9fi8yZM6e43SVp2rRpks+Dw1VZp7p6tcWIIwAAAACAb4iddtppqX+8njNnTu6+++48/fTTef/99zN//vwkyTvvvJNCoZDJkydXmaKrYcOGGThwYJVtrb/++sW6iywKT37xi1+kQ4cO2X333ZdrdMWECRPy8ccfZ6uttkrPnj2rlB922GE56aSTMnbs2CxcuLBKSLXXXnstdfs777xzlWXdunVLkmy11VZZY401SspatmyZ1q1bl+zrwoUL88ADD6RJkybZZZddlthOv379ctddd2X8+PFV7pezxx57pKysbKn9XJrPPvuseN+lY489drm3s8jYsWOTJIMHD17i6KDvfve7xWnhvmjBggV54IEH8thjj+Wdd94p3vvq5ZdfLvn3i5b0/7Ck42pJ5s+fn//7v/9L8vnxwLITHAEAAAAAfEN07ty52rIHH3wwBx54YHFU0JIsaTTEmmuuucQRTIsCoUUhQZLsuOOOOfnkk3PllVfmoIMOSoMGDdKrV68MGjQoRx11VDGc+TJTp05NkuK0f1/UqlWrtGzZMjNnzswHH3yQNm3alJQv7eeQJGuttVaVZYvCkiWVLSp/7733iq9nzJhRHGnSqFGjpbY3Y8aMKsu+rI9f5r333svHH3+c1q1bVwm6lseioGadddZZYnl1/X3rrbeyxx57FO8NtCTVjbJZ0tRuSzquluTee+/Ne++9l+7du2fbbbdd4jqL/k8/+uijJZbPnTu3pM1VVae6erVFcAQAAAAA8A3RuHHjJS6fM2dODjjggLz//vs577zzcuCBB6ZLly6pqKhIWVlZDj744Nxyyy0pFApV6n5xNM+XueKKK3LcccflzjvvzKhRozJ27NiMHz8+l112WW655ZYMGTJkufbti5Y2Wqe6n8MiS9unZd3fhQsXJvk8NPiyfVrStHlf1se64phjjsnEiRMzZMiQnHHGGenRo0eaN2+eevXq5fe//32OO+64JR5XSc2PrcXddNNNSVKcMnFJOnfunKeeeipvvfXWEssXLe/SpUtJncXLVkaduXPn5sMPP8waa6whOAIAAAAAoPY98sgjee+997LffvvlwgsvrFL+6quvrtT2evTokTPOOCNnnHFGPvnkk1xzzTU5/fTT8//+3/9bpuCoU6dOSZI33nhjieUzZ87Mhx9+mIqKipUy2mZ5tG3bNo0bN069evUyfPjwFZp2bnnbr6ioyPvvv58PP/wwrVq1WqHtdezYMUny5ptvLrF8Scvnzp2bkSNHpkOHDvnLX/5SZWTayj6uFpk1a1buvvvuJEsPjjbbbLPceeed+fe//73E8kXLN91005I6i5ctS50ePXqkvLw87777bt5+++0qo9aWVKc2LX9cBwAAAADAcls0fdlnn31W69v64IMPkix5arBXXnml2j+SrwyNGzfOaaedlo4dO+bdd9/N9OnTv7TOlltumYqKikyYMGGJ98dZNNqkb9++KzRqZUU0aNAgAwYMyKxZs/LAAw985e3Xr18/AwYMSJL8/ve/X6Y6SzuO+vTpkyT55z//WZxWbXGL7ie0uJkzZ2bhwoXp2LFjldBo/vz5uf3225epXzV122235ZNPPknfvn2XOv3h7rvvniS5++67q0x9N23atDzyyCNZY4010rdv3+Lyvn37pmXLlpk8eXKefvrpJbadJHvuuWdxWUVFRXbYYYckyV//+tdlqlObBEcAAAAAALWgbdu2adiwYSZPnpwFCxas0LYWjcCZNGnSctVff/31kyR/+9vfSu5x9OGHH+boo4/O/PnzV6h/i9xxxx0ZN25cleUTJkzItGnT0qxZs2UaGdO0adMcddRRWbhwYU444YSSIOOll17Kz372syTJSSedtFL6vbx+/OMfp169ejnyyCPz0EMPVSmfM2dO/vjHP+bjjz9eJe2feeaZKSsry0UXXZTRo0eXlH322We55557SpYt7Tjq3r17dtxxx3zwwQc588wzi1PxJcnIkSNz6623VqnTvn37tGzZMs8++2zGjh1bXL5gwYKceeaZeemll1Zo/6qzKDg87LDDlrreNttsk759+2b69Ok588wzi8s/++yzfP/738/8+fNz0kknpWHDhsWyRo0a5Qc/+EGSVDn2rrjiijzzzDPp379/ttxyy5K2TjnllCTJz372s5Kw8/HHH8/vfve7tGrVKkcfffRy7vHKZao6AAAAAGpF36v7fvlKq8jYE8d++UqwijVq1CiDBw/O3Xffnc022yy9evVKo0aN0rdv3xx55JE12tbOO++cxo0b51e/+lWeffbZdOrUKWVlZTn99NPTo0ePL62/1VZbZdCgQRk5cmTWX3/94kiVhx56KG3bts3ee++dO++8c3l2s8RDDz2UYcOGZa211soWW2yRFi1aZOrUqXnkkUeycOHCXHjhhcVRL1/m5z//ecaNG5eRI0emW7du6d+/f+bOnZsHH3wwn3zySU466aRaH8Hx7W9/O9dee21+8IMfZODAgdl4442z/vrrp2HDhnn99dfz9NNPZ968edl3331TUVGx0tvv379/LrvsspxxxhnZYYcdstVWW6V79+6ZMWNGJk6cmHnz5uXDDz8srr/XXntlzJgx2XHHHTNw4MA0bdo0bdu2zSWXXJIk+c1vfpO+ffvm2muvzf3335+tttqq+P/3/e9/P9dcc03J/1+DBg1yxhln5Mc//nH69++fHXbYIa1bt84TTzyRadOm5YQTTsi11167Uvf5rbfeypgxY9KoUaMccMABX7r+8OHDs91222XYsGF58MEHs+GGG+bJJ5/Mq6++mj59+uSss86qUuecc87JqFGj8thjj6V79+7p169f3njjjTzxxBNp165d/vjHP1aps9NOO+WHP/xhhg0bls033zyDBg3Kp59+mpEjR6ZQKGT48OErPJ3gymLEEQAAAABALbnuuuty2GGH5b333svNN9+c66+/PmPGjKnxdjp16pQ777wzvXv3zqOPPpo//vGPuf766/POO+8s8zbuvPPO/PjHP067du1y7733ZsKECTnwwAMzbty4lfYH7SOOOCKnnnpqOnXqlPHjx2fEiBF57bXXsttuu2XUqFHFURnLonnz5hkzZkwuvPDCtG3bNnfddVceeeSRbLXVVrn55pszbNiwldLnFXX88cfnX//6Vw4//PDMnj07f//73/PPf/4zc+bMySGHHJK///3vadmy5Spr/7TTTsuYMWPyne98J1OmTMltt92WZ599Nptsskl++ctflqx70kkn5ZxzzkmzZs0yYsSIXH/99SUjibp3754nnngiBx10UN5///3ccccdmTVrVm688cYceOCBSZI2bdqUbPPss8/OjTfemE033TRjx47NqFGjstlmm2XcuHHZaqutVvr+3nzzzVm4cGF23333Zbq/Vffu3fPUU0/liCOOyLvvvpvbb7899erVy7nnnpsHHngg5eXlVeo0btw4o0ePzrnnnpsmTZrkjjvuyBtvvJEjjjgi//73v6udHu/KK6/M8OHDs8EGG2TkyJF5/PHHs9NOO+Xhhx/OPvvss6K7vtKUFQqFQm13oq6ZNWtWWrZsmZkzZ6ZFixYrvL0tT//fldCr5fPT/QbUWtsb/3P3Wmu7eZ+lD1FclfZ4YcW/mbG8VudvU31Tz4Pdtu1ca23XJt8qXLJv6nngevDVcx4smesBq4Nv6jngWvDVcy1YMteCr97X4XeDlf13oq+DTz75JK+99lrWXXfdNG7cuLa7A3xFLrnkkpx11lm55JJLSqZ9Y/WxrO/PpqoDAIBvsA9GXVZrba+x0xm11jYAAFBzn3zySV599dVsuOGGJctHjx6diy++OA0aNCiOPKLuEhwBAAAAAABf6sMPP8xGG22UHj16pHv37mncuHFefvnlTJw4MUly+eWXp0uXLrXcS1aU4AgAAAAAYDUyY8aMnHbaacu0bs+ePTN06NBV3CNWF0ccccQyrde2bdtcfvnlK739li1b5rTTTsvIkSPz2GOPZdasWWnVqlV23XXXnHjiidl1111Xept89QRHALVoyk82qbW2O5/3n1prGwAAAKjenDlzcuONNy7Tuv379xccfYMs63HRpUuXVRIcVVRU5Be/+MVK3y6rF8ERAAAAAMBqpGvXrikUCrXdDVZDjgu+CvVquwMAAAAAAACsHgRHAAAAAAAAJBEcAQAAAAAAUElwBAAAAACsdO7FArB6Wdb35QaruB8AAAAArMY+GHVZbXeBr5n69esnSebPn5+Kiopa7g0Ai8yfPz/J//8+XR0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",
-      "text/plain": [
-       "<Figure size 1500x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(15, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(model_filter(\"ft_transformer\", models)))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model_data', ci=None, data=model_filter(\"ft_transformer\", models), palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=15)\n",
-    "plt.title('FT-Transformer Models with Different Oversampling Methods', fontsize=20, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>model</th>\n",
-       "      <th>data_sample</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "      <th>model_data</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.644251</td>\n",
-       "      <td>0.766097</td>\n",
-       "      <td>0.957526</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.681864</td>\n",
-       "      <td>0.804999</td>\n",
-       "      <td>0.974908</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.567129</td>\n",
-       "      <td>0.688707</td>\n",
-       "      <td>0.909057</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.576840</td>\n",
-       "      <td>0.715856</td>\n",
-       "      <td>0.975236</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.687967</td>\n",
-       "      <td>0.797628</td>\n",
-       "      <td>0.957317</td>\n",
-       "      <td>deepgbm_pure</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.460507</td>\n",
-       "      <td>0.638240</td>\n",
-       "      <td>0.947905</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.534332</td>\n",
-       "      <td>0.664453</td>\n",
-       "      <td>0.899439</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.445301</td>\n",
-       "      <td>0.617498</td>\n",
-       "      <td>0.967371</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.509262</td>\n",
-       "      <td>0.649690</td>\n",
-       "      <td>0.918171</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.492702</td>\n",
-       "      <td>0.637537</td>\n",
-       "      <td>0.927253</td>\n",
-       "      <td>resnet_like_ctgan7000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>150 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     region        model data_sample       CSI       MCC  Accuracy  \\\n",
-       "0     seoul      deepgbm        pure  0.644251  0.766097  0.957526   \n",
-       "1     busan      deepgbm        pure  0.681864  0.804999  0.974908   \n",
-       "2   incheon      deepgbm        pure  0.567129  0.688707  0.909057   \n",
-       "3     daegu      deepgbm        pure  0.576840  0.715856  0.975236   \n",
-       "4   daejeon      deepgbm        pure  0.687967  0.797628  0.957317   \n",
-       "..      ...          ...         ...       ...       ...       ...   \n",
-       "25    busan  resnet_like   ctgan7000  0.460507  0.638240  0.947905   \n",
-       "26  incheon  resnet_like   ctgan7000  0.534332  0.664453  0.899439   \n",
-       "27    daegu  resnet_like   ctgan7000  0.445301  0.617498  0.967371   \n",
-       "28  daejeon  resnet_like   ctgan7000  0.509262  0.649690  0.918171   \n",
-       "29  gwangju  resnet_like   ctgan7000  0.492702  0.637537  0.927253   \n",
-       "\n",
-       "               model_data  \n",
-       "0            deepgbm_pure  \n",
-       "1            deepgbm_pure  \n",
-       "2            deepgbm_pure  \n",
-       "3            deepgbm_pure  \n",
-       "4            deepgbm_pure  \n",
-       "..                    ...  \n",
-       "25  resnet_like_ctgan7000  \n",
-       "26  resnet_like_ctgan7000  \n",
-       "27  resnet_like_ctgan7000  \n",
-       "28  resnet_like_ctgan7000  \n",
-       "29  resnet_like_ctgan7000  \n",
-       "\n",
-       "[150 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "models['region'] = models['region'].apply(lambda x: x[0].upper() + x[1:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_plot(df, region):\n",
-    "    df = df.loc[df['region'] == region, :]\n",
-    "    df.sort_values(by=['CSI'], ascending=False, inplace=True)\n",
-    "\n",
-    "    plt.figure(figsize=(8, 8))\n",
-    "    palette = sns.color_palette(\"tab20\", n_colors=df['model_data'].shape[0])  # 더 많은 색상 지원\n",
-    "    ax = sns.barplot(x='region', y='CSI', hue='model_data', ci=None, data=df, palette=palette)\n",
-    "\n",
-    "    # 바 내부에 model_data 값 표시 (너무 아래로 내려가지 않도록 조정)\n",
-    "    for p, label in zip(ax.patches, df['model_data']):\n",
-    "        ax.annotate(f'{label}', \n",
-    "                    (p.get_x() + p.get_width() / 2., p.get_height() * 0.4),  # 높이를 20% 정도로 조정\n",
-    "                    ha='center', va='center', fontsize=10 ,rotation=90, color='black', fontweight='bold')  \n",
-    "\n",
-    "    # 범례 제거\n",
-    "    ax.get_legend().remove()\n",
-    "\n",
-    "    plt.ylabel('CSI', fontsize=20)  # y축 라벨 변경 및 스타일 조정\n",
-    "    plt.xlabel('', fontsize=20)  # x축 라벨 변경 및 스타일 조정\n",
-    "    plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "    plt.yticks(fontsize=10, fontweight='bold')  # y축(지역명) 폰트 크기 증가\n",
-    "\n",
-    "    plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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jR/n6668ZOHAgSUlJ9Z7z/PPP4+/vX/PRunXrRn4XIiIiInImaHFhuD5ms/mUz3nggQfIyspi7969HDp0iPfeew+AnJwcPvnkk3rPe+SRR8jLy6v5ODGQi4iIiEjL1eLC8Imjsunp6bU+j4mJqffcnj174uPjU/P11KlTaz4/2Yiyu7s7fn5+Vh8iIiIi0vK1uDA8cOBAgoODAZg7dy4AycnJrFu3DoAJEyYA0LVrV7p27crbb79dc+6TTz5JRkZGzddz5syp+bxt27anu+siIiIicoZpcWHYzc2tpmLE3Llzad++Pd26dSM/P5+QkJCaShP79+9n//79NYvtAJ555hkiIiLo1KkTHTt25OabbwYgIiKiplSbiIiIiDiOFheGAW655RZmz55Nnz59SE5OxjAMLrnkEtasWUNUVFS95z333HMMGzYMk8lEUlISHTt25LbbbmPTpk2qNSwiIiLigFrkphtgme974pzfP6trQd2jjz7Ko48+ejq7JSIiIiItSIscGRYRERERaQoKwyIiIiLisBSGRURERMRhKQyLiIiIiMNSGBYRERERh6UwLCIiIiIOS2FYRERERBxWi60zLCIiIvJ3Nfyt4U3a3uq7Vjdpe38nGhkWEREREYelMCwiIiIiDkthWEREREQclsKwiIiIiDgshWERERERcVgKwyIiIiLisBSGRURERMRhKQyLiIiIiMNSGBYRERERh6UwLCIiIiIOS2FYRERERByWwrCIiIiIOCyFYRERERFxWArDIiIiIuKwFIZFRERExGEpDIuIiIiIw1IYFhERERGHpTAsIiIiIg5LYVhEREREHJbCsIiIiIg4LIVhEREREXFYCsMiIiIi4rAUhkVERETEYSkMi4iIiIjDUhgWEREREYelMCwiIiIiDkthWEREREQclsKwiIiIiDgshWERERERcVgKwyIiIiLisBSGRURERMRhKQyLiIiIiMNSGBYRERERh6UwLCIiIiIOS2FYRERERByWwrCIiIiIOCyFYRERERFxWArDIiIiIuKwFIZFRERExGEpDIuIiIiIw1IYFhERERGHpTAsIiIiIg5LYVhEREREHJbCsIiIiIg4LIVhEREREXFYCsMiIiIi4rAUhkVERETEYSkMi4iIiIjDUhgWEREREYelMCwiIiIiDkthWEREREQclsKwiIiIiDgshWERERERcVgKwyIiIiLisBSGRURERMRhKQyLiIiIiMNSGBYRERERh6UwLCIiIiIOS2FYRERERByWwrCIiIiIOCyFYRERERFxWArDIiIiIuKwFIZFRERExGEpDIuIiIiIw2qxYXjOnDn069cPT09PgoKCuPTSSzl06JBN51ZWVjJs2DAMw8AwDB5++OHT3FsRERERORO1yDA8c+ZMrrrqKrZu3UpkZCSVlZXMnTuXYcOGkZqaesrzn3nmGdauXfsX9FREREREzmQtLgyXlZXVjOROmTKFuLg49u7di6+vL+np6UyfPv2k569Zs4bnnnuOyy+//K/oroiIiIicwVpcGN64cSOZmZmAJQwDREVFMWTIEADmz59f77kmk4lrrrmGqKgo3n//fZtfs7S0FJPJZPUhIiIiIi1fiwvDiYmJNZ+HhYXVfB4eHg5AQkJCvef+61//4siRI8yePZuAgACbX/P555/H39+/5qN169b2d1xEREREzjgtLgzXx2w2n/T4Dz/8wOzZs3n00Uc5++yz7Wr7kUceIS8vr+bjxEAuIiIiIi1XiwvDJ47Kpqen1/o8JiamzvO2b98OwKuvvoqPjw8+Pj41x1599VWio6PrfU13d3f8/PysPkRERESk5WtxYXjgwIEEBwcDMHfuXACSk5NZt24dABMmTACga9eudO3albffftvq/KKiIgoLCyksLKx5rLy8nIKCgr+i+yIiIiJyBmlxYdjNza2mYsTcuXNp37493bp1Iz8/n5CQkJpKE/v372f//v01i+2eeuopzGaz1ccxDz30ELm5uX/59yIiIiIizavFhWGAW265hdmzZ9OnTx+Sk5MxDINLLrmENWvWEBUV1dzdExEREZEWwsXeE5555pkm78R//vMfu8+ZOnUqU6dOrff4qRbU2focEREREfn7sjsMP/XUUxiG0aSdaEgYFhERERFpLLvDMDTtiGpTB2sREREREVvZHYaXLl16OvohIiIiIvKXszsMjxo16nT0Q0RERETkL9ciq0mIiIiIiDSFBs0ZFjlRUfoRTId3UmbKAsDNLxi/dmfhFdammXsmIiIicnKnPQwXFhayePFiDh48iGEYtG/fnrFjx1pthywtk7mqiiMLZpK9Z3WtY8mrviOo21DaTLgZw0k3IEREROTMZHcYNpvNLFq0CIDWrVvTrVu3ep/72Wefcf/995OTk2P1uLe3N8899xx33XWXvS8vZ5DUdT+Tvbt2ED4me+9a3APCiRx28V/XKRERERE72B2GN2zYwIQJEzAMg++++67eMDxr1ixuuOEGDMOoVYqtoKCAe+65h/Lycu67776G9VyaXfae1eDkRPToqwjsPBAXbz8wQ0WRiZzYjRxd+iVZu1cpDIuIiMgZy+7714sXLwYgLCyMiy++uM7n5OTkcPfddwOWkeSOHTvyxBNPMGPGDG666SZcXFwwm8088cQTJCUlNbz30qzK8rPxCAgnrN84XH0CMAwnDCcnXH0CCOs3Do/ACMoLcpu7myIiIiL1sjsMb9iwAcMwmDx5cr0bZnz22Wfk5uZiGAYjR45k27ZtPP3009x66618+OGH/Pbbbzg5OVFSUsKsWbMa/U1I83Dx8qM0L4O8uO21juXFbac0Lx0XL99m6JmIiIiIbeyeJhEbGwvA8OHD633ODz/8UPP566+/jpeXl9XxcePGcdlll/H111+zdOlSHn74YXu7IWeAwM4DSN+8iEM/vIaTixsuHpZFkRXF+VRVltc8R0RERORMZffIcHJyMgCdOnWq83h5eXnN6HGnTp3o27dvnc+76KKLANizZ4+9XZAzROTwKfhEdwEzVJWXUZafTVl+NlUV5WAGn1adiBw+pbm7KSIiIlIvu0eGi4qKAEtFiLps376d0tLSmikS9enYsSNArUoT0nI4u3nQ6YqHyT2wGVP8DspN2QC4+gXh17YXAZ36YRgqqyYiIiJnLrvDsJeXFwUFBfWG2PXr19d83r9///pf2MXy0uXl5fZ2Qc4ghmEQ2HmApkOIiIhIi2R3GI6Ojmbfvn1s3LiRUaNG1Tq+fPnyms+HDBlSbztZWZbdynx9tcCqJSsvNJG6/hdM8dY70Pm3O4vwQefj6h3QvB0UEREROQm772EPGTIEs9nMhx9+WGtUNzMzk99++w2AkJAQ+vTpU287u3btAqBNG23Z21KV5qaz9/PHydi6iNLsVMwV5ZgryinNTiV9yyL2fvYfSnPTmrubIiIiIvWyOwz/85//BODgwYNcfPHF7Nu3j/Lycnbs2MEll1xCcXExhmFw9dVXn7Sd5cuXYxgGPXv2bFjPpdklrfiGikITTq4e+LbtQVD3oQR1H4pv2x44uXpQUWQiacW3zd1NERERkXrZPU1i1KhRXHTRRfz000/Mnz+f+fPn13qOj48PDz74YL1t5OTkMG/ePICTLrKTM1t+wh6c3T3pfsPzuPoEWB0rK8hh7yePkp+gaiEiIiJy5mrQUv/Zs2czfvx4zGZzrQ8vLy++/PJLoqKi6j3/vffeo6ysDIAJEyY0rOfS7KoqynHx9KkVhAHcfAJx9vC2lFkTEREROUPZPTIMlrJq8+fPZ968efz0008kJCTg5uZGv379uOmmm4iOjj7p+QkJCUyZMoVWrVqd8rly5vIIjqQ4PYG4X94hoNMAXL38ACgvMpEbu5GyvEw8w2OauZciIiIi9WtQGD5m0qRJTJo0ye7zZsyY0ZiXlTNE+MBJxP/6HrmxG8mN3Vj7CQaED5j413dMRERExEaNCsPi2IK6DqGqvJTkFd9SUVxgdczF04eokZcS1G1oM/VORERE5NQaFIZ///13HnvsMQAeeOCBU1aOONGXX37Jyy+/DMCLL77I2LFjG9IFOUOEnDWK4O7DKUw9TFm+ZQc6N98gvCPaYTjrWktERETObHYvoDObzdx7771s376d0NBQu4IwwFVXXUVISAjbtm3j/vvvt/fl5UxkGBiGgQGWD8MAw2juXomIiIickt1Dd0uWLCE2NhZnZ2dee+01u1/QMAxef/11evfuza5du1i+fHmdO9lJy5C1a6Wl3nBxvtXjLp6+RI28jJCzzm6mnomIiIicmt0jw3PnzgVg3LhxdO/evUEv2r17d8477zwAvvvuuwa1Ic0vZ996jsyfSUVRPpix+qgoyidh4cdk71vfzL0UERERqZ/dI8MbNmzAMAwuvPDCRr3wBRdcwLx581i3bl2j2pHmk7rRsvV2QOcBBHTqj6u3P2azmYoiE7mxm8g9sJn0jfMI6jq4mXsqIiIiUje7w/CRI0cA6NKlS6NeuHPnzgDEx8c3qh1pPiVZKbgHhNJ+8p21jgV1G8quDx+gOCu5GXom0jAr1i5h/eY1pGWkABAeGsng/sM4e+iYZu6ZiIicLnaH4by8PACCgoIa9cLHzjeZTI1qR5qPk4srFcUFlBfm4ertb3WsvDCXypJCnFxcm6l3IrYrLinmoaf/zbZdm2sd+2XB9/Tu0Y8Xn3oLTw/PZuidiIicTnaHYT8/P3JycsjNzW3UCx8739fXt1HtSPPxbd2N3AOb2fPxw3hHdsClege6iiIThSmHqCwrJqDTgGbupcipfTTrHbbu3ASAm5s7/n4BYDaTl59HWVkp23dv4aNZ73DXzQ80b0dFRKTJ2R2GQ0NDycnJYc+ePYwePbrBL7x3714AwsLCGtyGNK9Woy6nICmWiqJ8TEd2WR80H9t447Lm6ZyIHZauWoSriyvPPvoywwaOtJQHxFJKcvWGFTwx/QGWrlqkMCwi8jdkdzWJQYMGYTab+eWXXxr1wj/99BOGYTBw4MBGtSPNxz0gnG7XPUto37G4B4RhOLtgOLvgHhBGaN+xdL3uWTwCw5u7myKnlJObTVRENMMHnV0ThMFSCnLE4FG0imxNbl5OM/ZQREROF7tHhidOnMisWbNYuHAhq1atYsSIEXa/6IoVK1i4cCGGYTBx4kS7z5czh6t3AK3Pvaa5uyHSKKEh4SQmJ/DDb98wati5BAZY1jTk5GazbPViEpOOEB4W2cy9FBGR08HuMDxlyhTatm1LfHw8l112GStWrKBTp042nx8bG8vll1+OYRi0bduWSy+91N4uyBkm98BmTPE7KTNlAeDmF4xfu14EdOzXzD0Tsc2Ecy/gky/f57X3XuC1916o9zkiIvL3Y/c0CVdXV15++WUA0tPT6d+/P2+88QaFhYUnPa+goIDXX3+dAQMGkJ6eDsArr7yCi4vdeVzOEJVlpcR+/QJxP79F5o5lmOJ3YorfSeaOZcT99CaxXz9PZVlpc3dT5JT+ecU0xo+ehNlsrvPj3LPP47orbm7uboqIyGnQoCR6ySWX8PTTT/Pkk09SWFjIfffdxxNPPMHIkSPp378/YWFheHt7U1hYSFpaGlu2bGHlypUUFhZiNpsBePrpp7n44oub8nuRv1jK6rkUJO4DwHBxxcXTB4CK4gLMFeUUHN1Pyuq5RJ9zdXN2U+SUXJxdeOKB57jiH9eybtMq0jPTAAgLCWdw/+F06ditmXsoIiKnS4OHZZ944gmio6O56667KCoqoqCggPnz5zN//vw6n38sBHt5efH2229z/fXXN/Sl5QyRE7sRw9mF9pPvxK99b6sV+Hlx2zj889vkxG5UGJYWo3OHrnRq36VmsVyAf6DVgrrmkmMy8cUvv7Nx5y7Ss7MBCAsKYlCvnlw1aQJBAf6naEFEROrTqDkKN9xwA+eddx6vvvoqn3/+OZmZmfU+NyQkhOuuu457772XqKioxrysnCEqiky4+4fh36GP1eOGYRDQoS/u/mGU5mU0T+dE7BR35CAffv42m7atp7R6eo+7mzsD+g5h2jV30KGt7WsjmlJyegb/fu5/5JryMZ/w+NG0dJIWLWHJuo28+fj/0UplKkVEGqTRE3ajoqJ4+eWXefnll9m9ezfbt28nKyuL/Px8fH19CQ4Opnfv3vTo0aMp+itnEFefQEpy08jY9gcBnQZYbbqRG7uJkpxU3PyCm7mXIqcWe2gf/3roRkpLS2ruYgGUlJawat0yNm1dx9v/+7hZpkt88M1cckz5eHm4061DewL9/DCbzeTm57P3UBy5+fl89O0PPPmvW//yvomI/B006eq1Hj16KPQ6kOAew0lZ8xOJf8wi8Y9Z9T5H5Ez3/mdvUVJSTERYFAP7DiEoIBgzZnJys9m4dR2p6cl88PnbvPLMO39537bt3Y+3pyefTH+K4IAAq2OZObnc8OiTbNmz9y/vl4jI34VKOUiDRQyZTGluOtl71tZ5PLDbECKGXPQX90rEfrv2bifAP5DP3vkWL08vq2NFRYVcPu0Cdu3d3ix9Ky0vIzggoFYQBggJDMDPx5vsvLy/vmMiIn8TCsPSYIaTM20n3UpY/wmYDu+gLL+6zrBvMH7tzsIrvG3zdrCFqCqvImNzBgXxBbj6uRI+JJyS7BK8Ir1w9XZt7u45hMrKClxdffD08Kx1zMPDEzdXN0pKipuhZxATGcmhhESeefcDRvbvS4CfLwC5pnxWbNpCamYWHWNaN0vfRET+DhSGpdG8wtvgFd6mubvRIpUXlrP7zd0UpRYB4NvGF992vux9by/R50UTMymmmXvoGDp16MrufTu448HrGT54NIEBgQDk5Oawev0yMrMz6Nmtd7P07YqJ43nu/Zms2LiZFRs31zpuAJdPHP/Xd0xE5G9CYVgapbKshPyEPRjOLvjFdMdwPv4jlblzBeX52UQOu7j5OniGO/LTEYpSi3BydaKqvAqAgC4BOLk5kbs3V2H4L3Lj1bfxwJN3snv/Tnbv32l1zGw242Q4ccNVtzRL38YMGURJaRkfffcDeQUFVsf8fLy5aco/OHfIoGbpm4jI34HCsDRYaW4GsXP+S3mhZb6ie0AYHf5xLx5BkQBk7VhGYWqcwvBJ5OzOwdnDmb6P9WXTE5sAMJwM3IPcKcksaebeOY6BfYfw4pNv8u7HrxF35KDVsfZtOnL7DfcwsO/QZuodTBo1gvHDh7Dv8BEyqusMhwYF0bVdG+3iKSLSSHoXlQZLWfsj5QXHF+6U5qRz4Jv/0fmqx3H3D2nGnrUcFUUVeEV44ebnZn2gCipLK5unUw5qcP9hDO4/jMysdNIyLDvQhYeGExJ8ZtTvNQwDJ8Oo2QTkxM9FRKThFIalwQoS94EBYf3G4xPdmcztyzDF7+LQ3FfofPXjzd29FsE9yJ2i1CJMh0w1j2XvzKY4vRjP0NqLueT0ysnLZtuuLaRlpAAQHhpJ/z6DCPQPatZ+zV+5hg+/nUtevvU0CX9fH6Zd+g8mnj2imXomItLyKQxLg5UX5uHuH1qz3bJ/x37E/fgGeXHbifvxTaoqK5q5h2e+kP4hHF1wlF1v7gIg/0g++z7aV3NM/jozv5jBF99+QsWffm5dnF24esr1TLv2jmbp17INm3jp48/qPJabX8Arn8zCw92dcwYP/It7JvL3tGCvieUH80nKKweglb8rozr6cl43v2bumZwuCsPSYC4e3lYL5gzDiXYX3EHsnOkUJO0HM5al7lKv6PHRFCQUkLs31+rxgK4BtBrXqsHtpidmErczHjcPV/qNaZ4qCC3J3F/n8OlXH9R5rLyinM+/+YjAgCCmXHjlX9wz+Oq3+QCM7N+XkQP6EejnB5jJMeWzctMWVm7eyte/L1QYFmmkorIqbvzyCOvjC2sd+2pzDoPaePPJ1DZ4uTk1Q+/kdFIYlgZzD4qg4GgsZfnZuPlabiM7ubrT4ZJ72f/Fs5SZspq5h2c+Jxcnut/WnbyDeRQcsdwC94nxwb+Tf4Paq6qs4tOnv2L1T+swm820P6stJQUlfPT4LK5+6FLGTh3dhL3/+/jht28wDIPLL7qG0cPPJTAgGKp3oFu6ejHf/DibH+Z906AwbDabKSwtBcDb3d3ueb4JKSlEhobw1J231Tp27pBBTH3wUY4kJ9vdLxGx9vKSNNZVB2F3F4MgLxfMZjM5xZWUVpjZcKSQl5ek8Z8Jkc3cU2lqCsPSYP7t+1CWl0n2ntVEDL6w5nFX7wA6XHIvcT+9hblKi8BOJvH3RNwC3AgfGo5/x+MBOP9wPhVFFQT2CLSrvV8/WsCqH6x3BOw3tjfOT37J1qU7FYbrkZxylOioGO6cdp/V460iW9OzW2/WblxJcspRu9pMyzWxePsuDqWmU15p+T1wdXamQ0Q45/bqTkSgbRc87q5umAoKyc4zEeRvfZs2OzcPU0Ehbq7anEWksebtzsPN2WDG5a0Z09m35sLVbDbzR2w+t3+dyLzdeQrDf0MKw9Jg4QMnEj5wYp3HPEOi6XHT//7iHrU8ifMT8W3jS/jQcKvHD/9wmIKEAoa9Psyu9lb9sA5nF2fuePUm3vq35ba/h5cHQREBpBxObbJ+/914e/uQkZlG3JGDtG/T0epYXPxB0jNS8fb2sbm95OxcPlq0nPLKCst0oWrlFZXsO5rModQ0po0bRVTQqS92+nTrwsrNW7nu4Sfo0bE9AX6WQJxrMrH7YBzFJSWM6N/X5r6JSN2yCiuJCXLj3C7WF52GYTC2ix8xQW4k5pQ1U+/kdFIYFjnDVJZVUm4qtwpRtspJyyWqQwR9z+ll9biHtwfZqc0bhkvKypj5xxIW79jB0UzLFJrokGDG9e7NDWPOwdPN7RQtnD5DB47k98U/c8OdVxAdFUNggGXaT05uNkeTEzBjZszZ59nc3qJtuyivqCDAx4sOEeH4eLgDUFBSyqHUNHILili0bTfXjTl1FYibL7+EnbEHyM0vYNOuPVbHzIC/jw83X3aJ7d+siNQpws+F+KxSZm3IYkJ3f0K8nQHILKzk9z15HM4qpZW/7sL8HSkMS6NVlpWStuFX8hP21GzAcaKeN7/cDL06vbLyCvngx9Vs2ptA6/BAbrl4ODsOJjGid3uiw0492rfm7jU1n+cfybf6+hhXX/vfdH0CvclIyqIg93gJrqyUbJLjUvENsn1ks6ll5OVx/nPPE1s9t/VYzj+QksLSnbv4+I8l/P74o4T6N2yudGPdfv3d7Nq7ncSkIyQkxZOYfMTST7Olp9FRMdx23b9tbi8hMwsvD3funDQOd1frt9nS8nJe/WkBCZm2zalvFRbGh8/8hy9+/Z2NO3eRnp0DQFhQIAPP6slV508gJDDA5r45qtjYWNatW0daWnUN6fBwhgwZQufOnZu5Z3KmmNI7kNeXp/OfeSn8Z15Kvc+Rvx+FYWm0xMWfkr23ep7qn0czG1FNoqS4iMMH9+Lk5EyXHn0a3lATS0jNZuI9M0jLyQegf9fWmAqL+ddL33LnZWfz9M2TmuR1woeFn/pJf9JzWDdW/7SeJ/4xHYDkuFSeuuwFKisq6Tm8e5P0qyGe+vob9icnYwDtI8IJ8/PHjJkMk4m41DQOpqTw1Nff8M4tNzdL/wIDgvj4ja/48ffvWL95NenVm26EhYYzuP9wLpp4KZ4ettd9rqoy4+LqhJuLc61jri4uODs7UVZhe+nBoAB/7rrmr69k8XdQWVnJ9OnTmT9/fq1j77//PuPHj+fxxx/H2bn2/5U4ljvPDiU+u5Qfd9Ye1AGY3NOfu0aF/sW9kr+CwrA0Wl7cdgC8wtriERwJRuPLzsz5+E2+nTWDstISOvfow8VX3MQn777AP299gNHnXdzo9hvjyQ/nkZqdT1SIH8mZls0yhvRsh6+XO8s2HwAb8lzHqy3zUg9+eRCPEA+ix0fXHHNyc8Iz3BPvKG+7+zbl7snsWbefnLRcAEoKLFs6B4T58487z7e7vaYyf+s2vNzcWPzUk/SIaW11bFdCAmOfepr5W7c1T+eqeXh4cuU/ruXKf1zb6LYigwJIzMziw4XL6RodiXf1NInCklL2HU0hv7iYmJBgu9pctXkrG3fuJq16O+bwoCAG9erJ8H59GtTHvSkHWRW7kZS8dEuf/cMY0Xkg3SI7nuLMluXTTz/l999/r/f4woULiY6O5qabbvoLeyVnIhdng9entGbasBCWHcgnJc9ywRrp78Kojr6cFaWNkP6uFIal0ZycXXHxD6XrtU81SXvzvp/N7A9ftXqs98DhZKalsHzRL80ehpdvPUiwvxfrZj5AzEX/qXm8dXggCWk5NrURNtiyxW/egTw8Qj1qvm6sgFB/np77CH98uZzDOy23+tv2jOHcq0bhG9h80yRMxcXEhITUCsIAPWNiaBUUTEJmZjP07LjSslJ+mvct6zavIT3DMr86LDSCIQOGc9GEKbi7e9jc1pizuvH50tUkZmWRmPWn6RBmy4Kcc87qZlNbxaWlPPb62+zYF1vr2G/LV9KrSyeeu/cuPN3dbWqvsqqSJ354mZ+3La517I3FH3NB73N57pIHcXb6e4yU/v777zg5OXH33XdzzjnnEBQUZCmXlZPD0qVLeeONN/j9998VhqVGz0hPekR4kFVoqQIT7O2src//5hSGpdGCe40mY8tCygtzcfUOaHR7P3/zCYaTEzf/+wk+eP1pAPz8AwkOjeDwwb0NbnfN4QK2Jhbj7+nMRWf5YyqpJMTHBXcX+0ayS0rLad8qBG9P6wVfhcWllJXbt+tep2s6UVVRRfr6dAoSq+sMt/YhpH8ITnb2q6K8kt8+WoCTkxOTb5t4Rr15twsLIzY5mf98NYcLBw4gtLoiQobJxM8bNnIwNZUuUVHN1r+c3GzuemQaCUfjgeNzhROS4tm0bR0/zfuOt/83s2Zh3al0jAzn2tHDmL91J+m5JqtjYQF+nNf3LDpG2jYN5pPvf2J7dRB2c3XB38cHM2AqKKCsvIId+w/wyfc/ccdVl9vU3vvLvuCnbYvqPf7r9j+ICYrijjH/tKm9M116ejqtW7fmsssus3o8JCSEyy67jO+//55k1WmWarHpJbz0Rxqr4gopKa8CwMPViZHtvblvTDhdw22/KJaWQ2FYGq0sL4OqijL2fPwIPjHdcHH3OuGoQZsJ9o24pCQl0KZdZyZfcUNNGAbw9fMnIf6g3f0rKa/ipi+PsOawpZh6n2hPgr2dueObRP7v3HBuH2nfHLC2UcHsO5LGN4u3AFBWXskHP67mSGoOPdpF2NVWRVEFu97cRVFKkdXjycuS6XlXT1y8bP8VdXF15rePFhIWE8qFt06wqx+n243njuGhWbN587d5vPnbvFrHjernNJf3Pn2TI4mHMQyDVpGtCQoIxly96UZSSiKJyUd479M3eeSep2xus1NUBJ2iIjAVFZNXVAyAv5cnfl723WpdvnEzLs7OPHXnbQzpfZZV7dO123bw9Dvvs3zjZpvD8E/bFuFsOPHQpDsY32Mkwd6BmDGTXZjLgl0r+N/v7/LT1oV/mzAcGBhIcnIya9euZejQoVbH1qxZQ1JSEoGB9i+Kio8vYtu2PDIzLRuqhIS406ePP23bep3izL+GubKSokOxlGdmAOAaEopXh84Ymhtdr10pxVz+8WGKy6uslr8Ul1excH8+K+MK+eaGdpou8TekMCyNlr1nDRhQWVpM3sEtxw9Ub8dsbxj29vElKzONstKSmscK8vNISjyMt7ev3f176Y80Vh+23l5zTGdfXJ0NlhzItzsM/3PiIB5771fueOkbDAN2Hkpm54xkDAOmTrBvS9yEXxNqgrCTq2UkuKq8iqLkIhJ+S6D9Ze3taq9jn3YcPZBMRXkFLq5nzq/3beeNJ8Nk4o1ff6vZgOIYFycn7r7gfG47b3wz9Q7WbFyBh7sH773yOR3adrI6dvBwLLfd/0/WbFxhd7uFJaXEp2eSV2j5P/b39qJDRFjNHGJb5JryiQoLZWgf63J5hmEwrG9vosJCSU7PsLm91LwMYoJbMXXIxVaPh/oGc83QfzBnw88czal7JX1LdM455/DNN9/wwAMP4O7ujn91xZLc3FzKyspqnmOrqioz7804zIoVtauBzPnqKCNGBnPHHe1wcmq+OzPZSxeS+sXHVJis70q4+PkRcfUNBI2xvUzg6VBVUUbm9qWYDu+s2anUzS8Yv3a9COk1GifX5imz+OLiNIrKq4gOcGVkex9CfFwwmyGrsIKVcQUczS3npT/S+Pzats3SPzl9zpy/ltJi+UR3aVTViD/r2Xcwa5fN576bLgYg9WgC9954EWWlJQwafq7d7f22Ow8PF4MfpnVg4nuWkWV3FyeiA1w5nGV/AfVb/zGcg0cz+PS39VTfTccwLCH51n8Mt6ut7F3ZGE4GXW7oQlAvyy34rB1ZxH4SS/aubLvD8JBJA5n13Ne8ets7jL5sBH7Bfpw4W6LLgE71n3yaPXHZpdw2fhxLd+0mqXoRWKugIEb37EFYM5VUO6awsICI8KhaQRigY7vOhIVGkJpm3630P3bsYeXu/VSaq6wedzacGNG9M2N797CpnZDAAI6mpfHTkmWM7N+PQD/LBWGOKZ+Vm7aQmJpGeLBt0zcAgr0DOJqTworY9ZzdebDVseX715OYnUJwE0x3OlPccsstxMbGsm3bNkpKSigpKbE63rt3b2655Rab2/v+++Q6g/Axq1ZmERHhzqWXtmpwnxsjd80Kjs54rc5jFaY8jr73Bk7uHgQMH/UX98yivNDEgW+epyS7+oKr+j20JCcF05FdZGxfQucrHsXV26/+Rk6TLYlFBHs5s+D2jni7W4+gF5RWcvYbsWxJLKrnbGnJFIal0Tpf+UiTtnftLfezbcMq4g/twzAMTHnZ5OVm4eXjy9XT7rG7vazCSjqGutMtwnqul4uTganE/u2iDcPg5X//g39fPoqtsZYtevt0iqZNpO2B5Jjy/HI8wzxrgjBAcK9gPMM8KU4vtru9T5/6EgzYt/EA+zYeqNXvmdvfsrvNphTq78+oHt1rFpW1Dg5u9iAMEBUZTcLReN79+HVGDTvXatON5WsWk5h0hDat29nc3rr9h1i2s+757ZXmKpbv3oePhztDupy6csP44UP5/KdfeWvWV7w166t6n2Or8T3PZtba77lj1uO4u7oR4Fm9o12RidKKsprnNKeKigo2bdrEwYMHycuzlLny9/enY8eO9O/fH1c7tp/28vLinXfeYfny5XXWGT777LNxcrJ9fv6K5Vk4ORn887rWDB4ciH/1Jgx5eeWsW5fD558lsGJ5VrOF4YwfvwHAf/Bw/AYPx9XfMg2mIi8X07rV5G1YTcbP3zVbGE5e+S0lWSlggHtAOK7efpjNUFFkojQ3jdKcVJJXfmv3HcWmUFFlxtfFGS+32j8PXq5OuDkbFJVV1XGmtHQKw3LGad22I69/8gtff/o2B/ZayrZ16taby6+7g1Yx9o2UAoT5unA4q5Qj2aU1j+1OKeZgZilRDdhN6M6Xv6V9VDD3XT2GmIjjIfaXlbtIz8nnpsm2BxMXbxdKMksoTCrEu5WllFrh0UJKMktw8W7gr2c9O9eZG7KlXRNaumsXj3/xFbsTE60e7xkTwzNXXcGYs85qpp7BxRMv440PXmTOD58z54fPax03DIOLJ15Wx5l123DgEBgwrGsnerRuhY+HO2Ys0yZ2JySxZt8BNhyIsykMX3PhJJLS0vlj3YY6j48ZPJBrJtteNu+uc29gb/JBNh3ZQUl5Kanl1lMs+sX05K5zb7C5vaZWWFjIZ599RmZ1dZFjixmzsrKIi4tj8+bNXH/99Xh721560DAMRo8ezejRoxvdv6ysMiIi3JkwwXoBZGCgGxMnhrNoYTrp6aX1nH36lSQl4hYWQZv7H691LHDEOey78wZKjiY0Q88s8uK24eTqRpern8Az1Lq6TFFGArFfPEte3LZm6VuPSE+2JBYxZeZhxnXxJdjH8h6cVVDBov35pOVX0L/1mTEnXJqWwrA0mrmyguRVc8nZt56ywlywui1s0O/+T2xuq6KinOULf8YwDO5+7EW7RmzqM66LH59uyGLcOwcxgN0pJVz0YRxmM4zvYv+tuK8WbmZAt9bcd7X1gq+3vl3Olv2JdoXhgC4BZGzKYMdLO/CoXqVcklaC2WwmuKt9dWgBXlzw9Kmf1AyW7drNpS++TGVVVa1IvjMhgcteeoW5//cgo3vaNnWgqV06+Spy8rL58rtPqai0rgji7OzMVZdcx6WTr7K5vez8QoJ9fZjYz3qeb7CvDzGhwexPTiE7v7Ces605Ozvz6K03cdmEcWzY8ecd6HrQuW0bm/sF4O3uyac3vcLiPatYdeBPdYY7DeTcbsOb5PeuoRYvXkxGRgaGYRAUFFQTegsLC8nOziYrK4vFixdz0UUX2dxmdnY2n332Wa2R4aFDh3LttdcSHGz775q/vwvp6aVs3ZpL374BVse2bsklLa0Uf//m+9Pq5OZGZUE+5bk5uAZYLwwsz8mmIt+Ekx0j602tsrQYN/+QWkEYwCs0Ble/YMrymqfM4r2jw7hudjxbjxax9aj1dAgz4GTA3aObpgymnFkUhqXRUtb9TNrGeoraG/aNRrq4uPLm8w8T2SqGMRMvaYLewf1jwlh/pJC9aZa5gmWVlj51C/fg3nNsf2M7mn68hnBZeaXV14XFZRxNy8Gwc/J0zAUx5MXmUWYqozj1+LQINz83Ys6PsastgJAo+wP0X+H577+noqqKAR06MKlfX8L8q3egyzMxb8tWNh06xAvf/9BsYRjg5mv/xaUXXsXGbeusdqAb0HswQYH2/bu6u7liKiomLddEeID1BVdabh55hcW4u9kXSDq1iaFTG/t/JupiGAbjeoxkXI+RTdJeUzpw4ACurq7cdNNNhIdbj76mpaUxc+ZMDhw4UM/ZtSUlJXHrrbeSk5NTM8oMkJiYyNGjR1m0aBHvv/8+0dHRJ2nluMGDg/j99zT+98IB3Nyc8PW1/BnNz6+grPoW+uDB9k+Zaio+PXqTt2E1+++ehnfnbrj4BwBQkZdLYexeqoqL8R88rNn65x4QSkl2CknLvyag8wBcqqfpVBSbyIndRGl2qmXzJjtVVlRwYN8OMlItc/tDI6Lo1LUXzi62x5wRHXz4eGobpi9MZf+fRve7hLnzyLgIRnZovnrtcvooDEuj5exbBwYEdRtK9p61uPoG4hnamsKUQ4T2sX/BW/tO3ciqDiNNwdfDmZ9v6cDPO3PZlmQJnL1beTK5pz9udtTy7XPt/wBqKkgc+/pErUID7Oqbe6A7vf+vNykrUyhIqK4zHOND5MhIXH3tH72Z+fiseo8ZhsGNz15jd5tNYXt8PK2Cglj05BO1Rh3vvuB8zrrnPrbFH26Wvp0oMCCI8aMbv512l6gItsYd4Z15iwn29bHagS4rvwAzZs6Ksi18vTjzU4L9/Zk8ZjShQfaXAKtLVkEOHyz/stbI8MjOg7hp5JWE+jZfmCspKSEgIKBWEAbLaK6fnx+5ubk2t/fOO++QnZ2Nl5cXPXv2rCmjlpOTw65du8jJyWHGjBk899xzNrV3xZWtiI8vYu/efMrKqsj60yLcLl19uOLK5pkvDBBxzY0U7ttFhSmP/O1b/nTUjLOvHxFX32h3u/H79rB91QoyUyxhMyQyit4jzqZtV/u2eQ/pPYajS74gbdPvpG2qYxDFsDzHHot+/ZZP33kBU1621eN+/kFcd8f/Mf7CK2xua1RHX0Z19CXNVE6yqRyAKD9Xwv2abzRdTj+FYWm0MlM2bj5BtJ10K9l71uLmG0SHf9zDrg/ux1xRbnd7U665lZefupdXnr6XCy69jsCgEE4siRAWYd8fmrnbcgjydmFKn0Cm9DkeJhJzyigur6JzmG1F1E+sHGGuY8Db1cWJe6+yvURTzXm+rsRMapoRv9U/ra+7skd1mbvmCsMuTs6UlpdTUl6O1592SistL6e0ohyXZt7xLDU9mRVrl+Lm6sao4ecS6H88EH4650NSUpNsrjM8vm9PEjKzyDIVkGnKJzM/33Kg+ucm2NeH8X162tTWglVrMYBv5i9i9KABXD5hHB3q2MnPVonZyUz94G6yC3Ot5pHHZx3lyNokftuxhC9ufpOY4ObZBCUoKIjMzEwWLVpEt27drKZJ7N27l6ysLEJDbS+HuHnzZnx8fPjyyy8JCQmxOpaRkcHVV1/Nxo0bbW7Pw8OZ/zzZhQ0bcti+zVQThoOD3ejdx4+BAwObtayae0QUnV56l/Qf5pC/bbNVnWHfPv0Ju/gKXINsv9NRVVnJ+/95hJW//Fjr2NdvvsqI8ydz23//h5ON9YvD+o2joshE2sZ5mP9UZtFwciJs4CTC+o2zuX8rF//KG889WOexvNws3nr+YTw8vDh73IU2t5lVWMH6I4Uk51WHYX9Xhrf3IbgB6zhKysr55Nd1/LExlqPpuQBEhwUwdmAXrjt/MJ7uCtlnAoVhaTTDyQlnL8utI8PZhfLCPAzDCcPJmaxdK2k1yvarcoAXHvsXhmGwbMGPLFvw459ezOCX1XF2tXf/j0n0jfbknE7WNYrv+i6RHcnFxD1pWyj5+aVbMGPmogc/pEubMF668+KaY54errSLDCbQz/7FFcVpxeQdzKM8v7zW4rfWE+0LPZ37d7QqpVZUUELSgSTMZujcr4PdfWsqAzt1ZOnOXQx5+FHOPasnIdU70GWaTPyxcxfZ+QWcc5Zt/w+nw8HDsdzx4A2UlFruHHw0+12mP/4qvbr3BWDtxpXsjd1lcxj28fDgjonnsvFAHLHJaZiqN93w8/Kkc1Q4Azu1x82O27dmoKKykj/WruePtevp16MbV0wcT/8e9o3KAbyy4EOyCnPwdvOid+tuBPsEYjZbNt3YnriX7MJcXlv0Ea9d+Z9TN3Ya9O/fn/nz57N27VrWrl1b67hhGPTv39/m9kpLSwkJCakVhAFCQ0Px9/evWaxnK8MwGDw4qFmnQ5yMa2AQrW68o0na+uGDd1n58w/1Hl/128+Ex7Rhyu132dxm1IgphPYdR37CbspMltFcN78gfGO64+ptX3WZb2fNAGDYORMZNnoCgUEhmM1mcnOyWLP0d9Ysm8/cL963OQy/tjSNGasyKa+0fjN2dTa4dXgI94+xbedIgIycAiY/+D4HEi0XJMcGUQ4ezWDZlgN88us6fn3lNkIDNfWiuSkMS6O5ePlRUWgpf+TmF0xpbhq7P36YsrxMnD0atvLWXNfQK01azpi84so6R3jrM7y3pZLF/117LlEh/jVfN0bq6lQOf3u43u/X3jD88Kf31HosJS6V5655hd6jmi9s/ufyy1i3P5YjGRl8smSp1TEz4Onmyn8ut71aQ11Ky0tIzojHMJxoG9nZrnM/+fJ9ikuOL5jJM+Xy4JN38dYLH9G5Q9cG9cfNxYXh3TozvJt9falLh9bRjB06mLmL/iAzJ5ctu/eyZfde2reO5vIJ4zlnyECcbVz0tj5uK77u3vxy98eE+lqPEKabMrnwzRtZe+jPt9f/OoMHD6awsJA1a9ZQ+aeRQycnJ4YNG8bgwYPrObu2tm3bcuDAAR5//HFGjx5tNU1i6dKlJCcn07mzff9HeXnl/PBDCtu35ZGZaRkZDglxo08ffy66OJKAgOYf7cvbsIb8bZusR4b7DsR/oO0LfAFW/vwDTs7OXPt/jzJo3AT8g0PAbCYvO4v1C+cz68XnWPHT93aFYQBXbz98Y7pZbbphbxAGOBp/kIhWMTw6fUatY6PHX8RNU0aSeNi2Oeafrc/ijeV1b2BTVmnm7RUZBHu7cP1g20bWn/n4d2ITMjAMaB8VTGigL2azmczcAuKSsziUlMkzH//OW/c37r1PGk9hWBrNM7Q1eQe3UpKVTEDnAaSt/43S7FQAAjr2s7u959+Z0yT9GvH6/prPd6eUWH1dUm4mq7CCQC/7b80/dO04sk2F/G/WIrbFJgHQt3M00y4aSpCf7eWeAI4uPIrZbMbJxalBc4RtEdk+gtZdo1n85XImXD/2tLzGqfRt147FTz3JM99+y/Lduykus9x+9HRzZVSPHjxx2aX0jGn4VJH5675m0ca5lFeU0TaiM6P7TebnVZ9xwbBrGNDt1PVU9+zfiWEY3HXzA/Tu0Y8ffvuaXxf+yP89fRfvv1L/POyTKa+sZOOBOA4kp5FbvQNdgLcXnaLCGdixPa4utv/subm5cvnE8UwZfy5/rNvAdwsWcyjxKIcSj/LChx/z0dwfmPPKCza1VVpRRqhvcK0gDBDmF0KApx8ZBdl1nPnXGTNmDIMHDyYuLs6qznC7du3w8bFvFG3q1Kk8+eSTLF26lKVLl9Y6bhgGV199tc3tpaWV8J8n9pGXZz0FLCWlhJSUElavzuKZZ7sREWHb9KumVlVSwuEXnqRwz85ax7IXz8e7e0/aPfwMTh629S8rNYWImDacd7X19tyBoWFMmPpPFn/9BelHE+s5u26mI7tJWjaH4kzr8zxDW9Pq7Cvwa2v7hbubuwemvBxysjIIDLaePpOdlU5+Xi6u7rbt+DhrYzYGcNPQYCZ29yfU2wUzZjILK5m3O4+Z67KYvTHb5jC8cN0+vNxdWfDmHXRvZ70ocHdcCufd/Q4L1+2zqS05vVpsGJ4zZw4vvvgie/fuxdPTkzFjxvC///2PDh3qvxX8yCOP8OOPP5KUlERZWRnh4eGce+65PPnkk7RpY195Ijmu3QW3Y66swMnVnagRU3Bydaco5RCeoa2JGGz7PK1jzuo3pEn6dTTX8sfKwHJVf+zrE03oZn9ptaPpuUy4511Ss45vdbpowz5m/b6B+W/cYdciusqSStwD3enzSB+c3Rs/Z3b1T+utvq6qqiI1Pp0DWw7i5tE8W5we0yOmNV/ffx+VVVVkVc+hDfb1tXlEsz6rtv/OvDVfWj3WJaYXOfmZbN6/0qYwnJefR0yrtlw22RKKHvr3k4DBrwt/4MEn76TKbF+h/YKSEj5evIIMk/Vc4cz8fA6mprHxwGFuGnc2PjYGkmOcnZ0ZP3wo44cPZdOuPXz9+wK27NlHZnbOqU+u1j40hn0ph7hvzrOM6zGCoOrd5rILc1m4eyVHc1PpFnnq+senm7e3N+3atbMKw/YGYYBx48ZRXFzMe++9V2vhnb+/P7fddhvjx9u+FfgXXxwlL68cDw9nOnX2tmy6YYY8UzkHYgsxmSr46suj3Htf8/wbps75jMI9OwAwXN1w8fUDzFTk52MuL6Nwzy5S53xG1PW32tSeX1Aw6UcT2bZyOX1GWv8ubV2xjLTEBPzsmINsOrKbQ3NfwWyuqjUtrDg9kUPfv0qHKffj18a2yjK9+g9lzbL53HrFGLr27EtAkGU6TG52Jvt2baW4qIChoyfY1FZCThntgt14/Dzr4NomCPq39mLpgXwScmzftdRUWEJMRGCtIAzQo30krUIDSEi1/XdXTp8WGYZnzpzJtGnTAGjXrh1ZWVnMnTuXlStXsn37diIiIuo8b8GCBRQWFtKpUydMJhMHDx7kk08+Yc2aNezbp6uzhnJycQOX40ErcsjkRrX35cw3Tnr86pvutqmde0ZZyqa9vjydSD9Xruh7fPGch6tBx1B3zu3sW9/p9frvx/NJyTThZBh0bG154z2YmElKlonnPlnAu/9n+xzpsEFhpG9Ip6KooknC8MzHZ9W7gK4xWzFvzc1lT74JHxcXxoaGUVBRQaCbG252Btkd8UdYvGOH1Q5043r34qxGXIwu3/orhmHwj1E38f2yjwDw9vQjwCeYpAzbKlQE+AVQUWl9sfTAvx4jPTONDVvWAJYRRFst3LabjLx8MCDYxwdvDw/AbKkmUVBAZn4+C7ft5pIhts99/bMBPbszoGd3DiUe5dv5i2w+78YRl/Pgt9NZuHsFC3evqHXcwOCG4Zc3uF9NIS4ujoULF5Kenm71eHh4OGPHjj3poEddJk+ezKRJk9izZ09Nm2FhYXTv3h0XO+ZuA+zeZcLLy5lXXu1JYKD1BWZ2dhn337eLnTtN9Zx9+uWtW4nh4kKb+x/Ht9+gmp9bs9lM/ub1HHnlOfLWrbQ5DA8eP4H5sz/jxX/djJu7B74BAQDk5+ZQVlpa8xxbpaz5EXNVFd6R7fHv2BcXL3/ATEWRibyDWylMiSN17Y82h+Eb/vUIu7dtJC83i60bVlodM5vN+AUEcf3tD9nUlq+7EymmCmLTS2otrN6fVkJyXjm+7ra/57WNCuJAYgZPfTiPC0b0JDTAcjGXkVvAL6t2cfBoJp1jbF8MKqdPiwvDZWVlPPzwwwBMmTKF7777juTkZLp27Up6ejrTp0/nzTffrPPcNWvW4HHCSMy1117L7Nmz2b9/P1lZWXYVXpfjUtb8WO8xJxc3PMNi7Lrt9eVHr500eNgchqtrCK+NL6BTqEfN1421bMsBPN1dmPfa7fTqaKlssf1AEpPuncGSTbF2tdVmchty9+ey5dkteEV64exxPBAbhkGPOxtQd/dPoy2+QT50H9KFKx+cYndTpZWVPLpnN1urR9S6+foR6OrGU3v3MK1tO65ubduc5sqqKv71wYfMWbW61rFnvvmWy4cPY8attzRolDgzL4WI4BhG97uwJgwDeHn4kJpl2+3btjEd2LRtHQlH44mJbgtYRmGffeQl7njweg7F217XFmB/UgquLs7cMv4cIgKt50Gm5uTx/oKl7E9KsavN+nRoHc3DN9u+Y9ykXmMoLivhtUUzySnKszoW4OnHPeNu4nw7S1s1pbi4OL744gvMZnOtufSpqal8+eWXTJ06lfbt7Z+zf2JZv4ZuLFJWZiYw0LVWEAYICnLD19eFnBz7q+g0lYq8PNzCI/Hrbz2v2jAM/AYMwS08krJ023/2Lr/rXuL37mHf5o2UlRSTlWq9TXyXfgO4/K57bW6vOC0eV99AOl/9OIZh/X8QPnASuz58gKLUeJvbi4xuw1uzf+ebT99h87rlZKZXl34Li6L/kFFc9s87CA61bdHbOZ19+W5bLhNmHKRtkDsh3pb348zCSuKzSzGb4YKetg+g3HDBEB559xfe/m4Fb39Xx4WnYXmONL8WF4Y3btxYs/J3yhTLH/eoqCiGDBnCokWLmD9/fr3nenh48O677/LZZ5+RnZ3NwYMHAejevTtBQfWvCi4tLaW09HgBbpOp+a76z0Qpa3485co2n+iudJxyn2UU+RRCw1tZVUQoLMynMN+E4eREaLj95Z6+vsHyR3NdfCE7qusM92rlyZC29s3vPSYnv5iO0aE1QRigd6dWtIkMIi7JvlXpR345QnGapU+FR23bkexkPt75dqPbONHMI/Fs+dOt5SFBQbg4ObEuO8vmMPzSjz/xVR1B+JhvVq+hfXg4D1/yD7v76OHmjakwm/KK47cvi0oKSM9JxtPdtgWc40ZNoLy8jPVb1tSEYQAvTy9efvptpr/+JOXltgec0rJyAny8agVhgIhAf/y9PcktKKrjzNr++OR9m1/XVlMGTGJy3/HsStpPanWd4Qj/MHq26oKrc/P+WVi+fDlVVVVER0fTuXNnfHx8MJvNFBYWEhsby9GjR1m+fLldYfi3337jnXfeqZlycYy/vz+33347F15o+3SuVq08iI8v4vXXDjJocCB+1fVnTaZy1q/LIT29lLZtm2/LXtfgEEpTk8ha8Ct+g4dbbbqRt34VpSlHcbMxHAJ4eHnzxCdfsHHxQravXkFWqiVIB0dE0nv42Qw4d5x9FxZOzpgrKzBXlGO4Ws/lraqwPI6dZRaDgsO47f7G77758NgItiQWEZdVRlxWKYctN7BqxhfaBbnx0Ni67zzX5ZaLh5ORW8Bb3yynvMJ6qpWLsxN3XXY2t1w8vNH9lsZrcWE4MfH4SE9Y2PGRvmMF2hMSTr7nekJCAhs2bKj5um/fvvz6668nHYl8/vnnefrpM3Ob2zPKSSozFBzdR9qG34gcduqw88mPtUPT1g2r+O9DN3PNzffZ3a2S8ipumZPAykMFVo+P7ODDh1fF4G7HxhsAYYE+HErKYP7aPUwYailt9fuaPRw6mkl4kH3TLtLXWYKIW4Ab7oHuGE1Un7S4sIS0eEvb4W3D8PRu2GKepRkZuDs58U6fPkzbYqkw4ObkRLi7O4nFxac4+7ivVq3C2cmJ56+ZykUDBxLm74cZyMgz8eOGDTwy+wu+XLmqQWG4Y3QPth9cx8tfPgBYRopf/vIByivK6Nl+oE1tTBw7mYlj657eExIcxqvP1l6pfjJBvt5kmPJZsHUn3Vu3stp0Y3dCElmmAkL97Z+i05QMDJwMg2NXsk6GYfcOiqdDSkoKfn5+3HjjjbXel4cPH84bb7xBSortI5uLFy+ud0ON3NxcXnjhBTw9PRk71rbFpRdOjuCtN+NYty6Hdevqnu954YW2B6amFjjqXNK+/YKkme+SNPPdep9jD8MwGDTuPAaNO6/R/fOJ6ojpyC72fPoYfm174uJVvQNdkQlT/C4qSgrwa2N/5Zu1yxewed1yMlIti5pDI1rRf+hohp5t+3zwEB8Xfru1I7M3ZbP8YD7JeZat2aP8XRjV0ZepA4LwcrPv78Vj15/HLRcNZ/nWAySlWy7GWoX5c3bfjoQFNu97gBzX4sJwfeorTfVnL7zwAs899xwHDx7k9ttvZ+nSpUydOpXFixfjXE/R8EceeYT77jsewkwmE61tHBFzBJ2vfJRDP7xGq9FXEdhlEAA5+9eTtGwObc+/jcqSQuJ//4Cc/RtsCsN16TtoBF169OHrT9+2e5vmN5ens+JPQRhg5aEC3lyewYPn2j5KAjBhSHc+/nUd1zz1OV7VBdOLSi2jhhOH2lf31dnDGVdfV/o9YX/VjbpUlFfw3es/s2TOCirKLW/kLq4ujLlyJJfecxEurvb9yueWl9PGy4sO3tYLl1wMg4KKCpvbScrKpkN4OLeOty6mHxEYwG3njWfm4j+Iz6i7pNGpXDD8GvYnbCcl8wgYBgXF+RQUmfBw92Li0Ksa1GZjDezUnnmbtrNqbyyr9tYxdcawPMceOSYTM776lq1795GT96e7U4bB4o/fs7mtH7bM59UFH5JTZN1OoJcf94yfxpT+E+3qW1NycnKisrKSiooKXF2tK6xUVFRQUVFh10jk7NmzARg9ejSjR48mKCgIs9lMTk4Oy5YtY9myZXzxxRc2h+Hhw4MpLa3iqy+Pkp9v/Tvg4+PCVVdFM3xE8025C7vkKkpTksldVbtyBkDA8NGEXWLf70VeVhY/fjiDHatr70A3+aZbCQixfd5r1IgpFCTFUpaXSeaOZdYHzeDk4krUCNundJUUF/H0Azeya+v6WscW/PQVPfoM4qlXPsHD07bRek83J24eFsLNw2rXpW6o0EAfRvbpaLXphoLwmaXFheETQ+iJiyuOfR5jQ3kmZ2dnunTpwj333MPSpUtZtmwZf/zxR70rit3d3XG3sTSLI0r8YxauPoGEnHV2zWMhZ40ifdMCkld+R7frniVj+1KK0uJtau/Pb2qVVZUkJxxm366tdi1iOuaXXXk4GfD4+Agu6hUAwI87cvnvglR+2ZVndxh+7IbxrNl5mH1H0igsOX7rvFvbcB693vZRCICYC2KI+zaO/MP5+LZr/JvjV/+by9JvVlqN0leUVbBw1lLKSyu49nH7NkAJcnPjaHExSSeMAh8oKOBIURHhdlRCCPXzIz4jg4XbtjO+T2+rYwu2beNwejqhfvZX9gAID4rmwatfYcGGb0lItcztjYnoxPhBlxIWaN9uhTm52bz90Sts2r6BnNwsq2MGBst/2WxTO0O7dKSwpJSVe2KpqrK+PerkZDCiW2eGdrGv2sBLMz9nw46ddd6AMewomP37zmU8/sPLdR7LLsrjyR9fxcvNg4ln2b+bYlOIjo4mLi6OGTNm0L59e6sd6OLi4iguLrZrikR8fDxRUVFMnz691rHx48dz6aWXcviwfVuBjxkTytlnB3PoUKHVDnQdOnjjYuedpqZmODsT8+//I/TCS8jfuomy6jrDbiGh+PTpj1d7+xbSpiUm8NS1V5CXnWW19WZK/GFSjsSzZt6vPD3ra8JjbFsE6xXRji5XP0Hyqu/IP7KHqupdSp1cXPFt053IEVPwCrW9zOKs919m55Z1ALi5ueMXYLnYyc/LoayslN3bNjDr/Ze5+R7bNpEpKa/ii03ZLD9YQFJ1+bxW/q6M7ujD1QOC8HC17/932ZYD/Of939gTn2r1eI92kTx180TO6d/4OuTSeC0uDA8cOJDg4OCaChJXXXUVycnJrFtn+WWYMMGyqrVrV0uh/DvvvJM777yTAwcOsHfvXi644AKcnJyoqqqyml9cWNj4+ZqOqiQnxVJa6PAO/Nv1Aizlc0pz02vmErt4eNscZB++44o6n2s2m+nZx/Zi+8ekmiroEOLOjUOPX+nfNDSErzbncCTb9jI5xwT4erHknbv4bsnW43WGu0Qz5Zw+uLvZ9yuVOC8RqmDn6ztx8XKxWkAH0P9J+6oNrP3Nsq3s4En9GTxxAADrf9/M+nmbWPvbRrvD8IjgYL5PTuaGLZsxgAOFBdy+bSsAw+0op3TRoIHMWLCQy19+BU83N4KqS2RlFeRTUl1z+KJBtk1p+LNsUwbubp5cc55tCytP5vk3nmLdplV132my8zpsbO8eDOnSgUMp6eRV70Dn7+VJh4gwfDztn7ayY79lhHlEvz60iYrC2blhoWvmSksd73HdRzKu+0jLDnSYyS7IZdGeFSzas4qPV37TbGF4zJgxJCQkkJOTw5Yt1pt/mM1mXF1dGTPG9gV+7u7umEwmsrOza60NycrKIi8vDzc3+8sOGoZh+aj+wTj29ZnCs11HPNp2oNJkuTXv7OffoP599dqL5GVl4uHtTadeffAPtuzwZsrO4sCObZiys/jqjZe555W3bO9baGs6/ONezFVVVBRbyg+6ePpiNGBR46ol83BxdePR6TMYOHyMVfWMDav+YPqjt7NqyTybwnBmQQVXfnqYQ5mWNULH3gXiMktZeaiA2Zty+OaGdoT42PY+v3zLQa547BMqq6pqbfC0Ky6FKx//lG+eu5FR/Zq/lKGja3Fh2M3NjenTp3Prrbcyd+5c2rdvT1ZWFvn5+YSEhNRUmti/37LBwrHFdklJSVx00UX4+PjQvn170tLSSEtLAywjEeeea98cKjnOKzSGwpQ4Dn3/Kk4u7mBAVbnlzcQ70lICqTjzKG5+toenusJI1579+Pej/7O7f97uTqTklZNmKie8erFLmqmcFFM5PnaUyTmRu5sLUycMZOqEhgW4Y0pzji/MrCiqoKLI9qkHdXFyciK0VTC3/u94dYE+o88ibsdhivJtn+N7zI1t2rI9L49D1ReL5dWjnO29vbnBjnJoj192KTuOHGH1vv0UlZVRlG29qcPQzp15/LJL7e4fwFMfTaNtVBfuu/JFq8ff/f4pjqYfYvpttm+asW2nZeT37KFjaBvTvt6pU7by8fCgd7uGbyZyIl9vb4ID/Hn6rtsb1U5cRgLRgZG8ftWTtY6d33sM571yDYcyjjTqNRojKiqKm266iSVLlnD48OGahYuurq60a9eOMWPG1KwRsUX//v1ZtmwZV1xxBT179rTagW7Xrl0UFRUxatSpa1GfaNmyTL78IhGTyfr31c/PMk3inDENL5e1YcN+du48jK+vF5MmDSQ/v5jgYF/c3GzflKckMZ7Urz6jYMdWqsosF/xObm749OpH+BXX4tmmnc1t7V6/Di9fX176aT6BodYVebLT03jwognsWrvG5vaOKUo/gunwTqsd6Pza9cIrzL7fl9ycLCJbxTBohPXfcMMwGDxyLJGtYkhNtq2qzP8Wp3IwsxQDaBvkRoiPC2YzZBVWEJ9dxuGsUv63OJWXLo62rb1Zi6iorKJ/19ZMHNqd0ECf6h3oCvl97R4270vkxdmLFYbPAC0uDAPccssteHt78/LLL7N37148PDy45JJLeOGFF4iKqrvaQExMDBdffDGbN29m//79mM1mOnTowNixY3n88cfxa+AtWoHW46/n0NxXKS/IrQnBAK6+gcSMu4HS3DQ8Q1vj27qbTe19/P0q6wcMg4DAYNzcG7YIbFAbbxbuM3Hu2wcY2MZyy3XjkUKKyqoY2YBNNwCWbo5l1bY40nPzra74DQO7ttYMHRjapHtMD57Un40LtlBaXIa7p2W0q7SolOLCEoZfZP+oureLC+/16csfGRnsrd4oo6uvD+eGhuFqxyiOj4cHvz32KL9u2syi7ds5mmUJw9HBQYzt3YsL+vdvcKkroM7FmwVFeRRUjzrZys/Xj5DgUJ577JWG9wWYuXgFvp4eDO3SgdYhTTN/9MpJ5/H+199x+GgS7aLtm/5xIncXN/KKTGQWZBPiYz1SmpGfTW6xCTcbqr6cTuHh4Vx11VVUVVVRVGSpuuHl5dWgn5E77riDbdu2kZuba7V4GiwX3QEBAdx+u+0XGGvXZPPejLqnVZhMFbz/fjzuHk4MG2bf/3tJSRl33z2DDRssAzlnndWOoCBfHnzwQ+666yJuvNG2xWvFhw9y6D8PUlVayom/GFVlpZg2raVgxxbaP/OSzdMlykpLCAwNqxWEAYLCwvHxDyA30/b5/uaqKo4smEn2ntoLpZNXfUdQt6G0mXCzzaPEIWERJCfG89vcWQwbPcFq043VS38nKfEwYRG2/b4sic3H09WJH6a1p2u49d+bvakl/OOjQyyJtf09ZcfBJKJC/Jj/+u21fnbvuvxs+lz7P7YfOGpze3L6tMgwDJYtNqdOnVrv8T+PLLZv354ffvjhdHfLIXmFxtBj2otk711HSaZl2oBnSCsCuw3FycUymtHhYttvYYdFHr/qNuVZVms3NAgDPDAmjNVxBRSUVbHsgOWNzAz4uDlxXwNqD7/w+SJe/uKPWo+bzfaH4U7XNHwjjLp4eHlQUljKU5e/QO+zLSuyd6zYRXlpBW4ebvw0Y17Ncy+6fdIp21uQlkaAqyvnhYdz3gmjcSklJZRWVtLW2/bydIZhcOHAAVw4cIAd31H9vlhwfHOWzLwUq69Ly0tJyjiMu6t9PzdXT7medz9+jbj4g7Rv2/DRmvi0DDBg55FEYkJCGNG9E92i7S8LeKLlGzdRWVXFLU8+S7voVvh4HV8QZGDwykO2VVoZ3L4Pi/as4vzXr6d36+4EV+9Al1WYy/bEPRSWFjO2+4hG9bUppKamcvDgQasd6Dp27Fjvpkr1iY6OZtasWXz22WesW7eu5o5geHg4Q4YM4dprryU01PaR3J9+slSyGDQ4kMGDAvEPcMVsBlNeOevX57BhQw6//Jxqdxh+++2fWb9+v9VjZ5/dE1dXF1au3GVzGE798lOqSktwCw3Hp1dfXPwDATMVebkU7NhKWUYaaV99RrvH/mtTe63adyB+317euP/fDBp7Hn7BlgsoU1Y26xfPJyPpKG272b5wOHXdz2Tvrr/MYvbetbgHhBM57GKb2jt34hS+nPk6773yH957pe6pEOdOtG1BXn5pFdEBrrWCMEC3CA+i/F3r3Mm0Ps5OTpSVV1JSVoHXn3YALS2rpKy8otE7cErTaLFhWM4cWbtX4eLpa7WADqA0L4Oq8jI8Q+wfxfrl20/55rN3yM22THMJCArhsn/eweTLbd9c4JjOYR78dHMH3l2ZwY5ky1SB3q08uX1EKB1D7V8Y+flv6zGboXV4AK3DAmnMNMGK4goqiytx8XHB2c2ZrG1ZmA6Z8IryInyofQv7AH7/eBEYkBafzsIjSywPmgEDfv1wgdVzbQnDL8Tup7uvH4P/NNfy2X172Zefz5KRZ9dz5p/a+f4HIgICuHz4MLyaaDHq+t1LOPaPX1Ccz/o9J6yer74YbhvZxa42l65aRGVlJTf8+0o6tO2Ij/fxRY2GYfDG9A9sb6z6ejwhI5MvV2QS7OvDiG6d6dOuDS4NmO+7ff/xjT8OJViPJtnzI3jf+FvYHL+T7KI81hy0XhBoxkyglz/3jp9md/+aSlVVFT///DM7duyodWzJkiWcddZZXHTRRXaNEgcHB1tVBGqMpKQSwsLcua+O7ZaHjwjm33ft4OjRErvbXbhwC+7ursya9SCXX25Z7Ofm5kpkZBBHjqSf4uzjCvfvwcXPn04vz8DZ09PqWGVxEfvuvIHC/Xtsbu+C62/m7YfuZf2i+axfVEcdf8Pg/Ott/3nJ3rManJyIHn0VgZ0H4uLtB2ZLabWc2I0cXfolWbtX2RyGr7j+TpKPxrNswY91Hh81bjJX3HCXTW3FBLpxKLOU5xemMqG7H8HeloiUVVjB73tMxGWV0THE9vevgd3bsGzLAUbc8hrn9O9MSIBl8CAzt5Clm2PJNhUxul/TDohIwygMS6Md+f0jvKM64N/eukpA/K8zKEw9TL/7P7GrvdkfvsrXn7xlNbqfk5XBh68/gykvp0G1hjuGuvPqJbbN8zqViqoq2kUGsemz/2t0W4e+PkTW1ix63d+Lsrwy9n9yfGSoorCCVmPtu5AIigz8Sxbx5NtRVg3g+e9/wACe+vobpo0by63jxhLqX3tDCnt0iO6BgcHBo7vwcPMkOvR4hQFXV3fCg1pxbn/7Svlt23U8HB6Isx6ls/fftVVwECO7d2blnliSsrLJMhXw04YtLN6+myFdOjKoU3u83G2fjjB+2BAadeVVLSY4iu/v/JAPln/JqgMbSM2z3OKO8A9lRKdB3Hz2lYT5NV1ZKXutXLmS7du313t8586dBAUF2T3Pd/ny5bVGhocOHcrZZ9t2QXeMm5tBQUEFubnlBARYz+PNzS0nP78CV1f7/5+ys/Np3z6Czp2t36dcXJzJz7dtgxYAc1UlhosrTnVUe3Fy98DJ1ZWKUtvD+rBJF1BaXMScN14mP8e6rrJPQABX3H0/wyfZvmlJWX42HgHhhPU7ocyiAa4+AYT1G0fmtiWU5tk+7cLZxYUHnnqdf1w1jU1rl5GZZhm5DwmPpP+QUXTsepbNbU0dEMTT81P4YE0mH6ypvYGSUf0cWz1+w3ms3xXPkdQcPptnXSXJbAZPdxcev6HxtZul8RSG5bSpKCnkpDtx1GPe95a6oD36DGL4OZZ6p2uWzWfX1vXM+352g8JwXnEl25OKyCysqLWqd0qfQLvauuXi4bwxZxkb9xxhYHfbF5HVpTCxEBdPF3xifDgw2zLyF9A1gNx9uaRvSLc7DL+88NlG9eeYqzYen1t5oLDA6uuSykryysvxc7V9QQ9YfhJyCgt5+cefeOu3eVw5Yjh3TpxIp6jIBvXx7urRs3+/ehERQa359+V1b6xgjwljLmiyiwnDgB4xregR04r49ExW7Y1lf1IKhSWl/LFjNyt27+c/V1xkc3sP2bHl8qmE+gbx2AV3Nll7JyosLmJ3XCzOTs7072Z7EDlm+/btODk5cd5559GtWzerHej27NnDggUL2L59u81huLi4mAcffJCtW7fWOvbzzz/Tp08fXn75ZTz/NIpanx49/NiwIYd779lJ584++PlZ/oyaTBXExhZQXFzJoMH2vacAhIb6ceRIOomJx4Pgvn2JHD6cSkSE7e15tu1AUew+Dj1xP34DhljtQGfatI7y7Gy8uti2fuOYc6ZczsjJ/yBu106rHeja9zwLFzvfB1y8/CjNyyAvbnutAZS8uO2U5qXXbMRhjw5detK+cw/yciwL8vwDg+3+Xb5hSDBZhRW8vzqT8irrPxQuTga3DAvhhiG2T3/p0zmaBW/ewX8/XsCKbQcpLrUMIni6u3B2n448dsN59GjfsPc/aVoKw9Jguz58oObzovQjVl9XlZdRUWzCxcOnrlNPqqy0lODQCKa//VXNav7zL7mWGy8ZQVFh7c0zTmXhPhP3fn+UorKqWscMw/4wfMeUkcxduo2J984gwMcTX6/jIzCGAVs+f8jmtsryyvAMtfwRLkouwjvam+63d2frc1utKk00pf9OfZnDu44wc3v9pZBSSywjRwaWChLHvj7RyGD7Rg87R0YyqmcPvli+gqKyMj5buozPly1nQt8+3H3++Qzp0rB6m2/e9xMA5RVlpGRZdqCMDI7BtQGLwB67r2kuJv6sbVgIbcNCyDTls2pvLNsPJ1Je2bDKIeUVFeTm59eqXxwebN8c1cV7VrHqwEZSqrdjjvQPY2TnQZzbreHbw748631e/2omxWWl9O96Frdfeg1Pf/g6j914F5ede75NbZhMJoKCghg0aFDNY4Zh4Ovry+DBg9m0aRM5fxqhPJkPPvigpkSbm5sbAQEBmM1m8vLyKCsrY9u2bXzwwQfcfbdt6xqunhrNvn35mEwVbN+eV+u4r68LV19t/12o0aN789VXy5gy5VkMwxKEp079H2azmXPO6X3qBqqFX34Nh6c/QVHsPopi9/3pqGVhQ/il9a+3qY9hGJYdMqvzpeHUsFJygZ0HkL55EYd+eA0nF7eavxEVxflUVZbXPMceR+Ji+fy9l9i2cRVl1aPebu4e9B00kmtuvo+2Hbva3NYD54Zz/eBgVsUVkFJdZzjS35Xh7X0ItbGk2om6t4vky2evp7KyiiyTpSpPsJ93g0sjyumhMCwNVpZXfRvJAHNFxfGvTxBg55sawOAR57Jr2wbrN9rqz4eNnmB3e9MXplJYRxAGao0S2+K+17/nQGIGZjPk5BeTc0LJMnv/NhjOBhXFFVSVV1GSUUJQr6Cax0/ndIdT7dh4XXUB/c8SjhDq7s6k8OOLljycnYjx9GJokO23CwH8vb14+bp/8uiUS/hw0WI+WryY9DwTv2/Zyu9btjKgYwcWP1W73JctFm74lgXrv6W8wlJGytXFjfMGX874QQ0r11ZeXk5uXjaVfwqcEWGNG8UJ8fPl4sH9Gdu7B+tj4+w6t6i4hJc/+ZzVW7ZRWVlpfdCOHeiKyor516zH2Rhfe07ud5vmMaDtWbx77XN4udk2UnrMxz9/w/RP37F6bFTfISSlp/H9kvk2h2Fvb29ycnI4cOAAnTpZz6c8cOAAOTk5NRtx2GLJkiW4urry3HPPMXz4cKs6tKtWreKxxx5jyZIlNofhiAgPXnypJz98n8z27XlkZlp+5kJC3Ojd25+LLo4kKMj+C7F//etCNm8+QGx17fKyMsvFUufOrbj99gtsbse3Vz/aPfw0KbM+oiTRukSeR+s2RF4zDd/e9u14ufzHuXz56ovk51pfhPgGBHLlPQ9wziW2LxqOHD6ForQECo7up6q8jLJy6zKLPtGdiBxu+w50h/bv4qHbL6e0pNjqfa20pJh1KxaydcNK/jfjG7umS4T4uHBx9QZNTWHnwWT+2LTfage6sQO70LND4xbVStNRGJYGixxmucWbsuYnXH2td6BzcnHHPTgS//Z97G63Y7derFk+n0f/dRXDxxyfJlFYkE/Hrj35Y97cmueeO+nUb5rp+RWE+rgw98b2xDTgj9Sf/bZ6NwCDe7QhJjywUVf4XuFe5B/JZ+PjG6ksq8SnjWWUpCy3DLeA5itvdX11DeGtebm08/Kq+bopBPn48NA/LubeCy/gyxUrefv3+RxISWHTwUMNam/trkX8umq21WPl5aX8uno2ft6BDOlhew3xoqJCXnjzaVauXUpllXXgtGcHOn9vL3xPsrGGj4cH5/ayb+vuj7//keUb6359e3age2vxJ2yIt8zJdXdxI8DLH8xmcotNlFaUsSl+J28t/oSHJt1hV/8++OELnAwnnrvjQR55x1IPPMg/gMiQMHYd2n+Ks4/r1q0b69ev56uvvsLFxQWv6qoZRUVFVFTPVe/Wzfbb/Dk5ObRq1YoRI6wrZBiGwciRI2nVqhXJyck2twcQEODKDTc23e8EgI+PJ1988TDz529k5854AHr2bMPEiQNxtXMbdd8+A/DtM4Dy7CzKsyzTLlyDQ3G1Y6OcY9bO/433n3i4zmP5Odl8+NRjeHh6MXSibRc7zm4edLriYXIPbMYUv4NykyUMu/oF4de2FwGd+mEYtr+nfjbjRUqKiwiLjKbvoJGW0mpmM7k5WWzdsJL0lKN8/t5LPPP65za3WZ8rPjlMcl4ZK++xbWFuZWUV/37lO77+Y0utY//9ZAGXjenL2w9cplHiM4DCsDRY5DDL4qT8hH14hrSq+bqxZr75XwzDYPf2Dezebl0X9IPXnj7+hWHYFIbHd/VlVVwhQd6N20DhmIhgP5ydnZj3WuM2PwCIPi+afTP3UVlSiUeIB6EDQ8k/nE9FcUXNKHFzeqOX5fbsttxc9hdYpqh08fGhT0BAo9t2c3Hh+jHncP2Yc5i3eQtvzZt36pPqsGLbbwD06jiE/l1HArBp3wp2HlzPiq2/2hWGP5j1NktXLar7oB0D9Q9cPNH2J9to9ZbtGMDVF0zii1/nERUWSv8e3Vi2YRM3XGL73OMFu1bg6uzC61c+yaguQ6xGSpftX8s9Xz3Dgl0r7A7Dh5MT6dq2A7deMrUmDAME+vqz/4jtFzpjxowhNTWVI0eOUF5eXlNa7ZiYmBi7dqALCwsjMTGR77//ntGjR1tturF06VISExPtLtcGsHFDDtu25ZFZvR1zSLAbffr6M3Cg/fOFy8srefbZL3Bzc+Gxx67iwguH2N3Gn1Xk5VK4d6f1dsw9+9TMIbbVLx9bKqgMGnceA8eeh3+wJWzmZWexYdF8Ni5eyC+ffGhzGAbLhUhg5wF2T4eoy96dW/APCOad2Qvw9LK+Y1BUWMC0S89m787aYbQhUk3ldpVWe+XLJcxZXP9rf7tkK+2igvm/a8c2RfekERSGpdHann8rFSUFVJQU4uJheTOqKCmkLD8LF09f3Hzs/+Nwqtv41U+yqa3/XhDFxR/GMfrNAwyI8bLadc4Am3cTOub5f03mpv9+wdyl2zhvSDd8PBteKiywRyADnhlAaU4pXhFeOLk64RXpRb/H++Hi3fy/nqVVVTy+Zzeb/zRHc0BgIP/t3gO3JqqROal/Pyb1t+/W7TFpWUcJ8g9j2uRHah7r23kET340jdRs23aeOmbVumUYhsG1l9/E519/RKvIaAb0GcLSVYuYdo194fBEFZWVFJTUngMe4O1Vx7Prlp2XR2RYKDdOuYgvfp2Hv48P9/xzKht37uZAfILN7WQV5tI6KJLRXYdaPW4YBud0HUbroEiO5qTY3N4xft6+pGZlUFJ2/PvMKzBx6Gg8fj62rx1wc3PjuuuuY9++fRw8eBCTyWRp38+Pjh070rVrV7umEE2cOJGZM2fyyiuv8MordW+mMnGi7RcvJSWVvPi/A+zZU3vzhT/+yKBbd18eeqgTHh62X3y7ujqzaNEWoqNDmmR6VOo3s8j48RvMFX+6u+HiTOjky4i48p82t5UUd4iw6Nbc8+rbtY4Nn3Qhd084h6S4g3b1r7zQROr6XzDFW+9A59/uLMIHnY9rde1rW1RWVuDq5ouHZ+3fJQ9PL1xd3Sgptq0ax4uLU096PKe48qTH/+zrxVtwdjJ47rYLuXBkT8ICfTCbISO3gJ9X7uSxGb8yZ9FmheEzQPP/tZUWL/639ylMPkCPaS9BdRiuKi9l/6yn8G7Vic5XPHKKFqz9uja+Sfs3a0N2zRabC/aaah6vLr9rdxi+8nFLqbhbX5hT65iBQcaC5+1qz9XHFVef4yuynT2ccT7hD+mOV3dQkFDAsNeH2dVuU/g84Qib6listCknh88TjjCtrW3buu587VXc7bzVaysnJ2fKK8qprKrE2cny71ZZWUFFZTlOhn13A7KyM4mKaMXN1/6Lz7/+CH+/QB7412Ns2LKW2EN77e5bpimfH9ZtJiEzq9YxA4Nnrr7E5rZcXVzw9LBceLm5upCRk0NFRQXlFRUs37iZB260LeBE+IWQkJXEnPU/M7bHCIK9LRerWYU5LNq9kiNZR4nyt7/G9fBe/fll1R+MveNqAOJTEjn3jqspLitl/FD7yqAZhkG3bt3smg5Rn+uuu46jR4+yYMGCOo+PGzeO66+/3ub2vvk6qSYIu7o64evrApjJz6+kvLyKvXvy+ebrJP55nX3bCg8Z0o2NG/dTUFCMj49987VPlDn/Z9K/+7LOY+aKCtK/n4OLfwAhEyfb1J6buwcFebnkZmYSEGK9aDY3M4OCvFxc3WwfECjNTWf/V/+loshkVWyoNDuV9JxUsveup8vVj+EeYNvPYIfOPdi3awsP3jKFwSPHEVA9FSQ3O4v1KxeRnZlGt7P629TWu6syT3oD6NjfDFslZeTSvlUIN19s/d4dEezHLRcP5+Nf1nEkJbues+WvpDAsjVackYB7YDhufsfno7n5BuEeGE5xuu0jVvb470O3cPjgXmbOXXnK5x6rF+nqbBDk5YyzU+NGXk46IG00YEWeTS/ahE2ZzTa3tzQjAwO4o30Hxlbv0rUoI50ZcXEsyciwOQzHhJ6+urWtwtpxOHkfb3z9CL07WUY7tx9YS35RHu2jbF9FDuDq6oaXp+WCzs3NnYzMNCoqyikvL2PpqkU89G/7Fvj9uH4LCRm1gzCA2c6flSB/PzKzcwGICgvlSFIK/7jrfopKSgjwtX3kdXLf8by79HP+++tb/PfXuiuKTO473q6+ATx6450s3byOPYcPYBgGWXm5ZObm4Oftw8PX2T6laPny5fj4+NCrVy9c7SzbVRcXFxeefPJJrrzyStauXUt6uqV6RlhYGEOHDqVLF/s2Zlm3LhsXF4N77+tIv37+VtNMtmzO49VXD7JuXbbdYbh373asWrWLa699icmThxAc7Gs1Smzr1ImsBb8CBiEX/AP/wcNxCQgEs2UHurx1q8j87QeyFv5qcxjuPmgwGxcv5P4Lx9Gpd1/8qsOmKTuLA9u3UlxYyKCxtv+8JK34hopCE05uHnhHdcC1uoxaeZGJwuRDVBSZSFrxLe0n21b67+pp9/Lkvdexf/dW9u+2Lp9nNpsxnJy46kbbd0BtynfwkAAfjqRks2jDPsYNsn4vWrR+H/EpWYQE2F9xSZqewrA0WlVlOZWlxbUerywtqimV09RystJJT7FtT3fDgFb+riz+Vyc83Rp/W///rrV9DupfqaK8kkcvfAZPX0+e+uahem+3PvHlgza3mVFaSoyXF5e2Ol7v+LJW0fyWmkpSce3/81MpLCnh1V9+Zfnu3aTnmayvLAyDHa/VfRv7ZMYO+Acf/jSd+NRY4lNjLQ9W7409dqDtq9IBggKDSc+0bMrQKiKa+MQ4Lrj6HAqLCgnwt3+6T3J2DoZhMLRLR0L9fe3aNe3PunfswJqt24lLPMp5w4fx/jdzKaoueTdu2NBTnH3craOmkpCVxK87am8pDjDprDHcNtr+0ludY9qz9L05vDL7A7bstywy7delB/dePY2Ordva3M6yZZapKn/88QcDBw5k0KBBdlWPqE+XLl3sDr51ycurIDzcnf79A6weNwyD/gMCCA93Jz3d/rKIr7/+I4YB8fGpvPnmj7XatjUMl6Wl4B4ZRdQ/b7Z63D0iCu8u3cnfsoGyNNunwVx97/+xb/Mm8nOy2bFmlfVBsxnfQEtFCVvlJ+zB2d2T7jc8j6tPgHXfC3LY+8mj5CfYvkNe30EjePKVj/n4rekc+dNGOW3ad+GGOx+h7+CRNrUV7uuCr7szi++se1e4UW/EkpBTZnPfJo88i/d/WM1VT3yKp5srgX6WqRzZpkJKqquFTB5pfy1uaXoKw9Jo7n6hlOSkkLjkCyIGWRZRpG2cR3lBHh7BzV9Q/JZhIbyzMoOc4go83RpfoeGha8ed+knNwMXVmZKiEjy83JusLJunszMZpaVklpYSUr2NcmZpKemlpXg52//2cc/Hn/LtmjVA7RGYhvb4rA6DuXbCPfy65gtyTJbFQoF+oVww/Bp6th9oV1s9u/Vi1brlHIo/wMSxk3n349coLLLUBp0wxvbyVsf4eXniZBhM7N/L7nP/7OETNt1o3zqaIH9/9sYdpn3rVkw6e8RJzrTm4uzM/y57hOuGX8rK2A2kVv+bRfiFMrLzQLpH2V/vubyinLlLfgcM3nrwmUaFfrCM6BUXF7Ny5UrWrFlDr169GDp0KCEhTX+H4amnniIzM5O33649J7YuwcFupKaWsnBhOoMGBeLvb/k9yMurYP36HFJSSghtwDbvUP9dJ5vWUFRz9vKmPCuTksR4PP50EVKSEE9ZVgbOXrZfXITHtOF/3//Kjx/MYPvqFVabbvQefjaTp91KUJjt02qqKspx8wmoFYQB3HwCcfbwprywdv3mk+k/ZBT9h4wiKyONjDRLZZDQ8CiCQ+2b7tM7ypPFsfkUllbi7V57ipW9o8aPXj+enQeTWbPzMEWl5RRlWH9fQ3q25dHr7b8LI01PYVgaLbD7UFJWfU/G1kVkbD1hJb4BQd3/+nmuf7b0QAGlFWZGv3mAzmHu+JzwJmcAX11v263+Y46kZJOUkUuXNuEE+3vz9rcrWLvzMD3aR/LgNefi6tI0VSsaYsRFQ1j0xTKOHkgmulPja1j29vdnVVYW123exFnV2yfvzMujpLKSAcH2j5Qu2LbN0m67tnSOjMKliUoKDex+DgO7n0N+keWPja9Xw7Z6fvy+/9Z83qFtJ4IDg9mzfxcd2nXigvH2V0sZ27sH363eyP6kFLq0atoLw7HDBjN22OAGn989qhPdo+oeAbOXq4srd7/yFO2iWnPleNu35q1PSEgI7dq1Y9u2bZSXl7N161a2bt1K586dGTZsGDEx9k1BOJndu3fbVVpt5NnBzP0umY9nHuHjmUfqfY69tm171+5z6uLbbxA5yxYR+8AduEe2wsUvAIAKUy6lKUlgBt9h9s3hDggJ5fpH/9Mk/fMIjqQ4PYG4X94hoNMAq2kSubEbKcvLxDPc/v/fvJwsdm1bT0ZqdRiOiKLPgOH4B9r+f/HAueFc2jew3tD75pRoSipsj8Q+nu78/PIt/LpqN39s2k9SRi4ArUIDOHdAF84f3r3RF47SNBSGpdEiBk6iMPkQprjtVo/7d+hN+MBJzdSr49YfKcTAclW/K+X4zmr2LoY45vH3f+X3tXtY/cG9LNkUy5MfWkqCLVi/l/KKSp6c1vRltWyVl2lZIPjMlS/SdVAn/IOPb2tqGAY3PnuNXe3d2KYtm3NzKaqsZEO2ZaGHGfBydubGBtQe9nB1JTAslOXPPmP3uadSXlFORWUZZjNkm45vaRvkF9rgNsefcz7jz7G9ZNSfzd+yEzMwe/kaPFxd8XA7cQ6swf0X2b6JzIszP633mLurGx1iWjN+2BDc3Bo3z/b/vp1ORn42n9z4sl3n9WzfhdSs9Ea99jEeHh5MmjSJc845hw0bNrBp0yYKCgqIjY0lNjaWVq1acdNNN9nU1o8//njS44WFhXb17ZJLokhLLWXVqrrngg8bHsSUKc23mULk1Bsp2r+H0pQkSpOPUpqcVH3EEuLcIqKIuNr2rb33btpAQEgokTauDziV8IGTiP/1PXJjN5Ibu7H2EwwIH2Dfe+gXH77Gt7NmUFlhPS3P2cWVKdfcyrW33G9TO53DPOgcVn998D7Rtld/OcYwDC4c2ZMLR/a0+1z56ygMS6MZzi50vOReCo7upzDFUk/UO7IDPtGNn5/XFAa38aLhN+Fr23komRB/b7q0CeflL5bg6uLE1PMG8vm8DfyycmfTh2E77s2t/XVjTdLftWrv8W+7OvnbG4bbeXszo09fvkxMOKHOsC9Xt25NGy/7/zBcP+YcZsxfQFpuLuFNUKsYIDX7KF8ufJP4lNhaxwzgjXt/tLmt6a/Xv0DO3c2dTu27cN6YC3C3cfV8XuHxkk4lZeWUlJ3wx9rOH8kFq9ae8pS5Cxfz5mP/h28j5tjuOLqPo9n2l1b795U3cOv0R7jt+Ue5+eKrCA0Mtpqu0zrc/pFxT09PRo0axYgRI9i2bRvr1q0jMzOTpKSkU59c7cUXXzzptCGz2WzXtCJnZ4M772rP+ReEs21rHlnVdYaDg93o3cef9u0b9m8/bdpr9R4zDIMPP7zHpnZc/APo9OLbZC38jfxtm6023fDt05/gcefj5FF/4PuzZ2+YCoZBr2EjuOCGm+k52Pb56XUJ6jqEqvJSkld8S0VxgXXfPX2IGnkpQd1sf41fv/uMrz5+o85jFeVlfPPp2wQEBnPhZdc3ptsNlpFTwKtfLWHJplirHejOHdiFu68YTXiQb7P0S6wpDEuT8YnuUm8APvTTmxSnJ9DzZvtGm+pjzzbKX9/Qvkle85j07Hy6tLHMRdsbn0rvTq145e5/sH53PPEpdY8W1aWqsoqt/92Ki6cLvR7sVe8f5F732z7ftHP/jnZvCX0qbby8eKSLfVUZ6nMkI4PisjIGPPgQZ3fvjv8JdXYN4J1bbq7/5Hp8ufBN4pPr3uHMbOc/xu+Lfz5lMPrmpy9498VP8fP1O+nzAM45q/GlwY4JCwoix2SivKIC3+p/t/zCItxcXfB09yCvoIDElFRm/fwbd1x1ef393/jrSV+nsMS+kdJjbnjmAQzD4Ns/fuPbP36zOmZgkLl4W4PaBXB2dqZ///7079+f/fv3s3btWrvOt2fOra3atfOmXbvGL+w7ZtOmAxhGrTWlx9aC2sXJ3YPQC6cQeqF9C0jrZTazY80qdqxZRduu3Tj/+mkMnXB+g2/xh5w1iuDuwylMPUxZvuWOk5tvEN4R7TDsXIvw29xZGIbBRVfexPBzJhIQFFq9A10mq5fM48c5M5n3/Wy7wnBmQQX/XZDC6sOFZBZUWB0zDIh70rZR3viULCbeM4OM3AKr/9eDRzM5lJTJ90u38fvrd9Auyv5pNdK0FIblL1FRkEuZKbPJ2nv8xQ+oKLN9Ve+JticVsfFIEV0jPBjR3v6yNl4ebqRlmUjNMnE4OYsp5/QBoMpsxs2OWrpOzk5Ullbi7O7cZAveHv70niZp50T55eXsK8gnp6y81iD1eeH2LVCZs2o1BmAqLua3zce3Fj42ZaUhYTgpIx43Vw+mnDONEP8IGnMXIDw0guzcbMrLy/Dztcw7NuXn4ebmjqeHJ3mmXBKOxvPZnA+46+ZTr6AfY+eWyydzx9WX8/wHH/PSg/fSr7vl4mTLnr08/vo73PPPqwkK8Of+/73K2m07ThqGn/75dYyT/BuZMZ/0+MnUGzqb8ALN3qoQAQEB+Pn58dprdY+8/utf/yItLc2uPpSUVLJzpwlXVyd69vTFxeV4KFy6JIOs7DIuvbTVSVqo7cILB3PiP1RBQTGbNsVSVFTCeec1fqe2YzJ//5nKgnzCL7O9YkhgWBg+/oEkHthP/N49vPPw/cx5/WUmXns9Y6ZcgUcD7hIZzi74tGr8nPWUpASiWrdj2r8ft3o8MroN3c7qz8Y1S0lJsq/E5//9lMTSA/l135Sz47rqqQ9/Jz2nAB9PNwZ0iyEs0Bez2UxGbgGb9iaQkVvIMzN/55Mn7LtjJ01PYVjOOJWVlSz65Rt2bFlDbnam1R9YwzCY/vZXBAWH2dzevd8f5ccduXxzQzvMwFWfHqaquskXJrfiin72LQTr2T6SVTvi6Hn1dAAG92hDVVUVSem5tI207wo/bHAYKctTKEwuxDuq6UaZ0hMzidsZj5uHK/3G9G5wO6uyMpm+fz8llXXvvGRvGB7etUuDg1Z9okPbYSrMYWjPxlf5uOvmB/nvK4/x0nPv07/3IAA2bVvPw8/czQP/eoygwGDufuQWVm9YblMYPia7oJD8omKq/hQW24XbPp/5w2+/JyIkuCYIA/Tr3o2I0BA+mvsjn7/wLGd17sjO2FPvBmZu0mqqFr+8OrNJ2rn77rtxcWm6P01du3Zl06ZNBAcH41ZHNRl7RzfT00v5zxN7ya3eljciwp3/e6gTUVGWjTL++CODgwcL7Q7Dzz57Xa3HcnIKuOyy/xIebv9i1fpkzvuRsrRUu8JwcEQUz3zxLdtWLefXTz5iz4Z1ZKUkM/ul5/l+xtuce9mVXHWv7SUbT2b3R/9HaV4G/e7/xKbne3v7kpmewpG4WNq0t66EEn9oPxlpyXh72zcVYf0Ry92R87r60SnUHZcG1qZfue0Qft4erP3oPiKCre8kpWSaGDrtFZZvtW/3Pjk9FIbljPP+q0/y+w9fALVHmhoygrojuRhvNycGxHjx2K/JVJqhQ4g7hzJL+WxDlt1h+PEbJ3Dl45+Qk1/MoO5tuHRMX1Ztj6OguIxB3e1bVFZusvxB3fnKTvw6+eHq63o8LBrQ8eqOdrVXVVnFp09/xeqf1mE2m2l/VltKCkr46PFZXP3QpYydOtqu9t47fJjieoJwQ8x7/LEma+uYq8ffxTtz/8OM75+me7v+eLhbj1IN7j7G5rbe+/QNIsKjaoIwwIA+g4mMaMX7n73FVx/8RK8e/di5Z+tJWjkuv7iEL5avJSm79i5T9u5Al56VTWVVJT8vWc6oQZYdtVZt3kpiaiouzpYKJm6urqe8OxHo5U+Alx/v//OFOo7+P3vnHd9U2f7/90mTpnvvXVoKZZYlU0BQcLBxgIjiXohb8VFBcaIIKgIuVEAEZO+99x4FSksp3Ttt2qQjadL8/kgHoeuctD7P8/09/bxevF455865etMm53zu676uz8fElMVvklUkvRGuf1dpMnYNwa2Fasmr8fjjj9O7d2/Ky8vrJcNTp06V1ES3ZnVGDREGyM7W8cmseGZ9Em21pFpDcHd3IjjYm02bjvPqq2NEXaPLbFx/3WQ0NDreGGIGDCJmwCCSr11ly++/cmLndko1xWz+/ZcWI8NVsxT9zl7972LP1jVMnXwvAUFhuHmY5ffUBflkpieDycTAu6XJIrrZ2+DrLOenCc1TLSnXV+Dn6VKHCAP4e7ng4eJATkFdW+9W/PvRSoZb8V+Hw3vMNY3RXXriFxDS7BKC7OIKgt1sEQSBq9nltPVWsvvlttz5bTypBdJLLXpGh3B9zQzUmrIaEfWB3SLJ3f45NlVSYbmFGvQVBoJ8GifaeWfMzS0mTKjj1HXGpZLhLb/u5Mh6y3rK7nd3xWbmX5zfHyuZDKv0ejxsbZnfpSsB9tZbxP6TyC/KoUxXSlzKeeJSLEmqgDQynJOXjdFoZMO21dw1wJxpPnR8H6npKTXZSqWtLQqFOL3qXRcuk6Gq325VqgNdl3ZtOXslju+X/cX3y2rtdk1A1w5RVFZWkpiahr9349nmjoFRnLhxHm9nd2zlzc+UVuOrpYsaHX/ncfEudAB6vZ4jR45w8+ZNSkpK6uwQTZs2TVScmJgYYmJiGhwfPHiwpHlVWzHff78v7aOd2bM7l0uXivni8wQ++dT6GvEff7Sss66srCQlJZfz52/g4iK+DCH+tWdpvC7FWh2dWoS178DU2XOZ+NpbbFv2O/vXrRZ97c0tjX9OKkqlaQxPeWk6cbHnyEhNIiM1icy0m0BtIiUgOJwnXnxXUszn+3vx+a5s4nPKaecrvtnwdkSF+BB7I5OnPl3OiAGd8K5ym8tTa9l8OJaU7EK6RP7nlEdaUYtWMtyK/zoo7exxcfPgqx/F32Cbgt5YCUCSSs+gCPMNyUlpQ36JdVlPQRBqiHA1bG7RzH1s5lLOx6eTt/OLRuO4RDTdhCUFR9afwEZuw0tzn2b+tJ8BsHOww8PPjayb2ZLjDfD05ExhIW4tYIsLUGEw8MnqNaw9foIstZrKysqaMUEQKFy2RHLMtft/RldRXm9XpdQGuphO3Tl9/gRzF33B3EW1fzuTyURMp95UVlZyPSmeAD9xW+CJWTkIgsCoO7qx8eQ5vF1diAkP4UhcAqPu6CZpbm89+Tgz5i/ieopl/WNUWAhvPvk4OaoCBvXqQXSbxiWwnh04kQGRPSmr0NVLht++93m0VjTRfblkUaMLV6lkeOvWrcTGxgIts0PUUlCrK/DxUdbYLffs6cacrxM5d07NnK8TqZCgQ3srfvxxa4ONcgMHSpXl+ods4W+Dp38Ak995n/EviVuYABReO9miXN3Nw4vvlmxl+7o/OXvioIXpRo8+g7hv7CTs7KXVNG+9UoyxEu7/MZF2vna42FmnTT/1oYE898VKNh2OZdPh2DrjggBTHxwoaW6t+GfQSoZb8W+BlFvzhCen8ePcmRzcvYk7+g/FXoJbUn0IcbflWk45Q+ZfR1NupFNVbV+upgJf53/uKyCmLrPTtJbVnizMURMQ4Ue3uywVKOwc7SjIlk6GX4+I5KWLF5h05jSdXVxxlFs+FN6Jkiaf99WGjXy3dVv9g1Z2/BeVFOKgdOTJEe/g4eKDjcx605N3p83kX5++QcKNOIvz7SKjeXfaDLJzs7hrwD10aCfOQrVUp8PLxYmekeFsPHkOW7mcgR3bcT4phdjkdDqFBImem4+nBz9+9D7nrl4jOcP8wA8PCqBbdG0N8cuPPtJknJ5hXegZ1rBCyd0dxLvZ3YogH38LklpcoqFIq0EmyAjy9ZMcLyHBLJXn7++Pl5dXs80JysrKWLp0KWfOnKGwsLBOpnnNmjWi4jg5yVEoav+fMpnAq6+14aOZ14iLs37L29/fnVtZoCCAh4czvXu35+mnh4uOI7NVInd1w6eBmuDsP3/DUCw++/r8p7Nxcfdo9D0OTtJqcmU2CuQO9ScCKrSFmEyV9Y41BDs7e8Y++ixjH5XegFsfqmuGAa5ml1uMSVmGjb8rhtJyPZ/8tgNVUanFmIeLAx88NZzxQ2KaMdNWtBRayXAr/i2IGDONSoO4WrW+g4ezcdVi5sx8te6gILD5aJKkn/1MX0/e2pBBkkqHm70N47q6cS2nHFWpkf5WqEn8EyjPL0eTosFGYYNHl8YfPI3Byd2RvAwVWnWtfqcqq4DMpGycPaT/XzdkZZFSWoqAuZmuGtXJG6lkeM3xEwjAw/37seroMQI9POgYHMzpxESeveduyfMDiGnbl+tpsUQGdWoWEQazmsTi7/7i7MVT3Ewxa2a3CY2k+y31sNOeE18bqZDLkQmymteF2hK0ZeWU6HRcz5KmYLB04xa83d24b+AAiya6K4k30JSU0qerOIL+T+HSip11zh04e5zHPnyV6U+8LDmeXC7H3t6eZ59tGYLz1VdfsWvXLqB5mWb/ADuuxWlQqfR4epoz60qlDe9Oj+KD96+Sn2+dys327Z9Zdd3tsAtrQ3nKTdwH3V3v/yt37QqQQIYHjRZf1y4Gtq5eCIJAx2e+rnfc3EAnvWa9RFuMjY28Thb4RsIVykq0dOom3q1xfFe3FmvznXzfHUy4pwfn4tPIqLJjDvR2pXu74P+oW2krLNFKhlvRbJgqK1FdPoQmNQ5DaXGdjEvbh99F4egmOt7cj18nPeVGvTJN1tygxse408HPnuQCHT1DHPF2klNpMvHn42GEuIur/fynYKo0cWPlDXJP5ZptUkOdMZQbSFyeSPi4cPwHSTMq6NQvmqMbT/LhWLPSRWZSNh899CVGg5FO/aXLfK1KNzfj2AgCbgoFNs3cnk5XqQj08ODnF19g1dFjBHi4s+rN1+n46uvoKiqaDlAPnB3c0JYW89Wfr9E+tBt2tpYPw/v6ThAd6/e/fsLby5cRw8ZYNNFdjruIRltM3153Spqbi709RaXmjJCXixNZhWpmrzfXhjpLrMFesmEz0RHh3DfQMnO7aMVqrt1MZs9vP0qKV6ov49dDKzlx4xwqbaHFPoYA7HzzT0nx6sPgHn3pEd2FeX/9ItmmuUePHpw4cQKtVouTU/MXrUePHgXM0myhoaHY2FhHRLp3dyMvV8ehQ/mMHVtb7+nmpuDd6VF8MycRo9H6MoXk5BwSE82mIpGRAYSFScuquw+6G835MxjUhSjqyei69u6PoUht1dwunzjG5RPHKFLl19FDfv6T+hoy68LBLxx1wmkM5SXI7erb9ZP2uysrLWH2h1M5e/wAgiBj0LBRvPzOZzWkeOFX75MQd0lSEuWbseJ3bMRAIbehd8ewFo3ZipZFKxluRbORtu9P8i/uMx/cfh+zgjvFnjsBgsDgYaPx9Q/CRqIIe32I9rMj2q+2EcLXWYGvc20d7HMrU4nLLuPwa/9e17z03enknrTMgnh29eTGihsUXC6QTIbHvzqKqyfiKcxRA1CuNW/xufm4MnaqdFthQQBfpZI/evTEzkrycCvkMhmezuYtVVu5nNyiYmQyGQobG5YdPMSsieKJazX2ndkAgkCWKo0sVVqdcSlk+Le/fqRju86MGDbG4vz8X7/hWsIVDm4+W/+FDaBdoB+JWTnkqIvp2y6SdSfO1HxH+rWT1hxZH3R6PSp1kVUlJh9v/Jatl8zf29tLeqyRvzt68YzFcWVlJYnpyZyJu2TVIlatVmMwGPjhhx8IDw/H7jbXtNGjR0uKp1QqcXFx4bfffrNiNrUYOdKPkSPrJ6jBwfZ8+511GXqNpoyPPlrGvn0XLM7fdVdXPvposugmOs977sfznvsbHPd/TJyN9e1Y/9NCVi/4tu5AlSuIWDIcPGQSfr1HIqunXh2g7YR/YZKgYLPmzx85c2x/1VSMHNi5gdzsDD75dim2SrvaOVoBvaGSglIjxkrL6wPdxCdRzsSlsu7ARZQKOY/c0532obVylG9/v4Hrabls+Po5q+bXipZDKxluRbNRGH8SAKfAtti6itf/bQiBoW0wVFTw1kffNjuWWORqKkhXW5eZbNbPPZGLIBNo91Q7rv16DQAbpQ1KdyVl2WWS47l5u/Lx2vfYs/wAyZfNjVZhnUIYOnEQzu7Ss2uPBAaxPD2NYkNFi5BhbxcXcorMW4XBXp4kZefQ8+13ScnPx60ZFsLWPuzEQKcrR1WQZ5U27/BunRnezUyOfN1ccHdyJENVgK+bK5H+4jSa737yecC8rrx242bN8a1wd5XeiHkwwfy97RDQlnCv4GaXmIx846l6t+VNJhP9uvSQHO/ixYsIgoBOpyM+vtZhsNo+WSoZHj16NKtWrUKlUuHp+d/n+PXpp3+xd++FOuf377+IQiFn9mzrSGxLYc/fy8FkwkYux8XDExsrtaAVjm6N7hTaOkmTujy8ZwuCIBDVMYao6K4c3b+dqxdP89WMaXww+2er5qjVGXl3Ywa7rmkwVN5eUiPege7YpSTGvvsrxqpG4Z83HGXRu48w6k7zPeFiYgbn4usu4Fvx70crGW5FsyGT2yJ3dyZqQstoyD4yZSrzPnmLv5cu5I7+Q3FwtCRxPiI7+f+T8PVwJsjHrcn36dV6HPwc8OhsuZ1po7RBV6iz6mc7uToy5iXpWeD6cLKwAH1lJZNOnybc0RFHm1sb6ATmdhFvFQ3QMSSYbefOk5CZyehevZi7eQvXs7IAeKBHd6vm+P0bG6267lYMHGn+2YIgcDXhcs3xrXB3az6BCvPxIszHS9I11Y9igYY3kB8YJK18A0Apt8XV3Zm/X1wo+dqGUF9pU68OXfn+rY8lxwoNDW1R1YjMzEx0Oh0TJkygR48eFqUXgiDw/vvS7l/l5UY2bsji8uViiooMdcoGvp8v7btx8GAsggBPPjmM++4z16jv2HGGxYt3cvBgXSUCMdDEnkcbewGDurDOWPBLb0iKVabV4uzuwZxNO3B2azkTkEpDBYbS4jrnbV3Efd/yczJxcfXgy4WrUChseWTKVN5+/kFOHt7Nz/Okf+4A5uzNZevVunMCJFVxfPPXPgzG2mbAcr2B579YiZO9kiE9oxq5UhpMRhOCzX9OYeX/B7SS4VY0G/59R5O2908Krp3AtU0MNrbW6zICfPn+ywiCwLIfv2bZj7c1WVjRQPdPITlLxdm4NOztFNzfr6PF2LKPHhcVQ+GkoLygnIqS2qy0rkBHaU4pCifr5MyybuYQf+Y6xSpNHXIy+sWGt0/rw8WiohoSdl1rbsqrPrbm1vv71JfRGQw4KpV8+NCDOCiVnLlxg47Bwbw1epQVEetCX6EjpzAdL1d/7JXitparf0+CIDRoKTzqXumNRIv3HGpwTGFjg7+7K33aReJs3/B35p2nzc5kXy1eQoCPN4+NrP0bKm1tCfH3o02w9BrHh3o+wLLja8nTFODtbH3TZjUuLN9hcSwIAt7uHtjZWmdEMWXKlGbP6Vbs2LEDQRAoKSnh8OHDNeerM81SyfCvv6Rw5Iiqxebn6KjE39+dadPG1Jxr2zaQvXsvoNVK3yXKWbuCnFXL6hkxf3ulkuHug4cSd+Ykjs4tIwdZXpBNys7FlGRer2dUEO1Ap7R3wNPbt0b7283Di1nzlvDms2PZvPoPbOTS76O744sRgJfv9OaHw3mEutsyIMKJrVeKePMu8bufV5KykAkCyz6aTJ9OYfy66ThfLNnNU58uZ+vcFyTP61YUXS8ibVsamhQNTsFOBN8fTP6ZfHz6+uDSpmUlO/8X0EqGW9FsuEX2IPfsTpK31tfAI/6mdisaIiT/DWtfo7GS179dx4pdZzFhokf7YDQlOl6e8zefvziS58b0Fx3Lrb0buadyufDFBQBKc0q5+PVFTEYTbtFukue2/+/DLP/8byor6//9SSXDXVxdW/R3bmdri90tLmBvj5G21V0f9p5Zz5WbZxgz8EkclE7MWzUdTakapULJC2NnEhHYdOPgv14zZ5A+/3Ymgf5BPPFIrYKBUmlHaHA4EWFtJc8tOSev/g9t1Z/nelY255JSeH74Xbg51k/chw/oB8CFawkE+njXHDcXGYVZlFfoGfHdFHq36Yaz3S2ZUuDTcdIcxUL8apvJCqoatKwlwv8EYmJiWjTTfO6cGoDwNg4EBtgja2Zm7sEH72T58n0UFGjw8DDX1atUxeTnF/Hkk8MkxyvYtRUwIdjIkbu6QjPLYMI7dOTkru3MmvIofYbfj8NtpHjg6LGS4qXu+o2SjPqIMCDBkMbHL5C0m9cpLyutaZrzDwrlw69+4f2pj6LX6yT/3XM1BkLcbXlrqC8/HM7D3cGGz0YEcChRw+Ws8qYDVEFTqqN9mC/39jXfg96aNJTKShOzl+1hwge/N+kW2RCKrhdxdeFVTNX3eRMo3ZXmRmxoJcNWoJUMt6LZSN7+M+UFWfVvH0l02QJ47YM5zZ/UP4h5K/ezfKdls9ADAzry6jwZ249flUSGQ0aEoE5Qo1eb5ZiM5ebGEVtXW0IekG4FuvWXnVQaTSiUcpw9nJv98P+uS9dmXX87vly3vsExe1tbOoeGMKSztAakc/GHycpPxc8jmB0nVqIpMW8J6/Tl7DixkpfHz2oyxn13m7PS52PPEOgfXHPcXIT6eJFZoMZYacTXzRWAHHURNjY2eLs6k6suRltezv7YOMb2abyu9sFhQ8lWqcgvVOPl7gZAXmEhCckp+Hl5ESExO7zp4h4EBLS6UvbFHas5b8KEgCCZDAP8vP4v5v31K7mF5oypj7snr098mufG1a952xiMRiP79u3jypUraDR1dzlmzJghKd7ChS1XDgJgayvDyUnOF190bPrNIpCRkY9eb2DMmI/p1cu8hX7mTAKVlSZSUnKZMWMpYM64f/zx5CbjGctKkbu4EjXvZ+QtkM1dPudLEAQSLp4n4eJtTo+CIJkMl+YkgyDg030Ydl4BCIJ1ZL1j117ciL/M9vXLLXSGozv34I2Zc/nyfemyfrZyAUelWRJRKRfILq6gwmhCbzSx7WoRs0eLK9Xzdnei6Las/juT7yYlu4CVu88BNGi00hjStqVhqjTh0cWDgktmh0t7H3sUzgqKbzZQ3tGKRtFKhlvRbGjSrgECHh36YOvihdBMcfy7H3iwZSb2D+GvnWdQyGX8/sFjPPaR+QHlZK8k0NuNhFRp+pi2rrZ0facr2Yey0aaayxCcQpzwu9PPqjKJUm05Hv7ufLbhA5QOLZ+Vu6bREFtcRBsHR3q4S68b/GLd+iYzzQOi27Pm7bcsMsiNIb8oB3cXb2wVSpKzE3B18uTtSd/wxdJppOfelDS/h0ZPIic3i3xVLl6e5u3QvPwc4hPj8PMNIDJcWp1fl9BgsgrUTH3gHrxdzNm+vKJiFu3YT/c2obT19+WHbXtJzGr6c/PNH8tISstg1bzZNeeUCls+WfgLkSHB/PDhdElz6xnaxaoHcUP44o8FzPnzZwvSmlOQz3sLv0JVrOa9KdJIyaFDhzh27Fi9Y/9JB7pqDB3qzbZtOajVFbi5Nd+hccuWUwgC6PWGGkWJ6l/l5s0nao4FAVFk2KVnb0quxGLTTNMiCzSwY9fQTl5jUDh7IAgCQXdNbNaUJj//FmMmPI2inl2IAUMeYOnmnhgkyjZ6O8nJLjZfE+puy/U8Hd2/ikOjq8TTQTxp79TGnx0n4jgfn063drWL1e/eGE9mXhGHLtyQNK9qaFO12Hna0f7p9hx7tfY7YutiS1mu9JKaVrSS4Va0AOw8/DAZDYTdX7fL3VokXb/KmmWLSL5h7iIPj2jP+Mkv0KatdK1cMfh5Qgh6kdqgmflFtAvx5b5+lnNxsleSkaeW/LMVjgqC7wuWfF19GDC6D0c3nUBbVNoiZPjz+Gvsyc3l26oM8euxl2oefG+1jeJ+P+nOYtB4D8qRuGvM27yF98aLq9GtqNBh62wmznmFmQT5RODi6I67izfZ+alNXG2Jr+Z/wo3k66xfUmsgoVTaMWP2u7Rt046fvlkqKd7BK/G4ONjXEGEAb1cXXB3sOXQlgd5REYR6e5KUk9dkrNTMbIJ8fXC9pfHLxcmRIF+fGkc6KVjyzFzJ1zSG3zb9DUDfzt0ZPfAeADYf2cvRi2f4bdPfksnw5cuXEQSBzp07c+nSJVxcXPD19SU9PZ1evXo1HeA2GAwGfvrpJ/bs2UNeXl4dPfQjR45Iipebq0Ovr+T112Lp2MkZR4fax6kgwAsvirPsrUaPHpG0ZCGYfXgkRcePcGPm27j1G4jNbY3I7oOkmdz8FdtASYOVCOg/nuRtP1GUdBHXNtbvQNk7ODbqUurhJU615VZ0D3Jgd3wx13LKeTDGnc93Z6PRmRvhxnUVnwSYfN8duDrZc/VmlgUZltvYsGTmZGb+shV9hXgZuWoIsrq9DaZKE/oiPYLsP79Q/L+IVjLcimbDr/coUnb8QvbJLeYGOqWlmYDYruBqHN23jdkfvoLJVFnzhU+7eZ3De7fy7ifz6T9EWt2rsdLE3+cKOZZcQr7WUMdcYMWUcHycxWd2PF0cSckuoKC41rIzPbeQhNRcPF2lZ2HKcsooSiyiQlNRhyVKJckPvT6aK8fjmP7ARwRFBmDnVNuYJQjwzuJ6XP0aQbxGi72NDZ1dXJibmEilyUSIvQOpZaWsz8yUTIZ3fPg+D8+Zy+eTHmVsH7Mj1LoTJ3h/+QoWv/wShSVanl/0E+tPnhJNhl2dPMhSpbFy9wKKSgrp7R0GgLa0CKcGLF8bQkraTYIDQnB1cas55+LsSnBASI0jnRSU6nQUlxnZeT62xnr5alom+cUaC/cpMU5UxkojBUXFGI3GGsMIg8FAQVExlZXS7Gv/Cej0Ovy9fNj0zeKa+T01+hG6PnovmtKSJq6ui6KiIlxcXBg7dmwNGZ44cSLffvstBpFulrfi999/Z/ny5ZKvawiHD5tLQcrKjJw5ra4zLpUML17cdENbfn4RFSLJU9ayXwGB0oRrlCZcsxwUpJPhlkbGwZWAiRvr52GjdMDmtmbXTs9KL5e7cPoIF88co7Agj9vlPV57v37Hu/owd1wtcW3va4e3s5wL6aW097VjQnfxZHh4n2iG94mud8zF0Y55r40XHetWOAY5UpxUTOKKRAAqSipIWJJAhbYCl8jWemFr0EqGW9Fs3Ny8AATIPLKGzCNrbhuV3kD3x8LZVFYacXR2oUv3voDZiEOrKeKPRV9JJsMzt2Wx/Iy5rqoFPEG4q2cUK3adZcCz8wCIT8ll8IvfU2E0MrSnNNOO7KPZ3Fx9s8FtRqlkeM13m8hKMtv8psRV6Vc2Q/4hT6/D384OQRBILNES6uDAHz168ujpU2SWS9+Oe+uPpQR4uDN58KCac48PHsyC7Tv4eNXfHP3iM37bu48LN5NFx+wW1Z89p9dxLHYXgiDQLao/RVoVaq2KDmHS5NqMlUZUhfkYjAbkVWYvBkMFqsJ8KiulZ3DaBfpzJTWdI3EJHIlLqDNmMBrJKCi0yBw3hGB/P5JS0/lk0S88dK8587p2116KtFoiQ6TvLFQYDXy/5ze2XdpPnkZFpUWmFGJn7ZYUb3jfQRy7dNaihKHavGPkgKGS5yeTyXBwMBMkGxsbSkpKEAQBmUzGhQsXuOeeeyTF2717N4IgMGzYMHbu3ImPjw8RERFcuXKF8eOlk5LoaOd/e0fv66//xJUrKZw7t0DkFQ3swVgpy33h8EGObd9CYW6uxfdBEAQ+WFyfckXD0BfXKnEYy0sxlpfWDlrxe135+3yW/1J3t6NaLUQKGb4dY7u4MbaLm9XXV0OnN5Cn1nL7HyDIR1rJWeA9gRT/VEzuCXN5VXl+OeX55sa+wKH//dKj/41oJcOtaBk0dHO1ooEuPy8LBydnflyxF3dPbwDUBfk898hdqHKzJMfbcsVs8tAzxIEQd9tmP78+eHI4B89dJzPf3KigKTXrAft7ufDeE9K6vtN3pWMymZDJZSgkZKcbwuF1x0AAD183PPw9sLFpXv02gL4q65heVkavqjphBxsbCvR6ybGuZ2VhAnZfvMQ9Xc06rPsvXyYpJ7fm7+Lu6IRMQk3oiAGTcXH0IF+dRcc2vQj0DiczL5lhdzxIZJC05qbQoDASbybw0ezpTBhrrsv8e+NyiorVtG3TXlIsgNG9u1FpqiQuzbKMoUNwIKPu6EaJTsfgTu1rmusawwMD7+T7P1dw5Ox5jpytbWASsE5n+McDf/Lbkb/rHzRJ/5Z0i+rIlsN7GfXm04yqKpPYcngvmhItXaM6sHLXppr3ThjWdIOio6Mj2io5Pzc3NwoKCliwYAFqtRp7iVbWADk5Ofj4+DBz5kx27tyJt7c3X3/9NWPHjkVvxWd55kfSPw8tAbH1uV3+3t6iP/fIlo0s/Fc9TZXVhcwS4d+v+Uoyt2Lbuj8xmUzYyBW4uXtabbcN8NaG9AbH7OQyOvrZMbarG3YKcffXxPQ8pn2zhlNXU+qMCQjk7fxC0vzcO7gT9UQUKZtSavTole5KQkaG4N6h5TSg/5fQSoZb0WyE3vtMi8Zr16EbhQV5NUQYzNqR7h7eFufEwl4hw8NBzuqn2rTI/Pw8XTj042v8svFYjXtQt3ZBPDOqH4724m06waweoXRXEvNeDDbK5ju82Tna4eLpwpdbZzY7FkCAnR1JJSU8fuY0JQYDUVX1qiq9Hi+l9JrkzqGhnLlxg4e+noODUokAlOjMN/OekREAXE1PJ9hLvDGFTJAxuPtIy3l7hxFQVS4BsOPEKlRFOUwaPq3RWCOGjWXej19y6Pg+Dh3fV3NeEASrdIbtbW15dGBfCjRacovMiycfN1c8nBxrxvu1FyfZNnroYFIys9i074CFEceYu+9i1JBBjV1aL7Zd2oeAwIiuQ9l8cQ++Lt608wvnYlocE3tLJyof/DgHQRA4HnuO47HnLMbeW1Db9CcgiCLDvr6+xMfHk5+fT3R0NEeOHCE/Px+Adu2k26bb2Njg6mpedCgUCgoLC5HJZMjlcrZs2cLLL0tXHfhfwo4/l4DJhG9IKDmpKdg5OmLn4EiFTkdIO+kLA/9+0tQnmkJpiRZXN08WrdyDi2vzCOGaC+omkyaLT6hY+3QbXO2bvm+/NnctJ6/UJcKA5ISRqdKEXq3HOcyZ7jO7YygxlwxZq0vfCjNayXArmg3PTgOaHSM3O6Pm9UNPvMQX/3qRpT9+zcB7zA/Nw3s2o8rL4fk3PpIce9pAb2Zuz2JTrJqhUc44NpN0zluxn9cn3sU7ky1r7oq0ZYx951e2f/ui6Fg+d/iQeyoXQ6mhRcjw+Gmj+POzVdy4eJOIrtJqFuvDQ4FBzE6IJ62sDGe5nGE+viSVlKCuqKCHm5vkeN89/SQPfjWHLLW6hgQDBLi78/3TT5GUk0OnkGAGRNdfZ2ctrtw8Q0r29SbJ8LgRj5CSdpP12/62MOIYN+IRxtz/kOSfu+74GTycnRjcqT0ezrUNTFdSM9CWl9M7KkJSvGmTJ/LwfcOIryojaRcehp+Xdc54WUW5+Lp48eWD09l8cQ9+Ll4smPQpd895FL1BeqYUxGUtxdpaP/jggxgMBmxtbRkyZAgKhYKMjAx8fX25807pmXB3d3dUKvPWvJ+fH+np6UyYMIGsrCycnZsuU7kdBkMlq1ZmcOxYAYWFFRba3oIAK1ZKb/JraRSfP03R0YNUFKgw3VJXLggCbWZ+KSlW+o1EHF3d+Gr9Vp7o0YmgiLa8s/BXXrtvCIPHWFf7CqBT51FRUmgxPwDnYGkEu/eddxN77gSOTs2vmQ10VZCnNaA3mnCrIrvqMiNKuYCjrYyCUiM38nV8dzCXGff6Nxnv4vUMZILA82P70y7UB3kzre3PzjqLrYstPWf1bCXBLYRWMtyKFkFpbio5p7ZSlm/eXrL3Dsa31/04+IjTyn16XF1CvXrpQlYvrdUGNZlMzHxjimQHuuHRLiw+oeLVtXW3vqT4zFfj0993YmMjY9rDtdm43EIN46cvJi45W1Ks0FGhqOPVnPvkHA7+DtjY3WJ3LAh0nCptm3/Dwq0YjZV8NvkbHF0csHe61dlM4Ksd0uxJh/v6EunkSEZZOZ1cXPCwtcWEiTmdOhPQiGtaQ+gUEsKFuXNYfew4cenmBVCH4CAe6tcXpcJ8U//r9dckx21JvP7idCaOf5y4hCsAREd1xM8noImr6sf5pBSCvDwY3MnywX4kLoF0VYFkMgzg5+XZIAGe+smXXLuZzJ7f6jPAsYSNzAZ3x6pMqY0clbYqU2ojZ93ZHbw5/DlJ8yrYe0nS+5uCXC5HLq99RA0cOLBZ8SIjIzl8+DDJyckMHjyYZcuWkZpqVhuxhlyvW5fF5s31f9+tUBprcRQe3kfa/Pqa0KxrIKg0GvAOaIPCVonMxgZdWRlOrq64+/iwdtF87hwlLdNbUaLmxobvKc2u734uvdcksl0njuzbxvQXH+bOoSPqOOUNvV88Yf/wXn9eX5fO8kmh9G9jXsQeSdLyzF8pfDoiAG8nOY/+kczeeI0oMhzg7YpMJvDpCyMk/Z/qgyATULorkcmbXwLXilq0kuFWNBuFCae5uWURmCpraofLVRkUxp8ifMSLuEc1nSERrVNpxVPm9fXp3MjX1Z+PsuKhZSMTmLV4OzJBYOpDA0nOUjF++mKSswpwc5JWy5iyOYWyHHMjWkm69I7726HKLKh5XVJUSklR85pSACIcnYi4RZbJ01aJ5y2anh9evcKNkhL+6nVHk7H+OnwEL2dniwY6gJS8PMp0etoH/Xc0f/j5BDRIgJ9/83GuJVzh4OazDV6vLqn9vRsrKy2O9QYD6pLSmuayFofI74inozv5GvPnJcDNl1RVJiO+e5IMdTYudk5NXG0dJs94jcs34jm/vOl61oMHDzY4JpfL8fPzIyJC/GJi1qxZVFRUYGdnx/PPP4+9vT1XrlwhMjKSJ554QnScahw7as4yD7jTkyOHVXh42BIaas/16yUMGy7esvefQv7WDYAJW78A9NmZyOzskdnZY6rQYxcqvWTM0dWNkmJz/4WLhyfpN66zeNYMMm8mYauUvjDOOLSa0qwGEhtW9Josnv8ZgiBw7fI5rl22LNNBECSR4dm7swlyU9QQYYABbZwIdrfl6z057J8WRa9QB86kljYSpRb/mjKMF75cxe6T17ind/NrzYPvCybxr0RyjuXg20+6dFwr6qKVDLei2cg8tBoqK7Gxc8A52Ly9rUmLw1heSubh1aLI8BcLVv5j8zuRXIIgwOhOrgS52SJvpg7jbx9M4tnPV/DRr9vIKShm3f6LZBdoCPByYfUXT0uKVd0NbOtmi9Jd2WyNyH6j7vi3GxKo9Hqyy8VZlL7408/0ioxgWIylruhTPyzkXFIShcuW/BNTbHE0tdX/zcYqsidAVqG69vgWuDrUb7/870KUXzj7rx0nKS+Vezrcya+HV3Iz31wDPyRavIuiFOQU5JGaI04T+cCBA01+lkNDQ5k0aZJFBrkhKJVKlLfUuU+ZMkXUPBpCfr4eT09bpk5tw5HDKjw9Fbz9TlumvnyRCv0/I3Xn6emCn5+HqPfq0lOxcXIm6ptFXJ40GrvgUMLem0X81CfxuEuaEgdAYJsIrp09TXGBig69enNs22b2rlkJJhORVjhValIugyAQMmwKqTt/x84zAI8O/ck5s42Qu6dIjgeNJFUkJlEyiiowVpr483QBD3Q0Z5h3xBWTlK9DXmW7rZTLsBVpwT3zl22YMDFxxh+4ONrh6libNBEEOLf0XUnzS9uWhiATuLHqBjfX3TSXStwylR4zG3ezbEVdtJLhVjQbem0BNkp7Ojz5BYqqbdeKkmKu/vYuek2hqBidu/f5x+bXxlNJhdHEt+NbxthixIBOLPv4cabMWsaidUcwmaBDuB9/f/YU/l7S6tVs7GxQOCvo/qE0CbD6UFlZydip5m04Dz/3/wqXLrEoLNFa5WL1X4tbO9zq+W/JZDIGdZTeBNaS+ObhD9Eb9dgr7Hn17qewt7UnNj2OKN82PDfo0f/o3G5FY5+LlJQUjhw5wuDBg5uMs3jx4gbHlEolUVFR3HFH07sb1ZDJBJydzY9QuVygqMiATCZgYyNj//58Jj1m3f0mPT2P2Nhk7OxsuesuS5L57bcviI5jqjSi9PZFprAFmYxKXTlyJ2fkHp7krF4uWWd48tv/Ii8zHZMJJr/zPkUqFTdiLxAS1Z6nZzRteX47DKUa7Dz88Oo8iNSdvyOztcOv9wMUXDlC4bUTuLeTVnO95Xiy5Dk0hN6hjhxO0vLh1kw+3Fq7eDMB/UMdqaw0cTW7jGB3cQ3TaTnqmtdF2nKKtLXJA2tu09UKEgCVFZUWx62wDq1kuBXNhqNfBBWlRTVEGEDh6ILc0dXinFj8tfi7BseUSiVt2nakW2/xNX5TB3rz1oYMFh7OY2iUM05Ky1qrQLemb2grd9fdEh87uCt/7TyLk70tj93bi4PnzQ5NE+4RvyoPGRFC0uokNDc1OIdLb+K5He/cOxNXLxfm7v2s2bFaEl1eqzUUuJScYnFcqteTX1xs0WDW4vg3E+2n7h6ICfh9zyG8XV0Y2SumZkwht8HDyQkHpTTlkZaGUmGLUlE7hxcGT/oPzqYunnzySf766y+GDx9Ox47m2vnLly+za9cuxo8fT1lZGRs2bODq1auiyXBTC8Ru3brxzTffWGSQG4KrqwK12mzZ6+WlJDu7nNdfjyUvT4ejo/QGKaOxkk8+Wc6mTScwmUx07hyOVlvOjBlLePvth3j00bskxbNxcsZYYpamk7u4UZ6WQvrP89FlpCGrx7q4KYS2jya0fW1j6/u/Nm8XR6ZQIshsal7r1blUlBRhKNNQnHy5WbGbi9mjA3huZSqXsyx3vDr72zN7VCDpRRU80MGVmCBxZXHvTJaus90Ygu9tmcROK2rRSoZbYRVuFUz36z2CpE0/kHlkDe7tzBnewviTVGgLCR4i/QH716/zmnxoderWm4/n/iGqVu3l1WkIwNd7c/h6b47FmNgGupe/Xl3vCl4QoKRcz/s/bjYfI0giw2nb0qASYr+NRe4gt2igA2nbXTKZDE9/D+SK5qtStDRSqiSxBEBnMNQc34pRPf+57vt7+0xAW1b0j8W/HeG+ZgnAuzpH4+roUHP834SF+xq2llYqlET7R9Avsue/cUaW2LZtGy4uLnTr1q3mXPfu3Tlx4gR79+7lhRde4OzZs2RmSrOibizTfP78ef7880+efrrpcqeQEHvOnlWTkVFG797ubNyYRVammTz17Cld2mvx4h1s2HDc4tzQoV35+GMbDh68JJkM2wWGoI2LxVCkxqlTF9RHDlCwZztgwqGt9F2JuDOnGhyzVdoRGBGBXSO2yLdD4eSOvqpmXenhS1luKrE/vlozZg3OHNvPoSrlodtNQT7/YYXoOAGutmx5PpKjSVoScs1Z13a+SvqF1y7YZ9zXdONcNd6dLL0spTFINWNqRdNoJcOtsAqXf3mzzrnsk1vIPrml9oQJEtfOldwVXHN5Iw+ty+dPsmbZjzz6zGviYkkeqG8+Yn6OtAzkrdtbhlIDhlLpNrO3YsxL97P4wz85uOYogx78Z+o+rcH0sWMA+HL9BgI9PJg8qFYZwEGpJCrAn3tvIT1icPTSDlHv69/lXjq2+c+QuhBvT/KKNBRotDWZb5VGS0JGNt6uzkT6t2zzi8kk/hO4YP/SJpv4eoV14cfHv7DIIP+7oFKpMJlMJCYmEhkZCUBSUhIFBQU1i2V7e3vR5UCLFi3irbfeYtq0aQwdas7U7dmzh/nz5zNr1iyKi4uZNWsWe/fuFUWGX30tAoOhEqXShkcmBKK0k5F4vYSQUHvGjhVPlKqxceNx5HIb5sx5htde+wkABwc7/PzcSUqSplID4P/Ec+jzzIv/gCeex6BWU5oYj11IGIHPNi4xWB8+eXJSo3v6coWCUU89x4Mvi7N8d42IoTg5lrL8dHy6Dydlxy8192Of7tLJ4/4d65k7q66ldbUDnRR8dyAXPxcFj3R3t2iiO5tWSlGZkSFR1u3ipWQVkKWqa5/er4u0hsaixMYX9q6R0ndk/9fRSoZbYR1Ecz7p29OzF63m47ee4plXP+DOoeYa2EN7NrP4+894Z9b3aIrVzJ31Bof3bhFFhueMab5CgWqXNE1OsfDu5d2ilq7rF2xFZiNjyawVrJi9BmcP51ueX9Kl1cRAzF/4vfFmw4rDcXFEBwXVHDcHq/YsarLgTsBMhlsSJpNJdH3zjvOxFGhK6BEZVnPO2d6O3Rev4OnsKJoMGwwGnnhvBo4O9vz00QcNPtwXzHhPVLxb0Rh9Pp18iV8Pr+TlIY9Ljlvvz5JwO6jWAv7rr79QVMnuVVSYyxICA83f6dzcXNxE6l1/8803+Pj4MHJkrUHLqFGjWLVqFYsWLWLp0qWsX7+ea9euiYpnayvD1ra25GrcOOvk96qRk6OmTRs/Bg+2rBN2cFCSnS2u9+JW2Ie1wT6slmS1mSHN5axeNPIHNOj1rPtpAT5BIQwc3bTMWuDAhwkc+LB5rl5BKF29KclOwt47GJdQaZKSAJv+/h2TyYR/UBhZ6cnYOzhh7+CAXqcjvK003fJ5B3LpFmTPI90tM9Sf7MjiUmaZZDnOnAINj81cwvmEeuQ9rXCguzL/SsODAvT7tp+keK1oJcOtsBJtH5n+j8Ve9M0MvHz8GDbykZpzw0dNYOPKxfyx6Ct+WLad7euXk3gtVlS8B2P+O+0pTZUmQu436zDbutu2SMPbrdJq+vIKi2NrSPeSlBS8lUru9/OzOH+luBiNwUAfDw8+7dCRikpx3fO/vvQiBVot6pIS3BzNW6rqkhLSVSo8nZ3xd7fib9XIA9ok4XdqMFTw6PNjcXJ0YvF3Kxr8e/w8d5nomKpiLZ7OTtjeonZgK5fj4eSISqMVHUcul1NWrsPezq7FGiOXPj2Pl/58n3fue5F7Ow0GYEfsfr7a8SNfP/w+RaUa3ls7mx2XD4giw18tXUSAtx+P3WdJhE5duYBaU8ywPgP585Pv0Im0Ph4xYgTLly9Ho9FY2CW7uLgwcuRICgoK8PX1JTQ0VFS8lBSzA9jx48fp27eveW6nTpGeXktQXFxckMnE6beuWZPR4JitrYywMAe6dBGfoXNzcyQzU4VaXfu5yMoq4ObNbNzdpdfTa682fH+U2dqiDAzBRoKt9UtfzGHxrBn0GnoPfYbfD8DxHVs5s28Pk96azs2rl9m/9m/2rl4higzfDqegKJyCoiRfV43Um9dxdnFj4fKdjB3UjpA2bfnomz945sE7uWfEw1bHrUZ5RSV5WoNV7Qezft3OufgGLJ6tkJFrFP8f9SH/O9FKhlthFaS6A0lBRuoNTCY4c/wAPfsOBuD8qSNkpqfUJAKdXNwQRD60AK5ml7HoSD7xOeaavva+drwwwIsOfuIeBqPf/pn2ob7Mnjqa0W//3OD7BGDD1+LNCm51EmoJjHrxvhaJU40/UlPo4OxShwwvSLrBNY2GfXcOxNNW/Bb6MwsXcTLhOhfnfVNDhkvKdQz6YAZ9oqLY+sG/pE3QZMLGRk5M237c2fU+3JzF2zjfDrlcQWlZCfb2Di1GOAVBQF1ait5gqCHEugoD6tJSpK5Ohg/oy7rd+7iZnkF4C+gxf7plPr4u3ozvUfuZGd/zfpYcW8u8XYtZP/VnVp3ezNXM66LifblkET2ju9Qhwx8s+ppz166Qv+cCvh7i/z6+vr5MmzaN2NhYcnPNEoQ+Pj507ty5RkrtkUceaSyEBaKiorhy5QpvvfUWdnbmXoPyKknADh06AOYyDL/bPusNYc3qpmuVO3RwZvp7URYZ5IbQr18HNm06wYMPflo1lywmTPgcg8FI//4dRM3pViR99A6NfcYEuRzvMQ/h9/BkUfGObt2Mm7c3L31Ra+TRffAQXr9/KGf37+HdRYtJuHCOtOvxouIlrGp4t00mt8XeJwSf7nejcHQTFc9oNOLjH2Q2BZGZTUGcXVzx9PLlr8XfMuS+pnejwj8yN+4JwIX0sprjW+HlJJ02HTh3HZkgMPe1sbw2bx3tQn14ZGh35q8+yDevSl84dJ9pqT5kLDOiOq8iY08GbZ8QZ+/eCku0kuFWNBtZxzY0OFZ9U3MJE7+t1KZtB+KvXODjN59EaWcPCOjKzeLmUR1jAEhJisfHTxwh2Ha1iFdWp1Fpql00X8/TsfVKEfMfCub+Dk1nb45cTKJcX1HzWhDqT0hK4VD/hJPQmJceaLFYDUFnNKISmd27HZdTUonw8yPIs9ZBLdDTgwg/P2KrMndi8d4T8zl0fgun4w5y9tohziccpUtkHwZ1G0FEoHTyAHD/3aNYs2kFScmJtAmLtCrGrfBzcyVNpWLJviPcUeU2dyohCV1FBcESbZQLiooBePHjz4mJboe7i0vN501A4O2npRlHJOenYcLE4YRT3BlllhQ7lniW1IKMmlpiV3uXZi0MynTlZKvyJdfSA1y8eBEHBweLBjoAtVpNRUUF3t7SmhLfffdd3njjDfLz8ykrK6s57+3tzfTp00lPTyciIoLu3Zsvc1iNq1c1bNyYxUMPNX2veuWV0Zw8eY2cKhkubZX8lo+PGy+9NLKRKxtDI7smhgpy16xA6esvSmbt6ukTKGxtKVKpcK36/hYXFqBRqynIPQmAX0gYOWmpomamTbtWP1evmnJx8iUKrhym3aMzsHVp+rvi7OKKVmOupXXz8CTlZgILZr9PWsoNlCJNQZpQRATg0R7Sd6/yi0qIDPZi8n138Nq8dTja2fLqhMGs2H2WdQcuMnpgF0nx7Dzq/n8cAx0pSiwi+1A2Xt2sTwr8r6KVDLei2cg6tqHJJJdTUHsix7+BTN50FnHqu18w840pFOTnUF5W6/Dj6e3HK9O/JCs9hfDIaNHaxLN352A0gYudDX3DzNnIE8klFJUb+WpPjigyPOHu7rQJ9Kp53VJ1vv+Ek5BWrWXPXwdJvmJ+KIV3CmXoxIE4uYnfah1y+BBg/m/GaYprjm+Fu4SMcDXKKyooLq3r2lRUWkp5VT2oWPh7hvDI3S8x6s4pHL+8myMXt3Hh+jEuXD9GoFcor0/4CluFNAkpVaFZJeWZ1yfRvUtPPNw8a1Y4AgLvvfaRpHh920WQdlRFar75n+WYNLK95/jJmof06dgrNR/BanNdqWS4vX8El9Kv8eKy97FTKBEEgTK9mYB1CTLv/CTm3CTArfHPpefd5hpXQRA4ey225vhW+LhLI/4AGzZsICgoiLZtLTNda9euJSMjgxkzZkiKFxkZyerVq9m1axc3b94EoE2bNgwbNgzbqs/y7NmzRcf76OP2zP7yOo8/HkzffmYjjGPHCli2NI1pr7ZBqzWy4IckThwvEEWGvb1d+fvv91mx4gCXLycD0KlTKI88MhgHB+lSaMGvvE3Gz/NxuaMfbv3MDavqowcpPn0c/8efoexGIgX7dqDavU0UGXb39iE3I503RtxN++7mnayEC+cp1WrwCQwCQJWdiauHuL+1U1A7SnOSMRkrsPc2qyOU5aUhyBXYefpTlp9BRUkRWcc3Ejr8qab/v2GRXD5/iqJCFZ279+Xgro3s2PgXJpOJqA4xouZU3V/y1oYMQt1teWVQ7YLLXiEjwktJe1/pbnsOdrbIbcwqP452ClKyC8gt1KAqKmH/mQTJ8W6HyWSiPLec8vxyjGXGpi9oRR20kuFWtBwaSf5o06+Rc2or/v2a3hIKbxvNr2sOsn/nBtJumrdoQyPaMXjYaBRV+pgfzG64VOF2ZBVX4KyUsXdqW7yrtrjytQbump9AVrE4ArbgnYfrfd1ctLSTkCqrkM8em4M6r7bb+NLhKxxae4z3/3wTDz9xWQ0xGZIRIreTb0WotzfXs7J4d9mfvD7C3Bz53datZKvVtAuwrgHJXunAnV3vQ6lQsvHwEsp1pWTkp1Bh0Esmw7v2b0UQBEwmEyfPHqvJilZ3pEslw53DgikqK2PfpTgqDGalEIVcztAu0XQODZIUq0tUW6sE+hvCR6Nf54Wl/yJXo6KsolZP1dfZi4/HvEGqKpMovzbcEd64u1h1M2H1760+PPHAgy0271uzulKwbds23N3dLRroALKysigvLyc8PFxSvN8Wp+DhactdQ2oJ05Ah3mzdmsOKv9L56utO7NmdS1KSOMvexYt38PTT9/LCC5a7O8XFpTz33HcsWfKWpPmpD+9H7u5ByCtv15xz6dGba688RfHpE4T/6xNKEq5SnposKt6E195i/juvU6bVcv5wlVW2yYQgkzHxjXfITk0mNz2dnkPEKUG4t+9NaW4y0VM+w87DrL5Rrsrk2p8f49nxTlzCOxO35EM0IjWHn3n1Q3Ky0jGZTDz76oeoC/KJv3KB8Mj2TH33c1ExqvtLTtwsIdTDtsX6Tfy9XMjIUwMQEeRN7I1MOkwwa8H7eUozagI49uqxBsfsfcTXgbeiFq1kuBXNRtSEf3Fj/TwCB0/EvZ15u7Uw/iQZB1YS9sALGMtLSN7+M4Xxp0SRYTDrVg4fNYHiInMXtYur9TelbkH25GmNNUQYzHVf3k4Ki3ONIT1XfDd3kI/4uba0k9Da7zaizi1CkAn4hfkAkJ2cS2GumrXfb+bZz8WpArwbZW5kmZ2QQICdHZNDQmrG7GQ2hDg40MZRvKZoNR7u349P16zlp527+GnnrprzAvBIf+kd0AXFuRy+sI3jl3dTqisBoH1YNwbFjMDRXrr8UddO3ZuUG5OKAdFR9G4bQW5VmYOPqwsKea0WtLasHENlJW6Ojdszz3tPGhlqCu38Itjx+jK2XNpLYm4yAG19whnRdQi2VTs48yc17Sy24J1PAHj5qw8JDwjmrcdqa+btlXa0DQmnYxvxjVHffVdrupOdnW1xXFFRQWlpKfYSGr+q8emnn9KxY8ea5rlqzJgxg7i4OI4cOSIpXmaVpvCFC0XExJh3ly5dKiInu3Zh4eQkF72AmT9/EzY2MqZMGVZzTqUq5oUXvicxUZqWMoD2yiVkCgWGIjVyVzcADMVFGDXFaAvMuxRKv0D02Vmi4vUZfj9+oWFsW/Ib6TfMSYqgyCgeeOJpQtuZdxJ+PVbXnKgh5Jzcgq2TRw0RBrDzDMDW2YOcU1vx7jYUp8C2aNLiRMVr07YDbdrWlkd9Nn+56Lncjqf6epKhriCnuAJfF7OSSXZxBbGZZQS52RLtJy07PLx3NPvOJBCXnM0LYwfw8py/a8rsnh/bchKYNrY2hI0Ja7F4/0toJcOtaDbS9i5D4eSOV+da7VivzoPIPbOTzMNriH7iE/Iu7qc0J1l0zM2r/+DvJQtQF5jNGdw8vHjo8ZcY9fCToq7PUNfWtL50pzcvrkrj6705jOpkfmhtvlxEjqaCj+4Tl92MmSxu+1SqTE5LOwldOX4NhZ2Cfy19g9Boc+zkq6l88fhcLh+9KjrOvb7m38t5dRGB9nY1x83FayMe4HRiIjsvXLT8ed1ieHWEtHrnnzd+xpWbZzCZTCgVdgyKeYCBMQ/g7W69xNUPXzZs2dscKOQ2BHrWv0hafug4GapCZj0qTm4uMzePuKSb2Nna0r97jNVz2nh+F+6OrhYNdAAZhdmUVZQT6RMmKs7E4aMBOHzhFG0CQmqOrYVarQbMmWaDwVBzfCuio6VJZTWG4uJiq6zAw8IcSEws4csvElAqZQgClJebVVXatjUvFNPSyvD2FldOZGMj8N13GxAEGU88cTfp6Xm88MJ80tPzcXFpfKFUHxTuHuhzc7g27Wkco809G6XxVzGWlmLrYy59qcjPrSHKTSE/KxM3Lx+LBrrmwFCmoVJbQMahv3GPMhvuqBPPUl6QZbaQroKY0jowa883BFulHcFhkdiLNAV5b1MmcTnlnHyz1pzETi7w8uo0OvjZseHZCFFxqjHzmfuY+Yz5exYd5keovztnr6XRsY0/g7tLb3iLfPS2EisBFE4KnMOckTu00jpr0Ppba0WzUV6YBSYounkJ13BzI0BxyhV06tyaLX+5naPoRpw/f5nLqt/nWzygClV5/PLtLIqLCnns2brC6rdjwLd167AWHs5j4eG8mmMTMGV5iijNSNHPSokyOS3tJFRSVIpfuG8NEQYI6xCCd5AXOSl5jVxZP6ZGRFBqMKAzGlHa2HAwP49LRUVEODrVUZgQA4Vczt9vvcmxa/GcuZEIQM+ISPq1l+6IdfmG2RHLxkZOZFBHNKVFbD32l8V7BEHgifvrGsQ0hYysdK7Gx2KntOPOvtKcv6yBmAYzY2Ul8/74kx1HjoHJRPuIcErKyvjq1z946dFHGHfPEEk/81/rvqJrUDQDo3pbnH/r70+5nBFP7KzdkuJ98fK7FJeUUKYrx15px6ZDuzl26SydItrVUZhoDIMGDQLg4MGDdRzoFAoFXl5eREWJzzSPHz++5nVCQoLFcXl5OWq1GldX6SYFzz4XxpdfJFBYWIFOVyst6OGh4NnnwsjOLickxJ4OHcXtUHz11bNMn76Yb79dR35+Edu3nyY/vxhfXzcWLnxF8vz8Jj1J6rdfUllWiubc6aqzJhAE/B97Gl1WJvrcbFzuELcjM23YINp27cbHf/5tcf7LF57i5tUr/HSoYTJaH1wiYlDHnybn9DZyTm+zGHNtE0OloYLSnGSLzHFjmP7SI40+Y+QKBQ8+9iKTnn29yViJ+TrCPW1xv4VYujnICfe05Xpe83bvAPp0CqdPJ2llObfCp7dPs+fQCku0kuFWNBsO3iGUZCVxY91cZHIlCFBZYb5hOPqbV9Bl+emiOoIBtq37E4COMXfQ/y7zavrYgR1cPn+Sbev+FEWGxVJSsSR3kwS5NKmoKKkg+1A22lSzvqhTiBN+A/1QOCokx3L1ciEnOZcLB2KJGdwZgPP7L5GdnIubt/TatLnXr3MwP49FMd1Q6fV8FBdXU0RQVFHBxGDryHy/9u0aJMCT5n1HbGoql+Z903QgQcBYaeTKzXq2Z03mB78UMmw0Gvn6h0/ZvmcTJkx0iOpESVkJn8+bwbRn3+bBURNFx2pp/LVlO9sPH7U4d2ePbnzz+zKOX7gomQw3hKIyjVVaqq/P+4SNB3exd+FfZOXnMeXjN2vIiaqokFcnNN0EBTB48GAAkpOT8fHxqTm2FllZ5jIAQRCoqKioOb4V1QRcCkJDHfju+y4cPaIiLd1cxxwcbM+AAZ4oFGaFmLfeFp/1Gzo0hm+/fYE33viZP//ci8kEbdsGsGDBVHx83CTPz63vQJR+geRtWYcuzazUYhcShtfIcdiHms04Ov6xRlLM+jLoxQUqNGrppiAh90yBykrU1y2/u25texB8zxMYyjT49RmFvbf42vrGMvwVej0rf/8ev8AQht4/vsH3ARgqTeRpDRiMJuQ25s9whdF8zlgp/cvRmBynna2CzhH+PDumP74e4ku7SjJKyNiTQWmmuSbdIcCBwKGBOAZJL19rRSsZbkULIHjYFG6snUuFVl1DggEUzu6E3PMkOnUO9t7BOAeL29rU63R4evvx+Q8rsKnqwH1g3GSeGjeA0hJxRgUrp1i/6q4P/btKs8ucs3wvKdkFzH/zoUbfpyvUETsvFn1RbVlH4dVCck7k0Pm1zijdpTWAxQzuzP5Vh/l+2k8o7czbi7pyc+yYu6TJ9wAkaDU4yeW0c3bmi/h4BKCnuzunCwvZmZtjNRluDNlqNal5TWex3V2kSWuJwbLVv7F19waLcwP7DmH2dx9z5OSB/ygZ3nH4KHIbG2a8/Bwzvl8EgL2dHd4e7qRkiqv7BBj2zWM1r+OyEi2OyyrKKSwpws1B+sLpYsJVXJ2ciYnqyM/r30cQBO7q2Zd9p4+xcucm0WS4GuPGjaOsrIzy8nILXeCioiIcHBxwdhZHHKqtlRcvXlzHgU6pVBIaGsqAAQMkzQ3g4MF8XFzkFg10ALm5OvT6SoKCmq5r3rz5RJ1z997bk40bj+PoqGTMmH6cPGl2xBs5Upx6TjX0ebl1GuiswY8f1Bos5aSlWhzrykpJib+GnYP0Mg65nSNtRr+CTp1LmcpsYGLvGYTSrer3aeeIT49hjUSwxJsz57Hgq/fpO2i4hXPpiUO7ePqV97l+LZZdm1ayff3yJslwhJeSuOxyXlmTxjP9zCpCvx3Pp6DUSEeJ9cJQK8d5O6q5+94z8azYdZad379MkIiFj+qCioQ/EizIf2l2KarzKqKmROEZI1295X8drWS4Fc2Gg3cIHZ/5ioK445Tnmxs97L0CcY/ui0xuzm5GjBHnVw/Qe8BQLl84ZbnlVfW632Bx1rp9wv6zq+NdJ69xLj6tSTKcujnVTISF2i7gstwy9Go9qVtTafuYtHqyca+MJP5sIpmJWejKagl2YKQ/46aOkPz/UOn1BFU1K90sLSHSyYmvOnXm8TOnydE1f7uwOfj4mV9bPOa23RuR28j55L2vee9T83aqg70DPt5+pKTdbPGfJwX5hWpCA/zp3y3G4ryDnR15BeIzcxnqbMBc3643VtQc34q7O0gnh9mqPCKCzG5wV5Ou0yWyPWu+/JHeU0aRniuerFdj3bp1pKWlMW3atBoyrNfr+fnnnwkJCeGJJ8RJyVWT4bNnz9KmTZua4+Zi0cKbtG3rSLdubhbnv//uBjdulLBiZa8mY3z44dJ6SZIgQGmpjjlz1lQdC5LJ8LWXn8AhKprIT+danL/52QeU3Uykw68rRcU5tHFtzf1Xoy7k0KZ1tYNVZKxtlxhJcwNI2fErSjdf/PqMROlWu+1fmHAGQ0kR3t2GSop3YNdG3D19eHPmvJpzdwwYyrMPDuLEoV18PG8J12LPknyjaVOQCd3dmbEtix1xxeyIK645LwATe3hImhdAv87hXLyejq7CSMdwc3nZlZvZKBU2RIX4EJecQ06hhq//3MN3bzStvJKyOQWTyYTcXo5LW/PCtfh6MYYyAymbU1rJsBVoJcOtaBHI5LZ4dR6EocycuZXbS7cPrUZkdBeOHdzBv16eSP8htWUSJVoNke07sXfb2pr3NrXCB/juQG6DY0q5QEd/e+6MsH6+zYE6Xo1MIaPTq51wCjbPQZum5fK3l1HHqSXHc3R1YOaqdzi57Qw3L1frDIfQ+/6eKGyll13IZTK0BgP6ykrSy8oYUCW2r5DJaDmrkP8e5OXnEBbShgF9Blucd7B3IDevLmn8d8LFyYmsvHyKtLW7IzkqFSmZWbg6i//8vnSXWVFk4f6ldRzo7BRK2ngHM7hd34YubxAKuZwirQadXk9SRir39x9SdV4hyS2yGjk5OXh6elrU87q4uODp6Ul2tvS/xUcffURxcTEajaYmq6zRaMjJycHV1VWyiUdD0GqNkspMxLzXmga/hoIbitQYiovreXP9aN+jF4IgEHfmFPaOjoS1r1VssLWzJ6BNG0ZMeUby1FSXj+AYEIFfH0upu9zT2yjJTpJMhmPPHkdua4u6IB+3KqfDInUBxcWFqM6ZPy8BweFkZTRt7vP4HZ4k5ulYdrrAQmbyiTs8eayXdDI8bnBXLiZmcOTn12kbbP6cJaTmcvfU+Uwc1pOhvaIY+Py37D8rzu1Rr9ZjY2dDzL9isHUx7wDqNXrOf3oevdo6Q6T/dbSS4Va0CHLP7Sbn5BYqSs36tgoHV3x7j8CnuzjNyVux+PtPEQSBKxdPceXiKYuxn+d9XHsgCKLI8LwDuU2KZfUOc+SPSaHYKf69FM9QasDex76GCAM4BTuh9FJSnlveyJW1eOfeGYRGB/PyvGeZ/dR3BEb689i/HmbAGOmE5naE2jsQpylm7InjlBuNRDubsxB5Oh3eSulGAC0NtUZFSnYCXm5+BHqHcy3lPDtOrKLCUEHXtn0Ydkfjmfnb4erqRlZOBkXF6ppz2blZpKTdxK0Z8n6NwcnODlcR28y9Ondg55HjPPOB+TuQkpHF8zM/xWg0ckdn8Q6PLw8xk+FTSRdo6xtWc9xcRIW04ey1WKLGD6akvIwe0eaa9cz8HAK9pBvKGAyGGrvkW1FeXo6hSrNZCj766CNiY2NZs2ZNDRkuKyvjySefpEuXLixYsEBUnFemXqp5ffNmqcWxTmekuNiAs7O4R+uFCwsl/A/EIW1hbSZYn5NlcVxZXk5ZShIyO/Fb/TP+MDelPtq5LYFtIvnwd+slywD0xbXmM5XGCotjY4Wu6li6vKGHty85mWk89/BddOxqzsrHxZ6lVKvBN8BczpWbnYGruzh3tlkPBPBcfy8uZpjrwbsG2hPkJt1oCGDeyv0EeLnWEGGAqBAfArzd+G7VAZ4e1Zc7OoZx5OINUfEcgx0xlBhqiDCArbMtti62KJylJz1a0UqGW9ECyDy6juwTmyy61ipKikjfvxxDmYaA/uIko26FqEyIxGxJY+8+mVzCj0fyee2uf2+XrsJFQVluGQWxBXh0NmccCmILKM8tR+Ei7qaWn1GAs7uZTMefvk6FTpqTW2OYHBLCh1evUGo04m9nxzAfH64UF6MxGGqyxP8pJKRe4qeNn1JhMGdC7uszgV2nVmOsNILJRFqu+cEihRDf0b0f2/ds4vGXzdckpyXx9KsTMRgN9O5hvR5ogUZLmqoQWxsbooMtpd8mDRK3aHl6/BjOXYkjr1ANQGkVUfRyd2PK2FGS5/TVw++hLi2muEyLS9VOTnGZlqyiHNwdXPFxkWbp+tZjz/H4zNfRlGoJ8w/ikXtGcvrqRdSaYh7oL725z83NDZVKxY4dO+jf3/y7P3bsGBqNxqosbmJiIsHBwfj61hJzHx8fgoODuX5dXEYOIO8WNQGDwWRxXI077vhnFk5iUHhgN9Vk0lBcTOGBPbeMmu+CDlHtJcf9K1b876gxXP6lqqFVgLLc1NrjW2DrLP3e8sSL7/LVjFcoLdFw5vh+oMosRybjyZemk5mWTE5WGn0Hiq9DDnKzbZAAj/nlBpcyy0SpERUUl5CZX8THv25n9EDzInHr0StcT8vDQVl7n7dXiiPbgUMDSfgjgdQtqXh2N/+uVOdV6NV6wseFoyuo/UwqPf7zSYv/C2glw61oNvIv7APAKSgKt2q9yOtn0KbFk39hn2QyvOV4covOb/WT4Tz1VwofDPdnREdzZnPz5SI+25XN9w8Goy4z8sa6dLZcKfq3k2GPjh5kH83m2q/XkNmas9KVerNMUzU5bgoOLg6kxKXz07u/A5CXls/iD5bVeZ8gCDz1yWN1zjeGPh4erO7dh1xdOWEOjtjKZIQ7OPBnz164KP6ZDITYLeEdJ1dRcUvD5vYTK8FkqmmsKyzO40zcIUlk+LnHp3Lmwkny8nMAKCk1G3l4e/rwzGMvio5TjcpKExtPneN8UgomTAR5elBeUcG6E2e4v0dXSZbMnm5u/DxrBuv37CP+ZjIA7cLDGDN0MK4im8luxTt/f8H5tCvsfH1ZDRku1Zfx0KKX6B7SkT+enttEBEsM6zOQK3/vIT0ni/ZhkShtbWkfFsnZpVvxsEK6rHPnzuzfv59Tp05x6lTtDpEgCHTu3FlyPJ1Oh1ZbtwFXo9Ggk1D/Pv5B82Jm7ZpMPDxsuWtI7aJBqZQRGGBP9x7i/r/PPDOPiIgA3nvvEZ55Zl6D7xMEgV9+eU1UTMfoTiAIlFyNRWZvj314rSauzFaJMjAY71FN76jdjh/efYOLRw/zwa9LCW1vboZOuRbHp09PpuuAgUydLfLz0oS9pWBjg19v6f0Ndw59gIDgMDas+JWUJLO0ZlhEO8ZOfJbwtub5rtp1qbEQkiE2HzOsdzQbD8Uyf/VB5q8+aDnWpz06vYGL1zMsMseN4dqv5qbK9N3ppO9Otxi7+uMtevIC9PtWupnR/yJayXArmo1KYwUKJ3faPjy9pjbQO2YIl395C6POOuvUahTk52A0GvH2td5IYca2LPxcFDzSvTZbM6GHB4tPqPhqTw7bX4xk+ekCYrOaN9dbIZbQhYwIofhGMaXZpTUkGMDB34GQ+0MaubIW7XpGcn7fJU5uPwsCaNRajm66TfPTBAhIJsMAbgoFZUYjx6tcq6KcnAm0wgFMLFa88Rq6iqa3wTPykpHLFUy8Zyp56ix2HF+Jo70LH0xZBJiY8cvT5BdJa9zy8vDmj/mrWLN5BdcSrgDQPqoj4x54xKoyiYNXrnHuRrLFuQ7BgWw4eY5r6VmSyDCAi5MjT4wZ2fQbRSA++wahnoH439K85OfqTahnINeyxG3X3g5PV3dKykrZecL8wI+J6kB4oHWKI/379ycjI4OEBEvN8KioqJpMsRQEBASQmprKvHnzmDx5MgDLly9HpVIRFhYmOs5DDwUCcPWKhuBg+5pja3DmzHX0ekPNa0Gon2BJseGO+PhrAC49fB92QSFEfPSV1fO7FZdPHMPe0bGGCAOEto/GwdmZKyePi47T9pHpYDJx/e/Z2HkGEHx3bZmOTG6L0s3Hqp6T3OwMPG5roPtvwbzXxmEwVrL16BWL8yMGdGTuq+PIL9Ly5qND6BDeMuZGNbCy1Px/Ea1kuBXNhmtEN7Tp8beVeZkP3Nr2sCrmvu3rWPrTHFS5WUR1jOGhyS+xcdVixk16jl79pG253sg3Z30OXNcwuK05g3bkhpaUgtpGAzd7G2QinzjHLiXh7GBH50hLgq7TGzBWVuJgZ8s7j91NflFJk7HkDnK6vNWF/HP5aFOqdIZDnfDq7oVMZP3ylI8exdPfg4zELOJOxmPnZEdoe/HanI2h0mRiXuJ1tt7WsPSAnz9vREaKNlKphrGykmUHDnLoahx5xUUWiwZBENj8r/fwdXMTFatcV0KwbyS9ogdTWWlkx/GVeLn5oahSMPFy9SUlJ1HS/ABcnF156tEXJF9XH84lpSCTyZhwZ2/+OmgmDEqFHFcHe/KKNJLjpWZlcyk+gcKi4jrPucdHS8um6Qx6tOV1P6OaMi06g/QmnMrKSt749hP+3La+xkREQGDyA+OY+9qHkj8rNjY2TJw4kZSUFDIyzNJbgYGBhIaGSp4bwPDhw/n5559Zs2YNa9bU6usKgsDw4cMlx3tlWhs0GgMlJQYcHc2P0pISA/n5epyd5Xh4NL3lPXJkb0JCfGpeW1Mr2xC6/L29xWIBlBQX4ehSV3JPrlBQkCO+odE52Fyi4d9vNApnz5rj5uKpsf1p36k7c35ZZ3F+xmuPcyP+Csu3i7eKbmm4OtmzdOZkkrNUXEs27zqZneg8asZfGCdewaXj1I7/yDz/l9FKhlvRbDj4hqG+fobrq2bjFtUTAPX1sxh1pTj4hqG6cqTmvZ4dm/7CH923jbmzLI01Itt35vL5k7i5e0kmwx387LiQUcaTy1OwV5htU0ursrAxVVqg8bnlBLqK2/Yf+dbP9IoOYcd3L912/ifOx6eTt/ML7und8A3+7MdncQxypP3T7bk8/zIO/g60ebCN1a5Czu5OPDrdLMfzVOepBLTx493fX7Mq1u34OyOdLfV07m/NziLI3p5HgqSR7reXLOW3veaymtvJnFQaYDKZqKw0UqjJq8mmGY2GmmNDpfQmK4DU9GTOx56hUF1QJ8P/5KPPS4pVXFqGj6sz0UGWCyelQk5RqTQyvHn/Qb7/cwWmBkT/pZLhQHc/kvPT+WLrAp4ZOAGA3w6vIk9bQBtvcbsSt2LB6qUs3brW4pwJE0u3riUiMJSpD4uTQrsdoaGhDRLgVatWkZOTw7Rp05qM89hjj3H58mWOHTtmcb5///5MmjRJ8rzmf59EfLyW7+d3riHD5eWVvDf9Ku3aOzFzZtMk75NPnqj3dUsg9fvZaC6cpc2ML7EPM+uklyUnkfTxuzh360nItHclxXP19CIrJZlTu3dyxz3mxcPpPbvISr6JhxV27Y6BUZSrMtCpc2uk1XTqHIqSLmLnGYhLqHTCV5+To7pQRXFRgeRYLYmpc1bTJsCTNx4dQph/bT305sOXyS3U8PQoac3Orm2bLsNJ25mGLl8n6r2taCXDrWgBZBxYCQJoM+LRZlhqOKbvv7XzWBBFhlctWYAgCIx6+Ek2rvoNAC8fPzy8fEmIuyh5fl+MDGTK8mRyNAZKK2pLEfyc5Xw5MpCUAh3RvnaStInru+mWlutF2erqCnQonMzEuzixGFNFy+1l/Rb7Q51zJUWlOLpKF8UH2J6dgwCMCwjkbh/zA2tPbi5rMzPYnpMtmQyvO2Eu3+gTFUWYj7fkbOHtSM+7ycxfnzUfCILlsRXYsH0N8xZ9iclUWe+4VDLsoLSlUFtK6S01qeqSUvKKNDhKVONYvmU7lZUmbBVy3JxdJG2d14cRXe9m/t7fWX5iA8tPbKg5LyAwsuvdkuMt37EeQRB4fuyjPDj0AQDW7N3Kj+uWs3z7eqvJcGPQarWo1WpR75XL5cyZM4cLFy5w5Yp5u7pjx47ExMRY9bNTUkrx91fi5VX7d/T0tMXfX0lKcqmoGFlZ4kmav780SS/tpQvI7OxriDCAfVgbbBwc0cZekBQLoOuAgexf+zffvvkKfiHmxUl2agoIAjF3DpYcL+PASnTqXLw617r/KRzdyDy8FqW7Dy6PfyIqzrefvlXzOjs91eK4vKyMm4lx2Nn/Z3XnV+w6S8/oYN541DKRM3/1Qc7Fp0kmw2JQeKUQbYqWyEnSSrH+V9FKhlvRMhDF58SRvrTk6wSGtOHZ12bUkGEAVzcP0lKkb3tH+9lxcFoUGy6pa3zl2/naMbqzK0q5uRTh54lNb73eaqkZn5JrcVxSricuOQdXp6Yli+T2ckrSS0hYYq6FLFeVk7i8nv+XAJGPSruRHdt0krhTCQx7fAguHs7MeXY+GTey8PB147WFLxHUVlrtdWZ5GYH29kyNqG3Cae/szMnCAjLKpNdYOyiVeLm4sGPGB5KvrRdN1WZLZIzLVi2mstKIra0Sd1f3ZpP1tv6+nE9KYf5Wc0d/XpGGhdv3YjRV0jZAmtxYSWkZPh4e/Pb5R9i3gKzd03c+wqW0qxxMsKwvH9yuD08NeERyvOTMdCICQ/j85dqMY/f2ndh98gg3M9OaPd+WQkxMTIMEePr06SQmJlqUUTQEvb6S0lJjnfMlJUb0+voXU7fj/vvFfQ8EQeDcOXHSb9Uwlmixdaz7GRPkcowFqnquaBwPTX2dS8eOoMrKJDsluea8d2AQD019TXI8XWE2SndfZIraz7JMoUTp5o2uMEd0nD1b19R8T4uLCix06Kt3dtp36iZ5fmLQ1BMtPbfWDEdfYbQ4LinTk55TiNCCpTGtsB6tZLgVzUb3t/5o0XgKWyWlJVoqK2sfKBV6HdlZaSiV1jVu2SlkTOjhQWGpeevc3UH6R7/aUlMQQFOq48jFpDrvGdStafLqEulCQWwB+efyAajQVpB7qn5jEKlk+MDqIyTFpjDxnfHsXn6AjERzA1lBtpr187fwyvfPSYpnK5NRVFFBqcGAg7yqLtJgoKiiAmWVVbYUvDNmNG8vXcaa48e5t1s3nCTond6Oe/tOsPrahlBSqsHX249li9Zhb9f8JsF7YjpyIzuX4lLzwkFXYZa9c3awZ2iXDo1dWgfDB/Rj19HjaLQlLUKGFTZyFk7+jDPJl7iUbu5O7xLUnp5h0m27AZS2SlRFajSlJTg7mDNxxSVaVMWF2P0XaFKLgUqlIitLXNOlj4+SzMxy/vgjldGjzWUCmzdlo1ZXEBgo7nMtXh1S+u6R3NUNXVYmRSeP4NrbvCNXdPIouqwMFB7SZPMA3Ly8+GL1Rnat+JPES+YdusguXRk28TGcXN0kx0OQoS9WYdTrsLE1fz6M+nLJOsOdYnqDAJfPn8TewYk2UbXfK6WdPcGhEYybJP6+V2E0MWR+As52Nmx9PqLBBfGGZyPqPX8rYibPBszPjNgbmTXHtyLQ20303Frxz6GVDLeiRVGhVWOqNGLrYr0GbXSn7pw7eYiZb0wBQJWbzfuvTKKsREuPvoOtivnHSRULDueRrzWTYS8nOS8N8ObJPuLnOeGe7gCs3H0OL1dH7r6jXc2Yg9KWtiHeTBretAVrxIQIlO5KSrNLKUoowsbOBsfAltnGy07JxdPfHQcXBxIvJOHk7sirP7zAN88v4MYl6XbCHZxdOKsu5Onz5+jtbt6mPVlYgNZgoIe7dHWFkb16snDHTp5ZsKjOmCAIFC5bIjrW/X0nSv75TeG+u0exfc9mijVFLUKGne3tefn+uzmRcIOMfPOWeKCnO72jIrBVSLv9PvfQOM5eucrkdz8gPCgQB/tawiUg8M27bzRydcPoGdalQQI87a+ZxGfdYOebfzYdJ7ozB86d4M5nx3PPHXcCsPvUYYq0Gu7q0fLbwP9pDLjTk79XZbBjew47tufUGRODX3997R+YmRnOMT0p2LeDlG8+x9bPHwB9dhYg4Ny96ftUfXBydWPcC1MbHF/30wLy0tN4/pMvm4xl7x1ESdYNEtfOwTvGXD6Qf3EfRn0ZjgHikwBfLloFwIi+YQSHR/LlwlWir60PChuBEn0ljrY2zd4Zql7sNKQSopDLeH3iXc36Ga1oGbSS4Va0CFRXj5J1eC16bQGO/hH43vEAuWd34dvrPlzbdJUUa+LTr3HhzDEunDqMIAio8rLJz83CRq5gwpNNN8rcjrn7cph/KM8it5KnNTBrRxaFpQbeGCJuu3rB2w8D5gxx17aBNcdSoXBSED4+HIBjrx7DwdeBTtPEO4g1hjJtOZ5+ZtKadTOHsA4hRHQJxzfYm/TETMnxJoeEcL5ITXZ5OZuyzNebALkg8ESI9Car5xb9SEJmZv15LmstZ6uQmH65zrlQv3Y16hJi8MKUVzl9/gQTnx1FeGgkjg61ixRBEPju858bubouDl6+xqBO7RnSOdrifJlez+97D/PcsMGiY/26dj2pWeZmxuspZqvtaqnWf2qjNU+jIkMtbsv6rcnPc/jCKVKzM/lt89+AeZtaIZfz9uSWUef4b8KoUX5cT9By/nyRxfnu3d0YNcpfVIyePaMk/cyff95GRoaKjz+e3OR7fSc8jubSWSry89Bn1373bb198XukZVwHb8eFQ/tJjL0kigx7dx9GyZZFlGRepyTT0tDDp5t059KW1Kd/MMadP06qiM8pp52v9btXm75+DhMmRr/9C+1Cffh66piaMXs7BeH+nri7WNfP0YqWRSsZbkWzUZhwmpRtv1icc/ANQ5t+DYWDi2Qy3L5TN7744S+W/jSH63FmkfS20V147Lk3rar9+vOMOSN3R4gD93Uwd9buiCvmZEoJf54pEE2Gq3Hxz+mAuebrys0sbGQyerS3Tku133d1BdENpQbkVpRxALh4OJOZlMWWX3ZSkF3IHcPN2eyS4hKcXKVnn7u4uvJ1p878kZJCvNasftDO2ZknQ0Lp5CK9S/lI3DUEQeChvn0J8fZCLrPe/vrk1X1sP76CUQOeoHu7AXz/9/t1aoQfv/d1ekYPaiBCXfy8ZD4paeYMesKNOMBMgk0mk1VZoj0XryCTCdzZoXYXQVtWzh/7jpBTVNTIlXWx/dARBMDLwx1fTw9smvG7+yfQt3N31n31M1/8sYALCWbh/27tOvLelJfo3SnmH/mZYvW8/wnI5TLenR5FXJyGxOtmWcTItk5ER0s3QBGLw4cvc/lysigyrHBzp+3sBah2bqL0urmx2aFtOzyHj0JuhUlLS8OjfW8qNAVkHdtAZZV5jkyhxL//WNzb95Yc7+uZr3LuxEE+++Ev2rQ1l0okXb/Kv16eSI++g3n74+9Ex8qr2kEc+fMN+oY74u0or1lxCsDXY8Q1Dvfvam5efGfyUAK93WqO/y1o1RiWhFYy3IpmI/vkZhDAp/swcs/uAsDW2QOFkzsl2XXrasWgQ9dezd7uqobOYMLPRcGKKeHYyMx3tMm9PBjwbTxanbhGl9sxZ/levlt5gDJ9BT3aB/PCuAHM+nUH7z85jAeHiCfsuadyKb5ejP9d/iicFFxdeJXSrFJs3WyJfj4axwBpBLbrwI4cWH2UdfM3AxBzV2e0RSUUZKtp19O6ruJubm50E6n92xTa+vujNxj45aXmZwovJByjUJNPVMgtW/y3kaML149JIsNbdm1AEAS8PX3x9fHDRia9LvpWCILArguXEQSBAdFRFGi0/LHvCIXaEuxsxVmvVsPB3h53FxeWzv60WXP6JzEgphdbv/2jRWIdPHgQFxcXunWz/D6lpaVRXl5O27ZtmTBhAgaDdRJ6LYXoaOcGCfCcOddJTSnj+/nW1WE3F3JnZ3wfbFg2LmftX+hzsgl+yboSm+bCt9d9eMcMpUxl1pG29wxEpqj9XlSUFGEyGkSV3V04fRR7B8caIgzQpm0HHJycuXjmqKR5rb+krtl1OZiordl5qd6FEUuGq9G7YxjxKbkkZ6lqpNVuZqrYdfIa7UJ9GNy9raR4YhB8bzAV2ooWj/v/K1rJcCuajXJVJnbu/gTd9WgNGQaQ2ztTXiB9ax7MDXMHdm3k2uXzeHj6cM/Ih8nNSie0TTucJTZrDI1y5lRKicVWcnWS797ouiLyTeH3zSf4Yslui3ODukWSkadm3f6LkshwzrEctClawsaGkXUwi9IssySTXq0nbVsa7Z+RJkj/yFvjUNjZkpuaR8zgzkR1jyQpNoU77u1O14HWlWIUVVSwPjOzJjPc3smZMQEBuFphx/zW6FG8+NPPzN20mXu7xeB8m5NdsJf4xp4sVQquju442df+DX09gxg36GlMJhNLts8lMz9F0vwcHRxxd/Ng5S+bJF3XECbc2Zu/j5xi5/lYNGXlXEpOQ1tWjouDPU8MES+yD/D0+DF8t+wvriYm0SHy35hhkoCCIjW/bFjB+XizdFn39h15ZvREPKxosDpw4ABBQUF1yPCuXbvIyMhgxowZODlJdyprDC2daVYXVpCbK97q+d8NzdlTlCYm/MfIMIBMYYujX3i9Y0kbvqMk+ybd3/y9yThaTRFOznV36BQKW1R54tUpAHqHOtCSxUczftpKcpaKyffV1mr7ejjz2e87CA/w4uCPr4qKk31UnLmJX38/3DtK7+n4X0YrGW5FsyGzUWDUl1los1YaKtAX5SGTS8t+ARQXFTL9pUdIu2muI4vqGEP7zt356I0pTHhyGpOefV1SvC4B9uyIK2bikpsWZRIaXSWdAuxZe6FW7mZ8TNM3kJ82HEUmCHz6wgj+tcicgfVwccTfy5XLSdLsf8tyy1C6K5E7yNHc1CB3lBP9XDRXF11FkyzdoUzpoGTiO+MtzrXpHMpzX06RHAsgV1fOyxcuoNLXOpKdLChga042P3SNwUeiSsAT839AAGb9vZpZf6+2GJPaQFdcUoive60VboB3GIHe4USHmUtDPF18yC2Uthh79vGpzF30BZevXaJT++Zn8zoEB/LooL6sOHSCY9eugwl83Vx5/K7+uDhIa9D7Y/0mjMZKpn02GydHBxxvW0gs//rzZs+3OUjPzWb4K4+RrcqrObf71GGWblvHzvnLCPRuvtVsRUUFGo3074VYzJ49G71euvteK/5JiFuguHt4kZl2k6P7t9P/rvsAOHZgBxmpSXj5iKvhrsaqJ1t2sZmUmU94gBcOdrXPQwc7W0L9PUnKyBcf528RO62CmQy3Qhr+z5LhlStX8tVXXxEXF4e9vT1Dhgxh9uzZREQ0LHcyffp0Dh48yI0bNyguLiYgIIAHHniADz/8EB8f69y/WgGOAZEUJ8dyY+1cAPSaAq6v/gqjvgyXcOmE4rcfPic1KQFbpR16XTkAMb0GoLSz58zxA5LJ8Ke7shGAUymlnEqxFMP/eHsteRUEcWQ4OUtF+zBfnh/bv4YMA7g72xOfUr9EWkMwlhtRupsJZVluGU7BTjiHOWPnZVeTJZaK0uJSkmJTKFYV1+lJ6z9aWi3eL8nJ5Ov1CECIg7nRI7W0lHydjsXJN3mvnXQr1QYfbRKzcjYyG1TFtb/v6ZMtawILNXmiTFBuxW/LF2E0Gnnp7Sk4O7ngYG/ZQPf34i1NxjifVDcb3TksmPM3krFVyOkREcaNbPO8u7URby2co6o1aNCUlKIpqf18/FMNdFL+JJ/8+h1Z+bnIBBltg8MAuJ6WTFZ+Lp8uns+i6Z+JijNr1izA/PvOyMioOb4Vjo7S69+NRiNbtmzh7NmzFBTUNbv44Ycf8PS0XgWnFf/ZGu7ufQaxa9NKvnz/JfyDwgDISk9GEAR69rNOsSG1QM/5jFLsFTKGtZe+i1gNmSCQnqumpEyPo72ZEGvLdKTnqpttnlMHrbXCVuH/JBlevHgxzzzzDADh4eGoVCrWrl3L4cOHuXjxIn5+9a+KZs+ejY2NDdHR0SgUCm7evMkPP/zAgQMHuHjxIrL/soaU/yvw7zcGTepVilMugwAV2kIqNIUINjb49x0tOd7pI/twcHLmx5V7eXzEHQDY2Njg4xdIdmaqVXMUc38Qex93cbQjW1VMub62HqtIW8aN9HxcHKV1HiucFZRll5G+Kx1doQ7PbuaHsbVNdBcPXuan6X9QXlJeZ0wQBMlk+ExhIUqZjPldY2hbtSWdoNXwysWLnCosbOLqulj4nPXucLfD2y2A9Lyb7Du7gSE9xliMHYvdRUmZBn8v8WQTIDu3dnFUrCmiWFPb5Ca2gW7d8TP1s1MB9AYD285drDoUJJHhYf36SDYRaQwL9y3F19Wb8T3uszh/IfUKRWVaBrXrzfxJs9AbxNUdHjh7HHulHdu/W0KXtmb1jIsJV7nv1SfYd1p8zWY1oapuXKwPPXr0EB2vGnPnzmXDhg0WP6MazZXQ+nfhP0k2xWDcC69QXPifsT6e/NybnD95mLycDDLTamUkffyDeOxZaWUgxkoT723OZM2FQkwmiAmyR6sz8ub6DGbc6y9JkhOgQ7g/Z66l8tC/FvP0SLPM4G9bTqApLadXtLR7FIBgI+AZ44nfAD9s3aTvvraiLv7PkWG9Xs/06eZu/vHjx7NmzRoyMzNp3749ubm5fP7553z//ff1Xvv+++/z6quv4u3tjdFo5JFHHmHt2rVcvnyZixcv1qlNa4U4OPpH0Pbhd8k8spbSbPNNyMEvnID+43D0b1qY/HZotcWEhEfi4WmZrTcajZSVlkiOl/xRy8iWVaNf5zZsOXqZe14xO0IlZxZw99QfKNNXMKyPtEypewd3co7lkLrVTPI9OnlQUVKBXq3HJUJ6JmLVnHWUa+sSYajfQropaAwGgu3ta4gwQJSTM/52dlY50E0aeKfkaxpCp4g7SM9NYsOhP0hMv0JkUCdkgkBSZhwXrx8HQaBLhDTyf++QES1DjET8qqX8PSorK5kybhQAPh4eLTLHBfuX0jUoug4Znr19EZcz4omdtRtvZ/EWwIWaIiKDw2qIMEDXqA6E+geRlCG+dnv0aPMCeuPGjXh4eHDnnbWfGYVCgZeXF76+0hRgAPbu3QtAly5dCAgI+K8jwGfPXsfR0Y72tynT6PUVGI0m7O1tef75Bygs/OfKROrD3tUrRb1v6EMT6DZw8D87mUbg7unN90u2smXtEuKvXACgXccYRox/QnKfyYLDefx93nKxP7y9C+/KMtkTXyyZDD83tj+nP0/l1NUUTl1NuW2srqJQQ4iZHkPWoSzyzuSRfzYf1XkVHl088B/ob9XzohW1+D9Hhk+fPk1+vrnGZvx4c21kQEAAffr0Yffu3ezYsaPBaz/9tLYL28bGhn79+rF2rdm6UdlI7aNOp0Onq22CKC4ubtb/4f9HOAVGEfXIey0Sy8cvkNSk61y5cLrm3MnDe8hITSIwpHm1XDmaCoyVJgJcrV9Nv//kcA6cu87Vm9kIAqiKS8gvKsHF0Y53J0vTxwwbE4ZMIaM8vxyPTh64RLigSdHg1c3LqgYIVVYBtnYKXvj6KQLa+CGTN2+3w8PWlvSyMo6pVPSr2kI+qlKRXlaGp0Q1hGrEpqQwb/NWrqaZLXo7hgTz2ogH6BwqLUNyV/dRnLi8B7Umn8tJp7mcVPt5wWTC3cWbu3qI35morKzk6cdeAsDX289qsvTJpPFNv8kKTHr7fTzdXFk1t66LVUuhvEJHnqbAKslnHw8vbqSnsP3YAe7rNxiAbUf3cyM9GV8Pb9Fxqq2Sk5OT8fDwaNA6WSrs7Oxwc3Nj0aK6hi//FKT8Gp9+eh5du4azZMnbdc5fuZLCuXMLuPPOll3Yi8HiWR82uSMhCAJDH2p5R0ipcHZ1Y+JTDTejrfzte7Kz0njt/a8bjbP6fCEKmcDCh4N5dqU5UeGotMHfVUFivvSGyHGDu5KZV8RXy3ZTUm7eaXG0U/Du4/cwdpB46VEHfwciHokgdFQouSdyyT6SjeqCCtUFFY4BjnR6vRM2ts1TwPlfxf85MpyWVutxf2udb3WmIDVV3DZ6SUkJS5cuBaB///506NCwNeoXX3zBxx9/bM10/2dQaaigMO44JVk3kDu64dV5ILqiPOy9gpDbS+v4HjRsFCt/+57pLz2MIAgkXLnAp+8+iyAIDLpnlFXzW3dRzZy9OWQVVxATZM9LA7xZfELFc/28GBIlTXOzbbA3+xa8wjd/7eN8QjoA3aKCeH3iXUQGiX/oA9gobQgfZ9lJ7RzqjPPj1umAhnUMobhAS8zgzlZdfzv6eXiwMSuLD65eqbFf1hmNVWPS6ys3njrNkz8soLKysoYoXMvIYP3JU/w+9WVG3yHeGcte6ci0hz9jybZvSMlKsBgLC2jH4/e9gYOdtM/eI0+PwNPDi3VLdkq67p+GTCbD19MDhbz5t+xOH5oXbAICl9Kv1RzfCk8n6Quxe/sO4rdNf/PYjFdxUJrLhUqrav6rybGkePfei06no6KiAoVCwdWrV0lJScHPz8+qXbwnn3ySuXPnsnv3bvr374+DQ/PMDtasycDTw5a7hlh+5xMStJRoDXTr7sbbb7elokK8fGN9i5CyMv0/Vh7h8+CjGIpF6l03Mod/qnhD7uiKrXPL1XGfPraPhKsXmyTD2cUGIr2V3HNbnbCjrYysIuvkyqY+NJCnR/XlWrJZ2aJ9mC/2ylpFntxCDfoKA0E+TX/35PZy/Ab4IbOVkbIpBWO5kZLMEiorKlvJsJX4P0eGG4KUm0VeXh4jR47k4sWLtG/fntWrVzf6/vfee4833qitOSouLiY42DqThf8fYSjTkrDqC8qrtCId/SNwCogkcd03+PUZTUD/sZLiPTJlKtfjLnH2+AGL8917D+ShJ16SPL9tV4t4Y326xbnOAfacTC7By1EumQwDtAn0stqB7nYYSg1oU7ToNXW72H3ukNbYee+Uu1n4xmL+/mY9fUfcgYOLpeKAp7/4bW+Ap0PDuFhURHJpKeVVJBgg3MGRp8LCJMUCmLlyFcbKSlwdHBjYwbydfjjuGuqSEj5a9bckMgzg5erHmxO/Jis/lewC80LYzzMEf0/p7ngymQxfH39JjnX1YfGeQ/i6ujCiVwyL9xxq8H0C8NTdA0XHfWLMSL5avIQtBw4zYrD15SbV5RkCQoOlGg/1fEBy3A+eeoVjl85yLfkGJeW1JTTR4ZG8/1TDFr4NYcuWLVy9epVnnnkGjUbD6tWra7L1paWl9O/fX1K8QYMGsWrVKj766KM6Y4IgcOTIEUnx1qzOpG1bxzpkeOmSVG7cKGHFyl64uTX9WXrmmXk1r5OSsiyOy8r0JCZm4uwsXnlEtXubqPd53nM/Lt3vEBfUZEKuUHDHPfdyzyOT8GigL8ca6NS5lGTdQKZQ4hbZ3WIsYow4ybGWhruDDelqPYWltRrWGWo9N/J1eDhaTzbtlQq6tatfo/ixmUs5H59O3s4vGo1RXlBO9uFsck/kYqian1t7N/wH+qNwbN69638Z/+fI8K0kNDc3t87rkCYsYuPj47n//vtJSkqiT58+bN68Ga8mtE2VSmWjZRT/68g4uIry/AxkcgWVVc02zqEdkcmVFN+8JJkMKxS2fDz3Dy6fP0n81QsAtOsQQ6du0l2JABYcykMAnuzjyW8nVAD4uSjwdZZzMcM6xYYibRlnr6WRV6itQygm3CO+uafgSgHXl17HWG6sOyhIJ8Pzp/0MAuxYspcdS/ZahhMEFl+cLymes0LBT926szcvl3hNrQPdUG8fbK1oOM0sKMDV3p4zX8/Gx9Usc5dXVESPt94hs54OfzHYfnwlbs6e9O1kmeG8mXmN0nItHdv0FB3rqUkv8MW8mWzasZZR91pX7pCck4ehauGQnJNX65l8OyRWYfyxfhM2MhnfLvmThSv+xs3ZyaKUQ6y02mdjzdvw76//mmCPAF4YVGvKYGdrRxuvYKL8pJcjuTm7sn/RKtbu28a5eLM1dvd2nRg/5H6UVpTUZGVlYWdnR0BAABs2mM1QIiIiSExM5OLFi5LJ8KxZs0hJSflHm9D0+krU6gpJZSZnzlxHEMyVCFptOWfOXK/znj4SehEyfplPkx8uwUyGxeCrDdvYuXwZR7Zs5Ni2zZzYuZ1eQ+9h+KTHad9d/HfrdpgqK0nd/Tuqy0cAE47+ERh1ZaTs+IWguybh0126JXNLYWCkE2suqBm2MBGAxDwdD/x0gwqjiUGR/5x7X1N9BNd+uUbhlUJMJhM2Shv8B/rjN9APe29pMo2tqIv/c2S4V69eeHp61ihITJw4kczMTE6cOAGYt9YA2rc33zymTp3K1KnmrMShQ4cYO3YsBQUFPPjggyxbtgw7O+t9x1thRlHSBWyU9nR46gtiF70GgCCTYeviib4or/GLq/Dey43Xm505tt8cVxD4/IcVkuZ3PU9HGy8lM+71ryHDAB6OchLzpNd/7ToZx3NfrERbVvdaAUESGU7ZkFI/EQbr9x4buM6aBjoAW5mM+3z9uM+3+dmgnpER5BYV1xBhAG9XV3zc3PC95ZwUbD++gjD/dnXI8LqDi0nNvs53r28QHWvxnwuxsbFhzoLPmP/LHNxc3akmFmKl1WLahOLp7FTzuqVwq7SaTq+3OJbCq8d0Hw7AqZsXCPEIrDluCShtbXn03jE8eu+YZsfSaDQ1Ume5ubn4+fkxadIkFixYQJFEK2uAc+fOIQgCw4YNw8/PD7mVJScTHqmtTb9+vcTiuBpiMsLVGDmyDwCbN5/A3d2JAQNq64Lt7RWEhfkxZoz4JqtaNPJ9N4n/xARFtOXpGbOY+MbbHFi3mt0rl3Ny9w5O7t5BSFQ7Zv25GlsrnqPZJzejij1scc6tbQ9Sd/1G0Y3z/1Ey/PZQX44mlZBVbE7uaKqcSv2c5bx5139OhrXgsvk7L9gIuES4UKGtIG1bWp33RT0R9e+e2v95/J8jw7a2tnz++ec8//zzrF27ljZt2qBSqdBoNHh5edUoTcTHm73Yq5vtAO655x70ej2CIJCamsrgwYNrxj788EMeeED61mArwKgrxc4zAIWjm8V5k6kSo75+ZYPbEXvuRB0ppdsbmEwmk1VNTUq5gFZnpLKyNrbOUElaoR57hfTs5oc/bUVT2gCJFqQRTl2BDplCRtQTUTj4OUAzy73e+a3lthX1lZWklJbirVTiplCQVFLCyvQ09JWV3OnpxVCR2txpt3wH3xg1kse/m8+sv1fzYF8zCVh74iRZBQXMnvxYy829QkdxSaFk+n+rtFq5rtziWOxnb3zfnvW+bi4eHz2ixWIBTL//ZbS6EsordNgplOy6cogzyZdo5xdRR2GiKej0euJTbhDo44enqztXkhKYv+oPdHodDwwYyoNDxWUhb4WNjQ3l5eUYDAZUKlVNgsPGxsaq+0BISAgGg4GZM2dKvlYqhg4V3zvwySePA3DmTDzR0SE1x82DCUEux7XPADyHjUDhKd7ZsSE4ODlz9yOTUNo78Nfc2ZRptaQmxKPXlVtFhlWXDyPY2BA+8mWSNpgVoGxs7bB19qBcZZ1zaUvB11nB9hci+ONkARczzSU/XQPsefwODzwc//O0yWQ0UXi1YWnLVjIsHf/5v6oVeO6553B0dGTOnDnExcVhZ2fHuHHj+PLLLwkICGjwumpnIZPJxKlTpyzG8vLEZTBbURe2Lp6U52egTa9tYlLfOI+uIBulh7hsYqeY3hbprcRrsVTo9YRFmh+AyYnXsLGxoV0n6Y0z3YMdOJSoZcpys6RNdrGBSUuS0eoqGdxW+pZXeq4aB6WCX/41kXahvshtrFdscApxokJTgUdnabW8DaF9r5bxuL+u1fLO5ViKKipQyGS8FhnJgqQkSg3mGrVD+fmUGo2M9G/a2anza3U1Pudu2szcTbWGJSbgwa/nSHKgmzZvjPmFIJCcnVB7fAucHdxExwN48tHnJb2/PqhLxJfeuDmKb+J6YsxIa6bTID7eNI+dlw+x6oUF5GryeX3lLISqL6G6tIin7xSnDhCbeI0H332B/KJClApb5rz6Pv9a+BWaKhnETYf3oC0rYcqIhyTNz8vLi4yMDObMmUNFRQWBgWa3weLiYlxcpMtITZkyhU8//ZSlS5fSv3//OsYdDenT344XXzQ3vC5adBNfXyXjxtU+c2yVMgID7QgJkd6cv820NQAAf0BJREFUt3272ZSktFTH9esZyGQyOncOkxwn6psfyd++CfXhfaiPHKDo2GFc7uiH132jcIy2To0iLzOD3SuXs3/dakqqGu669L+T4Y8+jpMVVttg1qO38wyoUycss7VDr/lntIqllK+4Och57T+YBa4P1QZNrWh5/J8kwwCTJk1i0qRJDY7XVxf23y5Y/n8V7u37kH18EwmrPgcBSrJukLThOxDAo724Ot8vF62qeb19/XISr11m4V+7aqTUMlKTeHXKCHrfKX3r7LXBPhxLKuHwDS0CkF1cQVZxBQqZwLRB0tQfAGKigshXa7m3b8MKJGIRMCSA+N/iSd6YjHdPb+T2ll9JpYf0m1+xSsPFQ5dR5xZRWWnZyT76RXEZusUpyagrzFuE+spKvk5IwAQoq+qEdZWVbM7OEkWGxX7rJH8/q98vCA0+5fp3HiYp5FOPviBtDvXgm43bRb1PQGDWo+MkxS7SatmwZz/xN80Lu/Ztwhg9dDCuTtJUMwCuZF7H2c6RjoFRLF+7HgGBfpE9OJp4hg3nd4kmw5/+Np88tZm8lOt1TJvzEZWmShyUdpiAMl05S7askUyGBw4cyKpVq9DpdLi7u9O1a1fS09MpKyujXbt2Uv+7fPDBBwiCwE8//cRPP/1kMSalgW7QYHOW9cqVYnz97GqOWwI//7yN337bhU6np3PncCZNGsJ3361n6tRR3H+/uGY3u+BQgp57Bf/HnqJg305UO7dQdOIIRSeOYBcaTuSnc5FJ6IH5ZtoLnD90gMrKSuwcHBj+6OMMf/Qx/ELCrPxfmiG3d0JflI+hTFtzTl+solyVhdz+n6nLnfj0NIpEmoLcyNdxMrmEPK2hztirg/8zJLnHR9LNZlohDv9nyXAr/nvg12cUpdnJFN+8ZHHeJawTvr2lZ7NWLVmAl4+fhaZwYEgbvHz8Wf/XL4yZ8LSkeN2CHPhrShhz9uZyqWrLq0uAPW8O8aFbkPQMztQHB/Lkp38y85dtPDy0G65OlluEYqRxqnHt12sAZO7LJHPfbVuDAvT7VlqtYFJsCnOem1+vAx2IJ8PxGg0yQWBKSCjZunK2ZWfjKJezvGcvTMBjZ06TJtJ0Y+v7LaM/fTsmDZ8GwPKd3+Pl5sfw3rXqHrYKJb7uQQR4h0mOW1SsZu2WlVy7fhWA6LYdGTfiEVxd3MQFEMnpTRJLanJVBbzy2WxUheqac6cuxbLt4GHmfzAdbw9pcmh5xSpCvcyd7Qk5N4n2j+TnJ75kxHdPkqUWbyt+Pv4yNjIb3n3iRdKyM1m2fR0ujk6cW7YNEyZ6TH6AxLRkSXMDaNu2LW+88QZFRUV4e3sjl8vx9vbmlVdesVoWrSUTIk9MCaGszIheX4mtrYyTJwqIi9MQGupQR2FCDP7++xALF1rWpPfu3Y6cnEJ27DgjmgxXw8bBEc9hI5Ap7cj6czGVZaWUp9ykUq+XRIbP7jc34soVCqJ79KK4QMXqHyytzwVBYOrsuZLm5xLWGdXlI8T98T4A5apM4pbNwFRpwCVcvDTk9g1/iXrffWMepVe/IaLeu/x0ATO2ZVLZwMflnyDDvh7OBPm4tXjcVohDKxluRbMhs5ETOf4NNGnXKM1OAsDBrw3OwdLc2KpRrC5ApdexZNFX9LvLXLt4/MAO0lNuYKu0ruGxV4gjq54Mb/qNIvDYR0sRBFiw5hAL1lhKZwkITUrjiIYVz+31P2xu0IFOSpdVscFAhKMjk0NCqKisZFt2NsH29rgqzI1Bwfb2NeoSTWFAdHTTb7ICvTsOBSAhLRZvN/+a4+YgJy+bF996gvyC2rKpE2eOsHnXen6cswQfr6adz6TIpUnBr2vWk1+oRhAEgv3M80jLziG/UM3itRuY/uyTkuIpbORoyrToDXpSVBkMje5fc14miC/9KSwuplNEFG899hz6igqWbV9HZFAYHlXb55FBYZxPuCJpbtVwcHBAr9eTkGAuwQoICMDDw7qSovfff9+q6xrCL7+kcOJ4AZ9/0YHCggrmzbtRM1asMTB6dNO7JrdixYr9yGQCb745nq+/XgOAm5sTPj5uJCRkSIqlz8tBtXMLBft2YtSaM6/OXXvged8o5M5WZF0FAYPBwPnDB+uOmUxgBRkOGPAgxSlXqNCYa1+NOvPiWuHsjn9/8TsmC2b/q+kackHgvjGPio95OA+jydxv4tWCNcLJWSrOxqVhb6fg/n4dLcaWfSSuVlyn1qFN0WLnZYdjoCPqa2rSd6ZTWVGJRxcPgobVL93WisbRSoZbYRUSVn3Z6HhR0iWyMGcM2j78rqTYvfoP4ei+baxZtog1yxbVGbMGOkMlG2OLOJ9Wio+znIe7uZOurqCdjxI3B+lfgwYTTBKzfR2ndmz6TRJwMzYFhVLOJ+veZ/oDH9OmSxgT3x3P/Gk/89qCF0XHqTSZUFQRIkVVaYTsFjZtrZHtl+vWNzhmb2tL59AQhnSWZhgy+d7XAKgwVKAtU9f523i4iM/S/bxkPnmqXGSCjOAgsxJEWnoK+apcfl46nw/e+LSJCBDuKy0ruD82jsKSUsb1aXwL9OyVOJQKBd+9/w5tQ80SkgnJKbz6+VecjpVONtt4h3Ap/Rp3fvkgZfpyugSZF6/ZRXn4uorf+jdWGmu0mW2rFkuyW2T3ZDLrPi0mk4ktW7Zw/vx5i/Pdu3fngQcekNxE19IN0jeTSnB0tKFNG0cWbjcnAbp2deXixSIOHcyXTIbT0vKJiPBn0qQhNWQYwMXFkaSkrEautETyVx9TfO4UVJqQ2dnhdd8oPO8dhdK/4X6axuDp/89YVyuc3Ih+/BPyzu+hpCqJ4ugXjnfM3chspZeHNZb1lzp7jc5IoKuC3S+3xcG2eS6eAEZjJa9/u44Vu86ad0vaB6Mp0fHynL/5/MWRPDdGnExgUUIRcT/HUVll5BJ8bzDpu9MxGc3/d22aeeHTSoilo5UMt8IqaNOu1dVPvf2OY6rnnAi8Mv0LKo1Gjh+0dAHrM3AYr0yXnnUtLDXwyO83uV4loxYTZE/3YAem/JnCtEHevH5X09m+W7Hp6+ckz6EhuLa1Tk6sIZSX6ghsG4BPiDcIUGmsJKJLOC4eziz7dCUfrnhHdKw4TTFDDpsz38Jtx9bii3Xrm/xIDIhuz5q338JOpDZtbmEmy3d9z83Ma3XGBJAkrXb6/AmUtkoWfv0HURFmchifGMdL7zzJqbPHRceRgoTMbNJVBU2SYU1JCcH+vjVEGCAqLBR/b28ycsSXNVTj+cGP8eqKmWh1pQS5+zMq5h4upl2luFxbkyUWi7PXYvG822wrKwiCxbG1OH78OOfOnatz/ty5c3h4eNCvn3S5sevXr7Ns2TJu3DBncSMjI3nsscdo21Z642lhYQX+/uadqtTUMsLDHXjvX1G8/nos+fl1DXSagpOTHXl5Reh0tQ5nxcWlpKTk4uQkXke2+IxZZlSQy3Hs0BlDkZqcVUst3yQIhLwqLkkxf1c92eAWQPbJzfj1Hol/vzEW5w3lJVxf/RXtJn4gOpbJZEKusKX/XffxwLjH8PKRthC5HQ/GuLPmQiHqMgMOVtrO34p5K/ezfOcZi3MPDOjIq/NkbD9+VTQZTtuZVkOEAdJ2mGXVqhvrdIU68s/mt5JhK9BKhlthFZyC2lkQ3dLsZEzGCuy9zaYoZXlpYGODo3+E9NjOrrz/5U9kZaSSmmTeHg0Jb4t/kHWarZ/vyiYhT4edXKDcYGbvA9o4Ya+QceC6VjIZ7t9VuiFBY9Br9BReKaSiqALTbUVqwfdJczq0d7bHUPUwdXB2IONGFie3nyUnLU9aKzVNV2k0J1fUWOwjcdeYt3kL740Xt1W6Ytd8bmbE1f9zJGa0irVFhASG1RBhgHaR0QT4BZKeIc7q/Z+Ch6sL6dm5HDt/kX7dzETz6PkLpGfn4OEmfVE1qF1v9r+9iqyiHCJ9wrCV2xLpE8b215bi5iBNraGpWlxrMovnz59HEATuuOMOOlftFsTGxnLy5EkuXLggmQzv37+fDz/8EJPJVDPf5ORk9u7dyyeffMJdd90lKZ5cLlBSYqCiopLs7HJ69nKvOW/N/7dnzyj27j3PY4/NBiAtLY9Jk2aj0+kZOFCqCoSAyWBEc66uBnJ1lkIsGf6nkHl4LYJgg+8dtX0MFSVFJK75mrL89EautMTCv3ax+e8l7N+5noO7NnJk71b6DhrOyIem0DFGmqNlNd6925fDN7QM/v46UT5KnJS1mpcCsGKKtJK7v3aeQSGX8fsHj/HYR+aFiZO9kkBvNxJSxS9kSzNKkcllREyIoDy/nLQdacgd5XR736yydGbmGcrzxcmZtsISrWS4FVYhakJtU1Texf2U5iQTPeVz7Kqk1MoLsrm2bCZukdKl0KrhHxiCf6B0W93bsS9Bg7NSxt6pbbnjG7P+tI1MINBNQWqh9AzOntPxnLuWxri7uuLr4cJzX6zg2KUkOkUE8PN7Ewj0dhMdS5Oi4erCqw0ab0glw96BnmTcyKJCV0FodDBxJ+P56d3fAQiIEJ8tGe4rbYEgFjs+fJ+H58zl80mPMraPWWlk3YkTvL98BYtffonCEi3PL/qJ9SdPiSbDqbk3EASBQd1H4u8RjExmvVizh7sXaRkpHDl5kAG9BwFw5MQB0jJS8HSX3hTVkvh/7d1ndFRVF4Dh96ZNeu+dkEDoXap0FAsqxYoNEXsHFXv7FBuKiGJDERsKqFgA6UV6r0lI773XmZT5fgxMGJKQuSESNPtZi+XcdnImJpl9zz1n78G9e/Hbpi28OP9jNBrDaJVWa/j5HdK7ZaOwbg4uVOgq2RxjGE3s5t+JYA91j9NvvuyaFn3t5hQWFuLu7m4spAQQEBBAXFwcBS2oVvjxxx9TV1eHo6Mj/foZRuEPHDhAaWkpCxcuVB0MBwTYEhtbzj0zDlFVVUdEuCFVW0G+Dg8P9WVxH3poArt2RREbm4GiQFFRGYWFZTg62nL//ebnmLb29OL8blUbKsjOIv7oEbyDggnpHMmRHX/zy6cfUa3V0n/MOK6bYf4ULCMLC9K3/QSKgs+AK9AW5RC3/B20RblY2pq/QDK4QycefPp1pj00m79++5FVK75h+6ZVbN+0itDwSN797Bc0KvMgv70hm7g8w5PEY5mG4PL0g9CWfGcz8orpHOzDFUNMMxA52mlIzy0yu52aqhocgxzxGuCFvk5P6ppUbD1tsTiVL9/Ww5aylLJmWhGNkWBYnLfsXb9j4+huDIQBbN19sXFyJ3vfGrz7tV6Fq5Yoqaoj3EuDt5PpB1RtHZTr6pq4qmkLftrC9iOJ3Hn1QBb/uYu1uw2P53cfT+LVRWv4dLZ5KakAUv9MbboCXQuMu3UkScdTKMwuYvKj1zD33g+pLK1CY2fDjbPML4s9u5P61FXmmLV4Cf7ubtw2coRx3+0jR/LR6jW88uNPbJ/zOl9u2MihxCSz23R3MkwJmTRCXZaRxgy9ZDi/rlrGs/97HM2pxZpareHD8HRw3FbumnwtR06eJCk9kypt/U1chwB/7pp0rer26urqeOW3efx8YLVJlrrJ/a7kpWseM3t086Onm59H3RJWVlZUVFSg1WrRnMp+oNVqqaiowNpafbCZk5ODo6MjS5cuNS7CKygo4KabbiInR/00k0mT/Jk7N47Kylq8vTVcOtyTkyfLKC+vZcAAdZk9AEJDffn++9l8/vkajh9PAqBbt1CmT7+c0FDzb067fLyk+ZNUOL5nJ+88eC+6U78Hk+9/mJWfL6Smpgb0ehJOGEpvqw2IwyY8QOIfn5C+9Ueqy4sojN5FdVkx1k5uhE+epbqf9g5OXDXpVmzt7PlqwRwqyktJiotGp61SHQz/dKAQBfBztsbfxRrLFs57P83D2YHkrAIKSsqN+9JyCjmZkoOHi8M5rjyL3lBwQ1uoNT5e09fUb5+eOyzUk2BYnLeaylLqygpI37YctwjDiEtR3H6qCjKxsFL/odXaAlytic3Vsje5/g/R+pgSEvK1hHmonw8Wk5KDv5cL3m5O7DiSiJO9hvcem8Qjc5fx9+H45hs4Q1lKGRZWFvSe3ZsD/zuAU4gToZNCif4imi73qM/CMPjqSxh8tSEFkzfw3vrXyUzKxjvQE3vnlqWjAsiuavjozUujwULl4+DYzEz0wLrDRxjXqycAm44dIyE7xzji4ubgqKrday69g6/+fJvjCfvoFnZ+Fd9m3PYQh48dIDElnqqq+tRxYSHh3H3bA+fV9vlycnBg4UvPsXHXXmJO3Sx07hDK6EEDjAvX1Fi8YznL968y2afXw/J9qwjxCGDasBuauPLcUrMbLvYK8PIxWVRnjsDAQBISEvjkk0+Mc3pjY2OpqqoiLEz9VKVu3bpRUFBgko3C3d0dd3d3Y9lnNfr0deXjhb3Iy9MRFGSHtbUFQUF2zPugB05OLftoDQ72bqUKdK3n508+QnfG78KKhR+CXo/HqQV5+ZkZ7Fj1u+pg2DWiP2HXPULiyg/J2f8X6MHOK5COk2di46juZiInM40/VnzDut9/pKzUUBSk78DhTLjhTpxaUBTEUWOBp6MVmx9pnUpuo/p34oe1+xk2430AYpJzGHn/fKpraxnTX93AQ3l6Oftf3t/ktmgZCYbFeXMO60XRyX1k7/mD7D1/NDjW1q7p7sL8rbnc8FUiCnAorZIZP6SgANd0d1XdXlFpJV07GEbBY1Ny6NMpkEkje7Hgpy1EJWWraqtWW4u9nz22XoaRC32dHqdQJ6wdrUlYlkDPmT3NbqumupZ7+j2Kk6sj87bMQVEUNPYaQruqn2qyIz+fZelpTAkIZKiHBzft3dPg8eCrXbsyTGWZ1x4hIeyLj+f6d97FXqNBAcq1hseR/cMN88tPpKUR5Hnudl9eNMNkW6/X8+nK/2FnY4+dbf1Ii4LCS9M/M7t/zk7OfPHB96zfvJqoWEOGhi4R3Rg78gpsrM9/IU1jzjXdduqTzxIREszLD93HE2/NJdTfn0duu5nxl6pfPHa2n/evRkHh1sETuaqnIUvLn0c28s3On/n5wBqzg+HVOzazcPk33Df5Vq4cOopet1zeYFT5m1fe50qVmWCGDx9OYmIiRUVF7NtnWHyk1+uxtLRkxAjzRumzsrKMr2+//Xaee+45Pv30U8aOHQvA+vXryc3N5fHHH1fVt9Ocna2pqqrjwP4iADqEOeDr27L0j2BYMHfsWBL5+aWcPbN+woRBZrdTnZ9HRVwMNr5+2IWEUXr4ADkrvqdOp8Nl4BC8J5r/9ColJhprjYYZL79OVkoyPy/8ECc3N977Yy16PTw87lKyU82bT59/vGFhE7fIQeQf24aFjS0ePYZTmmz4vfPoNsysNl97agZ7t29Er6/D1s6BCdffydVT7sA/KNTs93i2J8f48MKfmRxIraBvUMsHEU57ftrlbDkQS0ZeCQClFYa/eX6ezjxzh7rCQOKfIcGwOG/Bl00DvZ6iWNO7U5eIvoZjbeyh4V4cyahkc5zpXKrhHR154FL11aPcnOyIS8tlxcZDpGQXMm6gYbFVSXlVgwIczbG0tTSuDrays6Iiq4K8A3mGRRAqn3hZWVvi6uWCvZPdeadCWpuTzeHiYp7pXL+Q7OzubM7NUx0MfzB9GlPefpfMoiJjEAzg7+bG/Ol3kZCdTffgoGZzExcUN/5Yu1JbTqW2/gkAZnwfrr/rKjqHd+F/z77LI8/MoENwRx6/fzZXjlM/9eBsidm52Fpb4+fuarK/praWOr0eGysrRvWIpFzb+Nz1rLx8XJwM1eUOR59EV13d6HktkVaYSYhHALOvrB/x7hEYydaTu0ktyDjHlaZ+Wvc7O47sZ+Ezrxv3nb2g7tcta1UHw8HBwdx2221s3ryZjAxDf/z9/Rk5ciRBQebNpZ88eXKDfUuWLGHJkvqpBHq9npkzZ5pdge60ujo9X3yRzKaNuSbTTEaP9uLuGSGqfwe3bj3Ks89+RXkjBXMURTE7GC47dpikN1+iTmf4mfK5fio5vyxFX1ML6KlMiAMwOyCuKCulQ9fuDLv6Wupqa/l54Yf4BAVjfSr9mXdgEPHHjprVVvLqLxqfdKtAXXUVaZu+N+4wNxjevW0dAFbWNnTvfQlFhXl8+/ncs5pXePLV+Wa1B/Dephxq6vRMXpSAi50ljpr6pxoKsO0xdaO5vh7ObP3kMT5fuYMDMYYMEH06B3L3NUNwsDP/JjtovLo1JMJ8EgyL82Zl60DYtQ+jLcqhMt+QHN7Owx+N6z+zCEstGysLFt8ayu6kcg6lGx739Q6wY2CoirlaZxjWuyMrNh3m3reWAjC6fyfKK3Wk5xbTp7O6lDa2HrZUZFVQV12HQ5ADxSeLOfm1IYOGva/6EYlxU0eyYv5vHNseRfehLS92EVtWhruNDd5nVKrys7XlpkDD+1uYmEhsmfqFGt2Dgzn03rv8tGMH0WmGAKdrUCDXDxmM5tSj/u8ff6zZdsYPNn9kqzlZORm4uhgeyx48ug+dTtvMFeb7cv1Wgjw9uOfykSb7F63fSnp+Ia/eMonOAU0vbHSytycuOZXXP/kCgMycXN5etLjBeQoKT06/Q1XfNFY2FFYUU66twEFj+FkrqyqnsLwYWyvz87weiYvG282DAK/6NQOhfoE8fOOdALz4yVyOxDae7aM5oaGh3HnnnS26FsyvONeSynR//pHFxg25Jvv0etiwIRdfP1smTPBt4srGvffez5Q1VTBHxZ1x9vLvqTvjZzh72XeA/tTCOqjOy6Vo22azg2F9XR11tTXkZ2YYe1FTXW3crlF7g2bWW1H3/0NRFGprqtm3c1PDlvR6FEVdMJxeXP+eiiprKaqsX9fRkmGG93/YxOM3j+Kp28aa7C8uq2TiU1+wep55U0zULqgW5pNgWLQajas3Gte2qdl+tpsWJ57z+KZYQ/W0lqTJ+d99V1OlrSYhI5/xg7ow7pJIdh1Lok9nw3QJNfxG+FGWWoauSEfw1cHGzBIWNhaEXKc+ldyRbcdRLCx47/6P8Av1wdnDyTg6qijw1KJHzWonX6cjyK4+GLe3tCTA1o5rTs0T/CMri3QzyzGfzdbGhttHjiT/VAU7jxZUxLpy8M0t+tqNcXJ0JjYhmlfeMWRISc9K4415LzU4T0HhmcdeVt2+vpEPdl1NTaP7z9YzshPbDxxi0+69KEBxaRlr/zbNd3x6hbvaYLhnYBd2xh9g4oJ7GN7JMM9868k9lFaVMyT83DmPz5Sdn0vHwFDjtpO9Ix38g5g2wTDNYsmfK0hIT1XVt9MqKirYs2cPmZmGecj+/v4MGDDA7HLMCxYsaNHXNcemTXkAXHGFD0OHGeYhb/+7gNWrs9m8KVd1MJyZmY+trQ1vvnkXHTv6YWnZsmIPVckJKNY2BN73KLqsDLKXfYelkzOdP/gC9BB1/21oG5nXfS5J0VE8fPqGTlFMt1XoO2ux6mua4+UTYM4DIFUm93Jt1Xwc//vqLywtLXjkhvrpPTmFpUyevYiopKxzXHluxXHFDfY5hTgZs0sI80kwLP6TdiWV/1M1QfB2c2LJWaUzB3UPZdX76tMLeQ3wwmuAYcTGFlv6v9qfypxKbD1ssWpBZbyYfXHG15mJ2WQmnjGHWeWbzTljGsOfQ0yTwufrdNS0YDQN4NO165i78jdyig1/yL1dXHjimgncd3nL5s7FpR1r8pi1lQZf90A0Nk0XLejdvR/bdm1iw9a/UBSF4pIi1mz43eSc06NL5gbDi9bXFyfJLS412a6uqSGnqARbm+YXvc2881a83d1JzsjgwIlo7G1tiQhpndGh+0beyu7EQ6QXZbF0j+H96tFjZWHF/aNuU9VWem79B3ry7ztMjmXl51Jdo356R3FxMV9++SWlZ5T9jo2N5cCBA0yfPh1n5+ZzIfft21f11zVXTo4WPz9b7rizfk5+eLgjBw8Wk52t/ulC164hFBSUMnKk+esEGlNbUY5dWARul45GX1tL9rLv0Pj4YXFqzrvGx4+KuJPqGm3ud/0fqFBnrq9+3d6q7dXV6XlilGFQx9/FulWq71laKLy6aDUWisJD1w8nKTOfybMXkZRZgKuKgio5e3JIXZ1KyIQQPPt6cvzDhpUnI26LwKt/26aB/DeSYFj8Jw0MsefM6O9oRiW6Wj2RPobHv9HZWiwtoE9gyxZHaHU1LN94kH3RKXi7OXHbFQNIySqkS6gvbmZmbairrWPXE7uwdrSm///6oygKlhpLHIMcW9QngCHXXNIqf7z9bG1JrqhgQ04OY7xNR/t35OdToNMRaGf+H/HTXl++gnd+XWlyk5JdXMzsb74lv7SU56Y0nOPZnPk/PXfOD2MrCyvGDpjElUNuafT4Uw+/gI+3L4nJ8ew/vAcHewciwiIbPddcSdm5hh8/BbTV1Ybts3T0bf4piouTEw9NvRGAMdPuJSTAj/dmq0871Zh+oT344o63WLDxa46nG4Kj7gGdeWjMHfQJNr9MeLBfADHJCSzfsIopY640ObZ6x2ayC/IIb0HBnI0bN1JSUoKiKHieWlCZl5dHaWkpGzdu5LrrrlPV3qJFi5o8ptFo6NSpE5dcconZ7VlbW1BaWkNlZS12dobc1hUVtZSWVmPTghK+d9wxllmzvuD993/mqqsuwcnJ9O+In597E1eeRa+H2lp0efWFdvQ1NcbtOpU3JpPuf1jV+edy8sc3sfMMIGjMbZz88c0mz1MUhYgb2q4oyKUfnMTb0YpdM8/v78BpXz4/lRlv/MDLX6wiu6CEnzcdJqugFH9PZ5bNMT8lZP6hfHSFOlw6NV1kJ/9wvgTDLSDBsPhP+nFafeql7/YWcCyzkrUPhBPmaQiGE/K0XP1pPOM6q39EX1BSzoSZnxGTYhh17RcZxCVdQ7jx+a+YNXUMs28fZ1Y7FpYW2DjbYGlv2SoBLMDdr7dOWqYBbu4kVVTw5skYokpL6eXigqLAsZISfs3IQAEGuZv54XyGRRs2ADCkcyeuPRV4/L53H39HR7Now4YWBcPAOUeuamqrWbP7JzxcfRnYteEiLlcXNx69x1Cm+tKr+xAaFMaHb37Rsn6c0jvMEPwdSkjG3lZDJ//6R+Y2lpZ4ujjRr2OoqjY3fPVpg32l5eU4ObRs7jvAJWG9WRLWu8XXA4zpP5TopHgefPt5DkQfZUjP/lhYKOw6dohFK5eiKArjBl6qut34+Hisra2ZNm0afn6GedWZmZl8+eWXxMXFNXN1Q4sWLWr296xPnz7MnTvXmNf4XMIjHDh6pISnnjxOnz6G4OTgwWLKy2vp2VNdBT+Axx77FEWBJUvWs2TJepNjiqJw4MBHZrdVmZRA9AOnp80oZ22rM+WBR1p0XWPKUqPR11YbXzd4fHeayj+HeTlZnDxxCL+AEDpEdOHg7m388NV8qnVaBo+4nBvueNDstiwsFAJcrLFu4TSVxlw9rDvfvHI7d776DQt//hu9Hrp28OWn1+/Cz9P8n5WKzAqsXayxdqx/qmTnY0foxFDQQ+ySWCoyKlqt3+2JBMPiP++jbbn4OlsbA2GAME8Nfi7WfL4jj+mD1WVEeOnzVUQnZ2OnsaJSWwPAyL4R2GusWb83xuxgGMBvpB8pv6dQFFWEaxdXVf1ozFPjXySkSxAPvm+aemzF/N/JTs7hgbnmjULcGBjAmuwsympq+DkjnZ8z0o3H9ICTlRU3BqhbLAig1VXj7+7OH889i+WpvLN3jx1Dj8eeoLSFc5Bvu+Jxfly/kJ7hg+jb2bAC/UDMNo7E7ea6EdNIzY5j59F1bD+yptFg+Ezb/jjYYF9JaQnOTuqCm8mDDfmOE7Nz8Xd3NW6fj7Xbd3IoOoYpl43F1dmZp955n6T0DDzd3Zjz+MN0CAxQ3WZRRTHf7VrJ8XRDZcbuAZ25ZdC1uNqbX975wRvu4Pu/VlJUVsKnv3zPp798bzym1+txc3LhoRvuVN23yspKPD09jYEwgJ+fH25ubi2qQHdmn5py8OBBvv32W6ZPb/73ZPJkf44fKyU3V8vatfXZTSwtFSZPUVfFr75vTR5R21Izx1t+8x21b0+DfeE9exmzS5yLe7ehaNx8jK9bw+H9O3h11nRjUZCbpz/GT19/RG1NNXq9nrhoQ6YLNQHx46O8mfVrOj/sK+Dm/upv+gGWrmuY+3fiyF58/9d+HO1suHX8ALYcjAXgpnHmzdGvLqnGzrv+iZy9vz0OAQ64dTEsANa4a6jMbdnf0fZOgmHxn1dQUYO2RM/b67O5oqshqFkTVUJ8nhZbK/UfCmt3RePsYMuuRTPpepMhnZSlpQWBPm4kZ6r7kC48XggWcOKTE9h522HtZG38nFIUhW4Pmf+4GiAvvQBnj4aB24mdUSQeNy8XKICHjYa3unfn5agok7nDAD4aDS936YqnGaNnZxvftw87omNMPopPj9ZNGNCygHFf9FacHdy4/Yr6XLHdwwbw6pf3cjRuN/dPeonEjGgy85KbbWvNxj84cGQvN153K24u7jz+/H0kpsTj5eHDOy9/SFhouKq+zbruCsCwYC6rsBgLRSHQs2Ufrn9s3kp0QhIP3HQDP6/fSGK6IRtHbkEhX/68ktceUVcUJLMoh6mfPUJOab5x39aTe1i+fzXf3zMfXxfzHrX6enjx05yPmfbqLNJyTBdmBfn489WL7+LnqX5hraOjI/n5+cTExNC5syGVVUxMDPn5+Ti1YNHlwoULmTVrFo888ghjxowBDHmGP/zwQ1599VVKSkp49dVX2bBhg1nBcGSkE88934llP2WQkGBI59exowPX3+BP5xY8cfrii8dUX9MYn+untko7p21d+QsrFs7npsdmMXj8Vbw2bWqDaUkPznmXoVc1X5Y79IoZjb4+H0u/nI/2jKIgPyyah16vx8vHcHOYm53O5rUrVQXDczfmYGWh8OwfGbz6Vxbu9pbGt2xuarUH31nW6OwtRYHyKh3PffL7qfYUs4NhxUKhqqA+40jvp3ubHD+zMp1QR4Jh8Z83upMTq06UsPDvXBb+ndvgmFrF5ZV0DvbBx9302traOsoq1C2cKYkvMb6uzKmkMqdld/XbV+42vi4tLDPZ1lZqyUjIxsra/F/3itpaujg5823/AewrKiS5wvDoLdTegf6urlhZWJBdVYWPyjKnfcI68PvefVz9xpwzpknspaSigt4dOvD9tvpcr7dcal6e0djUI1hZWlNaUYSTvSsAZZUllFeWUlRmGBXycvUnr6j5VdsrVy8n6uQxHr57Jst/+4GEZMPj+Jy8LD7/9iPmPP++mrcLwKajUWw7EUN1bS2BHu4MiYxg7aGjjO3ZjV4dzC+IkpaVg7eHO44O9hyPjcfF0ZHXH3uQp979gKj4BNX9mrduEdmleVgoCqGehkV5SXmp5JTk8cG6RcyZMtusdsoqK+jXpQf7lvzBpv07iUkyVGGMDO3IqP6DsbayJjU7kyCfplPINaZTp07s27ePH3/80Vh+ufpUGq9OndRXBps7dy7e3t5MmDDBuO+aa67hxx9/ZOHChSxZsoRffvmF6Ohos9vs1s2Zbq+onxLRmP79W6famc/1t7ZKO6ftXrea/KxMug0cXL/zrCHsPev+MisY1pXkN3vOaTbO5lUFTIyNwsZGw8PPvElmWjLfL5qHs6s7n/20Eb1ez53XDSErrfkb4TOdmVqtsrqO9OI647aa4RNz1hibk1XmNFsvW8rTy8nYmIH/aNOnD9k7sqmpqMHe7/yLhLRHEgyL/7w5EwKorYO/oktM9l8W6cycCeofLQd5uxGdnM2uY/Xp29bsPEFcWh7hgeqmXHgN8DqfJ5ZGi57/xrhgKzctj0UvfGN6gh4CO5n/6Pau/ft4tnMkPV1cGOzuwWB30w+mVVlZfJQQ3yDLRHOe++4HFGBHdAw7omNMjj29pL7PiqKYHQy7OLiTX5LDa1/dT8eArgAkZERTqavAw9kwIllYmoujGY/+U9OT8fHyw8nRmaNRh3FxduWtF+cz88X7ORFtXmGBM+05mcDGIydM9nX09aK4vJKjyWmqguHyykq8PQyjyimZWXQKDaZLxzACfLxJTEtv5uqGdsTvx9ZKwzcz5tHV31Du+ETGSW79/DH+jttndjtDp09i4ezXGdKzH5cPGs7lg4abHP929S889/E7DbJMNGf06NGkpKSQk5ODTldflMTb25vRo9UV8ABITjYERDt37mTwYENgt2fPHtLS0oznODs7qyobXVpaw5o12STEnxoZDnfg8st9WlSOefv24xw9msT48f3x8nJh9uwvOXAglk6dAnnzzbvw8VFXovi0shMNf27tIzobs0s0Jy0uFldPL5zd6p9o+Id15LannkWv1/PR0zNJiY05Rwv1jn0+07xOo9B35ldmnVleXkp45x6MGj+R2tpavl80D7+A+qIgvv7BxEYdNvPrGjw24vxThOavbXqBYEu593CnPL2cpJVJlMSX4BzuDAqUJpSSfyTfeI5QT4Jh8Z/nYmfJpzcFk1Kg42Su4RFThJeGEHf1j/kBJo3qxbvfbeTqmYYFL/ujU7n15SUoiuGYGhG3RrSoD406nSvurIEGa1tr/Dr4MHX29WY3la3V8tiRw1wfEMjdoaFYnwoQinQ63omLZWe++SM8jXWz2XNUpG275tLbWfznu1RpKzieuP90AyiKwrWX3kluYQb5xdn0DB/YbFsVFeX4eBlGMFPSEukc3pVukT0I8AsmMVn9oq2dMXEoisIVfXuyar/hA9leo8HZ3o7MwoY5Qs/FzdmJ5IwMvv9jNbkFBYy8xPBotbS8HGdH9YvoiitL6eAZZAyEAbr6dyLQzY/kfPOD69TsDK55Yjr3T7mNF6Y/gs2pUdy8ogIenfsya3ZuUd03ADs7O2bMmMGxY8dITzf0JyAggO7du2Nlpf6jq1OnThw/fpxZs2Zhe+qJRlWV4e9B166nbqISEvD1NS8/cF6elhdfiKKgoH4U8eDBYjZuyOO1/3XBw0Nd+e7Fi9exf38s119/KcuXb2PbNkPKwEOH4pk371fmzDGvmmfhlvVk//QtvlPvwnXIcBJefoqz77iDHn4St0tHmdVeUV4ufqH1udiDO0USEtmFXkMNNz1eAQFkJiWZ1ZbZg6CK+b//+ro6amtryM3OMP7dqK6uNm7XVDde3fFcHht1ceTLP5vfSD+yd2WjK9JRcKyAgmOm0/I0bhr8R7Vsvnp7J8GwaDeC3W0Idlf3AdWYmbeM5nBsOuv2mI6GjO4fweM3mfcBc9r+V/bjEOhA5HTTFD4pf6RQmVtJ52nmlf388qihuMBdPR4irGcoz393fum3Ip2ciC4tZVl6GnsLC3mmc2eyqqp4Ly6W4upq9MA4b/UfGMXfLmn+JJX6dBqGl6s/G/evJCvfMC/azzOY0f2uI8DL8CH+1oPfn6sJI1dXd5JSE/jmp0Xk5GUz+lJD7uOS0mKcnMxfVHZaQVk53i7ODI4MNwbDAHY2NuQWl5zjyoYG9urJH5u38uWKXwEY0qcXJWXl5BYU0rOz+psqT0d3kvLS2BS9g1GRQwDYGLWDpPw0vBzNH13qF9mD/dFH+Xj5Ejbu284ns98gJSudx99/lfziIvR6PTeMvVp1/wCsrKzo3bs3vXv3btH1Z3r66ad54oknyMvLo/KMxZpeXl7Mnj2btLQ0OnbsaHZu4qU/pFNQUI2igL+/IbjOyKiioEDH0h/SePChsGZaMJWQkImPjyseHs7s2xeLo6Mtzz9/Cy+99A379pmfF7ho5zZ0+bk4dj/zxtw0uCze/bfZwbClpSW56fWj52+uMM3BnZeZYfbNa8SN5k29USsx9gR3TTQ8pVIUxWS7pQoravh6dwFHMgw/K70C7Lj9EnfczMz/fu2TnxEZ4sNbD13LtU9+1uR5CvDrO/eY1aaVnRXdH+nOya9PUpZsWgHUKdSJiNsjWpSfXkgwLIQq1TW13PDsl9hqrPn93XvYf6rOfN9OQQztpe7DD0BboMXGqWGAXhRTRFmK+nLHT335KHaO557H+9unq8lLy+eu15qeW/hRr958l5rKkpRkkirKuf/QQer0htltzlbWPB4ezkiv88tlmVVYRE1dLYEe5s0NPJdA7zCTBXQtNWTApaxcvZzPvzGksRo6cCQlpcXk5mfTq5v5VdlOs7W2pqSykura+nKulTod+aWlaMwounGm+26agsbGmvTsHAb37kWPThFEJyQx8pL+DOrVQ3XfRkUOYume33n4u5ewtTY8JamqNsx5H9VliNnt/PXhN7z3/Re8880nRCfFM+aBm6mtq0Ov1+Pu7Mrcx57n2hHqCqrU1NSQm5uLi4sL9vb2ZGdns2PHDmpqaoiMjKRHD/XvNzw8nGXLlvHXX3+RdGokMywsjMsuuwwbG8Pv4FtvvWV2e0eOFGNjY8Err0bSoYNhZD4hoZyXXozm8GF1o/4AJSUVREQYpm0lJWXRtWsw48f35+uv1xEXl2F2O9rUZKxd3bFyrr950wQE4X/HPaCHlPlvUZWSZHZ7viGhJEVH8efXi7jqDtOFhRuX/0hZURFBEebdtDsFqcvbm7lzJbriPELGn3tBY3PBuNrUlRnFOiZ9kUB2aY1x36bYUpYeKOTn6WH4uTT/u/v34QSqdNXG14rS+BxitVk1bT1s6flETyoyK6jIMqzlsPe1l7nC50mCYSFUsLay5HBcBkHergzpGcaQnuoDYDBUEjqturzaZLtOW0dlViUWVurzXEYOaH6E8MjW4yQcTTpnMGyhKNwWHMwANzcePXKY6ro69ICvrS0f9eqNu03LR9iX/r2d135aRnpBAf3DO/LEhAl8vGYND191JZebOQK4+8RGHO1c6NahH7tPbDznuc2lUzvTg9OfQGOjIS0jlaEDh9OrWx+iTh5jzKWXM3iA+ly5oT6enEhN59M1mwDDSPEnazZRXVtL5wB1C8rsNBoeuPkGk32RYaE8e6/5SfvP9MjYu9iXdJS4nCQqq+tXqEd4h/LIGPMeyQNYWFgw69Z7GN1/CFc/Pg1ttQ69Xk+wrz9rP/wWb3d18+izsrL49ttvqaiowNLSkquuuoq//voL7amsJlFRUeh0Ovr1U39zotFouOaaayg+Vf3QxUX9aP9pZWW1+PvbGgNhgLAwB7x9NGRlVp3jysa5uDiQnJzN6tV7ycjI59JLu5/6OpUNCnCcS3VRIRr/+rSHtsEdsAsNw6m3IVuLjZcP2kzzp8H0HTmGpKgTfDf3LaL376VL/0tQFAtiDu1n7/q1oCj0Hz3W7PbUKEk4THlWwjmD4VumP9bqX/ft9dlkldZgoUCYx6nc9PlaskqqeWdDNu9Naj6t5E1j+xIW4Gl83Zr1nVNXp2LjaoPPYB+T/aWJpdRU1ODWrWXzy9szCYaFUOmqod1YueUImXklqhKmnynuu/r5p1V5VSbbp9n7t+2dfkJ5Oe/GnjQGwgDZVVW8G3uSWRGdWhQQr9yzl3s/MS0e0btDKH9HRePp7Gx2MPzdmg8I9e9Mtw79+G7NB00OryioC4btbO14eIbpFJMunbrzwqzXzW7jTGN7dSM+M4fsomJQoEKrpaJKi8bGmtE9uqhur6y8gqjERAobmWJx2dDBjVzRNBc7J5bd/zF/HtnIsTPyDF/VczQ2Vur+355IjOWx914xBsIAqdmZPDr3FT6Y+ZKqgHjjxo2UlxsWpNXU1PDbb7+h1+tNMkrs37+/RcHwsmXLWLJkiTFPsbu7O7fffjvXX2/+fPrTXF2tycysYv++Ivr1dwVg375CMjOqcHNTN+oPMGBAJ1av3sezzxoWjg0Z0pWKCi1ZWYV062Z+BT/F0hJdTn3mlE7vfmxy/MzKdOa48rZpbPllOflZmezfvJH9m8+4+dTr8fDz58rb7zK7vdZ2y92PtXqbfyeUY2ulsHx6GN39DHl9j2ZUMuXLBLbGm/fE7qOnbmj0dWtIXZOKU4hTg2A48ZdEylLKGDLP/Cc7wkCCYSFU8nBxoKa2jpH3f8CES7vj5eaIcsZt/1O3nd8oiYW1BXbednSY0qH5k/8hP6Sm8lVKMtV1dVgqCrcFBZNWVcn6nBx2FRQwbf/+Fk2VmLvyNxTg/vGX8/GavwDwd3fHz82NAwmJ5774bGd+njfx4a5vQWW/0rISok4eo6CooMHj1yvGTGjiqsZ5OTtx/xWj2XwsmvT8QgACPNwY0a0zns7q0vrtOnSE1z9dRGVVI6OOiqI6GAawsbJhYt/xTOw7XvW1p32w9EveXPwx2modVpaWzLr1HuLTUli24U/W7t7KkOmTVE2VyMjIwMLCghEjRlBUVMTBgwextbXl4YcNZYE//PBD8luwgPPzzz9n8eLFJv9P8/PzmTdvHkVFRcyYoS7vbb9+rqxbl8M778Si0Rie4mi1hhRcp4NjNWbNmkJVVTWpqbmMGNGDYcO6c/BgPN26hTB+vPk5uDW+/lQmJZD7+wq8JphWdMzfsJrashJsg0PNbs/eyYkXvvqOBU8/TtwR06wMEb368OCbc3Fwbp30cq3h2MHdDfZ17tbbrKIgpxVV1hLmYWMMhAF6+NsR7GZDUr55C/LScgrN/nqB3uc/klurq6W6pFryDLeQBMNCqLRg2VYUBfKKy1n8Z8M/vOYEw0M+MNy573h0B04hTvR4Qv0cyH/SZ0mGwDTE3p5nO3emk6MhcBvm4cH7cXEUV1fzanSU6mA4Oj2dCD8/5tw61RgMA3g6OxGTbv68yPlPrGz09Wl1+jq2HvwDtc8md+zZyqvvPktFZcOSpgqK6mAYwMPJsVUq0H3y43IqGguEAUXFSB+ArkZHXE4yfi7euDm4cDIrgS///gltjY6xXYZxVS/zR9Nf+XweAJ2Dw/jkmTfo1cmQmeGqYaOZOe9/5BcXMv21p8wOhisrK/Hx8WH48OHU1tZy8OBBPDw8sLc3PCnx8PAgI8P8n5XTfvnlFwB69eplTM22efNmDh48yC+//KI6GL7xpgCiokpJS6s0BsEAQUF23Hij+pSNHh7OvP/+vSb7+vTpyOLF6hbDOg8YRGVSPJnfLKI86hgOXXugKBaUxxynePd2QMG5v7obJ+/AIF79bjmpcSdJjzc8xQoMjyCwYytmw2mhDatW8N0X7zPtgdlcOvZqZj9wY4M5wjNfep+Rl19ndptejlYk5utYH1PC2M6GQH9ddAmJ+Tq8Hc0Lm3rfZt78cwWF3L/mmHXujkfr0xOWJpeabJ9m7aT+qYSQYFgI1QK9XVUvemhKt4e6YWlrec5zUv9KRZuvJfwWddXPzocCTAkI4O7QDtickXd1hKcXPZ1deDv2JLtbUBLX1tqa0spK6urqgwdtdTXJObnYtaCiXVNqa2v5efMiFEVhZF/zA9iPFr1PeUV54wdb+P+8UqcjLb+QssqGgWyfMPMff2fn56OxseH5++4mJMAPS4tz/9w0JSozjnu+nk1heTE2Vta8MOER3lq1kDKt4QZg3fFtlOsquGGAeRkgFEXh/sm38sL0R9GcMXXmmuHjGNyjLw+/8xLr9mwzu391dXVYWhre2+n/nhncqF0MdZpWq8XLy4sFCxYY2504cSKTJ082TstQw9HRijlvdmX79gLi4+rzDA8d6o61tfr5/gA6XTWrVu3lyJFEPD2dmThxKBkZ+YSH++PiYl76PM8rJ1KwcS3V+bmU7NtNyb4zb9j1WHt64TVhkuq+rVj4Ie4+voyaZDql5OShA5SXlNBn+EjVbbaGvzeuIi87k57966cGnP1EZ/um1aqC4TGdnPh2XwEzfkjB7tT/y8pqw9+ssWZWFzT7/lRFGjlz+Azxaf4k0YAEw0KodPjb1ksP5BLR/AKewuOFlCWXtVowbE4apLk9etLH1bXRY242Nszp1p0/szIbPX4uAyLC2XDkKJPfeReAjIJCrpnzJiWVlYzrpS5HsznUfsxk5WZiq7HlpafepENwmDFoaqmY9EyWbd+Ltqa6wTEFRVUw3Ck0lKKSEob0Ob/v0/z1X1JQXgSAtkbHi7/OpU6vN2SU0ENVjZZle/80Oxhe+e4XDOs9oNFjXm4eLH1jAd+s+llVH9PT03n11VcBQ/B75nZLDRs2jEOHDjUaWI8cObJFbVpbWzBypCcjR6pbJNiYoqIypk9/n4QEw+9Vjx4d6NWrIw89tIB77rmS++837/+HpYMDHV9+m5QP3qQizjT9o32nLgQ//BSWDo6q+7fi4/mE9+zdIBj+9p03iD92lO8Om1d4Qw1zfn+TE2Jw9/TGxbU+HWBQaDh3P/oCer2ed196lKR4dX17cowPe5LLOZmrpaK6/sa9s7eGmaPNCzZ/MzNdmhqnPwPivo/D1tOWwMvqF/JZ2Fhg52OHg7/6nONCgmEh/lNi9sVi52hLcGSQyf5qXTV1tXo0djZcc98VlBaeexFIU4Hwma7yVZcNAWD2pIlsOX6CTUePoQAZBQWkFxRgbWnJUxOvVd1ea4sM70phcQHDBo5olfbWHDiKtrphIAygVzkidMMV43j1o8/49MfljB0yCEd7O5PjPmamqDuWFoOlYsEDo+8gozCLFQdW46RxYM0TS9Dr4Yr3bycxL9XsfjUVCJ/ptivVjUS2dqosgMjISDZv3sxDDz3EqFGGHLtbtmyhrKyMzp07s2rVKuO5V1555Tnbqq6uIy2tEg8PG5ydrUlJqeC337Ko1tVxySVuDB2mPl3g++//Qnx8JhqNNVqt4Wdm0KBIbG1t+Pvv42YHwwA2Pr6EvzGPqtQkqtIMubdtA0OwDTL/5sscuqoqCnNzVRXJOa00NRpLjR323qZ9qqupBn0dFtYa/AZfS01F6TnbKcrPJSCkPqtPaHgXwiK60m+Q4XfY2y+QjFR16xFc7Cz5/d6O/Ha0mMPp9XmGr+nhgsbMLD9qU22++90GkrMK+HBm04s5vQcacrsXxxZj62Vr3BbnT4JhIf5D3pr2AR17deC5b2c22J94LJlFhz+k1/DubdQ7GBAezh/PPsNry5ZzICEBgL5hYTw3ZTIDwi/cNJCm3Dzpdl6Y8yQff/k+l426CkcH00eivt7qbgCKyiuwtrLkhqGX4OXijMV5zK95cf5CFGDZmnUsW7PO9KCisP7LT8xqp7iylM6+Hblv5FR0NdWsOLCaEM9AXE+Vqw7xDOR4uvlFHlpbaxTYaMyHH36IoigcPnyYw4dNF4LNmzfP+FpRlHMGw0lJFbzxegwlJTVYW1sw/e4QlnydQkWFIZf07t2FVFbVMnasukBl69ajODra8uuvLzF27DMAWFpa4OfnTnp6nqq2ALKXfYe1hyfuoy832V8ec4La8jKc+15iVju39OxkeKEoxB09XL99BhcP9SPjsT++iYN/Rzrf8sJZ++dQnpVI35lf4RLW/FMQC0srsjPqi4Is+Ga1yfEzK9Ody7B5MXT3s+OTG4O5aXEinbw0vHqVP9f3uTBpytbujuZATOo5g+HTTlcurauuo7qs4aI5TQurq7ZnEgwL8R/T2B9+bYW2RaM3/4RBnTvx5/PPnnc7Ly9qesFTS9/rM/97HEVRWPrLNyz95RuTYwoKW37fr6o9f3c3yrVVRAa2TonUpt6VmgV0tfo6rC0Nf/ptrAyLbSyV+tGu8wnYW8O11/5zTwjM+blo7pwfl6ZRUmIoxlBdXcennySi14ONjeF7qNPVsWF9rupguLS0krAwXzw9TadO1dXpKS9Xn7c4e9m32EdENgiGM7/+jIr4k/T8cVUTV57l9PejqaoRwJgpN6ruX1Nqq7WomeDkHxRKYuwJfvn+cybeYvo34a+VP1BaXEhox+aLfaQVVeNxqnrbrqRytDV1zVzRdipzKon7Po7SxEZGzRUktVoLSDAsxH/AW3d9YHydkZBlsq2r1JIel4m9k11jl15w2upqlu3Yyd64OHxcXbltxHBScvPoEhSIu6P5cxkLinOaP6kFmgyGWhAjDusawdJtu1lz4Ci9OwRje1bVOVcH83NJv/f0E+o70IQjadF0f2EcYAjyz9y+2JwukHEmZ2dn1VMlduxouPK+JeLjy7GwUJhyvT+5OVo2bcrD3t6SD+b3RK/X89ijR8nIUB+8+vm5Ex+fyYED9TnHN28+QlJSNiEhrfM4vE6rpbqoQNVk+nv/Z8iK8OnzT+MTFMx19z5oPKaxtcW/Q0eCO5lXgQ7g5I9vGl9X5WeYbNdVa6nKS8fS1vzfi0GXjiPh5HG+XPAGxw7toXufgVhYWHDiyD52bF6DoigMHN78z7aLrSXHs6p4ZLlhilBKgY5Zv6Y1OE8B3rmu+aIb/6T4H+IbD4RBUqu1kATDQlzszPjjFrM31hisVZVVGbbP0nWw+R9Y/5T80lKu+t8bRKcbKmD1D+/IJRHhTHn7XZ6aeB3PTjZ/bmnHwG4m+Z1bw/w5n7dqe99v2QkKbI8+yfZo06kHCgqv3mL+++0V2Xr///TN/FC19vdVjZiYGHbt2sWgQYPo3Lkz8+bNaxD43nDDDURGqivte6a8vDxqa2vx8VG/8r6srJaQEDsmTfKnpqaOTZvy8POzxcnJ8HHq52dLfLz67BRXXNGfzz5bzfTp76EocPRoIo8//gmKYjhmriM3nJ7ioVARG3PGdj0rM9YEnDbiWsPP6Ik9u/AJDjFut1RZarThb5UCtdpKw/ZZnIK7mt3etTfdxdo/fiIvO4M9f69nz9/rjcf0ej1ePgFMvPnuZtsZGOrA2ugSfj9WjAIUVNSy4lCRyTl6Lo5guCy1DBTwG+GHva89ikXbPs35L5BgWIg2VBxXjJWtFQ6BpiuA66rr0Ov1WNpYEjQ+yDAvrAn5mQV0GxKJq5cL21fuxsnNkZ7DuxmP29ja4NfBh0snqi/K0Npe/GEpUenp2NlYU6kzvKdR3btjr9Gw7vBhVcHwoze80er969Pj/PMBN9BE3Kl2AR1AYUkJuw4fJb+wiLqzRrBvv9a8BVbX9jYv329bOXLkCMnJyUycONG47+zR+hMnTrQoGF6zZg2ffvopOTk5dO3aldtvv50ff/yRW265hSFDzHu0XFenx8rKEHxYnVpMdUb2wRanXZwx4wpOnEjh77+Pm+wfMqQL06erKYpy+nul0NQPn/vYK1T37/7X3wagWqelJD+/QcuefuZNBXLvNhSAguPbsbJ3wrlDT+MxC2sNtu5+eHQfbna/HBydefPjpbzz4iPEHD9kciyye19mvTIPR6fms/a8OcGfABdrTuZUsT2xHCeNBV19L46naWfTuGlAgQ4T264w03+NBMNCtKHjHx7HKdSJHo+bFt049uExY1nN5urMP3nZi3Ts1YHpn97G9pW7qa2tY/r/bvsnu91iaw4ewsXOjr3vvEWnhx4BwNLCgiBPD5Jyctu4dwaFRQXs2LuVvPxck3zIANNuubeJqxp311jzP9SbE52QxJPvvN94BTrMD4bfmPxUq/Xpn5CVlYWjoyPOZ1Q1c3NzMwar69atIzNTfVq/TZs2NUjP1rlzZw4ePGjSvjliY8u56ca9TW6rVV1dywMPLECjsWbRosc5diwJgG7dQujfv+GCtXMJfMAwnSbt4/ew8fHDe/LNxmMWGg0a/yDsQtQHUZnJSXz6wmxOHjrQ4JiiKGanVgu9wjCvtyw1GnufUOP2+fD1D2buF7+SnHCSlETDU7GQsAiCO5j/vXN3sOKlKwwLZENfPka4l4Yfp124YFPNOoeQa0I4ufgkhccLm/18EOaRYFiIttbI38A6XZ35c78UqDxjgY1v6MWbbqe4ooLIgAB8znpMW1unp6yJIO9Cijp5jMefv6/RCnSgPhju4KOuQt+5fPXzyqYr0J1HuxlF2Q32+Tp7YWHRssIR56u0tBSPM9LEaTQa3N3d6d/fMGp/4MABClpQ8OXrr79GURRuuOEGfvzxRwC8vb3x9PQkKiqqdTrfQtbWlkRFpeDv70G/fhH069fyym7uIw3zY8uPHcbGz9+4fb4+e+kZTh5sfAFpS6apdr9nLgC1Oi2VeakoioKDX8cW9+/7RR/g6e3LZRNMF/NFHd1PWWkxA4aYX1Ux6eWGGXeKK2txsWtZ3vEdRxJwsrelR7jp6LlWV0NtXR32tjY8detY8oqbnl6z/xXT771eryfqsyis7KywPKtf/V7q16J+tmcSDAvRBo59eMz4uiK7wmS7TldHRWYFVnbm/Xq6eDqTEZ/JE2OeAyAlOo2nxr/YyJkKb6955bz6fb6CPD2JSktjZ0z9KNLqAweIzcwk3Ne3DXtm8Pk3HzdZga4luW1PZmSRll9Iz5BAnOxs+Wn7HpJy8vB1deWGYQNwsTd/oVB0YiI21lZ88b+XuP3pF+jSsQMP3nwDL8z/mDcef9jsdjZF72DJ9hXcNmQyo7sMYdzcqQ3mCH9wy8uM6TLU7DZbW0lJifH17NmmRW5KS0upra1V3WZiYiLBwcE8+uijxmAYwNXVlaSkJLPbGT7i/AtsNGb06N6sXXuAnJwivL1dz7u9oIcMZZzrqnXUFBc3yARh46Xupjnx+HEUCwvG33oHgWHhWFidf/iQuXMl2Xv+pK5Gh4NfR7z7Xkb6tmX4D5uMexd107q+/+J9Onfv0yAY/uKD1zgZdYTftyeY3dbPhwvZkVjO3YM98XCw4tYliZzM0eLnbM1XU0Po7GOrqm8TZn3GgC7BrPnggbP2f8rBmDRy/5rDuIHnnvajLdA2ur+msoaayhpV/RENSTAsRBsoiav/sK+tqjXZPs2lU/Pz3AAGXdmfv77eSFFOMShQo6shL72RkbOLYI3FlMGDePvXlVzx2usowL64eG5+b56h/PPgQW3dPaJOHsPGRsOSj5Zx04xr6Na5Bw/PeJJn//c4b700X3V7f584SVJOHpeEd2BvbCIn07MASMnLY+3BY1w/1LxcrwCVVVpCA/0J8PZGAWpr6+jSMQxXZ2fmLfmOj180L13db4fWsy/5CHOm1AeZZy+o++vYljYLht3c3MjNzeXo0aP06GE6fSgmJoaysjKTkWNzaTQaysvLTaa+6HQ6MjMzsbU1P7h54IF/5tG5m5sjtbW13HjjG4wd2wcPDyfO/KW9776rVLWnzUwn9eP3qIhpZNRbwfzUaqd4+vmBonDbk+efFhEg99BGMrf/YrLPKaQbulWfUhi9S3Uw3BhtVRUFebkqaiMbfLevkMPplbxwuR+Ld+cTk2MIRDNKqpm7MZvPblZfvKSxRasVVbpmF7OeFjQ+qPmTRItJMCzEBaYt0OLa2RVrF2ty9+Ri7WiNa1dX43FLa0vsfOzwHmTeyM2NsyYR0acjaXEZ/LrgT9x8XLl0UtsvlmvMk9ddy8HERNYdPmKyf0yPHsy89po26lW9yqoKwkLCCfALQlEUautq6RbZA1dXd+Z+/Aafv/+tqvZyiktxtrfD0c6WxJxcNNbWXHtJH37etZ/EbHWFFBzs7aiuNowAOdrbk5yewabde8nIzjH7AxUgKjMODwc3fF3qp3AEuvlx17AbAHhnzaecyGiYjeRC6dixIzk5OaxcuZL09HRCQkJQFIXU1FT27t2LoihERKifRtC9e3d2797NE08Y5tTm5ubyyCOPUF5ezqBBLb8Ry8trOGLn7m6DhcoV/l9/vR5FgcLCMpYv39bguNpgOG3hPCpiTjR+UK/+zvjmJ55i/qxHObh1M32Gj1R9/dlyD6wDRSFw1M2kbfweACs7R2wc3ajMMb8C4oQhhpsTRVE4efyQcftMru7qRvMT87X4u1jjYmfJ/tQK3O0tWXRLCLd/k8SBtEqz27n2yc+Mr2OSc0y2y6t0RCVl4+Jo3o1Y0BUSDP+TJBgW4gLb/8p+nEKd6Dq1K7l7ctHX6omY2vI5ggB9x/Si75henNgZQ0CEH9c9oO6D80KxsbJi+ZOz2B4Vzf6EeAD6hXVkaJeWp8lqTY72Tuh0OsNrBycSk+PZsPUv0jNSWjQvskqnw8fVMMKfV1KKv4crPUKD2BZ1kpyihk8DzsXX05Pk9Ax0umoiQoM5cCKa1z/5AoCQAPMr4+WW5hPqUZ8aylFjT7C7PzdeMgGA5ftWkVKQrqpvrWnw4MEcOnSIqqoq9uzZw549e4zH9Ho9dnZ2qha7nTZ9+nT27dtnDKhzc3PJycnBysqKu+66y+x29u8r4s8/s7jyKh/693fjoQePNDhn5qxwBgxQt7DJz8+N1nx8U5kQC4qC55XXYRsYDJbq57s+On6Uyba+ro53HroHe0cnHM5Y4AjwwZpNqtrWFudg5xmAd9/LjMEwgKWtA1X5GWa3c3rhmaIoTS5CG3/tLar6Vqatw9/FkBM8Pk9Ldz87+gTaE+Juw8mcxqcrNObvwwkoiiHDSGmFlr8PN5yqMaKP+sqbxXENc2+fZmFtgb2vPZaals1vbq8kGBaiDdRW1c95tPNuvfQ9sxc/1mpttZarXz93CrS1hwylcRVF4fdnn7kQXWqSn68/icnxaHVaOnWMZP/hPbzyjqFPoUFhqtuz09iQV1rK4aRUCssq6ORvCFq11dUNCnA0Z/K4McQkJZFbVMj0yROJSZxHeWUlthob7rux+RKuZ8oqrs/csfv530yO5ZTmU13bdnMQnZycmDp1KsuWLWtQcMPV1ZUpU6bg5OTUxNVN69atGx9++CGfffaZccFcly5dmDFjBt26dWvm6npbt+YRFVXKgw81/fOwa2eB6mB49erXVZ3fHGtPb1DA/457WtxGbnrDohMAFaUlVJSecTPXgvn0ljZ2VJcVUVejM+6rqSpHW5iFpcb8v4mPPf8uAPP+Nwu/gBBunFY/f15ja0dQSEdCw9XdbHs4WBGbq+WjrblkFFdzVTfDDW2RykV0N43rC8DSdQfwdHFg7CX1ucLtNTZEBHsx9fIBqvoGhixE52JhZYH/GH+CrwxW3XZ7JcGwEBeYjbMNFVkV7HthHwDl6eUNVgqf9l9YFbwtKrpBxtOzPzr1jexrC9dfcwvRsSfIy8/h3jse5vHn76e8ogxbWzsenK6+AlwHHy+OJqWyfIdhdDPCzwddTQ3F5ZUEeKgLlsYOGcjYIQMNG97w4/tvkZqZhb+XF44qKtkFuvkRn5PMn4c3clUv0xX2m6J3kFdWYDJyfKHpdDoCAgJ4+OGHiY+PJzfXELh7e3sTFhaGpaUlxcXFuLiYN6f+TL169eKjjz46r/4lJVXg4mKNh4eNcZ+3t4YJ1xgWgH77TSqJiY1nI7mQ/G69i+T351ByYA/Ofc2fm36mSfebvzBTLcegSIpi9xHznSHdnbYoh5jvXqGuRodLWG+z2xl71RQAjuzfiX9giHH7fIyOcOK7/QW8u9GQaWVcZyeKKmrIKqnhkhDzf9c+etIw9ejvwwn0iggwbv/T6mrqSPsrDVtPW7wvuXizC11MJBgW4gLz7OdJxqYMdCWGEZG6mromVwr/FwyN7GySreBgYiLa6mq6BxtGLY6lpGBlacmA8JanVWotl426istGGaaYBPgF8cuStaSkJeHvG4CTo3MzVzd0Zd+e1NTWkl9aRmSAH50CfEnOySPAw40eIebPAaypqWH8jAdxcXJk+QfvoigKdhoNnULVL+QZGt6fuJwknv35bY6mR9M/tCeKonAw+Rg/7P4NBYXhnVoWPLWGhQsXct111xESEkKnTp3o1Mk0V+zBgwf566+/GmSZMIdOp2Pt2rUcO3YMT09Prr76ajIzMwkLCzM7uC4srMbPv36ep52dJT6+GsaNMwQdGzfkkpXVNr/P0Q/eabpDX0fSmy9jaW+PpcMZpc4VhcgFXzXb3pQHHmndDp7Bf9hkSpOPU5mbZlj4W1lKTUUplho7/IZcp7q9J140pGqr1mkpKsxvMGXC2zfA7Laeu9wXjbVCcoGOsZ2cGBDiwOH0Cq7u5szoTuqfShz+1vCzWl6p43hiJpYWFvSLbPkc4IhbI4j/KR6Pnh549DEsJs0/kE/+0XxCrwulPLWc7J3ZZG/PlmDYTBIMC3GBhV4XilOYExWZFaSuSsXG1QafQerLwv5brHr+OePrLzds5FBiIrvemkOEn2HKQGxmJiOef5Er+vZtqy4CUFNTzeiJA3FxduW3bzcYAk5bOzqHd2lxm452ttwy3HQxY4i3JzMuG6mqHSsrK9xdXXC0t29RirczTRt2Pb8e/IuSyjK+3fkL3+6sX9GvR4+LnRPThl2YEazGFBUV8fXXXzNo0CDGjBmD5am5ruXl5fz++++cPHmymRYaV1xczAMPPGBMo9a1a1e6d+/OzJkzmTZtGnff3XzJ3tPy8+of7X+12PTntrCwmpqalswwP3+63IY5owFqK8qpNUkZqP5nKGrfniaP2WhsCejYEVt7hybPOZutux+db32ZrF2/UZGVCIC9bwd8B07A1l19msX0lEQ+eP1Joo428pRNUVSlVrO3seDF8abz8HsF2DNvsvmjwmd797sNfLB0M5W6avpFBnHfpGG8+sUanpt2GVNG91HVVu7+XGycbYi4rX6tiXt3d0pfK6XgaAFd7+tKSWIJFRlt/4Ti30KCYSHagEdPDzx6elAcU4y9n327WSk8d+Vv+Lu7GwNhgAg/PwLc3VmwajUPXqGm7GzrsrKyxsPNE0dHp/MOOM9UU1vL4aRUUvMKcLKzpV/HUArLyvFxdcFeY9N8A6dMGjeGRSt+Ye+x4wzobv4c17N5OXnwye1zeGLpq2QW55gc83fx4b2bXsDb+Z/JpWuOgIAA0tPT2bVrF/Hx8UycOJGioiL++OMPKioq0Ov19OzZs/mGzrJgwQISExPRaDRotYaR2wEDBmBra8vOnTvNDoa9vTWkpVWy/e98hg4zTfG2f18RRUXV+Pmpy0PbWnyun/qPtf3atKnnnBtsZW3NNXfdw5QHHzW7TVs3n1apQAcwf87TnDiyr9FjLfltLq6s5XB6BXnlNQ0ys03urW6K01e/72LO1+tM9o3oE056bhE/bzqsOhguiS1BsVLQleqwcTL8Dakuq6amvIaSUwtz7bzs0DaS6UQ0ToJhIdpQ90caVjr6L8svKyW9oIBXf1rGhAGGimK/793HycxM7FQuKPsnXH/tLXz69QL2HNjBJX3VZyw4W4VWy6J1W8k5VUQi0MOdYE93lmzezsjuXRjTs6vZbe05chQLxYJn5s4nyM8XN2cnY3CioDD3afPmNJdrK+kZGMmqx75mZ/x+4nKSAQj3DmVIeD+sLa3IKMrG37VtnlZMnz6dbdu2sXXrVnJzc/n888/R6/Xo9Xrs7e256qqr6NrV/O/badu3b8fR0ZEffviBCRMMmTMsLS3x9fUlI8P87AW9ejmTllbJxx8nEhdXTpeuTigKxMSUsfYvw81Fnz7q5zO3Bp/rb/1nv8A58vXW6HT8/OlHeAcGM/zaiWY1V1NVTkVWAtXlJZxdx86j2zBVXYuLPopiYcG1N0wjqEMElpYtD282nCzlsRWplGnrGhxTFPXB8Ke/bsdCUfjffVfz7MLfAXB3dsDP04VjCepLi9u42FCVX8XB/x3EOcwwfas0sZSayhpsPQw3YtoCLVaOEuKZS75TQogL5vLevfl1z17e++133vvt9wbH2trOvX9jaWHBrJceIjggFDdXd+MosaIofPDGZ820YGrNwWPkFJdgZWlJzamqaR19fbC2tCI2I0tVMHw4pj73b0pmFimZWcZtNSNf1314N3OmPE3/0J6M6DyIEZ1Nc+yu2L+at1cvbJBl4kJRFIXhw4fTsWNHFi9eTG1tLXq9HldXV6ZPn46jo2PzjTSirKyM0NDQBgU7amtrqagw/3Hy1RN82bIln7KyGlavzmb1atOpCY6OVlw9oe2rKZadONrkMQsbGzQBwVjamZ+14YE577Lo1RcZMGYcgy6/EoCda/5k38b1TJ01m8QTx9i04ic2LPvBrGC4OP4QSas+pVbXWN5eRXUw7OXjj6Io3P3oC6qua8zrf2VR2kggDLSo9nRSZj6RoT7cO3GoMRgGcHOyIyY55xxXNi54QjAnvz5JbVUthScK6w8oEHJNCJW5lWgLtLj3cFff2XZKgmEhxAXzwfS7qK2r4/d9pvP6ru7Xjw+mm5/r9Z9y6Fh9v5LTEklOSzRut2TqREx6Jhobax69+jLe/vlPACwsFFwd7Ckoa7zsc1MuGzKoRSmszpZRnM20L2dy+5ApPDr2LmysDCPyBeVFvPjrXDZH7zrvr3G+cnJy+OOPP4yBMBjm/P7+++9MmDChRQGxr68viYmJHD582Lhv27ZtpKSkEBRk/jQlNzcbZj8Twbz348k7Y+4wgKenDY893hF3d/Onv/xTEl5+inPdJilWVnhddz2+N9xmVnvb//wdVy8vHpjzrnFf35GjefzKMezftJ6nFy7i5KEDpMbGnKOVemlbllKrbaKAhaI+4pz24DO89fyD7N2xkQFDRjd/wTmkF+uws7bgwymBRHhpsFRZQOVszg62ZOWXUKWrNu4rLqskPi0PZwf1U2o8+3hi52VHxqYMKjINN3L2/vb4j/LHIcAwb/uSN9tuEey/kQTDQogLxtXBgW8fe5TEnByi0ww5TDsHBBDmc3EsIBw/+upWnS9cpavG28UJJzvTDzy9Xo+uWl0u36dnTGuVPvUIiORoejRfb1/O9ti9zJkym/TCLF5Z+T6FFSXo0TOh19hW+VotsX37djZv3kxNTQ0WFhaMGDGCgoICjhw5QmxsLAsXLmzRVIlx48bx1Vdf8cADD6AoCidOnGD27NkoisJll11mdjtVVbWEhzsy74MeHDlSQtqpimRBgXb06OmMlZUFeXlaPD01qvr3z2g6qNTXVJOz/Ac0Pn64jWj+//eJvbuwtrGhOD8fl1Oj6yWFBZQWFVGQsxsA3+BQslNTzOqZriQfC2sbQq++HzsPf1DUF4m4a5Lp6HGdXs+rs6YbioKckf1FURQWrWhY1a8pPf3tyC+vYWxn9RlkGjOkRxh/bD/GuIcNaf2SMgoY+9ACKnXVXDaoZQWHHAIdTBbQifMjwbAQ4oLr4O1NB++LL+XPc0+81qrtuTrYk1NcQlJOfenl6LQM8kpK8XBWN7o59clniQgJ5uWH7jPZv2jFr6Rn5/DiA+YVV/j+nvl8tvUHFm76hricZG785EHq6urQo8fVzpkXr3mUy7uPUNW31rR+/XoAvLy8mDhxIn6nFlt27tyZP//8k4qKCpYvX86LL76oqt0777yT6Ohodu7cabJ/4MCB3H777Wa3M2vmMR58KIwuXZzo29eVvn1dTY5v2pjLkiWpDbJMXGhBDz9J+mcf4nzJEFyHDAegaPsWSvbuxO/2u6mMj6Ng4xry160yKxh28/ImJz2NJ64eS2Rfw3z/k4cOUlFWineAIS91flYGLu4e52rGyME3lOqKUlw7qls8dqaczMaLgpSXllB+RlEQtTe49wzx5IGfUnljbRYTe7ribGthcjzAVd3I/3PTLmfzgVhOJGahKJBfUk5ecTnODrY8fds4s9rI2ZODtaM1bl3dyNlz7qkVkk5NPQmGhRDilOvvuorO4V3437Pvmuz/bMkC0jJSeHX226ra6xkaxOajUSxavwUUSMsv4LutO0GBniryDANk5eXj5tJwpGr/8ROcTEw2ux0LCwvuGzmVoeH9uGPRE+hqqtGjJ8DVl+/vnY+nY9vOM1QUhYEDBzJmzBisrOo/orp27UpISAi//fYbsbGx52ihcdbW1sydO5eDBw9y4sQJY5t9+qgLxvLydLz6SjRXXeXLTTcHYGVlCJRKSqr59JMk9u8vUt23f0LRtk1YubkT/PCTxn3O/QYS/fBdlOzdRYdnX6P85AmqUpLMau+mx2bx4VOPU1lWxsFtWww79XoUCwtufuIpslKSyElLo/9o84I77/5XkPj7R6Rt+RH3roOx0pimZbNxbj6ovmX6Y2Z9LbVmLE1BAT7fkcfnO/JMjikKJLykbuFzRJAXGz96mLnfb+TgSUMA36dTII/fPIrwQC+z2oj7Lg6nUCfcuroR911c0ycqEgy3hATDQghxSlZOBh5uDT+E9x7cRUzcCdXtjegWSUZ+ISczskz2h/v5MLybeY9H126vH8ksLi0z2a7UaknJyDIJGs0Rm53ISyvfNwbCABlF2bz063u8ct0TbRoQ33777YSGhjZ6zMHBgZtvvpkDBw6Y1dZDDz10zuNnjhIvWLDArDY7hjsQH1fOH39kcfhwMQ8+FEZujpbPP0+ipMQw9WXYpeaNjv6Tyo4fwcLampriIqxcXAGoKSmmtrSEsoJ8ADS+AeiyzMtmMOjyK/ENCWXV11+SFm+4GQkM78RVd0wnpLPhZ/mLHY1X0mxMwq/zQYGcfavJ2bf6rKMKfWc2XxTklrsfM/vrqdXkBJMWppAOC/A8/wp05nzttklx/a8nwbAQot1bvaF+hXdRSaHJdlVVJcmpiVhZqUv9VltXx5JNf2NtacldY4eTnm9Y9R3g4UYHH/NGgwDe+mIxCoalUJk5ubz9xWKT43ogLND86lqLti1lwYav0dVWY2lhyX0jppKcn84fRzawJWY3186/u02nSjQVCJ+pr5kFWg4cOICiKCbVyM5+ZK7X61U9Rn/ttS78+msmK5ZnkJpaybPPnKCuztC+o6MVd88IYdCgtl/Fb+3mji4nm+hHpuPQxTCSWRFzgtqKCmy8DXP0q/NyjIGyOUIju5osoDtvTQVuLVhAd+zg7iaP2WhsCQoNx87MoiBL7+yg+us3p7iskv3RqeQWlhlvQE+7aVy/Zq8f8sGQRl+fpq/Tk7k1s1XXPLQnEgwLIdq9N95/EUVRUBSFjKx05sx7yeS4Xq+nY6i6xSqWFhZkFBbh6mBPBx8vVQHw2fQYguGzQwSNtTVBfr48fOtNZrf13tovAAjzCuatKbPp6m8odzym61Be/e0DCiuKmfXT/9p03nBr6d27t0lwEB0djU6nIzw8HIC4uDgsLS3p3t38x94WFgqTJvnTs6czr7wcQ3W1IQWXl5eG1/7XBVfXts+XDeA7dRop896krrKC0gN7T+3Vg6Lgd+t0tJkZ6HKycL6k6XzaW1f+grO7O70vHcHWlb80eR5gdm7h0yJuVF9O+1xmP3DjOQNBK2trptx6P1NnPN5sW4NCza+kZ461u6O4Z85SyiobFsFQUMwKhpujr9WT9EsSKOA3wq/Z84UpCYaFEIL6EUL9WYUFNDYaggM78Nh9T6lus2ugP8dS0iipqMTZ3vycrmfa8NWnAIyZdi9dOnZgwfPnF0QoKNw2ZBKPj5uOjVX9QqDLug2nX0gPXvjlXbaebLr07r/Jxx9/bHz9yy+/EB0dzXfffUdwcDAAKSkpTJs2jWHD1OW0TUmp4PPPko2BMEBurpbPPk3inntDL4qA2HXwcDS+AeT+8TPaVMOcctvgUDwnTMIuJAyAbouXn7ONT55/iohefeh96Qg+ef6pJlP7KYqiOhh2CmpZFoVzOft390zVOh1Lv5qPb0AwY66c3GxbeWU1bIwtJbukmrqzmn10pLo5uS98+ielFU1Ug2vBKPg5yTSJFpFgWAjR7m374yAAl17dh26de/DJ3CWt0q69RkNdnZ6PVm+gW1AAjrYak4BidI8uZrf13tNPYN9MkYRvfvuTrNw8npx+R5PnfDntHS4J693oMQ9HNz6+7XVW7Ftldr/+LZYsWYK3t7cxEAYIDg7G29ubH374gZtuMm90feXKTJb9lE5Njd4wSjzZj6xMLX//nc+BA0XMmnnsopkqYdeho8kCupYwCTCbCDZbEn8VJx6hIjMBt8hBWDu6kPjHJ5SlxWDvHUToVfdj46Tu+zfzpff56O3nGDzici4dczUAW9f/zq6ta5n+8HPERh9l7W9LWf3Ld80Gw4fTK7h1SVKjFehAfTCcllOEvcaaz5+9mc4hPlhZWjR/kbigJBgWQohT5s/5HAf7c6c8W7z0czKz0nnmsZebbW971ElQoKJKy964hAbH1QTDvSI7N3vO7sNHiU5IPGcw3FQgfKbJ/a80u1//FkVFReh0Oj755BNGjhwJwJYtW0hOTkajMT8n8A/fG7IBBATY8uBDYYSFGR6pDxjgyhdfJFNaWsMH8+IZtPTCB8OFW9Zj6eyCc58BFG5Zf85zzUmn9v3R2EZfn1ZXV8df3y1p0TzVnL2rKU2NxrPXKPIOb6IkwVAMpSw9lvStP9HhqvuaacHU5rUrcfPwZuZL7xv3XTJsDDOmjGDX1rW88v7XRB/dT1J880VB5m7MabICXUtm5PbuFEheURnjB6svIy4uDAmGhRDilD49+jd7zs6924g6ecysYNjFwb4VeiVaw5AhQ9i0aRPffPMN33zzTYNjalx5lQ833xyItXX9CN/AQe5EdnHik4WJHDxY3Cp9Viv1o7nYd+qCc58BpH40lyZDN8W8YLg5tTXVfPP26ygWFoy/tekbsMZU5qdj4+SOtYMLpakxWGrsCBp3JylrvqAsNVp1X47u34mVjQ1FBXm4unsCUFxUQElJIfkHDNlc/IM6kJnefBrCQ+mVaKwU1j4Qzoj5sfQJtOPF8X7M+CGFr6aGqO7bQ1OGM+1/3/LS56u4YUwfXBxNi/AEeruZ1c7+V86RrUOmR5wXCYaFEOIfMuu6K9q6C+KU2bNnU1dXx5YtW0z2Dx8+nNmzzZ+H/cKLnenWrfHKZC4u1jw9uxMbN+aeV1/Pi8lUhiYiJH3rZhw411zdptRWVWDjaSjWoS3IxN4nFPfIgeTsXUVlXrrq9ty9fMjOSOWeG0bRrdcAAKKO7qeirBQff0NO75ysdFzcPJttq0JXRydvDSHuGhSgtg76BNrj6WDF839ksPKejqr6duvLS1AU+Gj5Vj5avtXkmIJC7l9zzGpHW9DEvGNx3iQYFkII8Z/n5OTEnDlzSE9PJyHBMGWlQ4cOBAYGqmqnqUD4TKNHtzxzyPno+dPqRl+fpq+rI3/1by171t/KrGwdqCrMoiBqJ9qSPJzDegFQq63EUqP+icod9z/N2y8+TEV5Kft2bgJOLYq1sGDaA7PJSE0iOzOVwcObL73tpLFAW2MI8J1tLTmZU8Xvx4pJKtC2eAC2yfsFFQvonDu2Tnlo0ZAEw0IIIdqNgIAAAgLMz8v8X6KvrSHj609BUfC88ro27YtjcBcKo3aRtMqQLcU5tDu1Oi260gLsfdXn+b10zFX4B4Xy6w9fkJxwEoDQjp2ZePMMOkQY5ub/uPaIWW0FudlwMqeKquo6uvvZsj2xnEeWpwIQ4WX+/PLTfnvHvFLpzen+iLrKd8J8EgwLIcR/REseV4t2SMWPyaPjRzXdzHn8vAWOvJm6Gh3awmxcOvbBJawXZeknsfftgHvkwBa12bFTN5MFdC01bZAHR9MryS6t5qmxPty6JIlSbR321hY8d7mv6vaG9go77z6Jf5YEw0IIoUJbBZyHY07iYGtHeEiQyX5ddTV1dXXYajTcdu3VFJeWtkn/xH9TbnraP9KutYMLHa99xGSfY0AnOt/8nNltbFi1AhdXd/oPGcWGVSvOea45uYVPm9jTlYk9XQEIAXbPjCQ+T0uwmw0udpZmt3Pa+r0xHIhOZdKoXvi4O3PPnB/YcSSB7h39+eyZmwjwclXdpmhdEgwLIcQph47tx8HOgYiOpgUBdNU66mprsbW1Y9rN91BUXHjB+/bEm3Pp2jGMD59/usH+6MQk1n/5CYN69bjg/RL/bZH9BvxjJX7raqopjNpJeWY8Vg4uePYYgbY4FzvPQKzszp3iEOD912YS2b0v/YeM4v3XZjbdT0UxOxiurtXT6bXjuNtbsu/JSBRFwd7Ggh7+LSuaA7Dgpy1sP5LInVcPZPGfu1i725AtY/fxJF5dtIZPZ5tfQVL8MyQYFkKIUx6efTfdI3uy8N2vG+yPPnmcLb/vZ/CAS9uod6Bv5Pl2lVZ7jtU5or2JfvDOJo+15KnGi4u/P4/eNK2msoyTP86hKt+QOcLBryOO/hHE/TwX30HX4j/UvIp2Z/5ONPX+1ITy1pYK3k5WONtattpNQExKDv5eLni7ObHjSCJO9hree2wSj8xdxt+H41vla4jzI8GwEEKcobEP1KqqykYD0QvhibfmGl8np2eabFdptSSmZ+DYwlLP4r9Hl5vd1l0wS/qWH6nKS8fCypq6mmoAnEK6YWGloSTxiFnB8B87kxp9fVpdXR2/L1uMojJ9xrSBHryzIZutcaUMD3dSdW1jikor6drBMNc4NiWHPp0CmTSyFwt+2kJU0r/j/9d/nQTDQoh275FnZhhfJ6UmmGxXVlWSkByHo8P5fyi2xOHok8aP8oqqKg5Hn2xwTt+u5leyE/9tDl26m5T8vlgVJxzCUmNH17vmcHThYwAoFhbYOHugK26dPM21NdV8Pu9VFAsLrrlxmtnXbYotw9JC4Y5vkwnz1ODpYGX8lirAD3eqy3bh5mRHXFouKzYeIiW7kHEDDdOwSsqrGhTgEG1DgmEhRLt38Og+4yPR8opyDh7d1+Cc/r1btsL9fGTn59O/W1fcXV1Yu30nrk6OXNKzfl6wrY0NQX6+XDF86AXvm7g4dXzlnbbugllqtRXYevhj7eBqsl+vr6NWV9W6X0zl9JDdyeXG1/F5WuLz6otdtOQ2Y1jvjqzYdJh731oKwOj+nSiv1JGeW0yfzuryXIt/hgTDQoh2LSsnkwF9BuPp7snqDb/j6uLG4P7DjMc1GltCAjtw1WXXXfC+3TLrWbp2DOOtux9l7fad1NbW8fTdd17wfgjR2mycPajKS6csrf5JR1H8QbQFWWjc1acva02Te7m2al2S/913NVXaahIy8hk/qAvjLolk17Ek+nQ2TJcQbU+CYSFEu3b9XVfSPbInzz7+Mas3/E5tbS3PPv5qW3cLMIxCVVRVGrcD/XzarjNCtCK3yEFk7fyNkz++AQqUZ8aT8OsHoNDiPMOtZe7E1h2t9XZzYsnLt5vsG9Q9lFXv39+qX0e0nATDQoh2TVEUyivqH4sGB4S0YW9Mubm4kJyeyQ2PPwVAXHIqU598ttFzv3vnjQvZNSHOi++ga6jITqIkwbQqnHNod3wGTjC7nbsmDWv6YAuzrAybF0N3Pzs+uTHYZP87G7JJytfy0Q3BTVzZNK2uhuUbD7IvOgVvNyduu2IAKVmFdAn1xc1Zfflp0bokGBZCtGvubh4kpSYw8fbLADiZEMP1d13V4DxFUfhp0R8XtG9jBl3Csr/WkV9UjAJU19SQlZffsG8XtFdCnB99bQ3xK+aiWFkTceNsKrISALD3DcMpKLKZq03lZLZ+UZC0omq8HBuGR3/Hl3Eko7KRK86toKScCTM/IybFkDmiX2QQl3QN4cbnv2LW1DHMvn3cefdZnJ9/bTC8dOlS3n77baKiorCzs2P06NG89dZbdOzYsclrfv75Zz766CP27dtHSUkJAKtXr2b8+PEXqttCiIvMuBFXsPSXb8gryEVRFKqrdWTlZDQ4758qPHAu9900he4RHUlMz2DxL7/h5ebKFcPPMRImxL+AYmlFRXYSNs6eOAVFqg6Az9S998BWuxtccai+mE5Bea3JdoWujrg8LdaW6r/YS5+vIjo5GzuNFZXaGgBG9o3AXmPN+r0xEgxfBP6VwfCiRYu4++67AejQoQP5+fmsWLGCbdu2cfjwYXx9G598v3XrVrZv305gYKAxGBZCtG8PTn+CHl17k5Acz6JvP8bL04er22CxXFOG9evDsH59OHA8itBAf+64zvxHyEJcrFwj+lEYswddWSE2jm4tbufNhT+2Wp9m/pqOgiG2TinUMevXdJPjeqCLj/pUaGt3RePsYMuuRTPpetPrAFhaWhDo40ZyZsH5d1yct39dMKzT6Zg9ezYAkydPZvny5WRkZBAZGUlOTg5vvPEG8+fPb/TaZ555hrfffpsdO3YwatSoC9ltIcRFbPjg0QwfPJp9h3YRFhLOXbfc19ZdauD9Z2a1dReEaDWWdk7o6+qIXvIirhH9sXZw5swhXr8h17VJv/SnenH2bGNbK4WOnhpevtJPdZvF5ZV0DvbBx900V3ltbR1lFdomrhIX0r8uGN67dy95eXmAIRgG8Pf3Z9CgQaxbt441a9Y0ea2PT8tWYmu1WrTa+h9YGVUW4r9pwZuL2roLQrQLOXtXgwI1FaXkHdnU4HhbBMNJL3cHIPTlY/QJtOOXu5uedqlGkLcb0cnZ7DqWaNy3ZucJ4tLyCA/0bJWvIc6PRVt3QK3U1FTja29vb+Pr04FuSkpKq3/NOXPm4OLiYvwXFBTU6l9DCCGEaC9snD2wcfKo/+9Z/9rS0js78NpV/uc8Z/6WHJ781bzFe5NG9aKmto6rZ36KosD+6FRufXkJimI4Jtrev25kuCn6FqZQMcczzzzDE088YdwuKSmRgFgIIYRooe73zG3rLjRpUKhDs+dsPFnK4fRK3rmu+ZzEM28ZzeHYdNbtiTHZP7p/BI/fJFM2Lwb/umD4zCA0JyenwevgYPX5/5qj0WjQaDSt3q4QQggh/ruqa2q54dkvsdVY8/u797A/xvB0u2+nIIb2Cmvj3onT/nXTJAYMGICHh+ERyooVKwDIyMhg165dAMY0aZGRkURGRrJgwYK26agQQggh2jVrK0sOx2WQkVvMkJ5hPHz9CB6+foQEwheZf10wbGNjwxtvGCotrVixgrCwMLp06UJpaSmenp7GTBMxMTHExMQYF9sBzJ8/n/DwcKZOnWrcd9dddxEeHs7TTz99Yd+IEEIIIf7zrhrajcSMfDLzZPH9xepfN00C4J577sHBwYF3332XqKgobG1tmTRpEm+++Sb+/k1Pei8oKCA+Pt5kX2ZmJgDZ2dn/aJ+FEEII0f54uDhQU1vHyPs/YMKl3fFyc0Q5I43cU7eNbcPeCfiXBsMAU6dONRnhPVtjC+pefvllXn755X+wV0IIIYQQ9RYs24qiQF5xOYv/3N3guATDbe9fGwwLIYQQQrS23UnlOGos6OZnZ7JfW1NHXR3Y2VjwyAhvCipqzGov0NuVNqjmLlSQYFgIIYQQ4pQbFyfSN9Cen+82XeR241eJHMmoJOGl7ozu5NTE1Q0d/nZ2a3dRtLJ/3QI6IYQQQoh/kr5BQWaorK7jHyxpINqQjAwLIYQQot27aXF9ueS4XK3JdqWujpgcLc62lm3RNfEPk2BYCCGEEO3erqRyY46HUm0du5LKG5wzLKz56nTi30eCYSGEEEK0a+lFOoZ3dMTb0Yrlh4vwsLdkVET9vGBbaws6emq4sa9bG/ZS/FMkGBZCCCFEuzZ03kn6Btqz5LZAlh8uoqYO3p0Y2NbdEheILKATQgghRLumAGXaWuN2mKdN23VGXHAyMiyEEEKIds3L0YrYXC0D50YDcDyzimHzYhqcpwDbHut8gXsn/mkSDAshhBCiXbu2hyuf78wju7QGBdDV6kkrqm5wntTO+G+SYFgIIYQQ7dpzl/vSP9iemJwq3tuUg5+zNTf2kcVy7YUEw0IIIYRo9y7v4szlXZzZnlBGJ29bHhvl3dZdEheIBMNCCCGEEKf8OC2s+ZPEf4pkkxBCCCGEEO2WBMNCCCGEEKLdkmBYCCGEEEK0WxIMCyGEEEKIdkuCYSGEEEII0W5JMCyEEEIIIdotCYaFEEIIIUS7JcGwEEIIIYRotyQYFkIIIYQQ7ZYEw0IIIYQQot2SYFgIIYQQQrRbEgwLIYQQQoh2S4JhIYQQQgjRbkkwLIQQQggh2i0JhoUQQgghRLslwbAQQgghhGi3JBgWQgghhBDtlgTDQgghhBCi3ZJgWAghhBBCtFsSDAshhBBCiHZLgmEhhBBCCNFuSTAshBBCCCHaLQmGhRBCCCFEuyXBsBBCCCGEaLckGBZCCCGEEO2WBMNCCCGEEKLdkmBYCCGEEEK0WxIMCyGEEEKIdkuCYSGEEEII0W5JMCyEEEIIIdotCYaFEEIIIUS7JcGwEEIIIYRotyQYFkIIIYQQ7ZYEw0IIIYQQot2SYFgIIYQQQrRbEgwLIYQQQoh2S4JhIYQQQgjRbkkwLIQQQggh2i0JhoUQQgghRLslwbAQQgghhGi3JBgWQgghhBDtlgTDQgghhBCi3ZJgWAghhBBCtFsSDAshhBBCiHZLgmEhhBBCCNFuSTAshBBCCCHaLQmGhRBCCCFEuyXBsBBCCCGEaLckGBZCCCGEEO2WBMNCCCGEEKLdkmBYCCGEEEK0WxIMCyGEEEKIdkuCYSGEEEII0W5JMCyEEEIIIdotCYaFEEIIIUS7JcGwEEIIIYRotyQYFkIIIYQQ7ZYEw0IIIYQQot2SYFgIIYQQQrRb/9pgeOnSpfTt2xc7Ozvc3d2ZMmUK8fHxzV734Ycf0rVrVzQaDd7e3tx1111kZ2dfgB4LIYQQQoiLzb8yGF60aBE333wzBw8exM/Pj9raWlasWMGQIUPIyspq8roXXniBRx55hKioKEJCQigrK+Orr75i5MiRVFRUXMB3IIQQQgghLgZWbd0BtXQ6HbNnzwZg8uTJLF++nIyMDCIjI8nJyeGNN95g/vz5Da7Lzs7mrbfeAmDmzJm8++67HDlyhN69exMdHc0nn3zCE0880ejX1Gq1aLVa43ZxcTEAJSUlrf32Lnq12spWba+ivLRV2yutqm21tvTlVa3WFkBNZU2rtldZ1rr/L8prWrd/JRWt279KrXWrtVVeUdZqbQFUVVe3anvlla37vSurKm/V9krKW/f7d+bf19ZQXt5677eiovX+pgCUtfLvbW1F6/6/rShr3b/JF/NnRmt+XsDF/5nRHmOW0+9Zr9ef+0T9v8zff/+tB/SA/vvvvzfuHzdunB7QR0RENHrdt99+a7xux44dxv0RERF6QD9u3Lgmv+ZLL71kvFb+yT/5J//kn/yTf/JP/v17/qWmpp4ztvzXjQynpqYaX3t7extf+/j4AJCSkqL6utjY2CavA3jmmWdMRo3r6uooKCjAw8MDRVHUvwkhhLjIlJSUEBQURGpqKs7Ozm3dHSGEOG96vZ7S0lL8/f3Ped6/Lhhuir65IfDzuE6j0aDRaEz2ubq6tujrCSHExczZ2VmCYSHEf4aLi0uz5/zrFtAFBQUZX+fk5DR4HRwc3KrXCSGEEEKI/65/XTA8YMAAPDw8AFixYgUAGRkZ7Nq1C4Dx48cDEBkZSWRkJAsWLABgzJgxWFlZmVx35MgR4uLiTK4TQgghhBDtx78uGLaxseGNN94ADEFtWFgYXbp0obS0FE9PT2OmiZiYGGJiYsjLywPA19eXJ598EoC5c+fSuXNnBg0ahF6vJyIignvvvbdt3pAQQlwENBoNL730UoMpYUII8V/3rwuGAe655x6+/fZbevfuTUZGBoqiMGnSJHbs2HHOSdKvv/468+bNIzIyksTERBwcHLjjjjvYunUrDg4OF/AdCCHExUWj0fDyyy9LMCyEaHcUfUtXngkhhBBCCPEv968cGRZCCCGEEKI1SDAshBBCCCHaLQmGhRBCCCFEuyXBsBBCXIQSExN55JFH6NatG46Ojmg0Gnx8fOjatSuTJk3itdde49ixY23dzRYbOXIkiqKgKAqhoaFt3R0hRDv2n6lAJ4QQ/xXr1q1j4sSJlJeXm+zPyckhJyeHqKgofvnlF2pra+nevXsb9VIIIf4bJBgWQoiLSFlZGVOnTjUJhHv27ElwcDBarZaYmBhSUlLasIdCCPHfIsGwEEJcRNasWUNubq5xe8WKFUyaNMnknKSkJJYvX26sximEEKLlZM6wEEJcRE6XiD9tzJgxDc4JDQ1l1qxZTJs2rcGxtLQ0nnrqKXr37o2zszMajYbQ0FCmTZt2zjnGJSUlvP322wwdOhR3d3esra3x9vZm3LhxfPnll1RXVze4prl5v6GhocbjI0eObP7NCyFEG5BgWAghLiLW1tYm2zfddBMbN26kqqqq2Wt/++03unbtyjvvvMPhw4cpLS1Fp9ORnJzM4sWL6du3L1999VWD606cOEGvXr14+umn2bFjB4WFhdTU1JCbm8v69euZPn06w4cPp6CgoNXepxBCXCwkGBZCiIvIkCFDTLbXrFnDmDFjcHJyok+fPjz88MNs2LCBs4uHHj16lBtvvJHS0lLAEFQPHz6c8ePH4+LiAkB1dTUzZsxgx44dxusqKiq48sorSUpKMu4LCQnhsssuw8vLy7hv165dTJ06tbXfrhBCtDkJhoUQ4iIyePBgrrnmmgb7a2pqOHToEAsWLGDs2LH079+f2NhY4/FXXnnFOHrs6urK0aNH2bJlC6tXryY+Pp7g4GAAamtrefnll43XffHFFyQnJxu3r7/+euLi4vjrr7+IiYmhZ8+exmNr1qwxCaSFEOK/QIJhIYS4yCxbtoynnnoKBweHJs85cOAA48ePR6vVUldXx19//WU8Zmtry3PPPceUKVOYMmUK9957L3V1dcbjmzdvprKyEoDVq1ebtPv6669jZWVYW+3m5sbs2bNNjq9Zs+a8358QQlxMJJuEEEJcZGxsbHjrrbd4/vnnWbduHVu2bGHbtm0cOnTIZHpEQkICq1evZsiQIZSVlRn3Z2VlsWLFiibbr66uJjMzk7CwMJNRYY1GQ3h4uMm53bp1M9k+83whhPgvkJFhIYS4SDk5OTFp0iQ++OADDhw4QEJCAkOHDjU5JyYmpkVtV1RUtEYXjWpraxvsy8nJadWvIYQQ/wQJhoUQ4iKSm5tLTU1No8dCQ0N58MEHTfZZWVnh4eFhMqVi1KhR6PX6c/47Xbnu9FxiAK1WS3x8vEn7x48fN9k+83wbGxvj66KiIpPzDh06ZJyKIYQQFzMJhoUQ4iLy559/EhkZyfz5802Kb4Bh9PW3334z2RcZGYmlpSWXXXaZcd+WLVv49ttvG7Sdnp7O+++/z2uvvWbcd8UVV5ic8/zzzxuD8aKiIt5++22T4+PHjze+9vX1Nb4uKytj6dKlABQWFvLQQw+Z9X6FEKKtKfqz8/MIIYRoM4sXLzYW01AUhcjISEJCQlAUhcOHD5ORkWE819/fn/j4eGxtbTl06BADBw5Ep9MZj0dERNCpUydqamqIi4sjISEBvV7PHXfcweLFiwFDENu1a1dSU1ON14WGhtK5c2cOHjxoMtVh3LhxrF271rj95ZdfMn36dJP+BwUFkZWV1aBIx4gRI9i8ebNxe+TIkWzZsgUwpHI7M7WbEEJcSLKATgghLiKKohhf6/V6oqKiiIqKanCek5MT33//Pba2tgD07t2bH374gTvuuMO4mC42NtYk/dpplpaWxteOjo78+eefXH311aSkpACGcs9nB6cDBgzg+++/N9l388038/bbb5vMWz4dVF999dUcOHDAJHgXQoiLkQTDQghxEbn11lsJCwtj7dq17N69m9jYWLKzs9FqtTg6OtKxY0fGjh3Lww8/TFBQkMm1kyZN4pJLLuHjjz9m3bp1xMbGUlZWhoODA6GhofTv358rr7ySCRMmmFzXo0cPjhw5wsKFC1m5ciXR0dGUlZXh4uJCr169uPnmm7njjjsaVMezs7Nj06ZNzJ49m1WrVlFaWkp4eDgzZszgoYceomPHjv/490sIIc6XTJMQQgghhBDtliygE0IIIYQQ7ZYEw0IIIYQQot2SYFgIIYQQQrRbEgwLIYQQQoh2S4JhIYQQQgjRbkkwLIQQQggh2i0JhoUQQgghRLslwbAQQgghhGi3JBgWQgghhBDtlgTDQgghhBCi3ZJgWAghhBBCtFsSDAshhBBCiHbr/3bt7PAm687cAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "make_plot(models, 'Seoul')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "make_plot(models, 'Busan')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "make_plot(models, 'Incheon')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "make_plot(models, 'Daegu')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "make_plot(models, 'Daejeon')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "make_plot(models, 'Gwangju')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "file_path = \"./model_result/\"\n",
-    "file_list = os.listdir(file_path)\n",
-    "file_list = [file for file in file_list if file.endswith(\".csv\")]\n",
-    "\n",
-    "\n",
-    "models= pd.DataFrame()\n",
-    "for i in file_list:\n",
-    "    models = pd.concat([models, pd.read_csv(file_path + i)], axis=0)\n",
-    "\n",
-    "models = models.reset_index(drop=True)\n",
-    "models['model_data'] = models['model'] + '_' + models['data_sample']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df= models.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = df.loc[df['data_sample']=='pure', :].copy()\n",
-    "df.sort_values(by='CSI', ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **각 지역별 앙상블 모델 성능 비교 by 원데이터**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(15, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(df['model'].unique()))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model', ci=None, data=df.loc[(df['data_sample'] == 'pure'),:], palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=15)\n",
-    "plt.title('CSI Score Comparison of Models Averaged Over 3-Fold Cross-Validation', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "file_path = \"./model_result/\"\n",
-    "file_path_1 = \"./model_result/best_sample/\"\n",
-    "file_list = os.listdir(file_path)\n",
-    "file_list_1 = os.listdir(file_path_1)\n",
-    "file_list = [file for file in file_list if file.endswith(\".csv\")]\n",
-    "file_list_1 = [file for file in file_list_1 if file.endswith(\".csv\")]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "models= pd.DataFrame()\n",
-    "for i in file_list:\n",
-    "    models = pd.concat([models, pd.read_csv(file_path + i)], axis=0)\n",
-    "    \n",
-    "for i in file_list_1:\n",
-    "    models = pd.concat([models, pd.read_csv(file_path_1 + i)], axis=0)\n",
-    "\n",
-    "\n",
-    "models = models.reset_index(drop=True)\n",
-    "models = models.reset_index(drop=True)\n",
-    "models['model_data'] = models['model'] + '_' + models['data_sample']\n",
-    "df= models.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df= df.loc[df['model'] !=(\"deepgbm\"),:]\n",
-    "df= df.loc[df['model'] != (\"XGBoost\"),:]\n",
-    "df= df.loc[df['model']!=(\"LightGBM\"),:]\n",
-    "df= df.loc[df['model']!=(\"ft_transformer\"),:]\n",
-    "df= df.loc[df['model']!= (\"resnet_like\"),:]\n",
-    "\n",
-    "df.sort_values(by='CSI', ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table= df.loc[(df['region']=='seoul'),:'Accuracy']\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(df['model'].unique()))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model', ci=None, data=df.loc[(df['region']=='seoul'),:], palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=11)\n",
-    "plt.title('Soft Voting Ensemble: Model Performance Comparison in Seoul', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('')  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table = df.loc[(df['region']=='busan'),:'Accuracy']\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(df['model'].unique()))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model', ci=None, data=df.loc[(df['region']=='busan'),:], palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=11)\n",
-    "plt.title('Soft Voting Ensemble: Model Performance Comparison in Busan', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('')  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table = df.loc[(df['region']=='incheon'),:'Accuracy']\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(df['model'].unique()))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model', ci=None, data=df.loc[(df['region']=='incheon'),:], palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=11)\n",
-    "plt.title('Soft Voting Ensemble: Model Performance Comparison in Incheon', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('')  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "table = df.loc[(df['region']=='daegu'),:'Accuracy']\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(df['model'].unique()))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model', ci=None, data=df.loc[(df['region']=='daegu'),:], palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=11)\n",
-    "plt.title('Soft Voting Ensemble: Model Performance Comparison in Daegu', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('')  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>model</th>\n",
-       "      <th>data_sample</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>254</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.680</td>\n",
-       "      <td>0.789</td>\n",
-       "      <td>0.958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>255</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+resnet_like</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.666</td>\n",
-       "      <td>0.780</td>\n",
-       "      <td>0.955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>257</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.662</td>\n",
-       "      <td>0.776</td>\n",
-       "      <td>0.955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>264</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.661</td>\n",
-       "      <td>0.774</td>\n",
-       "      <td>0.955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>256</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.657</td>\n",
-       "      <td>0.773</td>\n",
-       "      <td>0.954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>266</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.656</td>\n",
-       "      <td>0.770</td>\n",
-       "      <td>0.955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>265</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.650</td>\n",
-       "      <td>0.765</td>\n",
-       "      <td>0.954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>274</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.646</td>\n",
-       "      <td>0.763</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>267</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.646</td>\n",
-       "      <td>0.764</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>268</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.644</td>\n",
-       "      <td>0.762</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>275</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.643</td>\n",
-       "      <td>0.760</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>276</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.627</td>\n",
-       "      <td>0.747</td>\n",
-       "      <td>0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>279</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost+Lig...</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>0.745</td>\n",
-       "      <td>0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>277</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.618</td>\n",
-       "      <td>0.741</td>\n",
-       "      <td>0.948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>269</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.610</td>\n",
-       "      <td>0.736</td>\n",
-       "      <td>0.947</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>270</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.593</td>\n",
-       "      <td>0.717</td>\n",
-       "      <td>0.945</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>258</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer+resnet_like</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.587</td>\n",
-       "      <td>0.712</td>\n",
-       "      <td>0.945</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>271</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.587</td>\n",
-       "      <td>0.712</td>\n",
-       "      <td>0.945</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>259</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.582</td>\n",
-       "      <td>0.708</td>\n",
-       "      <td>0.943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>260</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.581</td>\n",
-       "      <td>0.707</td>\n",
-       "      <td>0.944</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>278</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.578</td>\n",
-       "      <td>0.705</td>\n",
-       "      <td>0.943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>272</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.570</td>\n",
-       "      <td>0.698</td>\n",
-       "      <td>0.941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>261</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.562</td>\n",
-       "      <td>0.693</td>\n",
-       "      <td>0.939</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>273</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.557</td>\n",
-       "      <td>0.688</td>\n",
-       "      <td>0.938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>262</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.555</td>\n",
-       "      <td>0.686</td>\n",
-       "      <td>0.939</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>263</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.529</td>\n",
-       "      <td>0.663</td>\n",
-       "      <td>0.933</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      region                                              model data_sample  \\\n",
-       "254  daejeon                             deepgbm+ft_transformer        best   \n",
-       "255  daejeon                                deepgbm+resnet_like        best   \n",
-       "257  daejeon                                   deepgbm+LightGBM        best   \n",
-       "264  daejeon                 deepgbm+ft_transformer+resnet_like        best   \n",
-       "256  daejeon                                    deepgbm+XGBoost        best   \n",
-       "266  daejeon                    deepgbm+ft_transformer+LightGBM        best   \n",
-       "265  daejeon                     deepgbm+ft_transformer+XGBoost        best   \n",
-       "274  daejeon         deepgbm+ft_transformer+resnet_like+XGBoost        best   \n",
-       "267  daejeon                        deepgbm+resnet_like+XGBoost        best   \n",
-       "268  daejeon                       deepgbm+resnet_like+LightGBM        best   \n",
-       "275  daejeon        deepgbm+ft_transformer+resnet_like+LightGBM        best   \n",
-       "276  daejeon            deepgbm+ft_transformer+XGBoost+LightGBM        best   \n",
-       "279  daejeon  deepgbm+ft_transformer+resnet_like+XGBoost+Lig...        best   \n",
-       "277  daejeon               deepgbm+resnet_like+XGBoost+LightGBM        best   \n",
-       "269  daejeon                           deepgbm+XGBoost+LightGBM        best   \n",
-       "270  daejeon                 ft_transformer+resnet_like+XGBoost        best   \n",
-       "258  daejeon                         ft_transformer+resnet_like        best   \n",
-       "271  daejeon                ft_transformer+resnet_like+LightGBM        best   \n",
-       "259  daejeon                             ft_transformer+XGBoost        best   \n",
-       "260  daejeon                            ft_transformer+LightGBM        best   \n",
-       "278  daejeon        ft_transformer+resnet_like+XGBoost+LightGBM        best   \n",
-       "272  daejeon                    ft_transformer+XGBoost+LightGBM        best   \n",
-       "261  daejeon                                resnet_like+XGBoost        best   \n",
-       "273  daejeon                       resnet_like+XGBoost+LightGBM        best   \n",
-       "262  daejeon                               resnet_like+LightGBM        best   \n",
-       "263  daejeon                                   XGBoost+LightGBM        best   \n",
-       "\n",
-       "       CSI    MCC  Accuracy  \n",
-       "254  0.680  0.789     0.958  \n",
-       "255  0.666  0.780     0.955  \n",
-       "257  0.662  0.776     0.955  \n",
-       "264  0.661  0.774     0.955  \n",
-       "256  0.657  0.773     0.954  \n",
-       "266  0.656  0.770     0.955  \n",
-       "265  0.650  0.765     0.954  \n",
-       "274  0.646  0.763     0.953  \n",
-       "267  0.646  0.764     0.953  \n",
-       "268  0.644  0.762     0.953  \n",
-       "275  0.643  0.760     0.953  \n",
-       "276  0.627  0.747     0.950  \n",
-       "279  0.625  0.745     0.950  \n",
-       "277  0.618  0.741     0.948  \n",
-       "269  0.610  0.736     0.947  \n",
-       "270  0.593  0.717     0.945  \n",
-       "258  0.587  0.712     0.945  \n",
-       "271  0.587  0.712     0.945  \n",
-       "259  0.582  0.708     0.943  \n",
-       "260  0.581  0.707     0.944  \n",
-       "278  0.578  0.705     0.943  \n",
-       "272  0.570  0.698     0.941  \n",
-       "261  0.562  0.693     0.939  \n",
-       "273  0.557  0.688     0.938  \n",
-       "262  0.555  0.686     0.939  \n",
-       "263  0.529  0.663     0.933  "
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table= df.loc[(df['region']=='daejeon'),:'Accuracy']\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)\n",
-    "table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>model</th>\n",
-       "      <th>data_sample</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>280</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.680</td>\n",
-       "      <td>0.767</td>\n",
-       "      <td>0.954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>281</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+resnet_like</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.666</td>\n",
-       "      <td>0.771</td>\n",
-       "      <td>0.954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>283</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.662</td>\n",
-       "      <td>0.773</td>\n",
-       "      <td>0.954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>290</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.661</td>\n",
-       "      <td>0.774</td>\n",
-       "      <td>0.955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>282</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.657</td>\n",
-       "      <td>0.772</td>\n",
-       "      <td>0.954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>292</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.656</td>\n",
-       "      <td>0.770</td>\n",
-       "      <td>0.955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>291</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.650</td>\n",
-       "      <td>0.765</td>\n",
-       "      <td>0.954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>300</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.646</td>\n",
-       "      <td>0.763</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>293</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.646</td>\n",
-       "      <td>0.764</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>294</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.644</td>\n",
-       "      <td>0.762</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>301</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.643</td>\n",
-       "      <td>0.760</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>302</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.627</td>\n",
-       "      <td>0.747</td>\n",
-       "      <td>0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>305</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost+Lig...</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>0.745</td>\n",
-       "      <td>0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>303</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.618</td>\n",
-       "      <td>0.741</td>\n",
-       "      <td>0.948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>295</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.610</td>\n",
-       "      <td>0.736</td>\n",
-       "      <td>0.947</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>296</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.593</td>\n",
-       "      <td>0.717</td>\n",
-       "      <td>0.945</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>284</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer+resnet_like</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.587</td>\n",
