diff --git "a/IRIS FLOWER CLASSIFICATION using machine learning models.ipynb" "b/IRIS FLOWER CLASSIFICATION using machine learning models.ipynb"
new file mode 100644--- /dev/null
+++ "b/IRIS FLOWER CLASSIFICATION using machine learning models.ipynb"
@@ -0,0 +1,7412 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 406,
+   "id": "67fd971a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "%matplotlib inline\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.metrics import accuracy_score\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 407,
+   "id": "eafff9c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# columns = ['Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 408,
+   "id": "39845427",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df = pd.read_csv(r'D:\\archive\\iris.csv', names=columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 409,
+   "id": "3ea02098",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(r'D:\\archive\\iris.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 410,
+   "id": "a7b16c97-0f4d-439b-8aee-dc4e0b69c24e",
+   "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>Id</th>\n",
+       "      <th>SepalLengthCm</th>\n",
+       "      <th>SepalWidthCm</th>\n",
+       "      <th>PetalLengthCm</th>\n",
+       "      <th>PetalWidthCm</th>\n",
+       "      <th>Species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>146</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>147</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>148</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>149</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>150</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
+       "0      1            5.1           3.5            1.4           0.2   \n",
+       "1      2            4.9           3.0            1.4           0.2   \n",
+       "2      3            4.7           3.2            1.3           0.2   \n",
+       "3      4            4.6           3.1            1.5           0.2   \n",
+       "4      5            5.0           3.6            1.4           0.2   \n",
+       "..   ...            ...           ...            ...           ...   \n",
+       "145  146            6.7           3.0            5.2           2.3   \n",
+       "146  147            6.3           2.5            5.0           1.9   \n",
+       "147  148            6.5           3.0            5.2           2.0   \n",
+       "148  149            6.2           3.4            5.4           2.3   \n",
+       "149  150            5.9           3.0            5.1           1.8   \n",
+       "\n",
+       "            Species  \n",
+       "0       Iris-setosa  \n",
+       "1       Iris-setosa  \n",
+       "2       Iris-setosa  \n",
+       "3       Iris-setosa  \n",
+       "4       Iris-setosa  \n",
+       "..              ...  \n",
+       "145  Iris-virginica  \n",
+       "146  Iris-virginica  \n",
+       "147  Iris-virginica  \n",
+       "148  Iris-virginica  \n",
+       "149  Iris-virginica  \n",
+       "\n",
+       "[150 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 410,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 411,
+   "id": "9bbe38eb-a5ba-4deb-9b38-54e55a37a540",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=df.drop(columns=['Id'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 412,
+   "id": "034a8607",
+   "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>SepalLengthCm</th>\n",
+       "      <th>SepalWidthCm</th>\n",
+       "      <th>PetalLengthCm</th>\n",
+       "      <th>PetalWidthCm</th>\n",
+       "      <th>Species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
+       "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
+       "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
+       "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
+       "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
+       "4            5.0           3.6            1.4           0.2  Iris-setosa"
+      ]
+     },
+     "execution_count": 412,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 413,
+   "id": "aab81f22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
+       "       'Species'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 413,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 414,
+   "id": "cf3b0c75",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method DataFrame.info of      SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm         Species\n",
+       "0              5.1           3.5            1.4           0.2     Iris-setosa\n",
+       "1              4.9           3.0            1.4           0.2     Iris-setosa\n",
+       "2              4.7           3.2            1.3           0.2     Iris-setosa\n",
+       "3              4.6           3.1            1.5           0.2     Iris-setosa\n",
+       "4              5.0           3.6            1.4           0.2     Iris-setosa\n",
+       "..             ...           ...            ...           ...             ...\n",
+       "145            6.7           3.0            5.2           2.3  Iris-virginica\n",
+       "146            6.3           2.5            5.0           1.9  Iris-virginica\n",
+       "147            6.5           3.0            5.2           2.0  Iris-virginica\n",
+       "148            6.2           3.4            5.4           2.3  Iris-virginica\n",
+       "149            5.9           3.0            5.1           1.8  Iris-virginica\n",
+       "\n",
+       "[150 rows x 5 columns]>"
+      ]
+     },
+     "execution_count": 414,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 415,
+   "id": "2e6bbdaf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 150 entries, 0 to 149\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   SepalLengthCm  150 non-null    float64\n",
+      " 1   SepalWidthCm   150 non-null    float64\n",
+      " 2   PetalLengthCm  150 non-null    float64\n",
+      " 3   PetalWidthCm   150 non-null    float64\n",
+      " 4   Species        150 non-null    object \n",
+      "dtypes: float64(4), object(1)\n",
+      "memory usage: 6.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 416,
+   "id": "d8adc2ec",
+   "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>SepalLengthCm</th>\n",
+       "      <th>SepalWidthCm</th>\n",
+       "      <th>PetalLengthCm</th>\n",
+       "      <th>PetalWidthCm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.843333</td>\n",
+       "      <td>3.054000</td>\n",
+       "      <td>3.758667</td>\n",
+       "      <td>1.198667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.828066</td>\n",
+       "      <td>0.433594</td>\n",
+       "      <td>1.764420</td>\n",
+       "      <td>0.763161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>4.300000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>2.800000</td>\n",
+       "      <td>1.600000</td>\n",
+       "      <td>0.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5.800000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.350000</td>\n",
+       "      <td>1.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.400000</td>\n",
+       "      <td>3.300000</td>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>1.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.900000</td>\n",
+       "      <td>4.400000</td>\n",
+       "      <td>6.900000</td>\n",
+       "      <td>2.500000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
+       "count     150.000000    150.000000     150.000000    150.000000\n",
+       "mean        5.843333      3.054000       3.758667      1.198667\n",
+       "std         0.828066      0.433594       1.764420      0.763161\n",
+       "min         4.300000      2.000000       1.000000      0.100000\n",
+       "25%         5.100000      2.800000       1.600000      0.300000\n",
+       "50%         5.800000      3.000000       4.350000      1.300000\n",
+       "75%         6.400000      3.300000       5.100000      1.800000\n",
+       "max         7.900000      4.400000       6.900000      2.500000"
+      ]
+     },
+     "execution_count": 416,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 417,
+   "id": "f2d01ec1",
+   "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>SepalLengthCm</th>\n",
+       "      <th>SepalWidthCm</th>\n",
+       "      <th>PetalLengthCm</th>\n",
+       "      <th>PetalWidthCm</th>\n",
+       "      <th>Species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm         Species\n",
+       "0              5.1           3.5            1.4           0.2     Iris-setosa\n",
+       "1              4.9           3.0            1.4           0.2     Iris-setosa\n",
+       "2              4.7           3.2            1.3           0.2     Iris-setosa\n",
+       "3              4.6           3.1            1.5           0.2     Iris-setosa\n",
+       "4              5.0           3.6            1.4           0.2     Iris-setosa\n",
+       "..             ...           ...            ...           ...             ...\n",
+       "145            6.7           3.0            5.2           2.3  Iris-virginica\n",
+       "146            6.3           2.5            5.0           1.9  Iris-virginica\n",
+       "147            6.5           3.0            5.2           2.0  Iris-virginica\n",
+       "148            6.2           3.4            5.4           2.3  Iris-virginica\n",
+       "149            5.9           3.0            5.1           1.8  Iris-virginica\n",
+       "\n",
+       "[150 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 417,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4b79e7c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 418,
+   "id": "e19bcac1",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 418,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ZmSljDBfIAlBtMGIBWKD0wldZWVkaMWKEDh8+7HysadOmevbZZ13WA4CqihELwAKlF766++67rzgJ2d133+2yHgBUVYxYABZISkpScHCw6tatq4yMDAUHXzy0EhMTlZGRocaNG+vUqVMuX0MFgKqIEQvAAhs2bNCFCxeUk5OjIUOGuIxYDBkyRDk5Obpw4YI2bNjg61IBwKOuK1g899xzCggI0IQJEywqB/BPpedOvP3229q1a5eSkpIUGRmppKQk7d69W2+//bbLegBQVVX4o5AtW7Zo3rx5at++vZX1AH6p9NyJ+Ph4HTx4UOvXr9exY8fUsGFDJScna/PmzS7rAUBVVaERi4KCAo0cOVJvvPGGateubXVNgN+5dBKygIAApaSkaMSIEUpJSVFAQACTkAGoNio0YjF27Fj1799fvXv3dn6NrixFRUUqKipy3s/LY7pf+A935oZ55PFnNPH/jdKtt/fXPQ9NUFhMU53NOay3587UutWf6OV5C/X1D+XbF3PDAPBXbgeL9957T9u3b9eWLVvKtX56erqmTJnidmFAZeDe3DB1VG9QmtavfVOZn97uXBocVV/1BqVpZlYdzSznvpgbBoC/citYHD16VI888og+/fRThYaGlmubtLQ0TZw40Xk/Ly9PsbGx7lUJ+Ij7c8P0lMMxSX/7ZI1e+dsWPTLwJg3se6vbl/FmbhgA/sqtYLFt2zbl5OSoU6dOzmUOh0OfffaZXnvtNRUVFV32Amqz2WSz2aypFvCyis4NE3RHH/05q6YG38HIA4Dqxa1gceutt2rXrl0uy+677z61bNlSv//975lcCQCAas6tYBEREaG2bdu6LAsPD1fdunUvWw4AAKofrrwJAAAsc91zhWRmZlpQBgAAqAoYsQAAAJYhWAAAAMsQLAAAgGUIFgAAwDIECwAAYBmCBQAAsAzBAgAAWIZgAQAALEOwAAAAliFYAAAAyxAsAACAZQgWAADAMgQLAABgGYIFAACwDMECAABYhmABAAAsQ7AAAACWIVgAAADLECwAAIBlCBYAAMAyBAsAAGAZggUAALAMwQIAAFgm2NcFAN6SfbJQhUUXvNLWwZwCl389LdwWrGb1wr3SFgBcDcEC1UL2yUKlvpjp9XYnLNnhtbbWTk4hXADwOYIFqoXSkYqZw29UQozd4+2dK3bo29Nn1bh2mEJrBHm0rYM5BZqwZIfXRmMA4GoIFqhWEmLsantDlFfa6tLUK80AQKXCyZsAAMAyBAsAAGAZggUAALAMwQIAAFiGYAEAACzjVrCYM2eO2rdvr8jISEVGRqp79+5auXKlp2oDAAB+xq1g0bhxYz333HPatm2btm7dqltuuUWDBg3Snj17PFUfAADwI25dx2LgwIEu96dNm6Y5c+Zo06ZNatOmjaWFAQAA/1PhC2Q5HA799a9/VWFhobp3725lTQAAwE+5HSx27dql7t2769y5c7Lb7Vq2bJlat25d5vpFRUUqKipy3s/Ly6tYpcB1CgjOU3bePgWGev6S3t6UnVeggGCOKwCVg9vBokWLFtqxY4dyc3O1dOlSjRo1SuvWrSszXKSnp2vKlCnXXShwvWrU+kKPb57u6zI8okatWyXd4esyAMD9YBESEqKEhARJUufOnbVlyxa98sormjdv3hXXT0tL08SJE5338/LyFBsbW8FygYorPtNNL/W/S/FemITMm7JyCjT+3SxflwEAkiyYhKykpMTlo46fstlsstls19sMcN3MhUg1i2yh1nW9MwmZt5Scy5W5cMLXZQCAJDeDRVpamvr166e4uDjl5+dr0aJFyszM1CeffOKp+gAAgB9xK1jk5OTo3nvv1bFjxxQVFaX27dvrk08+0W233eap+gAAgB9xK1i8+eabnqoDAABUAcwVAgAALHPdJ28C/uBssUOStPu7XK+0d67YoW9Pn1Xj2mEKrRHk0bYO5hR4dP8A4A6CBaqFrP97830sY5ePK/GccBuHMwDf45UI1UKfNg0kSfExdoV5eARBujiKMGHJDs0cfqMSvHDdjHBbsJrVC/d4OwBwLQQLVAt1wkN0Z9c4r7ebEGNX2xuq1nUzAOBqOHkTAABYhmABAAAsQ7AAAACWIVgAAADLECwAAIBlCBYAAMAyBAsAAGAZggUAALAMwQIAAFiGK28CAKqls+cdyjrh/iR+1zvJYHy0XWEhnp9awFcIFgCAainrRIEGzPrc6+2uGNezSl/qn2ABAKiW4qPtWjGup9vbXe8kg/HRnp+Y0JcIFgCAaiksJOi6Rg6YZPDKOHkTAABYhmABAAAsQ7AAAACWIVgAAADLECwAAIBlCBYAAMAyBAsAAGAZggUAALAMwQIAAFiGK28CV1HRSYoO5hS4/Ouuqj5JEYCqi2ABXMX1TlI0YcmOCm1X1ScpAlB1ESyAq6joJEVWTKsMAP6IYAFcxfVMUtSlqbW1AIA/4ORNAABgGbeCRXp6um666SZFREQoJiZGgwcP1r59+zxVG+CXHA6HMjMztXjxYmVmZsrhcPi6JADwGreCxbp16zR27Fht2rRJn376qYqLi9WnTx8VFhZ6qj7Ar2RkZCghIUGpqam66667lJqaqoSEBGVkZPi6NADwCreCxapVqzR69Gi1adNGHTp00IIFC3TkyBFt27bNU/UBfiMjI0NDhw5Vu3bttHHjRuXn52vjxo1q166dhg4dSrgAUC1c1zkWubm5kqQ6depYUgzgrxwOhyZNmqQBAwZo+fLlSkxMlN1uV2JiopYvX64BAwZo8uTJfCwCoMqrcLAoKSnRhAkT1KNHD7Vt27bM9YqKipSXl+dyA6qa9evX6/Dhw3r88ccVGOh6WAUGBiotLU3Z2dlav369jyoEAO+ocLAYO3asdu/erffee++q66WnpysqKsp5i42NrWiTQKV17NgxSSozZJcuL10PAKqqCgWL3/72t1qxYoXWrl2rxo0bX3XdtLQ05ebmOm9Hjx6tUKFAZdawYUNJ0u7du6/4eOny0vUAoKpyK1gYY/Tb3/5Wy5Yt0z//+U81a9bsmtvYbDZFRka63ICqJjk5WU2bNtX06dNVUlLi8lhJSYnS09PVrFkzJScn+6hCAPAOt4LF2LFj9c4772jRokWKiIjQDz/8oB9++EFnz571VH2AXwgKCtJLL72kFStWaPDgwS7fChk8eLBWrFihF198UUFBTCwGoGpzK1jMmTNHubm5SklJUcOGDZ23JUuWeKo+wG8MGTJES5cu1a5du5SUlKTIyEglJSVp9+7dWrp0qYYMGeLrEgHA49yaK8QY46k6gCphyJAhGjRokNavX69jx46pYcOGSk5OZqQCQLXBJGSAxYKCgpSSkuLrMgDAJwgWAAC/l32yUIVFF7zS1sGcApd/PS3cFqxm9cK90pYVCBYAAL+WfbJQqS9mer3dCUt2eK2ttZNT/CZcECwAAH6tdKRi5vAblRBj93h754od+vb0WTWuHabQGp49f+pgToEmLNnhtdEYKxAsAABVQkKMXW1viPJKW12aeqUZv3Rdk5ABAABcimABAAAsQ7AAAACWIVgAAADLECwAAIBl+FYIgCrnbLFDkrT7u1yvtOftrx8ClRnBAkCVk/V/b76PZezycSWeE27j5RuVE7+ZAKqcPm0aSJLiY+wK8/AIgvSfixh56wJN/naJZ1QvBAsAVU6d8BDd2TXO6+168wJNQGXFyZsAAMAyBAsAAGAZggUAALAMwQIAAFiGYAEAACxDsAAAAJYhWAAAAMsQLAAAgGUIFgAAwDJceRMA4PcCgvOUnbdPgaGev6S6N2XnFSggOM/XZbiFYAEA8Hs1an2hxzdP93UZHlGj1q2S7vB1GeVGsAAA+L3iM930Uv+7FO+FSeC8KSunQOPfzfJ1GW4hWAAA/J65EKlmkS3Uum7VmgSu5FyuzIUTvi7DLZy8CQAALEOwAAAAliFYAAAAyxAsAACAZQgWAADAMgQLAABgGbeDxWeffaaBAweqUaNGCggI0PLlyz1QFgAA8EduB4vCwkJ16NBBs2fP9kQ9AADAj7l9gax+/fqpX79+nqgFAAD4OY9febOoqEhFRUXO+3l5/jWZCoDq4+x5h7JOFLi93cGcApd/3RUfbVdYSFCFtgUqG48Hi/T0dE2ZMsXTzQDAdcs6UaABsz6v8PYTluyo0HYrxvVU2xuq1qWoUX15PFikpaVp4sSJzvt5eXmKjY31dLMA4Lb4aLtWjOvp9nbnih369vRZNa4dptAa7o88xEdXrYmzUL15PFjYbDbZbDZPNwMA1y0sJKjCIwddmlpbC+CvuI4FAACwjNsjFgUFBTp48KDzfnZ2tnbs2KE6deooLi7O0uIAAIB/cTtYbN26Vampqc77pedPjBo1SgsWLLCsMAAA4H/cDhYpKSkyxniiFgAA4Oc4xwIAAFiGYAEAACxDsAAAAJYhWAAAAMsQLAAAgGUIFgAAwDIECwAAYBmCBQAAsAzBAgAAWIZgAQAALEOwAAAAliFYAAAAyxAsAACAZQgWAADAMgQLAABgGYIFAACwDMECAABYhmABAAAsQ7AAAACWIVgAAADLECwAAIBlCBYAAMAywb4uAACA63G22CFJ2v1drlfaO1fs0Lenz6px7TCF1gjyaFsHcwo8un9PIFgAAPxa1v+9+T6WscvHlXhOuM1/3q79p1IAAK6gT5sGkqT4GLvCPDyCIF0cRZiwZIdmDr9RCTF2j7cXbgtWs3rhHm/HKgQLAIBfqxMeoju7xnm93YQYu9reEOX1dis7Tt4EAACWIVgAAADLECwAAIBlCBYAAMAynLwJAKiWzp53KOuE+9eJKL22REWvMREfbVdYiOe/veIrBAsAQLWUdaJAA2Z9XuHtJyzZUaHtVozrWaW/TVKhYDF79mzNmDFDP/zwgzp06KBZs2apa9euVtcGAIDHxEfbtWJcT7e3u94rb8ZHe/7aF77kdrBYsmSJJk6cqLlz56pbt26aOXOm+vbtq3379ikmJsYTNQIAYLmwkKAKjxx0aWptLVWJ2ydvvvzyyxozZozuu+8+tW7dWnPnzlXNmjX11ltveaI+AADgR9wKFufPn9e2bdvUu3fv/+wgMFC9e/fWxo0br7hNUVGR8vLyXG4AAKBqcitYnDx5Ug6HQ/Xr13dZXr9+ff3www9X3CY9PV1RUVHOW2xsbMWrBQAAlZrHr2ORlpam3Nxc5+3o0aOebhIAAPiIWydv1qtXT0FBQTp+/LjL8uPHj6tBgwZX3MZms8lms1W8QgAA4DfcGrEICQlR586dtWbNGueykpISrVmzRt27d7e8OAAA4F/c/rrpxIkTNWrUKHXp0kVdu3bVzJkzVVhYqPvuu88T9QEAAD/idrAYPny4Tpw4oT/84Q/64YcfdOONN2rVqlWXndAJAACqnwBjjPFmg3l5eYqKilJubq4iIyO92TQAAKig8r5/M7spAACwDMECAABYhmABAAAsQ7AAAACWqdC06dej9FxR5gwBAMB/lL5vX+s7H14PFvn5+ZLEnCEAAPih/Px8RUWVPd28179uWlJSou+//14REREKCAjwZtM+lZeXp9jYWB09epSv2VYD9Hf1Qn9XL9W1v40xys/PV6NGjRQYWPaZFF4fsQgMDFTjxo293WylERkZWa1+Eas7+rt6ob+rl+rY31cbqSjFyZsAAMAyBAsAAGAZgoWX2Gw2PfXUU0whX03Q39UL/V290N9X5/WTNwEAQNXFiAUAALAMwQIAAFiGYAEAACxDsLiGgIAALV++3NdllIs/1eoPrHg+R48ercGDB191nZSUFE2YMOGq6yxYsEC1atW6rlogZWZmKiAgQGfOnPF1KdfkT7X6C6ue06ZNm2rmzJlXXac8rx/leX3wR5U2WJw4cUIPP/yw4uLiZLPZ1KBBA/Xt21f/+te/fFpXZXjzfvrpp3XjjTdWePsPPvhAKSkpioqKkt1uV/v27fXMM8/of//3f60r8jpUxr5PTEzUQw895LJs7ty5CggI0IIFC1yWjx49WsnJyZKkV1555bLHr6U8L1plycvL0xNPPKGWLVsqNDRUDRo0UO/evZWRkXHN6/t72+jRoxUQEKCAgACFhIQoISFBzzzzjC5cuHDNba0KWpXlzbs84bIs58+f1wsvvKAOHTqoZs2aqlevnnr06KH58+eruLjY2kIt5Ov+LygoUI0aNfTee++5LL/zzjsVEBCgw4cPuyxv2rSpnnzySUnSli1b9OCDD5a7rcOHDysgIEA7duyoUK0HDx7Ufffdp8aNG8tms6lZs2YaMWKEtm7dWqH9eVqlDRa//OUv9eWXX2rhwoXav3+/PvroI6WkpOjUqVO+Ls2vPfHEExo+fLhuuukmrVy5Urt379ZLL72knTt36u233/Z1eZIqZ9+npqYqMzPTZdnatWsVGxt72fLMzEzdcsstki5epc5bIw1nzpxRUlKS/vKXvygtLU3bt2/XZ599puHDh+vRRx9Vbm6uV+pwx+23365jx47pwIEDmjRpkp5++mnNmDHD12X5jfPnz6tv37567rnn9OCDD2rDhg3avHmzxo4dq1mzZmnPnj2+LvGqfNn/drtdXbp0ueLx+9PjOjs7W998843zuI6OjlbNmjW9UufWrVvVuXNn7d+/X/PmzdNXX32lZcuWqWXLlpo0aZJXanCbqYROnz5tJJnMzMyrrnP//febevXqmYiICJOammp27NjhfPypp54yHTp0MHPnzjWNGzc2YWFhZtiwYebMmTPOdTZv3mx69+5t6tatayIjI83NN99stm3b5tKOJLNs2bIy7//UG2+8YVq2bGlsNptp0aKFmT17tvOx7OxsI8l88MEHJiUlxYSFhZn27dubDRs2uOzj9ddfd9Y8ePBg89JLL5moqChjjDHz5883klxu8+fPd9b2xhtvmMGDB5uwsDCTkJBgPvzwQ+d+v/jiCyPJzJw5s8zn9NLn7s033zSxsbEmPDzcPPzww+bChQvm+eefN/Xr1zfR0dHm2WefLfN5qKjK2veffPKJkWSOHTvmfLx+/fpm9uzZpkmTJs5lhw4dMpLM2rVrjTHGjBo1ygwaNMj5eEFBgbnnnntMeHi4adCggXnxxRdNr169zCOPPGKMMaZXr16X9a8xF/s9KirKrFq1yrRs2dKEh4ebvn37mu+//96574cfftiEh4eb77777rLnLD8/3xQXFxtjjGnSpImZOnWqs464uDjz4YcfmpycHPOLX/zChIeHm3bt2pktW7aU2QdW+OlzY4wxt912m0lMTDTnzp0zkyZNMo0aNTI1a9Y0Xbt2dT6na9euvew5euqpp4wxxvzlL38xnTt3Nna73dSvX9+MGDHCHD9+3Ln/0m1Lf9d/ev+nrlaHMeXrl+LiYjNu3DgTFRVl6tSpYx599FFz7733On/2UaNGXfbzZGdnO2tbvXq16dy5swkLCzPdu3c3e/fude77+eefN4GBgWb79u2X1X7+/HlTUFBgjLn4e/Xb3/7WPPLII6ZWrVomJibGvP7666agoMCMHj3a2O12Ex8fbz7++ONy9Jw1KkP/p6WlmRYtWjgf/+qrr0xUVJSZPn26GTVqlHP5W2+9ZWw2mzl79qwx5uIx9Mc//tH5+P79+01ycrKx2WymVatW5h//+IfL68dP6+3Vq5fLczBjxgzToEEDU6dOHfOb3/zGnD9/3hhjTElJiWnTpo3p3LmzcTgclz2HpT9H6XvLkiVLTM+ePU1oaKjp0qWL2bdvn9m8ebPp3LmzCQ8PN7fffrvJyclxo5cqplIGi+LiYmO3282ECRPMuXPnrrhO7969zcCBA82WLVvM/v37zaRJk0zdunXNqVOnjDEX31zCw8PNLbfcYr788kuzbt06k5CQYO666y7nPtasWWPefvtt8/XXX5uvvvrK3H///aZ+/fomLy/PuY47weKdd94xDRs2NB988IE5dOiQ+eCDD0ydOnXMggULjDH/6fyWLVuaFStWmH379pmhQ4eaJk2aOF/0P//8cxMYGGhmzJhh9u3bZ2bPnm3q1KnjDBY//vijmTRpkmnTpo05duyYOXbsmPnxxx+dtTVu3NgsWrTIHDhwwIwfP97Y7Xbnc1J6v/SXtixPPfWUsdvtZujQoWbPnj3mo48+MiEhIaZv375m3LhxZu/eveatt94yksymTZuuui93Vda+LygoMDVq1DCLFi0yxhizZ88eExkZac6dO2fsdrs5dOiQMcaYN99804SGhjpr/+mL58MPP2zi4uLM6tWrzb///W8zYMAAExER4QwWp06dMo0bNzbPPPOMs3+NufgGVqNGDdO7d2+zZcsWs23bNtOqVSvnz+RwOEzt2rXNgw8+eM3nuEmTJqZOnTpm7ty5Zv/+/ebhhx82kZGR5vbbbzfvv/++2bdvnxk8eLBp1aqVKSkpueb+KupKbyy/+MUvTKdOncwDDzxgkpKSzGeffWYOHjxoZsyYYWw2m9m/f78pKioyM2fONJGRkc7nKD8/3xhz8fn/+OOPTVZWltm4caPp3r276devn3P/7gaLq9VhzLX7xRhjnn32WVOnTh2TkZFhvv76a/PQQw+ZyMhI589+5swZ0717dzNmzBjnz3PhwgVnbd26dTOZmZlmz549Jjk52SQlJTn33b59e9OnT59rPte9evUyERERZurUqWb//v1m6tSpJigoyPTr18+8/vrrzt+DunXrmsLCwmvuzwqVof9LA0BpEJw9e7bp37+/2bRpk8sfDPfcc49JSUlx3r80WDgcDtO2bVtz6623mh07dph169aZjh07urx+bN682RkSjx075nytGjVqlImMjDQPPfSQ+frrr83f/vY3U7NmTfP6668bY4zZvn27keR83SnLpe8tq1atMl999ZVJTEw0nTt3NikpKebzzz8327dvNwkJCeahhx5yq58qolIGC2OMWbp0qaldu7YJDQ01SUlJJi0tzezcudMYY8z69eudL+qXio+PN/PmzTPGXHxzCQoKMt9++63z8ZUrV5rAwECXvzov5XA4TEREhPnb3/7mXOZOsIiPj7/sF2Dq1Kmme/fuxpj/dP6f//xn5+N79uwxkszXX39tjDFm+PDhpn///i77GDlypDNYlP5sHTp0uKx9SeZ//ud/nPcLCgqMJLNy5UpjjDH9+vUz7du3v2Ltl3rqqadMzZo1Xd5k+/bta5o2beqSmlu0aGHS09OvuT93Vda+79Gjh/ONe/bs2eaOO+4wxhjTp08f89ZbbxljLr4ApaamOre59MUzPz/fhISEmPfff9/5+KlTp0xYWJgzWBhz+V9DxvxnpOrgwYPOZbNnzzb169c3xhhz/PhxI8m8/PLLV/z5LtWkSRNz9913O+8fO3bMSDJPPvmkc9nGjRsvG6Gx2qXPTUlJifn000+NzWYzo0ePNkFBQZeNvNx6660mLS3NGPOfkYJr2bJli5HkfONxJ1h888035arjav1izMWRrRkzZjjvX7hwwcTFxbm8qV46alXq0hGLUn//+9+NJOdfzmFhYWb8+PHXfB569eplevbs6VJDeHi4ueeee5zLSn8PNm7ceM39WaEy9H9hYaEJCQlxvm4PGzbMvPDCC6a4uNiEh4c7/2CIi4szU6ZMce730mP0k08+McHBwS71rly50uX1o/S1/8svv7zsOWjSpIm5cOGCc9mwYcPM8OHDjTHGLFmyxEi64ojUpa703rJ48WIjyaxZs8a5LD093WWExlMq9TkW33//vT766CPdfvvtyszMVKdOnbRgwQLt3LlTBQUFqlu3rux2u/OWnZ2trKws5z7i4uJ0ww03OO93795dJSUl2rdvnyTp+PHjGjNmjH7+858rKipKkZGRKigo0JEjR9yut7CwUFlZWbr//vtdanr22WddapKk9u3bO//fsGFDSVJOTo4kad++feratavL+j+9fzWX7js8PFyRkZHOfRs3Tt5r2rSpIiIinPfr16+v1q1bu0yVW79+fee+rVRZ+z4lJcX5uWtmZqZSUlIkSb169XJZnpqaesXts7KydP78eXXr1s25rE6dOmrRokW5npeaNWsqPj7eeb9hw4YV6lvJ9fekfv36kqR27dpdtswT/XupFStWyG63KzQ0VP369dPw4cM1dOhQORwONW/e3KWP161bd9mx9FPbtm3TwIEDFRcXp4iICPXq1UuSKnRM79q1q1x1XK1fcnNzdfz4cZdjOCgoSJ07dy53HVd7vXCn3y/dT1BQkOrWreuTPr+Ur/u/Zs2auummm5zH77p165SSkqLg4GAlJSUpMzNThw4d0pEjR8o8rr/++mvFxsaqUaNGzmXdu3cv93PQpk0bBQUFOe97+rj2Rv96fdp0d4SGhuq2227TbbfdpieffFIPPPCAnnrqKf3mN79Rw4YNLzvpRpJbJ8qNGjVKp06d0iuvvKImTZrIZrOpe/fuOn/+vNu1FhQUSJLeeOMNlzcOSS6/NJJUo0YN5/8DAgIkSSUlJW63eSWX7rt0/6X7bt68uT7//HMVFxdftl559nO1fVutMvZ9amqqpk2bpu+++06ZmZmaPHmypIvBYt68ecrKytLRo0edJ3hZ7UrPf+kLT3R0tGrVqqW9e/e6va/S30FP/l6WJTU1VXPmzFFISIgaNWqk4OBgLVmyREFBQdq2bdtlx47dbi9zX4WFherbt6/69u2rd999V9HR0Tpy5Ij69u1b4WO6PHVcrV+scLV+ad68eYX6vHRfvujzS1WG/k9NTdWSJUu0Z88enT17Vp06dZJ08bheu3atSkpKVLNmzcte161yrddsSdq7d686duzo1r7KOq690b+VdsTiSlq3bq3CwkJ16tRJP/zwg4KDg5WQkOByq1evnnP9I0eO6Pvvv3fe37RpkwIDA51/If7rX//S+PHjdccdd6hNmzay2Ww6efJkhWqrX7++GjVqpEOHDl1WU7Nmzcq9nxYtWmjLli0uy356PyQkRA6Hw+0a77rrLhUUFOhPf/rTFR/39VfurqYy9H1SUpJCQkL0pz/9SefOnXP+1XnTTTfpxIkTeuuttxQeHl7mCFN8fLxq1KihL774wrns9OnT2r9/v8t6FenfwMBA3XnnnXr33Xddfu5SBQUF5foan7eFh4crISFBcXFxCg6++HdOx44d5XA4lJOTc1kfN2jQQNKVn6O9e/fq1KlTeu6555ScnKyWLVte119n5anjWqKiolS/fn2XY9jhcGj79u0u613PMb169Wp9+eWXlz1WXFyswsJCt/fpTZWh/1NTU3XgwAEtWrRIPXv2dIaZm2++WevWrVNmZqZ69OihkJCQK27fqlUrHT16VMeOHXMu27Rpk8s6pdu628c33nijWrdurZdeeumKgaCyvmZXymBx6tQp3XLLLXrnnXf073//W9nZ2frrX/+qF154QYMGDVLv3r3VvXt3DR48WP/4xz90+PBhbdiwQU888YTL93pDQ0M1atQo7dy5U+vXr9f48eP1q1/9yvnL+fOf/1xvv/22vv76a33xxRcaOXKkwsLCrllfdna2duzY4XIrLCzUlClTlJ6erldffVX79+/Xrl27NH/+fL388svl/tnHjRunjz/+WC+//LIOHDigefPmaeXKlc70KV38mKK0hpMnT6qoqKhc++7WrZseffRRTZo0SY8++qg2btyob775RmvWrNGwYcO0cOHCctfpKZW578PCwpSYmKhZs2apR48ezhegkJAQl+VljQbZ7Xbdf//9+u///m/985//1O7duzV69GiXj5eki/372Wef6bvvvnMr6E6bNk2xsbHq1q2b/vKXv+irr77SgQMH9NZbb6ljx47OUbXKrnnz5ho5cqTuvfdeZWRkKDs7W5s3b1Z6err+/ve/S7r4HBUUFGjNmjU6efKkfvzxR8XFxSkkJESzZs3SoUOH9NFHH2nq1KnlanPXrl0ux/POnTvLVUd5jBs3Tunp6frwww+1b98+PfLIIzp9+vRlx/QXX3yhw4cP6+TJk+X+q3LChAnq0aOHbr31Vs2ePVs7d+7UoUOH9P777ysxMVEHDhwod52Vhbf7PykpSTabTbNmzXJ+dCJd/Ag6JydHH374YZkfg0hS79691bx5c5fXmyeeeMJlnZiYGIWFhWnVqlU6fvx4ub/6HRAQoPnz52v//v1KTk7Wxx9/rEOHDunf//63pk2bpkGDBpVrP95WKYOF3W5Xt27d9Mc//lE333yz2rZtqyeffFJjxozRa6+9poCAAH388ce6+eabdd9996l58+a688479c033zg/V5KkhIQEDRkyRHfccYf69Omj9u3bu/y1/uabb+r06dPq1KmT7rnnHo0fP14xMTHXrG/ixInq2LGjy+3LL7/UAw88oD//+c+aP3++2rVrp169emnBggVujVj06NFDc+fO1csvv6wOHTpo1apV+t3vfqfQ0FDnOr/85S91++23KzU1VdHR0Vq8eHG59//8889r0aJF+uKLL9S3b1+1adNGEydOVPv27TVq1Khy78dTKnvfp6amKj8/33l+RalevXopPz//qi9AkjRjxgwlJydr4MCB6t27t3r27HnZ5+3PPPOMDh8+rPj4eEVHR5fjWbuoTp062rRpk+6++249++yz6tixo5KTk7V48WLNmDFDUVFR5d6Xr82fP1/33nuvJk2apBYtWmjw4MHasmWL4uLiJF18M3jooYc0fPhwRUdH64UXXlB0dLQWLFigv/71r2rdurWee+45vfjii+Vq7+abb3Y5nkv75Fp1lMfvf/97jRgxQvfee6+6d+8uu92uvn37uhzTkydPVlBQkFq3bu0cwi8Pm82mTz/9VI8++qjmzZunxMRE3XTTTXr11Vc1fvx4tW3bttx1Vibe7P/Q0FAlJiZedlzbbDbn8qsd14GBgVq2bJnOnj2rrl276oEHHtC0adNc1gkODtarr76qefPmqVGjRm4Fgq5du2rr1q1KSEjQmDFj1KpVK/3iF7/Qnj17KnwhPU+rstOmP/3001q+fHmFr3RWmYwZM0Z79+7V+vXrfV2KX6hKfY+qp6SkRK1atdKvfvWrco+oAP6kUp+8WV29+OKLuu222xQeHq6VK1dq4cKFZZ4XAaBy++abb/SPf/xDvXr1UlFRkV577TVlZ2frrrvu8nVpgEcQLCqhzZs364UXXlB+fr5+9rOf6dVXX9UDDzzg67IAVEBgYKAWLFigyZMnyxijtm3bavXq1WrVqpWvSwM8osp+FAIAALyvUp68CQAA/BPBAgAAWIZgAQAALEOwAAAAliFYAAAAyxAsAACAZQgWAADAMgQLAABgGYIFAACwzP8HGrrRYux0awUAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind='box',sharex=False,sharey=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 419,