-       "      <td>0.763</td>\n",
-       "      <td>0.953</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>297</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.587</td>\n",
-       "      <td>0.712</td>\n",
-       "      <td>0.945</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>285</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.582</td>\n",
-       "      <td>0.755</td>\n",
-       "      <td>0.952</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>286</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.581</td>\n",
-       "      <td>0.749</td>\n",
-       "      <td>0.951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>304</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.578</td>\n",
-       "      <td>0.705</td>\n",
-       "      <td>0.943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>298</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.570</td>\n",
-       "      <td>0.698</td>\n",
-       "      <td>0.941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>287</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like+XGBoost</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.562</td>\n",
-       "      <td>0.743</td>\n",
-       "      <td>0.949</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>299</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.557</td>\n",
-       "      <td>0.688</td>\n",
-       "      <td>0.938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>288</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.555</td>\n",
-       "      <td>0.737</td>\n",
-       "      <td>0.948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>289</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>XGBoost+LightGBM</td>\n",
-       "      <td>best</td>\n",
-       "      <td>0.529</td>\n",
-       "      <td>0.730</td>\n",
-       "      <td>0.947</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      region                                              model data_sample  \\\n",
-       "280  gwangju                             deepgbm+ft_transformer        best   \n",
-       "281  gwangju                                deepgbm+resnet_like        best   \n",
-       "283  gwangju                                   deepgbm+LightGBM        best   \n",
-       "290  gwangju                 deepgbm+ft_transformer+resnet_like        best   \n",
-       "282  gwangju                                    deepgbm+XGBoost        best   \n",
-       "292  gwangju                    deepgbm+ft_transformer+LightGBM        best   \n",
-       "291  gwangju                     deepgbm+ft_transformer+XGBoost        best   \n",
-       "300  gwangju         deepgbm+ft_transformer+resnet_like+XGBoost        best   \n",
-       "293  gwangju                        deepgbm+resnet_like+XGBoost        best   \n",
-       "294  gwangju                       deepgbm+resnet_like+LightGBM        best   \n",
-       "301  gwangju        deepgbm+ft_transformer+resnet_like+LightGBM        best   \n",
-       "302  gwangju            deepgbm+ft_transformer+XGBoost+LightGBM        best   \n",
-       "305  gwangju  deepgbm+ft_transformer+resnet_like+XGBoost+Lig...        best   \n",
-       "303  gwangju               deepgbm+resnet_like+XGBoost+LightGBM        best   \n",
-       "295  gwangju                           deepgbm+XGBoost+LightGBM        best   \n",
-       "296  gwangju                 ft_transformer+resnet_like+XGBoost        best   \n",
-       "284  gwangju                         ft_transformer+resnet_like        best   \n",
-       "297  gwangju                ft_transformer+resnet_like+LightGBM        best   \n",
-       "285  gwangju                             ft_transformer+XGBoost        best   \n",
-       "286  gwangju                            ft_transformer+LightGBM        best   \n",
-       "304  gwangju        ft_transformer+resnet_like+XGBoost+LightGBM        best   \n",
-       "298  gwangju                    ft_transformer+XGBoost+LightGBM        best   \n",
-       "287  gwangju                                resnet_like+XGBoost        best   \n",
-       "299  gwangju                       resnet_like+XGBoost+LightGBM        best   \n",
-       "288  gwangju                               resnet_like+LightGBM        best   \n",
-       "289  gwangju                                   XGBoost+LightGBM        best   \n",
-       "\n",
-       "       CSI    MCC  Accuracy  \n",
-       "280  0.680  0.767     0.954  \n",
-       "281  0.666  0.771     0.954  \n",
-       "283  0.662  0.773     0.954  \n",
-       "290  0.661  0.774     0.955  \n",
-       "282  0.657  0.772     0.954  \n",
-       "292  0.656  0.770     0.955  \n",
-       "291  0.650  0.765     0.954  \n",
-       "300  0.646  0.763     0.953  \n",
-       "293  0.646  0.764     0.953  \n",
-       "294  0.644  0.762     0.953  \n",
-       "301  0.643  0.760     0.953  \n",
-       "302  0.627  0.747     0.950  \n",
-       "305  0.625  0.745     0.950  \n",
-       "303  0.618  0.741     0.948  \n",
-       "295  0.610  0.736     0.947  \n",
-       "296  0.593  0.717     0.945  \n",
-       "284  0.587  0.763     0.953  \n",
-       "297  0.587  0.712     0.945  \n",
-       "285  0.582  0.755     0.952  \n",
-       "286  0.581  0.749     0.951  \n",
-       "304  0.578  0.705     0.943  \n",
-       "298  0.570  0.698     0.941  \n",
-       "287  0.562  0.743     0.949  \n",
-       "299  0.557  0.688     0.938  \n",
-       "288  0.555  0.737     0.948  \n",
-       "289  0.529  0.730     0.947  "
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table= df.loc[(df['region']=='gwangju'),:'Accuracy']\n",
-    "table['CSI'] = table['CSI'].round(3)\n",
-    "table['MCC'] = table['MCC'].round(3)\n",
-    "table['Accuracy'] = table['Accuracy'].round(3)\n",
-    "table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(df['model'].unique()))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model', ci=None, data=df.loc[(df['region']=='daejeon'),:], palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=11)\n",
-    "plt.title('Soft Voting Ensemble: Model Performance Comparison in Daejeon', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('')  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 8))\n",
-    "palette = sns.color_palette(\"tab20\", n_colors=len(df['model'].unique()))  # 더 많은 색상 지원\n",
-    "sns.barplot(x='region', y='CSI', hue='model', ci=None, data=df.loc[(df['region']=='gwangju'),:], palette=palette)\n",
-    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=11)\n",
-    "plt.title('Soft Voting Ensemble: Model Performance Comparison in Gwangju', fontsize=15, fontweight='bold')  # 제목 볼드 및 크기 조정\n",
-    "plt.ylabel('CSI', fontsize=20,)  # y축 라벨 변경 및 스타일 조정\n",
-    "plt.xlabel('')  # y축 라벨 변경 및 스타일 조정\n",
-    "# x축(도시명) 글씨 크기 조정\n",
-    "plt.xticks(fontsize=15, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.yticks(fontsize=10, fontweight='bold')  # x축(지역명) 폰트 크기 증가\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "6it [00:00, 8513.47it/s]\n"
-     ]
-    },
-    {
-     "data": {
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-       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
-       "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var circle_marker_47460d79c6805e06f6b7ef1d5cb00331 = L.circleMarker(\n",
-       "                [37.57142, 126.9658],\n",
-       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.7, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var marker_2b2a90f64813c20a23ff74007db864a8 = L.marker(\n",
-       "                [37.57142, 126.9658],\n",
-       "                {}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var div_icon_0cd42476bb19dacb43454ee26a97f1d8 = L.divIcon({&quot;className&quot;: &quot;empty&quot;, &quot;html&quot;: &quot;\\u003cdiv style=\\&quot;font-size: 18pt; color: black;\\&quot;\\u003e108\\u003c/div\\u003e&quot;, &quot;iconAnchor&quot;: [0, 0], &quot;iconSize&quot;: [120, 30]});\n",
-       "            marker_2b2a90f64813c20a23ff74007db864a8.setIcon(div_icon_0cd42476bb19dacb43454ee26a97f1d8);\n",
-       "        \n",
-       "    \n",
-       "            var circle_marker_a2166893c44ea5420ad169c4fbc0b914 = L.circleMarker(\n",
-       "                [37.47772, 126.6249],\n",
-       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.7, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var marker_957f167293bbd1898ca7a19665469078 = L.marker(\n",
-       "                [37.47772, 126.6249],\n",
-       "                {}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var div_icon_c2ac2f46664cf0eae9bb0bed704472b3 = L.divIcon({&quot;className&quot;: &quot;empty&quot;, &quot;html&quot;: &quot;\\u003cdiv style=\\&quot;font-size: 18pt; color: black;\\&quot;\\u003e112\\u003c/div\\u003e&quot;, &quot;iconAnchor&quot;: [0, 0], &quot;iconSize&quot;: [120, 30]});\n",
-       "            marker_957f167293bbd1898ca7a19665469078.setIcon(div_icon_c2ac2f46664cf0eae9bb0bed704472b3);\n",
-       "        \n",
-       "    \n",
-       "            var circle_marker_09090fa39081d25b100002b6135ab510 = L.circleMarker(\n",
-       "                [36.37199, 127.3721],\n",
-       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.7, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var marker_b677f20e38ce7e599e8e1e6a496c7699 = L.marker(\n",
-       "                [36.37199, 127.3721],\n",
-       "                {}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var div_icon_7355463f8fff8ed2274886afc19ca14c = L.divIcon({&quot;className&quot;: &quot;empty&quot;, &quot;html&quot;: &quot;\\u003cdiv style=\\&quot;font-size: 18pt; color: black;\\&quot;\\u003e133\\u003c/div\\u003e&quot;, &quot;iconAnchor&quot;: [0, 0], &quot;iconSize&quot;: [120, 30]});\n",
-       "            marker_b677f20e38ce7e599e8e1e6a496c7699.setIcon(div_icon_7355463f8fff8ed2274886afc19ca14c);\n",
-       "        \n",
-       "    \n",
-       "            var circle_marker_22f52f6a1a9cb22da7d67d6848d17a40 = L.circleMarker(\n",
-       "                [35.87797, 128.65296],\n",
-       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.7, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var marker_4bdb88fe6ed4e708f5c5840cb66a1d92 = L.marker(\n",
-       "                [35.87797, 128.65296],\n",
-       "                {}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var div_icon_1ca6fdfb04d8b606e332defbd9dd6520 = L.divIcon({&quot;className&quot;: &quot;empty&quot;, &quot;html&quot;: &quot;\\u003cdiv style=\\&quot;font-size: 18pt; color: black;\\&quot;\\u003e143\\u003c/div\\u003e&quot;, &quot;iconAnchor&quot;: [0, 0], &quot;iconSize&quot;: [120, 30]});\n",
-       "            marker_4bdb88fe6ed4e708f5c5840cb66a1d92.setIcon(div_icon_1ca6fdfb04d8b606e332defbd9dd6520);\n",
-       "        \n",
-       "    \n",
-       "            var circle_marker_7f255e624ee41e5f1aa1724b6019aca0 = L.circleMarker(\n",
-       "                [35.17294, 126.89156],\n",
-       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;blue&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;blue&quot;, &quot;fillOpacity&quot;: 0.7, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
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-       "                [35.17294, 126.89156],\n",
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-       "        \n",
-       "    \n",
-       "            var div_icon_7e9d7d058db195bcab1ea6f0c84dac40 = L.divIcon({&quot;className&quot;: &quot;empty&quot;, &quot;html&quot;: &quot;\\u003cdiv style=\\&quot;font-size: 18pt; color: black;\\&quot;\\u003e156\\u003c/div\\u003e&quot;, &quot;iconAnchor&quot;: [0, 0], &quot;iconSize&quot;: [120, 30]});\n",
-       "            marker_ea0d3cdd91747084562f9e85e1cf11af.setIcon(div_icon_7e9d7d058db195bcab1ea6f0c84dac40);\n",
-       "        \n",
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-       "                [35.10468, 129.03203],\n",
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-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var marker_d725b8fb90e957c948e41a25f2ed9214 = L.marker(\n",
-       "                [35.10468, 129.03203],\n",
-       "                {}\n",
-       "            ).addTo(map_a7ab356614db630789360989eac1d1df);\n",
-       "        \n",
-       "    \n",
-       "            var div_icon_96bd66015b5d69d4a86c9d5ad1ce6181 = L.divIcon({&quot;className&quot;: &quot;empty&quot;, &quot;html&quot;: &quot;\\u003cdiv style=\\&quot;font-size: 18pt; color: black;\\&quot;\\u003e159\\u003c/div\\u003e&quot;, &quot;iconAnchor&quot;: [0, 0], &quot;iconSize&quot;: [120, 30]});\n",
-       "            marker_d725b8fb90e957c948e41a25f2ed9214.setIcon(div_icon_96bd66015b5d69d4a86c9d5ad1ce6181);\n",
-       "        \n",
-       "&lt;/script&gt;\n",
-       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
-      ],
-      "text/plain": [
-       "<folium.folium.Map at 0x7fbe820554c0>"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "df = pd.read_feather(\"../data/asos_air_merge.feather\") # asos와 air 병합한 데이터 불러오기\n",
-    "\n",
-    "df['지역_1'] = df['지역'].str.split(' ').str[0] # 도,시,광역시 이름 저장\n",
-    "df['지역_2'] = df['지역'].str.split(' ').str[1] # 시,군,구 이름 저장\n",
-    "data = df.loc[df['거리차이(km)'] <= 5,:].drop_duplicates(subset=['지점']).copy() # 5km 이내 데이터만 추출\n",
-    "# 데이터 필터링 (6개 지점만 선택)\n",
-    "data = data.loc[data['지점'].isin([112, 108, 133, 159, 143, 156]), :].copy()\n",
-    "\n",
-    "\n",
-    "import folium\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "# 지도 중심 설정 (첫 번째 지점 기준)\n",
-    "map_center = [data['위도'].iloc[0], data['경도'].iloc[0]]\n",
-    "\n",
-    "m = folium.Map(location=map_center, zoom_start=10, tiles=\"OpenStreetMap\")\n",
-    "\n",
-    "# CircleMarker + 텍스트 추가\n",
-    "for index, row in tqdm(data.iterrows()):\n",
-    "    # 원형 마커 추가\n",
-    "    folium.CircleMarker(\n",
-    "        location=[row['위도'], row['경도']],\n",
-    "        radius=6,  # 마커 크기\n",
-    "        color='blue',  \n",
-    "        fill=True,\n",
-    "        fill_color='blue',\n",
-    "        fill_opacity=0.7,\n",
-    "    ).add_to(m)\n",
-    "\n",
-    "    # 텍스트 항상 표시 (DivIcon 사용)\n",
-    "    folium.map.Marker(\n",
-    "        [row['위도'], row['경도']], \n",
-    "        icon=folium.DivIcon(\n",
-    "            icon_size=(120, 30),\n",
-    "            icon_anchor=(0, 0),\n",
-    "            html=f'<div style=\"font-size: 18pt; color: black;\">{row[\"지점\"]}</div>'\n",
-    "        )\n",
-    "    ).add_to(m)\n",
-    "\n",
-    "m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import Counter\n",
-    "\n",
-    "seoul= pd.read_feather(\"../data/data_for_modeling/df_seoul.feather\")\n",
-    "busan= pd.read_feather(\"../data/data_for_modeling/df_busan.feather\")\n",
-    "incheon= pd.read_feather(\"../data/data_for_modeling/df_incheon.feather\")\n",
-    "daegu = pd.read_feather(\"../data/data_for_modeling/df_daegu.feather\")\n",
-    "daejeon = pd.read_feather(\"../data/data_for_modeling/df_daejeon.feather\")\n",
-    "gwangju = pd.read_feather(\"../data/data_for_modeling/df_gwangju.feather\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "seoul = seoul.loc[seoul['year'].isin([2018, 2019, 2020, 2021]), :]\n",
-    "busan = busan.loc[busan['year'].isin([2018, 2019, 2020, 2021]), :]\n",
-    "incheon = incheon.loc[incheon['year'].isin([2018, 2019, 2020, 2021]), :]\n",
-    "daegu = daegu.loc[daegu['year'].isin([2018, 2019, 2020, 2021]), :]\n",
-    "daejeon = daejeon.loc[daejeon['year'].isin([2018, 2019, 2020, 2021]), :]\n",
-    "gwangju = gwangju.loc[gwangju['year'].isin([2018, 2019, 2020, 2021]), :]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "seoul :\n",
-      "multi_class\n",
-      "2    91.12\n",
-      "1     8.73\n",
-      "0     0.15\n",
-      "Name: count, dtype: float64\n",
-      "\n",
-      "busan :\n",
-      "multi_class\n",
-      "2    94.54\n",
-      "1     5.12\n",
-      "0     0.34\n",
-      "Name: count, dtype: float64\n",
-      "\n",
-      "incheon :\n",
-      "multi_class\n",
-      "2    83.46\n",
-      "1    14.54\n",
-      "0     2.00\n",
-      "Name: count, dtype: float64\n",
-      "\n",
-      "daegu :\n",
-      "multi_class\n",
-      "2    96.34\n",
-      "1     3.52\n",
-      "0     0.14\n",
-      "Name: count, dtype: float64\n",
-      "\n",
-      "daejeon :\n",
-      "multi_class\n",
-      "2    90.01\n",
-      "1     9.35\n",
-      "0     0.64\n",
-      "Name: count, dtype: float64\n",
-      "\n",
-      "gwangju :\n",
-      "multi_class\n",
-      "2    90.93\n",
-      "1     8.71\n",
-      "0     0.36\n",
-      "Name: count, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"seoul :\")\n",
-    "print(np.round(seoul['multi_class'].value_counts()*100 / seoul.shape[0],2))\n",
-    "print()\n",
-    "print('busan :')\n",
-    "print(np.round(busan['multi_class'].value_counts()*100 / busan.shape[0],2))\n",
-    "print()\n",
-    "print('incheon :')\n",
-    "print(np.round(incheon['multi_class'].value_counts()*100 / incheon.shape[0],2))\n",
-    "print()\n",
-    "print('daegu :')\n",
-    "print(np.round(daegu['multi_class'].value_counts()*100 / daegu.shape[0],2))\n",
-    "print()\n",
-    "print('daejeon :')\n",
-    "print(np.round(daejeon['multi_class'].value_counts()*100 / daejeon.shape[0],2))\n",
-    "print()\n",
-    "print('gwangju :')\n",
-    "print(np.round(gwangju['multi_class'].value_counts()*100 / gwangju.shape[0],2))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(35064, 31)"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "seoul.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "year\n",
-       "2020    8784\n",
-       "2018    8760\n",
-       "2019    8760\n",
-       "2021    8760\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "seoul['year'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Analysis_code/model_voting_test_best_sample/ensemble__voting_best_sample.ipynb b/Analysis_code/model_voting_test_best_sample/ensemble__voting_best_sample.ipynb
deleted file mode 100644
index 375cc1aa33f21f4a8579e70f99091c8582a272dc..0000000000000000000000000000000000000000
--- a/Analysis_code/model_voting_test_best_sample/ensemble__voting_best_sample.ipynb
+++ /dev/null
@@ -1,2642 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
-    "import torch\n",
-    "from torch.utils.data import DataLoader, TensorDataset\n",
-    "import random\n",
-    "from collections import Counter\n",
-    "import sys\n",
-    "sys.path.append('../../../../../../../../mnt/workspace/LightGBM/python-package')\n",
-    "from lightgbm import LGBMClassifier\n",
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.inspection import permutation_importance\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "from xgboost import XGBClassifier\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "import sys\n",
-    "sys.path.append('../')\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "import optuna\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import random\n",
-    "from ft_transformer import FTTransformer\n",
-    "from resnet_like import ResNetLike\n",
-    "from deepgbm import DeepGBM\n",
-    "from pytorch_tabnet.tab_model import TabNetClassifier\n",
-    "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Python 및 Numpy 시드 고정\n",
-    "seed = 42\n",
-    "random.seed(seed)\n",
-    "np.random.seed(seed)\n",
-    "\n",
-    "# PyTorch 시드 고정\n",
-    "torch.manual_seed(seed)\n",
-    "torch.cuda.manual_seed(seed)\n",
-    "torch.cuda.manual_seed_all(seed)  # Multi-GPU 환경에서 동일한 시드 적용\n",
-    "\n",
-    "# PyTorch 연산의 결정적 모드 설정\n",
-    "torch.backends.cudnn.deterministic = True  # 실행마다 동일한 결과를 보장\n",
-    "torch.backends.cudnn.benchmark = True  # 성능 최적화를 활성화 (가능한 한 빠른 연산 수행)\n",
-    "\n",
-    "# 전처리 함수\n",
-    "def preprocessing(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df['lm_cloudcover'] = df['lm_cloudcover'].astype('int')\n",
-    "    df['cloudcover'] = df['cloudcover'].astype('int')\n",
-    "    return df\n",
-    "\n",
-    "# 데이터셋 준비 함수\n",
-    "def prepare_dataset(region, data_sample='pure', target='multi', fold=3):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    return X_train, X_val, X_test, y_train, y_val, y_test, categorical_cols, numerical_cols\n",
-    "\n",
-    "# 데이터 변환 및 dataloader 생성 함수\n",
-    "def prepare_dataloader(region, data_sample='pure', target='multi', fold=3, random_state=None):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    # 연속형 변수와 범주형 변수 분리\n",
-    "    X_train_num = torch.tensor(X_train[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_train_cat = torch.tensor(X_train[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_val_num = torch.tensor(X_val[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_val_cat = torch.tensor(X_val[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_test_num = torch.tensor(X_test[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_test_cat = torch.tensor(X_test[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    # 레이블 변환\n",
-    "    if target == \"binary\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.float32)  # 이진 분류 → float32\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.float32)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32)\n",
-    "    elif target == \"multi\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.long)  # 다중 분류 → long\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.long)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.long)\n",
-    "    else:\n",
-    "        raise ValueError(\"target must be 'binary' or 'multi'\")\n",
-    "\n",
-    "    # TensorDataset 생성\n",
-    "    train_dataset = TensorDataset(X_train_num, X_train_cat, y_train_tensor)\n",
-    "    val_dataset = TensorDataset(X_val_num, X_val_cat, y_val_tensor)\n",
-    "    test_dataset = TensorDataset(X_test_num, X_test_cat, y_test_tensor)\n",
-    "\n",
-    "    # DataLoader 생성\n",
-    "    if random_state == None:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
-    "    else:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, generator=torch.Generator().manual_seed(random_state))\n",
-    "    val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n",
-    "    test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
-    "    \n",
-    "    return X_train, categorical_cols, numerical_cols, train_loader, val_loader, test_loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "# 디바이스 설정 (CUDA 사용 가능하면 GPU로, 아니면 CPU로)\n",
-    "import glob\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
-    "def calculate_csi(Y_test, pred):\n",
-    "\n",
-    "    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경\n",
-    "    # 혼동 행렬에서 H, F, M 추출\n",
-    "    H = (cm[0, 0] + cm[1, 1])\n",
-    "    \n",
-    "    F = (cm[1, 0] + cm[2, 0] +\n",
-    "         cm[0, 1] + cm[2, 1])\n",
-    "\n",
-    "    M = (cm[0, 2] + cm[1, 2])\n",
-    "    \n",
-    "    # CSI 계산\n",
-    "    CSI = H / (H + F + M + 1e-10)\n",
-    "    return CSI\n",
-    "\n",
-    "def csi_metric(y_true, pred):\n",
-    "    y_pred_binary = np.argmax(pred, axis=1)\n",
-    "    score = calculate_csi(y_true, y_pred_binary)\n",
-    "    return 'CSI', score, True  # higher_better=True\n",
-    "\n",
-    "\n",
-    "def eval_metric_csi(y_true, pred_prob):\n",
-    "\n",
-    "    pred = np.argmax(pred_prob, axis=1)\n",
-    "    y_true = y_true\n",
-    "    y_pred = pred\n",
-    "    csi = calculate_csi(y_true, y_pred)\n",
-    "    return -1*csi\n",
-    "\n",
-    "from sklearn.metrics import matthews_corrcoef, accuracy_score\n",
-    "\n",
-    "def multiclass_mcc(y_val, y_pred):\n",
-    "    \"\"\"\n",
-    "    다중 분류에서도 sklearn의 matthews_corrcoef를 그대로 사용할 수 있음.\n",
-    "    \"\"\"\n",
-    "    return matthews_corrcoef(y_val, y_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "# 디바이스 설정 (CUDA 사용 가능하면 GPU로, 아니면 CPU로)\n",
-    "import glob\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "\n",
-    "def calculate_csi(Y_test, pred):\n",
-    "\n",
-    "    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경\n",
-    "    # 혼동 행렬에서 H, F, M 추출\n",
-    "    H = (cm[0, 0] + cm[1, 1])\n",
-    "    \n",
-    "    F = (cm[1, 0] + cm[2, 0] +\n",
-    "         cm[0, 1] + cm[2, 1])\n",
-    "\n",
-    "    M = (cm[0, 2] + cm[1, 2])\n",
-    "    \n",
-    "    # CSI 계산\n",
-    "    CSI = H / (H + F + M + 1e-10)\n",
-    "    return CSI\n",
-    "\n",
-    "# Soft Voting 앙상블\n",
-    "def get_proba(region, model_choose, data_sample, fold, target='multi'):\n",
-    "    _, _, _, _,val_loader , test_loader = prepare_dataloader(region=region, data_sample=data_sample, target=target,fold=fold ,random_state=120)\n",
-    "    _ ,_,_ ,_ , y_val,_ ,_ ,_ = prepare_dataset(region=region, data_sample=data_sample, target=target, fold=fold)\n",
-    "\n",
-    "    folder_path = f'../save_model/{model_choose}/{data_sample}'\n",
-    "    model_paths = [path for path in glob.glob(f'{folder_path}/*.pth') if f'{region}' in path]\n",
-    "\n",
-    "    model = torch.load(model_paths[fold-1], weights_only=False).to(device)\n",
-    "    model.eval()\n",
-    "\n",
-    "    val_preds = []\n",
-    "\n",
-    "\n",
-    "    with torch.no_grad():\n",
-    "        for x_num_batch, x_cat_batch, _ in val_loader:\n",
-    "            output = model(x_num_batch.to(device), x_cat_batch.to(device))\n",
-    "            output = torch.softmax(output, dim=1)\n",
-    "            val_preds.extend(output.cpu().numpy())\n",
-    "\n",
-    "\n",
-    "    return val_preds, y_val\n",
-    "\n",
-    "\n",
-    "from sklearn.metrics import matthews_corrcoef\n",
-    "\n",
-    "def multiclass_mcc(y_val, y_pred):\n",
-    "    \"\"\"\n",
-    "    다중 분류에서도 sklearn의 matthews_corrcoef를 그대로 사용할 수 있음.\n",
-    "    \"\"\"\n",
-    "    return matthews_corrcoef(y_val, y_pred)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul = pd.read_csv(\"../../data/data_for_modeling/seoul_train.csv\")\n",
-    "df_seoul_test = pd.read_csv(\"../../data/data_for_modeling/seoul_test.csv\")\n",
-    "\n",
-    "df_busan = pd.read_csv(\"../../data/data_for_modeling/busan_train.csv\")\n",
-    "df_busan_test = pd.read_csv(\"../../data/data_for_modeling/busan_test.csv\")\n",
-    "\n",
-    "df_daegu = pd.read_csv(\"../../data/data_for_modeling/daegu_train.csv\")\n",
-    "df_daegu_test = pd.read_csv(\"../../data/data_for_modeling/daegu_test.csv\")\n",
-    "\n",
-    "df_daejeon = pd.read_csv(\"../../data/data_for_modeling/daejeon_train.csv\")\n",
-    "df_daejeon_test = pd.read_csv(\"../../data/data_for_modeling/daejeon_test.csv\")\n",
-    "\n",
-    "df_incheon = pd.read_csv(\"../../data/data_for_modeling/incheon_train.csv\")\n",
-    "df_incheon_test = pd.read_csv(\"../../data/data_for_modeling/incheon_test.csv\")\n",
-    "\n",
-    "df_gwangju = pd.read_csv(\"../../data/data_for_modeling/gwangju_train.csv\")\n",
-    "df_gwangju_test = pd.read_csv(\"../../data/data_for_modeling/gwangju_test.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocessing_df(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df['year'] = df['year'].astype('int')\n",
-    "    df['month'] = df['month'].astype('int')\n",
-    "    df['hour'] = df['hour'].astype('int')\n",
-    "    df['binary_class'] = df['binary_class'].astype('int')\n",
-    "    df['multi_class'] = df['multi_class'].astype('int')\n",
-    "\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',\n",
-    "       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',\n",
-    "       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',\n",
-    "       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',\n",
-    "       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',\n",
-    "       'month_sin', 'month_cos','multi_class']]\n",
-    "    return df\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul_test= preprocessing_df(df_seoul_test).copy()\n",
-    "df_busan_test= preprocessing_df(df_busan_test).copy()\n",
-    "df_daegu_test= preprocessing_df(df_daegu_test).copy()\n",
-    "df_gwangju_test= preprocessing_df(df_gwangju_test).copy()\n",
-    "df_daejeon_test= preprocessing_df(df_daejeon_test).copy()\n",
-    "df_incheon_test= preprocessing_df(df_incheon_test).copy()\n",
-    "\n",
-    "df_seoul= preprocessing_df(df_seoul).copy()\n",
-    "df_busan= preprocessing_df(df_busan).copy()\n",
-    "df_daegu= preprocessing_df(df_daegu).copy()\n",
-    "df_gwangju= preprocessing_df(df_gwangju).copy()\n",
-    "df_daejeon= preprocessing_df(df_daejeon).copy()\n",
-    "df_incheon= preprocessing_df(df_incheon).copy()\n",
-    "\n",
-    "df_seoul_test.drop(columns=['year'], inplace=True)\n",
-    "df_busan_test.drop(columns=['year'], inplace=True)\n",
-    "df_daegu_test.drop(columns=['year'], inplace=True)\n",
-    "df_daejeon_test.drop(columns=['year'], inplace=True)\n",
-    "df_incheon_test.drop(columns=['year'], inplace=True)\n",
-    "df_gwangju_test.drop(columns=['year'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import joblib\n",
-    "\n",
-    "lgb_seoul= joblib.load('../save_model/LGB_optima/lgb_seoul_smote.pkl')\n",
-    "lgb_busan= joblib.load('../save_model/LGB_optima/lgb_busan_smote.pkl')\n",
-    "lgb_incheon= joblib.load('../save_model/LGB_optima/lgb_incheon_smote.pkl')\n",
-    "lgb_daegu= joblib.load('../save_model/LGB_optima/lgb_daegu_smote.pkl')\n",
-    "lgb_daejeon= joblib.load('../save_model/LGB_optima/lgb_daejeon_smote.pkl')\n",
-    "lgb_gwangju= joblib.load('../save_model/LGB_optima/lgb_gwangju_smote.pkl')\n",
-    "\n",
-    "xgb_seoul= joblib.load('../save_model/XGB_optima/xgb_seoul_smote.pkl')\n",
-    "xgb_busan= joblib.load('../save_model/XGB_optima/xgb_busan_ctgan20000.pkl')\n",
-    "xgb_incheon= joblib.load('../save_model/XGB_optima/xgb_incheon_smote.pkl')\n",
-    "xgb_daegu= joblib.load('../save_model/XGB_optima/xgb_daegu_smote.pkl')\n",
-    "xgb_daejeon= joblib.load('../save_model/XGB_optima/xgb_daejeon_smote.pkl')\n",
-    "xgb_gwangju= joblib.load('../save_model/XGB_optima/xgb_gwangju_smote.pkl')\n",
-    "\n",
-    "lgb_seoul_1= lgb_seoul[0]\n",
-    "lgb_seoul_2= lgb_seoul[1]\n",
-    "lgb_seoul_3= lgb_seoul[2]\n",
-    "\n",
-    "lgb_busan_1= lgb_busan[0]\n",
-    "lgb_busan_2= lgb_busan[1]\n",
-    "lgb_busan_3= lgb_busan[2]\n",
-    "\n",
-    "lgb_incheon_1= lgb_incheon[0]\n",
-    "lgb_incheon_2= lgb_incheon[1]\n",
-    "lgb_incheon_3= lgb_incheon[2]\n",
-    "\n",
-    "lgb_daegu_1= lgb_daegu[0]\n",
-    "lgb_daegu_2= lgb_daegu[1]\n",
-    "lgb_daegu_3= lgb_daegu[2]\n",
-    "\n",
-    "lgb_daejeon_1= lgb_daejeon[0]\n",
-    "lgb_daejeon_2= lgb_daejeon[1]\n",
-    "lgb_daejeon_3= lgb_daejeon[2]\n",
-    "\n",
-    "lgb_gwangju_1= lgb_gwangju[0]\n",
-    "lgb_gwangju_2= lgb_gwangju[1]\n",
-    "lgb_gwangju_3= lgb_gwangju[2]\n",
-    "\n",
-    "\n",
-    "xgb_seoul_1= xgb_seoul[0]\n",
-    "xgb_seoul_2= xgb_seoul[1]\n",
-    "xgb_seoul_3= xgb_seoul[2]\n",
-    "\n",
-    "xgb_busan_1= xgb_busan[0]\n",
-    "xgb_busan_2= xgb_busan[1]\n",
-    "xgb_busan_3= xgb_busan[2]\n",
-    "\n",
-    "xgb_incheon_1= xgb_incheon[0]\n",
-    "xgb_incheon_2= xgb_incheon[1]\n",
-    "xgb_incheon_3= xgb_incheon[2]\n",
-    "\n",
-    "xgb_daegu_1= xgb_daegu[0]\n",
-    "xgb_daegu_2= xgb_daegu[1]\n",
-    "xgb_daegu_3= xgb_daegu[2]\n",
-    "\n",
-    "xgb_daejeon_1= xgb_daejeon[0]\n",
-    "xgb_daejeon_2= xgb_daejeon[1]\n",
-    "xgb_daejeon_3= xgb_daejeon[2]\n",
-    "\n",
-    "xgb_gwangju_1= xgb_gwangju[0]\n",
-    "xgb_gwangju_2= xgb_gwangju[1]\n",
-    "xgb_gwangju_3= xgb_gwangju[2]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **Soft Voting**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deepgbm=[] #DeepGBM\n",
-    "ft_transformer=[] #FT-transformer\n",
-    "resnet_like=[] #ResNet-like\n",
-    "XGBoost=[] #XGBoost\n",
-    "LightGBM=[] #LightGBM\n",
-    "val= []\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "\n",
-    "# 1 Fold\n",
-    "probas = []\n",
-    "val_preds, y_val = get_proba('seoul', 'deepgbm', 'ctgan20000', 1)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('seoul', 'resnet_like', 'smote', 1)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('seoul', 'ft_transformer', 'smote', 1)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul.loc[df_seoul['year'].isin([2018, 2019]), df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "XGBoost.append(xgb_seoul_1.predict_proba(X_val))\n",
-    "LightGBM.append(lgb_seoul_1.predict_proba(X_val))\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "probas = []\n",
-    "val_preds, y_val = get_proba('seoul', 'deepgbm', 'ctgan20000', 2)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('seoul', 'resnet_like', 'smote', 2)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('seoul', 'ft_transformer', 'smote', 2)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul.loc[df_seoul['year'].isin([2018, 2020]), df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "XGBoost.append(xgb_seoul_2.predict_proba(X_val))\n",
-    "LightGBM.append(lgb_seoul_2.predict_proba(X_val))\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "# 3 Fold\n",
-    "probas = []\n",
-    "val_preds, y_val = get_proba('seoul', 'deepgbm', 'ctgan20000', 3)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('seoul', 'resnet_like', 'smote', 3)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('seoul', 'ft_transformer', 'smote', 3)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul.loc[df_seoul['year'].isin([2019, 2020]), df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "XGBoost.append(xgb_seoul_3.predict_proba(X_val))\n",
-    "LightGBM.append(lgb_seoul_3.predict_proba(X_val))\n",
-    "val.append(Y_val)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "model = [deepgbm, ft_transformer, resnet_like, XGBoost, LightGBM]\n",
-    "model_name = [\"deepgbm\", \"ft_transformer\", \"resnet_like\", \"XGBoost\", \"LightGBM\"]\n",
-    "voting = []\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        soft = []\n",
-    "        hard = []\n",
-    "        mcc= []\n",
-    "        accuracy = []\n",
-    "        for k in range(3):\n",
-    "            result = []\n",
-    "            result.append(model[i][k])\n",
-    "            result.append(model[j][k])\n",
-    "            \n",
-    "            soft.append(calculate_csi(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            hard.append(calculate_csi(val[k],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "            mcc.append(multiclass_mcc(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            accuracy.append(accuracy_score(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            \n",
-    "        # 모델 조합과 함께 평균 CSI 저장\n",
-    "        voting.append((f\"{model_name[i]}+{model_name[j]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "        #voting.append((f\"{model_name[i]}+{model_name[j]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            soft = []\n",
-    "            hard = []\n",
-    "            mcc= []\n",
-    "            accuracy = []\n",
-    "            for l in range(3):\n",
-    "                result = []\n",
-    "                result.append(model[i][l])\n",
-    "                result.append(model[j][l])\n",
-    "                result.append(model[k][l])\n",
-    "\n",
-    "                soft.append(calculate_csi(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                hard.append(calculate_csi(val[l],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                mcc.append(multiclass_mcc(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                accuracy.append(accuracy_score(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                \n",
-    "            # 모델 조합과 함께 평균 CSI 저장\n",
-    "            voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "            #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                soft = []\n",
-    "                hard = []\n",
-    "                mcc= []\n",
-    "                accuracy = []\n",
-    "                for m in range(3):\n",
-    "                    result = []\n",
-    "                    result.append(model[i][m])\n",
-    "                    result.append(model[j][m])\n",
-    "                    result.append(model[k][m])\n",
-    "                    result.append(model[l][m])\n",
-    "               \n",
-    "                    soft.append(calculate_csi(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    hard.append(calculate_csi(val[m],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                    mcc.append(multiclass_mcc(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    accuracy.append(accuracy_score(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    \n",
-    "                # 모델 조합과 함께 평균 CSI 저장\n",
-    "                voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                for m in range(l+1, len(model)):\n",
-    "                    soft = []\n",
-    "                    hard = []\n",
-    "                    mcc= []\n",
-    "                    accuracy = []\n",
-    "                    for n in range(3):\n",
-    "                        result = []\n",
-    "                        result.append(model[i][n])\n",
-    "                        result.append(model[j][n])\n",
-    "                        result.append(model[k][n])\n",
-    "                        result.append(model[l][n])\n",
-    "                        result.append(model[m][n])\n",
-    "\n",
-    "                        soft.append(calculate_csi(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        hard.append(calculate_csi(val[n],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                        mcc.append(multiclass_mcc(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        accuracy.append(accuracy_score(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        \n",
-    "                    # 모델 조합과 함께 평균 CSI 저장\n",
-    "                    voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                    # voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "       \n",
-    "\n",
-    "            "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "      <th>region</th>\n",
-       "      <th>data_sample</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>deepgbm+ft_transformer</td>\n",
-       "      <td>0.699242</td>\n",
-       "      <td>0.804978</td>\n",
-       "      <td>0.961705</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>deepgbm+resnet_like</td>\n",
-       "      <td>0.709072</td>\n",
-       "      <td>0.813500</td>\n",
-       "      <td>0.963958</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>deepgbm+XGBoost</td>\n",
-       "      <td>0.669227</td>\n",
-       "      <td>0.782838</td>\n",
-       "      <td>0.957995</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>deepgbm+LightGBM</td>\n",
-       "      <td>0.693515</td>\n",
-       "      <td>0.801572</td>\n",
-       "      <td>0.962094</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>ft_transformer+resnet_like</td>\n",
-       "      <td>0.625273</td>\n",
-       "      <td>0.750936</td>\n",
-       "      <td>0.948804</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>ft_transformer+XGBoost</td>\n",
-       "      <td>0.614284</td>\n",
-       "      <td>0.740312</td>\n",
-       "      <td>0.947094</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>ft_transformer+LightGBM</td>\n",
-       "      <td>0.608569</td>\n",
-       "      <td>0.735933</td>\n",
-       "      <td>0.946637</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>resnet_like+XGBoost</td>\n",
-       "      <td>0.619427</td>\n",
-       "      <td>0.744657</td>\n",
-       "      <td>0.947822</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>resnet_like+LightGBM</td>\n",
-       "      <td>0.613685</td>\n",
-       "      <td>0.741096</td>\n",
-       "      <td>0.945773</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>XGBoost+LightGBM</td>\n",
-       "      <td>0.586111</td>\n",
-       "      <td>0.714906</td>\n",
-       "      <td>0.942197</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like</td>\n",
-       "      <td>0.682169</td>\n",
-       "      <td>0.794663</td>\n",
-       "      <td>0.959626</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost</td>\n",
-       "      <td>0.673033</td>\n",
-       "      <td>0.786757</td>\n",
-       "      <td>0.958448</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>deepgbm+ft_transformer+LightGBM</td>\n",
-       "      <td>0.676476</td>\n",
-       "      <td>0.790202</td>\n",
-       "      <td>0.959055</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>deepgbm+resnet_like+XGBoost</td>\n",
-       "      <td>0.678438</td>\n",
-       "      <td>0.791505</td>\n",
-       "      <td>0.959138</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>deepgbm+resnet_like+LightGBM</td>\n",
-       "      <td>0.677563</td>\n",
-       "      <td>0.791321</td>\n",
-       "      <td>0.959024</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>deepgbm+XGBoost+LightGBM</td>\n",
-       "      <td>0.632538</td>\n",
-       "      <td>0.754727</td>\n",
-       "      <td>0.951200</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>0.633231</td>\n",
-       "      <td>0.756134</td>\n",
-       "      <td>0.950061</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>0.630655</td>\n",
-       "      <td>0.754589</td>\n",
-       "      <td>0.949681</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>0.605288</td>\n",
-       "      <td>0.731968</td>\n",
-       "      <td>0.945577</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.610349</td>\n",
-       "      <td>0.736946</td>\n",
-       "      <td>0.946037</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>0.672997</td>\n",
-       "      <td>0.787585</td>\n",
-       "      <td>0.958111</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>0.671956</td>\n",
-       "      <td>0.787372</td>\n",
-       "      <td>0.958034</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>0.653134</td>\n",
-       "      <td>0.771641</td>\n",
-       "      <td>0.954997</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>deepgbm+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.649528</td>\n",
-       "      <td>0.769203</td>\n",
-       "      <td>0.953974</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.623331</td>\n",
-       "      <td>0.747511</td>\n",
-       "      <td>0.948277</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost+Lig...</td>\n",
-       "      <td>0.656912</td>\n",
-       "      <td>0.775223</td>\n",
-       "      <td>0.955378</td>\n",
-       "      <td>seoul</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                model       CSI       MCC  \\\n",
-       "0                              deepgbm+ft_transformer  0.699242  0.804978   \n",
-       "1                                 deepgbm+resnet_like  0.709072  0.813500   \n",
-       "2                                     deepgbm+XGBoost  0.669227  0.782838   \n",
-       "3                                    deepgbm+LightGBM  0.693515  0.801572   \n",
-       "4                          ft_transformer+resnet_like  0.625273  0.750936   \n",
-       "5                              ft_transformer+XGBoost  0.614284  0.740312   \n",
-       "6                             ft_transformer+LightGBM  0.608569  0.735933   \n",
-       "7                                 resnet_like+XGBoost  0.619427  0.744657   \n",
-       "8                                resnet_like+LightGBM  0.613685  0.741096   \n",
-       "9                                    XGBoost+LightGBM  0.586111  0.714906   \n",
-       "10                 deepgbm+ft_transformer+resnet_like  0.682169  0.794663   \n",
-       "11                     deepgbm+ft_transformer+XGBoost  0.673033  0.786757   \n",
-       "12                    deepgbm+ft_transformer+LightGBM  0.676476  0.790202   \n",
-       "13                        deepgbm+resnet_like+XGBoost  0.678438  0.791505   \n",
-       "14                       deepgbm+resnet_like+LightGBM  0.677563  0.791321   \n",
-       "15                           deepgbm+XGBoost+LightGBM  0.632538  0.754727   \n",
-       "16                 ft_transformer+resnet_like+XGBoost  0.633231  0.756134   \n",
-       "17                ft_transformer+resnet_like+LightGBM  0.630655  0.754589   \n",
-       "18                    ft_transformer+XGBoost+LightGBM  0.605288  0.731968   \n",
-       "19                       resnet_like+XGBoost+LightGBM  0.610349  0.736946   \n",
-       "20         deepgbm+ft_transformer+resnet_like+XGBoost  0.672997  0.787585   \n",
-       "21        deepgbm+ft_transformer+resnet_like+LightGBM  0.671956  0.787372   \n",
-       "22            deepgbm+ft_transformer+XGBoost+LightGBM  0.653134  0.771641   \n",
-       "23               deepgbm+resnet_like+XGBoost+LightGBM  0.649528  0.769203   \n",
-       "24        ft_transformer+resnet_like+XGBoost+LightGBM  0.623331  0.747511   \n",
-       "25  deepgbm+ft_transformer+resnet_like+XGBoost+Lig...  0.656912  0.775223   \n",
-       "\n",
-       "    Accuracy region data_sample  \n",
-       "0   0.961705  seoul        best  \n",
-       "1   0.963958  seoul        best  \n",
-       "2   0.957995  seoul        best  \n",
-       "3   0.962094  seoul        best  \n",
-       "4   0.948804  seoul        best  \n",
-       "5   0.947094  seoul        best  \n",
-       "6   0.946637  seoul        best  \n",
-       "7   0.947822  seoul        best  \n",
-       "8   0.945773  seoul        best  \n",
-       "9   0.942197  seoul        best  \n",
-       "10  0.959626  seoul        best  \n",
-       "11  0.958448  seoul        best  \n",
-       "12  0.959055  seoul        best  \n",
-       "13  0.959138  seoul        best  \n",
-       "14  0.959024  seoul        best  \n",
-       "15  0.951200  seoul        best  \n",
-       "16  0.950061  seoul        best  \n",
-       "17  0.949681  seoul        best  \n",
-       "18  0.945577  seoul        best  \n",
-       "19  0.946037  seoul        best  \n",
-       "20  0.958111  seoul        best  \n",
-       "21  0.958034  seoul        best  \n",
-       "22  0.954997  seoul        best  \n",
-       "23  0.953974  seoul        best  \n",
-       "24  0.948277  seoul        best  \n",
-       "25  0.955378  seoul        best  "
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df= pd.DataFrame(voting, columns=['model', 'CSI', 'MCC','Accuracy'])\n",
-    "df['region'] = 'seoul'\n",
-    "df['data_sample'] = 'best'\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deepgbm=[] #DeepGBM\n",
-    "ft_transformer=[] #FT-transformer\n",
-    "resnet_like=[] #ResNet-like\n",
-    "XGBoost=[] #XGBoost\n",
-    "LightGBM=[] #LightGBM\n",
-    "val= []\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba('busan', 'deepgbm', 'pure', 1)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('busan', 'resnet_like', 'ctgan10000', 1)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('busan', 'ft_transformer', 'pure', 1)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan.loc[df_busan['year'].isin([2018, 2019]), df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "LightGBM.append(lgb_busan_1.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_busan_1.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba('busan', 'deepgbm', 'pure', 2)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('busan', 'resnet_like', 'ctgan10000', 2)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('busan', 'ft_transformer', 'pure', 2)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan.loc[df_busan['year'].isin([2018, 2020]), df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "LightGBM.append(lgb_busan_2.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_busan_2.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba('busan', 'deepgbm', 'pure', 3)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('busan', 'resnet_like', 'ctgan10000', 3)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('busan', 'ft_transformer', 'pure', 3)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan.loc[df_busan['year'].isin([2019, 2020]), df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "LightGBM.append(lgb_busan_3.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_busan_3.predict_proba(X_val))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "model = [deepgbm, ft_transformer, resnet_like, XGBoost, LightGBM]\n",
-    "model_name = [\"deepgbm\", \"ft_transformer\", \"resnet_like\", \"XGBoost\", \"LightGBM\"]\n",
-    "voting = []\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        soft = []\n",
-    "        hard = []\n",
-    "        mcc= []\n",
-    "        accuracy= []\n",
-    "        for k in range(3):\n",
-    "            result = []\n",
-    "            result.append(model[i][k])\n",
-    "            result.append(model[j][k])\n",
-    "            \n",
-    "            soft.append(calculate_csi(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            hard.append(calculate_csi(val[k],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "            mcc.append(multiclass_mcc(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            accuracy.append(accuracy_score(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "        # 모델 조합과 함께 평균 CSI 저장\n",
-    "        voting.append((f\"{model_name[i]}+{model_name[j]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "        #voting.append((f\"{model_name[i]}+{model_name[j]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            soft = []\n",
-    "            hard = []\n",
-    "            mcc= []\n",
-    "            accuracy= []\n",
-    "            for l in range(3):\n",
-    "                result = []\n",
-    "                result.append(model[i][l])\n",
-    "                result.append(model[j][l])\n",
-    "                result.append(model[k][l])\n",
-    "\n",
-    "                soft.append(calculate_csi(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                hard.append(calculate_csi(val[l],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                mcc.append(multiclass_mcc(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                accuracy.append(accuracy_score(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            # 모델 조합과 함께 평균 CSI 저장\n",
-    "            voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "            #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                soft = []\n",
-    "                hard = []\n",
-    "                mcc= []\n",
-    "                accuracy= []\n",
-    "                for m in range(3):\n",
-    "                    result = []\n",
-    "                    result.append(model[i][m])\n",
-    "                    result.append(model[j][m])\n",
-    "                    result.append(model[k][m])\n",
-    "                    result.append(model[l][m])\n",
-    "               \n",
-    "                    soft.append(calculate_csi(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    hard.append(calculate_csi(val[m],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                    mcc.append(multiclass_mcc(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    accuracy.append(accuracy_score(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                # 모델 조합과 함께 평균 CSI 저장\n",
-    "                voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                for m in range(l+1, len(model)):\n",
-    "                    soft = []\n",
-    "                    hard = []\n",
-    "                    mcc= []\n",
-    "                    accuracy= []\n",
-    "                    for n in range(3):\n",
-    "                        result = []\n",
-    "                        result.append(model[i][n])\n",
-    "                        result.append(model[j][n])\n",
-    "                        result.append(model[k][n])\n",
-    "                        result.append(model[l][n])\n",
-    "                        result.append(model[m][n])\n",
-    "\n",
-    "                        soft.append(calculate_csi(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        hard.append(calculate_csi(val[n],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                        mcc.append(multiclass_mcc(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        accuracy.append(accuracy_score(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    # 모델 조합과 함께 평균 CSI 저장\n",
-    "                    voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                    # voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "       \n",
-    "\n",
-    "\n",
-    "\n",
-    "tm_df= pd.DataFrame(voting, columns=['model', 'CSI', 'MCC','Accuracy'])\n",
-    "tm_df['region'] = 'busan'\n",
-    "tm_df['data_sample'] = 'best'\n",
-    "\n",
-    "df = pd.concat([df, tm_df], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deepgbm=[] #DeepGBM\n",
-    "ft_transformer=[] #FT-transformer\n",
-    "resnet_like=[] #ResNet-like\n",
-    "XGBoost=[] #XGBoost\n",
-    "LightGBM=[] #LightGBM\n",
-    "val= []\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "\n",
-    "# 1 Fold\n",
-    "\n",
-    "val_preds, y_val = get_proba('incheon', 'deepgbm', 'pure', 1)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('incheon', 'resnet_like', 'smote', 1)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('incheon', 'ft_transformer', 'pure', 1)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon.loc[df_incheon['year'].isin([2018, 2019]), df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "LightGBM.append(lgb_incheon_1.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_incheon_1.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "\n",
-    "val_preds, y_val = get_proba('incheon', 'deepgbm', 'pure', 2)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('incheon', 'resnet_like', 'smote', 2)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('incheon', 'ft_transformer', 'pure', 2)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon.loc[df_incheon['year'].isin([2018, 2020]), df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "LightGBM.append(lgb_incheon_2.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_incheon_2.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "# 3 Fold\n",
-    "\n",
-    "val_preds, y_val = get_proba('incheon', 'deepgbm', 'pure', 3)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('incheon', 'resnet_like', 'smote', 3)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('incheon', 'ft_transformer', 'pure', 3)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon.loc[df_incheon['year'].isin([2019, 2020]), df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "LightGBM.append(lgb_incheon_3.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_incheon_3.predict_proba(X_val))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "model = [deepgbm, ft_transformer, resnet_like, XGBoost, LightGBM]\n",
-    "model_name = [\"deepgbm\", \"ft_transformer\", \"resnet_like\", \"XGBoost\", \"LightGBM\"]\n",
-    "voting = []\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        soft = []\n",
-    "        hard = []\n",
-    "        mcc= []\n",
-    "        accuracy = []\n",
-    "        for k in range(3):\n",
-    "            result = []\n",
-    "            result.append(model[i][k])\n",
-    "            result.append(model[j][k])\n",
-    "            \n",
-    "            soft.append(calculate_csi(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            hard.append(calculate_csi(val[k],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "            mcc.append(multiclass_mcc(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            accuracy.append(accuracy_score(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "        # 모델 조합과 함께 평균 CSI 저장\n",
-    "        voting.append((f\"{model_name[i]}+{model_name[j]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "        #voting.append((f\"{model_name[i]}+{model_name[j]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            soft = []\n",
-    "            hard = []\n",
-    "            mcc= []\n",
-    "            accuracy = []\n",
-    "            for l in range(3):\n",
-    "                result = []\n",
-    "                result.append(model[i][l])\n",
-    "                result.append(model[j][l])\n",
-    "                result.append(model[k][l])\n",
-    "\n",
-    "                soft.append(calculate_csi(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                hard.append(calculate_csi(val[l],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                mcc.append(multiclass_mcc(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                accuracy.append(accuracy_score(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            # 모델 조합과 함께 평균 CSI 저장\n",
-    "            voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "            #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                soft = []\n",
-    "                hard = []\n",
-    "                mcc= []\n",
-    "                accuracy = []\n",
-    "                for m in range(3):\n",
-    "                    result = []\n",
-    "                    result.append(model[i][m])\n",
-    "                    result.append(model[j][m])\n",
-    "                    result.append(model[k][m])\n",
-    "                    result.append(model[l][m])\n",
-    "               \n",
-    "                    soft.append(calculate_csi(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    hard.append(calculate_csi(val[m],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                    mcc.append(multiclass_mcc(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    accuracy.append(accuracy_score(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                # 모델 조합과 함께 평균 CSI 저장\n",
-    "                voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                for m in range(l+1, len(model)):\n",
-    "                    soft = []\n",
-    "                    hard = []\n",
-    "                    mcc= []\n",
-    "                    accuracy = []\n",
-    "                    for n in range(3):\n",
-    "                        result = []\n",
-    "                        result.append(model[i][n])\n",
-    "                        result.append(model[j][n])\n",
-    "                        result.append(model[k][n])\n",
-    "                        result.append(model[l][n])\n",
-    "                        result.append(model[m][n])\n",
-    "\n",
-    "                        soft.append(calculate_csi(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        hard.append(calculate_csi(val[n],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                        mcc.append(multiclass_mcc(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        accuracy.append(accuracy_score(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    # 모델 조합과 함께 평균 CSI 저장\n",
-    "                    voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                    # voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "       \n",
-    "\n",
-    "\n",
-    "\n",
-    "tm_df= pd.DataFrame(voting, columns=['model', 'CSI', 'MCC','Accuracy'])\n",
-    "tm_df['region'] = 'incheon'\n",
-    "tm_df['data_sample'] = 'best'\n",
-    "\n",
-    "df = pd.concat([df, tm_df], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deepgbm=[] #DeepGBM\n",
-    "ft_transformer=[] #FT-transformer\n",
-    "resnet_like=[] #ResNet-like\n",
-    "XGBoost=[] #XGBoost\n",
-    "LightGBM=[] #LightGBM\n",
-    "val= []\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba('daegu', 'deepgbm', 'smote', 1)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daegu', 'resnet_like', 'smote', 1)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daegu', 'ft_transformer', 'pure', 1)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu.loc[df_daegu['year'].isin([2018, 2019]), df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "LightGBM.append(lgb_daegu_1.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_daegu_1.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba('daegu', 'deepgbm', 'smote', 2)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daegu', 'resnet_like', 'smote', 2)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daegu', 'ft_transformer', 'pure', 2)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu.loc[df_daegu['year'].isin([2018, 2020]), df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "\n",
-    "LightGBM.append(lgb_daegu_2.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_daegu_2.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba('daegu', 'deepgbm', 'smote', 3)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daegu', 'resnet_like', 'smote', 3)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daegu', 'ft_transformer', 'pure', 3)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu.loc[df_daegu['year'].isin([2019, 2020]), df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "\n",
-    "LightGBM.append(lgb_daegu_3.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_daegu_3.predict_proba(X_val))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "model = [deepgbm, ft_transformer, resnet_like, XGBoost, LightGBM]\n",
-    "model_name = [\"deepgbm\", \"ft_transformer\", \"resnet_like\", \"XGBoost\", \"LightGBM\"]\n",
-    "voting = []\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        soft = []\n",
-    "        hard = []\n",
-    "        mcc= []\n",
-    "        accuracy= []\n",
-    "        for k in range(3):\n",
-    "            result = []\n",
-    "            result.append(model[i][k])\n",
-    "            result.append(model[j][k])\n",
-    "            \n",
-    "            soft.append(calculate_csi(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            hard.append(calculate_csi(val[k],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "            mcc.append(multiclass_mcc(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            accuracy.append(accuracy_score(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "        # 모델 조합과 함께 평균 CSI 저장\n",
-    "        voting.append((f\"{model_name[i]}+{model_name[j]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "        #voting.append((f\"{model_name[i]}+{model_name[j]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            soft = []\n",
-    "            hard = []\n",
-    "            mcc= []\n",
-    "            accuracy= []\n",
-    "            for l in range(3):\n",
-    "                result = []\n",
-    "                result.append(model[i][l])\n",
-    "                result.append(model[j][l])\n",
-    "                result.append(model[k][l])\n",
-    "\n",
-    "                soft.append(calculate_csi(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                hard.append(calculate_csi(val[l],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                mcc.append(multiclass_mcc(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                accuracy.append(accuracy_score(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            # 모델 조합과 함께 평균 CSI 저장\n",
-    "            voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "            #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                soft = []\n",
-    "                hard = []\n",
-    "                mcc= []\n",
-    "                accuracy= []\n",
-    "                for m in range(3):\n",
-    "                    result = []\n",
-    "                    result.append(model[i][m])\n",
-    "                    result.append(model[j][m])\n",
-    "                    result.append(model[k][m])\n",
-    "                    result.append(model[l][m])\n",
-    "               \n",
-    "                    soft.append(calculate_csi(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    hard.append(calculate_csi(val[m],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                    mcc.append(multiclass_mcc(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    accuracy.append(accuracy_score(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                # 모델 조합과 함께 평균 CSI 저장\n",
-    "                voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                for m in range(l+1, len(model)):\n",
-    "                    soft = []\n",
-    "                    hard = []\n",
-    "                    mcc= []\n",
-    "                    accuracy= []\n",
-    "                    for n in range(3):\n",
-    "                        result = []\n",
-    "                        result.append(model[i][n])\n",
-    "                        result.append(model[j][n])\n",
-    "                        result.append(model[k][n])\n",
-    "                        result.append(model[l][n])\n",
-    "                        result.append(model[m][n])\n",
-    "\n",
-    "                        soft.append(calculate_csi(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        hard.append(calculate_csi(val[n],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                        mcc.append(multiclass_mcc(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        accuracy.append(accuracy_score(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    # 모델 조합과 함께 평균 CSI 저장\n",
-    "                    voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                    # voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "       \n",
-    "\n",
-    "\n",
-    "\n",
-    "tm_df= pd.DataFrame(voting, columns=['model', 'CSI', 'MCC','Accuracy'])\n",
-    "tm_df['region'] = 'daegu'\n",
-    "tm_df['data_sample'] = 'best'\n",
-    "\n",
-    "df = pd.concat([df, tm_df], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deepgbm=[] #DeepGBM\n",
-    "ft_transformer=[] #FT-transformer\n",
-    "resnet_like=[] #ResNet-like\n",
-    "XGBoost=[] #XGBoost\n",
-    "LightGBM=[] #LightGBM\n",
-    "val= []\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "\n",
-    "# 1 Fold\n",
-    "\n",
-    "val_preds, y_val = get_proba('daejeon', 'deepgbm', 'pure', 1)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daejeon', 'resnet_like', 'pure', 1)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daejeon', 'ft_transformer', 'pure', 1)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon.loc[df_daejeon['year'].isin([2018, 2019]), df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "LightGBM.append(lgb_daejeon_1.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_daejeon_1.predict_proba(X_val))\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "# 2 Fold\n",
-    "\n",
-    "val_preds, y_val = get_proba('daejeon', 'deepgbm', 'pure', 2)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daejeon', 'resnet_like', 'pure', 2)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daejeon', 'ft_transformer', 'pure', 2)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon.loc[df_daejeon['year'].isin([2018, 2020]), df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "LightGBM.append(lgb_daejeon_2.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_daejeon_2.predict_proba(X_val))\n",
-    "val.append(Y_val)\n",
-    "\n",
-    "# 3 Fold\n",
-    "\n",
-    "val_preds, y_val = get_proba('daejeon', 'deepgbm', 'pure', 3)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daejeon', 'resnet_like', 'pure', 3)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('daejeon', 'ft_transformer', 'pure', 3)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon.loc[df_daejeon['year'].isin([2019, 2020]), df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "LightGBM.append(lgb_daejeon_3.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_daejeon_3.predict_proba(X_val))\n",
-    "val.append(Y_val)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "model = [deepgbm, ft_transformer, resnet_like, XGBoost, LightGBM]\n",
-    "model_name = [\"deepgbm\", \"ft_transformer\", \"resnet_like\", \"XGBoost\", \"LightGBM\"]\n",
-    "voting = []\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        soft = []\n",
-    "        hard = []\n",
-    "        mcc= []\n",
-    "        accuracy= []\n",
-    "        for k in range(3):\n",
-    "            result = []\n",
-    "            result.append(model[i][k])\n",
-    "            result.append(model[j][k])\n",
-    "            \n",
-    "            soft.append(calculate_csi(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            hard.append(calculate_csi(val[k],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "            mcc.append(multiclass_mcc(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            accuracy.append(accuracy_score(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "        # 모델 조합과 함께 평균 CSI 저장\n",
-    "        voting.append((f\"{model_name[i]}+{model_name[j]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "        #voting.append((f\"{model_name[i]}+{model_name[j]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            soft = []\n",
-    "            hard = []\n",
-    "            mcc= []\n",
-    "            accuracy= []\n",
-    "            for l in range(3):\n",
-    "                result = []\n",
-    "                result.append(model[i][l])\n",
-    "                result.append(model[j][l])\n",
-    "                result.append(model[k][l])\n",
-    "\n",
-    "                soft.append(calculate_csi(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                hard.append(calculate_csi(val[l],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                mcc.append(multiclass_mcc(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                accuracy.append(accuracy_score(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            # 모델 조합과 함께 평균 CSI 저장\n",
-    "            voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "            #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                soft = []\n",
-    "                hard = []\n",
-    "                mcc= []\n",
-    "                accuracy= []\n",
-    "                for m in range(3):\n",
-    "                    result = []\n",
-    "                    result.append(model[i][m])\n",
-    "                    result.append(model[j][m])\n",
-    "                    result.append(model[k][m])\n",
-    "                    result.append(model[l][m])\n",
-    "               \n",
-    "                    soft.append(calculate_csi(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    hard.append(calculate_csi(val[m],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                    mcc.append(multiclass_mcc(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    accuracy.append(accuracy_score(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                # 모델 조합과 함께 평균 CSI 저장\n",
-    "                voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                for m in range(l+1, len(model)):\n",
-    "                    soft = []\n",
-    "                    hard = []\n",
-    "                    mcc= []\n",
-    "                    accuracy= []\n",
-    "                    for n in range(3):\n",
-    "                        result = []\n",
-    "                        result.append(model[i][n])\n",
-    "                        result.append(model[j][n])\n",
-    "                        result.append(model[k][n])\n",
-    "                        result.append(model[l][n])\n",
-    "                        result.append(model[m][n])\n",
-    "\n",
-    "                        soft.append(calculate_csi(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        hard.append(calculate_csi(val[n],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                        mcc.append(multiclass_mcc(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        accuracy.append(accuracy_score(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    # 모델 조합과 함께 평균 CSI 저장\n",
-    "                    voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                    # voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "       \n",
-    "\n",
-    "\n",
-    "tm_df= pd.DataFrame(voting, columns=['model', 'CSI', 'MCC','Accuracy'])\n",
-    "tm_df['region'] = 'daejeon'\n",
-    "tm_df['data_sample'] = 'best'\n",
-    "\n",
-    "df = pd.concat([df, tm_df], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba('gwangju', 'deepgbm', 'pure', 1)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('gwangju', 'resnet_like', 'pure', 1)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('gwangju', 'ft_transformer', 'pure', 1)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju.loc[df_gwangju['year'].isin([2018, 2019]), df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "LightGBM.append(lgb_gwangju_1.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_gwangju_1.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba('gwangju', 'deepgbm', 'pure', 2)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('gwangju', 'resnet_like', 'pure', 2)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('gwangju', 'ft_transformer', 'pure', 2)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju.loc[df_gwangju['year'].isin([2018, 2020]), df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "LightGBM.append(lgb_gwangju_2.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_gwangju_2.predict_proba(X_val))\n",
-    "\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba('gwangju', 'deepgbm', 'pure', 3)\n",
-    "deepgbm.append(val_preds)\n",
-    "val_preds, y_val = get_proba('gwangju', 'resnet_like', 'pure', 3)\n",
-    "resnet_like.append(val_preds)\n",
-    "val_preds, y_val = get_proba('gwangju', 'ft_transformer', 'pure', 3)\n",
-    "ft_transformer.append(val_preds)\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju.loc[df_gwangju['year'].isin([2019, 2020]), df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "LightGBM.append(lgb_gwangju_3.predict_proba(X_val))\n",
-    "XGBoost.append(xgb_gwangju_3.predict_proba(X_val))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "model = [deepgbm, ft_transformer, resnet_like, XGBoost, LightGBM]\n",
-    "model_name = [\"deepgbm\", \"ft_transformer\", \"resnet_like\", \"XGBoost\", \"LightGBM\"]\n",
-    "voting = []\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        soft = []\n",
-    "        hard = []\n",
-    "        for k in range(3):\n",
-    "            result = []\n",
-    "            result.append(model[i][k])\n",
-    "            result.append(model[j][k])\n",
-    "            \n",
-    "            soft.append(calculate_csi(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            hard.append(calculate_csi(val[k],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "            mcc.append(multiclass_mcc(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            accuracy.append(accuracy_score(val[k], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "        # 모델 조합과 함께 평균 CSI 저장\n",
-    "        voting.append((f\"{model_name[i]}+{model_name[j]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "        #voting.append((f\"{model_name[i]}+{model_name[j]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            soft = []\n",
-    "            hard = []\n",
-    "            mcc= []\n",
-    "            accuracy = []\n",
-    "            for l in range(3):\n",
-    "                result = []\n",
-    "                result.append(model[i][l])\n",
-    "                result.append(model[j][l])\n",
-    "                result.append(model[k][l])\n",
-    "\n",
-    "                soft.append(calculate_csi(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                hard.append(calculate_csi(val[l],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                mcc.append(multiclass_mcc(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                accuracy.append(accuracy_score(val[l], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "            # 모델 조합과 함께 평균 CSI 저장\n",
-    "            voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "            #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                soft = []\n",
-    "                hard = []\n",
-    "                mcc= []\n",
-    "                accuracy = []\n",
-    "                for m in range(3):\n",
-    "                    result = []\n",
-    "                    result.append(model[i][m])\n",
-    "                    result.append(model[j][m])\n",
-    "                    result.append(model[k][m])\n",
-    "                    result.append(model[l][m])\n",
-    "               \n",
-    "                    soft.append(calculate_csi(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    hard.append(calculate_csi(val[m],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                    mcc.append(multiclass_mcc(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    accuracy.append(accuracy_score(val[m], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                # 모델 조합과 함께 평균 CSI 저장\n",
-    "                voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                #voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "\n",
-    "\n",
-    "for i in range(len(model)-1):\n",
-    "    for j in range(i+1, len(model)):\n",
-    "        for k in range(j+1, len(model)):\n",
-    "            for l in range(k+1, len(model)):\n",
-    "                for m in range(l+1, len(model)):\n",
-    "                    soft = []\n",
-    "                    hard = []\n",
-    "                    mcc= []\n",
-    "                    accuracy = []\n",
-    "                    for n in range(3):\n",
-    "                        result = []\n",
-    "                        result.append(model[i][n])\n",
-    "                        result.append(model[j][n])\n",
-    "                        result.append(model[k][n])\n",
-    "                        result.append(model[l][n])\n",
-    "                        result.append(model[m][n])\n",
-    "\n",
-    "                        soft.append(calculate_csi(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        hard.append(calculate_csi(val[n],mode(np.argmax(result, axis=2), axis=0).mode[0]))\n",
-    "                        mcc.append(multiclass_mcc(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                        accuracy.append(accuracy_score(val[n], np.argmax(np.mean(result, axis=0), axis=1)))\n",
-    "                    # 모델 조합과 함께 평균 CSI 저장\n",
-    "                    voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\", np.mean(soft), np.mean(mcc), np.mean(accuracy)))\n",
-    "                    # voting.append((f\"{model_name[i]}+{model_name[j]}+{model_name[k]}+{model_name[l]}+{model_name[m]}\"+\"_hard_voting\", np.mean(hard)))\n",
-    "       \n",
-    "\n",
-    "\n",
-    "\n",
-    "tm_df= pd.DataFrame(voting, columns=['model', 'CSI', 'MCC','Accuracy'])\n",
-    "tm_df['region'] = 'gwangju'\n",
-    "tm_df['data_sample'] = 'best'\n",
-    "\n",
-    "df = pd.concat([df, tm_df], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "      <th>region</th>\n",
-       "      <th>data_sample</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>deepgbm+XGBoost</td>\n",
-       "      <td>0.657407</td>\n",
-       "      <td>0.773279</td>\n",
-       "      <td>0.954158</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>deepgbm+LightGBM</td>\n",
-       "      <td>0.661543</td>\n",
-       "      <td>0.776346</td>\n",
-       "      <td>0.955109</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>ft_transformer+resnet_like</td>\n",
-       "      <td>0.587478</td>\n",
-       "      <td>0.712493</td>\n",
-       "      <td>0.944881</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>ft_transformer+XGBoost</td>\n",
-       "      <td>0.581913</td>\n",
-       "      <td>0.707980</td>\n",
-       "      <td>0.943359</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>ft_transformer+LightGBM</td>\n",
-       "      <td>0.580533</td>\n",
-       "      <td>0.707099</td>\n",
-       "      <td>0.943739</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>resnet_like+XGBoost</td>\n",
-       "      <td>0.562109</td>\n",
-       "      <td>0.692599</td>\n",
-       "      <td>0.938989</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>resnet_like+LightGBM</td>\n",
-       "      <td>0.555271</td>\n",
-       "      <td>0.686253</td>\n",
-       "      <td>0.938609</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>XGBoost+LightGBM</td>\n",
-       "      <td>0.528844</td>\n",
-       "      <td>0.663474</td>\n",
-       "      <td>0.932714</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like</td>\n",
-       "      <td>0.660600</td>\n",
-       "      <td>0.774370</td>\n",
-       "      <td>0.955334</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost</td>\n",
-       "      <td>0.649524</td>\n",
-       "      <td>0.765199</td>\n",
-       "      <td>0.953663</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>deepgbm+ft_transformer+LightGBM</td>\n",
-       "      <td>0.655685</td>\n",
-       "      <td>0.769959</td>\n",
-       "      <td>0.955032</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>deepgbm+resnet_like+XGBoost</td>\n",
-       "      <td>0.646377</td>\n",
-       "      <td>0.764436</td>\n",
-       "      <td>0.952637</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>deepgbm+resnet_like+LightGBM</td>\n",
-       "      <td>0.644172</td>\n",
-       "      <td>0.762363</td>\n",
-       "      <td>0.952752</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>deepgbm+XGBoost+LightGBM</td>\n",
-       "      <td>0.610494</td>\n",
-       "      <td>0.735516</td>\n",
-       "      <td>0.947010</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>0.592672</td>\n",
-       "      <td>0.716951</td>\n",
-       "      <td>0.945338</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>0.586850</td>\n",
-       "      <td>0.712067</td>\n",
-       "      <td>0.944805</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>0.569775</td>\n",
-       "      <td>0.697854</td>\n",
-       "      <td>0.940813</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.556842</td>\n",
-       "      <td>0.687964</td>\n",
-       "      <td>0.938342</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>0.646448</td>\n",
-       "      <td>0.762862</td>\n",
-       "      <td>0.953283</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>0.643276</td>\n",
-       "      <td>0.760303</td>\n",
-       "      <td>0.953017</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>0.626859</td>\n",
-       "      <td>0.747025</td>\n",
-       "      <td>0.950128</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>deepgbm+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.617990</td>\n",
-       "      <td>0.741269</td>\n",
-       "      <td>0.948493</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.577877</td>\n",
-       "      <td>0.704685</td>\n",
-       "      <td>0.942678</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost+Lig...</td>\n",
-       "      <td>0.625171</td>\n",
-       "      <td>0.745410</td>\n",
-       "      <td>0.950090</td>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>deepgbm+ft_transformer</td>\n",
-       "      <td>0.679848</td>\n",
-       "      <td>0.767237</td>\n",
-       "      <td>0.954138</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>deepgbm+resnet_like</td>\n",
-       "      <td>0.665680</td>\n",
-       "      <td>0.771460</td>\n",
-       "      <td>0.954373</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>deepgbm+XGBoost</td>\n",
-       "      <td>0.657407</td>\n",
-       "      <td>0.771914</td>\n",
-       "      <td>0.954319</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>deepgbm+LightGBM</td>\n",
-       "      <td>0.661543</td>\n",
-       "      <td>0.772801</td>\n",
-       "      <td>0.954477</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>ft_transformer+resnet_like</td>\n",
-       "      <td>0.587478</td>\n",
-       "      <td>0.762750</td>\n",
-       "      <td>0.952878</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>ft_transformer+XGBoost</td>\n",
-       "      <td>0.581913</td>\n",
-       "      <td>0.754925</td>\n",
-       "      <td>0.951518</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>ft_transformer+LightGBM</td>\n",
-       "      <td>0.580533</td>\n",
-       "      <td>0.748947</td>\n",
-       "      <td>0.950546</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>resnet_like+XGBoost</td>\n",
-       "      <td>0.562109</td>\n",
-       "      <td>0.742686</td>\n",
-       "      <td>0.949262</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>resnet_like+LightGBM</td>\n",
-       "      <td>0.555271</td>\n",
-       "      <td>0.737043</td>\n",
-       "      <td>0.948196</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>XGBoost+LightGBM</td>\n",
-       "      <td>0.528844</td>\n",
-       "      <td>0.730355</td>\n",
-       "      <td>0.946789</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like</td>\n",
-       "      <td>0.660600</td>\n",
-       "      <td>0.774370</td>\n",
-       "      <td>0.955334</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost</td>\n",
-       "      <td>0.649524</td>\n",
-       "      <td>0.765199</td>\n",
-       "      <td>0.953663</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>deepgbm+ft_transformer+LightGBM</td>\n",
-       "      <td>0.655685</td>\n",
-       "      <td>0.769959</td>\n",
-       "      <td>0.955032</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>deepgbm+resnet_like+XGBoost</td>\n",
-       "      <td>0.646377</td>\n",
-       "      <td>0.764436</td>\n",
-       "      <td>0.952637</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>deepgbm+resnet_like+LightGBM</td>\n",
-       "      <td>0.644172</td>\n",
-       "      <td>0.762363</td>\n",
-       "      <td>0.952752</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>deepgbm+XGBoost+LightGBM</td>\n",
-       "      <td>0.610494</td>\n",
-       "      <td>0.735516</td>\n",
-       "      <td>0.947010</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>0.592672</td>\n",
-       "      <td>0.716951</td>\n",
-       "      <td>0.945338</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>0.586850</td>\n",
-       "      <td>0.712067</td>\n",
-       "      <td>0.944805</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>0.569775</td>\n",
-       "      <td>0.697854</td>\n",
-       "      <td>0.940813</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.556842</td>\n",
-       "      <td>0.687964</td>\n",
-       "      <td>0.938342</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost</td>\n",
-       "      <td>0.646448</td>\n",
-       "      <td>0.762862</td>\n",
-       "      <td>0.953283</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+LightGBM</td>\n",
-       "      <td>0.643276</td>\n",
-       "      <td>0.760303</td>\n",
-       "      <td>0.953017</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>deepgbm+ft_transformer+XGBoost+LightGBM</td>\n",
-       "      <td>0.626859</td>\n",
-       "      <td>0.747025</td>\n",
-       "      <td>0.950128</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>deepgbm+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.617990</td>\n",
-       "      <td>0.741269</td>\n",
-       "      <td>0.948493</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>ft_transformer+resnet_like+XGBoost+LightGBM</td>\n",
-       "      <td>0.577877</td>\n",
-       "      <td>0.704685</td>\n",
-       "      <td>0.942678</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>deepgbm+ft_transformer+resnet_like+XGBoost+Lig...</td>\n",
-       "      <td>0.625171</td>\n",
-       "      <td>0.745410</td>\n",
-       "      <td>0.950090</td>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>best</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                model       CSI       MCC  \\\n",
-       "2                                     deepgbm+XGBoost  0.657407  0.773279   \n",
-       "3                                    deepgbm+LightGBM  0.661543  0.776346   \n",
-       "4                          ft_transformer+resnet_like  0.587478  0.712493   \n",
-       "5                              ft_transformer+XGBoost  0.581913  0.707980   \n",
-       "6                             ft_transformer+LightGBM  0.580533  0.707099   \n",
-       "7                                 resnet_like+XGBoost  0.562109  0.692599   \n",
-       "8                                resnet_like+LightGBM  0.555271  0.686253   \n",
-       "9                                    XGBoost+LightGBM  0.528844  0.663474   \n",
-       "10                 deepgbm+ft_transformer+resnet_like  0.660600  0.774370   \n",
-       "11                     deepgbm+ft_transformer+XGBoost  0.649524  0.765199   \n",
-       "12                    deepgbm+ft_transformer+LightGBM  0.655685  0.769959   \n",
-       "13                        deepgbm+resnet_like+XGBoost  0.646377  0.764436   \n",
-       "14                       deepgbm+resnet_like+LightGBM  0.644172  0.762363   \n",
-       "15                           deepgbm+XGBoost+LightGBM  0.610494  0.735516   \n",
-       "16                 ft_transformer+resnet_like+XGBoost  0.592672  0.716951   \n",
-       "17                ft_transformer+resnet_like+LightGBM  0.586850  0.712067   \n",
-       "18                    ft_transformer+XGBoost+LightGBM  0.569775  0.697854   \n",
-       "19                       resnet_like+XGBoost+LightGBM  0.556842  0.687964   \n",
-       "20         deepgbm+ft_transformer+resnet_like+XGBoost  0.646448  0.762862   \n",
-       "21        deepgbm+ft_transformer+resnet_like+LightGBM  0.643276  0.760303   \n",
-       "22            deepgbm+ft_transformer+XGBoost+LightGBM  0.626859  0.747025   \n",
-       "23               deepgbm+resnet_like+XGBoost+LightGBM  0.617990  0.741269   \n",
-       "24        ft_transformer+resnet_like+XGBoost+LightGBM  0.577877  0.704685   \n",
-       "25  deepgbm+ft_transformer+resnet_like+XGBoost+Lig...  0.625171  0.745410   \n",
-       "0                              deepgbm+ft_transformer  0.679848  0.767237   \n",
-       "1                                 deepgbm+resnet_like  0.665680  0.771460   \n",
-       "2                                     deepgbm+XGBoost  0.657407  0.771914   \n",
-       "3                                    deepgbm+LightGBM  0.661543  0.772801   \n",
-       "4                          ft_transformer+resnet_like  0.587478  0.762750   \n",
-       "5                              ft_transformer+XGBoost  0.581913  0.754925   \n",
-       "6                             ft_transformer+LightGBM  0.580533  0.748947   \n",
-       "7                                 resnet_like+XGBoost  0.562109  0.742686   \n",
-       "8                                resnet_like+LightGBM  0.555271  0.737043   \n",
-       "9                                    XGBoost+LightGBM  0.528844  0.730355   \n",
-       "10                 deepgbm+ft_transformer+resnet_like  0.660600  0.774370   \n",
-       "11                     deepgbm+ft_transformer+XGBoost  0.649524  0.765199   \n",
-       "12                    deepgbm+ft_transformer+LightGBM  0.655685  0.769959   \n",
-       "13                        deepgbm+resnet_like+XGBoost  0.646377  0.764436   \n",
-       "14                       deepgbm+resnet_like+LightGBM  0.644172  0.762363   \n",
-       "15                           deepgbm+XGBoost+LightGBM  0.610494  0.735516   \n",
-       "16                 ft_transformer+resnet_like+XGBoost  0.592672  0.716951   \n",
-       "17                ft_transformer+resnet_like+LightGBM  0.586850  0.712067   \n",
-       "18                    ft_transformer+XGBoost+LightGBM  0.569775  0.697854   \n",
-       "19                       resnet_like+XGBoost+LightGBM  0.556842  0.687964   \n",
-       "20         deepgbm+ft_transformer+resnet_like+XGBoost  0.646448  0.762862   \n",
-       "21        deepgbm+ft_transformer+resnet_like+LightGBM  0.643276  0.760303   \n",
-       "22            deepgbm+ft_transformer+XGBoost+LightGBM  0.626859  0.747025   \n",
-       "23               deepgbm+resnet_like+XGBoost+LightGBM  0.617990  0.741269   \n",
-       "24        ft_transformer+resnet_like+XGBoost+LightGBM  0.577877  0.704685   \n",
-       "25  deepgbm+ft_transformer+resnet_like+XGBoost+Lig...  0.625171  0.745410   \n",
-       "\n",
-       "    Accuracy   region data_sample  \n",
-       "2   0.954158  daejeon        best  \n",
-       "3   0.955109  daejeon        best  \n",
-       "4   0.944881  daejeon        best  \n",
-       "5   0.943359  daejeon        best  \n",
-       "6   0.943739  daejeon        best  \n",
-       "7   0.938989  daejeon        best  \n",
-       "8   0.938609  daejeon        best  \n",
-       "9   0.932714  daejeon        best  \n",
-       "10  0.955334  daejeon        best  \n",
-       "11  0.953663  daejeon        best  \n",
-       "12  0.955032  daejeon        best  \n",
-       "13  0.952637  daejeon        best  \n",
-       "14  0.952752  daejeon        best  \n",
-       "15  0.947010  daejeon        best  \n",
-       "16  0.945338  daejeon        best  \n",
-       "17  0.944805  daejeon        best  \n",
-       "18  0.940813  daejeon        best  \n",
-       "19  0.938342  daejeon        best  \n",
-       "20  0.953283  daejeon        best  \n",
-       "21  0.953017  daejeon        best  \n",
-       "22  0.950128  daejeon        best  \n",
-       "23  0.948493  daejeon        best  \n",
-       "24  0.942678  daejeon        best  \n",
-       "25  0.950090  daejeon        best  \n",
-       "0   0.954138  gwangju        best  \n",
-       "1   0.954373  gwangju        best  \n",
-       "2   0.954319  gwangju        best  \n",
-       "3   0.954477  gwangju        best  \n",
-       "4   0.952878  gwangju        best  \n",
-       "5   0.951518  gwangju        best  \n",
-       "6   0.950546  gwangju        best  \n",
-       "7   0.949262  gwangju        best  \n",
-       "8   0.948196  gwangju        best  \n",
-       "9   0.946789  gwangju        best  \n",
-       "10  0.955334  gwangju        best  \n",
-       "11  0.953663  gwangju        best  \n",
-       "12  0.955032  gwangju        best  \n",
-       "13  0.952637  gwangju        best  \n",
-       "14  0.952752  gwangju        best  \n",
-       "15  0.947010  gwangju        best  \n",
-       "16  0.945338  gwangju        best  \n",
-       "17  0.944805  gwangju        best  \n",
-       "18  0.940813  gwangju        best  \n",
-       "19  0.938342  gwangju        best  \n",
-       "20  0.953283  gwangju        best  \n",
-       "21  0.953017  gwangju        best  \n",
-       "22  0.950128  gwangju        best  \n",
-       "23  0.948493  gwangju        best  \n",
-       "24  0.942678  gwangju        best  \n",
-       "25  0.950090  gwangju        best  "
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.tail(50)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv(\"../model_result/best_sample/ensemble_best_sample.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Analysis_code/models/best_resnet_model.pth b/Analysis_code/models/best_resnet_model.pth
deleted file mode 100644
index c5fa0cf08ec45984cb4363bbd8705a57c2a20d32..0000000000000000000000000000000000000000
--- a/Analysis_code/models/best_resnet_model.pth
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:38869206f39923ce83985e58f681ff776759b2f93917150cb8c093accdf7346e
-size 627842
diff --git a/Analysis_code/models/tabnet_model.zip b/Analysis_code/models/tabnet_model.zip
deleted file mode 100644
index 941be9fa3a12404e6ab39895af23d8707b346a59..0000000000000000000000000000000000000000
--- a/Analysis_code/models/tabnet_model.zip
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:77e0653fc91feb84f87c6f185f237cc2f05cb37b25633f7561292dc986d95e94
-size 499040
diff --git a/Analysis_code/optima/LGB_smote_busan.py b/Analysis_code/optima/LGB_smote_busan.py
deleted file mode 100644
index 3dad088abd72f837d49f84aab602176cf2ab3bfb..0000000000000000000000000000000000000000
--- a/Analysis_code/optima/LGB_smote_busan.py
+++ /dev/null
@@ -1,204 +0,0 @@
-
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-import pandas as pd
-import numpy as np
-from collections import Counter
-from xgboost import XGBClassifier
-from warnings import filterwarnings
-filterwarnings('ignore')
-from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix
-import matplotlib.pyplot as plt
-import sys
-sys.path.append('../../../../../../../../mnt/workspace/LightGBM/python-package')
-from lightgbm import LGBMClassifier
-
-def calculate_csi(Y_test, pred):
-
-    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경
-    # 혼동 행렬에서 H, F, M 추출
-    H = (cm[0, 0] + cm[1, 1])
-    
-    F = (cm[1, 0] + cm[2, 0] +
-         cm[0, 1] + cm[2, 1])
-
-    M = (cm[0, 2] + cm[1, 2])
-    
-    # CSI 계산
-    CSI = H / (H + F + M + 1e-10)
-    return CSI
-
-def csi_metric(y_true, pred):
-    y_pred_binary = np.argmax(pred, axis=1)
-    score = calculate_csi(y_true, y_pred_binary)
-    return 'CSI', score, True  # higher_better=True
-
-
-def eval_metric_csi(y_true, pred_prob):
-
-    pred = np.argmax(pred_prob, axis=1)
-    y_true = y_true
-    y_pred = pred
-    csi = calculate_csi(y_true, y_pred)
-    return -1*csi
-
-def preprocessing(df):
-    df = df[df.columns].copy()
-    df['year'] = df['year'].astype('int')
-    df['month'] = df['month'].astype('int')
-    df['hour'] = df['hour'].astype('int')
-    df['binary_class'] = df['binary_class'].astype('int')
-    df['multi_class'] = df['multi_class'].astype('int')
-
-    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
-    df['wind_dir'] = df['wind_dir'].astype('int')
-    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
-       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
-       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
-       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
-       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
-       'month_sin', 'month_cos','multi_class']]
-    return df
-
-df_busan = pd.read_csv("../../data/data_for_modeling/busan_train.csv")
-
-df_smote_busan_1 = pd.read_csv("../../data/data_oversampled/smote/smote_1_busan.csv")
-df_smote_busan_2 = pd.read_csv("../../data/data_oversampled/smote/smote_2_busan.csv")
-df_smote_busan_3 = pd.read_csv("../../data/data_oversampled/smote/smote_3_busan.csv")
-
-df_smote_busan_1 = preprocessing(df_smote_busan_1)
-df_smote_busan_2 = preprocessing(df_smote_busan_2)
-df_smote_busan_3 = preprocessing(df_smote_busan_3)
-
-df_busan = preprocessing(df_busan)
-
-
-from hyperopt import fmin, tpe, Trials, hp
-
-lgb_search_space = {'max_depth': hp.quniform('max_depth', 3, 20, 1), 
-                    'min_child_weight': hp.quniform('min_child_weight', 1, 10, 1),
-                    'num_leaves' : hp.quniform('num_leaves', 2,100,1),
-                    'subsample': hp.uniform('subsample', 0.3, 0.9),
-                    'learning_rate': hp.uniform('learning_rate', 0.01, 0.3)
-                    }
-
-def objective_func(search_space):
-    lgb_model = LGBMClassifier(
-        n_estimators=4000,
-        device='gpu',
-        objective='multiclassova',
-        random_state= 120,
-        early_stopping_rounds= 400,
-        max_depth= int(search_space['max_depth']),
-        min_child_weight= int(search_space['min_child_weight']),
-        learning_rate= search_space['learning_rate'],
-        subsample= search_space['subsample'],
-        num_leaves= int(search_space['num_leaves']),
-        verbose=-1
-    ) 
-
-    optima = []
-
-    # Fold 1
-    X_tr, X_val, Y_tr, Y_val = df_smote_busan_1.loc[df_smote_busan_1['year'].isin([2018, 2019]), df_smote_busan_1.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_smote_busan_1.loc[df_smote_busan_1['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_busan_2.loc[df_smote_busan_2['year'].isin([2018, 2020]), df_smote_busan_2.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_smote_busan_2.loc[df_smote_busan_2['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_busan_3.loc[df_smote_busan_3['year'].isin([2019, 2020]), df_smote_busan_3.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_smote_busan_3.loc[df_smote_busan_3['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    return -1*round(np.mean(optima),4)
-
-from hyperopt import fmin, tpe, Trials
-
-trials = Trials()
-
-# fmin()함수를 호출. max_evals지정된 횟수만큼 반복 후 목적함수의 최소값을 가지는 최적 입력값 추출.
-lgb_best = fmin(fn=objective_func,
-            space=lgb_search_space,
-            algo=tpe.suggest,
-            max_evals=150, 
-            trials=trials)
-
-
-
-model= []
-
-#fold 1
-X_tr, X_val, Y_tr, Y_val = df_smote_busan_1.loc[df_smote_busan_1['year'].isin([2018, 2019]), df_smote_busan_1.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_smote_busan_1.loc[df_smote_busan_1['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-#fold 2
-X_tr, X_val, Y_tr, Y_val = df_smote_busan_2.loc[df_smote_busan_2['year'].isin([2018, 2020]), df_smote_busan_2.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_smote_busan_2.loc[df_smote_busan_2['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-#fold 3
-X_tr, X_val, Y_tr, Y_val = df_smote_busan_3.loc[df_smote_busan_3['year'].isin([2019, 2020]), df_smote_busan_3.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_smote_busan_3.loc[df_smote_busan_3['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-
-import joblib
-
-joblib.dump(model, '../save_model/LGB_optima/lgb_busan_smote.pkl')
\ No newline at end of file
diff --git a/Analysis_code/optima/LGB_smote_daegu.py b/Analysis_code/optima/LGB_smote_daegu.py
deleted file mode 100644
index 42386a15773c6728c94afcd0b4a0c61cc2e684f2..0000000000000000000000000000000000000000
--- a/Analysis_code/optima/LGB_smote_daegu.py
+++ /dev/null
@@ -1,204 +0,0 @@
-
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-import pandas as pd
-import numpy as np
-from collections import Counter
-from xgboost import XGBClassifier
-from warnings import filterwarnings
-filterwarnings('ignore')
-from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix
-import matplotlib.pyplot as plt
-import sys
-sys.path.append('../../../../../../../../mnt/workspace/LightGBM/python-package')
-from lightgbm import LGBMClassifier
-
-def calculate_csi(Y_test, pred):
-
-    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경
-    # 혼동 행렬에서 H, F, M 추출
-    H = (cm[0, 0] + cm[1, 1])
-    
-    F = (cm[1, 0] + cm[2, 0] +
-         cm[0, 1] + cm[2, 1])
-
-    M = (cm[0, 2] + cm[1, 2])
-    
-    # CSI 계산
-    CSI = H / (H + F + M + 1e-10)
-    return CSI
-
-def csi_metric(y_true, pred):
-    y_pred_binary = np.argmax(pred, axis=1)
-    score = calculate_csi(y_true, y_pred_binary)
-    return 'CSI', score, True  # higher_better=True
-
-
-def eval_metric_csi(y_true, pred_prob):
-
-    pred = np.argmax(pred_prob, axis=1)
-    y_true = y_true
-    y_pred = pred
-    csi = calculate_csi(y_true, y_pred)
-    return -1*csi
-
-def preprocessing(df):
-    df = df[df.columns].copy()
-    df['year'] = df['year'].astype('int')
-    df['month'] = df['month'].astype('int')
-    df['hour'] = df['hour'].astype('int')
-    df['binary_class'] = df['binary_class'].astype('int')
-    df['multi_class'] = df['multi_class'].astype('int')
-
-    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
-    df['wind_dir'] = df['wind_dir'].astype('int')
-    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
-       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
-       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
-       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
-       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
-       'month_sin', 'month_cos','multi_class']]
-    return df
-
-df_daegu = pd.read_csv("../../data/data_for_modeling/daegu_train.csv")
-
-df_smote_daegu_1 = pd.read_csv("../../data/data_oversampled/smote/smote_1_daegu.csv")
-df_smote_daegu_2 = pd.read_csv("../../data/data_oversampled/smote/smote_2_daegu.csv")
-df_smote_daegu_3 = pd.read_csv("../../data/data_oversampled/smote/smote_3_daegu.csv")
-
-df_smote_daegu_1 = preprocessing(df_smote_daegu_1)
-df_smote_daegu_2 = preprocessing(df_smote_daegu_2)
-df_smote_daegu_3 = preprocessing(df_smote_daegu_3)
-
-df_daegu = preprocessing(df_daegu)
-
-
-from hyperopt import fmin, tpe, Trials, hp
-
-lgb_search_space = {'max_depth': hp.quniform('max_depth', 3, 20, 1), 
-                    'min_child_weight': hp.quniform('min_child_weight', 1, 10, 1),
-                    'num_leaves' : hp.quniform('num_leaves', 2,100,1),
-                    'subsample': hp.uniform('subsample', 0.3, 0.9),
-                    'learning_rate': hp.uniform('learning_rate', 0.01, 0.3)
-                    }
-
-def objective_func(search_space):
-    lgb_model = LGBMClassifier(
-        n_estimators=4000,
-        device='gpu',
-        objective='multiclassova',
-        random_state= 120,
-        early_stopping_rounds= 400,
-        max_depth= int(search_space['max_depth']),
-        min_child_weight= int(search_space['min_child_weight']),
-        learning_rate= search_space['learning_rate'],
-        subsample= search_space['subsample'],
-        num_leaves= int(search_space['num_leaves']),
-        verbose=-1
-    ) 
-
-    optima = []
-
-    # Fold 1
-    X_tr, X_val, Y_tr, Y_val = df_smote_daegu_1.loc[df_smote_daegu_1['year'].isin([2018, 2019]), df_smote_daegu_1.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_smote_daegu_1.loc[df_smote_daegu_1['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_daegu_2.loc[df_smote_daegu_2['year'].isin([2018, 2020]), df_smote_daegu_2.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_smote_daegu_2.loc[df_smote_daegu_2['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_daegu_3.loc[df_smote_daegu_3['year'].isin([2019, 2020]), df_smote_daegu_3.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_smote_daegu_3.loc[df_smote_daegu_3['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    return -1*round(np.mean(optima),4)
-
-from hyperopt import fmin, tpe, Trials
-
-trials = Trials()
-
-# fmin()함수를 호출. max_evals지정된 횟수만큼 반복 후 목적함수의 최소값을 가지는 최적 입력값 추출.
-lgb_best = fmin(fn=objective_func,
-            space=lgb_search_space,
-            algo=tpe.suggest,
-            max_evals=150, 
-            trials=trials)
-
-
-
-model= []
-
-#fold 1
-X_tr, X_val, Y_tr, Y_val = df_smote_daegu_1.loc[df_smote_daegu_1['year'].isin([2018, 2019]), df_smote_daegu_1.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_smote_daegu_1.loc[df_smote_daegu_1['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-#fold 2
-X_tr, X_val, Y_tr, Y_val = df_smote_daegu_2.loc[df_smote_daegu_2['year'].isin([2018, 2020]), df_smote_daegu_2.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_smote_daegu_2.loc[df_smote_daegu_2['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-#fold 3
-X_tr, X_val, Y_tr, Y_val = df_smote_daegu_3.loc[df_smote_daegu_3['year'].isin([2019, 2020]), df_smote_daegu_3.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_smote_daegu_3.loc[df_smote_daegu_3['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-
-import joblib
-
-joblib.dump(model, '../save_model/LGB_optima/lgb_daegu_smote.pkl')
\ No newline at end of file
diff --git a/Analysis_code/optima/LGB_smote_daejeon.py b/Analysis_code/optima/LGB_smote_daejeon.py
deleted file mode 100644
index 1cdd70862934091c9ace8f13a1e0144ed2e26c58..0000000000000000000000000000000000000000
--- a/Analysis_code/optima/LGB_smote_daejeon.py
+++ /dev/null
@@ -1,204 +0,0 @@
-
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-import pandas as pd
-import numpy as np
-from collections import Counter
-from xgboost import XGBClassifier
-from warnings import filterwarnings
-filterwarnings('ignore')
-from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix
-import matplotlib.pyplot as plt
-import sys
-sys.path.append('../../../../../../../../mnt/workspace/LightGBM/python-package')
-from lightgbm import LGBMClassifier
-
-def calculate_csi(Y_test, pred):
-
-    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경
-    # 혼동 행렬에서 H, F, M 추출
-    H = (cm[0, 0] + cm[1, 1])
-    
-    F = (cm[1, 0] + cm[2, 0] +
-         cm[0, 1] + cm[2, 1])
-
-    M = (cm[0, 2] + cm[1, 2])
-    
-    # CSI 계산
-    CSI = H / (H + F + M + 1e-10)
-    return CSI
-
-def csi_metric(y_true, pred):
-    y_pred_binary = np.argmax(pred, axis=1)
-    score = calculate_csi(y_true, y_pred_binary)
-    return 'CSI', score, True  # higher_better=True
-
-
-def eval_metric_csi(y_true, pred_prob):
-
-    pred = np.argmax(pred_prob, axis=1)
-    y_true = y_true
-    y_pred = pred
-    csi = calculate_csi(y_true, y_pred)
-    return -1*csi
-
-def preprocessing(df):
-    df = df[df.columns].copy()
-    df['year'] = df['year'].astype('int')
-    df['month'] = df['month'].astype('int')
-    df['hour'] = df['hour'].astype('int')
-    df['binary_class'] = df['binary_class'].astype('int')
-    df['multi_class'] = df['multi_class'].astype('int')
-
-    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
-    df['wind_dir'] = df['wind_dir'].astype('int')
-    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
-       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
-       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
-       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
-       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
-       'month_sin', 'month_cos','multi_class']]
-    return df
-
-df_daejeon = pd.read_csv("../../data/data_for_modeling/daejeon_train.csv")
-
-df_smote_daejeon_1 = pd.read_csv("../../data/data_oversampled/smote/smote_1_daejeon.csv")
-df_smote_daejeon_2 = pd.read_csv("../../data/data_oversampled/smote/smote_2_daejeon.csv")
-df_smote_daejeon_3 = pd.read_csv("../../data/data_oversampled/smote/smote_3_daejeon.csv")
-
-df_smote_daejeon_1 = preprocessing(df_smote_daejeon_1)
-df_smote_daejeon_2 = preprocessing(df_smote_daejeon_2)
-df_smote_daejeon_3 = preprocessing(df_smote_daejeon_3)
-
-df_daejeon = preprocessing(df_daejeon)
-
-
-from hyperopt import fmin, tpe, Trials, hp
-
-lgb_search_space = {'max_depth': hp.quniform('max_depth', 3, 20, 1), 
-                    'min_child_weight': hp.quniform('min_child_weight', 1, 10, 1),
-                    'num_leaves' : hp.quniform('num_leaves', 2,100,1),
-                    'subsample': hp.uniform('subsample', 0.3, 0.9),
-                    'learning_rate': hp.uniform('learning_rate', 0.01, 0.3)
-                    }
-
-def objective_func(search_space):
-    lgb_model = LGBMClassifier(
-        n_estimators=4000,
-        device='gpu',
-        objective='multiclassova',
-        random_state= 120,
-        early_stopping_rounds= 400,
-        max_depth= int(search_space['max_depth']),
-        min_child_weight= int(search_space['min_child_weight']),
-        learning_rate= search_space['learning_rate'],
-        subsample= search_space['subsample'],
-        num_leaves= int(search_space['num_leaves']),
-        verbose=-1
-    ) 
-
-    optima = []
-
-    # Fold 1
-    X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_1.loc[df_smote_daejeon_1['year'].isin([2018, 2019]), df_smote_daejeon_1.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_smote_daejeon_1.loc[df_smote_daejeon_1['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_2.loc[df_smote_daejeon_2['year'].isin([2018, 2020]), df_smote_daejeon_2.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_smote_daejeon_2.loc[df_smote_daejeon_2['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_3.loc[df_smote_daejeon_3['year'].isin([2019, 2020]), df_smote_daejeon_3.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_smote_daejeon_3.loc[df_smote_daejeon_3['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    return -1*round(np.mean(optima),4)
-
-from hyperopt import fmin, tpe, Trials
-
-trials = Trials()
-
-# fmin()함수를 호출. max_evals지정된 횟수만큼 반복 후 목적함수의 최소값을 가지는 최적 입력값 추출.
-lgb_best = fmin(fn=objective_func,
-            space=lgb_search_space,
-            algo=tpe.suggest,
-            max_evals=150, 
-            trials=trials)
-
-
-
-model= []
-
-#fold 1
-X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_1.loc[df_smote_daejeon_1['year'].isin([2018, 2019]), df_smote_daejeon_1.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_smote_daejeon_1.loc[df_smote_daejeon_1['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-#fold 2
-X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_2.loc[df_smote_daejeon_2['year'].isin([2018, 2020]), df_smote_daejeon_2.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_smote_daejeon_2.loc[df_smote_daejeon_2['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-#fold 3
-X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_3.loc[df_smote_daejeon_3['year'].isin([2019, 2020]), df_smote_daejeon_3.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_smote_daejeon_3.loc[df_smote_daejeon_3['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-
-import joblib
-
-joblib.dump(model, '../save_model/LGB_optima/lgb_daejeon_smote.pkl')
\ No newline at end of file
diff --git a/Analysis_code/optima/LGB_smote_gwangju.py b/Analysis_code/optima/LGB_smote_gwangju.py
deleted file mode 100644
index 5c2371a23c543a1df7fd0d8c19edb2eeb2eec5e6..0000000000000000000000000000000000000000
--- a/Analysis_code/optima/LGB_smote_gwangju.py
+++ /dev/null
@@ -1,204 +0,0 @@
-
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-import pandas as pd
-import numpy as np
-from collections import Counter
-from xgboost import XGBClassifier
-from warnings import filterwarnings
-filterwarnings('ignore')
-from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix
-import matplotlib.pyplot as plt
-import sys
-sys.path.append('../../../../../../../../mnt/workspace/LightGBM/python-package')
-from lightgbm import LGBMClassifier
-
-def calculate_csi(Y_test, pred):
-
-    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경
-    # 혼동 행렬에서 H, F, M 추출
-    H = (cm[0, 0] + cm[1, 1])
-    
-    F = (cm[1, 0] + cm[2, 0] +
-         cm[0, 1] + cm[2, 1])
-
-    M = (cm[0, 2] + cm[1, 2])
-    
-    # CSI 계산
-    CSI = H / (H + F + M + 1e-10)
-    return CSI
-
-def csi_metric(y_true, pred):
-    y_pred_binary = np.argmax(pred, axis=1)
-    score = calculate_csi(y_true, y_pred_binary)
-    return 'CSI', score, True  # higher_better=True
-
-
-def eval_metric_csi(y_true, pred_prob):
-
-    pred = np.argmax(pred_prob, axis=1)
-    y_true = y_true
-    y_pred = pred
-    csi = calculate_csi(y_true, y_pred)
-    return -1*csi
-
-def preprocessing(df):
-    df = df[df.columns].copy()
-    df['year'] = df['year'].astype('int')
-    df['month'] = df['month'].astype('int')
-    df['hour'] = df['hour'].astype('int')
-    df['binary_class'] = df['binary_class'].astype('int')
-    df['multi_class'] = df['multi_class'].astype('int')
-
-    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
-    df['wind_dir'] = df['wind_dir'].astype('int')
-    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
-       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
-       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
-       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
-       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
-       'month_sin', 'month_cos','multi_class']]
-    return df
-
-df_gwangju = pd.read_csv("../../data/data_for_modeling/gwangju_train.csv")
-
-df_smote_gwangju_1 = pd.read_csv("../../data/data_oversampled/smote/smote_1_gwangju.csv")
-df_smote_gwangju_2 = pd.read_csv("../../data/data_oversampled/smote/smote_2_gwangju.csv")
-df_smote_gwangju_3 = pd.read_csv("../../data/data_oversampled/smote/smote_3_gwangju.csv")
-
-df_smote_gwangju_1 = preprocessing(df_smote_gwangju_1)
-df_smote_gwangju_2 = preprocessing(df_smote_gwangju_2)
-df_smote_gwangju_3 = preprocessing(df_smote_gwangju_3)
-
-df_gwangju = preprocessing(df_gwangju)
-
-
-from hyperopt import fmin, tpe, Trials, hp
-
-lgb_search_space = {'max_depth': hp.quniform('max_depth', 3, 20, 1), 
-                    'min_child_weight': hp.quniform('min_child_weight', 1, 10, 1),
-                    'num_leaves' : hp.quniform('num_leaves', 2,100,1),
-                    'subsample': hp.uniform('subsample', 0.3, 0.9),
-                    'learning_rate': hp.uniform('learning_rate', 0.01, 0.3)
-                    }
-
-def objective_func(search_space):
-    lgb_model = LGBMClassifier(
-        n_estimators=4000,
-        device='gpu',
-        objective='multiclassova',
-        random_state= 120,
-        early_stopping_rounds= 400,
-        max_depth= int(search_space['max_depth']),
-        min_child_weight= int(search_space['min_child_weight']),
-        learning_rate= search_space['learning_rate'],
-        subsample= search_space['subsample'],
-        num_leaves= int(search_space['num_leaves']),
-        verbose=-1
-    ) 
-
-    optima = []
-
-    # Fold 1
-    X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_1.loc[df_smote_gwangju_1['year'].isin([2018, 2019]), df_smote_gwangju_1.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_smote_gwangju_1.loc[df_smote_gwangju_1['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_2.loc[df_smote_gwangju_2['year'].isin([2018, 2020]), df_smote_gwangju_2.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_smote_gwangju_2.loc[df_smote_gwangju_2['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_3.loc[df_smote_gwangju_3['year'].isin([2019, 2020]), df_smote_gwangju_3.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_smote_gwangju_3.loc[df_smote_gwangju_3['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    return -1*round(np.mean(optima),4)
-
-from hyperopt import fmin, tpe, Trials
-
-trials = Trials()
-
-# fmin()함수를 호출. max_evals지정된 횟수만큼 반복 후 목적함수의 최소값을 가지는 최적 입력값 추출.
-lgb_best = fmin(fn=objective_func,
-            space=lgb_search_space,
-            algo=tpe.suggest,
-            max_evals=150, 
-            trials=trials)
-
-
-
-model= []
-
-#fold 1
-X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_1.loc[df_smote_gwangju_1['year'].isin([2018, 2019]), df_smote_gwangju_1.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_smote_gwangju_1.loc[df_smote_gwangju_1['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-#fold 2
-X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_2.loc[df_smote_gwangju_2['year'].isin([2018, 2020]), df_smote_gwangju_2.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_smote_gwangju_2.loc[df_smote_gwangju_2['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-#fold 3
-X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_3.loc[df_smote_gwangju_3['year'].isin([2019, 2020]), df_smote_gwangju_3.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_smote_gwangju_3.loc[df_smote_gwangju_3['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-
-import joblib
-
-joblib.dump(model, '../save_model/LGB_optima/lgb_gwangju_smote.pkl')
\ No newline at end of file
diff --git a/Analysis_code/optima/LGB_smote_incheon.py b/Analysis_code/optima/LGB_smote_incheon.py
deleted file mode 100644
index dba7999d6353f9fd51f593dc016be2ca0b3c0cce..0000000000000000000000000000000000000000
--- a/Analysis_code/optima/LGB_smote_incheon.py
+++ /dev/null
@@ -1,202 +0,0 @@
-
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-import pandas as pd
-import numpy as np
-from collections import Counter
-from xgboost import XGBClassifier
-from warnings import filterwarnings
-filterwarnings('ignore')
-from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix
-import matplotlib.pyplot as plt
-import sys
-sys.path.append('../../../../../../../../mnt/workspace/LightGBM/python-package')
-from lightgbm import LGBMClassifier
-
-def calculate_csi(Y_test, pred):
-
-    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경
-    # 혼동 행렬에서 H, F, M 추출
-    H = (cm[0, 0] + cm[1, 1])
-    
-    F = (cm[1, 0] + cm[2, 0] +
-         cm[0, 1] + cm[2, 1])
-
-    M = (cm[0, 2] + cm[1, 2])
-    
-    # CSI 계산
-    CSI = H / (H + F + M + 1e-10)
-    return CSI
-
-def csi_metric(y_true, pred):
-    y_pred_binary = np.argmax(pred, axis=1)
-    score = calculate_csi(y_true, y_pred_binary)
-    return 'CSI', score, True  # higher_better=True
-
-
-def eval_metric_csi(y_true, pred_prob):
-
-    pred = np.argmax(pred_prob, axis=1)
-    y_true = y_true
-    y_pred = pred
-    csi = calculate_csi(y_true, y_pred)
-    return -1*csi
-
-def preprocessing(df):
-    df = df[df.columns].copy()
-    df['year'] = df['year'].astype('int')
-    df['month'] = df['month'].astype('int')
-    df['hour'] = df['hour'].astype('int')
-    df['binary_class'] = df['binary_class'].astype('int')
-    df['multi_class'] = df['multi_class'].astype('int')
-
-    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
-    df['wind_dir'] = df['wind_dir'].astype('int')
-    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
-       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
-       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
-       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
-       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
-       'month_sin', 'month_cos','multi_class']]
-    return df
-
-df_incheon = pd.read_csv("../../data/data_for_modeling/incheon_train.csv")
-
-df_smote_incheon_1 = pd.read_csv("../../data/data_oversampled/smote/smote_1_incheon.csv")
-df_smote_incheon_2 = pd.read_csv("../../data/data_oversampled/smote/smote_2_incheon.csv")
-df_smote_incheon_3 = pd.read_csv("../../data/data_oversampled/smote/smote_3_incheon.csv")
-
-df_smote_incheon_1 = preprocessing(df_smote_incheon_1)
-df_smote_incheon_2 = preprocessing(df_smote_incheon_2)
-df_smote_incheon_3 = preprocessing(df_smote_incheon_3)
-
-df_incheon = preprocessing(df_incheon)
-
-
-from hyperopt import fmin, tpe, Trials, hp
-
-lgb_search_space = {'max_depth': hp.quniform('max_depth', 3, 20, 1), 
-                    'min_child_weight': hp.quniform('min_child_weight', 1, 10, 1),
-                    'num_leaves' : hp.quniform('num_leaves', 2,100,1),
-                    'subsample': hp.uniform('subsample', 0.3, 0.9),
-                    'learning_rate': hp.uniform('learning_rate', 0.01, 0.3)
-                    }
-
-def objective_func(search_space):
-    lgb_model = LGBMClassifier(
-        n_estimators=4000,
-        device='gpu',
-        objective='multiclassova',
-        random_state= 120,
-        early_stopping_rounds= 400,
-        max_depth= int(search_space['max_depth']),
-        min_child_weight= int(search_space['min_child_weight']),
-        learning_rate= search_space['learning_rate'],
-        subsample= search_space['subsample'],
-        num_leaves= int(search_space['num_leaves']),
-        verbose=-1
-    ) 
-    optima = []
-    # Fold 1
-    X_tr, X_val, Y_tr, Y_val = df_smote_incheon_1.loc[df_smote_incheon_1['year'].isin([2018, 2019]), df_smote_incheon_1.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_smote_incheon_1.loc[df_smote_incheon_1['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_incheon_2.loc[df_smote_incheon_2['year'].isin([2018, 2020]), df_smote_incheon_2.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_smote_incheon_2.loc[df_smote_incheon_2['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_incheon_3.loc[df_smote_incheon_3['year'].isin([2019, 2020]), df_smote_incheon_3.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_smote_incheon_3.loc[df_smote_incheon_3['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    return -1*round(np.mean(optima),4)
-
-from hyperopt import fmin, tpe, Trials
-
-trials = Trials()
-
-# fmin()함수를 호출. max_evals지정된 횟수만큼 반복 후 목적함수의 최소값을 가지는 최적 입력값 추출.
-lgb_best = fmin(fn=objective_func,
-            space=lgb_search_space,
-            algo=tpe.suggest,
-            max_evals=150, 
-            trials=trials)
-
-
-
-model= []
-
-#fold 1
-X_tr, X_val, Y_tr, Y_val = df_smote_incheon_1.loc[df_smote_incheon_1['year'].isin([2018, 2019]), df_smote_incheon_1.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_smote_incheon_1.loc[df_smote_incheon_1['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-#fold 2
-X_tr, X_val, Y_tr, Y_val = df_smote_incheon_2.loc[df_smote_incheon_2['year'].isin([2018, 2020]), df_smote_incheon_2.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_smote_incheon_2.loc[df_smote_incheon_2['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-#fold 3
-X_tr, X_val, Y_tr, Y_val = df_smote_incheon_3.loc[df_smote_incheon_3['year'].isin([2019, 2020]), df_smote_incheon_3.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_smote_incheon_3.loc[df_smote_incheon_3['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-
-import joblib
-
-joblib.dump(model, '../save_model/LGB_optima/lgb_incheon_smote.pkl')
\ No newline at end of file
diff --git a/Analysis_code/optima/LGB_smote_seoul.py b/Analysis_code/optima/LGB_smote_seoul.py
deleted file mode 100644
index 7a08676cdc8ba306339c6156754eaba12d9a4129..0000000000000000000000000000000000000000
--- a/Analysis_code/optima/LGB_smote_seoul.py
+++ /dev/null
@@ -1,204 +0,0 @@
-
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-import pandas as pd
-import numpy as np
-from collections import Counter
-from xgboost import XGBClassifier
-from warnings import filterwarnings
-filterwarnings('ignore')
-from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix
-import matplotlib.pyplot as plt
-import sys
-sys.path.append('../../../../../../../../mnt/workspace/LightGBM/python-package')
-from lightgbm import LGBMClassifier
-
-def calculate_csi(Y_test, pred):
-
-    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경
-    # 혼동 행렬에서 H, F, M 추출
-    H = (cm[0, 0] + cm[1, 1])
-    
-    F = (cm[1, 0] + cm[2, 0] +
-         cm[0, 1] + cm[2, 1])
-
-    M = (cm[0, 2] + cm[1, 2])
-    
-    # CSI 계산
-    CSI = H / (H + F + M + 1e-10)
-    return CSI
-
-def csi_metric(y_true, pred):
-    y_pred_binary = np.argmax(pred, axis=1)
-    score = calculate_csi(y_true, y_pred_binary)
-    return 'CSI', score, True  # higher_better=True
-
-
-def eval_metric_csi(y_true, pred_prob):
-
-    pred = np.argmax(pred_prob, axis=1)
-    y_true = y_true
-    y_pred = pred
-    csi = calculate_csi(y_true, y_pred)
-    return -1*csi
-
-def preprocessing(df):
-    df = df[df.columns].copy()
-    df['year'] = df['year'].astype('int')
-    df['month'] = df['month'].astype('int')
-    df['hour'] = df['hour'].astype('int')
-    df['binary_class'] = df['binary_class'].astype('int')
-    df['multi_class'] = df['multi_class'].astype('int')
-
-    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
-    df['wind_dir'] = df['wind_dir'].astype('int')
-    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
-       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
-       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
-       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
-       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
-       'month_sin', 'month_cos','multi_class']]
-    return df
-
-df_seoul = pd.read_csv("../../data/data_for_modeling/seoul_train.csv")
-
-df_smote_seoul_1 = pd.read_csv("../../data/data_oversampled/smote/smote_1_seoul.csv")
-df_smote_seoul_2 = pd.read_csv("../../data/data_oversampled/smote/smote_2_seoul.csv")
-df_smote_seoul_3 = pd.read_csv("../../data/data_oversampled/smote/smote_3_seoul.csv")
-
-df_smote_seoul_1 = preprocessing(df_smote_seoul_1)
-df_smote_seoul_2 = preprocessing(df_smote_seoul_2)
-df_smote_seoul_3 = preprocessing(df_smote_seoul_3)
-
-df_seoul = preprocessing(df_seoul)
-
-
-from hyperopt import fmin, tpe, Trials, hp
-
-lgb_search_space = {'max_depth': hp.quniform('max_depth', 3, 20, 1), 
-                    'min_child_weight': hp.quniform('min_child_weight', 1, 10, 1),
-                    'num_leaves' : hp.quniform('num_leaves', 2,100,1),
-                    'subsample': hp.uniform('subsample', 0.3, 0.9),
-                    'learning_rate': hp.uniform('learning_rate', 0.01, 0.3)
-                    }
-
-def objective_func(search_space):
-    lgb_model = LGBMClassifier(
-        n_estimators=4000,
-        device='gpu',
-        objective='multiclassova',
-        random_state= 120,
-        early_stopping_rounds= 400,
-        max_depth= int(search_space['max_depth']),
-        min_child_weight= int(search_space['min_child_weight']),
-        learning_rate= search_space['learning_rate'],
-        subsample= search_space['subsample'],
-        num_leaves= int(search_space['num_leaves']),
-        verbose=-1
-    ) 
-
-    optima = []
-
-    # Fold 1
-    X_tr, X_val, Y_tr, Y_val = df_smote_seoul_1.loc[df_smote_seoul_1['year'].isin([2018, 2019]), df_smote_seoul_1.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_smote_seoul_1.loc[df_smote_seoul_1['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_seoul_2.loc[df_smote_seoul_2['year'].isin([2018, 2020]), df_smote_seoul_2.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_smote_seoul_2.loc[df_smote_seoul_2['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_seoul_3.loc[df_smote_seoul_3['year'].isin([2019, 2020]), df_smote_seoul_3.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_smote_seoul_3.loc[df_smote_seoul_3['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)
-    csi = calculate_csi(Y_val, lgb_model.predict(X_val))
-    optima.append(csi)
-
-    return -1*round(np.mean(optima),4)
-
-from hyperopt import fmin, tpe, Trials
-
-trials = Trials()
-
-# fmin()함수를 호출. max_evals지정된 횟수만큼 반복 후 목적함수의 최소값을 가지는 최적 입력값 추출.
-lgb_best = fmin(fn=objective_func,
-            space=lgb_search_space,
-            algo=tpe.suggest,
-            max_evals=150, 
-            trials=trials)
-
-
-
-model= []
-
-#fold 1
-X_tr, X_val, Y_tr, Y_val = df_smote_seoul_1.loc[df_smote_seoul_1['year'].isin([2018, 2019]), df_smote_seoul_1.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_smote_seoul_1.loc[df_smote_seoul_1['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-#fold 2
-X_tr, X_val, Y_tr, Y_val = df_smote_seoul_2.loc[df_smote_seoul_2['year'].isin([2018, 2020]), df_smote_seoul_2.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_smote_seoul_2.loc[df_smote_seoul_2['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-#fold 3
-X_tr, X_val, Y_tr, Y_val = df_smote_seoul_3.loc[df_smote_seoul_3['year'].isin([2019, 2020]), df_smote_seoul_3.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_smote_seoul_3.loc[df_smote_seoul_3['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-lgb_model = LGBMClassifier(
-    n_estimators=4000,
-    device='gpu',
-    objective='multiclassova',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    verbose= -1,
-    learning_rate= lgb_best['learning_rate'],
-    max_depth= int(lgb_best['max_depth']),
-    min_child_weight= int(lgb_best['min_child_weight']),
-    subsample= lgb_best['subsample'] 
-)
-lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)])
-model.append(lgb_model)
-
-
-import joblib
-
-joblib.dump(model, '../save_model/LGB_optima/lgb_seoul_smote.pkl')
\ No newline at end of file
diff --git a/Analysis_code/optima/XGB_ctgan20000_busan.py b/Analysis_code/optima/XGB_ctgan20000_busan.py
deleted file mode 100644
index 34a2440b4ef922bbc150246496f463b1f577bb5d..0000000000000000000000000000000000000000
--- a/Analysis_code/optima/XGB_ctgan20000_busan.py
+++ /dev/null
@@ -1,207 +0,0 @@
-
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-import pandas as pd
-import numpy as np
-from collections import Counter
-from xgboost import XGBClassifier
-from warnings import filterwarnings
-filterwarnings('ignore')
-from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix
-import matplotlib.pyplot as plt
-
-def calculate_csi(Y_test, pred):
-
-    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경
-    # 혼동 행렬에서 H, F, M 추출
-    H = (cm[0, 0] + cm[1, 1])
-    
-    F = (cm[1, 0] + cm[2, 0] +
-         cm[0, 1] + cm[2, 1])
-
-    M = (cm[0, 2] + cm[1, 2])
-    
-    # CSI 계산
-    CSI = H / (H + F + M + 1e-10)
-    return CSI
-
-def csi_metric(y_true, pred):
-    y_pred_binary = np.argmax(pred, axis=1)
-    score = calculate_csi(y_true, y_pred_binary)
-    return 'CSI', score, True  # higher_better=True
-
-
-def eval_metric_csi(y_true, pred_prob):
-
-    pred = np.argmax(pred_prob, axis=1)
-    y_true = y_true
-    y_pred = pred
-    csi = calculate_csi(y_true, y_pred)
-    return -1*csi
-
-def preprocessing(df):
-    df = df[df.columns].copy()
-    df['year'] = df['year'].astype('int')
-    df['month'] = df['month'].astype('int')
-    df['hour'] = df['hour'].astype('int')
-    df['binary_class'] = df['binary_class'].astype('int')
-    df['multi_class'] = df['multi_class'].astype('int')
-
-    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
-    df['wind_dir'] = df['wind_dir'].astype('int')
-    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
-       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
-       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
-       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
-       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
-       'month_sin', 'month_cos','multi_class']]
-    return df
-
-df_busan = pd.read_csv("../../data/data_for_modeling/busan_train.csv")
-
-df_ctgan20000_busan_1 = pd.read_csv("../../data/data_oversampled/ctgan20000/ctgan20000_1_busan.csv")
-df_ctgan20000_busan_2 = pd.read_csv("../../data/data_oversampled/ctgan20000/ctgan20000_2_busan.csv")
-df_ctgan20000_busan_3 = pd.read_csv("../../data/data_oversampled/ctgan20000/ctgan20000_3_busan.csv")
-
-df_ctgan20000_busan_1 = preprocessing(df_ctgan20000_busan_1)
-df_ctgan20000_busan_2 = preprocessing(df_ctgan20000_busan_2)
-df_ctgan20000_busan_3 = preprocessing(df_ctgan20000_busan_3)
-
-df_busan = preprocessing(df_busan)
-
-
-from hyperopt import fmin, tpe, Trials, hp
-
-xgb_search_space = {'max_depth': hp.quniform('max_depth', 3, 20, 1), 
-                    'min_child_weight': hp.quniform('min_child_weight', 1, 10, 1),
-                    'learning_rate': hp.uniform('learning_rate', 0.01, 0.3),
-                    'subsample': hp.uniform('subsample', 0.3, 0.9)
-                    }
-
-def objective_func(search_space):
-    xgb_model = XGBClassifier(
-        n_estimators=4000,
-        tree_method='hist',
-        device='cuda',
-        enable_categorical=True,
-        eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-        objective='multi:softprob',
-        random_state= 120,
-        early_stopping_rounds= 400,
-        max_depth= int(search_space['max_depth']),
-        min_child_weight= int(search_space['min_child_weight']),
-        learning_rate= search_space['learning_rate'],
-        subsample= search_space['subsample']
-    ) 
-
-    optima= []
-
-    # Fold 1
-    X_tr, X_val, Y_tr, Y_val = df_ctgan20000_busan_1.loc[df_ctgan20000_busan_1['year'].isin([2018, 2019]), df_ctgan20000_busan_1.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_ctgan20000_busan_1.loc[df_ctgan20000_busan_1['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-    csi = calculate_csi(Y_val, xgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_ctgan20000_busan_2.loc[df_ctgan20000_busan_2['year'].isin([2018, 2020]), df_ctgan20000_busan_2.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_ctgan20000_busan_2.loc[df_ctgan20000_busan_2['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-    csi = calculate_csi(Y_val, xgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_ctgan20000_busan_3.loc[df_ctgan20000_busan_3['year'].isin([2019, 2020]), df_ctgan20000_busan_3.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_ctgan20000_busan_3.loc[df_ctgan20000_busan_3['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-    csi = calculate_csi(Y_val, xgb_model.predict(X_val))
-    optima.append(csi)
-
-    return -1*round(np.mean(optima),4)
-
-from hyperopt import fmin, tpe, Trials
-
-trials = Trials()
-
-# fmin()함수를 호출. max_evals지정된 횟수만큼 반복 후 목적함수의 최소값을 가지는 최적 입력값 추출.
-xgb_best = fmin(fn=objective_func,
-            space=xgb_search_space,
-            algo=tpe.suggest,
-            max_evals=150, 
-            trials=trials)
-
-
-
-
-model= []
-
-#fold 1
-X_tr, X_val, Y_tr, Y_val = df_ctgan20000_busan_1.loc[df_ctgan20000_busan_1['year'].isin([2018, 2019]), df_ctgan20000_busan_1.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_ctgan20000_busan_1.loc[df_ctgan20000_busan_1['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-#fold 2
-X_tr, X_val, Y_tr, Y_val = df_ctgan20000_busan_2.loc[df_ctgan20000_busan_2['year'].isin([2018, 2020]), df_ctgan20000_busan_2.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_ctgan20000_busan_2.loc[df_ctgan20000_busan_2['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-#fold 3
-X_tr, X_val, Y_tr, Y_val = df_ctgan20000_busan_3.loc[df_ctgan20000_busan_3['year'].isin([2019, 2020]), df_ctgan20000_busan_3.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_ctgan20000_busan_3.loc[df_ctgan20000_busan_3['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-
-import joblib
-
-joblib.dump(model, '../save_model/XGB_optima/xgb_busan_ctgan20000.pkl')
\ No newline at end of file
diff --git a/Analysis_code/optima/XGB_smote_daegu.py b/Analysis_code/optima/XGB_smote_daegu.py
deleted file mode 100644
index 778caf9663c851d5b47817bd1f38a009ce0a538b..0000000000000000000000000000000000000000
--- a/Analysis_code/optima/XGB_smote_daegu.py
+++ /dev/null
@@ -1,209 +0,0 @@
-
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-import pandas as pd
-import numpy as np
-from collections import Counter
-from xgboost import XGBClassifier
-from warnings import filterwarnings
-filterwarnings('ignore')
-from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix
-import matplotlib.pyplot as plt
-
-def calculate_csi(Y_test, pred):
-
-    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경
-    # 혼동 행렬에서 H, F, M 추출
-    H = (cm[0, 0] + cm[1, 1])
-    
-    F = (cm[1, 0] + cm[2, 0] +
-         cm[0, 1] + cm[2, 1])
-
-    M = (cm[0, 2] + cm[1, 2])
-    
-    # CSI 계산
-    CSI = H / (H + F + M + 1e-10)
-    return CSI
-
-def csi_metric(y_true, pred):
-    y_pred_binary = np.argmax(pred, axis=1)
-    score = calculate_csi(y_true, y_pred_binary)
-    return 'CSI', score, True  # higher_better=True
-
-
-def eval_metric_csi(y_true, pred_prob):
-
-    pred = np.argmax(pred_prob, axis=1)
-    y_true = y_true
-    y_pred = pred
-    csi = calculate_csi(y_true, y_pred)
-    return -1*csi
-
-def preprocessing(df):
-    df = df[df.columns].copy()
-    df['year'] = df['year'].astype('int')
-    df['month'] = df['month'].astype('int')
-    df['hour'] = df['hour'].astype('int')
-    df['binary_class'] = df['binary_class'].astype('int')
-    df['multi_class'] = df['multi_class'].astype('int')
-
-    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
-    df['wind_dir'] = df['wind_dir'].astype('int')
-    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
-       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
-       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
-       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
-       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
-       'month_sin', 'month_cos','multi_class']]
-    return df
-
-df_daegu = pd.read_csv("../../data/data_for_modeling/daegu_train.csv")
-
-df_smote_daegu_1 = pd.read_csv("../../data/data_oversampled/smote/smote_1_daegu.csv")
-df_smote_daegu_2 = pd.read_csv("../../data/data_oversampled/smote/smote_2_daegu.csv")
-df_smote_daegu_3 = pd.read_csv("../../data/data_oversampled/smote/smote_3_daegu.csv")
-
-df_smote_daegu_1 = preprocessing(df_smote_daegu_1)
-df_smote_daegu_2 = preprocessing(df_smote_daegu_2)
-df_smote_daegu_3 = preprocessing(df_smote_daegu_3)
-
-df_daegu = preprocessing(df_daegu)
-
-
-from hyperopt import fmin, tpe, Trials, hp
-
-xgb_search_space = {'max_depth': hp.quniform('max_depth', 3, 20, 1), 
-                    'min_child_weight': hp.quniform('min_child_weight', 1, 10, 1),
-                    'learning_rate': hp.uniform('learning_rate', 0.01, 0.3),
-                    'subsample': hp.uniform('subsample', 0.3, 0.9)
-                    }
-
-def objective_func(search_space):
-    xgb_model = XGBClassifier(
-        n_estimators=4000,
-        tree_method='hist',
-        device='cuda',
-        enable_categorical=True,
-        eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-        objective='multi:softprob',
-        random_state= 120,
-        early_stopping_rounds= 400,
-        max_depth= int(search_space['max_depth']),
-        min_child_weight= int(search_space['min_child_weight']),
-        learning_rate= search_space['learning_rate'],
-        subsample= search_space['subsample']
-    ) 
-
-    optima= []
-
-    # Fold 1
-    X_tr, X_val, Y_tr, Y_val = df_smote_daegu_1.loc[df_smote_daegu_1['year'].isin([2018, 2019]), df_smote_daegu_1.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_smote_daegu_1.loc[df_smote_daegu_1['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-    csi = calculate_csi(Y_val, xgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_daegu_2.loc[df_smote_daegu_2['year'].isin([2018, 2020]), df_smote_daegu_2.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_smote_daegu_2.loc[df_smote_daegu_2['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-    csi = calculate_csi(Y_val, xgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_daegu_3.loc[df_smote_daegu_3['year'].isin([2019, 2020]), df_smote_daegu_3.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_smote_daegu_3.loc[df_smote_daegu_3['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    optima.append(csi)
-
-
-    return -1*round(np.mean(optima),4)
-
-
-
-
-from hyperopt import fmin, tpe, Trials
-
-trials = Trials()
-
-# fmin()함수를 호출. max_evals지정된 횟수만큼 반복 후 목적함수의 최소값을 가지는 최적 입력값 추출.
-xgb_best = fmin(fn=objective_func,
-            space=xgb_search_space,
-            algo=tpe.suggest,
-            max_evals=150, 
-            trials=trials)
-
-
-
-model= []
-
-#fold 1
-X_tr, X_val, Y_tr, Y_val = df_smote_daegu_1.loc[df_smote_daegu_1['year'].isin([2018, 2019]), df_smote_daegu_1.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_smote_daegu_1.loc[df_smote_daegu_1['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-#fold 2
-X_tr, X_val, Y_tr, Y_val = df_smote_daegu_2.loc[df_smote_daegu_2['year'].isin([2018, 2020]), df_smote_daegu_2.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_smote_daegu_2.loc[df_smote_daegu_2['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-#fold 3
-X_tr, X_val, Y_tr, Y_val = df_smote_daegu_3.loc[df_smote_daegu_3['year'].isin([2019, 2020]), df_smote_daegu_3.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_smote_daegu_3.loc[df_smote_daegu_3['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-
-import joblib
-
-joblib.dump(model, '../save_model/XGB_optima/xgb_daegu_smote.pkl')
\ No newline at end of file
diff --git a/Analysis_code/optima/XGB_smote_daejeon.py b/Analysis_code/optima/XGB_smote_daejeon.py
deleted file mode 100644
index f3c8c13389b42f5990739920529bfc89d49d5919..0000000000000000000000000000000000000000
--- a/Analysis_code/optima/XGB_smote_daejeon.py
+++ /dev/null
@@ -1,209 +0,0 @@
-
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-import pandas as pd
-import numpy as np
-from collections import Counter
-from xgboost import XGBClassifier
-from warnings import filterwarnings
-filterwarnings('ignore')
-from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix
-import matplotlib.pyplot as plt
-
-def calculate_csi(Y_test, pred):
-
-    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경
-    # 혼동 행렬에서 H, F, M 추출
-    H = (cm[0, 0] + cm[1, 1])
-    
-    F = (cm[1, 0] + cm[2, 0] +
-         cm[0, 1] + cm[2, 1])
-
-    M = (cm[0, 2] + cm[1, 2])
-    
-    # CSI 계산
-    CSI = H / (H + F + M + 1e-10)
-    return CSI
-
-def csi_metric(y_true, pred):
-    y_pred_binary = np.argmax(pred, axis=1)
-    score = calculate_csi(y_true, y_pred_binary)
-    return 'CSI', score, True  # higher_better=True
-
-
-def eval_metric_csi(y_true, pred_prob):
-
-    pred = np.argmax(pred_prob, axis=1)
-    y_true = y_true
-    y_pred = pred
-    csi = calculate_csi(y_true, y_pred)
-    return -1*csi
-
-def preprocessing(df):
-    df = df[df.columns].copy()
-    df['year'] = df['year'].astype('int')
-    df['month'] = df['month'].astype('int')
-    df['hour'] = df['hour'].astype('int')
-    df['binary_class'] = df['binary_class'].astype('int')
-    df['multi_class'] = df['multi_class'].astype('int')
-
-    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
-    df['wind_dir'] = df['wind_dir'].astype('int')
-    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
-       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
-       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
-       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
-       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
-       'month_sin', 'month_cos','multi_class']]
-    return df
-
-df_daejeon = pd.read_csv("../../data/data_for_modeling/daejeon_train.csv")
-
-df_smote_daejeon_1 = pd.read_csv("../../data/data_oversampled/smote/smote_1_daejeon.csv")
-df_smote_daejeon_2 = pd.read_csv("../../data/data_oversampled/smote/smote_2_daejeon.csv")
-df_smote_daejeon_3 = pd.read_csv("../../data/data_oversampled/smote/smote_3_daejeon.csv")
-
-df_smote_daejeon_1 = preprocessing(df_smote_daejeon_1)
-df_smote_daejeon_2 = preprocessing(df_smote_daejeon_2)
-df_smote_daejeon_3 = preprocessing(df_smote_daejeon_3)
-
-df_daejeon = preprocessing(df_daejeon)
-
-
-from hyperopt import fmin, tpe, Trials, hp
-
-xgb_search_space = {'max_depth': hp.quniform('max_depth', 3, 20, 1), 
-                    'min_child_weight': hp.quniform('min_child_weight', 1, 10, 1),
-                    'learning_rate': hp.uniform('learning_rate', 0.01, 0.3),
-                    'subsample': hp.uniform('subsample', 0.3, 0.9)
-                    }
-
-def objective_func(search_space):
-    xgb_model = XGBClassifier(
-        n_estimators=4000,
-        tree_method='hist',
-        device='cuda',
-        enable_categorical=True,
-        eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-        objective='multi:softprob',
-        random_state= 120,
-        early_stopping_rounds= 400,
-        max_depth= int(search_space['max_depth']),
-        min_child_weight= int(search_space['min_child_weight']),
-        learning_rate= search_space['learning_rate'],
-        subsample= search_space['subsample']
-    ) 
-
-    optima= []
-
-    # Fold 1
-    X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_1.loc[df_smote_daejeon_1['year'].isin([2018, 2019]), df_smote_daejeon_1.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_smote_daejeon_1.loc[df_smote_daejeon_1['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-    csi = calculate_csi(Y_val, xgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_2.loc[df_smote_daejeon_2['year'].isin([2018, 2020]), df_smote_daejeon_2.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_smote_daejeon_2.loc[df_smote_daejeon_2['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-    csi = calculate_csi(Y_val, xgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_3.loc[df_smote_daejeon_3['year'].isin([2019, 2020]), df_smote_daejeon_3.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_smote_daejeon_3.loc[df_smote_daejeon_3['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    optima.append(csi)
-
-
-    return -1*round(np.mean(optima),4)
-
-
-
-
-from hyperopt import fmin, tpe, Trials
-
-trials = Trials()
-
-# fmin()함수를 호출. max_evals지정된 횟수만큼 반복 후 목적함수의 최소값을 가지는 최적 입력값 추출.
-xgb_best = fmin(fn=objective_func,
-            space=xgb_search_space,
-            algo=tpe.suggest,
-            max_evals=150, 
-            trials=trials)
-
-
-
-model= []
-
-#fold 1
-X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_1.loc[df_smote_daejeon_1['year'].isin([2018, 2019]), df_smote_daejeon_1.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_smote_daejeon_1.loc[df_smote_daejeon_1['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-#fold 2
-X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_2.loc[df_smote_daejeon_2['year'].isin([2018, 2020]), df_smote_daejeon_2.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_smote_daejeon_2.loc[df_smote_daejeon_2['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-#fold 3
-X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_3.loc[df_smote_daejeon_3['year'].isin([2019, 2020]), df_smote_daejeon_3.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_smote_daejeon_3.loc[df_smote_daejeon_3['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-
-import joblib
-
-joblib.dump(model, '../save_model/XGB_optima/xgb_daejeon_smote.pkl')
\ No newline at end of file
diff --git a/Analysis_code/optima/XGB_smote_gwangju.py b/Analysis_code/optima/XGB_smote_gwangju.py
deleted file mode 100644
index 6dc6c0efc65bacf4044a0f67fc6bb0c0ab170d58..0000000000000000000000000000000000000000
--- a/Analysis_code/optima/XGB_smote_gwangju.py
+++ /dev/null
@@ -1,209 +0,0 @@
-
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-import pandas as pd
-import numpy as np
-from collections import Counter
-from xgboost import XGBClassifier
-from warnings import filterwarnings
-filterwarnings('ignore')
-from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix
-import matplotlib.pyplot as plt
-
-def calculate_csi(Y_test, pred):
-
-    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경
-    # 혼동 행렬에서 H, F, M 추출
-    H = (cm[0, 0] + cm[1, 1])
-    
-    F = (cm[1, 0] + cm[2, 0] +
-         cm[0, 1] + cm[2, 1])
-
-    M = (cm[0, 2] + cm[1, 2])
-    
-    # CSI 계산
-    CSI = H / (H + F + M + 1e-10)
-    return CSI
-
-def csi_metric(y_true, pred):
-    y_pred_binary = np.argmax(pred, axis=1)
-    score = calculate_csi(y_true, y_pred_binary)
-    return 'CSI', score, True  # higher_better=True
-
-
-def eval_metric_csi(y_true, pred_prob):
-
-    pred = np.argmax(pred_prob, axis=1)
-    y_true = y_true
-    y_pred = pred
-    csi = calculate_csi(y_true, y_pred)
-    return -1*csi
-
-def preprocessing(df):
-    df = df[df.columns].copy()
-    df['year'] = df['year'].astype('int')
-    df['month'] = df['month'].astype('int')
-    df['hour'] = df['hour'].astype('int')
-    df['binary_class'] = df['binary_class'].astype('int')
-    df['multi_class'] = df['multi_class'].astype('int')
-
-    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
-    df['wind_dir'] = df['wind_dir'].astype('int')
-    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
-       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
-       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
-       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
-       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
-       'month_sin', 'month_cos','multi_class']]
-    return df
-
-df_gwangju = pd.read_csv("../../data/data_for_modeling/gwangju_train.csv")
-
-df_smote_gwangju_1 = pd.read_csv("../../data/data_oversampled/smote/smote_1_gwangju.csv")
-df_smote_gwangju_2 = pd.read_csv("../../data/data_oversampled/smote/smote_2_gwangju.csv")
-df_smote_gwangju_3 = pd.read_csv("../../data/data_oversampled/smote/smote_3_gwangju.csv")
-
-df_smote_gwangju_1 = preprocessing(df_smote_gwangju_1)
-df_smote_gwangju_2 = preprocessing(df_smote_gwangju_2)
-df_smote_gwangju_3 = preprocessing(df_smote_gwangju_3)
-
-df_gwangju = preprocessing(df_gwangju)
-
-
-from hyperopt import fmin, tpe, Trials, hp
-
-xgb_search_space = {'max_depth': hp.quniform('max_depth', 3, 20, 1), 
-                    'min_child_weight': hp.quniform('min_child_weight', 1, 10, 1),
-                    'learning_rate': hp.uniform('learning_rate', 0.01, 0.3),
-                    'subsample': hp.uniform('subsample', 0.3, 0.9)
-                    }
-
-def objective_func(search_space):
-    xgb_model = XGBClassifier(
-        n_estimators=4000,
-        tree_method='hist',
-        device='cuda',
-        enable_categorical=True,
-        eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-        objective='multi:softprob',
-        random_state= 120,
-        early_stopping_rounds= 400,
-        max_depth= int(search_space['max_depth']),
-        min_child_weight= int(search_space['min_child_weight']),
-        learning_rate= search_space['learning_rate'],
-        subsample= search_space['subsample']
-    ) 
-
-    optima= []
-
-    # Fold 1
-    X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_1.loc[df_smote_gwangju_1['year'].isin([2018, 2019]), df_smote_gwangju_1.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_smote_gwangju_1.loc[df_smote_gwangju_1['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-    csi = calculate_csi(Y_val, xgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_2.loc[df_smote_gwangju_2['year'].isin([2018, 2020]), df_smote_gwangju_2.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_smote_gwangju_2.loc[df_smote_gwangju_2['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-    csi = calculate_csi(Y_val, xgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_3.loc[df_smote_gwangju_3['year'].isin([2019, 2020]), df_smote_gwangju_3.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_smote_gwangju_3.loc[df_smote_gwangju_3['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    optima.append(csi)
-
-
-    return -1*round(np.mean(optima),4)
-
-
-
-
-from hyperopt import fmin, tpe, Trials
-
-trials = Trials()
-
-# fmin()함수를 호출. max_evals지정된 횟수만큼 반복 후 목적함수의 최소값을 가지는 최적 입력값 추출.
-xgb_best = fmin(fn=objective_func,
-            space=xgb_search_space,
-            algo=tpe.suggest,
-            max_evals=150, 
-            trials=trials)
-
-
-
-model= []
-
-#fold 1
-X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_1.loc[df_smote_gwangju_1['year'].isin([2018, 2019]), df_smote_gwangju_1.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_smote_gwangju_1.loc[df_smote_gwangju_1['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-#fold 2
-X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_2.loc[df_smote_gwangju_2['year'].isin([2018, 2020]), df_smote_gwangju_2.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_smote_gwangju_2.loc[df_smote_gwangju_2['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-#fold 3
-X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_3.loc[df_smote_gwangju_3['year'].isin([2019, 2020]), df_smote_gwangju_3.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_smote_gwangju_3.loc[df_smote_gwangju_3['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-
-import joblib
-
-joblib.dump(model, '../save_model/XGB_optima/xgb_gwangju_smote.pkl')
\ No newline at end of file
diff --git a/Analysis_code/optima/XGB_smote_incheon.py b/Analysis_code/optima/XGB_smote_incheon.py
deleted file mode 100644
index 0e84e34ca26bc68da5836db34e4f7bf00993db16..0000000000000000000000000000000000000000
--- a/Analysis_code/optima/XGB_smote_incheon.py
+++ /dev/null
@@ -1,209 +0,0 @@
-
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-import pandas as pd
-import numpy as np
-from collections import Counter
-from xgboost import XGBClassifier
-from warnings import filterwarnings
-filterwarnings('ignore')
-from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix
-import matplotlib.pyplot as plt
-
-def calculate_csi(Y_test, pred):
-
-    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경
-    # 혼동 행렬에서 H, F, M 추출
-    H = (cm[0, 0] + cm[1, 1])
-    
-    F = (cm[1, 0] + cm[2, 0] +
-         cm[0, 1] + cm[2, 1])
-
-    M = (cm[0, 2] + cm[1, 2])
-    
-    # CSI 계산
-    CSI = H / (H + F + M + 1e-10)
-    return CSI
-
-def csi_metric(y_true, pred):
-    y_pred_binary = np.argmax(pred, axis=1)
-    score = calculate_csi(y_true, y_pred_binary)
-    return 'CSI', score, True  # higher_better=True
-
-
-def eval_metric_csi(y_true, pred_prob):
-
-    pred = np.argmax(pred_prob, axis=1)
-    y_true = y_true
-    y_pred = pred
-    csi = calculate_csi(y_true, y_pred)
-    return -1*csi
-
-def preprocessing(df):
-    df = df[df.columns].copy()
-    df['year'] = df['year'].astype('int')
-    df['month'] = df['month'].astype('int')
-    df['hour'] = df['hour'].astype('int')
-    df['binary_class'] = df['binary_class'].astype('int')
-    df['multi_class'] = df['multi_class'].astype('int')
-
-    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
-    df['wind_dir'] = df['wind_dir'].astype('int')
-    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
-       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
-       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
-       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
-       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
-       'month_sin', 'month_cos','multi_class']]
-    return df
-
-df_incheon = pd.read_csv("../../data/data_for_modeling/incheon_train.csv")
-
-df_smote_incheon_1 = pd.read_csv("../../data/data_oversampled/smote/smote_1_incheon.csv")
-df_smote_incheon_2 = pd.read_csv("../../data/data_oversampled/smote/smote_2_incheon.csv")
-df_smote_incheon_3 = pd.read_csv("../../data/data_oversampled/smote/smote_3_incheon.csv")
-
-df_smote_incheon_1 = preprocessing(df_smote_incheon_1)
-df_smote_incheon_2 = preprocessing(df_smote_incheon_2)
-df_smote_incheon_3 = preprocessing(df_smote_incheon_3)
-
-df_incheon = preprocessing(df_incheon)
-
-
-from hyperopt import fmin, tpe, Trials, hp
-
-xgb_search_space = {'max_depth': hp.quniform('max_depth', 3, 20, 1), 
-                    'min_child_weight': hp.quniform('min_child_weight', 1, 10, 1),
-                    'learning_rate': hp.uniform('learning_rate', 0.01, 0.3),
-                    'subsample': hp.uniform('subsample', 0.3, 0.9)
-                    }
-
-def objective_func(search_space):
-    xgb_model = XGBClassifier(
-        n_estimators=4000,
-        tree_method='hist',
-        device='cuda',
-        enable_categorical=True,
-        eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-        objective='multi:softprob',
-        random_state= 120,
-        early_stopping_rounds= 400,
-        max_depth= int(search_space['max_depth']),
-        min_child_weight= int(search_space['min_child_weight']),
-        learning_rate= search_space['learning_rate'],
-        subsample= search_space['subsample']
-    ) 
-
-    optima= []
-
-    # Fold 1
-    X_tr, X_val, Y_tr, Y_val = df_smote_incheon_1.loc[df_smote_incheon_1['year'].isin([2018, 2019]), df_smote_incheon_1.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_smote_incheon_1.loc[df_smote_incheon_1['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-    csi = calculate_csi(Y_val, xgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_incheon_2.loc[df_smote_incheon_2['year'].isin([2018, 2020]), df_smote_incheon_2.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_smote_incheon_2.loc[df_smote_incheon_2['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-    csi = calculate_csi(Y_val, xgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_incheon_3.loc[df_smote_incheon_3['year'].isin([2019, 2020]), df_smote_incheon_3.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_smote_incheon_3.loc[df_smote_incheon_3['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    optima.append(csi)
-
-
-    return -1*round(np.mean(optima),4)
-
-
-
-
-from hyperopt import fmin, tpe, Trials
-
-trials = Trials()
-
-# fmin()함수를 호출. max_evals지정된 횟수만큼 반복 후 목적함수의 최소값을 가지는 최적 입력값 추출.
-xgb_best = fmin(fn=objective_func,
-            space=xgb_search_space,
-            algo=tpe.suggest,
-            max_evals=150, 
-            trials=trials)
-
-
-
-model= []
-
-#fold 1
-X_tr, X_val, Y_tr, Y_val = df_smote_incheon_1.loc[df_smote_incheon_1['year'].isin([2018, 2019]), df_smote_incheon_1.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_smote_incheon_1.loc[df_smote_incheon_1['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-#fold 2
-X_tr, X_val, Y_tr, Y_val = df_smote_incheon_2.loc[df_smote_incheon_2['year'].isin([2018, 2020]), df_smote_incheon_2.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_smote_incheon_2.loc[df_smote_incheon_2['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-#fold 3
-X_tr, X_val, Y_tr, Y_val = df_smote_incheon_3.loc[df_smote_incheon_3['year'].isin([2019, 2020]), df_smote_incheon_3.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_smote_incheon_3.loc[df_smote_incheon_3['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-
-import joblib
-
-joblib.dump(model, '../save_model/XGB_optima/xgb_incheon_smote.pkl')
\ No newline at end of file
diff --git a/Analysis_code/optima/XGB_smote_seoul.py b/Analysis_code/optima/XGB_smote_seoul.py
deleted file mode 100644
index d78a076cc1467e0118fb4c7bb66b594f341b3dab..0000000000000000000000000000000000000000
--- a/Analysis_code/optima/XGB_smote_seoul.py
+++ /dev/null
@@ -1,209 +0,0 @@
-
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
-import pandas as pd
-import numpy as np
-from collections import Counter
-from xgboost import XGBClassifier
-from warnings import filterwarnings
-filterwarnings('ignore')
-from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix
-import matplotlib.pyplot as plt
-
-def calculate_csi(Y_test, pred):
-
-    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경
-    # 혼동 행렬에서 H, F, M 추출
-    H = (cm[0, 0] + cm[1, 1])
-    
-    F = (cm[1, 0] + cm[2, 0] +
-         cm[0, 1] + cm[2, 1])
-
-    M = (cm[0, 2] + cm[1, 2])
-    
-    # CSI 계산
-    CSI = H / (H + F + M + 1e-10)
-    return CSI
-
-def csi_metric(y_true, pred):
-    y_pred_binary = np.argmax(pred, axis=1)
-    score = calculate_csi(y_true, y_pred_binary)
-    return 'CSI', score, True  # higher_better=True
-
-
-def eval_metric_csi(y_true, pred_prob):
-
-    pred = np.argmax(pred_prob, axis=1)
-    y_true = y_true
-    y_pred = pred
-    csi = calculate_csi(y_true, y_pred)
-    return -1*csi
-
-def preprocessing(df):
-    df = df[df.columns].copy()
-    df['year'] = df['year'].astype('int')
-    df['month'] = df['month'].astype('int')
-    df['hour'] = df['hour'].astype('int')
-    df['binary_class'] = df['binary_class'].astype('int')
-    df['multi_class'] = df['multi_class'].astype('int')
-
-    df.loc[df['wind_dir']=='정온', 'wind_dir'] = "0"
-    df['wind_dir'] = df['wind_dir'].astype('int')
-    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',
-       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',
-       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',
-       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',
-       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',
-       'month_sin', 'month_cos','multi_class']]
-    return df
-
-df_seoul = pd.read_csv("../../data/data_for_modeling/seoul_train.csv")
-
-df_smote_seoul_1 = pd.read_csv("../../data/data_oversampled/smote/smote_1_seoul.csv")
-df_smote_seoul_2 = pd.read_csv("../../data/data_oversampled/smote/smote_2_seoul.csv")
-df_smote_seoul_3 = pd.read_csv("../../data/data_oversampled/smote/smote_3_seoul.csv")
-
-df_smote_seoul_1 = preprocessing(df_smote_seoul_1)
-df_smote_seoul_2 = preprocessing(df_smote_seoul_2)
-df_smote_seoul_3 = preprocessing(df_smote_seoul_3)
-
-df_seoul = preprocessing(df_seoul)
-
-
-from hyperopt import fmin, tpe, Trials, hp
-
-xgb_search_space = {'max_depth': hp.quniform('max_depth', 3, 20, 1), 
-                    'min_child_weight': hp.quniform('min_child_weight', 1, 10, 1),
-                    'learning_rate': hp.uniform('learning_rate', 0.01, 0.3),
-                    'subsample': hp.uniform('subsample', 0.3, 0.9)
-                    }
-
-def objective_func(search_space):
-    xgb_model = XGBClassifier(
-        n_estimators=4000,
-        tree_method='hist',
-        device='cuda',
-        enable_categorical=True,
-        eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-        objective='multi:softprob',
-        random_state= 120,
-        early_stopping_rounds= 400,
-        max_depth= int(search_space['max_depth']),
-        min_child_weight= int(search_space['min_child_weight']),
-        learning_rate= search_space['learning_rate'],
-        subsample= search_space['subsample']
-    ) 
-
-    optima= []
-
-    # Fold 1
-    X_tr, X_val, Y_tr, Y_val = df_smote_seoul_1.loc[df_smote_seoul_1['year'].isin([2018, 2019]), df_smote_seoul_1.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_smote_seoul_1.loc[df_smote_seoul_1['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-    csi = calculate_csi(Y_val, xgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_seoul_2.loc[df_smote_seoul_2['year'].isin([2018, 2020]), df_smote_seoul_2.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_smote_seoul_2.loc[df_smote_seoul_2['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-    csi = calculate_csi(Y_val, xgb_model.predict(X_val))
-    optima.append(csi)
-
-    X_tr, X_val, Y_tr, Y_val = df_smote_seoul_3.loc[df_smote_seoul_3['year'].isin([2019, 2020]), df_smote_seoul_3.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_smote_seoul_3.loc[df_smote_seoul_3['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']
-    X_tr.drop(columns=['year'], inplace=True)
-    X_val.drop(columns=['year'], inplace=True)
-    optima.append(csi)
-
-
-    return -1*round(np.mean(optima),4)
-
-
-
-
-from hyperopt import fmin, tpe, Trials
-
-trials = Trials()
-
-# fmin()함수를 호출. max_evals지정된 횟수만큼 반복 후 목적함수의 최소값을 가지는 최적 입력값 추출.
-xgb_best = fmin(fn=objective_func,
-            space=xgb_search_space,
-            algo=tpe.suggest,
-            max_evals=150, 
-            trials=trials)
-
-
-
-model= []
-
-#fold 1
-X_tr, X_val, Y_tr, Y_val = df_smote_seoul_1.loc[df_smote_seoul_1['year'].isin([2018, 2019]), df_smote_seoul_1.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_smote_seoul_1.loc[df_smote_seoul_1['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-#fold 2
-X_tr, X_val, Y_tr, Y_val = df_smote_seoul_2.loc[df_smote_seoul_2['year'].isin([2018, 2020]), df_smote_seoul_2.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_smote_seoul_2.loc[df_smote_seoul_2['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-#fold 3
-X_tr, X_val, Y_tr, Y_val = df_smote_seoul_3.loc[df_smote_seoul_3['year'].isin([2019, 2020]), df_smote_seoul_3.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_smote_seoul_3.loc[df_smote_seoul_3['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']
-X_tr.drop(columns=['year'], inplace=True)
-X_val.drop(columns=['year'], inplace=True)
-
-xgb_model = XGBClassifier(
-    n_estimators=4000,
-    tree_method='hist',
-    device='cuda',
-    enable_categorical=True,
-    eval_metric=eval_metric_csi,  # 사용자 정의 평가 지표 사용
-    objective='multi:softprob',
-    random_state= 120,
-    early_stopping_rounds= 400,
-    learning_rate= xgb_best['learning_rate'],
-    max_depth= int(xgb_best['max_depth']),
-    min_child_weight= int(xgb_best['min_child_weight']),
-    subsample= xgb_best['subsample'] 
-)
-xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose= False)
-model.append(xgb_model)
-
-
-import joblib
-
-joblib.dump(model, '../save_model/XGB_optima/xgb_seoul_smote.pkl')
\ No newline at end of file
diff --git a/Analysis_code/resnet_like.py b/Analysis_code/resnet_like.py
deleted file mode 100644
index ddc815275174cef2b865c21aff16c9dd9747ac24..0000000000000000000000000000000000000000
--- a/Analysis_code/resnet_like.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-# ResNet-like Implementation
-class ResNetLike(nn.Module):
-    def __init__(self, num_classes, input_dim, d_main=128, d_hidden=64, n_blocks=4, dropout_first=0.25, dropout_second=0.0):
-        super(ResNetLike, self).__init__()
-
-        self.num_classes = num_classes  # 클래스 개수 저장
-
-        # Input layer
-        self.input_layer = nn.Linear(input_dim, d_main)
-
-        # Residual blocks
-        self.blocks = nn.ModuleList([
-            nn.Sequential(
-                nn.Linear(d_main, d_hidden),
-                nn.ReLU(),
-                nn.Dropout(dropout_first),
-                nn.Linear(d_hidden, d_main),
-                nn.Dropout(dropout_second)
-            ) for _ in range(n_blocks)
-        ])
-        
-        if num_classes == 2:
-            self.output_layer = nn.Linear(d_main, 1)  # Binary classification
-        elif num_classes > 2:
-            self.output_layer = nn.Linear(d_main, num_classes)  # Multi classification
-
-    def forward(self, x_num, x_cat):  # 두 개의 입력을 받음
-        x = torch.cat([x_num, x_cat], dim=1)  # 두 개를 하나로 합쳐서 모델에 전달
-        x = F.relu(self.input_layer(x))
-        
-        for block in self.blocks:
-            x = x + block(x)  # Residual connection
-        x = self.output_layer(x)
-        
-        return x
diff --git a/Analysis_code/sampling_data_test/deepgbm_sampled_data_test.ipynb b/Analysis_code/sampling_data_test/deepgbm_sampled_data_test.ipynb
deleted file mode 100644
index 7a6f9e04138412917bfc66ae5d4d0b9d62ae16cb..0000000000000000000000000000000000000000
--- a/Analysis_code/sampling_data_test/deepgbm_sampled_data_test.ipynb
+++ /dev/null
@@ -1,2885 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
-    "import torch\n",
-    "from torch.utils.data import DataLoader, TensorDataset\n",
-    "import random\n",
-    "\n",
-    "# Python 및 Numpy 시드 고정\n",
-    "seed = 42\n",
-    "random.seed(seed)\n",
-    "np.random.seed(seed)\n",
-    "\n",
-    "# PyTorch 시드 고정\n",
-    "torch.manual_seed(seed)\n",
-    "torch.cuda.manual_seed(seed)\n",
-    "torch.cuda.manual_seed_all(seed)  # Multi-GPU 환경에서 동일한 시드 적용\n",
-    "\n",
-    "# PyTorch 연산의 결정적 모드 설정\n",
-    "torch.backends.cudnn.deterministic = True  # 실행마다 동일한 결과를 보장\n",
-    "torch.backends.cudnn.benchmark = True  # 성능 최적화를 활성화 (가능한 한 빠른 연산 수행)\n",
-    "\n",
-    "# 전처리 함수\n",
-    "def preprocessing(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df['lm_cloudcover'] = df['lm_cloudcover'].astype('int')\n",
-    "    df['cloudcover'] = df['cloudcover'].astype('int')\n",
-    "    return df\n",
-    "\n",
-    "# 데이터셋 준비 함수\n",
-    "def prepare_dataset(region, data_sample='pure', target='multi', fold=3):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    return X_train, X_val, X_test, y_train, y_val, y_test, categorical_cols, numerical_cols\n",
-    "\n",
-    "# 데이터 변환 및 dataloader 생성 함수\n",
-    "def prepare_dataloader(region, data_sample='pure', target='multi', fold=3, random_state=None):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    # 연속형 변수와 범주형 변수 분리\n",
-    "    X_train_num = torch.tensor(X_train[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_train_cat = torch.tensor(X_train[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_val_num = torch.tensor(X_val[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_val_cat = torch.tensor(X_val[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_test_num = torch.tensor(X_test[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_test_cat = torch.tensor(X_test[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    # 레이블 변환\n",
-    "    if target == \"binary\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.float32)  # 이진 분류 → float32\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.float32)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32)\n",
-    "    elif target == \"multi\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.long)  # 다중 분류 → long\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.long)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.long)\n",
-    "    else:\n",
-    "        raise ValueError(\"target must be 'binary' or 'multi'\")\n",
-    "\n",
-    "    # TensorDataset 생성\n",
-    "    train_dataset = TensorDataset(X_train_num, X_train_cat, y_train_tensor)\n",
-    "    val_dataset = TensorDataset(X_val_num, X_val_cat, y_val_tensor)\n",
-    "    test_dataset = TensorDataset(X_test_num, X_test_cat, y_test_tensor)\n",
-    "\n",
-    "    # DataLoader 생성\n",
-    "    if random_state == None:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
-    "    else:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, generator=torch.Generator().manual_seed(random_state))\n",
-    "    val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n",
-    "    test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
-    "    \n",
-    "    return X_train, categorical_cols, numerical_cols, train_loader, val_loader, test_loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append('../')\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "import optuna\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import random\n",
-    "from ft_transformer import FTTransformer\n",
-    "from resnet_like import ResNetLike\n",
-    "from deepgbm import DeepGBM\n",
-    "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul = pd.read_csv(\"../../data/data_for_modeling/seoul_train.csv\")\n",
-    "df_seoul_test = pd.read_csv(\"../../data/data_for_modeling/seoul_test.csv\")\n",
-    "\n",
-    "df_busan = pd.read_csv(\"../../data/data_for_modeling/busan_train.csv\")\n",
-    "df_busan_test = pd.read_csv(\"../../data/data_for_modeling/busan_test.csv\")\n",
-    "\n",
-    "df_daegu = pd.read_csv(\"../../data/data_for_modeling/daegu_train.csv\")\n",
-    "df_daegu_test = pd.read_csv(\"../../data/data_for_modeling/daegu_test.csv\")\n",
-    "\n",
-    "df_daejeon = pd.read_csv(\"../../data/data_for_modeling/daejeon_train.csv\")\n",
-    "df_daejeon_test = pd.read_csv(\"../../data/data_for_modeling/daejeon_test.csv\")\n",
-    "\n",
-    "df_incheon = pd.read_csv(\"../../data/data_for_modeling/incheon_train.csv\")\n",
-    "df_incheon_test = pd.read_csv(\"../../data/data_for_modeling/incheon_test.csv\")\n",
-    "\n",
-    "df_gwangju = pd.read_csv(\"../../data/data_for_modeling/gwangju_train.csv\")\n",
-    "df_gwangju_test = pd.read_csv(\"../../data/data_for_modeling/gwangju_test.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "# 디바이스 설정 (CUDA 사용 가능하면 GPU로, 아니면 CPU로)\n",
-    "import glob\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "from sklearn.metrics import matthews_corrcoef\n",
-    "\n",
-    "def calculate_csi(Y_test, pred):\n",
-    "\n",
-    "    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경\n",
-    "    # 혼동 행렬에서 H, F, M 추출\n",
-    "    H = (cm[0, 0] + cm[1, 1])\n",
-    "    \n",
-    "    F = (cm[1, 0] + cm[2, 0] +\n",
-    "         cm[0, 1] + cm[2, 1])\n",
-    "\n",
-    "    M = (cm[0, 2] + cm[1, 2])\n",
-    "    \n",
-    "    # CSI 계산\n",
-    "    CSI = H / (H + F + M + 1e-10)\n",
-    "    return CSI\n",
-    "\n",
-    "# Soft Voting 앙상블\n",
-    "def get_proba(region, model_choose, data_sample, fold, target='multi'):\n",
-    "    _, _, _, _,val_loader , test_loader = prepare_dataloader(region=region, data_sample=data_sample, target=target,fold=fold ,random_state=120)\n",
-    "    _ ,_,_ ,_ , y_val,_ ,_ ,_ = prepare_dataset(region=region, data_sample=data_sample, target=target, fold=fold)\n",
-    "\n",
-    "    folder_path = f'../save_model/{model_choose}/{data_sample}'\n",
-    "    model_paths = [path for path in glob.glob(f'{folder_path}/*.pth') if f'{region}' in path]\n",
-    "\n",
-    "    model = torch.load(model_paths[fold-1], weights_only=False).to(device)\n",
-    "    model.eval()\n",
-    "\n",
-    "    val_preds = []\n",
-    "\n",
-    "\n",
-    "    with torch.no_grad():\n",
-    "        for x_num_batch, x_cat_batch, _ in val_loader:\n",
-    "            output = model(x_num_batch.to(device), x_cat_batch.to(device))\n",
-    "            val_proba = torch.softmax(output, dim=1)\n",
-    "            val_preds.extend(val_proba.cpu().numpy())\n",
-    "            \n",
-    "    val_preds = np.array(val_preds)\n",
-    "\n",
-    "    return val_preds, y_val\n",
-    "\n",
-    "\n",
-    "# 예시 입력(길이 동일해야 함)\n",
-    "# y_val = 실제 레이블 (배열, 예: [0, 2, 1, 0, 2, ...])\n",
-    "# y_pred = 예측 레이블 (배열, 예: [0, 2, 1, 0, 1, ...])\n",
-    "\n",
-    "def multiclass_mcc(y_val, y_pred):\n",
-    "    \"\"\"\n",
-    "    다중 분류에서도 sklearn의 matthews_corrcoef를 그대로 사용할 수 있음.\n",
-    "    \"\"\"\n",
-    "    return matthews_corrcoef(y_val, y_pred)\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deepgbm_pure= dict()\n",
-    "deepgbm_smote = dict()\n",
-    "deepgbm_ctgan20000 = dict()\n",
-    "deepgbm_ctgan10000 = dict()\n",
-    "deepgbm_ctgan7000 = dict()\n",
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df= pd.DataFrame(columns=['region','model','data_sample','CSI','MCC','Accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **원데이터**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.7104578833855647\n",
-      "mean of accuracy :  0.9665059344095949\n",
-      "mean of mcc :  0.8188418354471367\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'seoul'\n",
-    "datasample = 'pure'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.6818642050717673\n",
-      "mean of accuracy :  0.9749078856534504\n",
-      "mean of mcc :  0.8049988566475562\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'busan'\n",
-    "datasample = 'pure'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5671286321477068\n",
-      "mean of accuracy :  0.9090567324566875\n",
-      "mean of mcc :  0.6887072986816571\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'incheon'\n",
-    "datasample = 'pure'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5768403304029271\n",
-      "mean of accuracy :  0.9752356921259908\n",
-      "mean of mcc :  0.7158561147888857\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daegu'\n",
-    "datasample = 'pure'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.6879666308365847\n",
-      "mean of accuracy :  0.9573174763580109\n",
-      "mean of mcc :  0.797628286801526\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daejeon'\n",
-    "datasample = 'pure'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.6169039999207967\n",
-      "mean of accuracy :  0.9513605060259002\n",
-      "mean of mcc :  0.7404995782864335\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'gwangju'\n",
-    "datasample = 'pure'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **SMOTE 증강 데이터**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.6178386591753761\n",
-      "mean of accuracy :  0.9473890885046287\n",
-      "mean of mcc :  0.7437507767516012\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'seoul'\n",
-    "datasample = 'smote'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5771081499427251\n",
-      "mean of accuracy :  0.9590890660478578\n",
-      "mean of mcc :  0.7328290895553372\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'busan'\n",
-    "datasample = 'smote'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.559840116480077\n",
-      "mean of accuracy :  0.9007683126647871\n",
-      "mean of mcc :  0.6829120896700739\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'incheon'\n",
-    "datasample = 'smote'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.6425770288404345\n",
-      "mean of accuracy :  0.9787012085069575\n",
-      "mean of mcc :  0.7686582033950807\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daegu'\n",
-    "datasample = 'smote'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5569398315371836\n",
-      "mean of accuracy :  0.9299289492726501\n",
-      "mean of mcc :  0.6906810572279379\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daejeon'\n",
-    "datasample = 'smote'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5526168770566259\n",
-      "mean of accuracy :  0.9341257662333341\n",
-      "mean of mcc :  0.6910361569617187\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'gwangju'\n",
-    "datasample = 'smote'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **GAN 20000**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(8760,)\n",
-      "mean of csi :  0.7095252362606882\n",
-      "mean of accuracy :  0.9619258968152972\n",
-      "mean of mcc :  0.8058537207489471\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'seoul'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "print(np.argmax(val_preds, axis=1).shape)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5799669524449778\n",
-      "mean of accuracy :  0.9640024203408438\n",
-      "mean of mcc :  0.7334430424969706\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'busan'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5289538894298621\n",
-      "mean of accuracy :  0.8935004283421081\n",
-      "mean of mcc :  0.6580659191964314\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'incheon'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.44609227542135127\n",
-      "mean of accuracy :  0.9682904159492977\n",
-      "mean of mcc :  0.616365605274888\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daegu'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5716353901847903\n",
-      "mean of accuracy :  0.9385713751029269\n",
-      "mean of mcc :  0.7095774395951602\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daejeon'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4571493095973662\n",
-      "mean of accuracy :  0.9200951709625637\n",
-      "mean of mcc :  0.620850435638757\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'gwangju'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **GAN 10000**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5616336216829804\n",
-      "mean of accuracy :  0.9383062604486363\n",
-      "mean of mcc :  0.700657840332931\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'seoul'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5879352056252677\n",
-      "mean of accuracy :  0.9672767422711281\n",
-      "mean of mcc :  0.7351657312694786\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'busan'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0.4653465346533995, 0.5820895522387269, 0.7163695299836764]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "csi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5290269463089048\n",
-      "mean of accuracy :  0.8928201753291581\n",
-      "mean of mcc :  0.6582877751557777\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'incheon'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5557972102197738\n",
-      "mean of accuracy :  0.9747075421480318\n",
-      "mean of mcc :  0.7032214449405951\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daegu'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5413249801262358\n",
-      "mean of accuracy :  0.9286541116683718\n",
-      "mean of mcc :  0.6761149432485588\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daejeon'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.41816087724732665\n",
-      "mean of accuracy :  0.9039835816054095\n",
-      "mean of mcc :  0.5735307327535618\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'gwangju'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **GAN 7000**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5813133937275518\n",
-      "mean of accuracy :  0.9403897497317663\n",
-      "mean of mcc :  0.7156872321691327\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'seoul'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.597435747844303\n",
-      "mean of accuracy :  0.9670328367891807\n",
-      "mean of mcc :  0.7454746315945583\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'busan'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5123361043389418\n",
-      "mean of accuracy :  0.8907633014779882\n",
-      "mean of mcc :  0.646967905975133\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'incheon'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.6146700981464019\n",
-      "mean of accuracy :  0.9766398640949504\n",
-      "mean of mcc :  0.7399778687018386\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daegu'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5581763081057168\n",
-      "mean of accuracy :  0.9308861691244354\n",
-      "mean of mcc :  0.6896808823936648\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daejeon'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5798647223742114\n",
-      "mean of accuracy :  0.9421613394216134\n",
-      "mean of mcc :  0.7012366964380711\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'gwangju'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'deepgbm'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>model</th>\n",
-       "      <th>data_sample</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.644251</td>\n",
-       "      <td>0.766097</td>\n",
-       "      <td>0.957526</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.681864</td>\n",
-       "      <td>0.804999</td>\n",
-       "      <td>0.974908</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.567129</td>\n",
-       "      <td>0.688707</td>\n",
-       "      <td>0.909057</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.576840</td>\n",
-       "      <td>0.715856</td>\n",
-       "      <td>0.975236</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.687967</td>\n",
-       "      <td>0.797628</td>\n",
-       "      <td>0.957317</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.616904</td>\n",
-       "      <td>0.740500</td>\n",
-       "      <td>0.951361</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.617839</td>\n",
-       "      <td>0.743751</td>\n",
-       "      <td>0.947389</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.577108</td>\n",
-       "      <td>0.732829</td>\n",
-       "      <td>0.959089</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.559840</td>\n",
-       "      <td>0.682912</td>\n",
-       "      <td>0.900768</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.642577</td>\n",
-       "      <td>0.768658</td>\n",
-       "      <td>0.978701</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.556940</td>\n",
-       "      <td>0.690681</td>\n",
-       "      <td>0.929929</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.552617</td>\n",
-       "      <td>0.691036</td>\n",
-       "      <td>0.934126</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.709525</td>\n",
-       "      <td>0.805854</td>\n",
-       "      <td>0.961926</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.579967</td>\n",
-       "      <td>0.733443</td>\n",
-       "      <td>0.964002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.528954</td>\n",
-       "      <td>0.658066</td>\n",
-       "      <td>0.893500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.446092</td>\n",
-       "      <td>0.616366</td>\n",
-       "      <td>0.968290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.571635</td>\n",
-       "      <td>0.709577</td>\n",
-       "      <td>0.938571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.457149</td>\n",
-       "      <td>0.620850</td>\n",
-       "      <td>0.920095</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.561634</td>\n",
-       "      <td>0.700658</td>\n",
-       "      <td>0.938306</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.587935</td>\n",
-       "      <td>0.735166</td>\n",
-       "      <td>0.967277</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.529027</td>\n",
-       "      <td>0.658288</td>\n",
-       "      <td>0.892820</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.555797</td>\n",
-       "      <td>0.703221</td>\n",
-       "      <td>0.974708</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.541325</td>\n",
-       "      <td>0.676115</td>\n",
-       "      <td>0.928654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.418161</td>\n",
-       "      <td>0.573531</td>\n",
-       "      <td>0.903984</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.581313</td>\n",
-       "      <td>0.715687</td>\n",
-       "      <td>0.940390</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.597436</td>\n",
-       "      <td>0.745475</td>\n",
-       "      <td>0.967033</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.512336</td>\n",
-       "      <td>0.646968</td>\n",
-       "      <td>0.890763</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.614670</td>\n",
-       "      <td>0.739978</td>\n",
-       "      <td>0.976640</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.558176</td>\n",
-       "      <td>0.689681</td>\n",
-       "      <td>0.930886</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>deepgbm</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.579865</td>\n",
-       "      <td>0.701237</td>\n",
-       "      <td>0.942161</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     region    model data_sample       CSI       MCC  Accuracy\n",
-       "0     seoul  deepgbm        pure  0.644251  0.766097  0.957526\n",
-       "1     busan  deepgbm        pure  0.681864  0.804999  0.974908\n",
-       "2   incheon  deepgbm        pure  0.567129  0.688707  0.909057\n",
-       "3     daegu  deepgbm        pure  0.576840  0.715856  0.975236\n",
-       "4   daejeon  deepgbm        pure  0.687967  0.797628  0.957317\n",
-       "5   gwangju  deepgbm        pure  0.616904  0.740500  0.951361\n",
-       "6     seoul  deepgbm       smote  0.617839  0.743751  0.947389\n",
-       "7     busan  deepgbm       smote  0.577108  0.732829  0.959089\n",
-       "8   incheon  deepgbm       smote  0.559840  0.682912  0.900768\n",
-       "9     daegu  deepgbm       smote  0.642577  0.768658  0.978701\n",
-       "10  daejeon  deepgbm       smote  0.556940  0.690681  0.929929\n",
-       "11  gwangju  deepgbm       smote  0.552617  0.691036  0.934126\n",
-       "12    seoul  deepgbm  ctgan20000  0.709525  0.805854  0.961926\n",
-       "13    busan  deepgbm  ctgan20000  0.579967  0.733443  0.964002\n",
-       "14  incheon  deepgbm  ctgan20000  0.528954  0.658066  0.893500\n",
-       "15    daegu  deepgbm  ctgan20000  0.446092  0.616366  0.968290\n",
-       "16  daejeon  deepgbm  ctgan20000  0.571635  0.709577  0.938571\n",
-       "17  gwangju  deepgbm  ctgan20000  0.457149  0.620850  0.920095\n",
-       "18    seoul  deepgbm  ctgan10000  0.561634  0.700658  0.938306\n",
-       "19    busan  deepgbm  ctgan10000  0.587935  0.735166  0.967277\n",
-       "20  incheon  deepgbm  ctgan10000  0.529027  0.658288  0.892820\n",
-       "21    daegu  deepgbm  ctgan10000  0.555797  0.703221  0.974708\n",
-       "22  daejeon  deepgbm  ctgan10000  0.541325  0.676115  0.928654\n",
-       "23  gwangju  deepgbm  ctgan10000  0.418161  0.573531  0.903984\n",
-       "24    seoul  deepgbm   ctgan7000  0.581313  0.715687  0.940390\n",
-       "25    busan  deepgbm   ctgan7000  0.597436  0.745475  0.967033\n",
-       "26  incheon  deepgbm   ctgan7000  0.512336  0.646968  0.890763\n",
-       "27    daegu  deepgbm   ctgan7000  0.614670  0.739978  0.976640\n",
-       "28  daejeon  deepgbm   ctgan7000  0.558176  0.689681  0.930886\n",
-       "29  gwangju  deepgbm   ctgan7000  0.579865  0.701237  0.942161"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv('../model_result/deepgbm_sampled_data_test.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py39",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Analysis_code/sampling_data_test/ft-transformer_sampled_data_test.ipynb b/Analysis_code/sampling_data_test/ft-transformer_sampled_data_test.ipynb
deleted file mode 100644
index 74b6eec7c580be568132a2b9e65d3a0f9e8f7708..0000000000000000000000000000000000000000
--- a/Analysis_code/sampling_data_test/ft-transformer_sampled_data_test.ipynb
+++ /dev/null
@@ -1,2903 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
-    "import torch\n",
-    "from torch.utils.data import DataLoader, TensorDataset\n",
-    "import random\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "from sklearn.metrics import matthews_corrcoef\n",
-    "# Python 및 Numpy 시드 고정\n",
-    "seed = 42\n",
-    "random.seed(seed)\n",
-    "np.random.seed(seed)\n",
-    "\n",
-    "# PyTorch 시드 고정\n",
-    "torch.manual_seed(seed)\n",
-    "torch.cuda.manual_seed(seed)\n",
-    "torch.cuda.manual_seed_all(seed)  # Multi-GPU 환경에서 동일한 시드 적용\n",
-    "\n",
-    "# PyTorch 연산의 결정적 모드 설정\n",
-    "torch.backends.cudnn.deterministic = True  # 실행마다 동일한 결과를 보장\n",
-    "torch.backends.cudnn.benchmark = True  # 성능 최적화를 활성화 (가능한 한 빠른 연산 수행)\n",
-    "\n",
-    "# 전처리 함수\n",
-    "def preprocessing(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df['lm_cloudcover'] = df['lm_cloudcover'].astype('int')\n",
-    "    df['cloudcover'] = df['cloudcover'].astype('int')\n",
-    "    return df\n",
-    "\n",
-    "# 데이터셋 준비 함수\n",
-    "def prepare_dataset(region, data_sample='pure', target='multi', fold=3):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    return X_train, X_val, X_test, y_train, y_val, y_test, categorical_cols, numerical_cols\n",
-    "\n",
-    "# 데이터 변환 및 dataloader 생성 함수\n",
-    "def prepare_dataloader(region, data_sample='pure', target='multi', fold=3, random_state=None):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    # 연속형 변수와 범주형 변수 분리\n",
-    "    X_train_num = torch.tensor(X_train[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_train_cat = torch.tensor(X_train[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_val_num = torch.tensor(X_val[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_val_cat = torch.tensor(X_val[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_test_num = torch.tensor(X_test[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_test_cat = torch.tensor(X_test[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    # 레이블 변환\n",
-    "    if target == \"binary\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.float32)  # 이진 분류 → float32\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.float32)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32)\n",
-    "    elif target == \"multi\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.long)  # 다중 분류 → long\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.long)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.long)\n",
-    "    else:\n",
-    "        raise ValueError(\"target must be 'binary' or 'multi'\")\n",
-    "\n",
-    "    # TensorDataset 생성\n",
-    "    train_dataset = TensorDataset(X_train_num, X_train_cat, y_train_tensor)\n",
-    "    val_dataset = TensorDataset(X_val_num, X_val_cat, y_val_tensor)\n",
-    "    test_dataset = TensorDataset(X_test_num, X_test_cat, y_test_tensor)\n",
-    "\n",
-    "    # DataLoader 생성\n",
-    "    if random_state == None:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
-    "    else:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, generator=torch.Generator().manual_seed(random_state))\n",
-    "    val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n",
-    "    test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
-    "    \n",
-    "    return X_train, categorical_cols, numerical_cols, train_loader, val_loader, test_loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append('../')\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "import optuna\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import random\n",
-    "from ft_transformer import FTTransformer\n",
-    "from resnet_like import ResNetLike\n",
-    "from deepgbm import DeepGBM\n",
-    "from pytorch_tabnet.tab_model import TabNetClassifier\n",
-    "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul = pd.read_csv(\"../../data/data_for_modeling/seoul_train.csv\")\n",
-    "df_seoul_test = pd.read_csv(\"../../data/data_for_modeling/seoul_test.csv\")\n",
-    "\n",
-    "df_busan = pd.read_csv(\"../../data/data_for_modeling/busan_train.csv\")\n",
-    "df_busan_test = pd.read_csv(\"../../data/data_for_modeling/busan_test.csv\")\n",
-    "\n",
-    "df_daegu = pd.read_csv(\"../../data/data_for_modeling/daegu_train.csv\")\n",
-    "df_daegu_test = pd.read_csv(\"../../data/data_for_modeling/daegu_test.csv\")\n",
-    "\n",
-    "df_daejeon = pd.read_csv(\"../../data/data_for_modeling/daejeon_train.csv\")\n",
-    "df_daejeon_test = pd.read_csv(\"../../data/data_for_modeling/daejeon_test.csv\")\n",
-    "\n",
-    "df_incheon = pd.read_csv(\"../../data/data_for_modeling/incheon_train.csv\")\n",
-    "df_incheon_test = pd.read_csv(\"../../data/data_for_modeling/incheon_test.csv\")\n",
-    "\n",
-    "df_gwangju = pd.read_csv(\"../../data/data_for_modeling/gwangju_train.csv\")\n",
-    "df_gwangju_test = pd.read_csv(\"../../data/data_for_modeling/gwangju_test.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "# 디바이스 설정 (CUDA 사용 가능하면 GPU로, 아니면 CPU로)\n",
-    "import glob\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "from sklearn.metrics import matthews_corrcoef\n",
-    "\n",
-    "def calculate_csi(Y_test, pred):\n",
-    "\n",
-    "    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경\n",
-    "    # 혼동 행렬에서 H, F, M 추출\n",
-    "    H = (cm[0, 0] + cm[1, 1])\n",
-    "    \n",
-    "    F = (cm[1, 0] + cm[2, 0] +\n",
-    "         cm[0, 1] + cm[2, 1])\n",
-    "\n",
-    "    M = (cm[0, 2] + cm[1, 2])\n",
-    "    \n",
-    "    # CSI 계산\n",
-    "    CSI = H / (H + F + M + 1e-10)\n",
-    "    return CSI\n",
-    "\n",
-    "# Soft Voting 앙상블\n",
-    "def get_proba(region, model_choose, data_sample, fold, target='multi'):\n",
-    "    _, _, _, _,val_loader , test_loader = prepare_dataloader(region=region, data_sample=data_sample, target=target,fold=fold ,random_state=120)\n",
-    "    _ ,_,_ ,_ , y_val,_ ,_ ,_ = prepare_dataset(region=region, data_sample=data_sample, target=target, fold=fold)\n",
-    "\n",
-    "    folder_path = f'../save_model/{model_choose}/{data_sample}'\n",
-    "    model_paths = [path for path in glob.glob(f'{folder_path}/*.pth') if f'{region}' in path]\n",
-    "\n",
-    "    model = torch.load(model_paths[fold-1], weights_only=False).to(device)\n",
-    "    model.eval()\n",
-    "\n",
-    "    val_preds = []\n",
-    "\n",
-    "\n",
-    "    with torch.no_grad():\n",
-    "        for x_num_batch, x_cat_batch, _ in val_loader:\n",
-    "            output = model(x_num_batch.to(device), x_cat_batch.to(device))\n",
-    "            val_proba = torch.softmax(output, dim=1)\n",
-    "            val_preds.extend(val_proba.cpu().numpy())\n",
-    "            \n",
-    "    val_preds = np.array(val_preds)\n",
-    "\n",
-    "    return val_preds, y_val\n",
-    "\n",
-    "\n",
-    "# 예시 입력(길이 동일해야 함)\n",
-    "# y_val = 실제 레이블 (배열, 예: [0, 2, 1, 0, 2, ...])\n",
-    "# y_pred = 예측 레이블 (배열, 예: [0, 2, 1, 0, 1, ...])\n",
-    "\n",
-    "def multiclass_mcc(y_val, y_pred):\n",
-    "    \"\"\"\n",
-    "    다중 분류에서도 sklearn의 matthews_corrcoef를 그대로 사용할 수 있음.\n",
-    "    \"\"\"\n",
-    "    return matthews_corrcoef(y_val, y_pred)\n",
-    "\n",
-    "\n",
-    "\n",
-    "def multiclass_brier_score(y_val, y_proba):\n",
-    "    \"\"\"\n",
-    "    다중분류용 Brier Score 계산\n",
-    "    - y_val : 실제 레이블 (0, 1, 2)\n",
-    "    - y_proba : (N, 3) 형태의 예측 확률\n",
-    "    \"\"\"\n",
-    "    n_samples = len(y_val)\n",
-    "    y_val= np.array(y_val)\n",
-    "    n_class = y_proba.shape[1]\n",
-    "    \n",
-    "    # 실제 레이블을 one-hot으로 변환\n",
-    "    y_true_oh = np.zeros((n_samples, n_class))\n",
-    "    for i in range(n_samples):\n",
-    "        y_true_oh[i, y_val[i]] = 1\n",
-    "    \n",
-    "    # Brier Score 계산\n",
-    "    # (1 / N) * Σᵢ Σₖ (pᵢₖ - oᵢₖ)²\n",
-    "    bs = np.mean(np.sum((y_proba - y_true_oh) ** 2, axis=1))\n",
-    "    return bs\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ft_transformer_pure = dict()\n",
-    "ft_transformer_smote = dict()\n",
-    "ft_transformer_ctgan20000 = dict()\n",
-    "ft_transformer_ctgan10000 = dict()\n",
-    "ft_transformer_ctgan7000 = dict()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df= pd.DataFrame(columns=['region','model','data_sample','CSI','MCC','Accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **원데이터**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5998006360910436\n",
-      "mean of accuracy :  0.9481156066239157\n",
-      "mean of mcc :  0.7351618125239984\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'seoul'\n",
-    "datasample = 'pure'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.525891811597548\n",
-      "mean of accuracy :  0.9593201836464805\n",
-      "mean of mcc :  0.6847515269647338\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'busan'\n",
-    "datasample = 'pure'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.574439584484584\n",
-      "mean of accuracy :  0.9142701341584117\n",
-      "mean of mcc :  0.6997555499382345\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'incheon'\n",
-    "datasample = 'pure'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5325909397972257\n",
-      "mean of accuracy :  0.9751660345501576\n",
-      "mean of mcc :  0.6846558598973905\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daegu'\n",
-    "datasample = 'pure'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5841417763225328\n",
-      "mean of accuracy :  0.9446515457743843\n",
-      "mean of mcc :  0.710099560862753\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daejeon'\n",
-    "datasample = 'pure'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5123621312403577\n",
-      "mean of accuracy :  0.9400000831732248\n",
-      "mean of mcc :  0.6524953764059983\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'gwangju'\n",
-    "datasample = 'pure'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **SMOTE 증강 데이터**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.6027209460369666\n",
-      "mean of accuracy :  0.9459808618409561\n",
-      "mean of mcc :  0.7326561727884195\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'seoul'\n",
-    "datasample = 'smote'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.49442102850607217\n",
-      "mean of accuracy :  0.9448999218171686\n",
-      "mean of mcc :  0.6656582087002536\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'busan'\n",
-    "datasample = 'smote'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5695903712638072\n",
-      "mean of accuracy :  0.9045313812577455\n",
-      "mean of mcc :  0.6933882645176865\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'incheon'\n",
-    "datasample = 'smote'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5152208156718004\n",
-      "mean of accuracy :  0.9707628440252515\n",
-      "mean of mcc :  0.680387175933169\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daegu'\n",
-    "datasample = 'smote'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5604907640952831\n",
-      "mean of accuracy :  0.9305719822674684\n",
-      "mean of mcc :  0.6919263799254157\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daejeon'\n",
-    "datasample = 'smote'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4852746797180565\n",
-      "mean of accuracy :  0.9290042709450974\n",
-      "mean of mcc :  0.6329231459699102\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'gwangju'\n",
-    "datasample = 'smote'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **GAN 20000**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5803899707808177\n",
-      "mean of accuracy :  0.9414579018722292\n",
-      "mean of mcc :  0.7130066787294806\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'seoul'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4639353486936595\n",
-      "mean of accuracy :  0.9446345992298159\n",
-      "mean of mcc :  0.6467071571334145\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'busan'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5640202503368519\n",
-      "mean of accuracy :  0.9072701715863629\n",
-      "mean of mcc :  0.68857629970368\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'incheon'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.3506001798809408\n",
-      "mean of accuracy :  0.9495035598140248\n",
-      "mean of mcc :  0.5079853139279686\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daegu'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5239938645095054\n",
-      "mean of accuracy :  0.9276992830468016\n",
-      "mean of mcc :  0.669429458663152\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daejeon'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4145097694671273\n",
-      "mean of accuracy :  0.9129590122347814\n",
-      "mean of mcc :  0.5668829126361233\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'gwangju'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **GAN 10000**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5205630914565772\n",
-      "mean of accuracy :  0.9380401061290349\n",
-      "mean of mcc :  0.6720401513203828\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'seoul'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4161261582289995\n",
-      "mean of accuracy :  0.9366729462451447\n",
-      "mean of mcc :  0.5928041147082687\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'busan'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5623027751442908\n",
-      "mean of accuracy :  0.9051445758581398\n",
-      "mean of mcc :  0.6844425259764174\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'incheon'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4662085962650442\n",
-      "mean of accuracy :  0.9675943600236212\n",
-      "mean of mcc :  0.6230709704268124\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daegu'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5229201335739658\n",
-      "mean of accuracy :  0.9264041719689597\n",
-      "mean of mcc :  0.6589209425719041\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daejeon'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4575447167412667\n",
-      "mean of accuracy :  0.9180866415483528\n",
-      "mean of mcc :  0.6033826231809732\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'gwangju'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **GAN 7000**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5679676743815468\n",
-      "mean of accuracy :  0.9415375402350475\n",
-      "mean of mcc :  0.7057250825135037\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'seoul'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4934746936691438\n",
-      "mean of accuracy :  0.9561240445475793\n",
-      "mean of mcc :  0.6650891221099499\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'busan'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5469994030503834\n",
-      "mean of accuracy :  0.9032061198858864\n",
-      "mean of mcc :  0.6742953373159715\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'incheon'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.37617290206033815\n",
-      "mean of accuracy :  0.9553074914123645\n",
-      "mean of mcc :  0.5389485667787934\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daegu'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5437623015445493\n",
-      "mean of accuracy :  0.9309658074872537\n",
-      "mean of mcc :  0.6785419094519339\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daejeon'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.46978186086856627\n",
-      "mean of accuracy :  0.9244421155941479\n",
-      "mean of mcc :  0.6149128334271473\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'gwangju'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'ft_transformer'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>model</th>\n",
-       "      <th>data_sample</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.599801</td>\n",
-       "      <td>0.735162</td>\n",
-       "      <td>0.948116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.525892</td>\n",
-       "      <td>0.684752</td>\n",
-       "      <td>0.959320</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.574440</td>\n",
-       "      <td>0.699756</td>\n",
-       "      <td>0.914270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.532591</td>\n",
-       "      <td>0.684656</td>\n",
-       "      <td>0.975166</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.584142</td>\n",
-       "      <td>0.710100</td>\n",
-       "      <td>0.944652</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.512362</td>\n",
-       "      <td>0.652495</td>\n",
-       "      <td>0.940000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.602721</td>\n",
-       "      <td>0.732656</td>\n",
-       "      <td>0.945981</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.494421</td>\n",
-       "      <td>0.665658</td>\n",
-       "      <td>0.944900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.569590</td>\n",
-       "      <td>0.693388</td>\n",
-       "      <td>0.904531</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.515221</td>\n",
-       "      <td>0.680387</td>\n",
-       "      <td>0.970763</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.560491</td>\n",
-       "      <td>0.691926</td>\n",
-       "      <td>0.930572</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.485275</td>\n",
-       "      <td>0.632923</td>\n",
-       "      <td>0.929004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.580390</td>\n",
-       "      <td>0.713007</td>\n",
-       "      <td>0.941458</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.463935</td>\n",
-       "      <td>0.646707</td>\n",
-       "      <td>0.944635</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.564020</td>\n",
-       "      <td>0.688576</td>\n",
-       "      <td>0.907270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.350600</td>\n",
-       "      <td>0.507985</td>\n",
-       "      <td>0.949504</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.523994</td>\n",
-       "      <td>0.669429</td>\n",
-       "      <td>0.927699</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.414510</td>\n",
-       "      <td>0.566883</td>\n",
-       "      <td>0.912959</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.520563</td>\n",
-       "      <td>0.672040</td>\n",
-       "      <td>0.938040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.416126</td>\n",
-       "      <td>0.592804</td>\n",
-       "      <td>0.936673</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.562303</td>\n",
-       "      <td>0.684443</td>\n",
-       "      <td>0.905145</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.466209</td>\n",
-       "      <td>0.623071</td>\n",
-       "      <td>0.967594</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.522920</td>\n",
-       "      <td>0.658921</td>\n",
-       "      <td>0.926404</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.457545</td>\n",
-       "      <td>0.603383</td>\n",
-       "      <td>0.918087</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.567968</td>\n",
-       "      <td>0.705725</td>\n",
-       "      <td>0.941538</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.493475</td>\n",
-       "      <td>0.665089</td>\n",
-       "      <td>0.956124</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.546999</td>\n",
-       "      <td>0.674295</td>\n",
-       "      <td>0.903206</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.376173</td>\n",
-       "      <td>0.538949</td>\n",
-       "      <td>0.955307</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.543762</td>\n",
-       "      <td>0.678542</td>\n",
-       "      <td>0.930966</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>ft_transformer</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.469782</td>\n",
-       "      <td>0.614913</td>\n",
-       "      <td>0.924442</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     region           model data_sample       CSI       MCC  Accuracy\n",
-       "0     seoul  ft_transformer        pure  0.599801  0.735162  0.948116\n",
-       "1     busan  ft_transformer        pure  0.525892  0.684752  0.959320\n",
-       "2   incheon  ft_transformer        pure  0.574440  0.699756  0.914270\n",
-       "3     daegu  ft_transformer        pure  0.532591  0.684656  0.975166\n",
-       "4   daejeon  ft_transformer        pure  0.584142  0.710100  0.944652\n",
-       "5   gwangju  ft_transformer        pure  0.512362  0.652495  0.940000\n",
-       "6     seoul  ft_transformer       smote  0.602721  0.732656  0.945981\n",
-       "7     busan  ft_transformer       smote  0.494421  0.665658  0.944900\n",
-       "8   incheon  ft_transformer       smote  0.569590  0.693388  0.904531\n",
-       "9     daegu  ft_transformer       smote  0.515221  0.680387  0.970763\n",
-       "10  daejeon  ft_transformer       smote  0.560491  0.691926  0.930572\n",
-       "11  gwangju  ft_transformer       smote  0.485275  0.632923  0.929004\n",
-       "12    seoul  ft_transformer  ctgan20000  0.580390  0.713007  0.941458\n",
-       "13    busan  ft_transformer  ctgan20000  0.463935  0.646707  0.944635\n",
-       "14  incheon  ft_transformer  ctgan20000  0.564020  0.688576  0.907270\n",
-       "15    daegu  ft_transformer  ctgan20000  0.350600  0.507985  0.949504\n",
-       "16  daejeon  ft_transformer  ctgan20000  0.523994  0.669429  0.927699\n",
-       "17  gwangju  ft_transformer  ctgan20000  0.414510  0.566883  0.912959\n",
-       "18    seoul  ft_transformer  ctgan10000  0.520563  0.672040  0.938040\n",
-       "19    busan  ft_transformer  ctgan10000  0.416126  0.592804  0.936673\n",
-       "20  incheon  ft_transformer  ctgan10000  0.562303  0.684443  0.905145\n",
-       "21    daegu  ft_transformer  ctgan10000  0.466209  0.623071  0.967594\n",
-       "22  daejeon  ft_transformer  ctgan10000  0.522920  0.658921  0.926404\n",
-       "23  gwangju  ft_transformer  ctgan10000  0.457545  0.603383  0.918087\n",
-       "24    seoul  ft_transformer   ctgan7000  0.567968  0.705725  0.941538\n",
-       "25    busan  ft_transformer   ctgan7000  0.493475  0.665089  0.956124\n",
-       "26  incheon  ft_transformer   ctgan7000  0.546999  0.674295  0.903206\n",
-       "27    daegu  ft_transformer   ctgan7000  0.376173  0.538949  0.955307\n",
-       "28  daejeon  ft_transformer   ctgan7000  0.543762  0.678542  0.930966\n",
-       "29  gwangju  ft_transformer   ctgan7000  0.469782  0.614913  0.924442"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv(\"../model_result/ft_transformer_sampled_data_test.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Analysis_code/sampling_data_test/lgb_non_hierarchy_sampled_test.ipynb b/Analysis_code/sampling_data_test/lgb_non_hierarchy_sampled_test.ipynb
deleted file mode 100644
index 199b6bfc71f7638d80b99cc8d39aad5f1989fc58..0000000000000000000000000000000000000000
--- a/Analysis_code/sampling_data_test/lgb_non_hierarchy_sampled_test.ipynb
+++ /dev/null
@@ -1,3449 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **lightGBM**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append('../../../../../../../../mnt/workspace/LightGBM/python-package')\n",
-    "from lightgbm import LGBMClassifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score\n",
-    "from collections import Counter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul = pd.read_csv(\"../../data/data_for_modeling/seoul_train.csv\")\n",
-    "#df_seoul_test = pd.read_csv(\"../../data/data_for_modeling/seoul_test.csv\")\n",
-    "\n",
-    "df_busan = pd.read_csv(\"../../data/data_for_modeling/busan_train.csv\")\n",
-    "#df_busan_test = pd.read_csv(\"../../data/data_for_modeling/busan_test.csv\")\n",
-    "\n",
-    "df_daegu = pd.read_csv(\"../../data/data_for_modeling/daegu_train.csv\")\n",
-    "#df_daegu_test = pd.read_csv(\"../../data/data_for_modeling/daegu_test.csv\")\n",
-    "\n",
-    "df_daejeon = pd.read_csv(\"../../data/data_for_modeling/daejeon_train.csv\")\n",
-    "#df_daejeon_test = pd.read_csv(\"../../data/data_for_modeling/daejeon_test.csv\")\n",
-    "\n",
-    "df_incheon = pd.read_csv(\"../../data/data_for_modeling/incheon_train.csv\")\n",
-    "#df_incheon_test = pd.read_csv(\"../../data/data_for_modeling/incheon_test.csv\")\n",
-    "\n",
-    "df_gwangju = pd.read_csv(\"../../data/data_for_modeling/gwangju_train.csv\")\n",
-    "#df_gwangju_test = pd.read_csv(\"../../data/data_for_modeling/gwangju_test.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "seoul           :  Counter({2: 23686, 1: 2579, 0: 39})\n",
-      "\n",
-      "busan           :  Counter({2: 24694, 1: 1516, 0: 94})\n",
-      "\n",
-      "daegu           :  Counter({2: 25149, 1: 1107, 0: 48})\n",
-      "\n",
-      "gwangju         :  Counter({2: 23798, 1: 2411, 0: 95})\n",
-      "\n",
-      "daejeon         :  Counter({2: 23471, 1: 2660, 0: 173})\n",
-      "\n",
-      "incheon         :  Counter({2: 21893, 1: 3892, 0: 519})\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"seoul           : \", Counter(df_seoul['multi_class']))\n",
-    "print()\n",
-    "print(\"busan           : \", Counter(df_busan['multi_class']))\n",
-    "print()\n",
-    "print(\"daegu           : \", Counter(df_daegu['multi_class']))\n",
-    "print()\n",
-    "print(\"gwangju         : \", Counter(df_gwangju['multi_class']))\n",
-    "print()\n",
-    "print(\"daejeon         : \", Counter(df_daejeon['multi_class']))\n",
-    "print()\n",
-    "print(\"incheon         : \", Counter(df_incheon['multi_class']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocessing(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df['year'] = df['year'].astype('int')\n",
-    "    df['month'] = df['month'].astype('int')\n",
-    "    df['hour'] = df['hour'].astype('int')\n",
-    "    df['binary_class'] = df['binary_class'].astype('int')\n",
-    "    df['multi_class'] = df['multi_class'].astype('int')\n",
-    "\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',\n",
-    "       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',\n",
-    "       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',\n",
-    "       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',\n",
-    "       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',\n",
-    "       'month_sin', 'month_cos','multi_class']]\n",
-    "    return df\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# df_seoul_test= preprocessing(df_seoul_test).copy()\n",
-    "# df_busan_test= preprocessing(df_busan_test).copy()\n",
-    "# df_daegu_test= preprocessing(df_daegu_test).copy()\n",
-    "# df_gwangju_test= preprocessing(df_gwangju_test).copy()\n",
-    "# df_daejeon_test= preprocessing(df_daejeon_test).copy()\n",
-    "# df_incheon_test= preprocessing(df_incheon_test).copy()\n",
-    "\n",
-    "df_seoul= preprocessing(df_seoul).copy()\n",
-    "df_busan= preprocessing(df_busan).copy()\n",
-    "df_daegu= preprocessing(df_daegu).copy()\n",
-    "df_gwangju= preprocessing(df_gwangju).copy()\n",
-    "df_daejeon= preprocessing(df_daejeon).copy()\n",
-    "df_incheon= preprocessing(df_incheon).copy()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# df_seoul_test.drop(columns=['year'], inplace=True)\n",
-    "# df_busan_test.drop(columns=['year'], inplace=True)\n",
-    "# df_daegu_test.drop(columns=['year'], inplace=True)\n",
-    "# df_daejeon_test.drop(columns=['year'], inplace=True)\n",
-    "# df_incheon_test.drop(columns=['year'], inplace=True)\n",
-    "# df_gwangju_test.drop(columns=['year'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1, 2])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.unique(df_seoul['multi_class'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import confusion_matrix\n",
-    "from sklearn.utils.class_weight import compute_class_weight\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "from sklearn.metrics import matthews_corrcoef\n",
-    "\n",
-    "def calculate_csi(Y_test, pred):\n",
-    "\n",
-    "    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경\n",
-    "    # 혼동 행렬에서 H, F, M 추출\n",
-    "    H = (cm[0, 0] + cm[1, 1])\n",
-    "    \n",
-    "    F = (cm[1, 0] + cm[2, 0] +\n",
-    "         cm[0, 1] + cm[2, 1])\n",
-    "    \n",
-    "    M = (cm[0, 2] + cm[1, 2])\n",
-    "    \n",
-    "    # CSI 계산\n",
-    "    CSI = H / (H + F + M + 1e-10)\n",
-    "    return CSI\n",
-    "\n",
-    "def eval_metric_csi(y_true, pred_prob):\n",
-    "\n",
-    "    pred = np.argmax(pred_prob, axis=1)\n",
-    "    y_true = y_true\n",
-    "    y_pred = pred\n",
-    "    csi = calculate_csi(y_true, y_pred)\n",
-    "    return -1*csi\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "def multiclass_mcc(y_val, y_pred):\n",
-    "    \"\"\"\n",
-    "    다중 분류에서도 sklearn의 matthews_corrcoef를 그대로 사용할 수 있음.\n",
-    "    \"\"\"\n",
-    "    return matthews_corrcoef(y_val, y_pred)\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 사용자 정의 평가 지표 함수 정의\n",
-    "def csi_metric(y_true, pred):\n",
-    "    y_pred_binary = np.argmax(pred, axis=1)\n",
-    "    score = calculate_csi(y_true, y_pred_binary)\n",
-    "    return 'CSI', score, True  # higher_better=True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lgb_model = LGBMClassifier(\n",
-    "    n_estimators=4000, # 약한 학습기 개수\n",
-    "    tree_method='hist', \n",
-    "    device='cuda', # GPU 사용\n",
-    "    objective='multiclassova',\n",
-    "    early_stopping_rounds=400, # 과적합 방지를 위한 조기 종료 설정\n",
-    "    random_state= 120, verbose= -1\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pre_sampled_data= []\n",
-    "smote_sample_data= []\n",
-    "gan20000_sample_data= []\n",
-    "gan10000_sample_data= []\n",
-    "gan7000_sample_data= []"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df= pd.DataFrame(columns=['region','model','data_sample','CSI','MCC','Accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4830701326492519\n",
-      "mean of accuracy :  0.9297619790237127\n",
-      "mean of mcc :  0.6294046921291281\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "\n",
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul.loc[df_seoul['year'].isin([2018, 2019]), df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul.loc[df_seoul['year'].isin([2018, 2020]), df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul.loc[df_seoul['year'].isin([2019, 2020]), df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'seoul',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'pure',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.43506984201427806\n",
-      "mean of accuracy :  0.9573520972128652\n",
-      "mean of mcc :  0.603839321192473\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "\n",
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan.loc[df_busan['year'].isin([2018, 2019]), df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan.loc[df_busan['year'].isin([2018, 2020]), df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan.loc[df_busan['year'].isin([2019, 2020]), df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'busan',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'pure',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5522436617260514\n",
-      "mean of accuracy :  0.9117255533098785\n",
-      "mean of mcc :  0.6857225827228159\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "\n",
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon.loc[df_incheon['year'].isin([2018, 2019]), df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon.loc[df_incheon['year'].isin([2018, 2020]), df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon.loc[df_incheon['year'].isin([2019, 2020]), df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'incheon',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'pure',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.2974860165425261\n",
-      "mean of accuracy :  0.9538572622701301\n",
-      "mean of mcc :  0.4862244089903067\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "\n",
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu.loc[df_daegu['year'].isin([2018, 2019]), df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu.loc[df_daegu['year'].isin([2018, 2020]), df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu.loc[df_daegu['year'].isin([2019, 2020]), df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daegu',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'pure',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.48570589458995483\n",
-      "mean of accuracy :  0.9337746712578286\n",
-      "mean of mcc :  0.6313963695110991\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "\n",
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon.loc[df_daejeon['year'].isin([2018, 2019]), df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon.loc[df_daejeon['year'].isin([2018, 2020]), df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon.loc[df_daejeon['year'].isin([2019, 2020]), df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daejeon',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'pure',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4841000529921969\n",
-      "mean of accuracy :  0.9430086666500319\n",
-      "mean of mcc :  0.6363547628117262\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "\n",
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju.loc[df_gwangju['year'].isin([2018, 2019]), df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju.loc[df_gwangju['year'].isin([2018, 2020]), df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju.loc[df_gwangju['year'].isin([2019, 2020]), df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'gwangju',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'pure',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **SMOTE 증강기법을 적용시킨 데이터셋에 대한 성능**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocessing(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df['year'] = df['year'].astype('int')\n",
-    "    df['month'] = df['month'].astype('int')\n",
-    "    df['hour'] = df['hour'].astype('int')\n",
-    "    df['binary_class'] = df['binary_class'].astype('int')\n",
-    "    df['multi_class'] = df['multi_class'].astype('int')\n",
-    "\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',\n",
-    "       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',\n",
-    "       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',\n",
-    "       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',\n",
-    "       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',\n",
-    "       'month_sin', 'month_cos','multi_class']]\n",
-    "    return df\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_smote_busan_1 = pd.read_csv(\"../../data/data_oversampled/smote/smote_1_busan.csv\")\n",
-    "df_smote_busan_2 = pd.read_csv(\"../../data/data_oversampled/smote/smote_2_busan.csv\")\n",
-    "df_smote_busan_3 = pd.read_csv(\"../../data/data_oversampled/smote/smote_3_busan.csv\")\n",
-    "\n",
-    "df_smote_seoul_1 = pd.read_csv(\"../../data/data_oversampled/smote/smote_1_seoul.csv\")\n",
-    "df_smote_seoul_2 = pd.read_csv(\"../../data/data_oversampled/smote/smote_2_seoul.csv\")\n",
-    "df_smote_seoul_3 = pd.read_csv(\"../../data/data_oversampled/smote/smote_3_seoul.csv\")\n",
-    "\n",
-    "df_smote_daegu_1 = pd.read_csv(\"../../data/data_oversampled/smote/smote_1_daegu.csv\")\n",
-    "df_smote_daegu_2 = pd.read_csv(\"../../data/data_oversampled/smote/smote_2_daegu.csv\")\n",
-    "df_smote_daegu_3 = pd.read_csv(\"../../data/data_oversampled/smote/smote_3_daegu.csv\")\n",
-    "\n",
-    "df_smote_daejeon_1 = pd.read_csv(\"../../data/data_oversampled/smote/smote_1_daejeon.csv\")\n",
-    "df_smote_daejeon_2 = pd.read_csv(\"../../data/data_oversampled/smote/smote_2_daejeon.csv\")\n",
-    "df_smote_daejeon_3 = pd.read_csv(\"../../data/data_oversampled/smote/smote_3_daejeon.csv\")\n",
-    "\n",
-    "df_smote_gwangju_1 = pd.read_csv(\"../../data/data_oversampled/smote/smote_1_gwangju.csv\")\n",
-    "df_smote_gwangju_2 = pd.read_csv(\"../../data/data_oversampled/smote/smote_2_gwangju.csv\")\n",
-    "df_smote_gwangju_3 = pd.read_csv(\"../../data/data_oversampled/smote/smote_3_gwangju.csv\")\n",
-    "\n",
-    "df_smote_incheon_1 = pd.read_csv(\"../../data/data_oversampled/smote/smote_1_incheon.csv\")\n",
-    "df_smote_incheon_2 = pd.read_csv(\"../../data/data_oversampled/smote/smote_2_incheon.csv\")\n",
-    "df_smote_incheon_3 = pd.read_csv(\"../../data/data_oversampled/smote/smote_3_incheon.csv\")\n",
-    "\n",
-    "\n",
-    "df_smote_busan_1 = preprocessing(df_smote_busan_1)\n",
-    "df_smote_busan_2 = preprocessing(df_smote_busan_2)\n",
-    "df_smote_busan_3 = preprocessing(df_smote_busan_3)\n",
-    "\n",
-    "df_smote_seoul_1 = preprocessing(df_smote_seoul_1)\n",
-    "df_smote_seoul_2 = preprocessing(df_smote_seoul_2)\n",
-    "df_smote_seoul_3 = preprocessing(df_smote_seoul_3)\n",
-    "\n",
-    "df_smote_daegu_1 = preprocessing(df_smote_daegu_1)\n",
-    "df_smote_daegu_2 = preprocessing(df_smote_daegu_2)\n",
-    "df_smote_daegu_3 = preprocessing(df_smote_daegu_3)\n",
-    "\n",
-    "df_smote_daejeon_1 = preprocessing(df_smote_daejeon_1)\n",
-    "df_smote_daejeon_2 = preprocessing(df_smote_daejeon_2)\n",
-    "df_smote_daejeon_3 = preprocessing(df_smote_daejeon_3)\n",
-    "\n",
-    "df_smote_gwangju_1 = preprocessing(df_smote_gwangju_1)\n",
-    "df_smote_gwangju_2 = preprocessing(df_smote_gwangju_2)\n",
-    "df_smote_gwangju_3 = preprocessing(df_smote_gwangju_3)\n",
-    "\n",
-    "df_smote_incheon_1 = preprocessing(df_smote_incheon_1)\n",
-    "df_smote_incheon_2 = preprocessing(df_smote_incheon_2)\n",
-    "df_smote_incheon_3 = preprocessing(df_smote_incheon_3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "smote_oversample=[] # smote 적용 전 f1 score 저장 리스트"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm', 'vap_pressure',\n",
-       "       'dewpoint_C', 'loc_pressure', 'sea_pressure', 'solarRad', 'snow_cm',\n",
-       "       'cloudcover', 'lm_cloudcover', 'low_cloudbase', 'groundtemp', 'O3',\n",
-       "       'NO2', 'PM10', 'PM25', 'year', 'month', 'hour', 'ground_temp - temp_C',\n",
-       "       'hour_sin', 'hour_cos', 'month_sin', 'month_cos', 'multi_class'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_smote_seoul_1.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm', 'vap_pressure',\n",
-       "       'dewpoint_C', 'loc_pressure', 'sea_pressure', 'solarRad', 'snow_cm',\n",
-       "       'cloudcover', 'lm_cloudcover', 'low_cloudbase', 'groundtemp', 'O3',\n",
-       "       'NO2', 'PM10', 'PM25', 'year', 'month', 'hour', 'ground_temp - temp_C',\n",
-       "       'hour_sin', 'hour_cos', 'month_sin', 'month_cos', 'multi_class'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_seoul.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5811441298770988\n",
-      "mean of accuracy :  0.9400710923139624\n",
-      "mean of mcc :  0.7097862880128495\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "\n",
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_seoul_1.loc[df_smote_seoul_1['year'].isin([2018, 2019]), df_smote_seoul_1.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_smote_seoul_1.loc[df_smote_seoul_1['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_seoul_2.loc[df_smote_seoul_2['year'].isin([2018, 2020]), df_smote_seoul_2.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_smote_seoul_2.loc[df_smote_seoul_2['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_seoul_3.loc[df_smote_seoul_3['year'].isin([2019, 2020]), df_smote_seoul_3.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_smote_seoul_3.loc[df_smote_seoul_3['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'seoul',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'smote',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.468977364244475\n",
-      "mean of accuracy :  0.9506557169116118\n",
-      "mean of mcc :  0.6345551645621824\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "\n",
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_busan_1.loc[df_smote_busan_1['year'].isin([2018, 2019]), df_smote_busan_1.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_smote_busan_1.loc[df_smote_busan_1['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_busan_2.loc[df_smote_busan_2['year'].isin([2018, 2020]), df_smote_busan_2.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_smote_busan_2.loc[df_smote_busan_2['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_busan_3.loc[df_smote_busan_3['year'].isin([2019, 2020]), df_smote_busan_3.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_smote_busan_3.loc[df_smote_busan_3['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'busan',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'smote',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5845373158941275\n",
-      "mean of accuracy :  0.9115271851685506\n",
-      "mean of mcc :  0.7079881355705151\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "\n",
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_incheon_1.loc[df_smote_incheon_1['year'].isin([2018, 2019]), df_smote_incheon_1.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_smote_incheon_1.loc[df_smote_incheon_1['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_incheon_2.loc[df_smote_incheon_2['year'].isin([2018, 2020]), df_smote_incheon_2.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_smote_incheon_2.loc[df_smote_incheon_2['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_incheon_3.loc[df_smote_incheon_3['year'].isin([2019, 2020]), df_smote_incheon_3.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_smote_incheon_3.loc[df_smote_incheon_3['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'incheon',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'smote',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.45130042516888275\n",
-      "mean of accuracy :  0.9638063394632\n",
-      "mean of mcc :  0.6228321490115053\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "\n",
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_daegu_1.loc[df_smote_daegu_1['year'].isin([2018, 2019]), df_smote_daegu_1.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_smote_daegu_1.loc[df_smote_daegu_1['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_daegu_2.loc[df_smote_daegu_2['year'].isin([2018, 2020]), df_smote_daegu_2.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_smote_daegu_2.loc[df_smote_daegu_2['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_daegu_3.loc[df_smote_daegu_3['year'].isin([2019, 2020]), df_smote_daegu_3.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_smote_daegu_3.loc[df_smote_daegu_3['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daegu',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'smote',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5294747877274647\n",
-      "mean of accuracy :  0.9322567599038517\n",
-      "mean of mcc :  0.6632804903422373\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "\n",
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_1.loc[df_smote_daejeon_1['year'].isin([2018, 2019]), df_smote_daejeon_1.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_smote_daejeon_1.loc[df_smote_daejeon_1['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_2.loc[df_smote_daejeon_2['year'].isin([2018, 2020]), df_smote_daejeon_2.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_smote_daejeon_2.loc[df_smote_daejeon_2['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_3.loc[df_smote_daejeon_3['year'].isin([2019, 2020]), df_smote_daejeon_3.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_smote_daejeon_3.loc[df_smote_daejeon_3['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daejeon',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'smote',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5204263336367001\n",
-      "mean of accuracy :  0.9371142841696402\n",
-      "mean of mcc :  0.658370785349228\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "\n",
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_1.loc[df_smote_gwangju_1['year'].isin([2018, 2019]), df_smote_gwangju_1.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_smote_gwangju_1.loc[df_smote_gwangju_1['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_2.loc[df_smote_gwangju_2['year'].isin([2018, 2020]), df_smote_gwangju_2.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_smote_gwangju_2.loc[df_smote_gwangju_2['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_3.loc[df_smote_gwangju_3['year'].isin([2019, 2020]), df_smote_gwangju_3.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_smote_gwangju_3.loc[df_smote_gwangju_3['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'gwangju',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'smote',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **CTGAN을 통해 데이터 증강을 진행한 데이터셋에 대한 성능**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **2만개**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1 Fold\n",
-    "df_busan_gan20000_1= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_1_busan.csv\")\n",
-    "df_seoul_gan20000_1= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_1_seoul.csv\")\n",
-    "df_incheon_gan20000_1= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_1_incheon.csv\")\n",
-    "df_daegu_gan20000_1= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_1_daegu.csv\")\n",
-    "df_daejeon_gan20000_1= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_1_daejeon.csv\")\n",
-    "df_gwangju_gan20000_1= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_1_gwangju.csv\")\n",
-    "\n",
-    "# 2 Fold\n",
-    "df_busan_gan20000_2= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_2_busan.csv\")\n",
-    "df_seoul_gan20000_2= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_2_seoul.csv\")\n",
-    "df_incheon_gan20000_2= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_2_incheon.csv\")\n",
-    "df_daegu_gan20000_2= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_2_daegu.csv\")\n",
-    "df_daejeon_gan20000_2= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_2_daejeon.csv\")\n",
-    "df_gwangju_gan20000_2= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_2_gwangju.csv\")\n",
-    "\n",
-    "# 3 Fold\n",
-    "df_busan_gan20000_3= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_3_busan.csv\")\n",
-    "df_seoul_gan20000_3= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_3_seoul.csv\")\n",
-    "df_incheon_gan20000_3= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_3_incheon.csv\")\n",
-    "df_daegu_gan20000_3= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_3_daegu.csv\")\n",
-    "df_daejeon_gan20000_3= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_3_daejeon.csv\")\n",
-    "df_gwangju_gan20000_3= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_3_gwangju.csv\")\n",
-    "\n",
-    "\n",
-    "# for validation\n",
-    "df_busan = pd.read_csv(\"../../data/data_for_modeling/busan_train.csv\")\n",
-    "df_seoul = pd.read_csv(\"../../data/data_for_modeling/seoul_train.csv\")\n",
-    "df_incheon = pd.read_csv(\"../../data/data_for_modeling/incheon_train.csv\")\n",
-    "df_daegu = pd.read_csv(\"../../data/data_for_modeling/daegu_train.csv\")\n",
-    "df_daejeon = pd.read_csv(\"../../data/data_for_modeling/daejeon_train.csv\")\n",
-    "df_gwangju = pd.read_csv(\"../../data/data_for_modeling/gwangju_train.csv\")\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_busan_gan20000_1= preprocessing(df_busan_gan20000_1).copy()\n",
-    "df_seoul_gan20000_1= preprocessing(df_seoul_gan20000_1).copy()\n",
-    "df_incheon_gan20000_1= preprocessing(df_incheon_gan20000_1).copy()\n",
-    "df_daegu_gan20000_1= preprocessing(df_daegu_gan20000_1).copy()\n",
-    "df_daejeon_gan20000_1= preprocessing(df_daejeon_gan20000_1).copy()\n",
-    "df_gwangju_gan20000_1= preprocessing(df_gwangju_gan20000_1).copy()\n",
-    "\n",
-    "df_busan_gan20000_2= preprocessing(df_busan_gan20000_2).copy()\n",
-    "df_seoul_gan20000_2= preprocessing(df_seoul_gan20000_2).copy()\n",
-    "df_incheon_gan20000_2= preprocessing(df_incheon_gan20000_2).copy()\n",
-    "df_daegu_gan20000_2= preprocessing(df_daegu_gan20000_2).copy()\n",
-    "df_daejeon_gan20000_2= preprocessing(df_daejeon_gan20000_2).copy()\n",
-    "df_gwangju_gan20000_2= preprocessing(df_gwangju_gan20000_2).copy()\n",
-    "\n",
-    "df_busan_gan20000_3= preprocessing(df_busan_gan20000_3).copy()\n",
-    "df_seoul_gan20000_3= preprocessing(df_seoul_gan20000_3).copy()\n",
-    "df_incheon_gan20000_3= preprocessing(df_incheon_gan20000_3).copy()\n",
-    "df_daegu_gan20000_3= preprocessing(df_daegu_gan20000_3).copy()\n",
-    "df_daejeon_gan20000_3= preprocessing(df_daejeon_gan20000_3).copy()\n",
-    "df_gwangju_gan20000_3= preprocessing(df_gwangju_gan20000_3).copy()\n",
-    "\n",
-    "df_busan = preprocessing(df_busan).copy()\n",
-    "df_seoul = preprocessing(df_seoul).copy()\n",
-    "df_incheon = preprocessing(df_incheon).copy()\n",
-    "df_daegu = preprocessing(df_daegu).copy()\n",
-    "df_daejeon = preprocessing(df_daejeon).copy()\n",
-    "df_gwangju = preprocessing(df_gwangju).copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5278642102510234\n",
-      "mean of accuracy :  0.9409731059377364\n",
-      "mean of mcc :  0.6732186063208289\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan20000_1.loc[df_seoul_gan20000_1['year'].isin([2018, 2019]), df_seoul_gan20000_1.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_seoul_gan20000_1.loc[df_seoul_gan20000_1['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan20000_2.loc[df_seoul_gan20000_2['year'].isin([2018, 2020]), df_seoul_gan20000_2.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_seoul_gan20000_2.loc[df_seoul_gan20000_2['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan20000_3.loc[df_seoul_gan20000_3['year'].isin([2019, 2020]), df_seoul_gan20000_3.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_seoul_gan20000_3.loc[df_seoul_gan20000_3['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'seoul',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan20000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4608874696247123\n",
-      "mean of accuracy :  0.958718945197162\n",
-      "mean of mcc :  0.6276000553143347\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan20000_1.loc[df_busan_gan20000_1['year'].isin([2018, 2019]), df_busan_gan20000_1.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_busan_gan20000_1.loc[df_busan_gan20000_1['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan20000_2.loc[df_busan_gan20000_2['year'].isin([2018, 2020]), df_busan_gan20000_2.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_busan_gan20000_2.loc[df_busan_gan20000_2['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan20000_3.loc[df_busan_gan20000_3['year'].isin([2019, 2020]), df_busan_gan20000_3.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_busan_gan20000_3.loc[df_busan_gan20000_3['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'busan',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan20000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5536461635852592\n",
-      "mean of accuracy :  0.8955189385433041\n",
-      "mean of mcc :  0.6798438480715302\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan20000_1.loc[df_incheon_gan20000_1['year'].isin([2018, 2019]), df_incheon_gan20000_1.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_incheon_gan20000_1.loc[df_incheon_gan20000_1['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan20000_2.loc[df_incheon_gan20000_2['year'].isin([2018, 2020]), df_incheon_gan20000_2.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_incheon_gan20000_2.loc[df_incheon_gan20000_2['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan20000_3.loc[df_incheon_gan20000_3['year'].isin([2019, 2020]), df_incheon_gan20000_3.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_incheon_gan20000_3.loc[df_incheon_gan20000_3['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'incheon',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan20000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4051438319036602\n",
-      "mean of accuracy :  0.965851153196763\n",
-      "mean of mcc :  0.5791827944687878\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan20000_1.loc[df_daegu_gan20000_1['year'].isin([2018, 2019]), df_daegu_gan20000_1.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_daegu_gan20000_1.loc[df_daegu_gan20000_1['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan20000_2.loc[df_daegu_gan20000_2['year'].isin([2018, 2020]), df_daegu_gan20000_2.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_daegu_gan20000_2.loc[df_daegu_gan20000_2['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan20000_3.loc[df_daegu_gan20000_3['year'].isin([2019, 2020]), df_daegu_gan20000_3.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_daegu_gan20000_3.loc[df_daegu_gan20000_3['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daegu',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan20000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4682791445662134\n",
-      "mean of accuracy :  0.9317582403872545\n",
-      "mean of mcc :  0.6207261950649575\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan20000_1.loc[df_daejeon_gan20000_1['year'].isin([2018, 2019]), df_daejeon_gan20000_1.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_daejeon_gan20000_1.loc[df_daejeon_gan20000_1['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan20000_2.loc[df_daejeon_gan20000_2['year'].isin([2018, 2020]), df_daejeon_gan20000_2.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_daejeon_gan20000_2.loc[df_daejeon_gan20000_2['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan20000_3.loc[df_daejeon_gan20000_3['year'].isin([2019, 2020]), df_daejeon_gan20000_3.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_daejeon_gan20000_3.loc[df_daejeon_gan20000_3['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daejeon',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan20000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4817029529408228\n",
-      "mean of accuracy :  0.9420201528723874\n",
-      "mean of mcc :  0.6339890466307277\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan20000_1.loc[df_gwangju_gan20000_1['year'].isin([2018, 2019]), df_gwangju_gan20000_1.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_gwangju_gan20000_1.loc[df_gwangju_gan20000_1['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan20000_2.loc[df_gwangju_gan20000_2['year'].isin([2018, 2020]), df_gwangju_gan20000_2.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_gwangju_gan20000_2.loc[df_gwangju_gan20000_2['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan20000_3.loc[df_gwangju_gan20000_3['year'].isin([2019, 2020]), df_gwangju_gan20000_3.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_gwangju_gan20000_3.loc[df_gwangju_gan20000_3['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'gwangju',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan20000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **1만개**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1 Fold\n",
-    "df_busan_gan10000_1= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_1_busan.csv\")\n",
-    "df_seoul_gan10000_1= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_1_seoul.csv\")\n",
-    "df_incheon_gan10000_1= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_1_incheon.csv\")\n",
-    "df_daegu_gan10000_1= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_1_daegu.csv\")\n",
-    "df_daejeon_gan10000_1= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_1_daejeon.csv\")\n",
-    "df_gwangju_gan10000_1= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_1_gwangju.csv\")\n",
-    "\n",
-    "# 2 Fold\n",
-    "df_busan_gan10000_2= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_2_busan.csv\")\n",
-    "df_seoul_gan10000_2= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_2_seoul.csv\")\n",
-    "df_incheon_gan10000_2= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_2_incheon.csv\")\n",
-    "df_daegu_gan10000_2= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_2_daegu.csv\")\n",
-    "df_daejeon_gan10000_2= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_2_daejeon.csv\")\n",
-    "df_gwangju_gan10000_2= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_2_gwangju.csv\")\n",
-    "\n",
-    "# 3 Fold\n",
-    "df_busan_gan10000_3= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_3_busan.csv\")\n",
-    "df_seoul_gan10000_3= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_3_seoul.csv\")\n",
-    "df_incheon_gan10000_3= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_3_incheon.csv\")\n",
-    "df_daegu_gan10000_3= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_3_daegu.csv\")\n",
-    "df_daejeon_gan10000_3= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_3_daejeon.csv\")\n",
-    "df_gwangju_gan10000_3= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_3_gwangju.csv\")\n",
-    "\n",
-    "\n",
-    "# for validation\n",
-    "df_busan = pd.read_csv(\"../../data/data_for_modeling/busan_train.csv\")\n",
-    "df_seoul = pd.read_csv(\"../../data/data_for_modeling/seoul_train.csv\")\n",
-    "df_incheon = pd.read_csv(\"../../data/data_for_modeling/incheon_train.csv\")\n",
-    "df_daegu = pd.read_csv(\"../../data/data_for_modeling/daegu_train.csv\")\n",
-    "df_daejeon = pd.read_csv(\"../../data/data_for_modeling/daejeon_train.csv\")\n",
-    "df_gwangju = pd.read_csv(\"../../data/data_for_modeling/gwangju_train.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_busan_gan10000_1= preprocessing(df_busan_gan10000_1).copy()\n",
-    "df_seoul_gan10000_1= preprocessing(df_seoul_gan10000_1).copy()\n",
-    "df_incheon_gan10000_1= preprocessing(df_incheon_gan10000_1).copy()\n",
-    "df_daegu_gan10000_1= preprocessing(df_daegu_gan10000_1).copy()\n",
-    "df_daejeon_gan10000_1= preprocessing(df_daejeon_gan10000_1).copy()\n",
-    "df_gwangju_gan10000_1= preprocessing(df_gwangju_gan10000_1).copy()\n",
-    "\n",
-    "df_busan_gan10000_2= preprocessing(df_busan_gan10000_2).copy()\n",
-    "df_seoul_gan10000_2= preprocessing(df_seoul_gan10000_2).copy()\n",
-    "df_incheon_gan10000_2= preprocessing(df_incheon_gan10000_2).copy()\n",
-    "df_daegu_gan10000_2= preprocessing(df_daegu_gan10000_2).copy()\n",
-    "df_daejeon_gan10000_2= preprocessing(df_daejeon_gan10000_2).copy()\n",
-    "df_gwangju_gan10000_2= preprocessing(df_gwangju_gan10000_2).copy()\n",
-    "\n",
-    "df_busan_gan10000_3= preprocessing(df_busan_gan10000_3).copy()\n",
-    "df_seoul_gan10000_3= preprocessing(df_seoul_gan10000_3).copy()\n",
-    "df_incheon_gan10000_3= preprocessing(df_incheon_gan10000_3).copy()\n",
-    "df_daegu_gan10000_3= preprocessing(df_daegu_gan10000_3).copy()\n",
-    "df_daejeon_gan10000_3= preprocessing(df_daejeon_gan10000_3).copy()\n",
-    "df_gwangju_gan10000_3= preprocessing(df_gwangju_gan10000_3).copy()\n",
-    "\n",
-    "df_busan = preprocessing(df_busan).copy()\n",
-    "df_seoul = preprocessing(df_seoul).copy()\n",
-    "df_incheon = preprocessing(df_incheon).copy()\n",
-    "df_daegu = preprocessing(df_daegu).copy()\n",
-    "df_daejeon = preprocessing(df_daejeon).copy()\n",
-    "df_gwangju = preprocessing(df_gwangju).copy()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5442974261785484\n",
-      "mean of accuracy :  0.9422277740349827\n",
-      "mean of mcc :  0.6853617099956927\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan10000_1.loc[df_seoul_gan10000_1['year'].isin([2018, 2019]), df_seoul_gan10000_1.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_seoul_gan10000_1.loc[df_seoul_gan10000_1['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan10000_2.loc[df_seoul_gan10000_2['year'].isin([2018, 2020]), df_seoul_gan10000_2.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_seoul_gan10000_2.loc[df_seoul_gan10000_2['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan10000_3.loc[df_seoul_gan10000_3['year'].isin([2019, 2020]), df_seoul_gan10000_3.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_seoul_gan10000_3.loc[df_seoul_gan10000_3['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'seoul',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan10000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4518917046571256\n",
-      "mean of accuracy :  0.9571202518485249\n",
-      "mean of mcc :  0.6185787696267423\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan10000_1.loc[df_busan_gan10000_1['year'].isin([2018, 2019]), df_busan_gan10000_1.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_busan_gan10000_1.loc[df_busan_gan10000_1['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan10000_2.loc[df_busan_gan10000_2['year'].isin([2018, 2020]), df_busan_gan10000_2.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_busan_gan10000_2.loc[df_busan_gan10000_2['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan10000_3.loc[df_busan_gan10000_3['year'].isin([2019, 2020]), df_busan_gan10000_3.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_busan_gan10000_3.loc[df_busan_gan10000_3['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'busan',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan10000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5472322273711279\n",
-      "mean of accuracy :  0.9102826018248206\n",
-      "mean of mcc :  0.6822157410012658\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan10000_1.loc[df_incheon_gan10000_1['year'].isin([2018, 2019]), df_incheon_gan10000_1.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_incheon_gan10000_1.loc[df_incheon_gan10000_1['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan10000_2.loc[df_incheon_gan10000_2['year'].isin([2018, 2020]), df_incheon_gan10000_2.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_incheon_gan10000_2.loc[df_incheon_gan10000_2['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan10000_3.loc[df_incheon_gan10000_3['year'].isin([2019, 2020]), df_incheon_gan10000_3.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_incheon_gan10000_3.loc[df_incheon_gan10000_3['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'incheon',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan10000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.42595146846779247\n",
-      "mean of accuracy :  0.9694990268732688\n",
-      "mean of mcc :  0.5975633254437179\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan10000_1.loc[df_daegu_gan10000_1['year'].isin([2018, 2019]), df_daegu_gan10000_1.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_daegu_gan10000_1.loc[df_daegu_gan10000_1['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan10000_2.loc[df_daegu_gan10000_2['year'].isin([2018, 2020]), df_daegu_gan10000_2.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_daegu_gan10000_2.loc[df_daegu_gan10000_2['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan10000_3.loc[df_daegu_gan10000_3['year'].isin([2019, 2020]), df_daegu_gan10000_3.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_daegu_gan10000_3.loc[df_daegu_gan10000_3['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daegu',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan10000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.48065206351943335\n",
-      "mean of accuracy :  0.9330893238848551\n",
-      "mean of mcc :  0.6295068759845875\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan10000_1.loc[df_daejeon_gan10000_1['year'].isin([2018, 2019]), df_daejeon_gan10000_1.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_daejeon_gan10000_1.loc[df_daejeon_gan10000_1['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan10000_2.loc[df_daejeon_gan10000_2['year'].isin([2018, 2020]), df_daejeon_gan10000_2.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_daejeon_gan10000_2.loc[df_daejeon_gan10000_2['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan10000_3.loc[df_daejeon_gan10000_3['year'].isin([2019, 2020]), df_daejeon_gan10000_3.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_daejeon_gan10000_3.loc[df_daejeon_gan10000_3['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daejeon',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan10000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.48182318531993423\n",
-      "mean of accuracy :  0.9421339962239356\n",
-      "mean of mcc :  0.6331992271211336\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan10000_1.loc[df_gwangju_gan10000_1['year'].isin([2018, 2019]), df_gwangju_gan10000_1.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_gwangju_gan10000_1.loc[df_gwangju_gan10000_1['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan10000_2.loc[df_gwangju_gan10000_2['year'].isin([2018, 2020]), df_gwangju_gan10000_2.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_gwangju_gan10000_2.loc[df_gwangju_gan10000_2['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan10000_3.loc[df_gwangju_gan10000_3['year'].isin([2019, 2020]), df_gwangju_gan10000_3.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_gwangju_gan10000_3.loc[df_gwangju_gan10000_3['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'gwangju',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan10000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **7천개**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1 Fold\n",
-    "df_busan_gan7000_1= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_1_busan.csv\")\n",
-    "df_seoul_gan7000_1= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_1_seoul.csv\")\n",
-    "df_incheon_gan7000_1= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_1_incheon.csv\")\n",
-    "df_daegu_gan7000_1= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_1_daegu.csv\")\n",
-    "df_daejeon_gan7000_1= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_1_daejeon.csv\")\n",
-    "df_gwangju_gan7000_1= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_1_gwangju.csv\")\n",
-    "\n",
-    "# 2 Fold\n",
-    "df_busan_gan7000_2= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_2_busan.csv\")\n",
-    "df_seoul_gan7000_2= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_2_seoul.csv\")\n",
-    "df_incheon_gan7000_2= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_2_incheon.csv\")\n",
-    "df_daegu_gan7000_2= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_2_daegu.csv\")\n",
-    "df_daejeon_gan7000_2= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_2_daejeon.csv\")\n",
-    "df_gwangju_gan7000_2= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_2_gwangju.csv\")\n",
-    "\n",
-    "# 3 Fold\n",
-    "df_busan_gan7000_3= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_3_busan.csv\")\n",
-    "df_seoul_gan7000_3= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_3_seoul.csv\")\n",
-    "df_incheon_gan7000_3= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_3_incheon.csv\")\n",
-    "df_daegu_gan7000_3= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_3_daegu.csv\")\n",
-    "df_daejeon_gan7000_3= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_3_daejeon.csv\")\n",
-    "df_gwangju_gan7000_3= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_3_gwangju.csv\")\n",
-    "\n",
-    "\n",
-    "# for validation\n",
-    "df_busan = pd.read_csv(\"../../data/data_for_modeling/busan_train.csv\")\n",
-    "df_seoul = pd.read_csv(\"../../data/data_for_modeling/seoul_train.csv\")\n",
-    "df_incheon = pd.read_csv(\"../../data/data_for_modeling/incheon_train.csv\")\n",
-    "df_daegu = pd.read_csv(\"../../data/data_for_modeling/daegu_train.csv\")\n",
-    "df_daejeon = pd.read_csv(\"../../data/data_for_modeling/daejeon_train.csv\")\n",
-    "df_gwangju = pd.read_csv(\"../../data/data_for_modeling/gwangju_train.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_busan_gan7000_1= preprocessing(df_busan_gan7000_1).copy()\n",
-    "df_seoul_gan7000_1= preprocessing(df_seoul_gan7000_1).copy()\n",
-    "df_incheon_gan7000_1= preprocessing(df_incheon_gan7000_1).copy()\n",
-    "df_daegu_gan7000_1= preprocessing(df_daegu_gan7000_1).copy()\n",
-    "df_daejeon_gan7000_1= preprocessing(df_daejeon_gan7000_1).copy()\n",
-    "df_gwangju_gan7000_1= preprocessing(df_gwangju_gan7000_1).copy()\n",
-    "\n",
-    "df_busan_gan7000_2= preprocessing(df_busan_gan7000_2).copy()\n",
-    "df_seoul_gan7000_2= preprocessing(df_seoul_gan7000_2).copy()\n",
-    "df_incheon_gan7000_2= preprocessing(df_incheon_gan7000_2).copy()\n",
-    "df_daegu_gan7000_2= preprocessing(df_daegu_gan7000_2).copy()\n",
-    "df_daejeon_gan7000_2= preprocessing(df_daejeon_gan7000_2).copy()\n",
-    "df_gwangju_gan7000_2= preprocessing(df_gwangju_gan7000_2).copy()\n",
-    "\n",
-    "df_busan_gan7000_3= preprocessing(df_busan_gan7000_3).copy()\n",
-    "df_seoul_gan7000_3= preprocessing(df_seoul_gan7000_3).copy()\n",
-    "df_incheon_gan7000_3= preprocessing(df_incheon_gan7000_3).copy()\n",
-    "df_daegu_gan7000_3= preprocessing(df_daegu_gan7000_3).copy()\n",
-    "df_daejeon_gan7000_3= preprocessing(df_daejeon_gan7000_3).copy()\n",
-    "df_gwangju_gan7000_3= preprocessing(df_gwangju_gan7000_3).copy()\n",
-    "\n",
-    "df_busan = preprocessing(df_busan).copy()\n",
-    "df_seoul = preprocessing(df_seoul).copy()\n",
-    "df_incheon = preprocessing(df_incheon).copy()\n",
-    "df_daegu = preprocessing(df_daegu).copy()\n",
-    "df_daejeon = preprocessing(df_daejeon).copy()\n",
-    "df_gwangju = preprocessing(df_gwangju).copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5395567725547653\n",
-      "mean of accuracy :  0.9410132370187391\n",
-      "mean of mcc :  0.6810123059399511\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan7000_1.loc[df_seoul_gan7000_1['year'].isin([2018, 2019]), df_seoul_gan7000_1.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_seoul_gan7000_1.loc[df_seoul_gan7000_1['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan7000_2.loc[df_seoul_gan7000_2['year'].isin([2018, 2020]), df_seoul_gan7000_2.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_seoul_gan7000_2.loc[df_seoul_gan7000_2['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan7000_3.loc[df_seoul_gan7000_3['year'].isin([2019, 2020]), df_seoul_gan7000_3.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_seoul_gan7000_3.loc[df_seoul_gan7000_3['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'seoul',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan7000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4584344058363401\n",
-      "mean of accuracy :  0.9581093894253563\n",
-      "mean of mcc :  0.6253420379252826\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan7000_1.loc[df_busan_gan7000_1['year'].isin([2018, 2019]), df_busan_gan7000_1.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_busan_gan7000_1.loc[df_busan_gan7000_1['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan7000_2.loc[df_busan_gan7000_2['year'].isin([2018, 2020]), df_busan_gan7000_2.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_busan_gan7000_2.loc[df_busan_gan7000_2['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan7000_3.loc[df_busan_gan7000_3['year'].isin([2019, 2020]), df_busan_gan7000_3.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_busan_gan7000_3.loc[df_busan_gan7000_3['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'busan',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan7000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5498019261194692\n",
-      "mean of accuracy :  0.911005065249395\n",
-      "mean of mcc :  0.6849206337149152\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan7000_1.loc[df_incheon_gan7000_1['year'].isin([2018, 2019]), df_incheon_gan7000_1.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_incheon_gan7000_1.loc[df_incheon_gan7000_1['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan7000_2.loc[df_incheon_gan7000_2['year'].isin([2018, 2020]), df_incheon_gan7000_2.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_incheon_gan7000_2.loc[df_incheon_gan7000_2['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan7000_3.loc[df_incheon_gan7000_3['year'].isin([2019, 2020]), df_incheon_gan7000_3.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_incheon_gan7000_3.loc[df_incheon_gan7000_3['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'incheon',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan7000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.39073560033282706\n",
-      "mean of accuracy :  0.9644477089935207\n",
-      "mean of mcc :  0.5690411701305705\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan7000_1.loc[df_daegu_gan7000_1['year'].isin([2018, 2019]), df_daegu_gan7000_1.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_daegu_gan7000_1.loc[df_daegu_gan7000_1['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan7000_2.loc[df_daegu_gan7000_2['year'].isin([2018, 2020]), df_daegu_gan7000_2.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_daegu_gan7000_2.loc[df_daegu_gan7000_2['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan7000_3.loc[df_daegu_gan7000_3['year'].isin([2019, 2020]), df_daegu_gan7000_3.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_daegu_gan7000_3.loc[df_daegu_gan7000_3['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daegu',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan7000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4739750751092528\n",
-      "mean of accuracy :  0.9325196912609893\n",
-      "mean of mcc :  0.6246889480882462\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan7000_1.loc[df_daejeon_gan7000_1['year'].isin([2018, 2019]), df_daejeon_gan7000_1.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_daejeon_gan7000_1.loc[df_daejeon_gan7000_1['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan7000_2.loc[df_daejeon_gan7000_2['year'].isin([2018, 2020]), df_daejeon_gan7000_2.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_daejeon_gan7000_2.loc[df_daejeon_gan7000_2['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan7000_3.loc[df_daejeon_gan7000_3['year'].isin([2019, 2020]), df_daejeon_gan7000_3.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_daejeon_gan7000_3.loc[df_daejeon_gan7000_3['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daejeon',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan7000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.48000404186608364\n",
-      "mean of accuracy :  0.9417549342515658\n",
-      "mean of mcc :  0.6311702463153694\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan7000_1.loc[df_gwangju_gan7000_1['year'].isin([2018, 2019]), df_gwangju_gan7000_1.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_gwangju_gan7000_1.loc[df_gwangju_gan7000_1['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan7000_2.loc[df_gwangju_gan7000_2['year'].isin([2018, 2020]), df_gwangju_gan7000_2.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_gwangju_gan7000_2.loc[df_gwangju_gan7000_2['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan7000_3.loc[df_gwangju_gan7000_3['year'].isin([2019, 2020]), df_gwangju_gan7000_3.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_gwangju_gan7000_3.loc[df_gwangju_gan7000_3['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "lgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], eval_metric=csi_metric)\n",
-    "csi.append(calculate_csi(Y_val, lgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, lgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, lgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'gwangju',\n",
-    "    'model': 'LightGBM',\n",
-    "    'data_sample': 'ctgan7000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>model</th>\n",
-       "      <th>data_sample</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.483070</td>\n",
-       "      <td>0.629405</td>\n",
-       "      <td>0.929762</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.435070</td>\n",
-       "      <td>0.603839</td>\n",
-       "      <td>0.957352</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.552244</td>\n",
-       "      <td>0.685723</td>\n",
-       "      <td>0.911726</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.297486</td>\n",
-       "      <td>0.486224</td>\n",
-       "      <td>0.953857</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.485706</td>\n",
-       "      <td>0.631396</td>\n",
-       "      <td>0.933775</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.484100</td>\n",
-       "      <td>0.636355</td>\n",
-       "      <td>0.943009</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.581144</td>\n",
-       "      <td>0.709786</td>\n",
-       "      <td>0.940071</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.468977</td>\n",
-       "      <td>0.634555</td>\n",
-       "      <td>0.950656</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.584537</td>\n",
-       "      <td>0.707988</td>\n",
-       "      <td>0.911527</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.451300</td>\n",
-       "      <td>0.622832</td>\n",
-       "      <td>0.963806</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.529475</td>\n",
-       "      <td>0.663280</td>\n",
-       "      <td>0.932257</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.520426</td>\n",
-       "      <td>0.658371</td>\n",
-       "      <td>0.937114</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.527864</td>\n",
-       "      <td>0.673219</td>\n",
-       "      <td>0.940973</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.460887</td>\n",
-       "      <td>0.627600</td>\n",
-       "      <td>0.958719</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.553646</td>\n",
-       "      <td>0.679844</td>\n",
-       "      <td>0.895519</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.405144</td>\n",
-       "      <td>0.579183</td>\n",
-       "      <td>0.965851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.468279</td>\n",
-       "      <td>0.620726</td>\n",
-       "      <td>0.931758</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.481703</td>\n",
-       "      <td>0.633989</td>\n",
-       "      <td>0.942020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.544297</td>\n",
-       "      <td>0.685362</td>\n",
-       "      <td>0.942228</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.451892</td>\n",
-       "      <td>0.618579</td>\n",
-       "      <td>0.957120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.547232</td>\n",
-       "      <td>0.682216</td>\n",
-       "      <td>0.910283</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.425951</td>\n",
-       "      <td>0.597563</td>\n",
-       "      <td>0.969499</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.480652</td>\n",
-       "      <td>0.629507</td>\n",
-       "      <td>0.933089</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.481823</td>\n",
-       "      <td>0.633199</td>\n",
-       "      <td>0.942134</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.539557</td>\n",
-       "      <td>0.681012</td>\n",
-       "      <td>0.941013</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.458434</td>\n",
-       "      <td>0.625342</td>\n",
-       "      <td>0.958109</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.549802</td>\n",
-       "      <td>0.684921</td>\n",
-       "      <td>0.911005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.390736</td>\n",
-       "      <td>0.569041</td>\n",
-       "      <td>0.964448</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.473975</td>\n",
-       "      <td>0.624689</td>\n",
-       "      <td>0.932520</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>LightGBM</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.480004</td>\n",
-       "      <td>0.631170</td>\n",
-       "      <td>0.941755</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     region     model data_sample       CSI       MCC  Accuracy\n",
-       "0     seoul  LightGBM        pure  0.483070  0.629405  0.929762\n",
-       "1     busan  LightGBM        pure  0.435070  0.603839  0.957352\n",
-       "2   incheon  LightGBM        pure  0.552244  0.685723  0.911726\n",
-       "3     daegu  LightGBM        pure  0.297486  0.486224  0.953857\n",
-       "4   daejeon  LightGBM        pure  0.485706  0.631396  0.933775\n",
-       "5   gwangju  LightGBM        pure  0.484100  0.636355  0.943009\n",
-       "6     seoul  LightGBM       smote  0.581144  0.709786  0.940071\n",
-       "7     busan  LightGBM       smote  0.468977  0.634555  0.950656\n",
-       "8   incheon  LightGBM       smote  0.584537  0.707988  0.911527\n",
-       "9     daegu  LightGBM       smote  0.451300  0.622832  0.963806\n",
-       "10  daejeon  LightGBM       smote  0.529475  0.663280  0.932257\n",
-       "11  gwangju  LightGBM       smote  0.520426  0.658371  0.937114\n",
-       "12    seoul  LightGBM  ctgan20000  0.527864  0.673219  0.940973\n",
-       "13    busan  LightGBM  ctgan20000  0.460887  0.627600  0.958719\n",
-       "14  incheon  LightGBM  ctgan20000  0.553646  0.679844  0.895519\n",
-       "15    daegu  LightGBM  ctgan20000  0.405144  0.579183  0.965851\n",
-       "16  daejeon  LightGBM  ctgan20000  0.468279  0.620726  0.931758\n",
-       "17  gwangju  LightGBM  ctgan20000  0.481703  0.633989  0.942020\n",
-       "18    seoul  LightGBM  ctgan10000  0.544297  0.685362  0.942228\n",
-       "19    busan  LightGBM  ctgan10000  0.451892  0.618579  0.957120\n",
-       "20  incheon  LightGBM  ctgan10000  0.547232  0.682216  0.910283\n",
-       "21    daegu  LightGBM  ctgan10000  0.425951  0.597563  0.969499\n",
-       "22  daejeon  LightGBM  ctgan10000  0.480652  0.629507  0.933089\n",
-       "23  gwangju  LightGBM  ctgan10000  0.481823  0.633199  0.942134\n",
-       "24    seoul  LightGBM   ctgan7000  0.539557  0.681012  0.941013\n",
-       "25    busan  LightGBM   ctgan7000  0.458434  0.625342  0.958109\n",
-       "26  incheon  LightGBM   ctgan7000  0.549802  0.684921  0.911005\n",
-       "27    daegu  LightGBM   ctgan7000  0.390736  0.569041  0.964448\n",
-       "28  daejeon  LightGBM   ctgan7000  0.473975  0.624689  0.932520\n",
-       "29  gwangju  LightGBM   ctgan7000  0.480004  0.631170  0.941755"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv(\"../model_result/lightgbm_sampled_data_test.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Analysis_code/sampling_data_test/resnet_like_sampled_data_test.ipynb b/Analysis_code/sampling_data_test/resnet_like_sampled_data_test.ipynb
deleted file mode 100644
index 5195ac8b4d663371f3a1b3e5f88eeeae1a42fda0..0000000000000000000000000000000000000000
--- a/Analysis_code/sampling_data_test/resnet_like_sampled_data_test.ipynb
+++ /dev/null
@@ -1,2867 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
-    "import torch\n",
-    "from torch.utils.data import DataLoader, TensorDataset\n",
-    "import random\n",
-    "\n",
-    "# Python 및 Numpy 시드 고정\n",
-    "seed = 42\n",
-    "random.seed(seed)\n",
-    "np.random.seed(seed)\n",
-    "\n",
-    "# PyTorch 시드 고정\n",
-    "torch.manual_seed(seed)\n",
-    "torch.cuda.manual_seed(seed)\n",
-    "torch.cuda.manual_seed_all(seed)  # Multi-GPU 환경에서 동일한 시드 적용\n",
-    "\n",
-    "# PyTorch 연산의 결정적 모드 설정\n",
-    "torch.backends.cudnn.deterministic = True  # 실행마다 동일한 결과를 보장\n",
-    "torch.backends.cudnn.benchmark = True  # 성능 최적화를 활성화 (가능한 한 빠른 연산 수행)\n",
-    "\n",
-    "# 전처리 함수\n",
-    "def preprocessing(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df['lm_cloudcover'] = df['lm_cloudcover'].astype('int')\n",
-    "    df['cloudcover'] = df['cloudcover'].astype('int')\n",
-    "    return df\n",
-    "\n",
-    "# 데이터셋 준비 함수\n",
-    "def prepare_dataset(region, data_sample='pure', target='multi', fold=3):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    return X_train, X_val, X_test, y_train, y_val, y_test, categorical_cols, numerical_cols\n",
-    "\n",
-    "# 데이터 변환 및 dataloader 생성 함수\n",
-    "def prepare_dataloader(region, data_sample='pure', target='multi', fold=3, random_state=None):\n",
-    "\n",
-    "    # 데이터 경로 지정\n",
-    "    dat_path = f\"../../data/data_for_modeling/{region}_train.csv\"\n",
-    "    if data_sample == 'pure':\n",
-    "        train_path = dat_path\n",
-    "    else:\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "        train_path = f'../../data/data_oversampled/{data_sample}/{data_sample}_{fold}_{region}.csv'\n",
-    "    test_path = f\"../../data/data_for_modeling/{region}_test.csv\"\n",
-    "    drop_col = ['binary_class','multi_class','visi','year']\n",
-    "    target_col = f'{target}_class'\n",
-    "    \n",
-    "    # 데이터 로드\n",
-    "    region_dat = preprocessing(pd.read_csv(dat_path, index_col=0))\n",
-    "    if data_sample == 'pure':\n",
-    "        region_train = region_dat.loc[~region_dat['year'].isin([2021-fold]), :]\n",
-    "    else:\n",
-    "        region_train = preprocessing(pd.read_csv(train_path))\n",
-    "    region_val = region_dat.loc[region_dat['year'].isin([2021-fold]), :]\n",
-    "    region_test = preprocessing(pd.read_csv(test_path))\n",
-    "\n",
-    "    # 컬럼 정렬 (일관성 유지)\n",
-    "    common_columns = region_train.columns.to_list()\n",
-    "    train_data = region_train[common_columns]\n",
-    "    val_data = region_val[common_columns]\n",
-    "    test_data = region_test[common_columns]\n",
-    "\n",
-    "    # 설명변수 & 타겟 분리\n",
-    "    X_train = train_data.drop(columns=drop_col)\n",
-    "    y_train = train_data[target_col]\n",
-    "    X_val = val_data.drop(columns=drop_col)\n",
-    "    y_val = val_data[target_col]\n",
-    "    X_test = test_data.drop(columns=drop_col)\n",
-    "    y_test = test_data[target_col]\n",
-    "\n",
-    "    # 범주형 & 연속형 변수 분리\n",
-    "    categorical_cols = X_train.select_dtypes(include=['object', 'category', 'int64']).columns\n",
-    "    numerical_cols = X_train.select_dtypes(include=['float64']).columns\n",
-    "\n",
-    "    # 범주형 변수 Label Encoding\n",
-    "    label_encoders = {}\n",
-    "    for col in categorical_cols:\n",
-    "        le = LabelEncoder()\n",
-    "        le.fit(X_train[col])  # Train 데이터 기준으로 학습\n",
-    "        label_encoders[col] = le\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    for col in categorical_cols:\n",
-    "        X_train[col] = label_encoders[col].transform(X_train[col])\n",
-    "        X_val[col] = label_encoders[col].transform(X_val[col])\n",
-    "        X_test[col] = label_encoders[col].transform(X_test[col])\n",
-    "\n",
-    "    # 연속형 변수 Standard Scaling\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train[numerical_cols])  # Train 데이터 기준으로 학습\n",
-    "\n",
-    "    # 변환 적용\n",
-    "    X_train[numerical_cols] = scaler.transform(X_train[numerical_cols])\n",
-    "    X_val[numerical_cols] = scaler.transform(X_val[numerical_cols])\n",
-    "    X_test[numerical_cols] = scaler.transform(X_test[numerical_cols])\n",
-    "\n",
-    "    # 연속형 변수와 범주형 변수 분리\n",
-    "    X_train_num = torch.tensor(X_train[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_train_cat = torch.tensor(X_train[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_val_num = torch.tensor(X_val[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_val_cat = torch.tensor(X_val[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    X_test_num = torch.tensor(X_test[numerical_cols].values, dtype=torch.float32)\n",
-    "    X_test_cat = torch.tensor(X_test[categorical_cols].values, dtype=torch.long)\n",
-    "\n",
-    "    # 레이블 변환\n",
-    "    if target == \"binary\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.float32)  # 이진 분류 → float32\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.float32)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.float32)\n",
-    "    elif target == \"multi\":\n",
-    "        y_train_tensor = torch.tensor(y_train.values, dtype=torch.long)  # 다중 분류 → long\n",
-    "        y_val_tensor = torch.tensor(y_val.values, dtype=torch.long)\n",
-    "        y_test_tensor = torch.tensor(y_test.values, dtype=torch.long)\n",
-    "    else:\n",
-    "        raise ValueError(\"target must be 'binary' or 'multi'\")\n",
-    "\n",
-    "    # TensorDataset 생성\n",
-    "    train_dataset = TensorDataset(X_train_num, X_train_cat, y_train_tensor)\n",
-    "    val_dataset = TensorDataset(X_val_num, X_val_cat, y_val_tensor)\n",
-    "    test_dataset = TensorDataset(X_test_num, X_test_cat, y_test_tensor)\n",
-    "\n",
-    "    # DataLoader 생성\n",
-    "    if random_state == None:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
-    "    else:\n",
-    "        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, generator=torch.Generator().manual_seed(random_state))\n",
-    "    val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n",
-    "    test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
-    "    \n",
-    "    return X_train, categorical_cols, numerical_cols, train_loader, val_loader, test_loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append('../')\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "import optuna\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import random\n",
-    "from ft_transformer import FTTransformer\n",
-    "from resnet_like import ResNetLike\n",
-    "from deepgbm import DeepGBM\n",
-    "from pytorch_tabnet.tab_model import TabNetClassifier\n",
-    "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul = pd.read_csv(\"../../data/data_for_modeling/seoul_train.csv\")\n",
-    "df_seoul_test = pd.read_csv(\"../../data/data_for_modeling/seoul_test.csv\")\n",
-    "\n",
-    "df_busan = pd.read_csv(\"../../data/data_for_modeling/busan_train.csv\")\n",
-    "df_busan_test = pd.read_csv(\"../../data/data_for_modeling/busan_test.csv\")\n",
-    "\n",
-    "df_daegu = pd.read_csv(\"../../data/data_for_modeling/daegu_train.csv\")\n",
-    "df_daegu_test = pd.read_csv(\"../../data/data_for_modeling/daegu_test.csv\")\n",
-    "\n",
-    "df_daejeon = pd.read_csv(\"../../data/data_for_modeling/daejeon_train.csv\")\n",
-    "df_daejeon_test = pd.read_csv(\"../../data/data_for_modeling/daejeon_test.csv\")\n",
-    "\n",
-    "df_incheon = pd.read_csv(\"../../data/data_for_modeling/incheon_train.csv\")\n",
-    "df_incheon_test = pd.read_csv(\"../../data/data_for_modeling/incheon_test.csv\")\n",
-    "\n",
-    "df_gwangju = pd.read_csv(\"../../data/data_for_modeling/gwangju_train.csv\")\n",
-    "df_gwangju_test = pd.read_csv(\"../../data/data_for_modeling/gwangju_test.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "# 디바이스 설정 (CUDA 사용 가능하면 GPU로, 아니면 CPU로)\n",
-    "import glob\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "from sklearn.metrics import matthews_corrcoef\n",
-    "\n",
-    "def calculate_csi(Y_test, pred):\n",
-    "\n",
-    "    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경\n",
-    "    # 혼동 행렬에서 H, F, M 추출\n",
-    "    H = (cm[0, 0] + cm[1, 1])\n",
-    "    \n",
-    "    F = (cm[1, 0] + cm[2, 0] +\n",
-    "         cm[0, 1] + cm[2, 1])\n",
-    "\n",
-    "    M = (cm[0, 2] + cm[1, 2])\n",
-    "    \n",
-    "    # CSI 계산\n",
-    "    CSI = H / (H + F + M + 1e-10)\n",
-    "    return CSI\n",
-    "\n",
-    "# Soft Voting 앙상블\n",
-    "def get_proba(region, model_choose, data_sample, fold, target='multi'):\n",
-    "    _, _, _, _,val_loader , test_loader = prepare_dataloader(region=region, data_sample=data_sample, target=target,fold=fold ,random_state=120)\n",
-    "    _ ,_,_ ,_ , y_val,_ ,_ ,_ = prepare_dataset(region=region, data_sample=data_sample, target=target, fold=fold)\n",
-    "\n",
-    "    folder_path = f'../save_model/{model_choose}/{data_sample}'\n",
-    "    model_paths = [path for path in glob.glob(f'{folder_path}/*.pth') if f'{region}' in path]\n",
-    "\n",
-    "    model = torch.load(model_paths[fold-1], weights_only=False).to(device)\n",
-    "    model.eval()\n",
-    "\n",
-    "    val_preds = []\n",
-    "\n",
-    "\n",
-    "    with torch.no_grad():\n",
-    "        for x_num_batch, x_cat_batch, _ in val_loader:\n",
-    "            output = model(x_num_batch.to(device), x_cat_batch.to(device))\n",
-    "            val_proba = torch.softmax(output, dim=1)\n",
-    "            val_preds.extend(val_proba.cpu().numpy())\n",
-    "            \n",
-    "    val_preds = np.array(val_preds)\n",
-    "\n",
-    "    return val_preds, y_val\n",
-    "\n",
-    "\n",
-    "# 예시 입력(길이 동일해야 함)\n",
-    "# y_val = 실제 레이블 (배열, 예: [0, 2, 1, 0, 2, ...])\n",
-    "# y_pred = 예측 레이블 (배열, 예: [0, 2, 1, 0, 1, ...])\n",
-    "\n",
-    "def multiclass_mcc(y_val, y_pred):\n",
-    "    \"\"\"\n",
-    "    다중 분류에서도 sklearn의 matthews_corrcoef를 그대로 사용할 수 있음.\n",
-    "    \"\"\"\n",
-    "    return matthews_corrcoef(y_val, y_pred)\n",
-    "\n",
-    "\n",
-    "\n",
-    "def multiclass_brier_score(y_val, y_proba):\n",
-    "    \"\"\"\n",
-    "    다중분류용 Brier Score 계산\n",
-    "    - y_val : 실제 레이블 (0, 1, 2)\n",
-    "    - y_proba : (N, 3) 형태의 예측 확률\n",
-    "    \"\"\"\n",
-    "    n_samples = len(y_val)\n",
-    "    y_val= np.array(y_val)\n",
-    "    n_class = y_proba.shape[1]\n",
-    "    \n",
-    "    # 실제 레이블을 one-hot으로 변환\n",
-    "    y_true_oh = np.zeros((n_samples, n_class))\n",
-    "    for i in range(n_samples):\n",
-    "        y_true_oh[i, y_val[i]] = 1\n",
-    "    \n",
-    "    # Brier Score 계산\n",
-    "    # (1 / N) * Σᵢ Σₖ (pᵢₖ - oᵢₖ)²\n",
-    "    bs = np.mean(np.sum((y_proba - y_true_oh) ** 2, axis=1))\n",
-    "    return bs\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df= pd.DataFrame(columns=['region','model','data_sample','CSI','MCC','Accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **원데이터**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5556513128454227\n",
-      "mean of accuracy :  0.939922004308373\n",
-      "mean of mcc :  0.6944843892225018\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'seoul'\n",
-    "datasample = 'pure'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5246252526595989\n",
-      "mean of accuracy :  0.9591643378163702\n",
-      "mean of mcc :  0.6874494677398264\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'busan'\n",
-    "datasample = 'pure'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5799566950805518\n",
-      "mean of accuracy :  0.9153711397227005\n",
-      "mean of mcc :  0.7030239128662202\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'incheon'\n",
-    "datasample = 'pure'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4394531403147615\n",
-      "mean of accuracy :  0.9659717543728323\n",
-      "mean of mcc :  0.6143074280063717\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daegu'\n",
-    "datasample = 'pure'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5430465060488938\n",
-      "mean of accuracy :  0.9349603055784282\n",
-      "mean of mcc :  0.6757025454804458\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daejeon'\n",
-    "datasample = 'pure'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5577057247702664\n",
-      "mean of accuracy :  0.9437623200339346\n",
-      "mean of mcc :  0.689775566381261\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'gwangju'\n",
-    "datasample = 'pure'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **SMOTE 증강 데이터**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.6137456175954706\n",
-      "mean of accuracy :  0.9452800234548494\n",
-      "mean of mcc :  0.7422130871955783\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'seoul'\n",
-    "datasample = 'smote'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5023305919728182\n",
-      "mean of accuracy :  0.9473319069125267\n",
-      "mean of mcc :  0.6726508273995124\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'busan'\n",
-    "datasample = 'smote'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5860766625532056\n",
-      "mean of accuracy :  0.9089022381914814\n",
-      "mean of mcc :  0.7078408929922378\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'incheon'\n",
-    "datasample = 'smote'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4901581106489206\n",
-      "mean of accuracy :  0.9662389483577446\n",
-      "mean of mcc :  0.6522246131616541\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daegu'\n",
-    "datasample = 'smote'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5108150204429799\n",
-      "mean of accuracy :  0.9180820670209847\n",
-      "mean of mcc :  0.6519519041478157\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daejeon'\n",
-    "datasample = 'smote'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.50385899296261\n",
-      "mean of accuracy :  0.9282384534770567\n",
-      "mean of mcc :  0.6510080124725456\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'gwangju'\n",
-    "datasample = 'smote'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **GAN 20000**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5072327540718837\n",
-      "mean of accuracy :  0.9275254510068119\n",
-      "mean of mcc :  0.6620612708395112\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'seoul'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.38986584909236405\n",
-      "mean of accuracy :  0.9392384243664279\n",
-      "mean of mcc :  0.5796375719673601\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'busan'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5432016410802436\n",
-      "mean of accuracy :  0.8980009315401186\n",
-      "mean of mcc :  0.6676977064251691\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'incheon'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.2688077976760475\n",
-      "mean of accuracy :  0.9229636075554558\n",
-      "mean of mcc :  0.440646061562418\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daegu'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5006204822135542\n",
-      "mean of accuracy :  0.9202535119894204\n",
-      "mean of mcc :  0.6492987602101151\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daejeon'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.444672867325423\n",
-      "mean of accuracy :  0.9136339629546457\n",
-      "mean of mcc :  0.5955785036774024\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'gwangju'\n",
-    "datasample = 'ctgan20000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **GAN 10000**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5304050574661009\n",
-      "mean of accuracy :  0.9339477755321007\n",
-      "mean of mcc :  0.6815565974252659\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'seoul'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5675115951993178\n",
-      "mean of accuracy :  0.965488621902837\n",
-      "mean of mcc :  0.7176500871011381\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'busan'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5608852333036517\n",
-      "mean of accuracy :  0.9037794953048714\n",
-      "mean of mcc :  0.6835219927895569\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'incheon'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.3723832547505761\n",
-      "mean of accuracy :  0.9578420914739127\n",
-      "mean of mcc :  0.5464472897265699\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daegu'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5181167801633592\n",
-      "mean of accuracy :  0.9187646072976188\n",
-      "mean of mcc :  0.6610880657167911\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daejeon'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.40351880029410764\n",
-      "mean of accuracy :  0.9066332726168792\n",
-      "mean of mcc :  0.5649610782208994\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'gwangju'\n",
-    "datasample = 'ctgan10000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **GAN 7000**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5731369888329633\n",
-      "mean of accuracy :  0.9410729138075871\n",
-      "mean of mcc :  0.7091329811132822\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'seoul'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4605071450423024\n",
-      "mean of accuracy :  0.9479047624988564\n",
-      "mean of mcc :  0.638239831036732\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'busan'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5343315289869124\n",
-      "mean of accuracy :  0.8994391005647463\n",
-      "mean of mcc :  0.6644525961066555\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'incheon'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.44530071901663565\n",
-      "mean of accuracy :  0.9673712478478929\n",
-      "mean of mcc :  0.6174979682782439\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daegu'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5092618652909245\n",
-      "mean of accuracy :  0.9181710623716013\n",
-      "mean of mcc :  0.6496900098750791\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'daejeon'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.49270227739086486\n",
-      "mean of accuracy :  0.9272533705949381\n",
-      "mean of mcc :  0.6375371144090152\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "region = 'gwangju'\n",
-    "datasample = 'ctgan7000'\n",
-    "model= 'resnet_like'\n",
-    "\n",
-    "# 1 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 1)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "# 2 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 2)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "# 3 Fold\n",
-    "val_preds, y_val = get_proba(region, model, datasample, 3)\n",
-    "csi.append(calculate_csi(y_val, np.argmax(val_preds, axis=1)))\n",
-    "accuracy.append(accuracy_score(y_val, np.argmax(val_preds, axis=1)))\n",
-    "mcc.append(multiclass_mcc(y_val, np.argmax(val_preds, axis=1)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': region,\n",
-    "    'model': model,\n",
-    "    'data_sample': datasample,\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv(\"../model_result/resnet_like_sampled_data_test.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>model</th>\n",
-       "      <th>data_sample</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.555651</td>\n",
-       "      <td>0.694484</td>\n",
-       "      <td>0.939922</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.524625</td>\n",
-       "      <td>0.687449</td>\n",
-       "      <td>0.959164</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.579957</td>\n",
-       "      <td>0.703024</td>\n",
-       "      <td>0.915371</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.439453</td>\n",
-       "      <td>0.614307</td>\n",
-       "      <td>0.965972</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.543047</td>\n",
-       "      <td>0.675703</td>\n",
-       "      <td>0.934960</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.557706</td>\n",
-       "      <td>0.689776</td>\n",
-       "      <td>0.943762</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.613746</td>\n",
-       "      <td>0.742213</td>\n",
-       "      <td>0.945280</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.502331</td>\n",
-       "      <td>0.672651</td>\n",
-       "      <td>0.947332</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.586077</td>\n",
-       "      <td>0.707841</td>\n",
-       "      <td>0.908902</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.490158</td>\n",
-       "      <td>0.652225</td>\n",
-       "      <td>0.966239</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.510815</td>\n",
-       "      <td>0.651952</td>\n",
-       "      <td>0.918082</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.503859</td>\n",
-       "      <td>0.651008</td>\n",
-       "      <td>0.928238</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.507233</td>\n",
-       "      <td>0.662061</td>\n",
-       "      <td>0.927525</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.389866</td>\n",
-       "      <td>0.579638</td>\n",
-       "      <td>0.939238</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.543202</td>\n",
-       "      <td>0.667698</td>\n",
-       "      <td>0.898001</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.268808</td>\n",
-       "      <td>0.440646</td>\n",
-       "      <td>0.922964</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.500620</td>\n",
-       "      <td>0.649299</td>\n",
-       "      <td>0.920254</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.444673</td>\n",
-       "      <td>0.595579</td>\n",
-       "      <td>0.913634</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.530405</td>\n",
-       "      <td>0.681557</td>\n",
-       "      <td>0.933948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.567512</td>\n",
-       "      <td>0.717650</td>\n",
-       "      <td>0.965489</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.560885</td>\n",
-       "      <td>0.683522</td>\n",
-       "      <td>0.903779</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.372383</td>\n",
-       "      <td>0.546447</td>\n",
-       "      <td>0.957842</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.518117</td>\n",
-       "      <td>0.661088</td>\n",
-       "      <td>0.918765</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.403519</td>\n",
-       "      <td>0.564961</td>\n",
-       "      <td>0.906633</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.573137</td>\n",
-       "      <td>0.709133</td>\n",
-       "      <td>0.941073</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.460507</td>\n",
-       "      <td>0.638240</td>\n",
-       "      <td>0.947905</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.534332</td>\n",
-       "      <td>0.664453</td>\n",
-       "      <td>0.899439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.445301</td>\n",
-       "      <td>0.617498</td>\n",
-       "      <td>0.967371</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.509262</td>\n",
-       "      <td>0.649690</td>\n",
-       "      <td>0.918171</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>resnet_like</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.492702</td>\n",
-       "      <td>0.637537</td>\n",
-       "      <td>0.927253</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     region        model data_sample       CSI       MCC  Accuracy\n",
-       "0     seoul  resnet_like        pure  0.555651  0.694484  0.939922\n",
-       "1     busan  resnet_like        pure  0.524625  0.687449  0.959164\n",
-       "2   incheon  resnet_like        pure  0.579957  0.703024  0.915371\n",
-       "3     daegu  resnet_like        pure  0.439453  0.614307  0.965972\n",
-       "4   daejeon  resnet_like        pure  0.543047  0.675703  0.934960\n",
-       "5   gwangju  resnet_like        pure  0.557706  0.689776  0.943762\n",
-       "6     seoul  resnet_like       smote  0.613746  0.742213  0.945280\n",
-       "7     busan  resnet_like       smote  0.502331  0.672651  0.947332\n",
-       "8   incheon  resnet_like       smote  0.586077  0.707841  0.908902\n",
-       "9     daegu  resnet_like       smote  0.490158  0.652225  0.966239\n",
-       "10  daejeon  resnet_like       smote  0.510815  0.651952  0.918082\n",
-       "11  gwangju  resnet_like       smote  0.503859  0.651008  0.928238\n",
-       "12    seoul  resnet_like  ctgan20000  0.507233  0.662061  0.927525\n",
-       "13    busan  resnet_like  ctgan20000  0.389866  0.579638  0.939238\n",
-       "14  incheon  resnet_like  ctgan20000  0.543202  0.667698  0.898001\n",
-       "15    daegu  resnet_like  ctgan20000  0.268808  0.440646  0.922964\n",
-       "16  daejeon  resnet_like  ctgan20000  0.500620  0.649299  0.920254\n",
-       "17  gwangju  resnet_like  ctgan20000  0.444673  0.595579  0.913634\n",
-       "18    seoul  resnet_like  ctgan10000  0.530405  0.681557  0.933948\n",
-       "19    busan  resnet_like  ctgan10000  0.567512  0.717650  0.965489\n",
-       "20  incheon  resnet_like  ctgan10000  0.560885  0.683522  0.903779\n",
-       "21    daegu  resnet_like  ctgan10000  0.372383  0.546447  0.957842\n",
-       "22  daejeon  resnet_like  ctgan10000  0.518117  0.661088  0.918765\n",
-       "23  gwangju  resnet_like  ctgan10000  0.403519  0.564961  0.906633\n",
-       "24    seoul  resnet_like   ctgan7000  0.573137  0.709133  0.941073\n",
-       "25    busan  resnet_like   ctgan7000  0.460507  0.638240  0.947905\n",
-       "26  incheon  resnet_like   ctgan7000  0.534332  0.664453  0.899439\n",
-       "27    daegu  resnet_like   ctgan7000  0.445301  0.617498  0.967371\n",
-       "28  daejeon  resnet_like   ctgan7000  0.509262  0.649690  0.918171\n",
-       "29  gwangju  resnet_like   ctgan7000  0.492702  0.637537  0.927253"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Analysis_code/sampling_data_test/xgb_non_hierarchy_sampled_test.ipynb b/Analysis_code/sampling_data_test/xgb_non_hierarchy_sampled_test.ipynb
deleted file mode 100644
index e7c39f13ddc1ab165858bf629745a8ee1003eac4..0000000000000000000000000000000000000000
--- a/Analysis_code/sampling_data_test/xgb_non_hierarchy_sampled_test.ipynb
+++ /dev/null
@@ -1,3348 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **XGBoost**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from xgboost import XGBClassifier\n",
-    "from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score\n",
-    "from collections import Counter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_seoul = pd.read_csv(\"../../data/data_for_modeling/seoul_train.csv\")\n",
-    "# df_seoul_test = pd.read_csv(\"../../data/data_for_modeling/seoul_test.csv\")\n",
-    "\n",
-    "df_busan = pd.read_csv(\"../../data/data_for_modeling/busan_train.csv\")\n",
-    "#df_busan_test = pd.read_csv(\"../../data/data_for_modeling/busan_test.csv\")\n",
-    "\n",
-    "df_daegu = pd.read_csv(\"../../data/data_for_modeling/daegu_train.csv\")\n",
-    "#df_daegu_test = pd.read_csv(\"../../data/data_for_modeling/daegu_test.csv\")\n",
-    "\n",
-    "df_daejeon = pd.read_csv(\"../../data/data_for_modeling/daejeon_train.csv\")\n",
-    "#df_daejeon_test = pd.read_csv(\"../../data/data_for_modeling/daejeon_test.csv\")\n",
-    "\n",
-    "df_incheon = pd.read_csv(\"../../data/data_for_modeling/incheon_train.csv\")\n",
-    "#df_incheon_test = pd.read_csv(\"../../data/data_for_modeling/incheon_test.csv\")\n",
-    "\n",
-    "df_gwangju = pd.read_csv(\"../../data/data_for_modeling/gwangju_train.csv\")\n",
-    "#df_gwangju_test = pd.read_csv(\"../../data/data_for_modeling/gwangju_test.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "seoul           :  Counter({2: 23686, 1: 2579, 0: 39})\n",
-      "\n",
-      "busan           :  Counter({2: 24694, 1: 1516, 0: 94})\n",
-      "\n",
-      "daegu           :  Counter({2: 25149, 1: 1107, 0: 48})\n",
-      "\n",
-      "gwangju         :  Counter({2: 23798, 1: 2411, 0: 95})\n",
-      "\n",
-      "daejeon         :  Counter({2: 23471, 1: 2660, 0: 173})\n",
-      "\n",
-      "incheon         :  Counter({2: 21893, 1: 3892, 0: 519})\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"seoul           : \", Counter(df_seoul['multi_class']))\n",
-    "print()\n",
-    "print(\"busan           : \", Counter(df_busan['multi_class']))\n",
-    "print()\n",
-    "print(\"daegu           : \", Counter(df_daegu['multi_class']))\n",
-    "print()\n",
-    "print(\"gwangju         : \", Counter(df_gwangju['multi_class']))\n",
-    "print()\n",
-    "print(\"daejeon         : \", Counter(df_daejeon['multi_class']))\n",
-    "print()\n",
-    "print(\"incheon         : \", Counter(df_incheon['multi_class']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocessing(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df['year'] = df['year'].astype('int')\n",
-    "    df['month'] = df['month'].astype('int')\n",
-    "    df['hour'] = df['hour'].astype('int')\n",
-    "    df['binary_class'] = df['binary_class'].astype('int')\n",
-    "    df['multi_class'] = df['multi_class'].astype('int')\n",
-    "\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',\n",
-    "       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',\n",
-    "       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',\n",
-    "       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',\n",
-    "       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',\n",
-    "       'month_sin', 'month_cos','multi_class']]\n",
-    "    return df\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# df_seoul_test= preprocessing(df_seoul_test).copy()\n",
-    "# df_busan_test= preprocessing(df_busan_test).copy()\n",
-    "# df_daegu_test= preprocessing(df_daegu_test).copy()\n",
-    "# df_gwangju_test= preprocessing(df_gwangju_test).copy()\n",
-    "# df_daejeon_test= preprocessing(df_daejeon_test).copy()\n",
-    "# df_incheon_test= preprocessing(df_incheon_test).copy()\n",
-    "\n",
-    "df_seoul= preprocessing(df_seoul).copy()\n",
-    "df_busan= preprocessing(df_busan).copy()\n",
-    "df_daegu= preprocessing(df_daegu).copy()\n",
-    "df_gwangju= preprocessing(df_gwangju).copy()\n",
-    "df_daejeon= preprocessing(df_daejeon).copy()\n",
-    "df_incheon= preprocessing(df_incheon).copy()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# df_seoul_test.drop(columns=['year'], inplace=True)\n",
-    "# df_busan_test.drop(columns=['year'], inplace=True)\n",
-    "# df_daegu_test.drop(columns=['year'], inplace=True)\n",
-    "# df_daejeon_test.drop(columns=['year'], inplace=True)\n",
-    "# df_incheon_test.drop(columns=['year'], inplace=True)\n",
-    "# df_gwangju_test.drop(columns=['year'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1, 2])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.unique(df_seoul['multi_class'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import confusion_matrix\n",
-    "from sklearn.utils.class_weight import compute_class_weight\n",
-    "\n",
-    "def calculate_csi(Y_test, pred):\n",
-    "\n",
-    "    cm = confusion_matrix(Y_test, pred)  # 변수 이름을 cm으로 변경\n",
-    "    # 혼동 행렬에서 H, F, M 추출\n",
-    "    H = (cm[0, 0] + cm[1, 1])\n",
-    "    \n",
-    "    F = (cm[1, 0] + cm[2, 0] +\n",
-    "         cm[0, 1] + cm[2, 1])\n",
-    "    \n",
-    "    M = (cm[0, 2] + cm[1, 2])\n",
-    "    \n",
-    "    # CSI 계산\n",
-    "    CSI = H / (H + F + M + 1e-10)\n",
-    "    return CSI\n",
-    "\n",
-    "def eval_metric_csi(y_true, pred_prob):\n",
-    "\n",
-    "    pred = np.argmax(pred_prob, axis=1)\n",
-    "    y_true = y_true\n",
-    "    y_pred = pred\n",
-    "    csi = calculate_csi(y_true, y_pred)\n",
-    "    return -1*csi\n",
-    "\n",
-    "def sample_weight(y_train):\n",
-    "    class_weights = compute_class_weight(\n",
-    "        class_weight='balanced',\n",
-    "        classes=np.unique(y_train),  # 고유 클래스\n",
-    "        y=y_train                   # 학습 데이터 레이블\n",
-    "    )\n",
-    "    sample_weights = np.array([class_weights[label] for label in y_train])\n",
-    "\n",
-    "    return sample_weights\n",
-    "\n",
-    "\n",
-    "\n",
-    "from sklearn.metrics import matthews_corrcoef\n",
-    "\n",
-    "def multiclass_mcc(y_val, y_pred):\n",
-    "    \"\"\"\n",
-    "    다중 분류에서도 sklearn의 matthews_corrcoef를 그대로 사용할 수 있음.\n",
-    "    \"\"\"\n",
-    "    return matthews_corrcoef(y_val, y_pred)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 사용자 정의 평가 지표 함수 정의\n",
-    "def f1_metric(y_true, pred):\n",
-    "    y_pred_binary = (pred >= 0.5).astype(int)  # 확률 값을 이진 값으로 변환\n",
-    "    score = f1_score(y_true, y_pred_binary)\n",
-    "    return 'f1', score, True  # higher_better=True\n",
-    "\n",
-    "# 사용자 정의 평가 지표 함수 정의\n",
-    "def custom_metric(Y_true, preds): \n",
-    "    pred = (preds >= 0.5).astype(int)\n",
-    "    score = f1_score(Y_true, pred)\n",
-    "    return -score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pre_sampled_data= []\n",
-    "smote_sample_data= []\n",
-    "gan20000_sample_data= []\n",
-    "gan10000_sample_data= []\n",
-    "gan7000_sample_data= []"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import confusion_matrix,accuracy_score, recall_score, precision_score\n",
-    "from scipy.stats import mode  # 최다 득표수 계산을 위한 mode 함수\n",
-    "from warnings import filterwarnings\n",
-    "filterwarnings('ignore')\n",
-    "\n",
-    "# XGBoost 분류기 초기화\n",
-    "xgb_model = XGBClassifier(\n",
-    "    n_estimators=4000, # 약한 학습기 개수\n",
-    "    tree_method='hist', \n",
-    "    device='cuda', # GPU 사용\n",
-    "    enable_categorical=True, # 범주형 변수 지정\n",
-    "    objective='multi:softprob', \n",
-    "    early_stopping_rounds=400, # 과적합 방지를 위한 조기 종료 설정\n",
-    "    random_state= 120,\n",
-    "    num_class=3,\n",
-    "    eval_metric = eval_metric_csi\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df= pd.DataFrame(columns=['region','model','data_sample','CSI','MCC','Accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.565011886957529\n",
-      "mean of accuracy :  0.9455723773402865\n",
-      "mean of mcc :  0.7004827765196581\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul.loc[df_seoul['year'].isin([2018, 2019]), df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul.loc[df_seoul['year'].isin([2018, 2020]), df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul.loc[df_seoul['year'].isin([2019, 2020]), df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_seoul.loc[df_seoul['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'seoul',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'pure',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4602444563246328\n",
-      "mean of accuracy :  0.9597446789929386\n",
-      "mean of mcc :  0.6260853753404706\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan.loc[df_busan['year'].isin([2018, 2019]), df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan.loc[df_busan['year'].isin([2018, 2020]), df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan.loc[df_busan['year'].isin([2019, 2020]), df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_busan.loc[df_busan['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'busan',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'pure',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5581443524210103\n",
-      "mean of accuracy :  0.9130578844058522\n",
-      "mean of mcc :  0.6892177198119791\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon.loc[df_incheon['year'].isin([2018, 2019]), df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon.loc[df_incheon['year'].isin([2018, 2020]), df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon.loc[df_incheon['year'].isin([2019, 2020]), df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_incheon.loc[df_incheon['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'incheon',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'pure',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4166514505499186\n",
-      "mean of accuracy :  0.9683242050719033\n",
-      "mean of mcc :  0.600956509945712\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu.loc[df_daegu['year'].isin([2018, 2019]), df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu.loc[df_daegu['year'].isin([2018, 2020]), df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu.loc[df_daegu['year'].isin([2019, 2020]), df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_daegu.loc[df_daegu['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daegu',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'pure',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5044504047922097\n",
-      "mean of accuracy :  0.9357524265788357\n",
-      "mean of mcc :  0.6471930212882061\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon.loc[df_daejeon['year'].isin([2018, 2019]), df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon.loc[df_daejeon['year'].isin([2018, 2020]), df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon.loc[df_daejeon['year'].isin([2019, 2020]), df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daejeon',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'pure',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.49003643817143744\n",
-      "mean of accuracy :  0.943428795402184\n",
-      "mean of mcc :  0.6379812479286601\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju.loc[df_gwangju['year'].isin([2018, 2019]), df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju.loc[df_gwangju['year'].isin([2018, 2020]), df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju.loc[df_gwangju['year'].isin([2019, 2020]), df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'gwangju',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'pure',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **SMOTE 증강기법을 적용시킨 데이터셋에 대한 성능**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocessing(df):\n",
-    "    df = df[df.columns].copy()\n",
-    "    df['year'] = df['year'].astype('int')\n",
-    "    df['month'] = df['month'].astype('int')\n",
-    "    df['hour'] = df['hour'].astype('int')\n",
-    "    df['binary_class'] = df['binary_class'].astype('int')\n",
-    "    df['multi_class'] = df['multi_class'].astype('int')\n",
-    "\n",
-    "    df.loc[df['wind_dir']=='정온', 'wind_dir'] = \"0\"\n",
-    "    df['wind_dir'] = df['wind_dir'].astype('int')\n",
-    "    df= df[['temp_C', 'precip_mm', 'wind_speed', 'wind_dir', 'hm',\n",
-    "       'vap_pressure', 'dewpoint_C', 'loc_pressure', 'sea_pressure',\n",
-    "       'solarRad', 'snow_cm', 'cloudcover', 'lm_cloudcover', 'low_cloudbase',\n",
-    "       'groundtemp', 'O3', 'NO2', 'PM10', 'PM25', 'year',\n",
-    "       'month', 'hour', 'ground_temp - temp_C', 'hour_sin', 'hour_cos',\n",
-    "       'month_sin', 'month_cos','multi_class']]\n",
-    "    return df\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_smote_busan_1 = pd.read_csv(\"../../data/data_oversampled/smote/smote_1_busan.csv\")\n",
-    "df_smote_busan_2 = pd.read_csv(\"../../data/data_oversampled/smote/smote_2_busan.csv\")\n",
-    "df_smote_busan_3 = pd.read_csv(\"../../data/data_oversampled/smote/smote_3_busan.csv\")\n",
-    "\n",
-    "df_smote_seoul_1 = pd.read_csv(\"../../data/data_oversampled/smote/smote_1_seoul.csv\")\n",
-    "df_smote_seoul_2 = pd.read_csv(\"../../data/data_oversampled/smote/smote_2_seoul.csv\")\n",
-    "df_smote_seoul_3 = pd.read_csv(\"../../data/data_oversampled/smote/smote_3_seoul.csv\")\n",
-    "\n",
-    "df_smote_daegu_1 = pd.read_csv(\"../../data/data_oversampled/smote/smote_1_daegu.csv\")\n",
-    "df_smote_daegu_2 = pd.read_csv(\"../../data/data_oversampled/smote/smote_2_daegu.csv\")\n",
-    "df_smote_daegu_3 = pd.read_csv(\"../../data/data_oversampled/smote/smote_3_daegu.csv\")\n",
-    "\n",
-    "df_smote_daejeon_1 = pd.read_csv(\"../../data/data_oversampled/smote/smote_1_daejeon.csv\")\n",
-    "df_smote_daejeon_2 = pd.read_csv(\"../../data/data_oversampled/smote/smote_2_daejeon.csv\")\n",
-    "df_smote_daejeon_3 = pd.read_csv(\"../../data/data_oversampled/smote/smote_3_daejeon.csv\")\n",
-    "\n",
-    "df_smote_gwangju_1 = pd.read_csv(\"../../data/data_oversampled/smote/smote_1_gwangju.csv\")\n",
-    "df_smote_gwangju_2 = pd.read_csv(\"../../data/data_oversampled/smote/smote_2_gwangju.csv\")\n",
-    "df_smote_gwangju_3 = pd.read_csv(\"../../data/data_oversampled/smote/smote_3_gwangju.csv\")\n",
-    "\n",
-    "df_smote_incheon_1 = pd.read_csv(\"../../data/data_oversampled/smote/smote_1_incheon.csv\")\n",
-    "df_smote_incheon_2 = pd.read_csv(\"../../data/data_oversampled/smote/smote_2_incheon.csv\")\n",
-    "df_smote_incheon_3 = pd.read_csv(\"../../data/data_oversampled/smote/smote_3_incheon.csv\")\n",
-    "\n",
-    "\n",
-    "df_smote_busan_1 = preprocessing(df_smote_busan_1)\n",
-    "df_smote_busan_2 = preprocessing(df_smote_busan_2)\n",
-    "df_smote_busan_3 = preprocessing(df_smote_busan_3)\n",
-    "\n",
-    "df_smote_seoul_1 = preprocessing(df_smote_seoul_1)\n",
-    "df_smote_seoul_2 = preprocessing(df_smote_seoul_2)\n",
-    "df_smote_seoul_3 = preprocessing(df_smote_seoul_3)\n",
-    "\n",
-    "df_smote_daegu_1 = preprocessing(df_smote_daegu_1)\n",
-    "df_smote_daegu_2 = preprocessing(df_smote_daegu_2)\n",
-    "df_smote_daegu_3 = preprocessing(df_smote_daegu_3)\n",
-    "\n",
-    "df_smote_daejeon_1 = preprocessing(df_smote_daejeon_1)\n",
-    "df_smote_daejeon_2 = preprocessing(df_smote_daejeon_2)\n",
-    "df_smote_daejeon_3 = preprocessing(df_smote_daejeon_3)\n",
-    "\n",
-    "df_smote_gwangju_1 = preprocessing(df_smote_gwangju_1)\n",
-    "df_smote_gwangju_2 = preprocessing(df_smote_gwangju_2)\n",
-    "df_smote_gwangju_3 = preprocessing(df_smote_gwangju_3)\n",
-    "\n",
-    "df_smote_incheon_1 = preprocessing(df_smote_incheon_1)\n",
-    "df_smote_incheon_2 = preprocessing(df_smote_incheon_2)\n",
-    "df_smote_incheon_3 = preprocessing(df_smote_incheon_3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5833152545530768\n",
-      "mean of accuracy :  0.9418898828089262\n",
-      "mean of mcc :  0.7120196607674459\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_seoul_1.loc[df_smote_seoul_1['year'].isin([2018, 2019]), df_smote_seoul_1.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_smote_seoul_1.loc[df_smote_seoul_1['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_seoul_2.loc[df_smote_seoul_2['year'].isin([2018, 2020]), df_smote_seoul_2.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_smote_seoul_2.loc[df_smote_seoul_2['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_seoul_3.loc[df_smote_seoul_3['year'].isin([2019, 2020]), df_smote_seoul_3.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_smote_seoul_3.loc[df_smote_seoul_3['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'seoul',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'smote',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.46473886869778475\n",
-      "mean of accuracy :  0.9518734768903195\n",
-      "mean of mcc :  0.6265333263141976\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_busan_1.loc[df_smote_busan_1['year'].isin([2018, 2019]), df_smote_busan_1.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_smote_busan_1.loc[df_smote_busan_1['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_busan_2.loc[df_smote_busan_2['year'].isin([2018, 2020]), df_smote_busan_2.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_smote_busan_2.loc[df_smote_busan_2['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_busan_3.loc[df_smote_busan_3['year'].isin([2019, 2020]), df_smote_busan_3.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_smote_busan_3.loc[df_smote_busan_3['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'busan',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'smote',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5891561059817683\n",
-      "mean of accuracy :  0.9131620588700086\n",
-      "mean of mcc :  0.7103919570983672\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_incheon_1.loc[df_smote_incheon_1['year'].isin([2018, 2019]), df_smote_incheon_1.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_smote_incheon_1.loc[df_smote_incheon_1['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_incheon_2.loc[df_smote_incheon_2['year'].isin([2018, 2020]), df_smote_incheon_2.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_smote_incheon_2.loc[df_smote_incheon_2['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_incheon_3.loc[df_smote_incheon_3['year'].isin([2019, 2020]), df_smote_incheon_3.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_smote_incheon_3.loc[df_smote_incheon_3['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'incheon',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'smote',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4611201906216012\n",
-      "mean of accuracy :  0.9654021217489666\n",
-      "mean of mcc :  0.6275336823062336\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_daegu_1.loc[df_smote_daegu_1['year'].isin([2018, 2019]), df_smote_daegu_1.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_smote_daegu_1.loc[df_smote_daegu_1['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_daegu_2.loc[df_smote_daegu_2['year'].isin([2018, 2020]), df_smote_daegu_2.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_smote_daegu_2.loc[df_smote_daegu_2['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_daegu_3.loc[df_smote_daegu_3['year'].isin([2019, 2020]), df_smote_daegu_3.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_smote_daegu_3.loc[df_smote_daegu_3['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daegu',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'smote',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5188768068576874\n",
-      "mean of accuracy :  0.9321021616721145\n",
-      "mean of mcc :  0.6552123484066442\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_1.loc[df_smote_daejeon_1['year'].isin([2018, 2019]), df_smote_daejeon_1.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_smote_daejeon_1.loc[df_smote_daejeon_1['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_2.loc[df_smote_daejeon_2['year'].isin([2018, 2020]), df_smote_daejeon_2.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_smote_daejeon_2.loc[df_smote_daejeon_2['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_daejeon_3.loc[df_smote_daejeon_3['year'].isin([2019, 2020]), df_smote_daejeon_3.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_smote_daejeon_3.loc[df_smote_daejeon_3['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daejeon',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'smote',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5099103046808192\n",
-      "mean of accuracy :  0.9358984995550234\n",
-      "mean of mcc :  0.6499937333212472\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_1.loc[df_smote_gwangju_1['year'].isin([2018, 2019]), df_smote_gwangju_1.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_smote_gwangju_1.loc[df_smote_gwangju_1['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_2.loc[df_smote_gwangju_2['year'].isin([2018, 2020]), df_smote_gwangju_2.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_smote_gwangju_2.loc[df_smote_gwangju_2['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_smote_gwangju_3.loc[df_smote_gwangju_3['year'].isin([2019, 2020]), df_smote_gwangju_3.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_smote_gwangju_3.loc[df_smote_gwangju_3['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'gwangju',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'smote',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **CTGAN을 통해 데이터 증강을 진행한 데이터셋에 대한 성능**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **2만개**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1 Fold\n",
-    "df_busan_gan20000_1= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_1_busan.csv\")\n",
-    "df_seoul_gan20000_1= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_1_seoul.csv\")\n",
-    "df_incheon_gan20000_1= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_1_incheon.csv\")\n",
-    "df_daegu_gan20000_1= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_1_daegu.csv\")\n",
-    "df_daejeon_gan20000_1= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_1_daejeon.csv\")\n",
-    "df_gwangju_gan20000_1= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_1_gwangju.csv\")\n",
-    "\n",
-    "# 2 Fold\n",
-    "df_busan_gan20000_2= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_2_busan.csv\")\n",
-    "df_seoul_gan20000_2= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_2_seoul.csv\")\n",
-    "df_incheon_gan20000_2= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_2_incheon.csv\")\n",
-    "df_daegu_gan20000_2= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_2_daegu.csv\")\n",
-    "df_daejeon_gan20000_2= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_2_daejeon.csv\")\n",
-    "df_gwangju_gan20000_2= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_2_gwangju.csv\")\n",
-    "\n",
-    "# 3 Fold\n",
-    "df_busan_gan20000_3= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_3_busan.csv\")\n",
-    "df_seoul_gan20000_3= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_3_seoul.csv\")\n",
-    "df_incheon_gan20000_3= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_3_incheon.csv\")\n",
-    "df_daegu_gan20000_3= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_3_daegu.csv\")\n",
-    "df_daejeon_gan20000_3= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_3_daejeon.csv\")\n",
-    "df_gwangju_gan20000_3= pd.read_csv(\"../../data/data_oversampled/ctgan20000/ctgan20000_3_gwangju.csv\")\n",
-    "\n",
-    "\n",
-    "# for validation\n",
-    "df_busan = pd.read_csv(\"../../data/data_for_modeling/busan_train.csv\")\n",
-    "df_seoul = pd.read_csv(\"../../data/data_for_modeling/seoul_train.csv\")\n",
-    "df_incheon = pd.read_csv(\"../../data/data_for_modeling/incheon_train.csv\")\n",
-    "df_daegu = pd.read_csv(\"../../data/data_for_modeling/daegu_train.csv\")\n",
-    "df_daejeon = pd.read_csv(\"../../data/data_for_modeling/daejeon_train.csv\")\n",
-    "df_gwangju = pd.read_csv(\"../../data/data_for_modeling/gwangju_train.csv\")\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_busan_gan20000_1= preprocessing(df_busan_gan20000_1).copy()\n",
-    "df_seoul_gan20000_1= preprocessing(df_seoul_gan20000_1).copy()\n",
-    "df_incheon_gan20000_1= preprocessing(df_incheon_gan20000_1).copy()\n",
-    "df_daegu_gan20000_1= preprocessing(df_daegu_gan20000_1).copy()\n",
-    "df_daejeon_gan20000_1= preprocessing(df_daejeon_gan20000_1).copy()\n",
-    "df_gwangju_gan20000_1= preprocessing(df_gwangju_gan20000_1).copy()\n",
-    "\n",
-    "df_busan_gan20000_2= preprocessing(df_busan_gan20000_2).copy()\n",
-    "df_seoul_gan20000_2= preprocessing(df_seoul_gan20000_2).copy()\n",
-    "df_incheon_gan20000_2= preprocessing(df_incheon_gan20000_2).copy()\n",
-    "df_daegu_gan20000_2= preprocessing(df_daegu_gan20000_2).copy()\n",
-    "df_daejeon_gan20000_2= preprocessing(df_daejeon_gan20000_2).copy()\n",
-    "df_gwangju_gan20000_2= preprocessing(df_gwangju_gan20000_2).copy()\n",
-    "\n",
-    "df_busan_gan20000_3= preprocessing(df_busan_gan20000_3).copy()\n",
-    "df_seoul_gan20000_3= preprocessing(df_seoul_gan20000_3).copy()\n",
-    "df_incheon_gan20000_3= preprocessing(df_incheon_gan20000_3).copy()\n",
-    "df_daegu_gan20000_3= preprocessing(df_daegu_gan20000_3).copy()\n",
-    "df_daejeon_gan20000_3= preprocessing(df_daejeon_gan20000_3).copy()\n",
-    "df_gwangju_gan20000_3= preprocessing(df_gwangju_gan20000_3).copy()\n",
-    "\n",
-    "df_busan = preprocessing(df_busan).copy()\n",
-    "df_seoul = preprocessing(df_seoul).copy()\n",
-    "df_incheon = preprocessing(df_incheon).copy()\n",
-    "df_daegu = preprocessing(df_daegu).copy()\n",
-    "df_daejeon = preprocessing(df_daejeon).copy()\n",
-    "df_gwangju = preprocessing(df_gwangju).copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5570923548170014\n",
-      "mean of accuracy :  0.9440149587044938\n",
-      "mean of mcc :  0.6942604158597989\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan20000_1.loc[df_seoul_gan20000_1['year'].isin([2018, 2019]), df_seoul_gan20000_1.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_seoul_gan20000_1.loc[df_seoul_gan20000_1['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan20000_2.loc[df_seoul_gan20000_2['year'].isin([2018, 2020]), df_seoul_gan20000_2.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_seoul_gan20000_2.loc[df_seoul_gan20000_2['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan20000_3.loc[df_seoul_gan20000_3['year'].isin([2019, 2020]), df_seoul_gan20000_3.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_seoul_gan20000_3.loc[df_seoul_gan20000_3['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'seoul',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan20000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.46709831119818074\n",
-      "mean of accuracy :  0.9589464239671965\n",
-      "mean of mcc :  0.6290561623391081\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan20000_1.loc[df_busan_gan20000_1['year'].isin([2018, 2019]), df_busan_gan20000_1.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_busan_gan20000_1.loc[df_busan_gan20000_1['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan20000_2.loc[df_busan_gan20000_2['year'].isin([2018, 2020]), df_busan_gan20000_2.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_busan_gan20000_2.loc[df_busan_gan20000_2['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan20000_3.loc[df_busan_gan20000_3['year'].isin([2019, 2020]), df_busan_gan20000_3.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_busan_gan20000_3.loc[df_busan_gan20000_3['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'busan',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan20000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5523124093005438\n",
-      "mean of accuracy :  0.9114981785063753\n",
-      "mean of mcc :  0.6854497320743039\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan20000_1.loc[df_incheon_gan20000_1['year'].isin([2018, 2019]), df_incheon_gan20000_1.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_incheon_gan20000_1.loc[df_incheon_gan20000_1['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan20000_2.loc[df_incheon_gan20000_2['year'].isin([2018, 2020]), df_incheon_gan20000_2.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_incheon_gan20000_2.loc[df_incheon_gan20000_2['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan20000_3.loc[df_incheon_gan20000_3['year'].isin([2019, 2020]), df_incheon_gan20000_3.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_incheon_gan20000_3.loc[df_incheon_gan20000_3['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'incheon',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan20000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4182253863349025\n",
-      "mean of accuracy :  0.9664244246492171\n",
-      "mean of mcc :  0.5952665980456898\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan20000_1.loc[df_daegu_gan20000_1['year'].isin([2018, 2019]), df_daegu_gan20000_1.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_daegu_gan20000_1.loc[df_daegu_gan20000_1['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan20000_2.loc[df_daegu_gan20000_2['year'].isin([2018, 2020]), df_daegu_gan20000_2.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_daegu_gan20000_2.loc[df_daegu_gan20000_2['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan20000_3.loc[df_daegu_gan20000_3['year'].isin([2019, 2020]), df_daegu_gan20000_3.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_daegu_gan20000_3.loc[df_daegu_gan20000_3['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daegu',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan20000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4918910489160739\n",
-      "mean of accuracy :  0.933889866174281\n",
-      "mean of mcc :  0.6390630711637499\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan20000_1.loc[df_daejeon_gan20000_1['year'].isin([2018, 2019]), df_daejeon_gan20000_1.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_daejeon_gan20000_1.loc[df_daejeon_gan20000_1['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan20000_2.loc[df_daejeon_gan20000_2['year'].isin([2018, 2020]), df_daejeon_gan20000_2.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_daejeon_gan20000_2.loc[df_daejeon_gan20000_2['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan20000_3.loc[df_daejeon_gan20000_3['year'].isin([2019, 2020]), df_daejeon_gan20000_3.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_daejeon_gan20000_3.loc[df_daejeon_gan20000_3['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daejeon',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan20000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5013450661414979\n",
-      "mean of accuracy :  0.9428181999650672\n",
-      "mean of mcc :  0.6465339172273498\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan20000_1.loc[df_gwangju_gan20000_1['year'].isin([2018, 2019]), df_gwangju_gan20000_1.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_gwangju_gan20000_1.loc[df_gwangju_gan20000_1['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan20000_2.loc[df_gwangju_gan20000_2['year'].isin([2018, 2020]), df_gwangju_gan20000_2.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_gwangju_gan20000_2.loc[df_gwangju_gan20000_2['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan20000_3.loc[df_gwangju_gan20000_3['year'].isin([2019, 2020]), df_gwangju_gan20000_3.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_gwangju_gan20000_3.loc[df_gwangju_gan20000_3['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'gwangju',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan20000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **1만개**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1 Fold\n",
-    "df_busan_gan10000_1= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_1_busan.csv\")\n",
-    "df_seoul_gan10000_1= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_1_seoul.csv\")\n",
-    "df_incheon_gan10000_1= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_1_incheon.csv\")\n",
-    "df_daegu_gan10000_1= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_1_daegu.csv\")\n",
-    "df_daejeon_gan10000_1= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_1_daejeon.csv\")\n",
-    "df_gwangju_gan10000_1= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_1_gwangju.csv\")\n",
-    "\n",
-    "# 2 Fold\n",
-    "df_busan_gan10000_2= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_2_busan.csv\")\n",
-    "df_seoul_gan10000_2= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_2_seoul.csv\")\n",
-    "df_incheon_gan10000_2= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_2_incheon.csv\")\n",
-    "df_daegu_gan10000_2= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_2_daegu.csv\")\n",
-    "df_daejeon_gan10000_2= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_2_daejeon.csv\")\n",
-    "df_gwangju_gan10000_2= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_2_gwangju.csv\")\n",
-    "\n",
-    "# 3 Fold\n",
-    "df_busan_gan10000_3= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_3_busan.csv\")\n",
-    "df_seoul_gan10000_3= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_3_seoul.csv\")\n",
-    "df_incheon_gan10000_3= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_3_incheon.csv\")\n",
-    "df_daegu_gan10000_3= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_3_daegu.csv\")\n",
-    "df_daejeon_gan10000_3= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_3_daejeon.csv\")\n",
-    "df_gwangju_gan10000_3= pd.read_csv(\"../../data/data_oversampled/ctgan10000/ctgan10000_3_gwangju.csv\")\n",
-    "\n",
-    "\n",
-    "# for validation\n",
-    "df_busan = pd.read_csv(\"../../data/data_for_modeling/busan_train.csv\")\n",
-    "df_seoul = pd.read_csv(\"../../data/data_for_modeling/seoul_train.csv\")\n",
-    "df_incheon = pd.read_csv(\"../../data/data_for_modeling/incheon_train.csv\")\n",
-    "df_daegu = pd.read_csv(\"../../data/data_for_modeling/daegu_train.csv\")\n",
-    "df_daejeon = pd.read_csv(\"../../data/data_for_modeling/daejeon_train.csv\")\n",
-    "df_gwangju = pd.read_csv(\"../../data/data_for_modeling/gwangju_train.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_busan_gan10000_1= preprocessing(df_busan_gan10000_1).copy()\n",
-    "df_seoul_gan10000_1= preprocessing(df_seoul_gan10000_1).copy()\n",
-    "df_incheon_gan10000_1= preprocessing(df_incheon_gan10000_1).copy()\n",
-    "df_daegu_gan10000_1= preprocessing(df_daegu_gan10000_1).copy()\n",
-    "df_daejeon_gan10000_1= preprocessing(df_daejeon_gan10000_1).copy()\n",
-    "df_gwangju_gan10000_1= preprocessing(df_gwangju_gan10000_1).copy()\n",
-    "\n",
-    "df_busan_gan10000_2= preprocessing(df_busan_gan10000_2).copy()\n",
-    "df_seoul_gan10000_2= preprocessing(df_seoul_gan10000_2).copy()\n",
-    "df_incheon_gan10000_2= preprocessing(df_incheon_gan10000_2).copy()\n",
-    "df_daegu_gan10000_2= preprocessing(df_daegu_gan10000_2).copy()\n",
-    "df_daejeon_gan10000_2= preprocessing(df_daejeon_gan10000_2).copy()\n",
-    "df_gwangju_gan10000_2= preprocessing(df_gwangju_gan10000_2).copy()\n",
-    "\n",
-    "df_busan_gan10000_3= preprocessing(df_busan_gan10000_3).copy()\n",
-    "df_seoul_gan10000_3= preprocessing(df_seoul_gan10000_3).copy()\n",
-    "df_incheon_gan10000_3= preprocessing(df_incheon_gan10000_3).copy()\n",
-    "df_daegu_gan10000_3= preprocessing(df_daegu_gan10000_3).copy()\n",
-    "df_daejeon_gan10000_3= preprocessing(df_daejeon_gan10000_3).copy()\n",
-    "df_gwangju_gan10000_3= preprocessing(df_gwangju_gan10000_3).copy()\n",
-    "\n",
-    "df_busan = preprocessing(df_busan).copy()\n",
-    "df_seoul = preprocessing(df_seoul).copy()\n",
-    "df_incheon = preprocessing(df_incheon).copy()\n",
-    "df_daegu = preprocessing(df_daegu).copy()\n",
-    "df_daejeon = preprocessing(df_daejeon).copy()\n",
-    "df_gwangju = preprocessing(df_gwangju).copy()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5651854445647394\n",
-      "mean of accuracy :  0.9450405885337393\n",
-      "mean of mcc :  0.7027118144017459\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan10000_1.loc[df_seoul_gan10000_1['year'].isin([2018, 2019]), df_seoul_gan10000_1.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_seoul_gan10000_1.loc[df_seoul_gan10000_1['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan10000_2.loc[df_seoul_gan10000_2['year'].isin([2018, 2020]), df_seoul_gan10000_2.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_seoul_gan10000_2.loc[df_seoul_gan10000_2['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan10000_3.loc[df_seoul_gan10000_3['year'].isin([2019, 2020]), df_seoul_gan10000_3.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_seoul_gan10000_3.loc[df_seoul_gan10000_3['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'seoul',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan10000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.46692094075656104\n",
-      "mean of accuracy :  0.958222712944249\n",
-      "mean of mcc :  0.6325842231097966\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan10000_1.loc[df_busan_gan10000_1['year'].isin([2018, 2019]), df_busan_gan10000_1.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_busan_gan10000_1.loc[df_busan_gan10000_1['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan10000_2.loc[df_busan_gan10000_2['year'].isin([2018, 2020]), df_busan_gan10000_2.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_busan_gan10000_2.loc[df_busan_gan10000_2['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan10000_3.loc[df_busan_gan10000_3['year'].isin([2019, 2020]), df_busan_gan10000_3.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_busan_gan10000_3.loc[df_busan_gan10000_3['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'busan',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan10000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5578011155842045\n",
-      "mean of accuracy :  0.9120290316141428\n",
-      "mean of mcc :  0.6901038363927982\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan10000_1.loc[df_incheon_gan10000_1['year'].isin([2018, 2019]), df_incheon_gan10000_1.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_incheon_gan10000_1.loc[df_incheon_gan10000_1['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan10000_2.loc[df_incheon_gan10000_2['year'].isin([2018, 2020]), df_incheon_gan10000_2.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_incheon_gan10000_2.loc[df_incheon_gan10000_2['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan10000_3.loc[df_incheon_gan10000_3['year'].isin([2019, 2020]), df_incheon_gan10000_3.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_incheon_gan10000_3.loc[df_incheon_gan10000_3['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'incheon',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan10000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.424541930975018\n",
-      "mean of accuracy :  0.966994369172676\n",
-      "mean of mcc :  0.6005603470129561\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan10000_1.loc[df_daegu_gan10000_1['year'].isin([2018, 2019]), df_daegu_gan10000_1.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_daegu_gan10000_1.loc[df_daegu_gan10000_1['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan10000_2.loc[df_daegu_gan10000_2['year'].isin([2018, 2020]), df_daegu_gan10000_2.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_daegu_gan10000_2.loc[df_daegu_gan10000_2['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan10000_3.loc[df_daegu_gan10000_3['year'].isin([2019, 2020]), df_daegu_gan10000_3.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_daegu_gan10000_3.loc[df_daegu_gan10000_3['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daegu',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan10000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4845439338249185\n",
-      "mean of accuracy :  0.9338891384085635\n",
-      "mean of mcc :  0.63402545260005\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan10000_1.loc[df_daejeon_gan10000_1['year'].isin([2018, 2019]), df_daejeon_gan10000_1.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_daejeon_gan10000_1.loc[df_daejeon_gan10000_1['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan10000_2.loc[df_daejeon_gan10000_2['year'].isin([2018, 2020]), df_daejeon_gan10000_2.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_daejeon_gan10000_2.loc[df_daejeon_gan10000_2['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan10000_3.loc[df_daejeon_gan10000_3['year'].isin([2019, 2020]), df_daejeon_gan10000_3.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_daejeon_gan10000_3.loc[df_daejeon_gan10000_3['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daejeon',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan10000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.490651260954804\n",
-      "mean of accuracy :  0.9422102036912277\n",
-      "mean of mcc :  0.6368315107576099\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan10000_1.loc[df_gwangju_gan10000_1['year'].isin([2018, 2019]), df_gwangju_gan10000_1.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_gwangju_gan10000_1.loc[df_gwangju_gan10000_1['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan10000_2.loc[df_gwangju_gan10000_2['year'].isin([2018, 2020]), df_gwangju_gan10000_2.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_gwangju_gan10000_2.loc[df_gwangju_gan10000_2['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan10000_3.loc[df_gwangju_gan10000_3['year'].isin([2019, 2020]), df_gwangju_gan10000_3.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_gwangju_gan10000_3.loc[df_gwangju_gan10000_3['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'gwangju',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan10000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **7천개**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1 Fold\n",
-    "df_busan_gan7000_1= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_1_busan.csv\")\n",
-    "df_seoul_gan7000_1= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_1_seoul.csv\")\n",
-    "df_incheon_gan7000_1= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_1_incheon.csv\")\n",
-    "df_daegu_gan7000_1= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_1_daegu.csv\")\n",
-    "df_daejeon_gan7000_1= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_1_daejeon.csv\")\n",
-    "df_gwangju_gan7000_1= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_1_gwangju.csv\")\n",
-    "\n",
-    "# 2 Fold\n",
-    "df_busan_gan7000_2= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_2_busan.csv\")\n",
-    "df_seoul_gan7000_2= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_2_seoul.csv\")\n",
-    "df_incheon_gan7000_2= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_2_incheon.csv\")\n",
-    "df_daegu_gan7000_2= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_2_daegu.csv\")\n",
-    "df_daejeon_gan7000_2= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_2_daejeon.csv\")\n",
-    "df_gwangju_gan7000_2= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_2_gwangju.csv\")\n",
-    "\n",
-    "# 3 Fold\n",
-    "df_busan_gan7000_3= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_3_busan.csv\")\n",
-    "df_seoul_gan7000_3= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_3_seoul.csv\")\n",
-    "df_incheon_gan7000_3= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_3_incheon.csv\")\n",
-    "df_daegu_gan7000_3= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_3_daegu.csv\")\n",
-    "df_daejeon_gan7000_3= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_3_daejeon.csv\")\n",
-    "df_gwangju_gan7000_3= pd.read_csv(\"../../data/data_oversampled/ctgan7000/ctgan7000_3_gwangju.csv\")\n",
-    "\n",
-    "\n",
-    "# for validation\n",
-    "df_busan = pd.read_csv(\"../../data/data_for_modeling/busan_train.csv\")\n",
-    "df_seoul = pd.read_csv(\"../../data/data_for_modeling/seoul_train.csv\")\n",
-    "df_incheon = pd.read_csv(\"../../data/data_for_modeling/incheon_train.csv\")\n",
-    "df_daegu = pd.read_csv(\"../../data/data_for_modeling/daegu_train.csv\")\n",
-    "df_daejeon = pd.read_csv(\"../../data/data_for_modeling/daejeon_train.csv\")\n",
-    "df_gwangju = pd.read_csv(\"../../data/data_for_modeling/gwangju_train.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_busan_gan7000_1= preprocessing(df_busan_gan7000_1).copy()\n",
-    "df_seoul_gan7000_1= preprocessing(df_seoul_gan7000_1).copy()\n",
-    "df_incheon_gan7000_1= preprocessing(df_incheon_gan7000_1).copy()\n",
-    "df_daegu_gan7000_1= preprocessing(df_daegu_gan7000_1).copy()\n",
-    "df_daejeon_gan7000_1= preprocessing(df_daejeon_gan7000_1).copy()\n",
-    "df_gwangju_gan7000_1= preprocessing(df_gwangju_gan7000_1).copy()\n",
-    "\n",
-    "df_busan_gan7000_2= preprocessing(df_busan_gan7000_2).copy()\n",
-    "df_seoul_gan7000_2= preprocessing(df_seoul_gan7000_2).copy()\n",
-    "df_incheon_gan7000_2= preprocessing(df_incheon_gan7000_2).copy()\n",
-    "df_daegu_gan7000_2= preprocessing(df_daegu_gan7000_2).copy()\n",
-    "df_daejeon_gan7000_2= preprocessing(df_daejeon_gan7000_2).copy()\n",
-    "df_gwangju_gan7000_2= preprocessing(df_gwangju_gan7000_2).copy()\n",
-    "\n",
-    "df_busan_gan7000_3= preprocessing(df_busan_gan7000_3).copy()\n",
-    "df_seoul_gan7000_3= preprocessing(df_seoul_gan7000_3).copy()\n",
-    "df_incheon_gan7000_3= preprocessing(df_incheon_gan7000_3).copy()\n",
-    "df_daegu_gan7000_3= preprocessing(df_daegu_gan7000_3).copy()\n",
-    "df_daejeon_gan7000_3= preprocessing(df_daejeon_gan7000_3).copy()\n",
-    "df_gwangju_gan7000_3= preprocessing(df_gwangju_gan7000_3).copy()\n",
-    "\n",
-    "df_busan = preprocessing(df_busan).copy()\n",
-    "df_seoul = preprocessing(df_seoul).copy()\n",
-    "df_incheon = preprocessing(df_incheon).copy()\n",
-    "df_daegu = preprocessing(df_daegu).copy()\n",
-    "df_daejeon = preprocessing(df_daejeon).copy()\n",
-    "df_gwangju = preprocessing(df_gwangju).copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **서울**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5637353305761614\n",
-      "mean of accuracy :  0.9446616305279004\n",
-      "mean of mcc :  0.7003517728134977\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan7000_1.loc[df_seoul_gan7000_1['year'].isin([2018, 2019]), df_seoul_gan7000_1.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, df_seoul.columns != 'multi_class'], df_seoul_gan7000_1.loc[df_seoul_gan7000_1['year'].isin([2018, 2019]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan7000_2.loc[df_seoul_gan7000_2['year'].isin([2018, 2020]), df_seoul_gan7000_2.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, df_seoul.columns != 'multi_class'], df_seoul_gan7000_2.loc[df_seoul_gan7000_2['year'].isin([2018, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_seoul_gan7000_3.loc[df_seoul_gan7000_3['year'].isin([2019, 2020]), df_seoul_gan7000_3.columns != 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, df_seoul.columns != 'multi_class'], df_seoul_gan7000_3.loc[df_seoul_gan7000_3['year'].isin([2019, 2020]), 'multi_class'], df_seoul.loc[df_seoul['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'seoul',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan7000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **부산**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4616459581003174\n",
-      "mean of accuracy :  0.9586027106153988\n",
-      "mean of mcc :  0.6279091576031567\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan7000_1.loc[df_busan_gan7000_1['year'].isin([2018, 2019]), df_busan_gan7000_1.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2020, df_busan.columns != 'multi_class'], df_busan_gan7000_1.loc[df_busan_gan7000_1['year'].isin([2018, 2019]), 'multi_class'], df_busan.loc[df_busan['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan7000_2.loc[df_busan_gan7000_2['year'].isin([2018, 2020]), df_busan_gan7000_2.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2019, df_busan.columns != 'multi_class'], df_busan_gan7000_2.loc[df_busan_gan7000_2['year'].isin([2018, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_busan_gan7000_3.loc[df_busan_gan7000_3['year'].isin([2019, 2020]), df_busan_gan7000_3.columns != 'multi_class'], df_busan.loc[df_busan['year'] == 2018, df_busan.columns != 'multi_class'], df_busan_gan7000_3.loc[df_busan_gan7000_3['year'].isin([2019, 2020]), 'multi_class'], df_busan.loc[df_busan['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'busan',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan7000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **인천**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.5600003713221271\n",
-      "mean of accuracy :  0.9122589016143924\n",
-      "mean of mcc :  0.6911778795112914\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan7000_1.loc[df_incheon_gan7000_1['year'].isin([2018, 2019]), df_incheon_gan7000_1.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, df_incheon.columns != 'multi_class'], df_incheon_gan7000_1.loc[df_incheon_gan7000_1['year'].isin([2018, 2019]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan7000_2.loc[df_incheon_gan7000_2['year'].isin([2018, 2020]), df_incheon_gan7000_2.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, df_incheon.columns != 'multi_class'], df_incheon_gan7000_2.loc[df_incheon_gan7000_2['year'].isin([2018, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_incheon_gan7000_3.loc[df_incheon_gan7000_3['year'].isin([2019, 2020]), df_incheon_gan7000_3.columns != 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, df_incheon.columns != 'multi_class'], df_incheon_gan7000_3.loc[df_incheon_gan7000_3['year'].isin([2019, 2020]), 'multi_class'], df_incheon.loc[df_incheon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'incheon',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan7000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대구**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.41141211423242163\n",
-      "mean of accuracy :  0.9661207384118904\n",
-      "mean of mcc :  0.5889669181000196\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan7000_1.loc[df_daegu_gan7000_1['year'].isin([2018, 2019]), df_daegu_gan7000_1.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, df_daegu.columns != 'multi_class'], df_daegu_gan7000_1.loc[df_daegu_gan7000_1['year'].isin([2018, 2019]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan7000_2.loc[df_daegu_gan7000_2['year'].isin([2018, 2020]), df_daegu_gan7000_2.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, df_daegu.columns != 'multi_class'], df_daegu_gan7000_2.loc[df_daegu_gan7000_2['year'].isin([2018, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daegu_gan7000_3.loc[df_daegu_gan7000_3['year'].isin([2019, 2020]), df_daegu_gan7000_3.columns != 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, df_daegu.columns != 'multi_class'], df_daegu_gan7000_3.loc[df_daegu_gan7000_3['year'].isin([2019, 2020]), 'multi_class'], df_daegu.loc[df_daegu['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daegu',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan7000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **대전**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.4880797047418282\n",
-      "mean of accuracy :  0.9336605160066872\n",
-      "mean of mcc :  0.6352680209224976\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan7000_1.loc[df_daejeon_gan7000_1['year'].isin([2018, 2019]), df_daejeon_gan7000_1.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, df_daejeon.columns != 'multi_class'], df_daejeon_gan7000_1.loc[df_daejeon_gan7000_1['year'].isin([2018, 2019]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan7000_2.loc[df_daejeon_gan7000_2['year'].isin([2018, 2020]), df_daejeon_gan7000_2.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, df_daejeon.columns != 'multi_class'], df_daejeon_gan7000_2.loc[df_daejeon_gan7000_2['year'].isin([2018, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_daejeon_gan7000_3.loc[df_daejeon_gan7000_3['year'].isin([2019, 2020]), df_daejeon_gan7000_3.columns != 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, df_daejeon.columns != 'multi_class'], df_daejeon_gan7000_3.loc[df_daejeon_gan7000_3['year'].isin([2019, 2020]), 'multi_class'], df_daejeon.loc[df_daejeon['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'daejeon',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan7000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## **광주**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of csi :  0.49570507941388103\n",
-      "mean of accuracy :  0.9430463025342881\n",
-      "mean of mcc :  0.6441170200639816\n"
-     ]
-    }
-   ],
-   "source": [
-    "csi = []\n",
-    "accuracy = []\n",
-    "mcc = []\n",
-    "\n",
-    "# Fold 1\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan7000_1.loc[df_gwangju_gan7000_1['year'].isin([2018, 2019]), df_gwangju_gan7000_1.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, df_gwangju.columns != 'multi_class'], df_gwangju_gan7000_1.loc[df_gwangju_gan7000_1['year'].isin([2018, 2019]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2020, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "# Fold 2\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan7000_2.loc[df_gwangju_gan7000_2['year'].isin([2018, 2020]), df_gwangju_gan7000_2.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, df_gwangju.columns != 'multi_class'], df_gwangju_gan7000_2.loc[df_gwangju_gan7000_2['year'].isin([2018, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2019, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "# Fold 3\n",
-    "X_tr, X_val, Y_tr, Y_val = df_gwangju_gan7000_3.loc[df_gwangju_gan7000_3['year'].isin([2019, 2020]), df_gwangju_gan7000_3.columns != 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, df_gwangju.columns != 'multi_class'], df_gwangju_gan7000_3.loc[df_gwangju_gan7000_3['year'].isin([2019, 2020]), 'multi_class'], df_gwangju.loc[df_gwangju['year'] == 2018, 'multi_class']\n",
-    "X_tr.drop(columns=['year'], inplace=True)\n",
-    "X_val.drop(columns=['year'], inplace=True)\n",
-    "\n",
-    "xgb_model.fit(X_tr, Y_tr, eval_set=[(X_val, Y_val)], verbose=False)\n",
-    "csi.append(calculate_csi(Y_val, xgb_model.predict(X_val)))\n",
-    "accuracy.append(accuracy_score(Y_val, xgb_model.predict(X_val)))\n",
-    "mcc.append(multiclass_mcc(Y_val, xgb_model.predict(X_val)))\n",
-    "\n",
-    "\n",
-    "print(\"mean of csi : \", np.mean(csi))\n",
-    "print(\"mean of accuracy : \", np.mean(accuracy))\n",
-    "print(\"mean of mcc : \", np.mean(mcc))\n",
-    "\n",
-    "new_row = pd.DataFrame([{\n",
-    "    'region': 'gwangju',\n",
-    "    'model': 'XGBoost',\n",
-    "    'data_sample': 'ctgan7000',\n",
-    "    'CSI': np.mean(csi),\n",
-    "    'MCC': np.mean(mcc),\n",
-    "    'Accuracy': np.mean(accuracy)\n",
-    "\n",
-    "}])\n",
-    "\n",
-    "df = pd.concat([df, new_row], ignore_index=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>model</th>\n",
-       "      <th>data_sample</th>\n",
-       "      <th>CSI</th>\n",
-       "      <th>MCC</th>\n",
-       "      <th>Accuracy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.565012</td>\n",
-       "      <td>0.700483</td>\n",
-       "      <td>0.945572</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.460244</td>\n",
-       "      <td>0.626085</td>\n",
-       "      <td>0.959745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.558144</td>\n",
-       "      <td>0.689218</td>\n",
-       "      <td>0.913058</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.416651</td>\n",
-       "      <td>0.600957</td>\n",
-       "      <td>0.968324</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.504450</td>\n",
-       "      <td>0.647193</td>\n",
-       "      <td>0.935752</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>pure</td>\n",
-       "      <td>0.490036</td>\n",
-       "      <td>0.637981</td>\n",
-       "      <td>0.943429</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.583315</td>\n",
-       "      <td>0.712020</td>\n",
-       "      <td>0.941890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.464739</td>\n",
-       "      <td>0.626533</td>\n",
-       "      <td>0.951873</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.589156</td>\n",
-       "      <td>0.710392</td>\n",
-       "      <td>0.913162</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.461120</td>\n",
-       "      <td>0.627534</td>\n",
-       "      <td>0.965402</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.518877</td>\n",
-       "      <td>0.655212</td>\n",
-       "      <td>0.932102</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>smote</td>\n",
-       "      <td>0.509910</td>\n",
-       "      <td>0.649994</td>\n",
-       "      <td>0.935898</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.557092</td>\n",
-       "      <td>0.694260</td>\n",
-       "      <td>0.944015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.467098</td>\n",
-       "      <td>0.629056</td>\n",
-       "      <td>0.958946</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.552312</td>\n",
-       "      <td>0.685450</td>\n",
-       "      <td>0.911498</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.418225</td>\n",
-       "      <td>0.595267</td>\n",
-       "      <td>0.966424</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.491891</td>\n",
-       "      <td>0.639063</td>\n",
-       "      <td>0.933890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan20000</td>\n",
-       "      <td>0.501345</td>\n",
-       "      <td>0.646534</td>\n",
-       "      <td>0.942818</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.565185</td>\n",
-       "      <td>0.702712</td>\n",
-       "      <td>0.945041</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.466921</td>\n",
-       "      <td>0.632584</td>\n",
-       "      <td>0.958223</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.557801</td>\n",
-       "      <td>0.690104</td>\n",
-       "      <td>0.912029</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.424542</td>\n",
-       "      <td>0.600560</td>\n",
-       "      <td>0.966994</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.484544</td>\n",
-       "      <td>0.634025</td>\n",
-       "      <td>0.933889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan10000</td>\n",
-       "      <td>0.490651</td>\n",
-       "      <td>0.636832</td>\n",
-       "      <td>0.942210</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>seoul</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.563735</td>\n",
-       "      <td>0.700352</td>\n",
-       "      <td>0.944662</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>busan</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.461646</td>\n",
-       "      <td>0.627909</td>\n",
-       "      <td>0.958603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>incheon</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.560000</td>\n",
-       "      <td>0.691178</td>\n",
-       "      <td>0.912259</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>daegu</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.411412</td>\n",
-       "      <td>0.588967</td>\n",
-       "      <td>0.966121</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>daejeon</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.488080</td>\n",
-       "      <td>0.635268</td>\n",
-       "      <td>0.933661</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>gwangju</td>\n",
-       "      <td>XGBoost</td>\n",
-       "      <td>ctgan7000</td>\n",
-       "      <td>0.495705</td>\n",
-       "      <td>0.644117</td>\n",
-       "      <td>0.943046</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     region    model data_sample       CSI       MCC  Accuracy\n",
-       "0     seoul  XGBoost        pure  0.565012  0.700483  0.945572\n",
-       "1     busan  XGBoost        pure  0.460244  0.626085  0.959745\n",
-       "2   incheon  XGBoost        pure  0.558144  0.689218  0.913058\n",
-       "3     daegu  XGBoost        pure  0.416651  0.600957  0.968324\n",
-       "4   daejeon  XGBoost        pure  0.504450  0.647193  0.935752\n",
-       "5   gwangju  XGBoost        pure  0.490036  0.637981  0.943429\n",
-       "6     seoul  XGBoost       smote  0.583315  0.712020  0.941890\n",
-       "7     busan  XGBoost       smote  0.464739  0.626533  0.951873\n",
-       "8   incheon  XGBoost       smote  0.589156  0.710392  0.913162\n",
-       "9     daegu  XGBoost       smote  0.461120  0.627534  0.965402\n",
-       "10  daejeon  XGBoost       smote  0.518877  0.655212  0.932102\n",
-       "11  gwangju  XGBoost       smote  0.509910  0.649994  0.935898\n",
-       "12    seoul  XGBoost  ctgan20000  0.557092  0.694260  0.944015\n",
-       "13    busan  XGBoost  ctgan20000  0.467098  0.629056  0.958946\n",
-       "14  incheon  XGBoost  ctgan20000  0.552312  0.685450  0.911498\n",
-       "15    daegu  XGBoost  ctgan20000  0.418225  0.595267  0.966424\n",
-       "16  daejeon  XGBoost  ctgan20000  0.491891  0.639063  0.933890\n",
-       "17  gwangju  XGBoost  ctgan20000  0.501345  0.646534  0.942818\n",
-       "18    seoul  XGBoost  ctgan10000  0.565185  0.702712  0.945041\n",
-       "19    busan  XGBoost  ctgan10000  0.466921  0.632584  0.958223\n",
-       "20  incheon  XGBoost  ctgan10000  0.557801  0.690104  0.912029\n",
-       "21    daegu  XGBoost  ctgan10000  0.424542  0.600560  0.966994\n",
-       "22  daejeon  XGBoost  ctgan10000  0.484544  0.634025  0.933889\n",
-       "23  gwangju  XGBoost  ctgan10000  0.490651  0.636832  0.942210\n",
-       "24    seoul  XGBoost   ctgan7000  0.563735  0.700352  0.944662\n",
-       "25    busan  XGBoost   ctgan7000  0.461646  0.627909  0.958603\n",
-       "26  incheon  XGBoost   ctgan7000  0.560000  0.691178  0.912259\n",
-       "27    daegu  XGBoost   ctgan7000  0.411412  0.588967  0.966121\n",
-       "28  daejeon  XGBoost   ctgan7000  0.488080  0.635268  0.933661\n",
-       "29  gwangju  XGBoost   ctgan7000  0.495705  0.644117  0.943046"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv(\"../model_result/xgboost_sampled_data_test.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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