+   "id": "78182267-5d68-445e-8310-a91f1718784d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 419,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 420,
+   "id": "98576f72-7717-418e-8597-c01bb6ab8333",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 420,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.boxplot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 421,
+   "id": "760f68df-15aa-4b0b-8af8-1ad11c95662f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<Axes: title={'center': 'PetalLengthCm'}, xlabel='[Species]'>,\n",
+       "        <Axes: title={'center': 'PetalWidthCm'}, xlabel='[Species]'>],\n",
+       "       [<Axes: title={'center': 'SepalLengthCm'}, xlabel='[Species]'>,\n",
+       "        <Axes: title={'center': 'SepalWidthCm'}, xlabel='[Species]'>]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 421,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.boxplot(by=\"Species\",figsize=(10,10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 422,
+   "id": "20d6eb38-4848-4873-b030-06aa26ed9a9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<Axes: title={'center': 'SepalLengthCm'}>,\n",
+       "        <Axes: title={'center': 'SepalWidthCm'}>],\n",
+       "       [<Axes: title={'center': 'PetalLengthCm'}>,\n",
+       "        <Axes: title={'center': 'PetalWidthCm'}>]], dtype=object)"
+      ]
+     },
+     "execution_count": 422,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist(edgecolor='black',linewidth=1.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 423,
+   "id": "6c0484d7-a753-4b8c-881e-d0fc328636aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 423,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.violinplot(data=df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 424,
+   "id": "55ab3393-82fc-4562-9be1-9bd6e1d65741",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Species'>"
+      ]
+     },
+     "execution_count": 424,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.violinplot(data=df,x=\"Species\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 425,
+   "id": "788b5488-993a-485a-945f-9fbf6ef36976",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pandas.plotting import scatter_matrix\n",
+    "scatter_matrix(df,figsize=(10,10))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 426,
+   "id": "8e0207e0-4a27-4007-af45-005c8e22fda9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x1daac218b50>"
+      ]
+     },
+     "execution_count": 426,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1143x1000 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(df, hue='Species')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 427,
+   "id": "df2fe861-7ada-40d0-9bb8-304a914fdd3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1daae872830>"
+      ]
+     },
+     "execution_count": 427,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "t=['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']\n",
+    "for i in t:\n",
+    "    plt.bar(df['Species'],df[i])\n",
+    "plt.legend(bbox_to_anchor=(1.3,1))  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 428,
+   "id": "09293a3a-6927-41dd-bc4b-7eb149cfdfb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 428,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# histograms\n",
+    "df['SepalLengthCm'].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 429,
+   "id": "d07e8ffc-9d4f-41ad-a132-9b0d569a18b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "t=['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']\n",
+    "for i in t:\n",
+    "    df[i].hist()\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 430,
+   "id": "f86a936e-b68e-4118-a61d-7d03d68381f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 430,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['SepalWidthCm'].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 431,
+   "id": "0b523ad5-e443-4c39-8880-28911d5ff982",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "t=['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']\n",
+    "for i in t:\n",
+    "    plt.bar(df['Species'],df[i])\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 432,
+   "id": "8e5f9dad-959e-4ffb-a19a-eaec20f8c785",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plt.bar(X_axis, Y_Data[0], width, label = 'Setosa')\n",
+    "# plt.bar(X_axis+width, Y_Data[1], width, label = 'Versicolour')\n",
+    "# plt.bar(X_axis+width*2, Y_Data[2], width, label = 'Virginica')\n",
+    "# plt.xticks(X_axis, columns[:4])\n",
+    "# plt.xlabel(\"Features\")\n",
+    "# plt.ylabel(\"Value in cm.\")\n",
+    "# plt.legend(bbox_to_anchor=(1.3,1))\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "456a3e09-dc66-40fb-aec9-f8d9f2d2ccf3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 433,
+   "id": "d0b66ee4-db8a-4abd-a02b-ceb31346a6d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "one_hot_encoded_data = pd.get_dummies(df, columns = ['Species'],dtype=bool)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 434,
+   "id": "575da9d3-01a9-4039-aa00-72ea10cfdf2d",
+   "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>SepalLengthCm</th>\n",
+       "      <th>SepalWidthCm</th>\n",
+       "      <th>PetalLengthCm</th>\n",
+       "      <th>PetalWidthCm</th>\n",
+       "      <th>Species_Iris-setosa</th>\n",
+       "      <th>Species_Iris-versicolor</th>\n",
+       "      <th>Species_Iris-virginica</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</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>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
+       "0              5.1           3.5            1.4           0.2   \n",
+       "1              4.9           3.0            1.4           0.2   \n",
+       "2              4.7           3.2            1.3           0.2   \n",
+       "3              4.6           3.1            1.5           0.2   \n",
+       "4              5.0           3.6            1.4           0.2   \n",
+       "..             ...           ...            ...           ...   \n",
+       "145            6.7           3.0            5.2           2.3   \n",
+       "146            6.3           2.5            5.0           1.9   \n",
+       "147            6.5           3.0            5.2           2.0   \n",
+       "148            6.2           3.4            5.4           2.3   \n",
+       "149            5.9           3.0            5.1           1.8   \n",
+       "\n",
+       "     Species_Iris-setosa  Species_Iris-versicolor  Species_Iris-virginica  \n",
+       "0                   True                    False                   False  \n",
+       "1                   True                    False                   False  \n",
+       "2                   True                    False                   False  \n",
+       "3                   True                    False                   False  \n",
+       "4                   True                    False                   False  \n",
+       "..                   ...                      ...                     ...  \n",
+       "145                False                    False                    True  \n",
+       "146                False                    False                    True  \n",
+       "147                False                    False                    True  \n",
+       "148                False                    False                    True  \n",
+       "149                False                    False                    True  \n",
+       "\n",
+       "[150 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 434,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one_hot_encoded_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 435,
+   "id": "27222e64-f1e2-4f1f-bf39-b1bf9046d4ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "correlation_matrix = one_hot_encoded_data.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 436,
+   "id": "3ad77d30-1142-468f-ae05-4728b31913f4",
+   "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>SepalLengthCm</th>\n",
+       "      <th>SepalWidthCm</th>\n",
+       "      <th>PetalLengthCm</th>\n",
+       "      <th>PetalWidthCm</th>\n",
+       "      <th>Species_Iris-setosa</th>\n",
+       "      <th>Species_Iris-versicolor</th>\n",
+       "      <th>Species_Iris-virginica</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>SepalLengthCm</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.109369</td>\n",
+       "      <td>0.871754</td>\n",
+       "      <td>0.817954</td>\n",
+       "      <td>-0.717416</td>\n",
+       "      <td>0.079396</td>\n",
+       "      <td>0.638020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SepalWidthCm</th>\n",
+       "      <td>-0.109369</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.420516</td>\n",
+       "      <td>-0.356544</td>\n",
+       "      <td>0.595601</td>\n",
+       "      <td>-0.464700</td>\n",
+       "      <td>-0.130901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PetalLengthCm</th>\n",
+       "      <td>0.871754</td>\n",
+       "      <td>-0.420516</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.962757</td>\n",
+       "      <td>-0.922688</td>\n",
+       "      <td>0.201587</td>\n",
+       "      <td>0.721102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PetalWidthCm</th>\n",
+       "      <td>0.817954</td>\n",
+       "      <td>-0.356544</td>\n",
+       "      <td>0.962757</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.887510</td>\n",
+       "      <td>0.118376</td>\n",
+       "      <td>0.769134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Species_Iris-setosa</th>\n",
+       "      <td>-0.717416</td>\n",
+       "      <td>0.595601</td>\n",
+       "      <td>-0.922688</td>\n",
+       "      <td>-0.887510</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>-0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Species_Iris-versicolor</th>\n",
+       "      <td>0.079396</td>\n",
+       "      <td>-0.464700</td>\n",
+       "      <td>0.201587</td>\n",
+       "      <td>0.118376</td>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Species_Iris-virginica</th>\n",
+       "      <td>0.638020</td>\n",
+       "      <td>-0.130901</td>\n",
+       "      <td>0.721102</td>\n",
+       "      <td>0.769134</td>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         SepalLengthCm  SepalWidthCm  PetalLengthCm  \\\n",
+       "SepalLengthCm                 1.000000     -0.109369       0.871754   \n",
+       "SepalWidthCm                 -0.109369      1.000000      -0.420516   \n",
+       "PetalLengthCm                 0.871754     -0.420516       1.000000   \n",
+       "PetalWidthCm                  0.817954     -0.356544       0.962757   \n",
+       "Species_Iris-setosa          -0.717416      0.595601      -0.922688   \n",
+       "Species_Iris-versicolor       0.079396     -0.464700       0.201587   \n",
+       "Species_Iris-virginica        0.638020     -0.130901       0.721102   \n",
+       "\n",
+       "                         PetalWidthCm  Species_Iris-setosa  \\\n",
+       "SepalLengthCm                0.817954            -0.717416   \n",
+       "SepalWidthCm                -0.356544             0.595601   \n",
+       "PetalLengthCm                0.962757            -0.922688   \n",
+       "PetalWidthCm                 1.000000            -0.887510   \n",
+       "Species_Iris-setosa         -0.887510             1.000000   \n",
+       "Species_Iris-versicolor      0.118376            -0.500000   \n",
+       "Species_Iris-virginica       0.769134            -0.500000   \n",
+       "\n",
+       "                         Species_Iris-versicolor  Species_Iris-virginica  \n",
+       "SepalLengthCm                           0.079396                0.638020  \n",
+       "SepalWidthCm                           -0.464700               -0.130901  \n",
+       "PetalLengthCm                           0.201587                0.721102  \n",
+       "PetalWidthCm                            0.118376                0.769134  \n",
+       "Species_Iris-setosa                    -0.500000               -0.500000  \n",
+       "Species_Iris-versicolor                 1.000000               -0.500000  \n",
+       "Species_Iris-virginica                 -0.500000                1.000000  "
+      ]
+     },
+     "execution_count": 436,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "correlation_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 437,
+   "id": "2f911c6e-1403-43f4-ad0f-417e3cb05034",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Step 1: Encode categorical variables\n",
+    "# For example, if you have 'species' column containing categories 'Iris-setosa', 'Iris-versicolor', and 'Iris-virginica'\n",
+    "# You can encode them as numerical values using LabelEncoder from sklearn\n",
+    "# from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    " \n",
+    "# df.drop(columns = ['Species'])\n",
+    "\n",
+    "# encoder = LabelEncoder()\n",
+    "# df['Species_encoded'] = encoder.fit_transform(df['Species'])\n",
+    "# df.drop(columns=['Species\n",
+    "# Step 2: Compute correlations\n",
+    "\n",
+    "\n",
+    "# Step 3: Visualize the correlation matrix\n",
+    "\n",
+    "fig = plt.gcf()\n",
+    "fig.set_size_inches(10, 7)\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap='cubehelix', linewidths=1, linecolor='k', square=True, mask=False,\n",
+    "            vmin=-1, vmax=1, cbar_kws={\"orientation\": \"vertical\"}, cbar=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 438,
+   "id": "a8c5984a-74bd-4df8-98bf-a5bc04cd1894",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# one_hot_encoded_data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 439,
+   "id": "2145eb3a-d463-42f6-ad3a-3a27cf65f603",
+   "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>SepalLengthCm</th>\n",
+       "      <th>SepalWidthCm</th>\n",
+       "      <th>PetalLengthCm</th>\n",
+       "      <th>PetalWidthCm</th>\n",
+       "      <th>Species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm         Species\n",
+       "0              5.1           3.5            1.4           0.2     Iris-setosa\n",
+       "1              4.9           3.0            1.4           0.2     Iris-setosa\n",
+       "2              4.7           3.2            1.3           0.2     Iris-setosa\n",
+       "3              4.6           3.1            1.5           0.2     Iris-setosa\n",
+       "4              5.0           3.6            1.4           0.2     Iris-setosa\n",
+       "..             ...           ...            ...           ...             ...\n",
+       "145            6.7           3.0            5.2           2.3  Iris-virginica\n",
+       "146            6.3           2.5            5.0           1.9  Iris-virginica\n",
+       "147            6.5           3.0            5.2           2.0  Iris-virginica\n",
+       "148            6.2           3.4            5.4           2.3  Iris-virginica\n",
+       "149            5.9           3.0            5.1           1.8  Iris-virginica\n",
+       "\n",
+       "[150 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 439,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 440,
+   "id": "b16e2a8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data = one_hot_encoded_data\n",
+    "# X = one_hot_encoded_data.drop(columns=['Species_Iris-setosa', 'Species_Iris-versicolor',\n",
+    "#        'Species_Iris-virginica'])\n",
+    "# Y = one_hot_encoded_data[['Species_Iris-setosa', 'Species_Iris-versicolor',\n",
+    "#        'Species_Iris-virginica']]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 441,
+   "id": "181d94c2-b7b6-47fc-8e75-8d7bb7806ac3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# X1=data.drop(columns=['Species_Iris-setosa', 'Species_Iris-versicolor',\n",
+    "#         'Species_Iris-virginica'])\n",
+    "# Y1=data[['Species_Iris-setosa', 'Species_Iris-versicolor',\n",
+    "#      'Species_Iris-virginica']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 442,
+   "id": "2b1a5ca6-a4aa-40b3-b52e-0da075c45fcf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X=df.drop(\"Species\",axis=1)\n",
+    "Y=df[\"Species\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 443,
+   "id": "aa8665a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 444,
+   "id": "39505a1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x1daac28fdf0>"
+      ]
+     },
+     "execution_count": 444,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9i/VB0UKBoUFy/YLyhBgk6Ti2T/IXT31AMgRMZPtMHPHUtpSQyO1Ts2+0f/e736GmpgaLFi1SrMyXX34ZHR0domX29PSgp6fH92+Hw6HY9okoOmyfsRVN8I/WLo/gTQAwlJqjtcuTMANlsmL7JDHx1AckYsBEts/EFU9tSwmJ2D69NAuGlpqaioqKCkXLfPbZZ3H++edjzJgxIddZs2YNLBaL76+oqEjROhBR5Ng+45eeooWSOtg+SQz7gNhi+0xcbFuJQ7NHx++//340Nzfj4YcfVqS8gwcP4lvf+hZeeuklXHLJJSHXE/rEr6ioiI/WEOkA26e+NXe4YHf1wuHqhSXTAHOmwffbMKFc3CZDGnY2tYedi5v0ie2TxEjlwN3y67koyDYKBnMKJ8iT3HUTPXDUcGyfiUtufulEueaV2I+jDjfauz1wuPtgzkxHfpYRo8wmlWosn2aPjt9yyy248MILMWHCBEyZMgUGQ2DI9pdeeims8jZs2IDCwkJceOGFoutlZGQgIyMj7PoSkfrYPvXrYFs3ltc2YFtjm++1SlsBVlWVYnxBNqw5RtQsOh3rt+wPyEtbYStAzaLTEyJaaLJj+yQxYhGDz51cCGNaatDvTM+dXIg7LpqCFS9/LCvIk9yAUMkQOGo4ts/EJScad6Jc80rsR1NbN5YJ3K+srirFuIJsxescDs0eHb/xxhvx1ltv4eSTT0ZBQUHA4y4WiyWssgYGBrBhwwYsXLgQ6elxl6GMiEjXmjtcQZNsAKhrbMOK2gY0d7gAAI9taQxaZ1tjGx57qxFElNi8EYPnlFgDXp9TYsXdP5iKZbUNQb8znTjaLPj6O/tbsXTzbtidHt9rdqcn6AZcaF256xHFC7G2tW5+GQAkxDWvRNs96nAHTbKBofuV5bUNOOpwK1rncGk2S924cSM2b94s+Q20HG+88QaamppQXV2tQM2IiMif3dUbNGh51TW2we7qhcvTj62NwsFatiZgsBYiCjYmLxPrF5QHRQwOFcypvCgv4AkYf8ODPMkNCJVsgaMoOYRqW5YsIz5v6UqIa16Jttve7RG9X2nv9sT0EXLNJtojRozAhAkTFCnre9/7HjT6aTkRUdJxuKQDsRjSxB+IYrAWouQgFDH4i9ZuwXV7+gZEy/LvN+QGhGLgKEpUoaJxJ8o1r8R+ONx9US1Xm2aPjt99992466674HQ6tdokERFFwJxpEF2eazLAbJJeh4iSU6j+ISNd/LbTv9+Q28ewL6JkkyjXvBL7YTaJf2cstVxtmm39kUceweeff45Ro0bhpJNOCgqGtnPnTq2qQkSUEKKN1Bnq/ZZMAyptBagTeByr0lYAS6YB2cY0nDu5EBNHm4Oiju877GAwNKIk5O1T7C4PXrz6DGz7vA01dQfg9PQDAOoPdYTsW7xBnrzEAkLNLrEiPS0FdqdHVuCoRInOTMkl1HUr55qPB0rsR362EedMGonJYyxB9yJ7mu3Iz47tsdAsvdc999wjuvyuu+7SohpwOBywWCxMf0CkQ2yf8kUbqVPq/QfburGitiHghtg/6jig70ifpDy2TxIj1KdU2gqwqKIYN75YD6en3xd1/PaXPw64ufYGeRotEHV86ebdAetW2Aqw+FiZM8bnY938MgwCQevNKbHi/vllGEBw4Kh4jM4she0zsUiN0UJtI1Q70rOmYxlOht9rhHMfIZUlJZY0m2jrBTsiIv1i+5TH7vQEpc3xmlNixfoF5aLf1sh9vzePtjcQi8Uvj3a0daD4w/ZJoYj1B7NLrLjzoilITUnxBXPyflM3PMhTqLJbOnvQ9M3QTw/rD3UEfEvu7W8ABJUJIGn6KbbPxCF3fA2nHemR3enBr//yESb5PRmXkZ6K+kMd2HfYgd9cdork/uj9XkT1R8fb29vx3//931i4cGFQw7fb7fjDH/4guIyIiIRFG6lT7vvH5GWG/MaHkX6JyEusP9i6vxWpKSmYUJjjey1UkCch3sjiV238QHC5t7+ZUJgTVGaiRGem5CJ3fA2nHelRa5cHb+xpwRt7WkIul9o/vd+LqB4M7dFHH8U777wjOJG2WCzYunUr1q9fr3Y1iIgSRrSROpWJ9JkYUU+JKHpq9weRls9+iuJRsly3yXAvovo32ps3b8aDDz4Ycvl//dd/4ZZbbsGKFSvUrgoRkWbUDL4TbaROs8mALGMaqiuLg4KH1NQd8L3f++i4w9ULS6YBZr9Hx+XW4ajDjfZuDxzuPpgz05GfZYxpTksikkduH2Z3epBpSMPjl58a0I94H+0GhjIZiJUnta3h/c3w/isrIw3NHS443L1wuIb6mmxDGkwG+VHOifRC7viqxH2GVBlq38vIuRdRooxY7afqE+3PP/8cJSUlIZeXlJTg888/V7saRESaiTZQmZRoI3Vac4yoWXQ61m/Zj0e3NPper7AVoGbR6bDmGCWDi8ipA4OlEcUnuX2Y0HoVtgI8sqA8IACaMS016HeU3sBNg5AOVubf32QZ0/DIgnJs2HYAj25p9Pv3p0F9zcpLpuHsSSOxZe/XQfsYT9GZKblYc4yYXWINGfPAmmNU5D5DTsA1te9lpO5FlCgjlvup+qPjaWlpaG5uDrm8ubkZqamapfMmIlKV3ekJ6rCBod8KLd28G3anJ+ptWLKMWDu/DHNKrAGve29cpT6FdfcN4LEt+wNuSgFgW2MbHtvSiM6evqBJNgDUNbZhRW0DmjtcknVw9w0ETbK9ZSyvbcBRhzvc3SYiDcjtw0Ktt62xDRu2HUB1ZTHmlFhx9w+mYlltg2B5b3/2NZb8j/S2/Pub6spibNh2wNe3DP+3V11jG+7834+x7PzJqLAVBCyT21cSxcp1Z9mCrtsKWwGuO8sGd99A1PcZUu38qMOt+r0MADy2pVH4XuStxhDvCK8MqWOl9n6q/o12eXk5Xn75ZXznO98RXF5bW4vy8nK1q0FEpAmtAnOMycvE+gXlEUUcbe/2YKtAHlsA2NrYik53X9Cg5VXX2Aa7q9cXKC1UHfYedoiW0d7t4SPkRDoktw8TW29bYxvuuHAKrq4sFl2vMDcDWxvl9Zfe/uaw3R3wzVV5UV7Av/3VNbahp28A5ePyUV1RDEumAfnH8hBzkk161drlQfVzO1BdWYzqiuKAaNzVz+3AS9fMivo+Q6qdt3erfy/T2uUJ2f63hrMfImVI7Yfa+6n6RPv666/HT37yE5x44om45pprkJaWBgDo7+/H448/jt/+9rd44YUX1K4GEZEmtAzMEWnEUYe7T3R5p0v+PoSqg9Q2pJYTUWzI7cOk1nP39sOSZcQXrd0h1+npG5C1LS+h8qTL6PNNxF++dlZA9HMiPXK4e+H09If8AElyDFcgiJgS24i2Dlrsh9r7qfpEe/78+bjttttw4403YsWKFfjWt74FAPjiiy/Q1dWFW2+9FT/84Q/VrgYRkSaiDVSmBbNJvOvPzYx+H6S2IbWciGJDbh+mxHoZ6eEHKxtenlQZOaY00fKI9EaqbUmO4TKDiKm9jWjroMV+qL2fmtzprFq1Cpdccgmef/55NDY2YnBwEHPnzsVPf/pTfPvb39aiCkREmog2UJmSQkXRzM82otJWgDqBR7srbQXINaWLLrdITMQBSG4jP5uPbRLpkTXHiHMnF2LiaHNQFN99hx2w5hhhd3qQnpoSMmCTf18n1ie2dPaE3V8OL6/+UAcqbAWCP1WptBWgxdEjWh5RLIhFuZZqg/nZ0d9nSN2rKLGNaOugxX6ovZ8pg4ODg1GVEGccDgcsFgvsdrtgbm8iip1EaZ/NHS4s3bw7oOP2Bt8ZrUCkTrl1EIuiebCtGytqGwImwv5RxaWWy9F0LHL58DIYdTw+JUr7JGliGQOMaam4dfNufHiw3Rf92389ob4uVJ94//wyDABh95f+5Xmjjj+37UBQX3PnxVPx02few5TRZk3731hg+4wfcqJcS2XtUOI+Q6oMLe5l9LAfau6nphPtjo4O/Pvf/0ZLSwsGBgJ/U3PllVdqUgd2RET6lUjt0/tpdbiBypTa9vBUOl5zSqx44LJTsPLVT3DpqSei0JyBLnc/ckxpaHH0oHbnV1hVVQpLltGXR9u7Dxa/PNpyBeTRNqUjP5t5tONVIrVPCk2q/zi/dDSWvdQAIDCfNQCMG5GFwtyMkPm2Q/WJkfSX/u8xZxpgMqQdz6NtSke2MQ12lweZRm3731hh+4wPUu1r/YKhANFS61iyjIrcZ0iVocW9jB72Q6391OxHcq+++iouv/xydHV1wWw2IyUlxbcsJSVFs4k2EZEWIg1UpgQ50UT/2nAEf204IrjOr86dCEuW0RdZPBqjzCZOrIniiFT/sXDWSb5/Dw/Y9ObNc0P2e2J9YiT9pdB7xiCwvyoCn5whfZET1R+ArEjYStxnSJWhxb2MHvZDrf3ULIH1r3/9a1RXV6OrqwsdHR1ob2/3/X3zzTdaVYOIKOHpIZooEcUnqf5DLMo3+w4icXIibWuZvYTUpdk32v/5z39w4403IisrS6tNEhEltFDBVORE4SzKz8Rjl5+K9LRUdLqG3t/bP4Drnt8ZVpRNsYAuRBR/pPoPsSjfcvsO/37DkmlAdkY6utx9kv2I3P6G/RLplRKRtr3raHGdK7GNgJ+QZaYjPyu8n5DFc3vWbKJ93nnn4YMPPvCl9yIiosiJBVORisJZkG3EH686AyteDg608serzkB+lrybZTkBXYgovkj1Hy2dPYLvmy0zQq9/v+ENZCYUUG14PyK3v2G/RHomN9K21DpaXOdKbEMqqJsWdYglVYOhvfLKK77///rrr7Fy5UosXrwYpaWlMBgCb+R+8IMfqFWNAAwWEZmmpia0tgr/XiQaPT09yMjIULxcq9WKcePGKV4uqYvtUx45wVS6Pf0ho2gOArj1fz4KmQ7n/h+eIjmAyalDvHziTPKwfSaPUFF4V1eVotnuwu/e3B/Qf1TYCnDD2SWYfEKuaLsf3m9cf7YN9U3tgn3R8KBPcoNDJWu/xPYZP+REuRZbJ8uYpvp1rkRbOupw4+Y/7wp5r/Hgj6aLfrOdCO1Z1W+0582bF/TaypUrg15LSUlBf3+/mlWhKDQ1NWHSpMlwuZzKF56SAqjwWU9mZhb27t3DyTYlJDnBVCYU5mD9gnLBKJp7DjsEBz4AqGtsg93VKznRllMHvQ+ARCRsTF6mYP/R1u3Bog07UF1ZjOqKYvT0DSAjPRX1hzpQ/dwOvHp9pWi7H95vlBflBQRT8+ffj8jtb9gvUTwI1b78r02xdT5v6VL9OleiLbV3e0TvNdq7PaIT7URoz6pOtIen8KL41NraCpfLiTOq74J59EmKlXu4YTs+fuVpTP/pEowsnqRYuY7DX+L9mnvQ2trKiTYlJLmBUkJF0XS4og+0wmAtRIlNqP/4orU7KNK4P6l2P7zfEAus5l+e3P6G/RLFCzlRrkOO4Rpc50psQyrwqvTy+G/Pmv1G+w9/+AN+/OMfBz0m7PF4sGnTJqb3igPm0SdhxLiJipXnOPwlACCncJyi5RIlumiDqZgzow/GokRAFyKKL1H3PcOWiwVW8y9P7nbZL1Ey0OI6V2IbZpP4NFN6efy3Z80m2osXL8b3v/99FBYWBrze2dmJxYsXc6JNRORHLMqmNceIi0pPQNWpJ6LQnIEudz9yTek46nCjdudXvmAqocqwZBpQaStAXYjfTVkypSOaWnOMOHdyISaONqO8KA89fQMwGdKws6kd+w47ZAVFIiL9EGvv3mX9g4OYXWIN+ZtJqXY/PBBU/aEOVNgKBB8vnV1iRWrK0O88h/c3vf2DODE/EyZDKr7p8qB3YMC3npxAU0SxFk0kbS2uc7ljvNh+5GcbRe818rPFywjnPkOvkck1m2gPDg4iJSUl6PWvvvoKFotFq2oQEemeVJRNS5YRt35/EpYLRPJcVVUKS5Z0RNJVVaVYUdsQMAB63z8mL1NWHW6/aAqW1zYEPEbqXwciig9i7T0FwG3DooQPDg4G9B3eIE1yHoVdO7/MF+Sppu4AHllQjhQgoLwKWwEWzjoJF66vw6nj8rC2qhR3XDQFy4b1NxW2AiyuKMai53bg1HF5WF1Vivvnl+G2EEGk2C+RHkQbSduSZcR986ZhucAYft+8aYpc55Yso2Cb80YMl3OfMcpswuqqUsF6rq4qxSizSbIMqToA+o5MrmrUcQAoLy9HSkoKPvroI0ydOhXp6cfn9v39/Thw4AC+//3v489//rOa1fBhVMbw7dy5E6eddhrOXbFB0Ue8v3z/dbxfcw8qf/kYxk4uV6zcb5r24Z+rFuPDDz/Eqaeeqli5pD62T3lRNt19A6KRPO//4SlBg87wMryDpN3V6wu0Ysk0YExepuw6/PrPu0J+Ui0VTZTiD9tnYpJq7+eXjsaylxp8r2UZ01BdWYxZ3yqAyZAGS2ZwICc52/QGeTIfy6Ntd/biUPtQ0NX6Qx2oqTsAp2coUO6aqmn4W8MRbG0MrmOFrQDl4/Lx6JZGX99jSk8VDTSViNg+44MSkbTtTg9+/ZePMMnvm15vUMJ9hx34zWWnqB51/IHLTsEtf/lI1n4E5NE2pSM/eyiPthLbAKDryOSqf6PtjTy+a9cunHfeecjJyfEtMxqNOOmkkzB//ny1q0FEFBfkRNns7R+QjBouJ1LnmLxMwU975dZBaJLtrYNUNFEi0gep9r5w1kkBr3mDoT26pRFv3jwXEwpzBN8rRijIU3u3B1dt/EBw/UKzSXCSDQDbGttQXVEM4HjfM2m0OeEn1hSflIik3drlwRt7WvDGnpaQy9WOOt7eLX8/RplNgvcDSmwDgK4jk6s+0b7rrrsAACeddBJ+/OMfw2TijRcRUShyomx6+sUfRJKKxBluZOBI6iAVTZSI9EGqvYtFBlcy6q9YnyEVndx/Ofse0jNlonnHPuq4VDtTYj/kbEPqsexYRybX7DfaCxcu1GpTRERRUSKoRsCjUpnpyM8yyvqG12wy+B7NHB78o6buAHJNBvT2D0iuI7Vcqg5ivHUQLyNd1nHQawATomQh1N79+49sYzpqFp3u6z+8j3MDQxkMxNqwt/139fShINuI3v5BdHv6kJORDmNaKjpcHuSYjr0nM/QtqVR08qIRWbj+bBtq6g5IRjJmn0OxJGeMl1OGNceIdfPLggKiLtm821dGNNe61H2A2ZSuyH5IbUOMnG3IPRZq9QuaTbTz8/MFg6GlpKTAZDLBZrNh0aJFWLx4sVZVIiIKokRQjaa2biwTCFS2uqoU4wqyRd9rzTGiZtHpWL9lf1DQn5pFp8OaY0RP3wCeXTgDj77VGLTOswtnIC/TIFmGVB2kIpq6+wYko4lKHQc9BzAhShbD27s34NmGbQeC+o9HFpTjxhfr4fT049zJhTCmpQb9PnJOiRX3zy+Dp38Ay2obUN/UgUcWlOO3b3wW0Bd4A5kteOZ9zBifj/uqSnHOpJF4c+/XQXVscbhDRjuvsBXg9U+OoL6pHc8unIER2aH7N/Y5FGtyxng5ZWz6+Uzc9crHQePrpp/PhDVHOlCZnG2EanOzS6wYka3Mfojda+Rny4uuLrWO1LFQs18Q/4hQQXfeeSdSU1Nx4YUX4p577sE999yDCy+8EKmpqbjuuutw8skn45prrsEzzzyjVZWIiALYnR7BIGLv7G/F0s27YXd6JMs46nAHTS6Bod8OLq9twFGHW7KMx7Y0Br1/W2MbHntraDDLSE/F428Jr/P4258jNTVFsgwx3sjAc0qsAa/7R+71RhOttBUErOOdSKcAosdBaGADwjvWRBS94e29urIYG7YdEOw/Nmw7gOrKYswpseLuH0zFstoGwTb85TdOX/uXU947+1txe20D7r54qmCfUmkb6nuG90neyXpN3QFf/xfqUVIl+nciJUQzPgOAu28gaJINDI2vd7/yMbo9/VFf6z19A7j2zAmoGNYeK2wFuPZMG/oGBqPeD6l7jVFmk+S9iFQZAESPhfcpALX6Bc2+0a6rq8N9992HX/ziFwGvP/XUU/i///s/bN68GWVlZXjkkUdw9dVXa1UtIiIfJYKUtHd7RAOVSQUJa+3yhAz6s9U/+EeIbWz1BhCRKENqP8bkZWL9gnLRyL3jCrLx4I+mC0YT3XvYoUjANiJSn3977+nrD/iGyt+2xjbcceEUXF1ZLNpfZmek+dp/eVGeaHneQGbv7G+Fp38wZJ8CAOsXlKPZ7saB1m5flGXvN+zA8f4vksBL7HNIC3LG+GjvM5QYX785FpywurIY1RXFAZHNr9q4A5uvmRX1fgDS9xpy7kXE1vm8pUuxoG6R0Gyi/frrr2PdunVBr59zzjn49a9/DQC44IILsHTpUq2qREQUQJkgJeLBO6SXS9dBKviHEkFKAOHIwMOFiiYquZ8u9YO5EJF83vZe39Quup67tx+WLCO+aO0OuU6X+/jvuMMJZNbp7sWEwpyQH0ZasozYd7QL1z6/M2R5ofoeLQJIEUnR4j4j2oCo3m14MwwILldwDJe615BzLxJqHS2CuonR7NHxESNG4NVXXw16/dVXX8WIESMAAN3d3cjNzdWqSkREAeQEAZMuQ/zzS+nl0nXQIoBItCT3MzP6Y01EypPbD4qtl2NK8/2/VCAz/+Vq9rFK9O9E0dLiPkOqDEXaWZyM4bG+X9LsG+077rgD11xzDd566y18+9vfBgDs2LEDf/vb3/Dkk08CAP75z39i7ty5WlWJiJJUqOiScoKAScnPNkoGCRMjtw5KBBBRk9RxsGQacO7kQkwcbQ6KWLrvsEOTOhLRcd5+0e7y4MWrz8C2z9uCoozPLrEiPS0FdqdHMIiaNwKxs6cfL1x9Bt79vA0N/7GjwlYg+Khrha0A9Yc6AITum4b31+ZMQ8igaWJ9rBL9O1G0rDnGqMc+OeOrEvcy50waicljLEH13NNsV2QbSor0vk7t+6WUwcFBqacQFbNt2zY8+uij2LdvHwBg4sSJuOGGGzBr1iytqgCHwwGLxQK73Q6z2azZduPZzp07cdppp+HcFRswYtxExcr98v3X8X7NPaj85WMYO7lcsXK/adqHf65ajA8//BCnnnqqYuWS+rRon3KiTy7dvDug0/UG1RgdRtTx5bUNAYOg3Kjj3jpK1UFqG9HWQQly6hhpdHbSHsfPxCXUL1baCrCootj3G2hv4LEbX6zHjPH5WDu/DCkYCjT0wcF2X6Ty4e35qspvYRCDeLbugGDUcW95Qn1sqP763nnTsPLVTwIm23L6DiX6d71i+4wfSox9B9u6sUJgfF1VVYrxx7J6RHutHzw2hg+vp5LbUEK093Vq7oemE209YEcUPk60SStqt0+70xOUisZrTokV6xeUw5Jl9H0yGirwhhwB+aOHBfSRW9dQdbA7Pfj1Xz7CJL9PxL1BSvYdduC+qlKsqG0Iufw3l52iWdCfUMdB7rkg/eD4mZjE2uLsEituPW8ivu7sQf2hjoBvuL3tFAA6nL24/eUGwSCNs0usuP3CyTCmpaK3fxBOTx+yj+XRtrs8yM4Q7mOl+oi188vgcPWG3ccq0b/rEdtnfFBi7LM7PVhe24BLTz3Rl0c7x5SGFkcPand+hVVVpVHfy2h5vxQNpeqp1n5o9ug4AAwMDKCxsREtLS0YGAgMjjFnzhwtq0JESUhu1Fk5gTekhAoSJpdYHVq7PHhjTwve2NMiuPzX3xNfrmV03VDHgRGAifRBrC1u3d+KRbNOwlUbPwha5m2nEwpzjkVSDp0JIT01FcUjcwSWhv72TqqPcHn6MWl0+BNKJfp3okgpMfa1dnnw14Yj+GvDEcHlvzp3YtT3MlreL0VDqXqqtR+aTbTfe+89/PSnP8XBgwcx/Ev0lJQU9Pf3h3gnEZEyEiXqbKyjaCohUc4FUbyTaotiUcO97VSN9sw+ghKRMlHH1W8b8dL+9F5PzSbav/jFLzBjxgz89a9/xejRo5GSkqLVpomIAOgr6myowB1yKBVFM+Cx7sx05GeF93h7NPR0LogSmVRfI9UWh0cN9w961jswiM+OdiLTmBb0vuHrff51l6L9XHZGOj5v6YqoDyVSW6h2F87Y19zhgt3VC4erF5ZjgQDH5GVqMn6aTYaANuwfDK2m7oBuxmi930toNtHev38//ud//gc2m02rTRIRBdBL1FmpwB1SrDlGzC6xhvxNZX62+HJrjjHmgcj0ci6IEpmcvkaqLbZ09vj+nWVM8wU988+vu7pqGmbbrNja2Cq6Xrj9XKh6zS6x4oOD7Vj2UkNEZROpSazdSY3f3rFPLBCZFuOnNceImkWnY/2W/QFtuMJWgJpFp+tmjNb7vYRmebTPOOMMNDYKJz0nItKCJcuItfPLMKfEGvC6N7qkFt+G2J2eoAEYGPot0dLNu2F3eiTL6OkbwLVnTkCFrSDg9QpbAa4904bBgUFcd5ZNcPl1Z9nQ7ekPmmQDQF1jG5bXNuCowx3h3smnh3NBlMjk9jVSbfHMk0f6llVXFgdFFgeA+/66B9eeNQGzj/U5odYLp58Tq9d1Z9lw72ufRlw2kVqk2l1P34Do+AwMTdSHT7KBoTF6RW0Duj39moyfj21pDKrDtsY2PPaWfuZzer+X0Owb7RtuuAG//vWvceTIEZSWlsJgCPwqv6ysTKuqEFESG5OXifULymMWJVOJQCjfdHtw1cYPUF1ZjOqK4oCo4ldt3IHN18xC9XM7BJdXPze0XCinLTA0kLd3ezR5hDzW54IokYXT10i1Re+ynr7+gG+3vJyefly18QNs+vl3sKizByNzMwTXE9q2GKF6paem4PxHtgbk946kbCI1SLW7b7o9ouPzq9dXwtM/IDpG2129mDzarOr4ORTgMHSQRD21Mz3fS2g20Z4/fz4AoLq62vdaSkoKBgcHky4YWlNTE1pbhS/eaFmtVowbN06VsokSRSyjZCoTCKUPTo/wDa+3DLHlDld0wdSUFOuIpUSJKty+RqwtepfVN7WHLM/p6cdX7S5c+/xOPH65eGrNcAIUDa9XfVO74CQ7krKJlCYnWKnU+C0WgNC7DqDu+Kn3IGPD6fVeQrOJ9oEDBxQr6z//+Q+WLFmCv//973A6nbDZbNiwYQNmzJih2DbU0tTUhEmTJsPlcqpSfmZmFvbu3cPJNpFOKRG4Q26ws5Dvz4wumBoR6Z8aQYLkBk4bHkBNiW3LrUOsgx9RclMiWGlGv/hEW4trnO1MGZrdTY0fP16Rctrb21FRUYGzzjoLf//73zFy5Ejs378f+fn5ipSvttbWVrhcTpxRfRfMo09StGzH4S/xfs09aG1t5USbSEQso23LDdwhFik4P9uISlsB6gQeLau0FcCSacC5kwsxcbQ5KFrovsMOWDINOGfSSEweYwlavqfZjvxs7T4Vjib6OhGFpmSQIG+fCSBkIKezJ40EADy7cAZMhjS8ePUZ2PZ5G2rqDgR8Az27xIq0VOBgazc6XB7kmMJr93oPfkTxL5pxSer6zM+Wvn67Pf2SYzwgfS8Tzb2ONccoeh/BdiaPpl9b/PGPf8STTz6JAwcOYPv27Rg/fjwefvhhFBcX45JLLpFVxrp161BUVIQNGzb4XisuLlaryqoxjz4JI8ZNjHU1iJJOrKNtewN3LN28O2Cg9Q/cIRUpeJTZhNVVpVhe2xAwEHv3Y0xeJu64aAqW1TYEPJ7mv/zOi6diucDyVVWlmn3oEG30dSIKzZJlxH3zpgn2E/fNmyZ74uDfZ3qjiQ8ODgaUefakkVjy/clY+donQX3rIwvKceOL9XB6+lFhK8DCWSfhgkfqUD4uD4srirHgmfcxY3y+7HYvpw8lilS045LU9TnKbJK8fi1ZwKqqUqwQaLurjo3hUvcy0d7rWLKMovcRbGfypAwODg5qsaEnnngCd955J375y19i1apV+Pjjj/Gtb30Lzz33HDZu3Ii33npLVjlTpkzBeeedh6+++gr/+te/MHbsWFx77bW4+uqrZb3f4XDAYrHAbrfDbDZHs0sR2blzJ0477TScu2KD4hPtb5r24Z+rFuPDDz/EqaeK/z4qHGrV+cv3X8f7Nfeg8pePYezkcsXKVes4kPrUbp9HHW7c/OddgkFGKm0FePBH0zWbZHo/MR8euMPu9OD6F+sFvzGaU2LF+gXlvgEu4NNqUzrys4c+rZYq44HLTsEtf/lI1jbUEs5+kj7Eevyk8NidHvz6Lx9hkt83Ut6gS/sOO/Cby06RbGNCfaY3t+7MbxXAZEhFboYBJkMqVtQ2YKtA3zq7xIpbz5uIrzt7UH+oI+Ab7gpbAcrH5ePRLY1ht/tQfWiyYvuMnpLjktT1Kef69ebR9q5jOZZHW+peZt38Mty2eXdU9zoco5Wh2Tfa69evxzPPPIN58+Zh7dq1vtdnzJiBW265RXY5X3zxBZ544gncfPPNWL58OXbs2IEbb7wRRqMRCxcuDFq/p6cHPT3Hc0A6HI7odoSIFKN1+2zv9ugi2jYQOnBHOJGCR5lNgvWVKqO9O/rI59FSIvo6qYvjZ3xr7fLgjT0teGNPS8jlUm1MqM/0BnJ6dEsj/nHTbJx8Qi4+b+kSnGQDQxGKF806CVdt/CBo2bbGNlRXDD2VGG6712vwI62wfSpPyXFJ6vqUc/2OycsU/BZd6l7G4e6L+l6HY7QyNMujfeDAAZSXB39rmZGRge7ubtnlDAwM4NRTT8Xq1atRXl6On//857j66qvx5JNPCq6/Zs0aWCwW319RUVHE+0BEytK6fUpF09Yy2nboOigRlTy6qOJaRBONt4imyYjjZ3xTKsOBnOVS2xKLouy/jO1ePrZP5cXLuCQ5hiuQWSRejoXeafaNdnFxMXbt2hUUFO0f//gHJk+eLLuc0aNHY8qUKQGvTZ48GZs3bxZcf9myZbj55pt9/3Y4HOyMiHRC6/YpFe1TD9G2lYlKLh311Pv45/AgJzV1B8KKJhpp0BhGNNU/jp/xTU4bk2q/cvtMudHIAQT1PeNGZOH6s21h9z3Jju1TefEyLklGLlcgs4jZZFDsPiGZaXZXefPNN+O6666D2+3G4OAg/v3vf+PFF1/EmjVr8Pvf/152ORUVFdi3b1/Aa5999lnIqOYZGRnIyMiIqu5EpA6t26dUtG4to22HokREXakyRmQbUbPodKzfsj8gyEmFrQA1i06XHU00mqAxjBysfxw/45tUGzMZUoN+gzm8/crtM605xpDRyGeXWNHSOfSIszeY2oZtB6Lqe4jtUw3xMi5JtUuzKT3qex1rjjL3CclOs0fHf/azn2HdunW4/fbb4XQ68dOf/hRPPPEEfve73+EnP/mJ7HJ+9atf4b333sPq1avR2NiIF154AU8//TSuu+46FWtPRInAG6270lYQ8Lo3iqZWv88W441YOqfEGvB6OBF1pcoYBPDYlv1Bv+Ha1tiGx7Y0wi3ymKeX3ekJmmQDQ7/dWrp5N+xOT1R15G+/iKIj1sZWV5Xirlc+kWy/4fSZ151lQ8Ww9SpsBbj+LBvm2KyYU2JFdWUxNmw7INz3vNUIoliKl3HJlJ6Ku38wTbBd3v2DacjJSI/6XsfdNxD1fQJpnN7r8ssvx+WXXw6n04muri4UFhbC6XTi3XffxaxZs2SVcfrpp6O2thbLli3DypUrUVxcjIcffhiXX365yrUnokQwriAbD/5oumC0br0Yk5eJ9QvKo4qoK1bG3sOO0IGLGls1C5SixH4SUWih2lhbtydkkLTh7VdOn9na5UH1cztQXVmM6origAjni5/bgdduqMT6BeU4bHcHfDvmbysDLJEOxMO41NrlwU+e3o5188uw5PxJ6HL3I8eUhhZHD37y9Hb86eczMaEwJ6p7nfZuT9T3CaTxRNsrKysLWVlZAID9+/dj9uzZ6O/vl/3+iy66CBdddJFa1SOiBBcqWreeKBFRN1QZSgSFUypQSrJHDiZSm1Ab+6JVPAjt8PYr1Wc63L2+aOSCy129+NbInLC3SxQLeh+XHO5etHZ5BCP5A8fbUTT3OvEQPDYeaPboOBER6YMSQeHiJWgMEQVTuv3KLY/9BlH0tGhH8RA8Nh7wKBERxYBUtN//tDvhcPfB4eqFJdOAXFM6xuZnhVVGKEoEhYuXoDFaifRcJBMeI+14j3VXTy/ysozw9A2gq6cP5kwDcjLSMYhBPLtwBlJSUnxRhJ2eoScLI2m/cvsD9hv6xjY6RInjcNThPv7YdmY68rOU+4maFu1I7n2CmvuppFhd25xoJ6A9e/boujyiZCcVrftgWzeW1zYEBCGptBVgVVUpxhdkyypDjDfA0fLahoBBNJxAKd6gMUs37w4Y7PUWNEYL0ZyLZMFjpB3vsf7wYDseWVCO+1/fF9SXLKooxo0v1sPp6UeFrQCPLCjHjS/WY8b4/Ijar9z+wJJlxL3zpmGFQN9z77xpSdVv6A3b6BAljkNTWzeWCYzhq6tKMe7YGB4NLcZfOfcJau+nUmJ5bacMDg4OqrmBV155RXT5gQMHcPPNN4f1G+1oOBwOWCwW2O12mM1mTbbpb+fOnTjttNNw7ooNGDFuoqJl/6fhXdQ9eisAdU7pmbc8hVElpYqV9+X7r+P9mntQ+cvHMHZyuWLlftO0D/9ctRgffvghTj31VMXKJfXFun1qwe70BKXU8ZpTYsXqS0uxZPPuoEifwNAAtm5+GXIy0kXLWL+gXNZAG/BJdIRB4byfEus1aIzapM6n3HMRDyJtn8l0jGLN/1hff7YN9U3tgn1Jha0A5ePyfb+pnl1ixX2XTENeliGqcyHVHzR3uHD7yw2YMsbiy83rDZq2p9mOe+eVJtWkTknRjJ9so0OUOA5HHW7c/OddIcfwB380XbFvfLUYf0PdJ2i5n9GI9bWt+jfa8+bNk1wnJSVF7WokhV5nJ4BBTP/pEowsnqRYuYcbtuPjV55GXx8DHxBFSypad6e7T3DgAoC6xjY43H1w9w5EHfEbUCYonN6DxqhNiejriY7HSDv+x7q8KC9kcLJtjW2orij2/Xvr/lb0DQyqFoDRy+7qxZa9X2PL3q8Fl9/i6uVEOwbYRococRzauz2iY7iS0bq1GH9D3SdouZ/RiPW1rfpEe2CAeda0llM4TtFvyx2Hv1SsLKJkJxWt2+GSjuZtSBOPY8nIvdpRKvp6IuMx0o7/se6RyHM7fLkW50FO/0baYxsdosRxSJZo3fGyn7G+tvkbbSIiFYQKvCEVLdScKR1N1Cgx0fZGHJUK/sHAN8dFeiz0FkVZj+c03GMUah/0uG9qC3ef/Y91RnpwP5FlTEN1ZTHKi/KQk5GO126ohMmQirYuD0yGNBx1uAW/hVLi2NudHln9G2lPbhuVcx3Eczs1mwwBbaSnbwAmQ5ovYKCc69NsShctwxutu7nDBbur1xfw1JxpiKunOcKJSh7LayLWY7SqE22p32f7+8EPfqBiTYiItCMWeMOaY8TsEqvgo0yzS6zINaWLRvo0m9KRk5EuWoY1xygZ/IOBb46L5ljoKYqyXs9pOMdIaB/OnVyIOy6aghUvf6y7fVNTJOfT/1jXH+pAha3A93hnljENjywox4ZtBwIeKa+wFWBxRTEWP7cDp47LCwpkpMR15S1j0ayTRPs3i8REnNQhp43KuQ702gfJZc0xombR6Vi/ZX9QG6lZdLqs/nxEthHPLpyBR99qDCrj2YUzUJBtlBXwVO/kRiWP9TUR6zFa1Tza8+bNk/VXVVWlZjWIiDRjd3qCBhVg6LdASzfvhrtvANedZUOFrSBgeYWtANedZUNuRjpWVZWicthy7yA8Nj8LPX0DuPbMCYJlXHumDU5Pv2gdjjrcosvtTk+0hyFuSJ0vqWPhjf46p8Qa8LrW0dej3Q81yT1GofZh4mgzltU26HLf1BLp+fQ/1jV1B7C4otjXT1RXFmPDtgNBv6vc1tiGDdsOoLqyGHWNbVhe24CjDndU9Qi1L0s278YdF00N2b/Fw2QsEUm1UQCS14Ge+6BwPLalUbCNPPaWcLyD4TLSU/H4W8JlPP725/D0DwRNsoGh3zWvqG1Ac4cruh3QiDcquVBb9kYl18M1EesxWtVvtPn7bCJKNlKBN9q7Pah+bgeqK4tRXVEcEHW3+rkdePX6SkwozMG6+WVwuPt80UTNfnm0v+n24KqNHwiWcdXGHdh8zSzJOjDwzRAlAqWMycvE+gXlMY2+HuuAL1LkHKNQ+yAW1EsP+6aGaM6n/7Hu7unF6nml8PQPoLd/QFZwNP9ARkpcV/5ltHZ5cPnv38O6+WVYcv4kdLn7hyKdx9ljs4lIrI1+3tIleR0A0HUfJEdrlwdbG4X3YWs413uIIGFb97fCIRHw1B5HAQHHFWTjwR9ND5m9RC/jUizHaP5Gm4hIQZLBztx9cHr6Q97wegNzjM3PwtgoyxCrg5hkCXwDKBcoJdbR12Md8EUOqWMUah+kgnrpYd+UFu35FDrW9U3tou/xP87ePkKZ4FCB67R2DX1Q6PXytbMwZnRipnOMN6HaqJzrQCqxbDy0UzWu96DlCRYQUCx7iZ7GpViN0ZpOtLu7u/Gvf/0LTU1N8HgCHxe48cYbtawKEZEqJIOdSQQQkRtsJZoylKhDooh1oBSlJMJ+hNoHoaBe/uJh38KlxvmUKtP/OHv7CCXqkQjXZrJT4hzGw3nW4npPpoCAbPsaTrTr6+txwQUXwOl0oru7GyNGjEBrayuysrJQWFjIiTYRJQSpwBv52fICc4hF6czPNuKcSSMxeYwlKKrpnmY7LJkGReqQDGIdKEUpibAfofah/lBHyKA78bJv4RI7n7NLrOgfHMTnX3eFjN4r1H+IlVlhK0D9oQ4AgYGMrDlGnDu5EBNHm4P6mn2HHZLH3u70YGBwEM8unIGUlBRf5GWnpx9A4p6/RCO3f0nUPggI3odQY7RUGWaJgKfhBATUe4T3aPuPRKBqMDR/v/rVr3DxxRejvb0dmZmZeO+993Dw4EGcdtpp+M1vfqNVNYiIVGXJMuK+edMEA4TcN28aRplNkoE5mjtcuP7Fepzz0L9Q9fi7OOfBf+GGF+t9QVJGmU248+KpqG9qx1UbP8C1z+9E9XM7sKupHXdePBVj8jIl63BviOX3zpumq4FabbEOlKKURNiPUPuw77ADq6tK43rfwhXqWFTaCrBw1kmY99i2oH7BK1T/4fL0C/YL3qjjNXUHfEHJvI+CWrKMuOOiKYJ9zR0XTRE99t56nPvbd4ZiSjy3A/VN7XhkQTmyjGkJff4SjZz+JZH7oOH7IDZGS5UxNj9LNOBpOJH8xe4T9CCa/iNRpAwODkr9rEIReXl5eP/99zFx4kTk5eVh+/btmDx5Mt5//30sXLgQe/fu1aIacDgcsFgssNvtMJu1/03Qzp07cdppp+HcFRswYtxERcv+8v3X8X7NPaj85WMYO7k8acv9pmkf/rlqMT788EOceuqpipVL6ot1+1SC3enBr//yESb5fYLrDVS277ADv7nslICcwMMDc9idHlz/Yr1gAJE5JVasXzDUVsTWeeCyU7CitiFkHe65ZBpuf7kBU/y+Efcu39Nsx+pLy0L+5ipRhTof8UbN/dCqfYq1jUQ4R+Hw7rPd1Qt3bz/e/aIt4Bth4Hi/INV/rLm0FG9+ehSTxphx5skj4XD3YWSuEZmGNLR1eZCVkYYWRw9qd36FVVWlsvujUN+oh3rf7BIr7rtk2lAQtAQ/f1rSon3KaYOJ0E7F9kFumwhVRnOHC/e+9gl+eFoRCs0Z6HL3I8c01PY2f3gIt180VXKyHWm71Fq81FNNmj06bjAYkJo69AV6YWEhmpqaMHnyZFgsFhw6dEirahARqaq1y4M39rTgjT0tIZd7P/0XGmDkROkExKO7tneL1+Hm7/Viy96vsWXv14LLvRGHk0msg5kpJRH2I9Q+JMK+hcu7z5+3dOHSJ94VXMc/eq9Y/1GYm4E39rbgjb0tKC/KCwhINtyvzp0oWZ5Y1GCx923d34q+gcGkO5eJQE4bTIR2KrYPcttEqDLsrl78/eOj+PvHRwXLuOGckyUn2nqJ5i0lXuqpJs0m2uXl5dixYwdKSkowd+5c3HnnnWhtbcUf//hHTJs2TatqEEWlqakJra3CnUY0rFYrxo0bp3i5pL1oo2wqEd1VKqq4VNRTqfcTkbbk9iti6/lHFZcbyT3S/kxP0YaJlBT1GK9A1PF4aV/xUk81aTbRXr16NTo7OwEAq1atwpVXXolrrrkGJSUlqKmp0aoaRBFramrCpEmT4XI5FS87MzMLe/fu4WQ7AUQbZVOZqKfiXbtU1FOp9xORtuT2C2Lr+UcVlxvJPdL+iNGGKVFFPcYrEHU8XtpXvNRTTZrdTc2YMcP3/4WFhfjHP/6h1aaJFNHa2gqXy4kzqu+CefRJipXrOPwl3q+5B62trZxo64RUJM+jDjfauz1wuPtgzkxHfpbR96i13CibkUYslRPdVSqquCXTIBr11BtxWC/0HllVKcmyn0DgvuZkpMOYlooOlwc5psTe70gN7xeyjGmorixGeVEeAGBgcBBHHW4MIjDC96Z/N+En3x6H8qI8mAxpePHqM7Dt8zY0/MeOClsBtolEco8mYngiRMFPRmJjWzIR64ujHeOlxl85UcfjpX3FSz3VpPnXFi0tLdi3bx8AYNKkSRg5cqTWVSCKinn0SYoHsiP9aO5wYcnm3QG/K5pTYsXa+WUYk5eJprZuLKttCLhBrbQVYHVVKcYVZPuibC6rbcCjWxqD1vEGQxHbxtr5ZVi6eXfA4DQ86qnYOt7I5qGWj87LxOqqUiyvbQgY7L111NONldSxShTJsp+A8L56I18veOZ9zBifn5D7HQ1vJOOlm3fjg4NDkbs3bDsQ1McsqijGjS/Ww+npx9mTRuL5n30HK1/7JGi9qyq/hbITLQAQ0Jd5+winpx+3CZyjRxaU48YX6zFjfL5oJGn/+or1Y6QfUmNbspDqi5UY41dVlWKFwPgrN+p4vLSveKmnmjSbaHd2duLaa6/Fpk2b0N8/9GloWloafvzjH+Oxxx6DxWLRqipERILsTk/Q4AgMBe1Yunk31s4vC7oRAYC6xjYsr23Agz+aDlN6Kla8/LHgOre//DEeuOwU0W2sX1COMXmZWL+gXDRyq9Q6UsvHFWTjwR9NP/7thSkd+dn6+vZC6nwkSsTSZNlPIPS+ettLdWUxHt3SmHD7rQRvm+5w9uL2l4X7oUEcP4ZTxliw8rVPBNdLQQruvHgKVs8rhad/AN09fb4+AhDOarCtsQ2pKSn4+42zZUUMl9OPkT4cdbglxzY9jQ1qkdMXA4hqjF87vwwrX/0E08flY3FFcUDWj5WvfiI760e8tK94qadaNJto/+xnP0N9fT1ee+01zJw5EwCwfft23HTTTfiv//ovbNq0SauqEBEJkoqQaXf1Cj5qCQwNsu3dHhjSUiUjgkcTsdSf1DpSy0eZTbq+eUqWiKXJsp+A+L5ua2xDdUUxgMTbb6X4IoGH6If8j2F5UV7AN27+tja2on9gEONHBX9T+XlLl2IRwxMhAnUyaO/2SI5teh4rlKJU1g+p+4g3936NNxXI+hEv7Ste6qkGzSbar732Gl5//XVUVlb6XjvvvPPwzDPP4Pvf/75W1SAiCkkqQqacaN2GtBTJdcQkQxROuZIlYmmy7Ccgva/+0bATab+VJPcYSkUWD9UXJdP1SEMkM1UkSSYKLbJ+SGceSY5jnSw0m2gXFBQIPh5usViQn5+vVTWIiEKSipApJ1q3IU08mq9URG8to3DqIfiWWPAdpSKWKrGfagYJSqbIrFL76h8N25Ceir1HHL5jzUBNQ+QeQ6nI4qH6IqnyTcY02J182iCRSGaqCCMTRXOHC3ZXLxyuXlgyDTBnGuIm3oIWWT+kM4/IP9Z6GMNJnGYT7dtvvx0333wz/vjHP+KEE04AABw5cgS33nor7rjjDq2qQUQUklSETDnRuk3pqVFFBNcqCqcegm9JBd9RImKpEvupdpCgZIrMas0xYnaJVfDRygpbAeoPdfj+/+8fH8GjWxp9QYJWvvpJwOOWyRioCRC/XvyPYf2hjoiyC0iV/9ruw9h9qIMB6xJIfrZRkUwUB9u6sVygr1xVVYrxcdBO4ynrhx7GcJIm/nGngp544gm89957GDduHGw2G2w2G8aNG4d3330XTz31FE499VTfHxFRLHgjZM4psQa87o2QOeZYtO5KW0HAcv9o3VJleCOCh1quxafRUgFf7E6P6nWQCr5z1OGWPJZSx0qJ/ZRTz2hFu5/x5rqzbKgY1oa8Ucdr6g4E/D8wdKxX1DZg8pjAp+KUPAfxJNT1UjnsuO077MAqif4qnPL9z4uWfQWpLxXA3T+YKnit3P2DabImC80drqBJNnC8/TZ3uJSrsErk9MXRjvFy7iOk6GEMJ3k0+0Z73rx5Wm2KiChiSkTrjjYiuNr0EHxLbvCdaI6VEvupVZCgWF8TWmnt8qD6uR2orixGdUUxPP0DGJmTAWN6Kty9/Vi/oBz1hzp8Kaq86hrbsPhYkC9/yRSoyZ/Q9ZJjSkd3Tx9e+NkZAddPJNkFvOUftrvxRWu3Lyqy/3lhwLrE0dbtwRXPvo9188uw5PxJ6HL3I8eUhhZHD37y9Hb891VnYKTENSMVLNTu6o2Lb1rjIeuHHsZwkkezifZdd92l1aaIiKKiRLTuaCOCq0kPwY7CCb4T6bFSYj+1DBKUDJFZHe5eOD39gtGwH7/8VFz7/M6Q7w0V3CtZgwfJvV4izS5gyTLii9Zu0XPCwGiJweHuQ2uXB1dt/CDkcskyJIKFxtO1ovesH3oYw0kezR4dB4COjg78/ve/x7Jly/DNN98AAHbu3In//Oc/WlaDiCip6SH4lpLBd0KXoX5gGyXqmUzEzolU8K5Qy3kO1KOHvoLUp0Q/JxUslNeKctgu44dmo9Pu3bvx3e9+FxaLBV9++SWuvvpqjBgxAi+99BKamprwhz/8QauqEBElNT0E31Iq+I4Ya44R504uxMTRZpQX5aGnbwAmQxp2NrVj32GHrP3Uop7JROzaa+nswXcnjcSkMZag87Wn2e4L8uWP50BdeugrSH352UacM2kkJodoe3LamFSQL4vERJzkY7uMH5pNtG+++WYsWrQI999/P3Jzc32vX3DBBfjpT3+qVTUoiezZs0fX5RHFijeYy9LNuwMGai2Db40ym7C6qhTLaxsCbszCCQgjxZJlxB0XTcGy2oaAR5W925D72K3a9UwmYtfe2SePxKwJBVgucL5WVZVi3d8D+2CeA/Xpoa8g9Y0ym3DnxVNDtj05bWxMXiZWVZVihUBfuaqqNC5+nx0v2C7jh2YT7R07duCpp54Ken3s2LE4cuSIVtWgJOCytwFIwRVXXKFK+b09jOZI8U8PwbeiDQgjxe70YMXLHwtGwb395Y+xfkG5rP1Vu57JJtS1BwC3vLhb8Hzd8fLHWDu/DDeeczLPgcb00FeQuuxOD24P0VfeEUZfOb4gG/f/8BTYXb2+a8USR3m04wnbZXzQbKKdkZEBh8MR9Ppnn32GkSNHalUNSgK9zk4Ag5j+0yUYWTxJsXIPN2zHx688jb6+5Ay8Q4lHD8G3ogkII0XJyKxq1jMZCV17n7d0iZ4vl6cfk0abtageDaOHvoLUo2RfOSYvkxNrjbBd6p9mE+0f/OAHWLlyJf785z8DAFJSUtDU1IQlS5Zg/vz5WlWDkkhO4TiMGDdRsfIch79UrCwiUh8js8YXni+i2GDbI1KHZhPtBx98ED/84Q9RWFgIl8uFuXPn4vDhw5g5cyZWrVqlVTWIiOKC3elBa5cHDncvzJkGWLMT85NrOfsZ6bHQW2TWZDmn4fIeF6mo44ykKx+vNRIS6rrQW19JQ9iO459mE22LxYJ//vOfqKurw+7du9HV1YXTTjsN55xzjlZVICKKC80dLizZvDvgUb45JVasnV+WUI/kydnPaI6FniKzJss5DZf/cfnld0sw22bF1sbg8zWbkXRl47VGQsSuC2uOEbNLrIKPj7PtxQbbcWJQPY/29u3b8dprr/n+XVlZiezsbDz++ONYsGABfv7zn6Onp0ftahARxQW70xM0uAJDv5Nbunk37M7ECMYnZz+jPRbeyKxzSqwBr2sdmTVZzmm4hh+XlBTg2rMmoMJWELBeha0A155pQ0/fQCyqGVd4rZEQqevC6enHtWey7ekF23HiUP0b7ZUrV+LMM8/ERRddBABoaGjA1VdfjYULF2Ly5Ml44IEHMGbMGNx9991qV4WISPeUDEqjZ3L2E0DUx0IPkVmT5ZyGa/hxmTbGgqs2foDqymJUVxSjp28AGempqD/Ugas27sBL18xCIQPSieK1RkKkrosOVy/bno6wHScO1Sfau3btwr333uv796ZNm/Dtb38bzzzzDACgqKgId911FyfaRERInqA0cvZzUKIMucci1pFZk+Wchmv4cenpG4DT0x+QxzdwfWZ8kMJrjYTIuS7Y9vSD7ThxqD7Rbm9vx6hRo3z//te//oXzzz/f9+/TTz8dhw4dUrsaRERxIVmC0iixn/FyLJLlnIZr+HGRCoZmNmkWViZu8VojIdFeF2x72mI7Thyqt5xRo0bhwIEDKCoqgsfjwc6dO3HPPff4lnd2dsJg4AVDRIkl0mihegrgpYRQx8GaY8S5kwsxcbQZ5UV56OkbgMmQhp1N7dh32OHbTyWOhZxz0dzhgt3VC4erF5ZMA8yZBsUCziTaOVXK8ONSf6gDZ08aiSljLCgvykPfwCDGWDLR09cPh6sXqSkpONjajQ6XBzmmwPOox+i8sagTrzUSInVdWDINuGDaKMw/rQiF5gx0ufuRa0rHUYcbmz88hPzsoevmqMON9m4PHO4+mDPTkZ9lxKhhj5RLraPHthoJNfeD7ThxqD7RvuCCC7B06VKsW7cOL7/8MrKysjB79mzf8t27d2PChAlqV4OISDPRRAv1BvBaunl3wCCrdQAvJUgdhzsumoJltQ0BjytW2gqwuqrUt5/3zpuGFbUNqGtsC1jn3nnTZB0LOefiYFs3ltc2YNuwbayqKsX4guzID8AxiXROlWTJMgac303/bsLzP/sOVr72CWrqDuCRBeVY+489AeelwlaAxRXFWPDM+5gxPh/r5pdhENBddN5YRQzmtUZCpK6L0XmZWHL+5JD94CizCU1t3VgmsHx1VSnGHesnpdZJlEjaau8H23HiUH2ife+99+LSSy/F3LlzkZOTg40bN8JoPH6B1NTU4Hvf+57a1SAi0oRUtND1C8rjIoBXtKSOwwOXnYIVL38ccEMGAHWNbbj95Y+xfkE53H0DuOfVTzB9XD4WDwvQs/LVT7D60rKgb1PCqcP6BeXo9vQH3Vx667GitgH3//AURW6cEuGcKu2owx1wfsfkZeK+1z7BtsY2XH+2DRu2HQg6L95/V1cW49EtjXj7s6/xt92Hg1KChdPelKZEHxANXmskROy6aO5wifaDa+eXBU2gvcuX1zbgwR9NBwDRde7/4SkxbRdK0ap9sx0nBtUn2larFe+88w7sdjtycnKQlpYWsPwvf/kLcnJy1K4GEZEmlIoWGusAXtGSOg7t3dLHqbd/AFv2fo0te78WXK+92yM60ZZzLjz9A0E3hl51jW2wu3oV+6Yl3s+p0tq7PQHn9283zcbWY+eivCgvZGCmbY1tqK4oBgAU5mYI5t0GYhedVw8Rg3mtkZBQ14Xd1SvaD3a6+0SXt3cPZYmQ6ktj3S6UoGX7ZjuOf5pFN7BYLIKvjxgxQqsqEBGpjtFCh0gdB6kotp3uXnj6xeOOS5Uh51xI5YdNlvMVC8PPX6fr+LGWOi/e5Xo8f+wDKN44XBLXrMRyOVHJpa77eGkXbN8UjrgLI3j33XcHBFMDgIkTJ2Lv3r0xqhERaUmLQCrRbCOeooUqcSxDlWE2GZBlTEN1ZXFQsLOaugOSUWxzTQb09g/AmmPEuvllQQF6lmze7StDrA5S28joF5+o6el8xTvveerq6UVelhHmzHQ8feVpGG0eCng2OAjULDodO5vakWVIEy3LG6FcKlJ5LM5fJH1AqGs4UQJHkb6ZM8X761yJ5XKikku1Re9yPVzzYgHd4mmMp9iLu4k2AEydOhVvvPGG79/p6XG5G0QUJi0CqUS7jXiJFqrEsRQrw5pjRM2i07F+y/6AR4ArbAWoWXQ6RmQbMbvEKvgI3uxjx8nTN4BNP/8O7nrlk6DgOpt+/h1YTAbJOkidi25PPyptBQHB1vy3Y8nkTZMSvOfpw4PteGRBOe5/fR/qmzpCBjw7d/IofHdSId7Y2xJU1uwSKxpbugAALZ09umtv4fYBoa7h++ZNw8rXPsUbe1oCXo+3wFGkf3mZBjy7cAYefasxqL9+duEMWEzpostHZBsxCGC2zSr4U47ZtqHI5lLtQg/B0qQCusXLGE/6IP5RsE6lp6fjhBNO8P1ZrdZYV4mIVCYVgMTu9OhiG95ooXNKAvslPUULVWI/pcpw9w3gsS2NgsGsHnurEX0Dg7j2zAmosBUELK+wFeDaM23o6RvAABA0yQaGfu939yufoHdgULQOACTPxZi8TKyqKkXlsHp4o+1yQhM9/2ulurLYF+TM///9bWtsw29e34tff+/kkNdHha0Ac0qsOOvkkbprb+H0AWLtaHltAyaNNge9rlR/R+Q1MDiIx98S7q8ff/tzDAKiyzPSU2FKT8V1Z9kE2+x1Z9uQbUwTbRdAcPYAQNtr/qjDLRrQ7ajDHRdjPOlHXH4VvH//fowZMwYmkwkzZ87EmjVrMG7cOMF1e3p60NPT4/u3w+HQqppEJCGc9qlFABKltqH3aKFK7KesYGchglRt3d8Ku6sXV238ANWVxageFlH8qo078NI1swBEH1xnQmGO5LkYX5CN+394CuyuXt86FgXzaMcrpcZP/2vFP8iZWMCzrY1tOOzoQfm4fMHrY/M1swKi++qtvcntA8TaUV1jGxYfC/rmL54CR5F6lLy/dbj7fIEIh9u6v1VyeWvX0CS4euMOwT69+rkdePX6StH++POWrpgHS2vv9kgGfRtlNul+jCf9iLuJ9hlnnIHnnnsOEydOxOHDh3HPPfdg9uzZ+Pjjj5Gbmxu0/po1a4J+001E+hBO+9QiAImS29BztFAl9lOJYGdOT3/IiZaSwXXknIsxeZlJP7EeTqnx0/9a8Q9eJhXIzN0b+vrodPfC4vdtrx7bm5w6SbWjUMeIAZdIyfvbaIOhdbp7MQiI9ulS/bEegoxJB9g8vlyPfQ7pT9w9On7++efjsssuQ1lZGc477zz87W9/Q0dHB/785z8Lrr9s2TLY7Xbf36FDhzSuMRGFEk771CIASbIEOVFiP6XKkBPsTOr90ZaRKOcrVpQaP/2vFf/gZVKBzMSWJ8q5lWpHoY5Bouw/RU7J+1uzRCyKXKnlJkPU44oexl+pMUdO0Dcif3E30R4uLy8PJ598MhobhT9By8jIgNlsDvgjIn0Ip316A5AIUSoAidxt2J0efN7Shfqmdnz+dZfgb8fkrBMrcvezucOFPYcdeP+LNuw97EBzh0t2GfnZ4sstmYag30V7VdoKkJ9tRH62UXQdb3Adqf2gyCg1fvpfK/WHOny/4fT//+Fml1jR4ugRXJZIQerE2lGlrQD1hzqCXo/Fta3n/ixZRdI+Q/XpuaZ00b4215Qu2ddGO0ZrMcZLkRpz8rMTb0xh21ZX3E+0u7q68Pnnn2P06NGxrgoRqUiLACRyttHc4cL1L9bjnIf+harH38U5D/4LN7xYHzAJlbNOLFmyjLhv3jTBAGD3zZsGS5YRB9u6cev/fITzf7cVP376PXz/d1tx2/98hINt3b4yxI7VKLNJdPmYvEysDhGEbHVVKUaZTRhlNomuMyYvk0Fp4oD/tVJTdwCLK4qHos/7/b+/SlsBFlechBMsJsFliRSkTqwtrqoqxb7Dgb+7jcW1rff+jOQR69NPzM8SDQp5Yn6WZF8b7RithyBjo8wm0ePgTfGVKNi21Rd3z0DccsstuPjiizF+/Hg0NzfjrrvuQlpaGhYsWBDrqhGRyrQIQCK2DalI2+sXlAMQj5zqH8ApVuxOD1a+9immj8vH4mFBa+597VPc/YOpWB4i8uqK2gbc/8NTfL9pFjsfUsvHFWTjwR9NP56v1JSO/GxjwM2M1DoMShMf/M9Td08vVs8rRU/fAI7Y3bjzoinoHwCcnj709A1g+xdtuP6FegDwBVYCgBPzM5FrSqwgdWJt8YF/7MV9VaVYen5fzK5tOX0e25r+NXe4RPv0dfPL8OzWL3DfvGlw9Q6g09WL3EwDMg2peHbrF/j19ybK6muj7Y9j3Z/bnR7c/4+9WFxRjCXnT0KXux85pjS0OHrwwD/2YlVVacJc72zb2oi7ifZXX32FBQsWoK2tDSNHjkRlZSXee+89jBw5UvFtNTU1obVVOAJipPbs2aNoeUTJRosAJKG2ISdaN4CYR06V0trlwRt7WgLy8/q7+XsTJaN9eyc7UudDarn3m2sxUuswKE18GH6ePm/pwpUb/u37999umo0fPrk14D3+gZX+ftPshJpkA9Jt8VfnTsSEwhyNa3WcFtkeSH12V69on+5w9+EP7zXhD+81Ca6zcFaxr/3K+WY6mmsilv15a5cHf204gr82HBFc/qtzJybM9c62rY24m2hv2rRJk+00NTVh0qTJcLmcqpTf28PfQBDFGzlRUQclytBDtGDJiOEyIswSRWv4dSgnsnGi0UOkZTF6rx/JE22fniznOZmu92Ta11iKu4m2VlpbW+FyOXFG9V0wjz5JsXIPN2zHx688jb4+6dQ1RKQvSkRF1UO0YPOxx/HWzS9DoTkDXe5+5JrScdThxpLNu6Uj0Cq4D0cd7uOPhWemIz/LmHC/gyNhw69Dt2cANYtOx86mdtTUHYDT0x+wfq7JgKMOt+D1YXd60NrlgcPdC3OmAdbs+HjKQQ+RlsXovX4kj5w+vSg/E49dfirS01LR6RpqR739A7ju+Z2+8xyv7UyuZLrek2lfY4kTbQnm0SdhxLiJipXnOPylYmURkba8UVHfEXjcyj8qqpx1YsmaY8Smn8/EXa98HPA4YaWtAJt+PhNZhlRU2gpQJ/CooZIRn5vaurFs2O8GvYHOxhVkK7IN0q9Q12GFrQCPLCjHjS/W+ybblbYCNHe4sOqvnwZdH80drqDfGs4psWLtsaB7eia3T4kVvdeP5PFmeQjVp5tN6fjjVWdgxcvB/fEfrzoD+VmGuG5nciXT9Z5M+xpLcR91nIhIK3KiouohcqoUd99A0OQGGPqt3t2vfIyUlBTc/QPhSMh3/2Aaso1pUdfhqMMdNMn21mF5bQOOOtxRb4P0LdR1uK2xDRu2HUB15VAQtEpbAe68eCqWbN4ddH1IBfTRe6oavfcXeq8fyZNtTBPt01OBoEk2MNQf3/5yA7p6+uK6ncmVTNd7Mu1rLPEbbSKiMGgReVVt7d0e0cA4Ha5e/L9n38e6+WVBkVd/8vR2/OnnM6PeF6k6tHd7+Ah5ghO7BrY1tmH5+ZNx3tRRaHH04KfPvOcLNuh/fSRCQB+99xd6rx9Ja+3y4CdPbw/Zp//xqjMkg6XFezuTK5mu92Ta11jhRJuIKExaRF5Vk8MtHiOi092L1i4Prtr4QcjlatdBajnFP6lzfPAbJ659fqfoexMloI+e+wtA//UjcQ6JPl0qWFqyBchMpus9mfY1FvjoOBFRkjGbxD9jlQqCokSQFKk6SC2n+Cd1jjPSQ9+ieN/LgD5E0qTaiVSwNC0DZBIlEk60iYiSTH62Mei3el7eYGfDf7flNTxIylGHG3sPO/DvA99g7xGH7N9WS9UhP5ufsCcyu9ODbGNayGtgtq0ALSGuJf/rwxvQRwgD+hANkWonZlO6aH9sNqWznRFFgBNtIqIkM8pswuqqUsHAOKurSjEmL1NWkJSmtm7c/Odd+P7vtuJHT23H9x/eil//eRea2rqjrgN/n524mjtcuP7FelQ98S7uuGiq4DWwqqoUFTZryGXe64MBfYikSbWTsflZWBWiP15VVYqx+VlsZ0QR4LN5RERJaFxBNh780fTjOaxN6cjPPp7DWipIilTU8Ad/NF1ysixVB0o8w6OEX/7793wBmrp7+pGXaUB+thGm9FT8+i8fYfq4fCyuKEZP3wAy0lNRf6gD9732KX5z2Sm+a5EBfYikibWTow43Vr76iWB7W/nqJ1h9aRnbGVEEONEmIkpSo8wm0UmtWJAUpaKGS9WBEsvwKOHDAzS9efNcjDKb8HlLF97Y04I39rSELMf/2mRAHyJpodpJe7cHb+79Gm/u/Vrwfd7+nO2MKDx8dJyIiMLGqOEUCblRwhMlmjhRPGB/TqQOfqNNRESC7E4PWrs8cLh7Yc40wJp9/NsMsykdWcY0VFcWo7woDz19AzAZ0rCzqR01dQdkRw0X24YS9SRtSZ2L4dGPrTlGrJtfhkJzBrrc/TAZ0tDc4UJWRprodhjlmEg5cvtz9rVE4eFEmyjBNTU1obW1VXrFMFmtVowbN07xckkfmjtcAb+lBYYC36ydP/RbvRHZRjy7cAYefasRj25p9K1TYSvAswtnYISMqOFS21CinqQdOefCG/34nf2tsOYY8fzPvoOVr30S8DOESlsBVl4yDWdPGoktAo+yMsoxkbJGZBtRs+h0rN+yP6g/r1l0OkZkG9nXEkWAE22iBNbU1IRJkybD5XIqXnZmZhb27t3DyXYCGh6wyuud/a1Yunk31i8oR0Z6Kh5/qzHod9rbGtuQmpKCRxeUR70NqW9KlCiDlCH3XHijHy/dvBsLZ50UNMkGhn7jf+f/foy7Lp6Knr6BgOWMckykvL6BQTy2Zb9wf44UrJ1fiqUvNbCvJQoTJ9pECay1tRUulxNnVN8F8+iTFCvXcfhLvF9zD1pbWznRTkDDA1b5e2d/K1q7PACArSGCoW09to7YjZecbUjduClRBikjnHPhjV7cbHeLBtTr6RtA+bh8VB+LgvwtazZGW0w8p0QKs7t6Q/fnja1wuPvY1xJFgBNtoiRgHn0SRoybGOtqUJyQE4hqUKIMqWBVSgS7YsAs/Qj3XFiyjNh3tEviPX0Bj7G+fO0s3swTqcDhiq4vZV9LJIwTbSIiCjA8YNVwcgJRSa2jxDaUKIOUEcm5kAqYl2MKDIjG80mkDnNmdH0p2yaRME60iXRiz549cVFmMkj2yKr+AauG8w9EJWedaLehdhmkjHDPhd3pgcmQitk2K7Y2Br+n0laAFkePaBlEpAxLpgGVtgLUCTw+XmkrgNmUjnMnF2LiaHNQVPJ9hx1sm0QhcKJNFGMuexuAFFxxxRWqbaO3x6Na2YmGkVURELDqnWHHwT8QlZx1ot2G2mWQMsI5F9429uHBdjyyoBwDGAyKOn7nxVPx02feC1kGESlnTF4mVlWVYkVtQ8Bku9JWgFVVpRibn4U7LpqCZbUNAT/nqLQVYHVVKdsmUQicaBPFWK+zE8Agpv90CUYWT1K07MMN2/HxK0+jr69P0XITFaNYH+cNWNXa5UGnuxe5JgOsOYHf7MtZJ9ptaFEGKUPOuRjexm58sR7VlcWorigGAIzNz4TZZEBPbz9+f+UMnk8ijYwvyMb9PzwFdlevr/1aMg0Yk5cJu9ODFS9/LJgh4PaXP06qsZEoHJxoE+lETuE4xQOWOQ5/qWh5iY5RrAN50zFFu46a71eqDFKG1LkY3sacnv6Ab8jevHlu0jw5QqQ3Y/IyBdsfx0aiyKTGugJERHrBKNZE6mIbI4o/bLdEkeE32kRExyRaFGupoG5KBH1L9sBxFB6zyYAsYxqqK4uDgirV1B2AyZgGu5PfjhHFQnOHC3ZXLxyuXlgyDTAfe3Q80cZGIq1wok1EdEwiRbGWCuqmRNA3Bo6jcFlzjKhZdDrWb9kf8Mh4ha0ANQtPx5t7juL9L77hNUSksYNt3Vhe2xAUmHBVVWlCjY1EWuKj40REx3gjJ88psQa8Hm9Rj6WCuh11uEWX253SUeqltiGnDEo+7r4BPLZlf1BQpW2NbXjsrUZMGW3mNUSkseYOV9AkGxgKdraitgHdnv6EGBuJtMZvtImI/CRCFGupwDXt3dEHtmFwHIpEe7cHWwVy9QLA1sZW3Hb+UEBIXkNE2rG7eoMm2V51jW2wu3oxebQ57sdGIq1xok1ENEy8R7GWClzjcIune5MT2IbBcSgSUtdel7vf9/+8hoi04XDJ68/jfWwk0hofHSciSjBSgWvMJvHPWOUEtmFwHIqE1LWXY0rz/T+vISJtmDPZnxOpgRNtIqIE4w1cI2ROiRX52eLL5QS2kdoGg+OQ3enB5y1dqG9qx+dfd8Hu9CA/24hKW4Hg+pW2ArQ4egDwGiJSg1CbBABLpkG0XVokJuJEJIwTbSKiBCMV1G2U2YT75k0LurGqtBXgvnnTZD0amCiB40gdzR0uXP9iPc556F+oevxdnPPgv3DDi/UYHBjE6qpSwWvvzounYsnm3byGiFQQqk02d7gwJi8Tq0K0y1VVpcwAQBQh/kabiCgBiQV1szs9WPnap5g+Lh+LK4rR0zeAjPRU1B/qwL2vfYrfXHaKrElOIgSOI+WJRaS/bfNurF9Qjgd/NB3t3R443H0wm9KRbUyD3eXBn34+k9cQkcKkskSsX1CO8QXZuP+Hp8Du6vX155ZjebSJKDKcaBMRJahQgWtauzx4Y08L3tjTIvi+cKI9MzgODScnIv2EwhyMMpsClhUhW4vqESUduVkixuRlcmJNpCA+Ok5ElGQYMZzUxOuLSF/YJoligxNtIqIkw4jhpCZeX0T6wjZJFBucaBMRJRlGDCc18foi0he2SaLY4ESbiCjJMGI4qYnXF5G+sE0SxQaDoRERJSFGDCc18foi0he2SSLtcaJNRJSkGDGc1MTri0hf2CaJtMVHx4mIiIiIiIgUxIk2ERERERERkYI40SYiIiIiIiJSUNL9RntwcBAA4HA4RNfr6uoCANi/+gIDff2Kbb+r5T9D2z/yJTIyMhQrV82yWW58lqtm2Z1HDg6V39Ul2ZYAIDc3FykpKZLryW2fRKQctk8ifZLbNgG2TyKtyWmfKYPelpkkvvrqKxQVFcW6GkRJxW63w2w2S67H9kmkPbZPIn2S2zYBtk8irclpn0k30R4YGEBzc3NYnxLqjcPhQFFREQ4dOiS7A44XibxvQGLvn9i+yW1vcttnIh9HrfFYKidej6XS7VMv4vV8qIHHIlC8HI9w2trAwAD27duHKVOm6H6/4kG8XCPxIhGPp5z2mXSPjqempuLEE0+MdTUUYTabE+ZiHS6R9w1I7P2LZt/CbZ+JfBy1xmOpnEQ9lvE6fibq+YgEj0WgRDoeqampGDt2LIDE2q9Y47FUVrIdTwZDIyIiIiIiIlIQJ9pERERERERECuJEOw5lZGTgrrvuUjxCtR4k8r4Bib1/Wu5bIh9HrfFYKofHUl94Po7jsQiUqMcjUfcrFngslZWsxzPpgqERERERERERqYnfaBMREREREREpiBNtIiIiIiIiIgVxok1ERERERESkIE60iYiIiIiIiBTEiTYRERERERGRgjjRJiIiIiIiIlKQbibaa9euRUpKCn75y1+GXOe5555DSkpKwJ/JZNKukkREREREREQS0mNdAQDYsWMHnnrqKZSVlUmuazabsW/fPt+/U1JSwtrW4OAgOjs7kZubG/Z7iUhdbJ9E+sX2SaRfbJ9E+hPzb7S7urpw+eWX45lnnkF+fr7k+ikpKTjhhBN8f6NGjQpre52dnbBYLOjs7Iy0ykSkErZPIv1i+yTSL7ZPIv2J+UT7uuuuw4UXXojvfve7stbv6urC+PHjUVRUhEsuuQSffPKJ6Po9PT1wOBwBf0SkD2yfRPrF9kmkX2yfRPoX04n2pk2bsHPnTqxZs0bW+hMnTkRNTQ3+93//F//93/+NgYEBzJo1C1999VXI96xZswYWi8X3V1RUpFT1iShKbJ9E+sX2SaRfbJ9E+pcyODg4GIsNHzp0CDNmzMA///lP32+zzzzzTEyfPh0PP/ywrDJ6e3sxefJkLFiwAPfee6/gOj09Pejp6fH92+FwoKioCHa7HWazOer9IKLIsX0S6RfbJ5F+sX0S6V/MgqF9+OGHaGlpwamnnup7rb+/H++88w4effRR9PT0IC0tTbQMg8GA8vJyNDY2hlwnIyMDGRkZitWbiJTD9kmkX2yfRPrF9kmkfzGbaJ9zzjloaGgIeG3x4sWYNGkSlixZIjnJBoYm5g0NDbjgggvUqiYRERERERFRWGI20c7NzcW0adMCXsvOzkZBQYHv9SuvvBJjx471/YZ75cqV+M53vgObzYaOjg488MADOHjwIH72s59pXn8KZnd60NrlgcPdC3OmAdZsIyxZxlhXi4iI4gjHEqL4xfZLdJwu8miH0tTUhNTU4/Ha2tvbcfXVV+PIkSPIz8/HaaedhnfffRdTpkyJYS0JAJo7XFiyeTe27m/1vTanxIq188swJi8zhjUjIqJ4wbGEKH6x/RIFilkwtFhxOBywWCwMFqEgu9OD61+sD+hYveaUWLF+QTk/zSRZ2D6J9Evt9smxhChysR4/2X6JgsU8jzbFv9Yuj2DHCgDv7G9Fa5dH4xoREVG84VhCFL/YfomCcaJNUXO4e0WXd0osJyIi4lhCFL/YfomCcaJNUTObDKLLcyWWExERcSwhil9sv0TBONGmqFlzjJhTYhVcNqfECmsOf5NDRETiOJYQxS+2X6JgnGhT1CxZRqydXxbUwc4psWLd/DIGvyAiIkkcS4jiF9svUTBGHSfFeHMndrp7kWsywJrD3IkUHrZPIv3Sqn1yLCEKn17GT7ZfouN0nUeb4osli50pERFFh2MJUfxi+yU6jo+OExERERERESmIE20iIiIiIiIiBXGiTURERERERKQgTrSJiIiIiIiIFMSJNhEREREREZGCONEmIiIiIiIiUhAn2kREREREREQKYh5tEmV3etDa5YHD3QtzpgHWbOZHJCIi5XCcIdIHtkUiZXGiTSE1d7iwZPNubN3f6nttTokVa+eXYUxeZgxrRkREiYDjDJE+sC0SKY+PjpMgu9MT1OECwDv7W7F0827YnZ4Y1YyIiBIBxxkifWBbJFIHJ9okqLXLE9Ther2zvxWtXex0iYgochxniPSBbZFIHZxokyCHu1d0eafEciIiIjEcZ4j0gW2RSB2caJMgs8kgujxXYjkREZEYjjNE+sC2SKQOTrRJkDXHiDklVsFlc0qssOYwCiUREUWO4wyRPrAtEqmDE20SZMkyYu38sqCOd06JFevmlzHdAxERRYXjDJE+sC0SqSNlcHBwMNaV0JLD4YDFYoHdbofZbI51dVSlRD5Ebxmd7l7kmgyw5jCnIqknmdonUbxRq30OH2dyTOno7umD3cVcvkRyyWmfUveFvOcjUhbzaCcopfIhWrLYyRIRkXr8x5nmDhdu+ctHzOVLpDA594W85yNSFh8dT0DMh0hERPGGYxeROti2iGKDE+0ExHyIREQUbzh2EamDbYsoNjjRTkDMh0hERPGGYxeROti2iGKDE+0ExHyIREQUbzh2EamDbYsoNjjRTkDMh0hERPGGYxeROti2iGKDUccTkDcf4tLNu/HOsOiSzIdIlLwOHTqEtra2iN5bUFCAoqIihWtEdBzHLiJ1sG0RxYZu8mivXbsWy5Ytw0033YSHH3445Hp/+ctfcMcdd+DLL79ESUkJ1q1bhwsuuED2dpIpT68e8iEqkcubkkcytU+tHTp0CBMnTYbL2R3R+zOzsrFv7x5OtpNYtO1T7nigh7GLKN6Ek0c7VNviPRuRsnTxjfaOHTvw1FNPoaysTHS9d999FwsWLMCaNWtw0UUX4YUXXsC8efOwc+dOTJs2TaPaxo9Y50NUKpc3EUWvra0NLmc3Zv5sJXJPGBfWezuPNGH77+9EW1sbJ9oUkXDGg1iPXUSJSqxt8Z6NSHkx/412V1cXLr/8cjzzzDPIz88XXfd3v/sdvv/97+PWW2/F5MmTce+99+LUU0/Fo48+qlFtSS7mbCTSp9wTxiG/6OSw/sKdmBP543hApG9so0TqiPlE+7rrrsOFF16I7373u5Lrbt++PWi98847D9u3bw/5np6eHjgcjoA/Uh9zNpIcbJ9E+qVU++R4QKQ8JcdPtlEidcR0or1p0ybs3LkTa9askbX+kSNHMGrUqIDXRo0ahSNHjoR8z5o1a2CxWHx/fOxRG8zZSHKwfRLpl1Ltk+MBkfKUHD/ZRonUEbOJ9qFDh3DTTTfh+eefh8lkUm07y5Ytg91u9/0dOnRItW3RcczZSHKwfRLpl1Ltk+MBkfKUHD/ZRonUEbNgaB9++CFaWlpw6qmn+l7r7+/HO++8g0cffRQ9PT1IS0sLeM8JJ5yAo0ePBrx29OhRnHDCCSG3k5GRgYyMDGUrT5K8ORvfEXgUiTkbyYvtk0i/lGqfHA+IlKfk+Mk2SqSOmH2jfc4556ChoQG7du3y/c2YMQOXX345du3aFTTJBoCZM2fizTffDHjtn//8J2bOnKlVtUkmb87GOSXWgNeZs5GIKLlwPCDSN7ZRInXE7Bvt3NzcoJRc2dnZKCgo8L1+5ZVXYuzYsb7fcN90002YO3cuHnzwQVx44YXYtGkTPvjgAzz99NOa15+kjcnLxPoF5cyHSkSU5DgeEOkb2yiR8nSRRzuUpqYmpKYe/9J91qxZeOGFF3D77bdj+fLlKCkpwcsvv8wc2go46nCjvdsDh7sP5sx05GcZMcoc/W/nmQ+ViIiAwPHA7vSgtcuDL1q7Yc40wJotPVZ43+Nw98p+DxHJp8Q9m1r3k0TxSFcT7bffflv03wBw2WWX4bLLLtOmQkmiqa0by2obsK2xzfdapa0Aq6tKMa4gO4Y1IyKiRNPc4QrK2TunxIq188swJi9TsfcQkbZ4P0kUKOZ5tCm2jjrcQZ0iANQ1tmF5bQOOOtwxqhkRESUau9MTNGEGhnL1Lt28G3ZncL7eSN5DRNri/SRRME60k1x7tyeoU/Sqa2xDezdvYIiISBmtXZ6gCbPXO/tb0doVPOZE8h4i0hbvJ4mCcaKd5BzuvqiWExERyeVw94ou7xRYHsl7iEhbvJ8kCsaJdpIzm8R/pi+1nIiISC6zySC6PFdgeSTvISJt8X6SKBgn2kkuP9uISluB4LJKWwHysxnRlYiIlGHNMQbl6vWaU2KFNSd4zInkPUSkLd5PEgXjRDvJjTKbsLqqNKhz9EaJZEoGIiJSiiXLiLXzy4ImznNKrFg3v0wwtVAk7yEibfF+kigYn+NIEM0dLthdvXC4emHJNMCcaZCd8mRcQTYe/NH043kPTenIzw4/7yFznMavcM4dzzMRRWNMXibWLyhHa5cHne5e5JoMsOYE59ju6ulFXpYRnr4BdPX04faLpsCQmoK2bg9yTIH5edkvkZqSZYyUqrtUjuxxBdm4/4enRHw/Sokp2jYRz22KE+0EcLCtG8sF8hauqirFeJl5C0eZTVF92sgcp/ErnHPH80xESrBkCd8oefuYDw+245EF5bj/9X0BY1uFrQCLK4pxZc2/MWN8PtbNL8MgwH6JVJMsY6RU3eXkyI7n/Sd1RHtNxPs1xUfH41xzhytokg0MpVJYUduA5g6X6nVgjtP4Fc6543kmIjX59zHVlcXYsO1A0Ni2rbENG7YdQHVlMd7Z34q3P/saS/6H/RKpI1nGSKm6N3e4JHNkx/P+kzqivSYS4ZriRDvO2V29onkL7S71054wx2n8Cufc8TwTkZr8+5jyoryQY9u2xjaUF+UBAApzM7C1kf0SqSNZxkipukvda7Z3e+J6/0kd0V4TiXBN8dHxOOeQmEhrkV+UOU7jVzjnjueZiNTk38f09A2IrutdLrUe+yWKRrKMkdHW3eHugyEtJaoyKPFEf13Fb5vyimqi3dLSgpaWFgwMBA50ZWVlUVWK5DNnxj6/KHOcxq9wzh3PMxGpyb+PyUgXf+DOu1xqPfZLFI1kGSOjrbvZlA5DGtsiBYr+uorfNuUV0aPjH374IaZNm4bRo0ejrKwM06dPR3l5ue+/pB1LpkE0b6FFYiKuBOY4jV/hnDueZyJSk38fU3+oAxUhxrYKWwHqD3UAAFo6e9gvkWqSZYyUqrvUvWZ+tjGu95/UEe01kQjXVEQT7erqapx88sl499138cUXX+DAgQMB/yXtjMnLxKoQeQtXVZVqEpGPOU7jVzjnjueZiNTk38fU1B3A4orioMm2N+p4Td0BzCmx4qyTR7JfItUkyxgpVfcxeZmSObLjef9JHdFeE4lwTaUMDg4Ohvum3Nxc1NfXw2azqVEnVTkcDlgsFtjtdpjN5lhXRzH/aXfC4e7z5SQ1m9IxNj9LcF2hfHQAos5R5y1XKC8q6Vs4507N85yo7VMPdu3ahfLycnzv9ueQX3RyWO9tP/QZ/u++Raivr8f06dPVqSDpnlbt0zeeuXphyTLAmJaKTncvMo3pMKalwu7yIDtDOPc2xx9Sg17GSDFKtM/mDpdoDuyAPNqmdORnG4NSw0rl2qbkE22biOf+PaLfaJ9zzjn46KOP4nKinYiaO1xY+lJDxDkeZ5dYcd1ZNlQ/twNOT7/o+8WEyotK+hfOueN5JiK1HGzrDkpZ6X1Ca/yxXL1AdtD72C+RmpJhjJSTJ3uU2SQ6aY73nMekjmjbRLy2KSDCR8d///vfo6amBvfccw82b96MV155JeCPtKNEjset+1uxfst+VFcWi76fiIhILc0drqBJNjCUPmhFbQOaO1wxqhlRYjvqcEvmyZaSCDmPiZQW0Tfa27dvx7Zt2/D3v/89aFlKSgr6+/ujrhjJIyfHnPdTILF1tzW2obqiOOC14e8nIiJSi1SuXrurl9+KEamgvdsjmSdb6vHvcO5HiZJFRN9o33DDDbjiiitw+PBhDAwMBPxxkq0tJXM8CuUjjYccdUREFP8crvjPmUoUjxzuvqiWD63D9ks0XEQT7ba2NvzqV7/CqFGjlK4PhUnJHI9C+UjjIUcdERHFP7NEOkqOR0TqMJvEH3CVWj60Dtsv0XARTbQvvfRSvPXWW0rXhSKgVI5H/7ykod5PRESkFqlcvRaJiTgRRSY/2yiZJ1tKIuQ8JlJaRBPtk08+GcuWLcOiRYvw4IMP4pFHHgn4I+0okeNxdokVN5xdgpq6A6LvJyIiUsuYvEysCpGrd1VVKX+fTaSSUWaTZJ5sKYmQ85hIaRHl0S4uLg65LCUlBV988UVUlVJToubpjTbHI4C4zVFHiSNR26ceMI82RUur9unN5esdjyzDcvkSUTAl2qecPNlS4jnnMZHSIoo6fuDAAemVkpy3o3G4e2HONMCarUxHE6pcJXI8siPUD7WuHyIiNUXSdwm9hxNr0gLHWhEpkb0tnnMeU/JSqy8Ie6LtcDiQk5OD1NTAp84HBgbQ1dXFb6Ew9Gn88FyCc0qsWDu/LKqbB7XKJX3heSaieBRJ38X+jmKF116gprbuoFza3kfHxxVkx7BmROpSsy8I6zfatbW1mDFjBtzu4MT1LpcLp59+Ol599dWoKhTv7E5P0MkChnIILt28G3anR1flkr7wPBNRPIqk72J/R7HCay/QUYc7aJINDOXQXl7bgKOO4Pt+okSgdl8Q1kT7iSeewG233YasrKygZdnZ2ViyZAkeffTRqCoU71q7PEEny+ud/a1o7YrshKlVLukLzzMRxaNI+i72dxQrvPYCtXd7gibZXnWNbWjvTq7jQclD7b4grIn2xx9/jDPPPDPk8jlz5qChoSGqCsU7h7tXdHmnxHKtyyV94XkmongUSd/F/o5ihddeIIe7L6rlRPFK7b4grIl2e3s7+vpCN7be3l60t7dHVaF4ZzaJ5/nMlViudbmkLzzPRBSPIum72N9RrPDaC2Q2iYdsklpOFK/U7gvCmmifdNJJ+OCDD0Iu/+CDDzB+/PioKhTvrDnGoByCXnNKrL5UWnopl/SF55mI4lEkfRf7O4oVXnuB8rONQTm0vSptBcjPTq7jQclD7b4grIn2pZdeihUrVuDo0aNBy44cOYLbb78d8+fPl13eE088gbKyMpjNZpjNZsycORN///vfQ67/3HPPISUlJeDPZAovv5/aLFlGrJ1fFnTS5pRYsW5+WcSh4tUql/SF55mI4lEkfRf7O4oVXnuBRplNWF1VGjTZ9kYdDzeXNlG8ULsvSBkcHByUu3JnZydmzpyJpqYmXHHFFZg4cSIAYO/evXj++edRVFSE9957D7m5ubLKe/XVV5GWloaSkhIMDg5i48aNeOCBB1BfX4+pU6cGrf/cc8/hpptuwr59+47vQEoKRo0aJXcX4HA4YLFYYLfbVU1F5s3H1unuRa7JAGuOMvnYmjtcsLt64XD1wpJpgDnTgDF5mTjqcKO92wOHuw/mzHSMyDIiIz016pxwzDEZG2pdP3qnVftMRrt27UJ5eTm+d/tzyC86Oaz3th/6DP933yLU19dj+vTp6lSQdE9O+5TbdwWMWaZ0ZBrT0NrpRo7JgJyMdLh7B9DV04u8LCM8fQPo6unjGESKS6SxVonx86t2JzrdfXC4hu75ck3pODH/eADk4fea+VlGxSfhvO+kWFCrLwjrRxe5ubnYtm0bli1bhj/96U++32Pn5eXhiiuuwKpVq2RPsgHg4osvDvj3qlWr8MQTT+C9994TnGgDQxPrE044IZxqx4QlS/mOIVSOw1VVpVj79z34+8dDTxpkGdPw7MIZePytRmz1WzfcnHDMMRk7alw/RERqk9N3hRrL7rx4Ko50uLFy66eob+rAIwvKcf/r+wLW4xhESuJYe9zBtm4sD3GPOb4gW5M827zvpFhRqy8I69FxALBYLHj88cfR2tqKo0eP4siRI2hra8Pjjz+O/Pz8iCvS39+PTZs2obu7GzNnzgy5XldXF8aPH4+ioiJccskl+OSTTyLeZjwRy3G4orYBPzytyPdadWUxHh02yQbCywnHHJNERKQ0sbFs5aufwGhIwbbGNlRXFmPDtgNB63EMIlJec4craJINHL/H/KrdqXqebd53UiIKe6LtlZKSgpEjR6KwsBApKSkRV6ChoQE5OTnIyMjAL37xC9TW1mLKlCmC606cOBE1NTX43//9X/z3f/83BgYGMGvWLHz11Vchy+/p6YHD4Qj4i0dSOQ4LzRm+f5cX5YVcV25OOOaYJC0kSvskSkRqtE+psSw7Y+hBOyXGMaJEpmT7tLt6Rdtlp7tP9TzbvO+kRBTRRPvo0aP4f//v/2HMmDFIT09HWlpawF84Jk6ciF27duH999/HNddcg4ULF+LTTz8VXHfmzJm48sorMX36dMydOxcvvfQSRo4ciaeeeipk+WvWrIHFYvH9FRUVhVxXz6RyGHa5+33/39M3ILqunJxwzDFJWkiU9kmUiNRon3LHMiXGMaJEpmT7dLjE25PkcgXybPO+kxJRRInxFi1ahKamJtxxxx0YPXp0VN9oG41G2Gw2AMBpp52GHTt24He/+53o5NnLYDCgvLwcjY2NIddZtmwZbr75Zt+/HQ5HXN7MS+UwzDEd/4AjI1388xM5OeGYY5K0kCjtkygRqdE+5Y5lSoxjRIlMyfZpzhRvT5LLFcizzftOSkQRtYy6ujps3bpVleizAwMD6OnpkbVuf38/GhoacMEFF4RcJyMjAxkZGSGXxwtvjsM6gUd3Km0FaHEcP2b1hzpQYSsQfMxHbk44b165dwQe40nGHJOkjkRpn0SJSI32KTWWdfcMfTOmxDhGlMiUbJ+WTINou8w1pYsuVyLPNu87KRFF9Oh4UVERwsgKFtKyZcvwzjvv4Msvv0RDQwOWLVuGt99+G5dffjkA4Morr8SyZct8669cuRL/93//hy+++AI7d+7EFVdcgYMHD+JnP/tZ1HXRO7Ech6uqSrH5w0O+12rqDuD6s2yYHUVOOOaYJCIipYmNZXdePBWe3kFU2gpQU3cAiyuKUTFsPY5BRMobk5eJVSL3mCfmZ6meZ5v3nZSIIvpG++GHH8bSpUvx1FNP4aSTTop44y0tLbjyyitx+PBhWCwWlJWV4fXXX8e5554LAGhqakJq6vHPAtrb23H11VfjyJEjyM/Px2mnnYZ33303ZPC0WAonD6DcvITjCrKxbn4ZHO4+X543sykdY/OzcPtFU3HDOSf78mtbMg148LJT8I3MfIdCdRiTl4n1C8o1yzEZbu5EofUBMP8iEZGOjSvIxoM/mu4bc3JN6cg6lkf7hLwMrJ1fhk53H7rcvbjvkmnw9A/A7hoag7KNaWjtcsPZ2x/QvzP3rrpieXxDbZvnXFnjC7J9bU8oj/a4gmzc/8NTYHf1+u4JLZmGoLRb0eTa1vq+M9ElUxuR2tdYHQvZE+38/PyA32J3d3djwoQJyMrKgsEQ+LuJb775RlaZzz77rOjyt99+O+Dfv/3tb/Hb3/5WXoVjKJw8gOHkJRRa95xJI3HnxVNx+8sfB2xvdokV151lQ/VzO+D09EdVBy0uxHBzJwqtH84+ExFR7IwymzDKbAoYf6w5Rjz/s+9gyebdQePRHRdNxeW/fw8TT8jF4opi3Pj0+5gxPh/r5pdhEGDuXRXFMrdxqG3fN28aVr72Kd7Y06J5nRKVVB5tOdeBErm2mdtcGcmUk1xqX2N5LFIGZT4DvnHjRtmFLly4MOIKqc3hcMBiscBut8NsNitevt3pwfUv1gumKJhTYsX6BeW+DuSow42b/7xL8DdolbYCPPij6b5PAUOte/3ZNtQ3tQuWUWErQPm4fDy65XiwuGjqoJZwjpnU+nL2mfRL7faZzHbt2oXy8nJ87/bnkF90cljvbT/0Gf7vvkWor69XJTYHxQel2+fw8efZhTNQI5A7GxgajxZXFOOqjR8E9PNrLi3F33YfxtZGeeMHhSfc8VmrbVfaCjB92FivRZ30LJr2+VW7M+gDLq9KWwHWzi/DspcaRK8Dd99AzO8naUgs263WpPb1gctOwS1/+Shmx0L2N9p6njzriZw8gN4TKpVPtL3b4+uUQq1bXpQXNNB4bWtsQ3VFsWJ1UEs4x0xqfTn7TEREsTd8/Ck0Z4iOR0vOnwQgsJ8vzM0QnGQD7PuVEO74rNW26xrbsHjYWK9FnRKVVJ7sTnef5HXQ2z8Q8/tJGhLLdqs1qX1t747tsYgoGFpaWhpaWlqCXm9raws7j3aiCScPoFTeQf/lodaVyjUqtDzSOqgl3NyJUutL7TMREcXe8PHFm0M7FP/l3n6e+bbVFcvcxpGM9QDPeSSk8mRLHdNOd68u7idpSDLlJJfaV6nrTu1jEVEwtFBPm/f09MBoTIxPSCIVTh5AqbyD/stDrSuVa1RoeaR1UEu4uROl1pfaZyIiir3h44s3h3Yo/su9/TzzbasrlrmNIxnrAZ7zSEjlyZY6prkmA3r7xT/00uJ+koYkU05yqX2Vuu7UPhZhfaP9yCOP4JFHHkFKSgp+//vf+/79yCOP4Le//S2uu+46TJo0Sa26xgVvHkAhw/MAevOJChmelzDUuvWHOkKWUWErQP2hDsXqoJZwjpnU+nL2mYiIYm/4+NPi6BEdj1ocPQAC+/mWzp6wxg8KT7jjs1bbrhQY67WoU6Ly5skW4s2jLXUd6OF+kobEst1qTWpf87NjeyzCmmh7o34PDg7iySef9P37t7/9LZ588kk4nU48+eSTatU1LoSTB1Asn+jwvISh1t3TbMeqqtKg7c0useKGs0tQU3dAsTqoJdzciaHWl7vPREQUe8PHnyWbd+OOi6aGzLG9ZPNuVBwLilZTdwBzSqw46+SRzL2roljmNhbb9uqqUuw77NC8TonqxPwsyTzaUteBHu4naUgy5SSX2tdRZlNMj4XsqOP+zjrrLLz00kvIz89Xo06q0iqqsTdfm5w8gAE5B03pyM8OnXOwucMFu6vXly/bfCyHoVDeQlN6qip1UEs4xyzU+k5PPzokcjxGIpq8kCQfo46rh1HHKVpqtc+AcS3LgJyMdHT1DOXy9ebObuvqQVZGOoxpqWjr9iDHFNgPhzt+UHjEjq/a+WlDbdv/dXOmASZDGhzuXjhc4uN0ouYWVqJ9ftXuDJlHG5DXzqTuJxP1+OtRvNy7KnFNSF2bUsvVOlYR/WDirbfeinrDiS6cPIDefKJSIsknOaEwR9E6qCnc3InD11cif6MQtcolIkp2YvlNJ51wfLKQnpYqmgeVuXfVFer4apGfNtS2/V9vauvGrf/zkeQ4nUy5hcPV3OEKSuE1/NjIaWdi95M8/tqJl2OtVD2lrk2x5Wre58v+Rvvmm2+WXehDDz0UcYXUFq/fmDGfpDi18oHrIc94MonX9hkP+I02RUvp9ik312sy5YSNJ3o5L3LHab3UVy3RtE8tjk2iH389iZdjrYd6qn2fL/sb7fr6+oB/79y5E319fZg4cSIA4LPPPkNaWhpOO+20iCtDoTGfpDi18oHrIc84EVEikpvrNZlywsYTvZwXueO0XuqrR1ocGx5/7cTLsdZDPdW+z5c90fZ/XPyhhx5Cbm4uNm7c6Puddnt7OxYvXozZs2dHXBkKjfkkxamVv5F5IYmI1CE312sy5YSNJ3o5L3LHab3UV4+0ODY8/tqJl2Oth3qqfZ8fVtRxrwcffBBr1qwJCIaWn5+P++67Dw8++GBUFSJhzCcpTq184HrIM05ElIjk5npNppyw8UQv50XuOK2X+uqRFseGx1878XKs9VBPte/zI5poOxwOfP3110Gvf/311+js7IyqQiSM+STFqZW/kXkhiYjUITfXazLlhI0nejkvcsdpvdRXj7Q4Njz+2omXY62Heqp9nx/RRLuqqgqLFy/GSy+9hK+++gpfffUVNm/ejKuuugqXXnppVBUiYcwnKU6t/I3MC0l6cujQIezatSuiv3379sW6+kQB5OZ6TaacsPFEL+dF7jitl/rqkRbHhsdfO/FyrPVQT7Xv8yPKo+10OnHLLbegpqYGvb1Dz8+np6fjqquuwgMPPIDsbP2mPIr3qMZy8kkmcw5RtfKB6yHPeDKI9/appkOHDmHipMlwObujKufM257BqAlTw3oPo44ToF77lDt+cZzTJ72cF7njtF7qqzQl2qcWxyZRj78excux1kM91brPj+jB86ysLDz++ON44IEH8PnnnwMAJkyYoOsJthLCSagutG5P3wC+USgZ+iAApBz/d7zlEFUiOb2QUPkbhRLRm9JTZdchVLlC+wFAsNxor594Or+kvLa2Nric3Zj5s5XIPWFc2O8/8sn72F37BPr6GMCPYsPbr3X19MKSaYC7bwBd7j4UZBvRPzAYNK75v8fXF+YYMaEwJxbVT2pyxqSUFCA9LQUtnT34orVbdOwSKy/S8c+UngpDWioMaSkwpKfC5Be7xr/MnIx0GNNSkZKCoOst2XX29MHTP4CevgGY+gfQ2dOn+L1HvN2vxpJUW5BarsSxFrp/DnfuokU9mztcsLt64XANjS/mTENYebjF8r9HI6pfeGdnZ6OsrEypuuhaOAnVhdadXWLFtWdOwFUbP4DT0w8g/GTo8ZJ8XorW+yGUiH62zYrrzrKheuMO3/kItw6hzvN1Z9lQ/VxguffNm4aVr32KN/a0+NYN5/qJx/NM6sg9YVzYebABwHHkoAq1IZLH2699eLAdjywox/2v70N9UwceWVCO377xWUD/7O3vUgDcxr4w5sTGJO858j+vQufS/3yFKm/d/DIMAhGNf+GWWWErwOKKYix45n3MGJ/PawrAwbZuLB92r1RpK8CqqlKMl3mfSsqRuhfU4l5R6P5Zj3MXPV+7sn+jfemll8LhcPj+X+wv0didnqCLBBjK8bZ0827YnR7Jdbfub8WjbzWiuvJ4vuu6xjYsr23AUYdb0Tromdb7cdThDuokAGBrYyvWv7U/4HyEUwex87x+S3C5y2sbMGl04KNc4Vw/8XaeiYi8/Pu16spibNh2ANsa2wL+35+3v3v7s6/ZF8aY1JjkPUdS59J7vsTKe/uzr7Hkf8If/yIpc1tjGzZsO4DqymJeUwC+ancGTVSAofvUFbUN+KrdGaOaJSepdnfU4Vb9XjHU/bPe5i7NHS7Ra7e5wxX1NqIhe6JtsViQkpLi+3+xv0QjJ6G6nHW3NbahvCgv4DVvMnQl66BnWu+HWCJ6ofMhtw6RnOfhrwltL1HOMxGRl3+/Vl6U5+uT/f9/uHf2t6IwNyPkMvaF2pAak7znSOpces+XWHmFuRnY2hj++Bdpmf5jdbJfU53uvpDnr66xDZ1R5hKm8Ei1u/Zu9e8Vxe6f9TR3sbt6Retpd8U2Z7jsR8c3bNgg+P/JIJyE6lLr9vQNCJQv3YHpIam7ErTeD6ljK3Q+5NQhkvMs9Nrw7SXKeSYi8vLv1/z7wVB9opzl7Au1IXeskzqX3vMlVp7cMoaLpkz/5cl8TTkkJiPJfGxiQardSd3bKnG+pLahl7mL3q/dsNJ7zZ07FytXrsTWrVt90caTQTgJ1aXWzUgPPuRykqHrIam7ErTeD6ljK3Q+5NQhkvMs9Nrw7SXKeSYi8vLv1/z7wVB9opzl7Au1IXeskzqX3vMlVp7cMoaLpkz/5cl8TZkzee+hJ1LtTureVonzJbUNvcxd9H7thjXRLi4uxoYNGzB37lzk5eXhu9/9LlatWoXt27ejv79frTrGXDgJ1cXWrbAVoP5QR8BrcpOh6yGpuxK03g+xRPRC50NuHSI5z8NfE9peopxnIiIv/36t/lAHKo71yf7/P9ycEitaOntCLmNfqA2pMcl7jqTOpfd8iZXX0tkT0fgXaZn+Y3WyX1O5pvSQ90qVtgLkyphUkXKk2l1+tvr3imL3z3qau1gyDaL1tEhMxNUW1kT7ueeew4EDB/DFF19g/fr1GDt2LJ5++mlUVFQgPz8f559/Ph544AG16hoz4SRUD7Xu7BIrrj+rBDV1B3yvhZMMXQ9J3ZWg9X6ESkQ/22bFDWcHno9w6iB2noXKXV1Vin2HHQHrhnP9xNt5JiLy8u/XauoOYHFFMSpsBQH/78/b35158kj2hTEmNSZ5z5HUufSeL7Hyzjp5ZETjXyRleqOO19Qd4DUF4MT8LKwSuFfyRm4+MT8rRjVLTlLtbpTZpPq9Yqj7Z73NXcbkZYpeu7HOJpAyODg4GG0hX3zxBWpqarB+/Xp0dXXp+ttth8MBi8UCu90Os9ks/QY/4SRUF8o7lwIcz6Ptlww9VH45oZxw2ca0mCd1V4LWyemjPZah8vMJ7Ud3Tx/s7j7furmmdIzNzwprn7U+PnoRTftMdLt27UJ5eTm+d/tzEaX3Orjjn3jv93eh8uYnMHbiKWG9t/3QZ/i/+xahvr4e06dPD3vblBiibZ/efq27Z2is88+j3ds/CKenD7kmAzIMqejq6YPdOdSHZhnT0NrVg6yMyHK4UmSEck/bXR5kZwSOSf73O5bMdGSkp8Lh6kV2hgE5pvShMdEVeH8jlpvXf/wzZxqQnZGOLndfQD0cbg/MmUZ4+gbQ1dMHc6YBORlD23K4gsdN/zKzRfYlnslpn1K5hr9qd6LT3YdOVy9yj92/cJIdO1I5rKPNHS2HEtuQuqeVyrMtx3/anXD4XbvmY/feSm4jEhE/C3Lw4EG8/fbbvr+WlhZ85zvfwdy5c5Wsn67ITaguljNueIonoXUvKj0Bt35/UsiccBMKcxTYm9hSIjm9XGLnQ86xlMrP578f4awrRsvjQ0SkBTn92sG2bizZvDuoD73joqm4/PfvYcpoM3Mea0Bu7lupHNu3/OWjgGXnTi7EHRdNwYqXPw5Ztv910tzhCirj7EkjseT7k4PGWm8Z3xoZPK4LX3vJlRta6v5EiZzJpBypNqjF+VIqB7ZY36/ENpo7XFj6UkNMc46HEtaj43/4wx9QXV2Nb33rWygtLcWLL76Ik08+Gc8//zw6Ojrw5ptv4s4771SrrnFBiZzbVaeeqOuccPEk2hx+4eTn03suPyIiPfuPSC7fe1/7BOvmlzHnsQbkjptyc2z7mzjajGW1DVHdI00ZY8HK1z6RzNlNx0ndn3zV7ow6ZzIpR6ptNXe4VD9fWuTAVmIbesg5LiasifaiRYuwZcsW3HbbbWhra8M//vEPLFu2DLNmzYLBwIiEgDI5twvNGbrOCRdPos3hF05+Pr3n8iMi0jOHRC7fQvNQzuZkz3msNrnjptwc2/7k5tsWKz+cMmiI1P2JVB5tOTmTSTlSbUvqfCpxvrTIga3ENvSQc1xMWBPtxx9/HN/5zndwzz33oLCwEBdffDEefPBBfPDBB1Dgp94JQYmc211u8d+4xzonXDyJNodfOPn59J7Lj4hIz6T6UP+xkf2peuSOm3JzbEu9JlS2WPmR5ttOZpL3JxLL5eRMJuVI5tHW4HxpkgNbgW3oIee4mLAm2r/4xS+wadMmHD58GNu2bcMFF1yAf//737jwwguRn5+PCy+8EL/5zW/UqmtcUCLndo4pTXYZJC7aHH7h5OfTey4/IiI9k+pD/cdG9qfqkTtuys2xLfWaUNli5UeabzuZSd6fSCyXkzOZlCOZR1uD86VJDmwFtqGHnONiwppo+5syZQquueYa/OlPf0J9fT2uv/561NXVYcmSJUrWL+4okXO7xdGj65xw8STaHH7h5OfTey4/IiI9M0vk8m1xDOVsTvacx2qTO27KzbHtr/5QR8hzLPceKZwyaIjU/YlUHm05OZNJOVJtS+p8KnG+tMiBrcQ29JBzXExEE+2Wlhb86U9/wjXXXIPJkyejqKgIv/nNb1BeXp70wdCUyLldu/MrXeeEiyfR5vALJz+f3nP5ERHp2ViRXL53XjwVSzbvZs5jDcgdN+Xm2Pa377ADq6tKo7pH+rTZjjsumiqZs5uOk7o/OTE/K+qcyaQcqbY1Ji9T9fOlRQ5sJbahh5zjYsLKo33ttdfi7bffxr59+5Ceno5vf/vbOPPMM3HWWWdh1qxZMJnCO7FPPPEEnnjiCXz55ZcAgKlTp+LOO+/E+eefH/I9f/nLX3DHHXfgyy+/RElJCdatW4cLLrhA9jbl5BkMlWstnBxs4eRBDpWjzpcT7lgZZlM6cjLSVckDF86+hcrrF20ZpvRUwfdL5REMZ/8C8l17+kVzY/vXo7OnD53HcmOb/XJLCq3rLde7LYtIzkElrrVIjoNW2wsX82iHxjzaFGvhtk+xfsW7rOtYTu2evgFf/5qTkY4uv3zI2cyjrSj/Y285ls/c4QoeX71jb1fPUK7znv6h9SxZ6cjNMMDl6Q/Ibd3p9iDXZISn/3hu60xDGjrdvbC7jufY7u7phdk0NFZ6c297z7ldoB7+43emMQ2GtFR0OHuQl5WB3v4BuDz9QfdZw+8bzCYDenr70eFSfuyLp/FTKk+2d/nwex0vvexrPJA6VnKOZbR5z+VsQ2odqWtCiWOhRA5sqWMVzrxMSWE9xF9fX4958+bhrLPOQkVFBbKyoktif+KJJ2Lt2rUoKSnB4OAgNm7ciEsuuQT19fWYOnVq0PrvvvsuFixYgDVr1uCiiy7CCy+8gHnz5mHnzp2YNm1aVHXxEsq1Jifv43By8yCHyoO3qqoU9/9jL/7acAQAkGVMQ82i0/HYlkZsbVQ2D1w4+eWE6nvOpJG48+KpuF3m8REqY7bNiuvOsqF64w44Pf2+9987bxruefUTbNn7dcDxCTdP4PDzIZZP8ndvfIaX6psBDD2SsunnM3HXKx8Lrnvva5/ijT0tQfs8ebT0TahS15pcWm+PiJJXqHFl3fwyDAJYsnk3PjzYjkcWlOP+1/cJ9q8WkwG3xSj3aaLynhexY7+6qhSGtFTc5rfeb9/4DNsa25BlTMMjC8qxYdunAe8LNZZU2gqwqKIYN75YD6en35cDe6nA+Ou/nv847x2/5eYNDrXeHRdNxdV/+ACtXR7Fxr5Y5ucNl9Txk8qzHU/7GmtSx0rOsdTifEmtI7UNJY6FEvshp2+QOy9TWljfaGthxIgReOCBB3DVVVcFLfvxj3+M7u5uvPbaa77XvvOd72D69Ol48sknZZUv9omf3enB9S/WB4WBv/5sG+qb2gVD6c8psWL9gvKITt5Rhxs3/3mXYLmVtgIsrijGVRs/ULUOofZZqNxQ9Q2nbmL7XGErQPm4fDy6pdH3WqWtANOHveZ9/cEfTY/om43mDhdu/Z+PQh73274/CT94dBsA4NmFM1Cz7UDIdYXqJud8aH2tab29SPEb7dD4jTbFmtz2KTaurLm0FH/bfRhbG1tF+5/ZtgJcUDoGy2obgpbFom9KBP7nRe6xH75eqPeJlec/tstdDwgc56Xul+Su572vUmLsC+f+SQti7VPquKydX4Ylm3eHXL5ufhmWvhSc/xxgexxO6rp44LJTcMtfPhI9lu6+AdXPFwDReq6+tFRyG2MlvtmWOhZrLi3FbVHuh9SxinSuoBTZv9F+5ZVXZP9For+/H5s2bUJ3dzdmzpwpuM727dvx3e9+N+C18847D9u3bw9Zbk9PDxwOR8BfKFrnbGzv9sjKGapmHcLJYReqvuHUTWyftzW2obwoL+C1OoHXvK9HmidQKv9getrxZiGV01yobnLOh9bXml7zkYbTPolIW5G2T7FxpTA3w/dUllj/s3XYGOiPuZIj439e5B774euFep9Yef5ju9z1gMBxXup+Se56ofbLn1L5e/U0fkodF6k82g53X0z3NZ4okddZi/MlVU8525AitQ2HAvsht2+IFdmPjs+bN0/WeikpKejvF88D7a+hoQEzZ86E2+1GTk4OamtrMWXKFMF1jxw5glGjRgW8NmrUKBw5ciRk+WvWrME999wjqy5a52yUukj9c4aqV4dw8n4L1ze8vJji+xxO3s1I8wSGk09SKqd5qLpJ5ufW/FrTZz7ScNonEWkr0vYpNq749zlS/Y/YcuZKDp//eZF77IevF+p9kZYnVY53nJca7+Wu5x3TlRj7tMgzLCa8+1uJ4yeVl1nqvont0UeJvM6efvGHjSXznss4X1KPMytxziXbSJTbkHOsYp0DXvY32gMDA7L+wplkA8DEiROxa9cuvP/++7jmmmuwcOFCfPrpp2HvSCjLli2D3W73/R06dCjkulrnbJTK7eafM1S9OoST91u4vuHlxRTf53DybkaaJzCcfJJSOc1D1U0yP7fm15o+85GG0z6JSFuRtk+xccW/z5Hqf8SWM1dy+PzPi9xjP3y9UO+LtDypcrzjvNR4L3c975iuxNinRZ5hMeHd30ocP6m8zFL3TWyPPkrkdZZcR4HzFW2ubkXaSJTbkHOsYp0DPuI82koxGo2w2Ww47bTTsGbNGpxyyin43e9+J7juCSecgKNHjwa8dvToUZxwwgkhy8/IyIDZbA74C0XrnI352UZZOUO9dRieykKJOoSTwy5UfcM5PmL7XGErQP2hjoDXKgVe874eaZ5AqfyDff3HP+mWymkuVDc550Pra02v+UjDaZ9EpK1I26fYuNLS2eNbJjauzbYVCOZhBpgrOVL+50XusR++Xqj3iY0l/mO73PWAwHFe6n5J7nre+yolxj4t8gyLCad9Sh0XqTzaZlN6TPc1niiR11mL8yVVTznbkCK1DbMC+yG3b4iViCfa3d3d+Nvf/oYnn3wSjzzySMBfNAYGBtDTIzy4zpw5E2+++WbAa//85z9D/qY7XKFysYWT9zEco8ymkHnwVlWVonbnV77XauoO4IazSzBb4TqEk8MuVH33NNuxSubxCVXGbJsVN5xdgpq6AwHvX1VVij3N9oB1o80TKJVP8rltx+uwZPNu3P2DaSHX3Xc48DdRcs+H1tea1tsjouQlNq6cdfJI37KaugNYXFEcNHGrtBXgvqpSwTzM7Jsi539exI79Kr9jP3w977+Hj4mhxhJvADLv2B4qB/bw9YaP82L3S3LX8+ZiF6uvkvl79XSNSh2/E0Vy2K+qKsXY/Ky42ddYUyKvsxbnS6qecrYR7bEYq8B+yO0bYiWiqOP19fW44IIL4HQ60d3djREjRqC1tRVZWVkoLCzEF198IaucZcuW4fzzz8e4cePQ2dmJF154AevWrcPrr7+Oc889F1deeSXGjh2LNWvWABhK7zV37lysXbsWF154ITZt2oTVq1eHld4rnDzaw3OtqZWDLSDfoykd+dmBean9twdAlTqEs2/h1DecMrx5tIe/P9T2ouXNuTc837XQfrj7BqLeZyFaX2taby9cjDoeGqOOU6xFmkdbqF/xLuvuGVo2lKM5sC+WKoMi43/szf55tIeNr6HWs2SmI9c0lEdbaizJMaWj+1hO9GyBfNvdPX3INRmQaTyeb1tsnJd7PzB8PXPmUB5tu0v5sU8v16ic9il1/ELdF3npZV/jgdSxknMstThfUuv4clwfWz48x7USx0KJ/VBrrhCtiB5c/9WvfoWLL74YTz75JCwWC9577z0YDAZcccUVuOmmm2SX09LSgiuvvBKHDx+GxWJBWVmZb5INAE1NTUhNPf6l+6xZs/DCCy/g9ttvx/Lly1FSUoKXX35ZsRzaXlK51gYBIEW57Y0ymwQvhlD1UKNTCye/nCk9FYa0VBjSUmBIT4Vp2G+d5BwfoX22Oz2C7w+1PaEE9gAEk9oLrTsmL1M096N/PcI9R3KJnWOh14X2I5zth7s9IqJIifUr/su8/ZohLRXG9FRkG9NklZGMoh0DAOFjn5GeApMhFe1ODw62OWHOTEd+lhETCnMC1jOkpSA9LRVpqSlISQFSUoD0tBS0dPbgi9buoTrlHH+fsFD5d6VzMfuPxd46NXe0Bx2LUGO22LEYTu6xjqdrVOq4ZBvT4PL0C7ZFIL72NdakjlVnTx88/QPo6RuAqX8AnT19QetL3W8rcb6k1klPSz12K5xyrL2H/zC01Dak7sfl7IfcNq+1iCbau3btwlNPPYXU1FSkpaWhp6cH3/rWt3D//fdj4cKFuPTSS2WV8+yzz4ouf/vtt4Neu+yyy3DZZZdFUu2oyEmYngyEjsO5kwtxx0VTsOLljyM+PqGO733zpmHla5/ijT0totvLMqahZtHpeGxLoy91jFgZoeqm5/Os57oREUWC/Zp8Sh8rb3kfHmzHIwvKcf/r+wLS5HgfvTSkpeK2YduttBWgurIYKUgJep8W50/t6yYZr8tk3OdYOdjWjeW1DUHtbVVVKcYXDH0QJXU+tDhfTW3dWCZQz9VVpRhXEOoDM/IX0W+0DQaD75vmwsJCNDU1AQAsFktCRg22Oz1BFzMwlMNt6ebdvm9iE12o4zBxtBnLaoMTyss9PmLHd3ltAyaNDnwESmh71ZXFWL9lf8AkW6wMobrp+TzruW5EUg4dOoRdu3ZF/JeI4wqxXwuH0sfKv7zqymJs2HYgKBdtXWMbVtQ24O19Xwdtt66xDRvqDuCw3RX0PrXPn9rXTTJel8m4z7HyVbszaJINHG9vX7U7Jc/HUYdb9fN11OEOmmR767m8tgFHHe6ot5EMIvpGu7y8HDt27EBJSQnmzp2LO++8E62trfjjH/+o+GPceiCVcL21y5MUj9KEOg7lRXl4dEuj4HvkHB+x41vX2IbFFcWS2xOrg1AZQnXT83nWc92IxBw6dAgTJ02Gy9kdcRmZWdnYt3cPioqKFKwZxRr7NfmUPlb+5YmNn1sb27BIYPyUWqbm+VP7uknG6zIZ9zlWOt19QZNXr7rGNnS6+9DTOyB6Ptq71T9f7d0e0Xq2d3t0+ai23kQ00V69ejU6OzsBAKtWrcKVV16Ja665BiUlJaipqVG0gnogmXBdRtL2RBDqOPT0DQi+7iV1fKSO7/DyhbYnVYdQy/3rpufzrOe6EYlpa2uDy9mNmT9bidwTxoX9/s4jTdj++zvR1tbGiXaCYb8mn9LHyr+8SMdPqWVqnT+1r5tkvC6TcZ9jxeGSPtYGid9BO9x9kmVES2obUstpSEQT7RkzZvj+v7CwEP/4xz8Uq5AeSSZcl5G0PRGEOg4Z6eIdgtTxkTq+w8sX2p5UHUIt96+bns+znutGJEfuCeMiipZOiYv9mnxKHyv/8iIdP6WWqXX+1L5ukvG6TMZ9jhVzpvSxNkpMtKVyWCtxvqS2ISePNkWRRxsYihq+detWbN26FV9//bVSddIdqYTr3rRbiS7Ucag/1BEyWbyc4yN2fCttBag/1CG5vfpDHUF5OcXKEKqbns+znutGRBQJ9mvyKX2s/MsTGz9n2wrQ0tkTclmo32mqef7Uvm6S8bpMxn2OlVxTesh75kpbAXJN6ZLnIz9b/fOVn20UrWd+Nq8JOSKaaHd2duL//b//h7Fjx2Lu3LmYO3cuxowZgyuuuAJ2u13pOsacVML1ZPndSqjjsO+wA6urSiM+PmLHd3VVKfYddkhur6buAG44uwSzZZYhVDc9n2c9142IKBLs1+RT+lj5l1dTdwCLK4qDJtveKMhnnjwyaLuVtgIsrizGaEtm0PvUPn9qXzfJeF0m4z7Hyon5WVhVVRo0ifW2txPzsyTPxyizSfXzNcpswuoQ9VxdVcrfZ8uUMjg4OBjum3784x+jvr4e69evx8yZMwEA27dvx0033YTp06dj06ZNildUKQ6HAxaLBXa7HWazWfoNfuQkTE8GAUnhj+XaHGU2+RLOO1y9sGQOJZzPMqbJzvkZ6vgOL9d8LJH98HqMyDJiEJBVN3OmAdkh6hbOeVYip2k4hOoGCOcOj6RcrfYjlGjaZ6LbtWsXysvL8b3bn4voEeyDO/6J935/FypvfgJjJ54S1nvbD32G/7tvEerr6zF9+vSw3httvaPZNilLrfbJsVW+SI6Vf/9uyTQgOyMdXe4+ONy9yMlIhzEtFZ1uD3JMBrj7BuBw9cFsSkd+ttF3Mz18vM3N+P/s3Xl4G9W9P/63bEuWvMg2dhwSsMGNTVYnMUspsROWAi1bmzSFNkDJAr3floSl/FogC1CWbPRyewvccruQwP3eBtpeSNrmS8uFBggOS4EE4kCSxhBw2iQONrZkydqt3x9GipbZJM2MRtL79Tx5wDOjmTPSnE0653xK4PIH4fYGUVdRimF/CMP+4//v9AZRZStBpdUMbyAEhye+bhFrR6RCrG2QzvspVPel+1xmqz5VI38yL6pH7jn4x8AwhrxBOD/PG5XWEpxcUxZ3Drl8okYekCN3DSXPu9x9yJ1DjTyVrXyZ1gD7rVu34vnnn0dHR0d021e+8hX86le/wle/+lXVEmc0SgKm5zuxuH33z52Ge//0PrbtG51CUGYpxuMLz8TPX+rGqwrjawq9v0Ix/L48aQzuvmIqVimMo52YNmD0G7kff20avv3L19Hn8ielTcnnnI2Yk4nvkRppYOxMIsom1q3KpfpexZbvZZZiPLygLSmUl1x5L1RHdDTX4vqOLyCMMB564e/Yf3QIv7nhS4Ixdxe1N+Hmp3Zh2B/CRZPrseryKVi5uQudGcTmVSO+r1zdl85zmev1KfOiOuSeAzXiaOsR41qNWN5y6dQjXng282VaQ8dra2tRVVWVtL2qqgo1NTUZJ4qMSSqu38rNXZgy/vgzsaSjCY8mdLIjxyqN8ScWw2/y+CqsSCGOdmLagNHQBD/+4x6snz89rbQZIeakGmkwwn0QEZH6Est3sXjZUuW9WB3R2d2PIw4PNnSOnm/9/Om4b+v7gjF3N+44iCUdo2HAJo6zY0VCJztynNLYvGrE99Wi7mN9SoD8c/BPmTjahwc9suc4POjRPMa1GrG85fKqUAc41Wtkeh9a58u0OtqrVq3CbbfdhqNHj0a3HT16FD/60Y9w1113qZY4Mha5eNdtDdXRv9saqkXj70Vi/MkRi+EndG6p6yWmLXZ7vb00rbQpiTmpNTXSYIT7ICIi9SWW7+nUy1J1xFi7NdphrreXip57R0wdLFdXD7jTbxukcg4t6j7WpwTIPwdOmTjaDk9A9hwOTyDjPCBHLg1KYnnL5VWHJ5DxNTK9D63zZVpDxx977DF0d3ejsbERjY2jcVF7enpQWlqKTz/9FL/4xS+ix+7cuVOdlFLWpRLvOtPY2qPXE47Rp2YcbZc3lGbash9zUo00GOE+iIhIfYnlezr1slQdEXs+obpU6Fi5NCiJzatGfF8t6j7WpwTIPweqxNGWOYcaMa5l70NBLG9/SHoZsEzvIxfauWl1tOfOnatyMigXpBLvOtPY2qPXE3481YyjXWEtTjNt2Y85qUYajHAfRESkvsTyPZ16WaqOiD2fUF0qdKxcGpTE5lUjvq8WdR/rUwLknwNV4mjLnEONGNey96EglncgJP3FWqb3kQvt3LQ+iXvuuUftdFAOiMT12y4wBCMxVnUkLqfQkBGlMf4iMfwS53JF4mjHbpe6nlgc7Y7mWhxzxscHVZo2qfdCr5iTaqTBCPdBRETqSyzf06mXpeqIXqcXs5tr8Wp3P445fYL1NQC0x9TBQvV3hNLYvGJtg1TOoUXdx/qUAPnnwP55HG2x57fq86g4UueospkzzgOZ3kcklrfU8+4Njsjea6bXyPQ+tM6Xac3RBoDBwUH8+te/xvLly/HZZ58BGB0m/s9//lO1xJGxSMX1Wz2vFXsPH4+hvqHzIJad3ywY11ppjD+xGH57DzuwOoU42olpA46vOn7HM7vTSpsRYk6qkQYj3AcREakvsXyPxMtOrFOlynuxOqKjuRbjqmxY3DEaf/uOZ3bjrsunCsbcXdzehA2dBwEA+484RWMIK43Nq0Z8Xy3qPtanBMg/ByfJxNGOrHgvdY7x1TbNY1yrEctbLq+Or7ZpHi882/kyrTjau3fvxoUXXoiqqip8/PHH2L9/P77whS9g1apV6OnpwX/9139pkVZVFFKcXq1ixonFw4vbbi1BbbkFvtCIbJxAufQmnjcS21Mo3qM3OCIZRztybFVMHO1M4kUaIeakGmkwwn0AhZU/U8U42oyjnW3Mn7kjtt6sspWgtKQITk8AFVYzrOZiOL2B0XjZthJYS4rg+HyfWDshsY6osJbA7QvC5Q2gymaBPzQCXyCIKpsF7s/jaEfjbfuCcHji62SpGMJK2y5ibYNUaFH3Zas+Zf5UjxrtZ7nY0f8cGIbTG8SQJ4BKmxl2awlOSjGOtlwe0PM+InlZ9j4k0imWZ3K5nZvW0PHbbrsNixYtwoMPPojKysro9ksvvRRXX321aomj9GkVM07uvLEZR0mcwHTOG6E0pvQDc6fhvq0f4MW9x5LOPaG+Is13QjgN2aBGGoxwH0RElDmpuLXm4iLc/sxuvPPJAB5e0Iaf/fXvimJqqxVTWiqO9up5rbB8nj4lbZexdmvGv9xpUfexPs1tesRtPjzowZ3PdkleQ0k6pPKAHvehtJ0vl1fl8kwut3PTGjr+1ltv4f/8n/+TtP2kk06KC/lF2aFVzLhUzisXJ/CfA8OqplfqHCs2d2HSOHvSdsa1JCKifCIVt3bl5i68vP9TvHqgL62Y2qkQq5Ol4mivikmfFmkikqN1e1RpbOhM06HHfSiJB05pdrRLS0vhdDqTtv/973/HmDFjMk4UZUarmHGpnFcuTmDskv1ax4MWi6PNuJZERJRPpOLWvtrdj3p7KYD0YmqnQqxOlrpubPq0SBORHK3bo0pjQ2eaDj3uQ0k8cEqzo/21r30N9913HwKB0TfRZDKhp6cHd9xxB+bPn69qAil1WsWMS+W8SuIEpnPedNMmFruTcS2JiChfyMWdVRrPOtO6UaxOlruu1H7W16Q1PdqjSmJDZ5oOXe4jhXZ+IUtrjvZDDz2Eb37zm6ivr4fH48G5556LI0eO4JxzzsHq1avVTiOlSKuYcamcV0mcwHTOm27axGJ3Mq4l6e3QoUPo7xf+FljK/v37NUgNEeUTubizSuNZZ1o3itXJcteV2s/6mrSmR3tUjdjQcsfoch8ptPMLWVod7aqqKrzwwgvo7OzE7t274XK5cMYZZ+DLX/6y2umjNGgVMy6V88rFCYwtaLSOBy0WR5txLUlvhw4dwsRJk+EZdqd9Dr+f3xITkTCpGNOzm2txbMgHIL2Y2qkQq5Ol4mjHpk+LNBHJ0bo9mkps6EzSocd9KIkHTikOHX/99dexdevW6N8dHR0oLy/Hz3/+cyxYsAD/8i//Ap9PuJAk/WgVMy6V88rFCYxd+l/reNBr5rVi/xFn0nbGtSS99ff3wzPsxjk33IeLVz2R0r/p874PAAgGpYedEVHhkopbu3peK847bQzmtNRFY2q3pxBTOxVidbJUHO0HYtKnRZqI5GjdHlUaGzrTdOhxH0rigVOKv2jfd999OO+883D55ZcDALq6uvDd734XCxcuxOTJk/GTn/wE48ePx49//GMt0kopGF9twyML2lSLOxcbg0/svELHrps/HUMxcQLF4mhLpVdpHECpc/zrlTMMESfaqLSKuU7CKk9sTDmmtPPoJxqlhoiMKp2yubG2HA9dNRMDbj9cviBqyy0IhMLod/tht5nxkytnRGNgr5nbCn9oBG5fMKluzLRekKqTH/zmDDg8gej2Kps52jBX2nbJtXor19JbqMZX2/CTK2dIxo5Wco5186fD4QnA6QmgymaG3WbGuM+fcSVt9EzToUY/QO4cp9SWS+ZlPRk1f6XU0X733Xdx//33R/9++umn8cUvfhG/+tWvAAANDQ2455572NE2iExjxknFz0uMP514bJmlGBsWnYX/2NaNV7uVxfATSm+qcQDF7plxLcVpFXOdiIjSl0nZHIlbK3WOL4ypEH29WvWCWN07vtomeh4l9XWu1Vu5lt5CpsZnJRXLvvHz+NJyz7ka6dAj/rRUXtaLkfNXSkPHBwYGMHbs2Ojfr7zyCi655JLo32eddRYOHTqkXuooa1KJwSd07JKOJjyy7UBcJ1vs9WqkgdLD95hSsX//frz77rsp/eMibkSp0yMOrtg5jF4vGD19iXItvYVMjc9KKpb9is1d6HV6dUlHoTD6e5XSL9pjx47FwYMH0dDQAL/fj507d+Lee++N7h8aGoLZzMnv+UBJDL7IN1xCx7Y1VOPRbd2KXq9GGig9fI9JCa+jHzCZ8O1vfzvtc3ARNyLl1Cib0z2H0esFo6cvUa6lt5Cp8VlJxbLv7O7HgNsvO/ybz4xyRn+vUupoX3rppbjzzjuxfv16bNmyBWVlZZg9e3Z0/+7duzFhwgTVE0n6SylmtsCxasTo1CoeOB3H95iU8HtcQDiMtmtXYMypqc0tP/r+m9i9+TEu4kaUAj3i4Iqdw+j1gtHTlyjX0lvI1Ml30nWd3H610lEojP5epdTRvv/++/GNb3wD5557LioqKvDkk0/CYjn+LcGGDRtw8cUXq55I0l9KMbMFjlUjRqdW8cDpOL7HlIry+gYu4kakAz3i4Iqdw+j1gtHTlyjX0lvI1Ml30l0ruf1qpaNQGP29SmmOdl1dHbZv346BgQEMDAxg3rx5cft///vf45577lE1gZQdkfh5QhJj8AkdG4nRqeT1aqSB0sP3mIjIeNQom9M9h9HrBaOnL1GupbeQqfFZRWLZC+lorkVNOdu/ajL6e5VSRzuiqqoKxcXFSdtPOOGEuF+4KXelEoNP6NgNnQdx0wUtmK1hDD/OT8kc32MiIuPRIw6u2DmMXi8YPX2Jci29hUyNz0oqlv2aea2KwnPxmVHO6O9VSkPH1bZ27Vo8++yz2LdvH2w2G2bNmoX169dj4sSJoq954oknsHjx4rhtpaWl8HrlV/Gj1KQSg0/s2Ec1juFHmeN7TERkPHrEwdXy2loyevoS5Vp6C5kan1VsLHunNwi7tQQ15anH4uYzo4yR36usdrRfeeUVLF26FGeddRaCwSBWrFiBiy++GB988AHKy8tFX2e32+NCxphMJj2Sq7tUgq9rHag9DAAmZdeKPVaPGH6xep3e4wWbrQQ1ZakVbGK0fn+zjXHGiYjUpUa9kW7ZHLm2yxdAdZkFI+FwXN2c6rUj5/uoz53SvUReN+wPoNpmgdsfUq1+lnpvjFhns57NHUo+K7n2ZiSWvRglz6hcOtR4zuXOkel+tdIpx6j5K6sd7b/85S9xfz/xxBOor6/HO++8gzlz5oi+zmQy4cQTT9Q6eVmVSvB1rQK1C513dksdlp7fjCVPvIVhfwgAcNHketx1+RSs3LInq8Hie/rdSbELI0N1GmvFv7iRo9X7S0RE+Smb9Ubk2u98MoCHF7Thwef3x9WLqaYj3XuJvG7vESd+c8OXcKcG9bOa6SVSKtP2phrPqB7nyHS/WunMZWnN0daKw+EAMDrXW4rL5cIpp5yChoYGfP3rX8f777+vR/J0k0rwda0CtYud99UDfXhk2wEs6WiKbps4zo7lm7uyGiy+1+lNKvSA0ZiFKzZ3odeZ3tQCrd5fym2HDh3Cu+++m/K/2JE4RJSfsllvxF57SUcTNu44mFQvppKOdO8l9nXr50/HfVvfV71+VjO9REpl2t5U4xnV4xy9Tm9G+x3DfuZHZPkX7VgjIyO49dZb0d7ejmnTpokeN3HiRGzYsAHTp0+Hw+HAv/7rv2LWrFl4//33cfLJJycd7/P54PP5on87nU5N0q+mVIKvaxWoXeq8O7r7saT9eEe7raEaj27rVj0NqRhw+5MKvYjO7n4MuP1pDVHT6v2lUbmYPw8dOoSJkybDM+xO+xx+P2NgkvHlYv40gmzWG7HXVqNuTvdeYl9Xby/VpH5WM725iPkzOzJtb6rxjOpxjgF3Zvv7XKOd6ELJj2IM09FeunQp9uzZg87OTsnjzjnnHJxzzjnRv2fNmoXJkyfjF7/4Be6///6k49euXYt7771X9fRqKZXg61oFapc7ry84Ivj/aqYhFU5vMKP94q/T5v2lUbmYP/v7++EZduOcG+5D5YmNKb326PtvYvfmxxAMpvc8EukpF/OnEWSz3oi9thp1c7r3Evs6lzckcw31ysNCqrOZP7Mj0/amGs+oHufI9D6HvIHRdSFkjsl3hhg6vmzZMmzduhUvvfSS4K/SUsxmM9ra2tDdLfyt7fLly+FwOKL/Dh06pEaSNZVK8HWtArXLnbe0pEjw/9VMQyrsVunvjOT2i79Om/eXRuVi/oyoPLERNQ2npfSvrDa/15ag/JLL+TObsllvxF5bjbo53XuJfV2FNTkcbPyx6v3mU0h1NvNndmTa3lTjGdXjHJneZ6XVXFD5UUxWO9rhcBjLli3D5s2bsW3bNjQ1Ncm/KEEoFEJXVxfGjRsnuL+0tBR2uz3un9GlEnxdq0DtUudtb67FrkOD0b93HRpMiheoRhpSUVNuEU1DR3MtasrVfx/0urd8lov5k6hQMH+mJ5v1Ruy1dx0aRHuGdXO69xL7umNOnyb1s9x1E+Vbnc38mR2ZtjfVeEb1OEdNeWb76yosBZUfxWS1o7106VL893//NzZt2oTKykocPXoUR48ehcfjiR5z3XXXYfny5dG/77vvPvzv//4vPvroI+zcuRPXXnstPvnkE9xwww3ZuAVNpBJ8XatA7WLnnd1Sh5suaMGGzoPRbfuPOLFmXmtWg8WPtVuxZl5rUuEXWQUy3flfWr2/RESUn7JZb8Ree0PnQSxub0rqbKeSjnTvJfZ1dzyzG3ddPlX1+lnN9BIplWl7U41nVI9zjLVbM9ofCbdV6Pkxq3O0H3vsMQDAeeedF7d948aNWLRoEQCgp6cHRUXHvw8YGBjAd7/7XRw9ehQ1NTU444wz8Nprr2HKlCl6JVsX46tt+MmVMxTFhNYqUPv4ahvWzZ8OhycQPW+VzYxySzH+tKwj6VrZDhbfWFuOh66aefw9s5agpjzzONpavb9ERJSfUq030okzm/iaitISuH1BODwB3HX5FFiKizDk9WPN3Fb4QyNw+4Jp1V/p1oGxr/P4A1g7r/V4HG2V6mc100sUIRcju7G2HA9+c0ZS+1hpuCo1nlE9zpHpfrXSmcuy2tEOh+WmyQMvv/xy3N8//elP8dOf/lSjFBlHqnHntAjULhUncEJ9hS5pSNVYu1WTitsI90ZERLlDab2RTpxZodd0NNdiUXsTbn5qF4b9IVVj1aZbB2ar7mSdTelSEiNbjdjQajyjepwj0/1Kj8lXhlgMjeIZIe6cVnGpiYiIaFQ69b3Yazq7+7Fxx0Es6WiSPQcRJVPS9jVCG51yh2HCe9FxRogDqVVcaiKidOzfvz+t1/l8PpSWlqZ93draWjQ0NKT9eiIp6dT3Uq/Z0d2PJe3HF5YtlFi1RGpQ0vY1FxdlvY1OuYMdbQMyQhxIreJSExGlwuvoB0wmfPvb307vBCYToGCakhhbWTn279vLzjZpIp36Xu41ifGzCyFWLZEalLR9zcUmyWOY3ygWO9oGZIS4c1rFpSYiSoXf4wLCYbRduwJjTj0tpdceff9N7N78WFqvBYChoz14/dd3o7+/nx1t0kQ69b3caxLjZxdCrFoiNShp+5qLM49PT4WDvSUDisSd2y4wNEXvuNSdAkNo1I57SUQkp7y+ATUNqXWWnUc/Sfu1RHpIp76Xek17cy12HRqUPQcRJVPS9rWWFGW9jU65g4uhGZAR4s5pFZeaiIiIRqVT34u9pqO5Fovbm7Ch86DsOYgomZK2rxHa6JQ7+It2htKJfamEWNw5APjwmEv16wnJNE5gNmj1eRARkXpYVh83vtqGn1w5QzJur9BrEtsIFdbRONqbbjg7LlZtuu81PyNKVAjPRGzb1+kJoMpmhj2h7atGbGi5WN2UH9jRzoAacfSkJMad0/p6ifS+XqZyLb1ERIWIZXW8dN8PJbFp0z03PyNKVCjPhNL7zCQ2tJJY3ZQfOHQ8TXrH0cv362Uq19JLRFSIWFbH0/L9SPfc/IwoUaE8E3rcp5JY3ZQ/2NFOk5LYl7yefnItvUREhYhldTwt3490z83PiBIVyjOhx30qidVN+YMd7TTpHes636+XqVxLLxFRIWJZHU/L9yPdc/MzokSF8kzocZ9KYnVT/uAc7TTpHes636+XqVxLL2XXoUOH0N8v/I2ynP3796ucGqLCwbI6npbvR7rn5mdEiQrlmdDjPpXE6qb8wU8zTXrHus7362Uq19JL2XPo0CFMnDQZnmF3Rufx+/PjG3wiPbGsjqfl+5HuufkZUaJCeSb0uE8lsbopf7CjnaZIHL07n9kdlyG1iqOX79fLVK6ll7Knv78fnmE3zrnhPlSe2Jjy64++/yZ2b34MwSCHdxGlimV1PC3fj3TPzc+IEhXKM6HHfUZida/Y3BXX2Y6N1U35gx3tDKgRR4/XU0+upZeyq/LERtQ0nJby65xHP9EgNUSFg2V1PC3fj3TPzc+IEhXKM6HHfTbWluOhq2Yej6NtLUFNOeNo5yN2tDOUSRw9Xi9er9N7vNCxlaCmTLrQcQz70efyw+kNwG4zo67covv7Q0REqWNZHU+P9yMMACblx8emKVLfftTnjqtvUyFWZ1PuKLR8m2qeScVYuzXjjnWq7WbSHzvaZAg9/e6kuIKRYTSNteVJxx8e9CTFOpzTUod186djfLVNlzQTEREZmRp1pVHOQaSHXHlWU203U3YwvBdlXa/Tm1RYAKPxBFds7kKv0xu33THsTyoEgdEYh3c+sxuOYcYgJCKiwqZGXWmUcxDpIVee1VTbzZQ97GhT1g24/UmFRURndz8G3PEFW5/Ln1QIRmw/0Ic+lzEKQiIiomxRo640yjmI9JArz2qq7WbKHna0KeucXunVmxP3O73SYZWGZPYTERHlOzXqSqOcg0gPufKsptpupuxhR5uyzm6VXiogcb/dapY8vlJmPxERUb5To640yjmI9JArz2qq7WbKHna0Ketqyi3oaK4V3NfRXIua8vgVLusqLJjTUid4/JyWOtRVFM6KmERERELUqCuNcg4iPeTKs5pqu5myhx1tyrqxdivWzGtNKjQiqycmhiqoKrNg3fzpSYXhnJY6rJ8/vaBCTxAREQlRo640yjmI9JArz2qq7WbKHo4tIENorC3HQ1fNPB4P0FqCmnLxeIDjq214ZEEb+lx+DHkDqLSaUVdRWPEdiYiIpKhRVxrlHER6yJVnNdV2M2UHO9pkGGPt1pQKiKoy4xV8RERERqJGXWmUcxDpIVee1VTbzaQ/Dh0nIiIiIiIiUhE72kREREREREQqYkebiIiIiIiISEXsaBMRERERERGpiB1tIiIiIiIiIhVx1XEiIiIJ+/fvT+t1tbW1aGhoUDk1RERElAuy2tFeu3Ytnn32Wezbtw82mw2zZs3C+vXrMXHiRMnX/f73v8ddd92Fjz/+GC0tLVi/fj0uvfRSnVKdXY5hP/pcfji9AdhtZtSV50YIAiKiXON19AMmE7797W+n9XpbWTn279vLzjaljXU+kf6Y70gtWe1ov/LKK1i6dCnOOussBINBrFixAhdffDE++OADlJeXC77mtddew4IFC7B27Vpcfvnl2LRpE+bOnYudO3di2rRpOt+Bvg4PenDHM7vx6oG+6LY5LXVYN386xlfbspgyIqL84/e4gHAYbdeuwJhTT0vptUNHe/D6r+9Gf38/O9qUFtb5RPpjviM1ZbWj/Ze//CXu7yeeeAL19fV45513MGfOHMHX/OxnP8NXv/pV/OhHPwIA3H///XjhhRfw6KOP4j//8z81T3O2OIb9SRkfALYf6MOdz+zGIwva+G0bEZEGyusbUNOQWkebKBOs84n0x3xHajPUHG2HwwEAOOGEE0SPef3113HbbbfFbfvKV76CLVu2CB7v8/ng8/mifzudzswTmgV9Ln9Sxo/YfqAPfS4/Mz/lnHzJn0T5iPkze1jnkxzmT/Ux35HaDNPRHhkZwa233or29nbJIeBHjx7F2LFj47aNHTsWR48eFTx+7dq1uPfee1VNazY4vQHJ/UMy+4mMKF/yJ5GYXF5Ijfkze1jnkxzmT/Ux35HaDNPRXrp0Kfbs2YPOzk5Vz7t8+fK4X8CdTmfWGy/psFvNkvsrZfYTGVG+5E+iRPmwkBrzZ/awzic5zJ/qY74jtRmio71s2TJs3boV27dvx8knnyx57Iknnoje3t64bb29vTjxxBMFjy8tLUVpaalqac2WugoL5rTUYbvAkJY5LXWoq+BQFso9+ZI/iRLlw0JqzJ/Zwzqf5DB/qo/5jtRWlM2Lh8NhLFu2DJs3b8a2bdvQ1NQk+5pzzjkHf/3rX+O2vfDCCzjnnHO0SqYhVJVZsG7+dMxpqYvbPqelDuvnT+ecESIiA4ospJbKv8oTG7OdbMoy1vlE+mO+I7Vl9RftpUuXYtOmTfjDH/6AysrK6Dzrqqoq2GyjS+hfd911OOmkk7B27VoAwC233IJzzz0XDz30EC677DI8/fTTePvtt/HLX/4ya/ehl/HVNjyyoA19Lj+GvAFUWs2oq2BsPypMhw4dQn9/f8qvS3fOLJHeMnlWjTDHmzLDOp9If8x3pKasdrQfe+wxAMB5550Xt33jxo1YtGgRAKCnpwdFRcd/eJ81axY2bdqEVatWYcWKFWhpacGWLVvyPoZ2RFUZMzvRoUOHMHHSZHiG3Wmfw+/noiZkTJnO7waMMcebMsc6n0h/zHeklqx2tMPhsOwxL7/8ctK2K6+8EldeeaUGKSKiXNDf3w/PsBvn3HBfysNsj77/JnZvfgzBYFCj1BFlJpP53YBx5ngTEREVMkMshkZElI7KExtR05BaR8R59BONUkOkrsj8biIiIso9WV0MjYiIiIiIiCjfFNwv2pHh6k6nM8spISoclZWVMJlMsscpzZ8ulwsA4Dj8MUaCIymlxfXpYQDA0NFP0G9JPTRKJq/P1muzee1cTXc2r51puoeO9Yyex+VSVNepnT+JSB1K8ybA/EmkNyX50xRWMlE6j/zjH//gnDUinTkcDtjtdtnjmD+J9Mf8SWRMSvMmwPxJpDcl+bPgOtojIyM4fPhwSt8SGo3T6URDQwMOHTqkuADOFfl8b0B+35/UvSnNb0rzZz6/j3rje6meXH0v1c6fRpGrn4cW+F7Ey5X3I5W8NjIygv3792PKlCmGv69ckCvPSK7Ix/dTSf4suKHjRUVFOPnkk7OdDFXY7fa8eVgT5fO9Afl9f5ncW6r5M5/fR73xvVRPvr6XuVp/5uvnkQ6+F/Hy6f0oKirCSSedBCC/7ivb+F6qq9DeTy6GRkRERERERKQidrSJiIiIiIiIVMSOdg4qLS3FPffcg9LS1FejNbp8vjcgv+9Pz3vL5/dRb3wv1cP30lj4eRzH9yJevr4f+Xpf2cD3Ul2F+n4W3GJoRERERERERFriL9pEREREREREKmJHm4iIiIiIiEhF7GgTERERERERqYgdbSIiIiIiIiIVsaNNREREREREpCJ2tImIiIiIiIhUxI42ERERERERkYrY0SYiIiIiIiJSUcF1tMPhMJxOJ8LhcLaTQkQJmD+JjIv5k8i4mD+JjKfgOtpDQ0OoqqrC0NBQtpNCRAmYP4mMi/mTyLiYP4mMp+A62kRERERERERayrmO9qmnngqTyZT0b+nSpdlOGhERERERERFKsp2AVL311lsIhULRv/fs2YOLLroIV155ZRZTRURERERERDQq5zraY8aMift73bp1mDBhAs4999wspYiIiIiIiIjouJzraMfy+/347//+b9x2220wmUyCx/h8Pvh8vujfTqdTr+QRkQzmTyLjYv4kMi7mTyLjy7k52rG2bNmCwcFBLFq0SPSYtWvXoqqqKvqvoaFBvwQS5QjHsB8fHnNhV88APvzUBcewX5frFnL+zNZ7TqRUIedPIqNj/iQ6zqhtKlM4hwPufeUrX4HFYsGf/vQn0WOEvvFraGiAw+GA3W7XI5lEhnZ40IM7ntmNVw/0RbfNaanDuvnTMb7apum1CzV/ZvM9J1KqUPMnUS5g/iQaZeQ2Vc7+ov3JJ5/gxRdfxA033CB5XGlpKex2e9w/IhrlGPYnFU4AsP1AH+58Zrfm3wgWYv7M9ntOpFQh5k+iXMH8SWT8NlXOztHeuHEj6uvrcdlll2U7KUQ5q8/lTyqcIrYf6EOfy4+qMovOqcpvfM+pEPT09KCvT/g5z0RdXR0aGxtVPy8REeUeo7epcrKjPTIygo0bN2LhwoUoKcnJWyAyBKc3ILl/SGY/pY7vOeW7np4eTJo0GR7PsOrnttnKsG/fXna2iYjI8G2qnOylvvjii+jp6cGSJUuynRSinGa3miX3V8rsp9TxPad819fXB49nGGcvuQf2caeqdl7nkY/x5oZ70dfXx442EREZvk2Vkx3tiy++GDm8hhuRYdRVWHDR5HpMHGdHW0M1fMERWM3F2NkzgP1HnKir4BBmtdVVWDCnpQ7bBYY6zWmpS+k9dwz70efyw+kNwG4zo67cwmHnaeJ7qT77uFNxQuPEbCeDiIiyKNP6Ver1araptJCTHW0iUkdVmQWrLp+CFZu78Oi27uj2juZarJ7Xyo6GBqrKLFg3fzrufGZ3XMUwp6UO6+dPV/yeG3mVzVzD95KIiEh9mdavcq9Xq02lFXa0iQpYr9OLlZu7sKO7P257Z3c/Vm7uwkNXzcRYuzVLqctf46tteGRBG/pcfgx5A6i0mlFXofwbXrlVNh9Z0Jb1yiVX8L0kIiJSX6b1q9LXZ9qm0hI72kQFbMDtR2dCJzuis7sfA24/O9oaqSpLvxIw+iqbuYTvJRERkfoyrV9TeX0mbSotsaNNVEAS57nIrdbo9AZ1ShmlQq1VNjkv2fgrlhIREeUipfWrWFskH+pndrSJCoTQPJf/d1OH5GvsVhYRRqTGKpuclzzK6CuWEhER5SIl9atUWyQf6ueibCeAiLQnNs/lqNOLjuZawdd0NNeiprywft3MFZFVNoUoWWVTbt6TY9ivWlqNLtP3koiIiJLJ1a8V1hLJtkiFtSTn62d2tIkKgNg8lzue2Y27Lp+a1NnuaK7FmnmtnJ9tUJFVNhMrIKWrbCqZ91QoMn0viYiIKJlc/er2BSXbIm5fMOfrZ44LJSoAYvNc+lx+XPPrN/A/3zsH3sAInN4g7NYS1JRb2Mk2uExW2cyHeU9qMvKKpURERLlKqn7d1TMg+VqnJ4AvjKnI6fqZHW2iAiA1z6XP5UdoBJg0zq5jikgN6a6ymQ/zntRm1BVLiYiIcplY/aq0LZLL9TOHjhMVAM5DpVh8HoiIiCibCqEtwo42UQFQcx6qY9iPD4+5sKtnAB9+6iqohbPyBeclJ+NzTUREpD6x+rUQ2iIcOk5UINSYh8qQUPmD85KP43NNRESkPrn6Nd/bIvxFm6iAVJVZMKG+AjMbazChviLlX7IZEiq/ZPI85As+10REROpTWr/mc1uEv2gT5RDHsB99Lj+c3gDsNjPqyvX71k9JSKh8KhzzQTafl1zB55qIiEhYJu0I1q/saBPljGwPb2VIqNyS7eclV/C5JiIiSpZpO4L1K4eOE+UEIwxvZUio3GGE5yVX8LkmIiKKp0Y7gvUrO9pEOUHJ8ButFUIYhnxhhOclV/C5JiIiiqdGO4L1KzvaRDnBCMNvCiEMQ74wwvOSK/hcExERxVOjHcH6lXO0iXKCUYbf5HsYhnxhlOclV/C5JiIiOk6tdkSh16/saBPlgMjwm+0Cw3j0Hn5TVVY4BWSuMtLzkiv4XBMREY1Ssx1RyPUrO9pEBiQUTmH9/Om445ndcYXeRZPr8eOvTUWfy4+P+txph3BiGKjcJPa5RYZr3ZnwvCQO18qXzz1f7oOIiMgIcq0dkWk6tLoPdrSJDEYqnELs8Bu7zQxLcRHufLYroxBODAOVm+Q+N7nhWvnyuefLfRARERlJrrQjMk2HlvfBxdCIDEQunAIATKivwMzGGtSWW7B8c1dGoRcYBio3Kf3cqsos0edlQn1F3DfQ+fC558t9EBERGZHR2xGZpkPr+8jJX7T/+c9/4o477sCf//xnDA8Po7m5GRs3bsSZZ56Z7aRRlhllCEu6+lx+vPPJAJZd0Iy2hmr4giOwmouxs2cAGzoPos/lj96PktALcveuxjlIf5l+bmp+7nJ5Tss8yeeXiIhIO2J1uFHqXyO1h4TkXEd7YGAA7e3tOP/88/HnP/8ZY8aMwYEDB1BTU5PtpFGWGWUISyZcvgAeXtCGjTsO4tFt3dHt7c21eHhBG9y+4+EU1Ai9wDBQuSnTz02tz10uz2mdJ/n8EhERaUOqDjdK/WuU9pCYnBs6vn79ejQ0NGDjxo344he/iKamJlx88cWYMGFCtpNGWWSUISyZqrZZsHHHQezo7o/bvqO7Hxt3HESV7fi3amqEXmAYqNyU6eemxucul+d6nV7N8ySfXyIiIvXJ1fEVpdK/1epV/2baDpC7j3KZ/XJy7hftP/7xj/jKV76CK6+8Eq+88gpOOukk3Hjjjfjud78reLzP54PP54v+7XQ69Uoq6ShbQ1jUXuUwFAondbIjdnT3wx8aif6tRuiFbIeBYv5MT6afmxqfu9w0hwG39nky289vvmP+JDIu5k/Skly72lJcpFr9m0lbuq7Cgosm12PiOHtSW2T/EadsOizFRWhvrhVse7c318JSnNlv0jn3i/ZHH32Exx57DC0tLXj++efx/e9/HzfffDOefPJJwePXrl2Lqqqq6L+GhgadU0x6yMYQlsODHix7ahe+/G+vYN7PX8OXH3oFNz21C4cHPWm//h+Dw5KvcfuC0f+PhF6Y01IXd0xi6AUpapwjE8yf6cn0c6sqs+CBudPQ0Vwbt72juRYPzJ2m6HOPTHPY1TOA6598Gzf+ZieWPPEWdvUM4OEFbXDFPKtC1MiT2X5+8x3zJ5FxMX+SluTa1Q6PX5X6N9O2dFWZBXddPiWpLfJuzwDuunyKbDoGPX4sbm9Ce0J7qL25Fovbm+DwZDb6zhQOh8MZnUFnFosFZ555Jl577bXotptvvhlvvfUWXn/99aTjhb7xa2hogMPhgN1u1yXNpL0Pj7nw5X97RXT/X287FxPqK1S7nmPYj2VP7RL8tm9OSx0eWdAmmbnFXv/4wjNx/ZNvi75O6D4i3wQKhV5I5X4yPUc6mD8zk+7n5hj24//7/XuYFPMNcGlJEXYdGsT+I07865UzZM/zSZ8bK7Z0iX4L/MDXp+H8h/TJk9l6fvNduvlz586dOOOMM3DRyo04oXGiaun5rGc/Xli9GO+88w5OP/101c5LlItYf5KWlLarM6l/M21Lq3GOD4+5cMWjnVjS0ZTUHtrQeRB/WtaRUVsl54aOjxs3DlOmTInbNnnyZDzzzDOCx5eWlqK0tFSPpFEW6T2EVKtVDncdGhQdwiJ2H1VlmXcq1DhHOpg/M5Pu59bn8uPFvcfw4t5jovvlzusPjUhOcwiEwrrlyWw9v/mO+ZPIuJg/SUtK29WZ1L9GiJ5TV2HBmafUxC1AHKFGWyXnOtrt7e3Yv39/3La///3vOOWUU7KUIjKCyBDSO5/ZHVcoSA1hSWVOSOKxckNJ0l3lcEPnQfzH1afj8unjUV9ZGp1r0uv04vzTxrAzkUOMHGrO6Q2grsKC9fOno95eCpc3hEprSXQBMyXDuuWGhg/7gynnSSIiIlJHJu2QdNrVqVI67VPqPjKdOqr1feZcR/sHP/gBZs2ahTVr1uCqq67C3/72N/zyl7/EL3/5y2wnjbJsfLUNjyxoUzSEJZWwQ0LHbrrhbMm0ZLLqcxhhPLf7MF6N+bVwTksdzj1tjOQ5yTiMHmqu2mbGb274Eu7b+n7cr9IdzbX4zQ1fgqXYJHsOJSt9ppIniYiISB1qtENMAC5pHYeFs06NDqk+NuSTfZ1Scu0Iu80sex9qRB/Rsq2Sc4uhnXXWWdi8eTOeeuopTJs2Dffffz/+/d//Hddcc022k0YGUFVmwYT6CsxsrMGE+grRX7KVhh0SO/a1j/qTFpKKSGXV50RLOprweOfBuE62WNrImHIh1FypuTipkw0And39uH/r+yg1F8ueQ+wZBpKHlcnlSSIiIlKHGu0Qx7Aftz+zG8uf7YouMnb9k29j+bNduEOltoxcO6K8tET2PpS2ReRo1VbJuV+0AeDyyy/H5Zdfnu1kUI5KZT5Hn8uPvUeceHzhmXFDbD8d8uKsU2pgMpmSvmVbM68V/W4/Pupziw7VERuqMusLtYLzRITSRsaUrVBzqXB6AqLzqzu7++H0BGS/8VY63KrX6cWA2w+nNwi7rQQ1ZRaMtVvVuxkiIiKKMsLcZyWqykansL38908Fp0u6vEHZNEyor1Bl6LdW0/1ysqNNlIlU5nMM+wOiQ2zvvmIq7rliCkwwRYeaWM1FuOeP78ctMiU2VEdoqEqmc78p+7IRai5VTq/0/Gq5/RFyw8p6+t1YvrkrKe+smdeKxtrytNJORERE4tRoh+jVlgkDeG73EbzaHd9JPve0MXD5lKUh0yHuWk73Y0ebCk4q8zmqbRbcuTk5hFFndz/u+9P7WDuvFQ2fdxjEQgxEhrgIhRhIXK3xw2MuxWkjY1JjvpDW7Fbpol9uP3B8WJlYSI1186cndbKB0byzYnMXHrpqJn/ZJiIiUpka7RA92jLRIe7dwu3m+78+TTYNcm0RJeF2pYanKwkxJoUdbTK8VIZziA1TTdz+9L98Ccs27USfK/4X5MT5HG5/SHKIrdsfiv6txjAbvcOUkfqUfoZqDFOSG5Ytdo2acgs6mmvRKfBsdzTXoqZcPo1yz7tDZnj6gNuPsXaroVdnJyIiypZ060c12pJ6tEf7XH6888kAll3QHI1hbTUXY2fPADZ0HoQ/NIKLJtdj4jh70v79R5yoq7AobnuLtZe0HiLPjjYZWirDOcSGqa6e14p7//Q+tu37NG770/9yDr79y9ejnW2h+RypDLFVY5iNHuEUSFtKPkM1hinJDcuWu8aaea1YsbkrrrMdef1Yu1X29XLPu9MjF+IuaPjV2YmIiLIhk/pRjbZkVZkFD8ydJthOeGDuNFXaoy5fAA8vaMPGHQfj1idqb67Fwwva4AsEcdflU7B8c1fc/khbparMgo/63JLX8PgDku0lrYfIs6NNhpXKcI5ep1d0mOrKzV2Y2VgT19Hu7O7Hj/+4B5u/Pwv9br/oUv52m8wQ25j9ag2zYUik3Cf1GaoxTEnqeV+xuQsPfnOG7DUaa8vx0FUzj3/Day1BTbkl+iuz3OuVhOWQ3i+9mmimw7WIiIhykRrthEzbko5hP+7b+gFmNtZgcXtTdO7zrkODuH/rB/jXK2dkXEdX2yx48Pn9SW2ZyN/r5rVixZY9gm2dVVv2KGqLVElMAV2xuQt3Xz5V8vWZDpFnR5sMK5XhHANuv+Qw1cXtTYLb3f4QZjbWiKbBWlKE9uZawXO3N9fCWnI8Qp6aw2wS525T7hH7DNUYpiT3vDs8AUXXGGu3Cs6TVpJGuee9ymaWHJ5uLSky/OrsREREelNrOHMmbck+lx8v7j0Wt7hv4v5M62h/aES0LbPj8zZ6pm0RuSmg5mKTpkPkcy6ONhWOVIZzSA3xLrMU44RyCx5feCZ+fs3p2LDoLCy7oBlllmI4vQHs6hnAh5+6BGMCOjwBLG5vQntCzOz25losbm/C4HAAHx5zYVfPAPrdfqyZ15oUz4/DvimWOquBSk9pkDtHZH+v04t9R5z428HPsO+oE71Or+I0RoamiT3vkeHpifHmI8O1HDJDy42wOjsREZHejBC9xOkNoK5itO38p5va8dR3v4StN3Xg8YVnoq7CokoaXL7MIqAoaYvInaPf7Zd8faZtd/6iTYaVylBssVWSyyzFeHhBG/7tf/fj1ZhvtCLzP8pLi3HZwzsACM99qSg1Y8Gv3sSSjiYsSRg6c/NTu/Dr687E3J+/Fj3+osn1WPuNVngDIxz2TYLUWQ1UuuiWO0eVzSw5Z0lpGuWGpkkNTw+EwhndAxERUT4yQvSSaptZNLztb274EizFpoyvITsFTWFbR3K6nsyX+hWlJZpO2eQv2mRYkeEgQhKHc0RWUU60pKMJG3ccjOtkA6NDUp7YcRChkePbInNfYn/Zrquw4MxTavDotm5c/+TbuPE3O3H9k2/j0W3dOL2xGq99FH/eF/Yew/Jnu1BXYcHMxhpMqK9gJ5vipPJcixF73oHRSrDKZpa8Rqm5WHKOt9VcJHn+ipjKr6rMggn1FaLP+1i7FZPG2fHFphMwaZw9OlRdjfeBiIgo3xihfiw1Fyd1soHRdsL9W99Hqbk442tUWEsk2xp2mbZM7Psg1haRay9FoqzItWXSxY42GZbUcJAH508HgOiwbZcviLXzWvHlSWPijj3nC8Lzq4HRwuIzd/xw8cicD7k0zG6pw6L2JmzoPBi3vcxSjOkN1Tji8EoOSafCJTfMSUnhPtZulRyWPb7aJj2USib01pAviCUdTZidcP7ZzbVY0tEEt8xwLyWqyixYP3861n6jNW5ax9pvtOJBTrUgIqICpUY7IVNy7QS5yCJKuH1BXN/xBayZNy2uHbBm3jRc3/EF+AIhxe+DY9gf7RPEtr3l2ktC69SoiUPHydDEhnO4/SEse2pXUtiDB+a14vavBuHwjA5TdfulOwTDMXGwIxLnnQilIRQOY+5/7Ih7fWSYemKYAoYrokRqDFOSGpYtd41DAx7Jcw95AjDBhEtax2FRzJSJXqcXJpjgUml+WBjAc7uP4NXu+Hx87mljxF9ERESU57IdgSaV8LbpGvIGEEYYz3UdiVs4dXZzLRZ3NGHIG0DTmArZ90EuFJpce0lL7GiT4SWumigV9mDV5i48sqANE08cPf7DYy7Jc5eWJA/qEJr7kpiGD4+5kjrpkWHqid8AMlwRCVFjZXmxVcPlriE376m2ohT//tfkkBrA6PoGa+a2pp7YBNF83M3wXkRERImyGYFGrp0gt18JsfBer3b3YwSItjWk3gelodDk2kta4dBxyjlKwh5ESM1zaW+uxa5Dg3HblM59ETpvW0O16DCbxHQRZZPcnKWATMgNf+ziBmlKJR8TERGRfpTObc6EXHgvJW0No7cldP1F2+v1Yvfu3Th27BhGRuLfvK997Wt6JoVyWCphDyLzXBK/7ZrdUoel5zdjyRNvRbelMvclct47n9kdjb3nC0oXCAxXRKlwDPvR5/LD6Q3AbjOjrly9b7Yjc5ZWbO6KG64VmbPU75aumGLnaKebTiOELyEiIqJkcu2EyK/DmbRV5MJ7KVkPxuhtCd062n/5y19w3XXXoa8v+VsHk8mEUCh5riyRkFTDHoRCI7hk2olYNOvU6FzTfpcPJ1dZ8adlHWnPfUmcP2OVWYGR4YpIKbn5RmpQI/RWJuk0QvgSIiIiEiY3tznTtoo64U6N3ZbQraN900034corr8Tdd9+NsWPH6nVZykORYdvbBYaKJA797nV6cadAGCNg9Fu5h66aiQn1FWmnJXbeiGPYrzhdRGKUzjdSg9icJSV5LNN0ppKPiYiISH9i7QQ12ipqtAMiIcI6Rdr5FSrMJc+EbnO0e3t7cdttt7GTTRlLJezBgNsv2MkusxRjZmMNPnP7FYXhEgsbkG66iMToOd+o1+nFviNO/O3gZ9h31IlepxeAsmc503QyvxAREeWmVNoAYm1oNdoBbh3CkWZCt27+N7/5Tbz88suYMGGCXpekPKY07IFQ+IFUw3ClMjQm2+EYKPfpNd+op9+N5QmjPSJzrxpry2WfZTXSyfxCRESUe5S2AeTa0Jm2A4a8+oQjTZduHe1HH30UV155JV599VW0trbCbI4fM3/zzTfrlRTKE0rCHgiFH0glDFc6Q2OyGY6Bcp8e8416nd6kTjYAdHb3Y8XmLjx01UyMtVsln2W10sn8QkRElFuUtAGUtqEzaQeIhQgD1AtHmgndOtpPPfUU/vd//xdWqxUvv/wyTCZTdJ/JZGJHO08dHvTA4QnA6QmgymaG3WbG+Gqb4CqFADJeZTnxvHabGV+eNAZ/3fdp9Ji2huq4X7JjRYa7RK6rZGhMYhq1XC2a8l9dhQUXTa7HxHF2tDVUwxccgdVcjJ09A9h/xJm0BkF0kRJbCWrKLIriRA64/djVM4hlFzQnXWND50EMuP0Ya7dKPsucY01ERJQ9cu1NNdqjYudQ0lZJpw2dKqUhwrLVNteto71y5Urce++9uPPOO1FUxPDdheCTfjdWJPxq9uVJY3D3FVOxasse0XBbw/7RFehTXWVZbHjK/XOnAXg/2tlOJQxXqsNj9VgtmvJbVZkFqy6fghWbu+K+EOporsXqea3RikFu6LcUly8oOH2ivbkWDy9og8sXlH2Wq8oseGDuNMHQHw/MncYvl4iIiDQiV0er0R6VO4dcW+WjPrfk+dWYCqckRFg22+a69Xj9fj++9a1vsZNdIA4PepI62QAweXwVVmzuSvqG69UDfXhk2wEs6WiKbosMLZFapCxCanjKXVv24P65rfjLLbPxu/9zDprqpDsh5aXHv39KZXis3BAZJfdBdHjQg5Uiw7pXbu7C4UGP7NDvyKJmYmrLLYLTJ3Z092PjjoOoLbfIPsuOYT/u2/oBZjbW4PGFZ+Ln15yOxxeeiZmNNbh/6wd83omIiDQg197sdXozbo/KXUOurdLr9KKiVPr33HKZ/UrIXaPMUpLVtrluvd6FCxfit7/9bcbn+fGPfwyTyRT3b9KkSSqkkNTk8AQEh3K0NVRLDvFoa6iO26Z0lWW54SkefwiTxtnxxaYTUGYuRnvC6oQR7c21sBQfzxaR4bFCEofH6rlaNOUvhycgGKYCGK3AHJ6A6Gr6kWMG3NLPWiAUlh1qJfcs97n8eHHvMTy6rRvXP/k2bvzNTlz/5Nt4dFs3Xth7jM87ERGRBuTamwPuzNujcteQa6sMuP2wFBcpbm+nS+4a5mJTVtvmug0dD4VCePDBB/H8889j+vTpSYuh/du//Zvic02dOhUvvvhi9O+SkuzGSKNRsfMfAiLDs+WGbZeXluDn15weN19UydASpzeAMksxlnQ0Cc45jT3HoMePxe2jv5zHdjbam2uxuL0JDo8fwOiv3pHQA3c+sztuLqpQ6AG9VoumzOkxVyfd+dNOj5LnyCR5jNBq+7Hcfun9Do/0/iFvAGHJI/i8ExERaUGuvSnXBoitn8XaQ0ratFLtbqc3CF8whOs7mlAE4NWY9vbs5los7ohvb6dLrk3fL/PDg9ZtFd16qF1dXWhrawMA7NmzJ25f7MJoSpSUlODEE09ULW2UucT5D3+6qV3wuNIS6W+v3L4gbvzNTgDH54vabfKrF1fZzJJzTmPPUVFqxoJfvYklHU1YEhMKYNehQdz81C78aVlH3LlNAC5pHYeFs06NHntsyJeUBj1Wi6bM6TFXJ5P503LPe6XVLNPNFl5tP36/9DXkXq/kWebzTpSbenp60Ncn/AtQJurq6tDY2Kj6eYkKjWwdblNWh0u1h5S0aaXa3VW2EpiLi/CZOyAaeqtChXaCXJv+2e/Pkr0PLenW0X7ppZdUO9eBAwcwfvx4WK1WnHPOOVi7di0L7ywSmsdxzOlDR3Nt0rCSXYcGBbcDo5lz16HB6N87uvthAvDQVTNl01BeWiI65zTxHHUVFpx5So3gyuOJw8Edw37cLjC3I3JsbHgvrsJsfOmEa0uV0tBZYqpsZtE80tFciyqbGcVFJsljasql76HCWiL5ervNrOhZ5vNOlF96enowadJkeDzDqp/bZivDvn172V4jypBsHW6Vr8Pl2kM/uXKG5Dns1hI8INHufvCbM1BcZMKvOz8SnKrW0VyrqH0vR65NX1Oe3ba55h3tUCiE999/Hy0tLbDZ4n8t8ng8OHDgAKZNm6Z4kbSzzz4bTzzxBCZOnIgjR47g3nvvxezZs7Fnzx5UVlYmHe/z+eDzHf/10el0ZnZDlDTMpMRkwjufDMQdc8czu/GbG76E+7e+H1cQ7D3swOp5rbhry564hz4yxOPmp3bFnWdnzyC8/hA+POaSHObr8gYl56y6vEGMtY/+ncpw8FRCE6RyXhqld/7UI9SEkvnTUh3t8dU2rJ7XilWbu5KGWj0wrzX6q/uaea1YKXDM6nmtskPU3b4glnQ0wQTh4Vy+QEjRs8znPb+x/iw8fX198HiGcfaSe2Afd6pq53Ue+RhvbrgXfX197GirhPmzcLl9QSxqb0IYycOlF7U3waugDv/wmEuyPeT2BbFu/vSkzvjsz8/hCYQk2zqezyMISYUSjW2bp0uu7T3Wbs1qW0Xzjvb//b//F48++ijefPPNpH1msxlLlizBrbfeimuvvVbR+S655JLo/0+fPh1nn302TjnlFPzud7/D9ddfn3T82rVrce+996Z/AxRHaJjJ7JY6PLygDTc/tSsamqvP5cc1v34D6+dPx8rLpmDIG0Cl1Yyqz+NoP7KgDX0uP4a8AVhKivDcnqNxrweAMksxHl7QhlVb4jsTQsN8U50fnZiGSqsZdRXJHfhUz6t0mDmN0jt/6jGPXm5ulNx+YHRxj0tbx8cNtTo25ItbOMQscoxZweIiQ94ATDCJDuca8gZgNRfLPstK8xHlJtafhcs+7lSc0Dgx28kgCcyfhcvhCeDmp3aJDpfedMPZ+MKYCsn6Wa49NOwLwFxkwiXTTsSi2HaA04tgaAQOBevJmEyQHF7u9qkzP1quLZLNtormHe3HH38cP/zhD1FcXJx88ZIS3H777Xj00UcVd7QTVVdX47TTTkN3d/KQAQBYvnw5brvttujfTqcTDQ0NaV2r0IkNM3n1QB9GwmEs6WiKy0h9Lj+uf/Jt/PW2c/HFpvgVAavKjj/gHx5zCQ75WNLRJDgcXGiYb7lFfnn/RLFpEJNqeC+lw8xplN75U4959PLzo6X3K3mOAGT0rFXbLHjw+f2C30a3N9di9dxpis+vJB9RbmL9SWRczJ+Fq9xSgmF/SLDtDBxv80rVz3LtoSqbBXcKTIMDRod93335VMnXV1rNKCkyCbY1In+vmdsqeY5UyLVFstVW0byjvX//fnzpS18S3X/WWWdh7969aZ/f5XLhww8/xHe+8x3B/aWlpSgtLU37/HSc1LDbHd39WNLelLRdbP5D7PDzE8otuGhyPSaOs8cNLamrsGBD50HB620/0IdjQ77oOaptZsxuro375Tsisrx/OuTmXVdYS6LD2m2WYsxoqMY7nwzE/TIfSa8aw5Lzjd75U4959OWWYsm5U+WW5C8dYykNE/fOJwOiw7HknjV/aERyOJc3KB/eK3J+PVZwp+xg/UlkXMyf+U+sfjUXm9DeXCv6ZbmSNq9ce8jtD0m2E8zFJtn21LEhn2wo0XyneUfb7XZLzhsZGhrC8LDyRTd++MMf4oorrsApp5yCw4cP45577kFxcTEWLFigRnJJgtwwk0Ri8x8Sh5/XVVjw9L+cg3v+uCfu27nZzclD0mP1fDaM6598G8DoMPPHF54JwIRXu5Pnfve7/Wgak1LyAUjP/Xhg7jSs3NyFF/cei7ueWJoZ7ij79JhHP+jx467LpyatT9DRXIu7r5gKh8ePBolwFkqGt2c6HGvYH5R8vUthaBA9VnAnIiIqNFL168CwfEgruTavXHvon4MeyXbCwLBftj31UZ9bMg1un/xUulyneUe7paUFr732GqZPny64v7OzEy0tLYrP949//AMLFixAf38/xowZg46ODrzxxhsYMyaNXhSlRG6YSeMJZfjrbedKzn8QGn7+7S824p4/7kn61uvV7j6MIHlIupBhfwjXP/k2Vl02Gbdc2IJjQ76UlveXIjS3o8JaktTJBo4XeEJpZrgjY9B6rk6ZxYxv/fJ1rJ8/HXdcMgkubwgV1mIcc/pw9a/ewG//5RzJ1ysZ3l5cBMnhWKvnTpM8h9jQ8cjfD3xd+vWVVrMuK7gTEREVGrn69d6vTcW1j/8t7ZBWEVLtIW8ghJ+++HfJdoJce4phb3XoaF999dVYtWoVZs2aldTZfu+993D33Xfj9ttvV3y+p59+Wu0kkkJyw0zqK0tlG9ZCw2LbGqpFO9JiQ9ITQ4EBo53tFZv34PGFZ0ZjcQPKwh3JSZzb8eExV1InWyrNDHdkLFrO1amrsGDKOHt0tEUsJc+BkuHtR5xeyeFY3qD0cCx/aETy9YFQWDYNeqzgTkREVGjk6tdAKIzTG4Xbzqm2ecXaQz6ZdoLv82HfUu0phr3VoaP9gx/8AH/+859xxhln4MILL8SkSZMAAPv27cOLL76I9vZ2/OAHP9A6GaQCNYbdCg2L9cl0ChLNbqnDwlmnJoUCEzpfR3Mt1igIdyQncZ5MKBxGmaVYcEh7Yhoi7w8A2TBllPsyzSdKXr+/1yV5DqdndDiW2Pwul8xwrWF/EOvnT8fLf/8U9ZWl0blZvU4vzj9tjKIhYZHh5ZzDTUREhSaTuk92RXB/EGvntaKzuw/1duvxOtrhQUdzXbTNm1EaZFYVl9sPMOwtoENH22w243//93/x05/+FJs2bcL27dsRDodx2mmnYfXq1bj11lthNuf/0IF8kemwW6FhJKUl0uGIastL8fjCM6NDY8ZVWfHN/3xdtJN7al05nvrul1BhLYbbF4oLiZQOpSHNYn2hrhxbbpwVfX+G/SEse2oX57IWiEzzifxwLPmVzaXmdykZzhUG8NzuI3FrHsxpqcO5p435/Bry5+AcbiIiKjSZ1n1y9avdZkZxcRGe6zoaV0fPbqnDuRPrVUmD3LBupcO+Cz0MaGY9EIXMZjNuv/12vPvuu3C73RgeHsa7776L22+/HRZLYbzR+aSqzIIJ9RWY2ViDCfUVKWWWyDCSWLsODaK9uVbw+I7mWry4rxfXP/k2bvzNTlz/5Nt4bs9RnN5YLXh8e3Mtnus6ggW/egNXPLID3/7lG7j9md1wDPsVpzGWVEizJ3YcxJIO4ZXWx1VZo+8PIByKKTLXJt20kbFlkk/kXm+3mdEhkWcqrSWS87sqrCVJ+TAispr+Hc/sjqvAY1/vGPajwloimQabpVgyDXzuiYgo38jNr1ZS99k+j14ipKO5FqUlRYJ19KufX6PX6c04DVUy7Ywqm/IfSTNtD+UyzX/RTuT3+3Hs2DGMjMQPF25sbNQ7KZQFQsNINnQexIZFZ6HIZEr61Xhx+6no+qcj+ou21VyMrn8OYklHE0wJx0dWW0wcUi42X1RsSE3sdpulWHSeTGd3P248rzlujozQcBjOZSW1+QIhyZXN3f6g5DPn9gUlh3O5fdKv73P5YTIBi9qbEEbyqqeL2psw5A3wuSciooKitM0nNax7yBvA9R1fwKWt4zA2Zmj4UYcH46pscHql6+gBd+btzvHVNqye14qVm7uS2hmr57VyVJpCunW0//73v+P666/Ha6+9Frc9HA7DZDIhFBIeBkz5R2wYyaMJ24IjIzg86MXfDn6Gn75wIPr69uZaTD+pGqsum4ySoiIMeQOwlBThuT1HRYdyJ4bWEhpSc9Hketx1+RSs3LInuv3n15wueS9Wc7HsSutKwjURpWLQE8B3/+tt0ZXNf37NGZKvd3kDsJmLcUnrOCycdWp0WsaxIR8A+WdyyBtAGMDNT+0SXfX0icVflD0HERFRPlHS5pMb1j3kDSKMMJ7rOhLXyZ3dXIvFHU1wyVzDqTBEp5xTasvx4DdnwOEJRNu5VTYzO9kp0K2jvXjxYpSUlGDr1q0YN24cTCb5YOqUv8RWKYzddvBTF37d+ZFkaIGmMaNDsz885pIMARY7l0RsWM/EcXYs39wVdz25+eNVNnN0eLgYhjcgtVWUlqDP5Rdc2RxQMIfbZhaczgCMVvb3KwjvBYyu9C+W7+TSwOeeiIjyjVybr7xUemrXIwvaUFtuEQyt9Wp3P0YA2Tpazfp3fLWNHesM6DJHGwDeffdd/OIXv8All1yCmTNnYsaMGXH/iBIFQmHZEEQRQnO/IxJDCIgN62lrqE66ntT8caWhCVJJG5ESluIi0eeyvbkWZZZiyf3e4IjksDJ/aET2mZV7rmvK+dwTEVFhkasbLcVFssO65du/I5J1fLmlmPWvQej2i/aUKVPQ1yf8YJEwPcPipHItJXOb00lvr9OLAbcfTm8QdlsJ3DIhiFy+YFy4rLXfaMWP//g+XoiJby0UWis4EhY8n1CYsQ2dB/HwgjYA8fNQUwlNwPAGxqNH3tLyGoMeP67vaEIRRr/hjogMK+tz+XBDxxdwmdj8Lo903pKbwx25D6kQYGPtVkXnYPgvIiLKF3JtvmNDXsnXR6ZmSXF4gvjeuRME1zb6P3O+AIfHLxuiM3ou1sGa0rSj7XQ6o/+/fv163H777VizZg1aW1uTQnrZ7XYtk5Jz9AyLk8q1xI59YO403Lf1A7yY0MlVmt6efnfSsO3f3HC25Gu8gRC+8djxOf9zWuqwZl4rll86GU5PQDS01uMLzxQ8n9Aw8WF/KDoP9a7LpsAbCKUVmqDQwxsYiR55S+trVFrN+MwdwCWt47AoZn50r9MLE0wot5Zg0BMQnd9lt8kPK1PyzMqFAJM7B8N/ERFRvpGq+8RC00YoGdZtt5XAVlKKS6adiEWx66w4vWioKYMpHJatnwHWwXrQtKNdXV0dNxc7HA7jy1/+ctwxXAwtmVxogEcWtKnWQUvlWlLHrtjchZmNNXEdbaXp7XV6kzrZAPD6R/3oaK6N6yhEdDTX4rWP4rdH0vHIgjZ84fO5245hf9Jc1MhwcKFh4kLXG/aHsPvQIL7b0ZTR+y42L530o0fe0uMa5aUlgusXAKN5Y/386Xi886Dg/K4wgHXzp0vmLZulGID0Mxu9T5EQYJH7FDuHnuUcERGRnsTqvkhoTLH6t8JaAmtJEea01MX9Ih4xp6UOFZ/P8xZrA6ybP122fgbAOlgHmna0X3rpJS1Pn7f0DAclda23PxnA4HAgLtTVjIZqvPPJQNI3cp3d/VjcnhxTWkl6B9x+wcIiMmw7MYzX7JY6LJx1alIYrzJLMaY3VOOIw4uP+tyw28woMZnwzicDgucF4oeD7z/ixNp5rej8sD9uqE2/y4f2CbXoc/mj5+XQmtykRtgNI1zD5Q2Kzt/q7O7HkE96v8sXlA3NBdgk05hpOcWwd0RElK/E6k+3LygZusvtC2KsveJ4Zzmh/bt+/nQ4vAHpNoBM+K8+12gcbdbB2tO0o33uuedG/7+npwcNDQ1Jq42Hw2EcOnRIy2TkHD3DQYldq8xSjIcXtGHVlq64OaDtzbV4eEGbYBgtoTnOStIrFoYgMmz72e/Pgrm4KDr8JhQOY+5/7Ii7fiS9G3ccjFsFeXZLXVJ6Y4eDr7x0MvzBkeiwHrc/lDTUZnZLHRpOKMOSJ96KnoNDa3KTXN5y++TDbmR6DSWhPTK9htwcbMdwQDY0l1waMy2nGPaOiIjykVT96fYFFIXuCoVGBIeGB0MjcAzLtQGUheiUO4Yyp9tiaE1NTThy5Ajq6+vjtn/22Wdoamri0PEYeoaDErvWko4mbNyRPPQ08veSjqaksD5iobDk0isVhiDSsY0NofXhMVdSJ18sva8e6MNIOJyU3khYonkzT8KU8VUAxIfCCp2DQ2tyk1zeqrJZMh5KpUZoj0yvoSS8l2RoLpt8GjMtpxj2joiI8o3ctKjVc6dh3V/2i4buWjO3Fb1OL+4UmFIJjA4NX3nZFMk02G2Z16+sg9WhW0c7Mhc7kcvlgtVq1SsZOSESGkBsboaay/KLXautoVq0Eb6jux9LEoaJdzTXYtehwbTSW1NukZyvUlMe//q6CgsumlyPiePsaGuohi84gsYTygAAu3oGkzrhO7r78YMLT4seazUXY2fPAPYfcSoK+yV2zxxak3vk8pY/JB32SsnnLXcNJaE9Mr1GTXlyHol97ss/D/8lVIm3N9eiVEEaMy2n9CzniIiI9CA3LcrtD2FXzyCWXdCcVD9v6DwIf2gEHndIcmh4uaVYst1caS1RVL+yDtae5h3t2267DQBgMplw1113oaysLLovFArhzTffxMyZM7VORk7RMxyU2LXkxA4Tj6w6fv/WD+KOUZresXYr1sxrxYrNXXGFRkdzLdbMa8VYe/wXMVVlFtx1+RQs39wV92WA1LB2hyeA6598O+ncsWmTG8oqNDSeQ2tyi1zeOuqUD7uR6TWUhPbI9Bpj7VasunwKViTkkY7mWqye14ohrz+6pkLiHO3F7U3od/tl0zihviKjcoph74iIKN/ItSVdvqDgVMdIG9bjD8IblB7Y3e/yYfW8VqwUaDevnteKk2vKFNWvrIO1p3lHe9eu0QWrwuEwurq6YLEc/+AsFgtmzJiBH/7wh1onI+foGQ5K6FojYelM/oW6cmy5cVZcuv71yhlpp7exthwPXTXzeBxtawlqyi1JnWxgdFjOyi17UhrWnqizux+rtuyJG6YrN5RVaGg8h9bkHq3DbhjhGr1OL1YKDDvr7O7Hys1deGDuNNz81Juic7Sf/f4sRWnMtJxi2DsiIsoncm3J2nILfvri30XbsKvnToMlILzmUYTNUoJTasuxbv50DHmD0fqz0lqCk2tGf9BUUr+yDtae5h3tyMrjixcvxs9+9jPGy06BnuGgEq/lGPZLDikZV2VNSlum6R1rtwp2rBOlOsS7XWRYe+IwXamhrELn4NCa3CX2rKo5nDmb1xhw+wWHlAGjnW1vcARnnlIj+IVUZOi50jRmmu8Z9o6IiPKFkilqYsPCd3xePyudUhnpVItRUr+yDtaWbnO0N27cqNelSAViwzovmlyPH39tqmCoq16n9/gv0rYS1JQJ/yINiIc9UCKVId5iocAiYofpit3z7JY6LD2/GUueeCu6jUNr8pMew5mVXiOzPCK96rjTE8T6+dPx8t8/jQtl1+v04vzTxmCs3cohZURERCmqKrNI1q89Ax7J1zs9QUw60a5oSmUm7QTSh24d7W984xuC200mE6xWK5qbm3H11Vdj4sSJeiWJZCQOKbHbzLAUF+HOZ7viflG+vPVE/Oirk7AiYahqpEBorC2PO2+moY3khuXEDmsvKTLhkodfFR2umzhMV2wYDQD8aVkHh9YUAD2GUsldI+M8YpNbdbwEgdAIntt9OC583+zmWrRPqFWURiIiIkoWBpJCxc5pqcO5p41RVD8D8lMqM20nkD6E4zFpwG63Y9u2bdi5cydMJhNMJhN27dqFbdu2IRgM4re//S1mzJiBHTt26JUkUqCqzIIJ9RWY2ViD2nILlm/uShq2Pe/0k5M62cDoENUVm7vQG7PAlFzYA8ew9CJMwPFhOUIiw9pnNtZgQn0FqsvMOPOUGtFjhYbpxt7zhPqK6LCaxG2Uv/T4vMWuoUYesVvN6GiuFdzX0VyLitKS0bwsEF4kNs/yuSciIlJOLFRspA6vKC2RrJ8rS493xMfarZg0zo4vNp2ASePscb9kZ9pOIH3o9ov2iSeeiKuvvhqPPvooiopG+/cjIyO45ZZbUFlZiaeffhrf+973cMcdd6Czs1OvZFEKxOZG19tLJUMVfOb24/CgB3abGSUmE975ZEDw/EpDG6UyvJcrG1OukQsNEptHxIaNeQMhLGpvQhjJq4ovam+CyxeUDB0y4PYrWi+BiIgo32QyJFuuDnf5gri+4wu4tHUcxtqt0TbzUYcH46pscPmkp34puQZDzxqHbh3txx9/HDt27Ih2sgGgqKgIN910E2bNmoU1a9Zg2bJlmD17tl5JohSJzY0e9oUkQxU4PQF865dvABid7ywWggtQHi4rlWGtHAJLuURuDYJIHpEaNjbkDeDmp3aJrir+xOKzZNIgX9ETERHlm0yHZMvW4Z4Awgjjua4jcfOvZzfXYnFHE4YU1L9K2wmUfbp1tIPBIPbt24fTTjstbvu+ffsQCo12uKxWK0wmk15JohRVlAo/LrUVFjy87YBoqIJ7vzY1uu3VA30YCYdFQ3ClEi4rlZUSuaoi5QqxfBZRXloiO2zs/q9Pw7A/JBrmTi6fyc0hIyIiyjdydWtsSFgxsuG9Kkrx739NbjO/2t2PEQAPfH2abDrluRrX+gAAqkpJREFUrsHQs8ah2xzt73znO7j++uvx05/+FJ2dnejs7MRPf/pTXH/99bjuuusAAK+88gqmTp0qc6bj1q1bB5PJhFtvvVWjVFOv04t9R5z428HPUFJkwmyBeSWeQEgyVEEwFMbPrzkdGxadhWUXNGNXzyDaGqrjjiuzFGPtN1oxEg5jV88APvzUFZ1j4hj248NjrqTtROmSe6bUeObSvYaluAjtIvO32ptrYSkukh025g+NSK5jUG4plryGVSBmPBERUT5TMiRbjtw6QgGZ8F7+kHQMbSXXYOhZ49DtZ4uf/vSnGDt2LB588EH09vYCAMaOHYsf/OAHuOOOOwAAF198Mb761a8qOt9bb72FX/ziF5g+fbpmaS50Pf1uLI9Z5KzMUozHF54JwBS3yMOnQz7J83zcP4wbf7MTwPHh5MUxIxfKLMXYsOgs/Me2bix/tiu6/aLJ9bjr8ilYuWUPV1Uk1cgNC1NjJc9MruH0+rH481jwifOrF7c3weHxIxSWvr7bF5Rcm6DP5ZW8htPDYWdERFRY1BiSXVVmwQNzpwmG5npg7jT0OqXbzE6P/NBxrj+UO3TraBcXF2PlypVYuXIlnE4ngNGVyGM1NjYqOpfL5cI111yDX/3qV3jggQdUTyuN/pK9PGEl8WF/CNc/+TZWXTYZt1zYgmNDPpSWFMkOYTEXH+9UR863eu40/PW2czHkDaCmzIJVW/YkrdA4cZw9KQ1AakN4iGLJDQv7yZUzMh42luk17v/6NHzrl2+Kzq/+07IOjISle9rlpSWSaxMM+0O4+SnpaxARERUSJVO35DiG/bhv6weY2ViDxQn16/1bP8APvyIdxrhS4dQtrj+UG7IyES+xg52qpUuX4rLLLsOFF14o29H2+Xzw+Y5/exTp5JO0AbdfcGjLsD+EFZv34PGFZ0Z/pV52QTM6mmvjvrmLaG+uxa5Dg3HbdnT3IxAKo2VsJQDgw2OupE42ALQ1VIvOMeWqivlB7/wpNyxswJ35Sp6ZXsMfGsGZp9QIPvuRIWGDwwG0N9cK5tHI8HJAfG2CugqL7DWIWH8SGRfzp/oiU7fk6lYpfS4/Xtx7DC/uPSa4f/mlkyWvUargGhFcf8j4dOto9/b24oc//CH++te/4tixYwgn/CITWRBNztNPP42dO3firbfeUnT82rVrce+996ac3nyQSXgCuVWHfcHjc0j2H3FizbxWrNqyJ24IS2QY6s1P7Up6vTsmfIHYUJ3Yawjhqoq5T+/8KTcsTO65V/LMZXoNty+I9fOn4+W/f4r6ytJo6I9epxfnnzYGVWUWfNzvlh1eDpSLXoPDzkiJQq4/iYyO+VN9gx75qVuRulWsjS3XBvjM7ZO8Rr/bh6YxFZLXoNyhW0d70aJF6OnpwV133YVx48altbr4oUOHcMstt+CFF16A1aosxuvy5ctx2223Rf92Op1oaGhI+dq5JtN5pnar9KPRcIINP7/mdJSWFOHYkA+W4qK4ISxWczG2dh0RDeMVuyKi2NDzUpkFmbiqYu7TO3/KTXOQe+6VPHNqXCMQGsFzuw/j1YTQH+0TRhcwqyg1Y8GvMhv6zWFnJKdQ60+iXMD8qT6ldatUG1uuDVBeasZ1G94SvcYz358lew2uUZQ7dOtod3Z24tVXX8XMmTPTPsc777yDY8eO4fTTT49uC4VC2L59Ox599FH4fD4UFxfHvaa0tBSlpaVpXzMXqRGeoKbcIjkc/Pn3e+OGnc5pqcMjC9owoX70W7hepxfv9gwIdrI7mmtREdPZiKyeuD0hvbsODYqmgcNb84Pe+VPsWQNGn6macun9Sp65CmuJ6HPb0VwLu80seQ2bpRg/+p/3BEN/rNjchYeumqna0G8OOyMphVh/EuUK5k/1Kalb5drY6+ZPl2wDVFpLcHqj8NTIjuZaVNnMqrTjyRh0i+HS0NCQNFw8VV/+8pfR1dWFd999N/rvzDPPxDXXXIN33303qZNdqNQITzDWbsWaea1J4bxmN9fi+o4mbOg8KHlety+IRe1NSSGE2ptrsai9KW7oeGQYa2KogsiQ9MTtHN5K6RJ71iLP1Fi7VXJ/5JmTCt0l9+z7AiHJawx5A6KhPzq7+zHg9sveB/MGERFRapTUrX0uP975ZADLLmjG4wvPjAtf+/YnAxjyBiTbAIHgCFbPa0VHwv6O5lqsnteK8dU2VdrxZAy6/aL97//+77jzzjvxi1/8Aqeeempa56isrMS0afGB3MvLy1FbW5u0vZCpEZ4AAMzFRbi0dfxo5+DzoS29Ti9MEB72H3tehyeAm5/aJTo0ZtMNZ8e9VmoYK4e3kprkhkzL7ZcbzqXk2Z85pkL0GocOeiTTH5njzaHfRERE6pKrW12+AB5e0IaNOw7G/SodCV/r8ATl2wCNNXjwmzPg8ASi16iymaNDwtVqx1P26dbR/ta3voXh4WFMmDABZWVlMJvj5zB89tlneiUl78nND1Eyz9Qx7MftAsNWgNHCZElHU9Kwl8R518P+kOiq4UJpEBvGyuGtpDa5Z0psv5LhXEqffbFryM3hjt3PvEFERKQuqbq12mbBg8/vTxp5Fvn7rsunKGoDjK+2ic61VqMdT8ag6y/aWnj55Zc1OW8uk5uHqmT+ZuzQmLaG6ujKxzt7BrCh8yCWfL5ioth51UgDkdHI5Ys+lz/jZ19qfYSO5lrUlDPvEBERaUVqtW9/aER0eteO7n5YS4oybv+yDZ0/dOtoL1y4UK9LFTw1QvfIDY0Jjhyfby90XoYPonwkly/cvgAm1Fdk9OxH1kdYsbkrrrPd0VyLNfNaMdauLOICERERpUZuepgrZo0hIU5PIOP2L9vQ+UO3jjYAfPjhh9i4cSM+/PBD/OxnP0N9fT3+/Oc/o7GxEVOnTtUzKXkv0/mbckNjHvj6NGy5cZbkeTmHlPKNXL5YM7cVQObPfmNtOR66aiYG3H44vUHYrSWoKbewk01ERKQRpdPDpJSXmlVp/7INnR9062i/8soruOSSS9De3o7t27dj9erVqK+vx3vvvYfHH38c//M//6NXUgpGJvM35YbGBEfCmNlYo2kaiIxGLl/4QyPRvzN99sfarexYExER6UTJat9Kh3Wr0f5lGzr36Rbe684778QDDzyAF154ARbL8YfmggsuwBtvvKFXMkghuaExDk9AMLQRUT6Tyxdumf16kwpDRkRERMcpWe1brfCarJ8Lg26/aHd1dWHTpk1J2+vr69HXJ/ztEWWP3NAYhyeA6598G0D83BWifJZLK4HKzTMjItLb3r17VT9nXV0dGhsbVT8vFR6ldXymw7pZPxcO3Tra1dXVOHLkCJqa4ler3rVrF0466SS9kkEKSQ2NaW+uxa5Dg9G/Y+eucIgL5bNcWQlUyTwz5lUi0ovH0Q/AhGuvvVb1c9tsZdi3by8725SxVOr4dId1s34uLLp1tL/97W/jjjvuwO9//3uYTCaMjIxgx44d+OEPf4jrrrtOr2TkPKmQA2oSW/GwvbkWi9ubcPNTu+KOf/uTAQwOB3RJGxU2vfKAEDVXAtXyPpTMM2PeJCK9BIaHAIQx8+o7MKZpkmrndR75GG9uuBd9fX3saFPGInV8Ykd4toqrfbN+Liy6dbTXrFmDpUuXoqGhAaFQCFOmTEEoFMLVV1+NlStX6pWMnKb3UJPEoTGWkiI8t+cobn5qF4b9oehxZZZiPLygDau2dOHVmIWiOAyG1GaE4VZqrASq9X0omWdGRKS3ivpGnNA4MdvJIBIVCo3gkmknYtGsU+ELjqC0pAjHnF4EYxY7zQTr58KiW0fbYrHgV7/6Fe6++250dXXB5XKhra0NLS0teiUhp2VrqEns0JgPj7niYgdHLOlowsYdB5NWY+YwGFKTkYZbZbISqB73kUtzyYmIiIyg1+nFnZu7BKOLdDTX4qGrZmYcDYT1c2HRbdXxiIaGBlx66aW46qqr0NLSgt27d8etQk7ClAw10Vpk7kqitoZq0ZBHeqWN8p8R8oAa9LgPsbwKGGsuORERkVEMuP2i7dnO7n4MuFk/U2p072gnCofDCIVC8gcWOCMMNRELaSCHw2BIDUbIA2rQ4z7UCj9CRERUKJxe6RCdcvuVYP1cWHQbOk6ZMcpQE6H5qSPhsCHSRvnNKHkgU3rdhxpzyYmIiAqF3SrdLZLbrxTr58LBjnaOMFJYocT5qY5hv2HSRvnLSHkgE3reRyZzyYmIiApJTbkFHc216BSZo11TzvqZUqN5R9vpdEruHxoa0joJOSs2/E+VzYy181qxvbsP9ZWl8AVHYDUXo9fpxfmnjclqZlUz5BGRGD2fM7nQW5mE5mJ+ISJS1969e1U/Z11dHUOGFZixdivWzmtFZ3cf6u3W421thwcdzXUZL4RGhUfzjnZ1dTVMJpPo/nA4LLm/UCWG/ymzFGPjorPw3O4jeLU7vnF+7mljspXMKA6DIT3o8ZzJhd5SIzQX8wsRUeY8jn4AJlx77bWqn9tmK8O+fXvZ2S4wYQDPdR2JC1c7u7kO7c2prU9EBOjQ0X7ppZe0vkTeEQr/s6SjCQ9vO2DoEFocBkN60PI5kwu99ZMrZ6gWmov5hYgoM4HhIQBhzLz6DoxpmqTaeZ1HPsabG+5FX18fO9oFpNfpxXKB8F6vdvdhxeYuVcJ7UWHRvKN97rnnan2JvCMU/qetoVowhjVwPCQQG+1EmZELvTXglg/NxXxIRKSvivpGnNA4MdvJoBynJLwXO9qUCk072nLzs2PZ7XYNU5JbhML/+IIjkq/JldBGREYmF3pLLrQH8yEREVFu0iO8FxUWTTvacvOzgeNztBlL+zih8D+lJdIhz3MltBGRkcmF3pIL7cF8SERElJv0Cu9FhUPTJ4bzs9MjFP5n16FBtDfXCg5pyaXQRkRGJhd6q6Y8P0KMERERUTw9w3tRYdC0o8352ekRCv+zofMgNiw6C0UmU9JqxwwJRKQOudBbY+1WhuYiIiLKQ2PtVqyZ14oVm7viOtsdzbVYM6+V87MpZbqPgRgeHkZPTw/8fn/c9unTp+udFEMTC//zKEMCEWlKLvQWQ3MRERHlp8bacjx01UwMuP1weoOwW0tQU25hJ5vSoltH+9NPP8XixYvx5z//WXA/52gnEwv/wwY9kbbkQm8xNBcREVF+Gmu3smNNqtCto33rrbdicHAQb775Js477zxs3rwZvb29eOCBB/DQQw8pPs9jjz2Gxx57DB9//DEAYOrUqbj77rtxySWXaJTy3OAY9qPP5YfTG4DdZkZdOTsCRNnEPElEWunp6UFfn3CowUzs3btX9XMS5SPW8aSEbh3tbdu24Q9/+APOPPNMFBUV4ZRTTsFFF10Eu92OtWvX4rLLLlN0npNPPhnr1q1DS0sLwuEwnnzySXz961/Hrl27MHXqVI3vwpgOD3pwxzO7k+Zur5s/HeOrbVlMGVFhYp4kIq309PRg0qTJ8HiGNbtGwOeXP4ioQLGOJ6V062i73W7U19cDAGpqavDpp5/itNNOQ2trK3bu3Kn4PFdccUXc36tXr8Zjjz2GN954oyA72o5hf1JmB4DtB/pw5zO78ciCNn7DRqQj5kki0lJfXx88nmGcveQe2Medquq5j3S9jj1//CWCQcYLJhLCOp5SoVtHe+LEidi/fz9OPfVUzJgxA7/4xS9w6qmn4j//8z8xbty4tM4ZCoXw+9//Hm63G+ecc47gMT6fDz6fL/q30+lM61pG1efyJ2X2iO0H+tDn8jPDk2HlY/5knqR8kY/5M5/Yx52KExonqnpO55GPVT0faYf5MztYx1MqivS60C233IIjR44AAO655x78+c9/RmNjIx5++GGsWbMmpXN1dXWhoqICpaWl+N73vofNmzdjypQpgseuXbsWVVVV0X8NDQ0Z34uROL0Byf1DMvuJsikf8yfzJOWLfMyfRPmC+TM7WMdTKnTraF977bVYtGgRAOCMM87AJ598grfeeguHDh3Ct771rZTONXHiRLz77rt488038f3vfx8LFy7EBx98IHjs8uXL4XA4ov8OHTqU6a0Yit1qltxfKbOfKJvyMX8yT1K+yMf8SZQvmD+zg3U8pUK3jvZ9992H4eHjC3eUlZXh9NNPR3l5Oe67776UzmWxWNDc3IwzzjgDa9euxYwZM/Czn/1M8NjS0lLY7fa4f/mkrsKCOS11gvvmtNShroLDV8i48jF/Mk9SvsjH/EmUL5g/s4N1PKVCt472vffeC5fLlbR9eHgY9957b0bnHhkZiZunUkiqyixYN396Uqaf01KH9fOnc54Ikc6YJ4mIiPIT63hKhW6LoYXDYZhMpqTt7733Hk444QTF51m+fDkuueQSNDY2YmhoCJs2bcLLL7+M559/Xs3k5pTx1TY8sqANfS4/hrwBVFrNqKtgPD+ibGGeJCIiyk+s40kpzTvaNTU1MJlMMJlMOO200+I626FQCC6XC9/73vcUn+/YsWO47rrrcOTIEVRVVWH69Ol4/vnncdFFF2mR/JxRVcYMTmQkzJNERET5iXU8KaF5R/vf//3fEQ6HsWTJEtx7772oqqqK7rNYLDj11FNFQ3MJefzxx7VIJhEREREREZEqNO9oL1y4EADQ1NSE9vZ2lJToNlqdiIiIiIiISHe6LYZ27rnn4pNPPsGqVauwYMECHDt2DADw5z//Ge+//75eySAiIiIiIiLSlG4d7VdeeQWtra1488038eyzz0ZXIH/vvfdwzz336JUMIiIiIiIiIk3p1tG+88478cADD+CFF16AxXJ88YALLrgAb7zxhl7JICIiIiIiItKUbh3trq4uzJs3L2l7fX09+vr69EoGERERERERkaZ062hXV1fjyJEjSdt37dqFk046Sa9kEBEREREREWlKt472t7/9bdxxxx04evQoTCYTRkZGsGPHDvzwhz/Eddddp1cyiIiIiIiIiDSlW0d7zZo1mDx5MhobG+FyuTBlyhTMmTMHs2bNwqpVq/RKBhEREREREZGmNA9qPTIygp/85Cf44x//CL/fj+985zuYP38+XC4X2tra0NLSonUSiIiIiIiIiHSjeUd79erV+PGPf4wLL7wQNpsNmzZtQjgcxoYNG7S+NBEREREREZHuNB86/l//9V/4+c9/jueffx5btmzBn/70J/zmN7/ByMiI1pcmIiIiIiIi0p3mHe2enh5ceuml0b8vvPBCmEwmHD58WOtLExEREREREelO8452MBiE1WqN22Y2mxEIBLS+NBEREREREZHuNJ+jHQ6HsWjRIpSWlka3eb1efO9730N5eXl027PPPqt1UoiIiIiIiIg0p3lHe+HChUnbrr32Wq0vS0RERERERJQVmne0N27cqPUliIiIiIiIiAxD8znaRERERERERIWEHW0iIiIiIiIiFbGjTURERERERKQidrSJiIiIiIiIVMSONhEREREREZGK2NEmIiIiIiIiUlHOdbTXrl2Ls846C5WVlaivr8fcuXOxf//+bCeLiIiIiIiICEAOdrRfeeUVLF26FG+88QZeeOEFBAIBXHzxxXC73dlOGhERERERERFKsp2AVP3lL3+J+/uJJ55AfX093nnnHcyZMydLqSIiIiIiIiIalXO/aCdyOBwAgBNOOCHLKSEiIiIiIiLKwV+0Y42MjODWW29Fe3s7pk2bJniMz+eDz+eL/u10OvVKnmKOYT/6XH44vQHYbWbUlVtQVWbR/RxEesuF/EnaMkrZZZR0GAnzJ5FxaZE/2R4lUldOd7SXLl2KPXv2oLOzU/SYtWvX4t5779UxVak5POjBHc/sxqsH+qLb5rTUYd386RhfbdPtHETZYPT8SdoyStlllHQYDfMnkXGpnT/ZHiVSX84OHV+2bBm2bt2Kl156CSeffLLoccuXL4fD4Yj+O3TokI6plOYY9icVSACw/UAf7nxmNxzDfl3OQZQtRs6fpC2jlF1GSYcRMX8SGZea+ZPtUSJt5Nwv2uFwGDfddBM2b96Ml19+GU1NTZLHl5aWorS0VKfUpabP5U8qkCK2H+hDn8svO9xGjXMQZYuR8ydpyyhll1HSYUTMn0TGpWb+ZHuUSBs519FeunQpNm3ahD/84Q+orKzE0aNHAQBVVVWw2XJrWIrTG5DcPySzX61zEBHpzShll1HSQUSULWyPEmkj54aOP/bYY3A4HDjvvPMwbty46L/f/va32U5ayuxWs+T+Spn9ap2DiEhvRim7jJIOIqJsYXuUSBs519EOh8OC/xYtWpTtpKWsrsKCOS11gvvmtNShrkJ+iI0a5yAi0ptRyi6jpIOIKFvYHiXSRs51tPNJVZkF6+ZPTyqY5rTUYf386YrmsqhxDgDodXqx74gTfzv4GfYddaLX6VV+I59zDPvx4TEXdvUM4MNPXVz4gohEywW1yq5MKU0HyzciyldqlIMsS4mS5dwc7XwzvtqGRxa0oc/lx5A3gEqrGXUVqcUcNAG4pHUcFs46Fb7gCEpLinBsyCf7uoiefjeWb+7Cju7+6LaO5lqsmdeKxtpyRedgSAciSiRXLmRadqlFLh0s34go36lRDrIsJYrHjrYBVJWl1rGO5Rj243aBcArAaOH1yII2yXP3Or1JnWwA6Ozux4rNXXjoqpkYa7fKpkEqpINcGogo/8iVCz+5ckZGZZea6ZRKx0+unMHyjYjymhrlIACWpUQJ2NHWkWPYjz6XH05vAHabGXXlox3sXqcXA24/nN4g7LYS1JRZRDu3hwc9cHgCcHoCqLKZUW4pRq/Di2UXNKOtoRq+4Ais5mLs7BnAhs6DsuEUBtz+pE52RGd3PwbcftmONkM6EOUmsTJJ6X6pY+TKhQG3H+98MqCo7FKSjnT1uaTTMeBm+UZE+a3P5Uevw4sXbpuDYCgMp2e0rC0pNmHpf+9UVA4CUKUs1bK8J9IbO9o6ERouc9Hkeqy6fApWbu5Cp4Jh25/0u7Ei4dfnL08ag19edyZWbunCo9u6o9vbm2vx8II2uH3S4RSc3mBG+0ePYUgHolwjN4RPyRA/qWNcMmWPyxfEwwvasHHHQcmyS+uhhi5fQDIdLp90GcjyjYhynT8YiLYlE6cR/vK6M+H0SE/pGfIGYDIho7LU7QtwaDnlHS6GpgOxIZQTx9mxIqGTDRwfth27INnhQU9SJxsAJo+vSioYAWBHdz827jiIKpv0t4B2q/R3LXL7R49hSAeiXCI3rLvX6ZXc7xj2y56jWqbsqS23YOOOg6Jll91mlr2GGovoVNuk01FbLn0fLN+IKNdVlFoE25Kd3f1YtaULNWWlkq+vtJozLkurbBbNy3sivbGjrQOxIZRtDdWyw7YjHJ6A4LFS59jR3Q9/aEQybTXlFnQ01wru62iuRY1MwQgwpANRrlEyrFtuiJ/cOfyhEclywR8akSy7vMERRdNSMiWXjkAozPKNiPKayx+SbI+6/CHZcjDTstQfGtG8vCfSG4eOayR2jklwJCx4jC8o3gmuq7CgtKQIe4844fQEUGQypXwOABgcDuBvBz+Lm/udOP9l7bxWvPFRP06oKI3Oqel1eNDRXCc7Pxs4HtLhzmd2Y3vCcB89w/QQkTJy0z2c3iDqKixYP3866u2lcHlDqLSWRH/pHvIGIFyqHef2BSXLhUMDHslrOD1BmIuFy70IpcO2E9e2sNvM0WGIcsMZh/3S95FK+abH3EPObyQiMf8cGIbTG4yWhZXWEpxUUwanJ4AySzGWdDQJzq8e8gawfv50vPz3T1FfGdNWdHpx/mljUFVmwUd9bslzyJWlR2XCynKaDuUidrQ1kDjH5PGFZwoeV1oiPKCgrsKC39zwJaz6w57ot4OpniNiyBfEkifeAjA6n/vuK6Zi1ZY9cd8azm6pw9LzJmDJk29j2B8a3dZci/Zm4W8ehRglTA8RyZOb7lFdVoLf3PAl3Lf1/aT5er+54UuwFJsg8v1hVKXVLBm+cMgXkLyGyRRGsUm6fFMybFtobYuO5lqsnteKU2rLFU19USMMox5zDwtlfuPevXtVP2ddXR0aGxtVP29PTw/6+oR/pUuXFvdPwnLpWZMjVRZWl5sl51fbbWaEATy3+whe7Y4vX849bQwAoMomfw6psjTS/hTDaTqUi9jRVpnQnMJdhwbR3lybNKRm16FBdDTXJs3RXj9/elLjM9VzAKMFaDh8vDU8eXyV4DzvVw/0YSQcxpKOpmjh+GqK4b2MEKaHiJSJTPfYLpJnyywluOOZ3YLz9e7f+j7WzZ+OytISyXNEhlSLhS9Ucg1zcZFk+VYhs4aE2NoWnd39WLm5Cw9+cwYqrCWKrpFpGEatw9oUQphFj6MfgAnXXnut6ue22cqwb99eVTtAPT09mDRpMjyeYdXOGSvg41BareTasybnnwPDkmXhuvnTRedXmwCsmz99tHzpFi9fSkuKJM+xfv50AOJlqVy9xGk6lIvY0U6BkiF5fS4/9h5x4vGFZ0aHQ9qtJfj6jPFY++e92Lbv0+ix+484sWZeK3Z096Hebo0OsznlhDLs6hmMO++GzoP4j6tPx2Wt4zA25tj+IR++NmM87v1TfMe8vbkWi9qbUBwz5LytoTruW8ZYO7r7saS9KW7bzp5BDPuC+PCYS/aeGf6GyHjEyqyqMovkMMABkTUhgNGG2ZA3iJNrymSHEkqlYcgblL1GaUkRFrU3IQwIlm/uz4d9i11DbG2LyDUcnoDia2RCaRmZybDvQiiHA8NDAMKYefUdGNM0SbXzOo98jDc33Iu+vj5VOz99fX3weIZx9pJ7YB93qmrnPdL1Ovb88ZcIBjN/NklYrj1rstdVUN7u6hkUDc015A3KhmOUmqPd2d0PpzeIkyTSyGmIlI/Y0VZI6ZC8Yb/4cMj7vj4NP/pKCL7ASHS4jMMTwHNdR/BqzLGzm+vw8II23PzUrrihNGGE8VzXkbhfXma31GGMvRRfbDoBS9qbosO2dx0axM1P7cK/XjkjeqzcfO7Y/WWWYjy8oA13/2FPXNqE7pnhvYiMR67M8odG8NzuwwllTy1mTaiFS2GelhtKKBn+S8E1vMVFuPmpXVjS0SRYvm264WzJazg96lwjU3JlpBphbQqpHK6ob8QJjROznQzF7ONOVTW9ziMfq3YukpZrz5oYubLQ7ZUOc+iWCYPo9gXgCUi3MZWUQWpM0yEyEna0FUhlSF61zYI7RYbn3P2HPVg7rxWTx43Gx+51erFcaCh3dx9GED+Ue0lHEx7vTB6SExn23dZYg+uffDsp7bFzuOXmc8fuX9LRJDgESOieGd6LyFjkyqy132gVnkby+TDC+74+TfL8ldaY0FsiQwl/cuUMyTT8+GtTZa9RbDJh2B8SHYljsxRLXmP5pZNlr2EpLpK8hhrll1wZKRfWRsmwb5bDRCTGbpPO/7UVpfjpXw8IDvsGgAe+Pg0/fVF6v7lEuqOttAzKZJoOkdGwo62A0HDwSmsJPh3y4oMjThxxePFRnxt2mxkBmaEzLn8Iu3oGRo8NSodCiB3Kneqwb2D0m8Zdhwajf0vN5048tq2hGhs6D0oOE4oUhJxXQ/km11dulhtGLDeMMBAakZy3bLeWZBwiTO4a5ZZihEbCuGTaWHzzjIaklcn/551DMBcXSV5jtaVY8hpVNjPKLcWKyq9Mngm5MlJJWBu5a7EcJiIxFTJloU8mNJeS/VU2s2x5S1Ro2NFWQGw4+OzmOiw9fwK+8dhr0SHeYquDRwwOB7DgV28AAJ7+7pcUp0EuHnZi9K/ZzXW48fxmXP/kW9FtHxx24K7Lp+KB//dB0qrjN54Xf2yxySQ7TCiC82oon+TDys2yw4hlhhE6PAGsnteKlZu74hpNkRVqT6opw66eAZk0SM8fdXgCuOvyqbh/6/tJ17j7iqnoc/lQZAJu/+okrNqyJ2kqzv1zp8ExLB3ZYMjrl7yPyOcpV35l+kzIlZFqhLVhOUxEYpxeP+6fOw13bdmTVBbeP7cVnw5Jl0FyQ8+dngAmnWhXVN4SFRJ2tBUQGw4uNMRbToW1OPr/5TH/L+SkGhueu2U2hjwBVJVJfxM4vur4sXabGc+/fxR/+7gfjyxoi5tzeM2v38Az35uF4EgYQ94AyktL8O6hwaRjx1Vbcd/WD0SHCa2Z2xp/fc6roTyQLys3yw0jtsuUJ3abGafUlmP9/OlweoPRPG3/POYqAJRbpKsPu8yK4JVWM6759RtYP3867rhkElzeECqsxTjm9OHqX72B/77+bJRZigWn13R29+OuLXuwel6ryNlHlZpH7+PBb86AwxOI3kdVTBxtQLr8UuuZ0COsDcthIhJiNZtx3eN/w39cczpWFBdhyBNApc2MYGgE1z3+Jn55nfSPRHJlUJVtdDrRg3/Zh8XtTUll+k/+sg+r57WyLKKCw462Am5/SPEQb7EwXMDot3rHnMd/gTnm9EkOs/nngCc67/r5W2eLnre9uRb+0Ai+9ugOAMCyC5qxq2dA8Ng5LXWoLjNHC7sPj7nwo//ZnXTc/3zvHMl7FvqFnfNqKNfly8rNcsOIS4uLJMuT0uLR9RpOqikTXSXWXGySPEeZzJDsKpsZk06sFFxboqO5FjXlFgy4/ZJD3If9IUXDpcdX22R/TRErv9R8JvQIa8NymIgS1VVY0FRXHm0nxhoN6Sg9tLzCUixZ3luKi9Dn8uP/dR3F/+s6KpiGH1w0kWUTFRzp1bEIgPwQyNjVujd0HsTi9ibMbq6LOyYyHPKOZ453au94ZjfuunwqOpprk45dPa8Vm3f+I7rtk8+Gsbi9Ce0Jx7Y312JxexM+cx2Pp7mh8yCWnd+M2S3xaRAaQig2xPRTl/SQTKUhbxzDfnx4zIVdPQP48FMXHMOM+0nGlS8rN0eGEc8RKQOc3oBkeaLkPvvdfslz9Ll8kmkYX23DmnmtguXfmnmtGGu3ypa9Q96g5DXUaNQpfSYyKevkPi82TokoE3JlTN+QV7Q9evcVU9Hv8kqW9/1uf97Un0Rq4i/aCsgNgYxdrXvYH8LNT+3Cczd3wBsYgdMbRKW1BIcHPbj6V2+gL6ZD3Ofy45pfv4HN358Ftz8EpzcIu7UENeUWjLVbsXpeK35w0UQMeQOwWorxjZ+/JhqCZsuN7fjLLbOj5zih3IJHFQwhFBtiaimW/g5GyVDGfJjrSoUln1ZulhuqvOBXb4qWJ39a1iF7/gprCa7b8DfRczx74yzZocyNteV46KqZGHD7k8o/QL7stVtLNB8ureSZUKOs47BvItKSVBkz6AlITuX5v9efjZsff0u8vP/+LJhVaDcS5Rt2tBWoKbcoXq0bAM48pQY1McP3HMN+rH1ub1wnO2LKODvsNjMaasuT9sUOAex1enF6o/DK4x3NtagqM0cbp4nnkCI2ZFFqhXIlQxnzZa4rFZZ8W7lZaqjymafUCJYnSu/TbjVLlkmRDqrcUOaxdqtg2QVIl72R4eVKrpEJuWeiwlqCH/7+PVXKOg77JiItiZUxldYSyak8ldYSyfK+ptwCa0lRXtWfRGrg0HEBiUMArSVFgkMcZzfX4aYLWrCh82B0m9BQPzWGBbp9QSwSGbazqL1J8VDuRGJp23/EiTXzWtNOs5J5jURGUyhDeNW4T28gJFkmeQPSC3wpMdZulR1erjW598rtC7KsI6KcIDbFxRMI4v650wTL2vvntsIXDMqWxYVSfxKlgr9oJxAbAvjg/OmCQxytJUX407IO2aF+mQ4LdHgCuPmpXaLDdjbdcHba9yyVtnTTzLk6lKsKZQivCcAlreOwcNap0fLk2JD02gyxtCyTYskNL9eD1DMhF+aMZR0RGYHUFJcBdxD/3+/eFV2V/KGrZuKLTXbZsrhQ6k8ipdjRjiE13Pn2z4cAThpnT3qdHsMC7VYzhv0h0TBimc59EUtbumnOp7muVHjyfQivY9iP2wXKOmC04aVkuLPWZVIsqeHlehF7JljWEZHRyU3nW3HpZBwa8AiuSg4cXy9DSVmc7/UnUSpybuj49u3bccUVV2D8+PEwmUzYsmWLaudWc7iz2qttR+YJCjHi3JdcSy9RIVGjrDNSHs9mdAMjvQ9ERELkyvzyz8N7CYldD4OIUpNzHW23240ZM2bgP/7jP1Q/t1rDnQ8PerDsqV348r+9gnk/fw1ffugV3PTULhwe9KSdtlyb+5Jr6SUqJGqUdUbJ41qUt6kwyvtARCRGvsz3Y7XIHOzVOq2HQZSPcm7o+CWXXIJLLrlEk3OrMQRQy9W2c23uS66ll6hQqDXcOdt53CjRDbL9PhARSZEr88tLLbh/6weY2ViDxQlrbjyw9QP865UzWJ4RpSHnOtqp8vl88PmOL/DjdDpFj1UjtI+SIZmZFFa5Nvcl19JL+kolf5J61Axjls08rnV5m4p8LOuYP4mMS832rT80ghf3HsOLe48Jvl7PspQon+Tc0PFUrV27FlVVVdF/DQ0NoseqMQSQq20TKZdK/iT15MtwZ5a32mL+JDIuNdu3LpkQsSxLidKT979oL1++HLfddlv0b6fTKVkYZToEkCvQEimXav4k9eTDcGeWt9pi/iQyLjXbt8P+kOS1WJYSpSfvO9qlpaUoLS1N6TWZDAFUc0gmUb5LJ3+SenJ9uDPLW20xfxIZl5rtW5alRNrI+6HjesuXIZlEREbH8paIKHMsS4m0kXO/aLtcLnR3d0f/PnjwIN59912ccMIJaGxszGLKjsuHIZlERLmA5S0RUeZYlhKpL+c62m+//TbOP//86N+R+SkLFy7EE088kaVUJcv1IZlERLmC5S0RUeZYlhKpK+c62ueddx7C4XC2k0FEREREREQkiHO0iYiIiIiIiFTEjjYRERERERGRinJu6HimIsPOnU5nllNCVDgqKythMplkj2P+JNKf2vnT5XIBABz/+AgjQen4vKlwHfvn6PWPfqxq2LGho58AAHbu3BlNuxr+/ve/A8id90HLc/O8oyLPmsvlUlTPKc2bAOtPIr0pyZ+mcIFNeP7HP/6BhoaGbCeDqKA4HA7Y7XbZ45g/ifTH/ElkTErzJsD8SaQ3Jfmz4DraIyMjOHz4cErfEhqN0+lEQ0MDDh06pLgAzhX5fG9Aft+f1L0pzW9K82c+v49643upnlx9L9XOn0aRq5+HFvhexMuV9yOVvDYyMoL9+/djypQphr+vXJArz0iuyMf3U0n+LLih40VFRTj55JOznQxV2O32vHlYE+XzvQH5fX+Z3Fuq+TOf30e98b1UT76+l7laf+br55EOvhfx8un9KCoqwkknnQQgv+4r2/heqqvQ3k8uhkZERERERESkIna0iYiIiIiIiFTEjnYOKi0txT333KP6aqNGkM/3BuT3/el5b/n8PuqN76V6+F4aCz+P4/hexMvX9yNf7ysb+F6qq1Dfz4JbDI2IiIiIiIhIS/xFm4iIiIiIiEhF7GgTERERERERqYgdbSIiIiIiIiIVsaNNREREREREpCJ2tImIiIiIiIhUxI42ERERERERkYrY0SYiIiIiIiJSETvaRERERERERCoquI52OByG0+lEOBzOdlKIKAHzJ5FxMX8SGRfzJ5HxFFxHe2hoCFVVVRgaGsp2UogoAfMnkXExfxIZF/MnkfEUXEebiIiIiIiISEvsaBMRERERERGpiB1tIiIiIiIiIhWxo01ERERERESkIna0iYiIiIiIiFRUks2Lr127Fs8++yz27dsHm82GWbNmYf369Zg4caLoa5544gksXrw4bltpaSm8Xq/WyaUc4Bj2o8/lh9MbgN1mRl25BVVlFsOe1yjXI9JKr9OLAbcfTm8QdlsJasosGGu3qnoN5hciUloORI5z+QKoLrPAHxyByxdk2UFEqstqR/uVV17B0qVLcdZZZyEYDGLFihW4+OKL8cEHH6C8vFz0dXa7Hfv374/+bTKZ9EguGdzhQQ/ueGY3Xj3QF902p6UO6+ZPx/hqm+HOa5TrEWmlp9+N5Zu7sKO7P7qto7kWa+a1orFWvIxPBfMLESktByLHvfPJAB5e0IYHn98fVz6x7CAiNWV16Phf/vIXLFq0CFOnTsWMGTPwxBNPoKenB++8847k60wmE0488cTov7Fjx+qUYjIqx7A/qZIFgO0H+nDnM7vhGPYb6rxGuR6RVnqd3qRONgB0dvdjxeYu9DozH4XE/EJESsuB2OOWdDRh446DSeUTyw4iUpOh5mg7HA4AwAknnCB5nMvlwimnnIKGhgZ8/etfx/vvvy96rM/ng9PpjPtH+afP5U+qZCO2H+hDnyu9SlOr8xrletnG/Jm/Btz+pEZsRGd3PwbcmT/LhZZf9Mb8SblAaTkQe1xbQ7Vo+ZQrZQfzJ5HxGaajPTIygltvvRXt7e2YNm2a6HETJ07Ehg0b8Ic//AH//d//jZGREcyaNQv/+Mc/BI9fu3Ytqqqqov8aGhq0ugXKIqc3ILl/SGa/3uc1yvWyjfkzfzm9wYz2K7tGYeUXvTF/Ui5QWg7EHucLjih6jZExfxIZn2E62kuXLsWePXvw9NNPSx53zjnn4LrrrsPMmTNx7rnn4tlnn8WYMWPwi1/8QvD45cuXw+FwRP8dOnRIi+RTltmtZsn9lTL79T6vUa6Xbcyf+ctulV4CRG6/smsUVn7RG/Mn5QKl5UDscaUl0s3fXCg7mD+JjM8QHe1ly5Zh69ateOmll3DyySen9Fqz2Yy2tjZ0d3cL7i8tLYXdbo/7R/mnrsKCOS11gvvmtNShriK9VUS1Oq9RrpdtzJ/5q6bcgo7mWsF9Hc21qCnP/FkutPyiN+ZPygVKy4HY43YdGkS7SPmUK2UH8yeR8WW1ox0Oh7Fs2TJs3rwZ27ZtQ1NTU8rnCIVC6Orqwrhx4zRIIeWKqjIL1s2fnlTZzmmpw/r509MO16HVeY1yPSKtjLVbsWZea1JnO7LquBohvphfiEhpORB73IbOg1jc3pTU2WbZQURqMoXD4XC2Ln7jjTdi06ZN+MMf/hAXO7uqqgo222hoheuuuw4nnXQS1q5dCwC477778KUvfQnNzc0YHBzET37yE2zZsgXvvPMOpkyZIntNp9OJqqoqOBwOfvunA73j22oVs1ePWMCxIu/bkDeASqsZdRWFEduT+TM1WucvNc4fl3esJagpj887alyjUPOL3pg/SS1qly1VZSWoLDXD4w/JlgORa7t9AVTZLPCHRuD2BXO+7GD+JDKerMbRfuyxxwAA5513Xtz2jRs3YtGiRQCAnp4eFBUd/+F9YGAA3/3ud3H06FHU1NTgjDPOwGuvvaaok036ypf409mI01tVlruVPelD6+dSrfOPtVtFv5RS6xrML0S5Q41839PvTgofGBktM7OxRvK1LC+ISC9Z/UU7G/iNnz4cw34se2qXYMiNOS11eGRBm+q/vGlxPb3vo9Axfyqj9XOpx3PPvJV7mD8pU2rk+16nF7f97l3B8FwdzbV46KqZmo44MyrmTyLjMcRiaJR/8iX+NOP0khFp/Vzq8dwzbxEVHjXy/YDbLxoDu7O7HwNulh1EZAzsaJMm8iX+NOP0khFp/Vzq8dwzbxEVHjXyvdMbzGg/EZFe2NEmTeRL/GnG6SUj0vq51OO5Z94iKjxq5Hu7VXp5Ibn9RER6YUebNJEv8acZp5eMSOvnUo/nnnmLqPCoke9ryi1JYQMjOpprUVPOsoOIjIEdbdJEvsSfZpxeMiKtn0s9nnvmLaLCo0a+H2u3Ys281qTOdmTV8UJcCI2IjImrjpOm9I5vq9X1GKdXH8yfqdH6udTjuWfeyh3Mn6QWNfJ9bBxtu7UENeWWgu5kM38SGQ8nspCmtIxXGamond4A7DYz6sototcTOzZVYQAwSZ/XFxzBZ5HK31aCmjLjV/5xDZYcSXOuU+OZzDR/Kf3cE5/7WIcHPXB4AnB6AqiymWG3mdOK4y11DSLKT2EAJhPgDY6g75gLTm8AFaUlsBQXYdDjR6XVjPLSEri8waSycqzdGldeOYb9+PDzcySWqWLlrVptAyX0vBYRGQM72pSTDg96cMczu+PChMxpqcO6+dOTGvmpHJvKtR6YOw33bf0AL+49Ft0+u6UON543Adc/+TaG/SEAx4ezNdaWp32/Wurpd2P55q64cClGT3Ouy/SZVIPc564kjZ/0u7FC4Byr57XiFAXPjhHeByLSV2y+L7MU4+EFbdi442BcOdLeXIsbOr6Afrcfj3fG7xMqI8TKkvXzpyMMKK7DtSp/jFbW9fT0oK9POMxaJurq6tDY2Kj6eYlyFYeOU85xDPux7KldgrE457TU4ZEFbXHfYis9NtVrdTTXYmZjDR7d1h23vb25Fm0J2zuaa/HQVTMN9ytxr9OL2373rmBM0mykuRDyZ6bPpBrkPvcHvzkjqVGYmEa3P4Qf/c97kueQakAa4X2g1BRC/iRtJeb7ZRc0Y1fPgGA5smbeNDzXdQSdAvtiywipsmTtN1rx3O4jeLVbeR2udvmjV1mnNH/29PRg0qTJ8HiGM75mIputDPv27WVnm+hz/EWbck6fyy9YYQHA9gN96HP5o5VWKsemeq3O7n4sbm9K2r6jux9LErZ3dvdjwO03XEd7wO0XbOAAxk1zrsv0mVSD3Ofu8ARk0+gPjcieQ6qjbYT3gYj0lZjv2xqqkzq6EWPtVsFONhBfRkiVJfWVpYKdbEC8Dle7/DFaWdfX1wePZxhnL7kH9nGnqnZe55GP8eaGe9HX18eONtHn2NGmnOP0BiT3D8XsT+XYdK7lC44o3u70BiXPlQ1yaTJimnNdps+kOmmQ/lzl0jDkDYg++0rPYYT3gYj0lZjvpcoRpWWMVFkidw6x/WqWP0Yt6+zjTsUJjROzcm2iQsGONuUcu9Usub8yZn8qx6ZzrdIS4Qh5QtvtVuNlN7k0GTHNuS7TZ1KdNEh/rnJpqLSaURqSbsBmmrf0eB+ISF+J+V6sDpXbBxwvI6TKErlziO1Xs/xhWUdUuBhHm3JOXYUlKQZnxJyWOtRVWNI6NtVrdTTXYtehwaTt7QLbO5prUVNuvGGwNeWWpFikEUZNc67L9JlUg9znXmUzy6axymaWPYcUI7wPRKSvxHy/69Ag2kXKkV6nF7NF9sWWEVJlybEhX8p1uNrlD8s6osLFjjblnKoyC9bNn55UcUVWGI2d65TKsalea828Vuw/4ozbPrulDsvOb8GGzoPRbZGVnI0413ms3Yo181qTOkxGTnOuy/SZVIPc5z6+2iabxvHVNqwWOcfqz88hxQjvAxHpKzHfb+g8iMXtTUnlSHtzLcZV2bC4I3lfYhkhVZacf9qYlOpwLcoflnVEhYurjlPOisSkHPIGUGk1j/7KJlJhCcULtpYUKY5pKXYtoe1xcbStJagpN35M6kgs5Mh9VKUZCzlThZQ/U3l+5c6RblxWuc9dSRozfXbUeB/UwBi38gopf5K2YvO93XY8VvaQN4Dyz+NoOzx+VFjj9wnVv5E8W1FaArcvCKfn+DkSY3ErqcMzzfdyMbu1KuuU5s+dO3fijDPOwEUrN6o6R/uznv14YfVivPPOOzj99NNVOy9RLuMETMpZVWXKKimh+JWzW+qw9PxmLHnirWi8a6mYlmLXEtteb/COdSyjxfcsFEqfXzFaxYePfb2SNI6vtmX0nGT6PqiBeYBIX0L5fmxS37BcdJ9UnrWZi3G7yL4J9RWy6ciEXFmS7bKOiPTFoeOU1xzDfsF4wK8e6MMj2w5gScfx0B7bD/Thzmd2wzHs1zuZWSP2/hTie5FLMv3c+Lkfx/eCKLfI5dmX//5pVvIzyxIiSsSONuU1qfiVO7r70dZQHbctEtOyUCiJ70nGk+nnxs/9OL4XRLlFLs/WV5aK7tMyP7MsIaJE7GhTXksnDnYhxe81anxPkqZ1fPhC+tz5XhDllnTq9Qgt8zPLEiJKxI425bV04mAXUkxLxvfMTVrHhy+kz53vBVFuSadej9AyP7MsIaJE7GhTXpOKXykU77rQYloyvmdu0jI+fKF97nwviHKLXJ49NuQT3adlfmZZQkSJ2NGmvCYWv3J2Sx1uuiA+3nUhxrRkfM/cpGV8+EL73PleEOUWuTx73mljspKfWZYQUSLG0aaCIBVHW0lMS7m4mLHbAaQcn1uv2L3Ziu+pFPNnaoSe61Ritv9zYBhO72jc2SqbGZXWEpxUUxbdr8bzmSvxqY2SB4yM+ZOyIbYMqfg8PrbL54fdaoHbH4qWf3arGb5ACIOe48c5PH6Ul5pRYR2Nse3wqF8OxaavyiYes1trjKNNZDyMo015TyquZWJMzVRe/8Dcabhv6wd4ce8xAECZpRgbFp2F/9jWjVe75ePx6h27l/E980umz88n/W6s2NyFHd390W0dzbVYPa8Vp9SWq/J85lJ8aiPE8yaieEJlSHtzLe6+fCru+dP72Lbv0+j2juZaLGpvws1P7cKwPxQta0wAfvj79zQphzJtXxBRfuPQccprWsYbXrG5C5PGHf/WeElHEx7ZdiCuky12Lb3jbTK+Z37J9PM8POhJ6mQDQGd3P1Zu7sI/B4Yzfl74zBFRJsTKkB3d/bh/6/uYMr4qbntndz827jiIJR1NALSPq80yjojksKNNeU3LeMOdCXG42xqqkzouYtfSO94m43vml0w/T4cnIPqsdnb3w+kNZvy88JkjokykUv9G7EjYrmVcbZZxRCSHHW3Ka1rHG46N1ykVuzPxWnrH22R8z/yS8XPtyWy/kueFzxwRZSLdeNmJ27WKq80yjojksKNNeU3reMOx8TqlYncmXkvveJuM75lfMn6ubZntV/K88JkjokykGy87cbtWcbVZxhGRHHa0Ka9pGW+4IyEO965Dg2hvrlV0Lb3jbTK+Z37J9POsspnRIfKsdjTXwm4tyfh54TNHRJlIpf6NaE/YrmVcbZZxRCSHHW3Ka1rGG14zrxX7jzij2zZ0HsRNF7RgtoJr6R1vk/E980umn+f4ahtWz2tN6mxHVh0/qaYs4+eFzxwRZUKsDGlvrsXdV0zFB4cdcds7mmuxuL0JGzoPAtA+rjbLOCKSk9U42mvXrsWzzz6Lffv2wWazYdasWVi/fj0mTpSO6/f73/8ed911Fz7++GO0tLRg/fr1uPTSSxVdk3FAM5dKTGmjVDRCMXK9wRHFMYjFYuwKbQeQcnxuveJtyt1HtmN/51P+1CM/HB70wOEJRD/PKps5pXA1sXG07TYz7CJxtKWeT7n7zJX41EYuv4win/In6UMoBvagx48yy/H/r7Qejz0tlP9iy5Dyz88x5PWj0mqBPzQCty+ISuvxWNlOT3JZk2k5JFU+xJ7bbpO+Fy0xjjaR8WQ1jvYrr7yCpUuX4qyzzkIwGMSKFStw8cUX44MPPkB5ebnga1577TUsWLAAa9euxeWXX45NmzZh7ty52LlzJ6ZNm6bzHRQepTGlI9uNEi83MUZuT78bywViCK+Z14rG2uRnTyzGrtT2dNKlNaHrpRLrOJfiImeTHu9Tqs+wUBrvfLZLMo1yz6eS+8yF+NR8ronUJxYDe3F7E25+6k20NVbjho4voN/tx+OdB+PKstj8J1yGyJdxsTIph+TKh9hzHx70aBazm4hyT1aHjv/lL3/BokWLMHXqVMyYMQNPPPEEenp68M4774i+5mc/+xm++tWv4kc/+hEmT56M+++/H6effjoeffRRHVNemFKJKR3ZbsRYkr1Ob1IHBRgNF7Jicxd6nd4spUx/qcQBZcxQZfR4nzJ9htVIY748D/lyH0RGIhUDOxLrekd3P444PNiQ0MkGjJP/WEcSUSYMNUfb4Ridb3PCCSeIHvP666/jwgsvjNv2la98Ba+//rrg8T6fD06nM+4fpSedmJZGjCU54PZLxhAecBsrvVpKJQ6oFjFD8zF/6hFbNdNnWI005ksM2Xy5Dy3kY/4kfUjlq9hY12PtVnSKlGVGyH/ZriOlMH8SGZ9hOtojIyO49dZb0d7eLjkE/OjRoxg7dmzctrFjx+Lo0aOCx69duxZVVVXRfw0NDaqmu5CkG9PSaLEknd5gRvvzSSpxQLWIGZqP+VOP2KqZPsNqpDFfYsjmy31oIR/zJ+lDaXtBKsY1kP38l+06UgrzJ5HxGaajvXTpUuzZswdPP/20quddvnw5HA5H9N+hQ4dUPX8hSTempdFiSdqt0ksTyO3PJ6nEAdUiZmg+5k89Yqtm+gyrkcZ8iSGbL/ehhXzMn6QPpe0FqRjXQPbzX7brSCnMn0TGZ4iO9rJly7B161a89NJLOPnkkyWPPfHEE9Hb2xu3rbe3FyeeeKLg8aWlpbDb7XH/KD3pxLQ0YizJmnKLZAzhmnJjpVdLqcQB1SJmaD7mTz1iq2b6DKuRxnyJIZsv96GFfMyfpA+pfBUb67rX6cVskbLMCPkv23WkFOZPIuPLakc7HA5j2bJl2Lx5M7Zt24ampibZ15xzzjn461//GrfthRdewDnnnKNVMulzqcSUjmw3YizJsXYr1ojEEF4zr1U0xFc+SiUOKGOGKqPH+5TpM6xGGvPleciX+yAyEqkY2JFY1+3NtRhXZcPijqaksswo+Y91JBFlIqtxtG+88UZs2rQJf/jDH+JiZ1dVVcFmGw2DcN111+Gkk07C2rVrAYyG9zr33HOxbt06XHbZZXj66aexZs0axeG9GAc0c6nElDZyxdLr9B6Po20tQU25eBztfJfKZ6fl55xP+VOP/JDpM6xGGnMt34vJl/vQUj7lT9KHUAxsh8cPm+X4/1fExNE2av4zSh0phXG0iYwnq5NRH3vsMQDAeeedF7d948aNWLRoEQCgp6cHRUXHf3ifNWsWNm3ahFWrVmHFihVoaWnBli1bGENbR6nElI7rCNhKUFOmbWc2UsE5vQHYbWbUlcd/CRC7fazdmhcda7F7TkUqMUZzIS6ykYQBwJT66w4PeuDwBOD0BFBlM8NuMyfFYbWWFMFcXARzsQnmkiJYZeY7JlLjs8yX5yFf7oPISGLzVaSuCoVHy8WRcBj+UBj+0AjKAUyor4g77qM+d9p1mlJK60/WkUSUjqx2tJX8mP7yyy8nbbvyyitx5ZVXapAiUlNPvzsp1m9kaGtjbbnq1zs86EmKYTmnpQ4PzJ2G+7Z+gBf3Hovbvm7+9KSOS64Ru+d8uLdclunn8km/GysE8s7qea045fO8w8+eiHKFUHkVGUZ+3Ya/4fTGaqyZ1wpzcRFu16lcYxlKRFozxGJolH96nd6kTjYwGuN3xeYu9Dq9ql7PMexPqjCB0diVKzZ3YdI4e9L2O5/ZDcdw7sbIlbrnXL+3XJbp53J40JPUyQZG887KzV2jv3TzsyeiHCFWXu3o7sfGHQexpKMpWr69vP9TXco1lqFEpAd2tEkTA25/UkchorO7HwNudSuxPpc/qcKMvV5bQ3XS9u0H+tDnyt3KVOqec/3eclmmn4vDE5DMOw5PgJ89EeUMqfJqR0z9/Gp3P+rtpYLHqV2usQwlIj0UTsBg0pXTG8xof+rXC0ju9wVHBLcPybzOyOTuOZfvLZdl+rk4PfKvNxdLf0fKz56IjCKV+lmsrgbULddYfxKRHtjRJk3YrdKPltz+1K9nltxfKrJIVKXM64xM7p5z+d5yWaafi90m/3qLTEebnz0RGUUq9bNYXQ2oW66x/iQiPXDoOGmiptySFBczoqO5FjXl6q7IWVdhSYpdGXu9XYcGk7bPaalDXUXurgwqdc+5fm+5LNPPpcpmlsw7VTYzP3siyhlS5VV7TP08u7kWx4Z8gsepXa6xDCUiPbCjTZoYa7dizbzWpA5DZNVxtcNqVZVZsG7+9KSKc05LHdbMa8X+I86k7evnT8/pEBxS95zr95bLMv1cxlfbsFok76ye14rx1TZ+9kSUM8TKq8iq4xs6D0bLt/NOG6NLucYylIj0YAoribGVoL+/H3fffTdeeuklHDt2DCMj8XNqPvvsM9USqDan04mqqio4HA7Y7Xb5F1BG4uJoW0tQ83n8aqH42taSIsUxsMW2u/0hODwBDHkDqLSaUfV57OFUridEjVjVqUjlnoHRhV0i91xXkbsxPPMpf2YaQ/6fA8NweoMY8gRQaTPDbi3BSTVlKV1Dbn/kGpFY3ZUJ11Dy3Gd6n5Q78il/kjYOD3rg8ARGy5QyMypKS+D2BzHkCaK23IJAKAxvIIhKqxm+0AicngDsVjPsn9fVwPFyR6hOSyxvys3FGPT4UVYaXz5JlV2RfS5fANVlFviDI3D7gknX0rvez5TS/Llz506cccYZuGjlRpzQOFG163/Wsx8vrF6Md955B6effrpq5yXKZWlNlP3Od76D7u5uXH/99Rg7dixMJpPa6aI8MdZuTWp0C8XXnt1ch6XnN2PJk29h2B8CIB0DW2j77JY63HjeBFz/5NvRc3x50hjcfcVUrNqyJ26F0dktn1/vifjrCcXP1DvWZjrxwCfUV6ieDkpfps/M4UEP7ny2S/L1cteQi2MvF6tbyT3IXYOICodYmbKovQk3P7ULw/6QaJ0cW7ZUlQl3aMXKm7sun4pv/eJ1TBlnx/r50xEGBMsuqX2JZTNjbBORGtL6RbuyshKdnZ2YMWOGFmnSFL+Rz65epxe3/e5dwfBF7c21aGuswaPbuqPbOpprMTNhm9T2xHMsu6AZu3oGFF9vTksdHlnQFveN9rKndgmGAUk8Vg1S1xO7Zy3SkS35kD8zfWaUvB6A5DHr5k/Hj/7nPcHnvqO5FuvmT8cdz+yW3L88oaOfmAZvcEQ0L3c01+Khq2byl+08kw/5k7RxeNAjWubE1rVSdbJU+SjVduj4fAj69U++jbXfaMVzu4/g1e7ksktqX+y19a731cJftImMJ6052pMmTYLH41E7LVQApOJr7xCIdy0WA1tse+I52hqqU7peYvxMvWNtFmI88HyT6TOj5PVyx8jF4h7yBmX3y6VBKi93dvdjwM1nkqhQSJU5sXWtVJ0sVT7KlTeR+Nv1laWCHWm5fbHXZoxtIlJLWh3tn//851i5ciVeeeUV9Pf3w+l0xv0jEiMXP1sohqZYXE0l26Vicortj42fqXeszUKMB55vMo6jreD1msfqVhDLWy4vy+0novwhV6ZE6i65Olms7JIrT1zekOz5lV6bMbaJSC1pzdGurq6G0+nEBRdcELc9HA7DZDIhFAqpkjjKP3Lxs4ViaIrF1VSyXSomp9j+2PiZesfaLMR44Pkm4zjaKjxzGcfqVhDLOxCSbrTK5XUiyh9yZUqk7pKrk8XKLrnypMJaLHt+pddmjG0iUktav2hfc801MJvN2LRpE/76179i27Zt2LZtG1566SVs27ZN7TRSHpGKr90uEO9aLAa22PbEc+w6NJjS9RLjZ+oda7MQ44Hnm0yfGSWvlztGLhZ3pbVEdr9cGqTyckdzLWrK+UwSFQqpMie2rpWqk6XKR7ny5phzNP72sSGfaNkltS/22oyxTURqSaujvWfPHmzcuBHf+ta3cN555+Hcc8+N+0ckRiy+9uzmOtx0QQs2dB6MbpOKgS20fXZLHZadH3+OvYcdWD2vNanSnN0ifL3E+Jl6x9osxHjg+SbTZ0bJ6+WOGV9tk4xjf3JNmWSs7pNrymTTIJaXI9fgQmhEhWN8tU20TInEygbE62S58lGqvLn7iqm445ndmNNSh/NPGyNadknti702Y2wTkVrSWnV8zpw5uPvuu3HhhRdqkSZN5dKqqbkWwzEVQvG1I3GtE2NnisXUFNruC47gM4G43ULHAsrjT+sdKziVe86XZwLIrfwpJ9NnRslnLXeMWBz7iH8MDGPIG4y+vtJagpMF4mhLpUHuGpQ/8il/UmqUtkcicbRjyxS3PwjH8Gj5UG4phtPrR4VVPH61lMTyptxSDIfHD5tFOAa2UNmltB7NtfqWq44TGU9ak+huuukm3HLLLfjRj36E1tZWmM3x81WmT5+uSuIKWb7HcBSKrw1AsBITi6kptr1e5Lxi55CTjc8i1XsmY1HjmVHyWcsdI5bPImlMDOGVmEYlaZC6BhHlvlTKs/HVNsEyTq16VKi8aUB50nFSZZfSepT1LRFlKq2h49/61rewd+9eLFmyBGeddRZmzpyJtra26H8pM45hf1KFBIyGlbjzmd1wDDO0hF74WVCqcuGZyYU0ElH2qVFWsLwhokKV1i/aBw8elD+I0qYkhiO/ZdUHPwtKVS48M7mQRiLKPjXKCpY3RFSo0upon3LKKWqng2IwhqNx8LOgVOXCM5MLaSSi7FOjrGB5Q0SFKqWh4++88w7OP/98OJ3OpH0OhwPnn38+3nvvPdUSV6gYw9E4+FlQqnLhmcmFNBJR9qlRVrC8IaJClVJH+6GHHsIFF1wguJphVVUVLrroIvzkJz9RLXGFijEcjYOfBaUqF56ZXEgjEWWfGmUFyxsiKlQpdbTffPNNfP3rXxfdf8UVV+C1117LOFGFjjEcjYOfBaUqF56ZXEgjEWWfGmUFyxsiKlQpzdH+5z//icrKStH9FRUVOHLkSMaJotEQGY8saMupGI75ip8FpSoXnplcSCMRZZ8aZQXLGyIqRCl1tMeMGYP9+/ejqalJcP++fftQVyc8PIhSV2gxHHudXgy4/XB6g7DbSlBTZkk5Pq9j2I8+lx9ObwB2mxl15eq8h3p/FlrdB+kn02dGz2cgDAAmTU4tex981olyh1BZoTQPx5aJkdd81OfWJN+zXCEiI0ipo33hhRdi9erV+OpXv5q0LxwOY/Xq1bjwwgtVSxwVjp5+N5Zv7sKO7v7oto7mWqyZ14rG2nJF5zg86EmK1TmnpQ7r5k/H+Gqb6mnWSr7cB6VPj2fACNfgs05kfFL51ATg9hTzsNb5nuUKERlFSnO0V61aha6uLpx99tn43e9+h/feew/vvfcefvvb3+Lss8/Gnj17sHLlSq3SSnmq1+lN6mQDQGd3P1Zs7kKv0yt7DsewP6liBUZjdN75zG44hv2qplkr+XIflD49ngEjXKPX6eWzTmRwcvn45b9/mlIe1rrsYR1KREaSUkd7woQJePHFF+F2u/Htb38bp59+Ok4//XQsWLAAw8PDeOGFF9Dc3KxVWilPDbj9SZ3siM7ufgy45SvGPpc/qWKN2H6gD32u3Khc8+U+KH16PANGuMaAm886kdHJ5eP6ylLRfUJ5WOuyh3UoERlJSkPHAeDMM8/Enj178O677+LAgQMIh8M47bTTMHPmTA2SR4XA6Q1mtH/0mIDk/iGZ/UaRL/dB6dPjGTDCNeTyNZ91ouyTy8e+4IjoPqE8rHXZwzqUiIwk5Y52xMyZM9m5JlXYrdKPodz+0WPMkvsrZfYbRb7cB6VPj2fACNeQy9d81omyTy4fl5aID4wUysNalz2sQ4nISFIaOh4RCoXw+OOP4+qrr8aFF16ICy64IO4fUSpqyi3oaK4V3NfRXIuacvmVQusqLEkxOiPmtNShriI3VhvNl/ug9OnxDBjhGjXlfNaJjE4uHx8b8onuE8rDWpc9rEOJyEjS6mjfcsstuOWWWxAKhTBt2jTMmDEj7p9S27dvxxVXXIHx48fDZDJhy5Ytkse//PLLMJlMSf+OHj2azm2QQYy1W7FmXmtSZzuy6riSEF9VZRasmz89qYKd01KH9fOn50xYj3y5D0qfHs+AEa4x1m7ls05kcHL5+LzTxqSUh7Uue1iHEpGRpDV0/Omnn8bvfvc7XHrppRld3O12Y8aMGViyZAm+8Y1vKH7d/v37Ybfbo3/X19dnlA5KLeakGvEphc6xfv50OL1BOD2j2+zWEpxUU5bSOdbNnw6HJwCnJ4Aqmxl2mxnjqm2CxwLQ5J4zfX/GV9vwyII29Ln8GPIG8P+3d9/hUVX5/8DfkzKZSZkkJCQQIcASOgQCCIbQoihihUVFFGmKP1wQ+WIDURHpsAKC7AIqoK5YdilWsIChiYAU6SUQCAshkCzJpNfz+yPOMJMp905yMy3v1/PMA3PLuZ97555z7sncuZ8QjT8ig5kDVElK5GyXUpvzQO45ILUf9mKICdNi3l87QV9cjryiMoT8Wecam6S/kXOcpLZhbz94rhO5lpx2KiZMi7l/7YQ8k7YiROOHsrIK5JaUYc7gjigorbDaThjakPySckQEqVFWIVBUVo63H+6A4vJK6IuUb4PZrhCRu6jRQFutVivydPFBgwZh0KBBDq8XFRWFsLCwWm+fqjiSc1KJ/JTVy4gMVuPzZxMx4+vjsvNoO1LGnCGdsHDraXx3rOrOh0C1L9aMvh0rtqdiV6qy+6xU/s7QQF4U1BUlcrZLUeI8kDoHpPZDKoZL2QV4zcr6c4Z0QrOIIFnHSc5+Su0Hz3Ui15DbTtlsKwZ3Qk5ROeZvPWM2z1BGRUUlpm46hsPpOVg2PAFLfj5r/P/CH6yvo1Sea7YrROQOanTr+Isvvoh3330XQgil45GlS5cuaNy4Me6++27s2bPHJTF4C0dyTiqRn9JaGQuGxlsMkAHbebQdLWP6pmP4a9cmxmlje7fA8u3nzAbZSuwz83e6PyVytktxxnkgtR/WLqBNY7hys9Diwtmw/vRNx/Dfm4WSx4nnO5Hnklt//2unrfj1fBZW7zhvMW/nuayqslOzsCc1G2N7t8DaPWkW/7e3XSIibyD7G+3qt3Zv374dW7ZsQYcOHeDvb/4Ux40bNyoTXTWNGzfGypUr0b17d5SUlOCDDz5A//79sW/fPnTt2tXqOiUlJSgpufWwDr1eXyexeSo5OScNfxV2ZFlHthelC5DMo216S1lNynh1UFvj+4SmYXhve6qs/XD28alvnF0/5eRsr+3ti844D6T2I7eozG4M+uJyu+vnScy/WVAKf18fnu9ejv2n95LbTtlrC6J0GuyyMW/XuSyM7tUcgHmf60j/S/axfhK5P9kD7dDQULP3Q4YMUTwYKW3atEGbNm2M73v16oXz589jyZIl+OSTT6yuM2/ePMycOdNZIXocR3JOKpGf0loZ+cUVEuuUV3vveBmm8+3l/QRqvs/M3+k4Z9dPJXK2S2/DGTmqa5eDWl9Uy/nF5fD3VdUqBnJ/7D+9l9x2yl5bINWXGuabLudI/0v2sX4SuT/ZA+21a9fWZRw11qNHD+zevdvm/GnTpmHKlCnG93q9Hk2bNnVGaB7BkZyTSuSntFZGsMZXYh2/au8dL8N0vr28n0DN95n5Ox3n7PqpRM526W04I0d17XJQ67S1nK/xg7+v/HpEnon9p/eS207Zawuk+lLDfNPlHOl/yT7WTyL3V6OryjvvvBMbN260eCCZXq/H4MGDsX37diVik+XIkSNo3LixzfkBAQEICAhwWjyexpBzcqeVW8iq55x0ZFlHtnddX4LecRHYbeUWNGt5tGtSxnX9rdurDl/OQVJchNXb4Wqzz0ocn/rG2fXTkLNd7rlWE844D6T2I1TrbzcGncbP7vohEvPDg9TQ+PnwfPdy7D+9l9x2yl5bcF1fjD6tIq3egt6nVSSu//nMC9M+15H+l+xj/XRf6enpyMqy/tOM2oiMjERsbKzi5VLdqdHD0FJSUlBaavnAiuLiYuzatUt2Ofn5+Thy5AiOHDkCAEhLS8ORI0eQnp4OoOqvdSNHjjQuv3TpUnz11VdITU3F8ePHMXnyZGzfvh0TJkyoyW4QHMs5qUR+SmtlvLrhKN56qKPsPNqOljFnSCdsOvRf47Q1u9Pw/J2t0EfhfWb+TvenRM52Kc44D6T2IyZMazeG28IDMcfG+nOGdEKT8EDJ48Tznchzya2/Tey0Fb3iIvFs378gqdq8vq0isXBoPHrHRaJ3XATW7E7DmKQWSKr2f3vbJfJU6enpaNu2Hbp166b4q23bdsYxEnkGlXDg0eFHjx4FUPXU7+3bt6NBgwbGeRUVFdi6dStWrVqFixcvyiovJSUFycnJFtNHjRqFdevWYfTo0bh48SJSUlIAAAsXLsTq1atx5coVBAYGIj4+Hm+++abVMmzR6/UIDQ1Fbm6uWS5ub1ST3M9yck46sqxUbKZlFJdX3srZq/FDeFBVXs2rOUUWubFjTHJjyynD2rIArO6HteNmVq5Jzk97+bnllMuLCnPOqp9m+aFNzhMlSdUTqfNBzvliqBuGbYT+WTes7qeVXLX/vVmIPJPc9SEaPzQxyV0vVb6cbZD3qE/9p6eT29/Ya6cMdbuwtByRwQEoKK1A3p/lBQf4Ib+kHPqicoRqq35K8r+CEgRr/BHg6wN9cRmCNf7Q+vsir7gMecW38mgXl5UjVKtGaUUlCkrKa30dwT61itz6eejQIXTr1g13T1+LBrFtbC7nqP+ln8FPc8bg4MGDNh9QXB8Yjm/PsTOga9xcsXL1GRexb83Men98PY1Dt4536dIFKpUKKpUKd955p8V8rVaL5cuXyy6vf//+dlOErVu3zuz9K6+8gldeeUV2+fWZo3l8Hck5qUR+SmtlhAIWF+hSuX7llGEvZmsDl+rHrU+rSExIjsPYdQdQWFr1ULW720XhjQfaY/rm41aPccuoYMlylc4bSvJF6zR1Phi0V0+kzgc550tty6htHm45MRCR8zlSL221U7bah3lDOsHX18ei/KS4CLz5QAcs2HoK20/fsNhum0bK/mGGbQ+5O13j5or+IYM8k0O3jqelpeH8+fMQQmD//v1IS0szvq5cuQK9Xo+xY8fWVawkk7fkt72aU2Q31+/VnCJFt2fruO06l4Xl289hbO8WxmltGuswbdMx5tcmh0mdD5n6YsnzpbZlXM0pqlUebjkx8Lwmcj4l6mWmvthm+7A7NQuv/sey/D2p2Zj17Qm0jzHPUFMX7QHbHiLyFA59o92sWTMAQGWl/fQM5Freks85t6hMMlewkn+5tnfc9qRmY2zSrYF2XeXiJu8ndT7cLJA+XwDUqgw5dau2MfC8JnI+JfqbmwWlErmzrZe/OzUbY0z6SUe3Kxf7VCLyFLIH2l9//bXsQh966KEaBUPK8JZ8zlK5fJXeD6njVtNcoN7yeZAypM4HOTmypR6sIZkvvJZ1S04MPK+JnE+J/sZe+yE3d3ZNtisX+1Qi8hSyB9qDBw82e69Sqcx+X61SqYz/r6ioqH1kVGPeks9ZKpev0vshddxqmgvUWz4PUobU+VDbHNlyyqht3ZITA89rIudTor+x137IzZ1dk+3KxT6ViDyF7N9oV1ZWGl8//vgjunTpgi1btiAnJwc5OTn4/vvv0bVrV2zdurUu4yUZDPkxrfGkPJWhWn+LlCIGhlzBSrJ33JLiInD4co7x/eHLOTZjs5VfW86y5P2kzofwIOnzpbZlyKlbtY2B5zWR8ylRL8OD1DbbB0PubGt6V+snHd2uXGx7iMhT1CiP9uTJk/Huu+9i4MCB0Ol00Ol0GDhwIBYvXoxJkyYpHSM5yFvy28aEae3m+lX6yaK2jlufVpF4/s5WWLM7zTjtTIYec4d0Yn5tcpjU+RCt00ieL7UtIyZMW6s83HJi4HlN5HxK1MtoncZm+9A7rqqc6uUnxUXgzQc74OTV3BpvVy62PUTkKRzKo22g1Wpx4MABdOzY0Wz60aNH0bNnTxQVKfs0aCXVpzygSuS7rp4jt0GgGuWVwmpea1v5rmvrys1C6IvLjfuh0/jhtvBAuzmsq+fVdCTfprW8wBo/H7u5sZ2Vf9zbuUv9VCI3tNw82bbOBzkxGOqGIQ+2oW4YmObJDtX6I9jBPNlyzlme1/WHu9TP+szR/NhFpWXQadUoKK2AvqgMYUH+CFKb58BW+/rgZmEpwgOrclvn/jk96M9c2bmFVe1HkNoX+uJSBKr9EeDvC31RmbF9CjApo6xCoLC05vmxHT0WbHuqMI+2e+DxJVMOPXXc4Pbbb8eUKVPwySefIDo6GgCQmZmJl19+GT169FA0QKq52ua7rp5HM1DtizWjb8eK7eewy+SJpPd1jMarg9rZzXddU1dzijB1o3kaLVs5rK3lu+7bKhKzB3fE29+exM+nrhuXtZVv015uzuq5sQHn5x+nuieVX1oOOTlea5NnG5DOMS81v7YxGvC8JnKOmuTHvmTSngWqfbFseALW7kkzaxcMObBnfXfSLAd277gIjE5qgUmfHUZhaQWS4iLwdO8WUKEMH+y+YFaGK3JYs+0hIndXo1vH16xZg4yMDMTGxiIuLg5xcXGIjY3FlStX8OGHHyodI7mAtTyaY3u3wPJqg2wAGNqtaZ3ku7aVK9NWDmtr+a53nsvCa5uOoW1j87/uMt81WWMvf+xrm44hU18sWUZtzyM560vlmP/vzULJ+TzXiTxHTdqV6u3E2N4tLAbZgO0c2LtTs7F2T5qxT92Tmo21u9OQkVtkUQbbDiIiSzUaaMfFxeHo0aP45ptvMGnSJEyaNAnffvstjh07hri4OKVjJBewlkczoWmY1dyaUboAyZy8NWErV6atOICqC4GEpmEWMVSfBpjnA7a3PWvLkneylz92d2o2bhZInwO1PY/krC+VBzuvuFxyPs91Is9Rk3alejthr++01U9W71N3pWbb/BkN2w4iInM1unUcqErndc899+Cee+5RMh5yE9byaNrKj5lfbD+dW01zWtrKlVmTPJ5ycnsyNydJ5p+WmF+1TO3OIznrS+Zxl8qT7eQc9URUOzVpV/TV6nlNc2BXn26vHLYdRES3yB5oL1u2DM8++yw0Gg2WLVtmd1k+edzzWcujaSs/ZrDG125ZNc1paStXZk3yeMrJ7cncnCSZf1piftUytTuP5KwfUGH/gjlEKk+2k3PUE1Ht1KRd0VWr5zXNgV19ur1y2HYQEd0ie6C9ZMkSPPnkk9BoNFiyZInN5VQqFQfaXsCQR3O3yW1mhy/nICkuwuLWs+v6EotlDWqT79qQK3NntdvlDDmsrW2ver5rQwxycnva2p61Zck7WTvvDXrHRSA8SPocqO15JGf9gtIKu3GGaPwk5/NcJ/IcNWlXQrX+Zu2ArT4csN1PVu9T+8RF2HxWBdsOIiJzsn+jnZaWhoiICOP/bb0uXLhQZ8GS81jLo7lmdxqev7MV+sSZ567ccPByneS7tpUr01YOa2v5rvu2isTcIZ1wJkNvtizzXZM19vLHzh3SSVaKr9qeR3LWl8ox3yQ8UHI+z3Uiz1GTdqV6O7FmdxrGJLWwaBds5cDuHReBMUktjH1qUlwExvRugcahWosy2HYQEVlyKI/2jBkzcNddd+GOO+6AWu2ZjSnzgDrGLJevxg8Ngm7l0a6ee1cqJ29N2cqVaW06AOa79mDuUj+rn/fhQTXPo13T80jO+lJ1zpBH2zA/pFoebZ7r5Ah3qZ/1WU3qrGk7odP6I/jP/Nh5RVU5sKvn0dYXlSNE6wedxh/FZVX5t4MCqpbLLSpFsMa/Kse2SdvCtsP1mEfbPfD4kimHHob28ccfY9asWdBoNEhMTERycjKSk5PRs2dP+PnV+Llq5MY0fj7w9/WBv68K/n4+CPDzQdSf36hVF6T2RVFpBfx9faD280GQ2hfX9cX4n2HAovVDeKDjAxYDAQCqW+9t5dC0NY35rkmuaJ2mxuepgdR5ZDaYt1M3qp/3pqzVOVOmg2p77G2DiNyH3P7JMCDPLylDWKAafj6qP/tyH4QE3PqDm2E5lUqFSgCNdBq0aaQ2zisqrYAA4OOjQligP5pFBhm3Ea0z39aFrALotP6IDGIfSkQEODjQTktLw8WLF/HLL78gJSUFH3zwAd58800EBQUhKSnJOPDu0aNHXcVLTnQ1p8gib2ffVpGYPzTeYqBtbdk+rSLxt/4t8fRHv6OwtOrJ5IZbcGMjgiCHIzEQeYr07AKLfN2mdUPOeV/busG6ReSdDHX74KWbWDY8AQt/OGPW1hhu8xaAzTZABeAVGe0D2xEiItsczqPdvHlzjBkzBh999BEuXryI8+fP491330VUVBTmzp2LXr161UWc5GS5haUWnSdQlSdz6oajyC0slVx217ksvPdLKsb2bmGctjs1G69tOmbzYSo1jYHIU2Tqiy0G2cCtumHtwhUwP+9rWzdYt4i8k2ndHtu7BdbuSbNoa3aey0LK2Rt49T+224CUszck2we2I0RE9jk80DZ16dIl7Ny5Ezt27MDOnTtRVlaGvn37KhUbuVBWfqlF52mw81wWsvJLZS27JzUbCU3DzKbtTs3GzQLpDtiRGIg8xc2CUqtP/QWq6kZuUZnkeV/busG6ReSdTOt2QtMwm21NVEgAdqXabgOiQgJszjO0D2xHiIjsc+jW8fT0dKSkpBhvHc/KykKvXr3Qr18/jBs3Dj169PDYh6SROX1xmd35eSbzpZYtKbfM+asvLlc0BiJPIXXuS53XecVlkHqCpVQZrFtE3sm0blvre+XMk5pvaB/YjhAR2efQQLt58+aIjY3Fc889h+eeew7dunWDr6+v9IrkcXQa+7mvQ0zmSy0b4Gd544ROI33qORIDkaeQOvelzms5573UMqxbRN7JtG5b63vlzJOab2gf2I4QEdnn0K3jjz32GEpKSrBgwQLMnj0bS5cuxaFDh+BAhjDyEJHBaot8nQZ9W0UaU2lJLZsUF4HDl3PMpvWOi0B4kPSdD47EQOQpwoPUFjloDXrHRSBU6y953te2brBuEXkn07p9+HIOkmy0NdfzSuy2AdfzSmzOM7QPbEeIiOxzaKD9+eefIyMjA7/++isGDRqE/fv347777kN4eDgeeOABLFq0CAcOHKirWMmJQgPVmD803qITNTyt1DR1h61l+7SKxMTkVlizO804zfBkZTmpkxyJgchTROs0mDukk8Vg21A3YsK0kud9besG6xaRdzKt22t2p2FMUguLwXbfVpFIbt3QbhvQv3VDyfaB7QgRkX0qocDX0SdPnsT69euxfPlyFBQUoLxc+ve3rqLX6xEaGorc3FzodDpXh6MYQx5LfXGZonksreX61fj5WN2WIYa84jKEaPwRGaxGSXnlrTzaGj+EBzmeR9tauezAvZOn1E859U0qT7bZfCt1Q855X9u6wbpFjvCU+uku6qpfltpefkkZQrX+KC6vRH5xOSKC1CirECgsLbeo5/baALntA9sR9yC3fh46dAjdunXD3dPXokFsG8W2/7/0M/hpzhgcPHgQXbt2VaxcT8PjS6Yc+o22qczMTKSkpBgfjnb27FkEBASgT58+SsZHMtRVHktbubEnJMdh7LoDxtzYptuy1rlGOTiwrs7wDR6RO5BT36TyZANV32zb+6OTnPO+tnWDdYuobjg7v7S17SXFRWBMUguMXLMf3ZuFW922vTZAbvvAdoSIyDqHbh3/8ssv8be//Q3t27dHTEwMRo0ahePHj+Oxxx7Dtm3bkJOTg19++aWuYiUr6iqPpb3c2Mu3nzPLjc2cmVRfyKlvUnmy5eSQJyLP5ez80ra2tyc1G2v3pGFs7xbsp4mIXMChb7RHjBiB7t27Y8iQIUhOTkZSUhK0WuX/MkvyycljWZO/NEvlxh6b1MJsWm22ReQp5NS3sopKu3mybxaUOvzzCSLyHHXVL9dke6b9NftpIiLncmigffPmTQQFBdVVLFQDdZXHsia5sZkzk7ydnPpWWmH/sRdycsgTkedydn5pR/pr9tNERM4je6Ct1+vN/rWHD0lxnrrKY1mT3NjMmUneTk59K6uw/COUeRk1fjQGEXkAZ+eXdqS/Zj9NROQ8sn+jHRYWhvDwcLsvwzLkPHWVx9LR3NjMmUn1gZz6JpUnW04OeSLyXM7OLy23v2Y/TUTkXLK/WuFDztyTIY/l1A1HsbPa001rk8fSVrmmTx1XaltEnkJOfQsFMHdIJ7y26Rh2W3nqOH+fTeTd6qpfdnR7hqeOT/rsMPtpIiIXkD3Q7tevn+Ib37lzJxYtWoSDBw8iIyMDmzZtwuDBg+2uk5KSgilTpuDEiRNo2rQpXn/9dYwePVrx2NyBtRycxeWVFrl5Y8K0WD48QfE8ljFhWix6tLPVPNrfTOxtsS1r8QKQnUfU2TlHyTMpcZ5IlWFvfkyYFvOHxiO3qAz6oqp8tTqtPxqbpM2JjQjCO491kZUnuyYxEJF7c7RfztQXW/S10TqNzemAZRux6NHOKCgph76oDEEBflD7+iC3qBRfTUiC2tcH1/OKUVRWgaAAP+QXl7NtISKqY7X6sWBhYSHS09NRWmqeLiI+Pl7W+gUFBejcuTPGjh2Lv/71r5LLp6Wl4f7778f48ePx6aefYtu2bXjmmWfQuHFjDBw4sEb74K6q58SMDFbj82cTMePr4zZz8yrdUdrLA9oyKlhyWamc23K3VRc5R8kzKXGeSJUhNV9OjmzAfp7s2sZARO5Pbn5pW23KnCGdMPObE9h++obZ9HlDOsHX18dmG/GXhrf656s5t5YLVPti2fAErN2TZrYtti1ERHXDoTzaBjdu3MADDzyAkJAQdOjQAQkJCWYvuQYNGoTZs2djyJAhspZfuXIlWrRogXfeeQft2rXDxIkT8cgjj2DJkiU12Q23ZS0n5oKh8RaDbKDucvM6kge0tjm3nZ1zlDyTEueJVBmZ+mK786/mFNU6R3ZtY2B9IPIemfpim23K9E3H0D4m1GL67tQsvPof6TaielsztncLi0G2tfWIiEgZNfpGe/LkycjJycG+ffvQv39/bNq0CZmZmZg9ezbeeecdpWM02rt3LwYMGGA2beDAgZg8ebLNdUpKSlBSUmJ8L+ep6a5mLSdmlC7Aqbl5HckDWtuc287OOUruw5H6qcR5IlXGzQL783OLympdD2sbA+sDOYsn9p+e5mZBqd02ZUy1/hMAonQa7EqVbiOqtzUJTcPw3vZUyfXIM7B+1k+nTp2qk3IjIyMRGxtbJ2XXZzUaaG/fvh1fffUVunfvDh8fHzRr1gx33303dDod5s2bh/vvv1/pOAEA165dQ3R0tNm06Oho6PV6FBUVQau1vO1p3rx5mDlzZp3EU1es5cTML66QWEfZ3LyO5AGtbc5tZ+ccJffhSP1U4jyRKkOqHkltQ049rOsYiJTiif2np5Gq79b6T2vTTBnaiOptjdz1yDOwftYvRbnZAFQYMWJEnZSv1Qbi9OlTHGwrrEYD7YKCAkRFRQEAwsPDcePGDbRu3RqdOnXCoUOHFA2wtqZNm4YpU6YY3+v1ejRt2tSFEUmzlhMzWOMrsY6yuXkdyQNa25zbzs45Su7DkfqpxHkiVYZUPZLahpx6WNcxECnFE/tPTyNV3631n9ammTK0EdXbGrnrkWdg/axfygrzAAh0eeJVNGzRVtGy9RkXsW/NTGRlZXGgrbAajc7atGmDM2fOoHnz5ujcuTNWrVqF5s2bY+XKlWjcuLHSMRo1atQImZmZZtMyMzOh0+msfpsNAAEBAQgICKizmOqCISemaZqO6/oS9I6LMEsXZFAXuXmtxWBQPRenvWXl5Nx2ZFvkXRypn0qcJ1JlhAfZnx+q9a91PaxtDKwP5Cye2H96mvAgtd02pXr/CQDX9cXo0yrS6k9MTNuI6m3N4cs5SIqLsHqrOtsWz8P6WT8FR8WiQWwbV4dBMtXoYWgvvPACMjIyAAAzZszAli1bEBsbi2XLlmHu3LmKBmgqMTER27ZtM5v2008/ITExsc626QqGnJh9W0Uap7264SjeeqgjesdFmC1bV7l5rcUAWM8DamvZPq0i8fydrbBmd1qN1mfeTzKlxHkiVUa0TmN3fkyYFnOHdKpVPaxtDKwPRN4jWqex2abMGdIJp67mWkzvHVfVFki1EdXbmjW70zAmqYXFtti2EBFQ9fvvQ4cOKfpKT0939W65lEoIIWpbSGFhIU6fPo3Y2FhERkZKr/Cn/Px8pKZWPZgjISEBixcvRnJyMho0aIDY2FhMmzYNV65cwccffwygKr1Xx44dMWHCBIwdOxbbt2/HpEmT8N1338lO76XX6xEaGorc3FzodDrHd9aJDDkyTXNwmuXRtpKb1xkxSOXBNl0WQK3WZ8dfv8ipn0qcJ1JlSM03y21bw3pY2xiInM2T+k9PY6tNsdfWyG0jTJfTaf2NebTZtngXufXz0KFD6NatG+6evlbRb0b/l34GP80Zg4MHD6Jr166Kletp6ur4Xtz3A/atmYnek1fgtnbyMzzJceXYr9j93ssAaj0ktFDff/tdo1vH3377bbz00ksIDAwEAAQGBqJr164oKirC22+/jTfffFNWOb///juSk5ON7w2/NRk1ahTWrVuHjIwMs7+EtGjRAt999x3+7//+D++++y6aNGmCDz74wOtyaBtYy8EZCtTpwFpODI4uW9v1iUwpcZ5IlSE1316ObGfFQETew1abYq+tkdtGWFsumn8nIaI/1dXvv/nb7xoOtGfOnInx48cbB9oGhYWFmDlzpuyBdv/+/WHvC/V169ZZXefw4cMOxUtERERERETW8fffyqvRb7SFEFCpVBbT//jjDzRo0KDWQRERERERERF5Koe+0Q4PD4dKpYJKpULr1q3NBtsVFRXIz8/H+PHjFQ+SiIiIiIiIyFM4NNBeunQphBAYO3YsZs6cidDQUOM8tVqN5s2be90TwImIiIiIiIgc4dBAe9SoUQCqHkqWlJQEP78a/cSbiIiIiIiIyGvV6Dfa/fr1w6VLl/D6669j+PDhuH79OgBgy5YtOHHihKIBEhEREREREXmSGg20d+zYgU6dOmHfvn3YuHEj8vPzAVQ9DG3GjBmKBkhERERERETkSWp07/fUqVMxe/ZsTJkyBSEhIcbpd955J9577z3FgiMiIiIiIiLPdOrUKcXLjIyM9Ijc3DUaaB87dgzr16+3mB4VFYWsrKxaB0VERERERESeqSg3G4AKI0aMULxsrTYQp0+fcvvBdo0G2mFhYcjIyECLFi3Mph8+fBi33XabIoERERERERGR5ykrzAMg0OWJV9GwRVvFytVnXMS+NTORlZXlnQPtxx9/HK+++ir+/e9/Q6VSobKyEnv27MFLL72EkSNHKh0jEREREREReZjgqFg0iG3j6jBcokYD7blz52LixImIjY1FeXk52rdvj4qKCjzxxBN4/fXXlY6RiIiIiIiICIBn/PbboYF2ZWUlFi1ahK+//hqlpaV46qmnMHToUOTn5yMhIQGtWrVSLDAiIiIiIiIiA0/67bdDA+05c+bgrbfewoABA6DVarF+/XoIIbBmzRpFgiEiIiIiIiKyxpN+++3QQPvjjz/GP/7xD/y///f/AAA///wz7r//fnzwwQfw8alRSm4iIiIiIiIi2Tzht98OjY7T09Nx3333Gd8PGDAAKpUKV69eVTwwIiIiIiIiIk/k0EC7vLwcGo3GbJq/vz/KysoUDYqIiIiIiIjIUzl067gQAqNHj0ZAQIBxWnFxMcaPH4+goCDjtI0bNyoXIREREREREZEHcWigPWrUKItpdfHENyIiIiIiIiJP5dBAe+3atXUVBxEREREREZFX4KPCiYiIiIiIiBTEgTYRERERERGRgjjQJiIiIiIiIlKQQ7/RJtfLLSxFVn4p9MVl0Gn9ERmkRmig2tVhERHVGbZ79Rc/eyIi8lQcaHuQqzlFeHXDUew6l2Wc1rdVJOYPjUdMmNaFkRER1Q22e/UXP3siIvJkvHXcQ+QWllpccADAznNZmLrhKHILS10UGRFR3WC7V3/xsyciIk/HgbaHyMovtbjgMNh5LgtZ+bzoICLvwnav/uJnT0REno4DbQ+hLy6zOz9PYj4Rkadhu1d/8bMnIiJPx4G2h9Bp/O3OD5GYT0Tkadju1V/87ImIyNNxoO0hIoPV6Nsq0uq8vq0iERnMp7ASkXdhu1d/8bMnIiJPx6eOe4jQQDXmD43H1A1HsbPaE1gXDI1nuhMi8jps9+ovfvZEVNfS09ORlWX9WRA1derUKUXLI8/mFgPtFStWYNGiRbh27Ro6d+6M5cuXo0ePHlaXXbduHcaMGWM2LSAgAMXFxc4I1aViwrRYPjwBWfmlyCsuQ4jGH5HBzClKRN6L7V79xc+eiOpKeno62rZth6Kiwjopv6yED2wkNxhof/HFF5gyZQpWrlyJnj17YunSpRg4cCDOnDmDqKgoq+vodDqcOXPG+F6lUjkrXJcLDbR+kZFbWIqs/FLoi8ug0/ojMogXI0Tkekq0TbbaPfJ+pp+94Vy6kFXAfo6IaiUrKwtFRYXoOXYGdI2bK1ZuxrG9OP71apSXlytWJnkulw+0Fy9ejHHjxhm/pV65ciW+++47rFmzBlOnTrW6jkqlQqNGjZwZplu7mlNkkW+0b6tIzB8aj5gwrQsjI6L6jG0TKYXnEhHVBV3j5mgQ20ax8vQZFxUrizyfSx+GVlpaioMHD2LAgAHGaT4+PhgwYAD27t1rc738/Hw0a9YMTZs2xcMPP4wTJ044I1y3lFtYanHxAVTlGZ264ShyC3nrChE5H9smUgrPJSIi8kQu/UY7KysLFRUViI6ONpseHR2N06dPW12nTZs2WLNmDeLj45Gbm4u///3v6NWrF06cOIEmTZpYLF9SUoKSkhLje71er+xOuFhWfqnFxYfBznNZyMov5a115La8vX7WZ2ybPJ+71E+eS0SW3KV+VlcXDwOLjIxEbGys4uUS1TWX3zruqMTERCQmJhrf9+rVC+3atcOqVaswa9Ysi+XnzZuHmTNnOjNEp9IXl9mdnycxn8iVvL1+1mdsmzyfu9RPnktEltylfhoU5WYDUGHEiBGKl63VBuL06VMcbJPHcelAOzIyEr6+vsjMzDSbnpmZKfs32P7+/khISEBqaqrV+dOmTcOUKVOM7/V6PZo2bVrzoN2MTuNvd36IxHwiV/L2+lmfsW3yfO5SP3kuEVlyl/ppUFaYB0CgyxOvomGLtoqVq8+4iH1rZiIrK4sDbfI4Lh1oq9VqdOvWDdu2bcPgwYMBAJWVldi2bRsmTpwoq4yKigocO3YM9913n9X5AQEBCAgIUCpktxMZrEbfVpFmeUYN+raKRGQwb6cj9+Xt9bM+Y9vk+dylfvJcIrLkLvWzuuCoWEUfLkbkyVz6MDQAmDJlCt5//3189NFHOHXqFJ577jkUFBQYn0I+cuRITJs2zbj822+/jR9//BEXLlzAoUOHMGLECFy6dAnPPPOMq3bBpUID1Zg/NB59W0WaTe/bKhILhsbzd2tE5BJsm0gpPJeIiMgTufw32sOGDcONGzfw5ptv4tq1a+jSpQu2bt1qfEBaeno6fHxu/T3g5s2bGDduHK5du4bw8HB069YNv/76K9q3b++qXXAqazlpY8K0mD80HrlFZdAXlSFU6w+d1h+NHUh5cjWnyGJ9R1OmMJc3EZmKCdNi+fAEZOWXIq+4DCEaf0QGm7cLmfpi3Cwohb64HDqtH8ID1YjWaRSNQ07bJLWMM+JUgqe0w/biNPRHecVV/ZHa1wf64jK8/kB7+PuokF1QimCN+WfgKftNRET1h8sH2gAwceJEm7eKp6SkmL1fsmQJlixZ4oSo3I+1PKJ3t4vC6w+0x/RNx7A7Nds4vXdcBOYO6YTYiCDJci9lF+C1Tcewp9r6c4Z0QjMZ69uKjTlOiSg00PaAJz27ANOstD1y2y455LRNUss4I04leEo7bCvOhUPjUVJRadEfJcVFYExSC0z6bB8SYsMwJqkFRq7Zj+7NwrFgaDwE4BH7TURE9YvLbx0neWzlEW3TWIfXqg2yAWB3ajZe23QMmfpiu+VezSmyuKgxrD990zFczSmqcWzMcUpEtmTqiy0Gr4D8tksOOW2T1DJXc4rqPE4leEo7bC/Oi/8rtNof7UnNxto9aRjbu4XZ/3eey0LK2Rt49T/uv99ERFT/cKDtIWzlEU1oGmZxUWKwOzUbNwvsX2TkFpXZXT+3SDptipwcp0REpm4WlNaq7ZJDTtsktYxUG6lEnErwlHbYXpxBAb42j/We1GwkNA2z+H9USAB2pbr/fhMRUf3jFreOkzRbeURLyisl1iu3P19iIC0nPylznBKRoyTbJon58rYh3TYJiTKk2i8l4lSCp7TD9uLML66wu65pf2f4v1Qf6C77TURE9Q8H2h7CVh7RAD/7NyXoNPY/Yp229vlJmeOUiBwl2TZJzJe3jdq3TVLLKBGnEjylHbYXZ7DG1+66pv2d4f9SfaC77DcREdU/vHXcQxjyiFZ3+HIOesdFWF2nd1wEwoPsP3U1VOtvd/1QiYG4vdgA5jglIuvCg9S1arvkkNM2SS0j1UYqEacSPKUdthdnQUmFzWOdFBeBw5dzLP5/Pa/EI/abiIjqHw60PYStPKJnMvSYM6STxcWJ4Ym4UulnYsK0NtefM6STrCe2MscpETkqWqfB3Fq0XXLIaZuklokJ09Z5nErwlHbYXpwtGgRa7Y8MTx1fszvN7P99W0UiuXVDj9hvIiKqf9zjnjcPZit3pyM5PeUuGxOmxaJHO1vN5bpgaDz0xeXGXLU6jR9uCw+UtQ/NIoKw8JHOxrylIRp/hDqYR9tebESepr7k5HX1fsbKbHtqk8NaTi7vmDAt5g+Nt4ij8Z9xxEYE4Z3HutyKQeOH8CD3a9/k7Ks7MD3e+cVliAwOQGlFJS7dLIRO64/5Q+ORX1IOfVHVeRng64O84jJs/lsS/H2r8mhv/Fsvs/PAE/abiIjqFw60a8FWLtDZgzvi7W9P4udT182mW8vp6UjeU1vLzhrcETO/OYHtp28Ypzua4zUmTFurfKOekr+VSEp9OZfdYT/lxKBEDmt7ubzlbiNap3G7gbU1UvvqDgzH+3B6DpYNT8DSbecsjv3opBaY9NlhFJZWyOpXPWG/iYiofuGt4zVkLxfoa5uOoW1jncX06jk9Hcl7am/Z6ZuOoX1MqNl0Z+Z49ZT8rURS6su57A77KScGZ+TadsY26BbT4z22dwus3ZNm9dgbcmUDjvWrRERE7oID7Rqylwt0t0mOT1PVc3o6kve0JttzVo5XT8nfSiSlvpzL7rCfcmJwRq5tZ2yDbjE93glNw2TlzQbk96tERETugreO15BUzlJbuT1Nc3o6kve0pttzRo5XT8nfSiSlvpzL7rCfcmIorbCf5VqZXNt1n8+bbjE9nlI5sKvPl9OvEhERuQsOtGtIKmeprdyepjk9Hcl7WtPtOSPHq6fkbyWSUl/OZXfYTzkxlFXYH4gpk2u77vN50y2mx1MqB3b1+XL6VSIiInfBW8dryF4u0N4mOT5NVc/p6Uje05psz1k5Xj0lfyuRlPpyLrvDfsqJwRm5tp2xDbrF9HgfvpyDJBl5swH5/SoREZG74EC7huzlAp07pBPOZOgtplfP6elI3lN7y84Z0gmnruaaTXdmjldPyd9KJKW+nMvusJ9yYnBGrm1nbINuMT3ea3anYUxSC4vBdm+TXNmAY/0qERGRu+A9cbVgL2fp3x/tLCunpyN5T+0tO/ev8S7N8eop+VuJpNSXc9kd9lNODM7IYe0pebK9henxzi8px+yHO6K0ohK5RVXngE7jh5LySqx/pmeN+lUiIiJ3wIG2A67mFCG3qAz6ojKEav2h0/ob83dWV1BagdKKSpSUV0JTUYmC0gqUlBfjf4YLOa0fwv/8xsbasqGBVelvsvJLoS8ug07rj8ggNfJKys2WzSspR2igGhWVAgKAEFUPD6qoFLihL0a2le1ZK7ekvNJqbJn64lsXnybTrbGWx9TatnhhRO7OW3LyStU/W22PgZz6/9+bhcgrLje2i8EaPzQJv1WIVAy22jRT1to3U1JxytkPjZ8P/H194O+rgr+fDzQSvx929Fg7qwxXs7cPhs+huKwcEUEBEABUEPD1UaGyHBACUAEQAEorKlFaUYnyyqq+6VxmHoI1/ghS+yLAD1ULytwuERGRK3CgLdOl7AK8Vi3Xau+4CMwZ0gnNIoIkl+0TF4kJyS0x9qPfUVhaAQC4q21DvPlgB0zfdAy7rZT77s9nsfHwVQBA/G06LBveFdM3W49h5jcnsP30DbPpbz3UASM+3GdMfWLY3uubj5ul1enTKhJ/698ST5vEdl/HaLw6qJ3VfZ47pBNiq+2zNVdziizy5PZtFYn5Q+MRE6aVXJ+Iak6q/km1aenZBRb5pavXf6kyahuDnG1IxSlnP2rbVinR1nlDe2lrHxYMjUdZRSWmbTqG9OxCfPx0D0zddAxnruXh02fusPr5jE5qgUmfHUZhaQWS/ryVfOTaA+gaG4bZgzvh2Y9/R6NQDRYMjYcAPP7YERGR9+FvtGW4mlNkcaEHVOX1nL7pGK7mFEkuuys1C8t/ScXY3i2M09rFhOK1aoNs03JHJ91adtnwBItBtumy7WNCLaa/9fUJLBgab7G96rlrd53LwnvVYhvaranNfX5t0zFk6ostD5SJ3MJSiwsfoCrn6dQNR5FbyLynRHVFqv5duVlot037781Ci8GPYb6h/v9XRhn2YpCzvlTbKxXnFRn7Udu2Som2zhvaS3v7kHL2Bqb/+TmseLIrXt98HHtSs7FgaDze/vaE1c9n7Z40Y5+0x+T97tRsvL75GFaM6Gos+9X/ePaxIyIi78SBtgy5RWUWFwIGu1OzkVtUJmvZPanZSGgaZnyf0DTMbrl+vrc+nqKySrvLmpZrOj1KFyBre9Vji9IF2N3ezQL7Fy9Z+aUWFz4GO89lGb9lJyLlSdU/fXG53fqdJzH/ZkGp5DJ5xeV2Y5CzvlTbK1WG1H7eLCitdVulRFvnDe2lvX2ICgnArj8/Bz9fH+NnYq+fqd4nmb7fnZqN8j9zrFeV7dnHjoiIvBNvHZdBbzKQtiav+NZ8qWVLyiut/t9quUU1K9dUfnGF7O2Zzjddzxp9cbnEfPnHjIiUJVX/JNs0ifn64nLj76Xraht5xWWQ2IR0nDL2w99XZXcZqbZKibbOG9pLe/tg2reYfmZS/Uz1PstaOZL9qAccOyIi8k4caMug0/rbnR+iuTVfatkAkwfsBEg8bCdEW7NyTQVrfGVvz3S+6XrW6DT2Tx2dRv4xIyJlSdU/yTZNYr5O4weJMXCttyGnjZCMU8Z++PtKtMNSx1KBts4b2kt7+2Dat5h+ZlL9TPU+y1o5kv2oBxw7IiLyTrx1XIZQrb9FjlWD3nERCDW5cLC3bFJcBA5fzjG+P3w5x2655RW3/lKv9fexu6xpuabTr+tLZG2vemzX9SV2txceZP9prpHBaov8uAZ9W0UiMphPgyWqK1L1T6fxs1u/QyTmhwepJZcJ0fjZjUHO+lJtr1QZUvsZHqSudVulRFvnDe2lvX24nleCPn9+DuUVlcbPxF4/U71PMn3fOy4Cfn/eiXA9r8Tjjx0REXknDrRliAnTYs6QThYXBIYn35o+1dTWsn3iIvH8na2wZneacdqpq7l2y12359aykz47jNmDbS976mquxfS3HuqIVzcctdhe9YuSPq0iMTHZPLYNBy/bjG3ukE6S+WVDA9WYPzTeYluGJ9Ay7QpR3ZGqf7eFB9pte5qEB2KuRP1vIqMMezHIWV+q7ZWK8zYZ+1HbtkqJts4b2kt7+5DcuqHxc5zw6SHMGtwRveMi8OqGo3jjgQ5WP58xSS2MfVKSyfvecRGYPaQTJvzrkLFsTz92RETknXjruBXW8nE2iwjCwkc6I7eoDHnFZQjR+CP0zzza1dla1s9HhY3P9arK5arxQ3hQVS7XBUPjoS8uNy6r0/jhtvBAvHhPG4zr29KYn1btq7JYNuTPnLWzBnfCS9W25wPgk6d7WuT9Xj48AVn5pcZlI4Or8mhbi+2dx7rcyj9rMl0OW9vihQ9R3ZOqf80igjB/aDzyisuRV1SGEO2t9gQAYk3m64uq2sKQajmyzcqo1iY5HIOV9Q3L2Gt7YyOC7LZTUvPlxFnbY21gL9ezN7SX1fchKMAPal8fZOYVQ6fxx8JHOkNfVIbcwlLMG9IJ+aUVKCgpxZwhnVBYWmF2DuSXlGPdmNuh0/ojwNcH2QUl2PBcLwSrfZFfUop/juhmdnw8/dgREZH34UC7GqlcpnJzctpaNqraIPVqThGmbjRPufVAp0Z4+d62NnPHtmusk9ze1ZwivGxnP6xdgFSPDQCidRrZA2trQgN5sUPkKvbqn1R+aTk5rgGYDYodjUHO+oDt9tRAqp2S047Vtq2SWl9OnmxvaC8N+2Bvf9ua9GGmyzUN1+Ljp3vg1Q1HJc67W+df9e0SERG5C946bsLZuUxtbW9I1yay83Y7Ui7zihIRAGTqi+3ml5bKcS3VBpG5+tYmy93f6suZ5tg2ZTjvrtwsdM4OEBERKYADbRPOzmVqa3tSOaxzJVLWeENOViKqOzcLSmuVn1qqDSJz9a1Nlru/1ZczzbFdnSEvOhERkafgQNuEs3OZ2tqeVG5RZ+R1JSLvJTVgkcyBzTbEIfWtTZa7v9WXk5NbnYiIyFNwoG3C2blMbW1PKreoM/K6EpH30mnsP55DMgc22xCH1Lc2We7+Vl9OidzqRERE7oIDbRPOzmVqa3tSOaxDJS5GvCEnKxHVnfAgda3yU0u1QWSuvrXJcve3+nKmObarM+RFJyIi8hQcaJtwdi5TW9vbdOi/svN2O1Iu84oSEVD1FG57+aWlclzLzb5AVepbmyx3f6svZ5pj25ThvLtNxhPqiYiI3IVb/Hl4xYoVWLRoEa5du4bOnTtj+fLl6NGjh83l//3vf+ONN97AxYsX0apVKyxYsAD33XefIrE4O5epve3JzdvtDvtBRJ5FKr+0VP5qckx9a5Pl7m/15VQCFrnVdRo/DrKJiMjjuHyg/cUXX2DKlClYuXIlevbsiaVLl2LgwIE4c+YMoqKiLJb/9ddfMXz4cMybNw8PPPAA1q9fj8GDB+PQoUPo2LGjIjE5Ox+nre05krfbkXKJiADp/NK1bYPIXH1rk+Xub307LkREVD+4/NbxxYsXY9y4cRgzZgzat2+PlStXIjAwEGvWrLG6/Lvvvot7770XL7/8Mtq1a4dZs2aha9eueO+995wcOREREREREZEll36jXVpaioMHD2LatGnGaT4+PhgwYAD27t1rdZ29e/diypQpZtMGDhyIzZs3W12+pKQEJSUlxvd6vb72gRORIlg/idwX6yeR+2L9JHJ/Lv1GOysrCxUVFYiOjjabHh0djWvXrlld59q1aw4tP2/ePISGhhpfTZs2VSZ4Iqo11k8i98X6SeS+WD+J3J/Lf6Nd16ZNm2b2DXhubi5iY2P5lz8iJwoJCYFKpbKYzvpJ5Hqsn0TuyVbdBGpeP/Pz86uW/+8FVJZXKBZr/vUrAAD9tYsICAhQrNy8a5cAAIcOHTLGroSzZ88C8JzjUFfl1mXZnlau4VzLz8+X1c/Zq59GwoVKSkqEr6+v2LRpk9n0kSNHioceesjqOk2bNhVLliwxm/bmm2+K+Ph4Wdu8fPmyAMAXX3w58ZWbm8v6yRdfbvpi/eSLL/d8ya2brJ988eX8l5z66dJvtNVqNbp164Zt27Zh8ODBAIDKykps27YNEydOtLpOYmIitm3bhsmTJxun/fTTT0hMTJS1zZiYGFy+fFneXyHclF6vR9OmTXH58mXodDpXh6Mob943wLv3z96+hYSEyCpDbv305uPobDyWyvHUY6l0/XQXnvp51AUeC3Oecjzk1k2gqn6ePHkS7du3d/v98gSeco54Cm88nnLqp8tvHZ8yZQpGjRqF7t27o0ePHli6dCkKCgowZswYAMDIkSNx2223Yd68eQCAF154Af369cM777yD+++/H59//jl+//13rF69Wtb2fHx80KRJkzrbH2fS6XRec7JW5837Bnj3/tVm3xytn958HJ2Nx1I53nosPbX/9NbPoyZ4LMx50/Hw8fHBbbfdBsC79svVeCyVVd+Op8sH2sOGDcONGzfw5ptv4tq1a+jSpQu2bt1qfOBZeno6fHxuPbOtV69eWL9+PV5//XW89tpraNWqFTZv3qxYDm0iIiIiIiKi2nD5QBsAJk6caPNW8ZSUFItpjz76KB599NE6joqIiIiIiIjIcS5N70U1ExAQgBkzZij+1EF34M37Bnj3/jlz37z5ODobj6VyeCzdCz+PW3gszHnr8fDW/XIFHktl1dfjqRJCCFcHQUREREREROQt+I02ERERERERkYI40CYiIiIiIiJSEAfaRERERERERAriQNuDvPXWW1CpVGavtm3bujosxVy5cgUjRoxAREQEtFotOnXqhN9//93VYSmiefPmFp+dSqXChAkTXB1arVVUVOCNN95AixYtoNVq0bJlS8yaNQt18fiHefPm4fbbb0dISAiioqIwePBgnDlzRvHt1Af//Oc/ER8fb8xpmZiYiC1btrg6LK8wf/58qFQqTJ482dWh1DtsI+yr7+emN19nAMCKFSvQvHlzaDQa9OzZE/v373d1SB5p586dePDBBxETEwOVSoXNmze7OiSPxPaYA22P06FDB2RkZBhfu3fvdnVIirh58yaSkpLg7++PLVu24OTJk3jnnXcQHh7u6tAUceDAAbPP7aeffgIAr0hTt2DBAvzzn//Ee++9h1OnTmHBggVYuHAhli9frvi2duzYgQkTJuC3337DTz/9hLKyMtxzzz0oKChQfFverkmTJpg/fz4OHjyI33//HXfeeScefvhhnDhxwtWhebQDBw5g1apViI+Pd3Uo9RLbCNvq+7np7dcZX3zxBaZMmYIZM2bg0KFD6Ny5MwYOHIjr16+7OjSPU1BQgM6dO2PFihWuDsWjsT3mU8c9yltvvYXNmzfjyJEjrg5FcVOnTsWePXuwa9cuV4fiFJMnT8a3336Lc+fOQaVSuTqcWnnggQcQHR2NDz/80Dht6NCh0Gq1+Ne//lWn275x4waioqKwY8cO9O3bt063VR80aNAAixYtwtNPP+3qUDxSfn4+unbtin/84x+YPXs2unTpgqVLl7o6rHqNbUQVnpvef53Rs2dP3H777XjvvfcAAJWVlWjatCmef/55TJ061cXReS6VSoVNmzZh8ODBrg7F49XH9pjfaHuYc+fOISYmBn/5y1/w5JNPIj093dUhKeLrr79G9+7d8eijjyIqKgoJCQl4//33XR1WnSgtLcW//vUvjB071uMH2QDQq1cvbNu2DWfPngUA/PHHH9i9ezcGDRpU59vOzc0FUDVApJqrqKjA559/joKCAiQmJro6HI81YcIE3H///RgwYICrQ6E/sY2ownPTu68zSktLcfDgQbPP18fHBwMGDMDevXtdGBnRLfWxPfZzdQAkX8+ePbFu3Tq0adMGGRkZmDlzJvr06YPjx48jJCTE1eHVyoULF/DPf/4TU6ZMwWuvvYYDBw5g0qRJUKvVGDVqlKvDU9TmzZuRk5OD0aNHuzoURUydOhV6vR5t27aFr68vKioqMGfOHDz55JN1ut3KykpMnjwZSUlJ6NixY51uy1sdO3YMiYmJKC4uRnBwMDZt2oT27du7OiyP9Pnnn+PQoUM4cOCAq0OhP7GNqMJzs4o3X2dkZWWhoqIC0dHRZtOjo6Nx+vRpF0VFdEt9bY850PYgpt8QxsfHo2fPnmjWrBm+/PJLj7/Vs7KyEt27d8fcuXMBAAkJCTh+/DhWrlzp8R1gdR9++CEGDRqEmJgYV4eiiC+//BKffvop1q9fjw4dOuDIkSOYPHkyYmJi6vSzmzBhAo4fP+41zylwhTZt2uDIkSPIzc3Ff/7zH4waNQo7duzgYNtBly9fxgsvvICffvoJGo3G1eHQn9hG8Nw0VZ+uM4jcTX1tjznQ9mBhYWFo3bo1UlNTXR1KrTVu3Nji4r5du3bYsGGDiyKqG5cuXcLPP/+MjRs3ujoUxbz88suYOnUqHn/8cQBAp06dcOnSJcybN6/OLl4mTpyIb7/9Fjt37kSTJk3qZBv1gVqtRlxcHACgW7duOHDgAN59912sWrXKxZF5loMHD+L69evo2rWrcVpFRQV27tyJ9957DyUlJfD19XVhhPUP24gqPDdv8ebrjMjISPj6+iIzM9NsemZmJho1auSiqIiq1Of2mL/R9mD5+fk4f/48Gjdu7OpQai0pKcnikf9nz55Fs2bNXBRR3Vi7di2ioqJw//33uzoUxRQWFsLHx7wp8fX1RWVlpeLbEkJg4sSJ2LRpE7Zv344WLVoovo36rLKyEiUlJa4Ow+PcddddOHbsGI4cOWJ8de/eHU8++SSOHDlSbwYy7oBthDmem7d483WGWq1Gt27dsG3bNuO0yspKbNu2jc/dIJdhe8xvtD3KSy+9hAcffBDNmjXD1atXMWPGDPj6+mL48OGuDq3W/u///g+9evXC3Llz8dhjj2H//v1YvXo1Vq9e7erQFFNZWYm1a9di1KhR8PPznqr34IMPYs6cOYiNjUWHDh1w+PBhLF68GGPHjlV8WxMmTMD69evx1VdfISQkBNeuXQMAhIaGQqvVKr49bzZt2jQMGjQIsbGxyMvLw/r165GSkoIffvjB1aF5nJCQEIvfnAUFBSEiIqJe/RbNHbCNMMdz8xZvv86YMmUKRo0ahe7du6NHjx5YunQpCgoKMGbMGFeH5nHy8/PN7hZNS0vDkSNH0KBBA8TGxrowMs/C9hiAII8xbNgw0bhxY6FWq8Vtt90mhg0bJlJTU10dlmK++eYb0bFjRxEQECDatm0rVq9e7eqQFPXDDz8IAOLMmTOuDkVRer1evPDCCyI2NlZoNBrxl7/8RUyfPl2UlJQovi0AVl9r165VfFvebuzYsaJZs2ZCrVaLhg0birvuukv8+OOPrg7La/Tr10+88MILrg6j3mEbIa0+n5vefp2xfPlyERsbK9RqtejRo4f47bffXB2SR/rll1+stiOjRo1ydWgehe2xEMyjTURERERERKQg/kabiIiIiIiISEEcaBMREREREREpiANtIiIiIiIiIgVxoE1ERERERESkIA60iYiIiIiIiBTEgTYRERERERGRgjjQJiIiIiIiIlIQB9pERERERERECuJAm+qUSqXC5s2bXR2GLJ4UK5HSlDj/R48ejcGDB9tdpn///pg8ebLdZdatW4ewsLBaxULkaVJSUqBSqZCTk+PqUCR5UqxEdUGpOtC8eXMsXbrU7jJy+mc5/S85HwfaXurGjRt47rnnEBsbi4CAADRq1AgDBw7Enj17XBqXOwxm33rrLXTp0qXG62/YsAH9+/dHaGgogoODER8fj7fffhv/+9//lAuS6g13rKt33HEHxo8fbzZt5cqVUKlUWLdundn00aNHo0+fPgCAd99912K+FDkXGbbo9XpMnz4dbdu2hUajQaNGjTBgwABs3LgRQogalUkEVJ3XKpUKKpUKarUacXFxePvtt1FeXi65rlJ/KHKXwaycP47ZUlpaioULF6Jz584IDAxEZGQkkpKSsHbtWpSVlSkbKNVbrq6v+fn58Pf3x+eff242/fHHH4dKpcLFixfNpjdv3hxvvPEGAODAgQN49tlnZW/r4sWLUKlUOHLkSI1iTU1NxZgxY9CkSRMEBASgRYsWGD58OH7//fcalUf2caDtpYYOHYrDhw/jo48+wtmzZ/H111+jf//+yM7OdnVoHm369OkYNmwYbr/9dmzZsgXHjx/HO++8gz/++AOffPKJq8MjD+SOdTU5ORkpKSlm03755Rc0bdrUYnpKSgruvPNOAEBoaKjTvonOyclBr1698PHHH2PatGk4dOgQdu7ciWHDhuGVV15Bbm6uU+Ig73XvvfciIyMD586dw4svvoi33noLixYtcnVYHqO0tBQDBw7E/Pnz8eyzz+LXX3/F/v37MWHCBCxfvhwnTpxwdYjkRVxZX4ODg9G9e3er/WP1fjMtLQ2XLl0y9psNGzZEYGCgU+L8/fff0a1bN5w9exarVq3CyZMnsWnTJrRt2xYvvviiU2KodwR5nZs3bwoAIiUlxe4yTz/9tIiMjBQhISEiOTlZHDlyxDh/xowZonPnzmLlypWiSZMmQqvVikcffVTk5OQYl9m/f78YMGCAiIiIEDqdTvTt21ccPHjQbDsAxKZNm2y+r+79998Xbdu2FQEBAaJNmzZixYoVxnlpaWkCgNiwYYPo37+/0Gq1Ij4+Xvz6669mZaxevdoY8+DBg8U777wjQkNDhRBCrF27VgAwe61du9YY2/vvvy8GDx4stFqtiIuLE1999ZWx3H379gkAYunSpTaPqemx+/DDD0XTpk1FUFCQeO6550R5eblYsGCBiI6OFg0bNhSzZ8+2eRyofnDXuvrDDz8IACIjI8M4Pzo6WqxYsUI0a9bMOO3ChQsCgPjll1+EEEKMGjVKPPzww8b5+fn54qmnnhJBQUGiUaNG4u9//7vo16+feOGFF4QQQvTr18+iPgpRVU9DQ0PF1q1bRdu2bUVQUJAYOHCguHr1qrHs5557TgQFBYkrV65YHLO8vDxRVlYmhBCiWbNmYtasWcY4YmNjxVdffSWuX78uHnroIREUFCQ6deokDhw4YPMzoPqn+rkshBB33323uOOOO0RxcbF48cUXRUxMjAgMDBQ9evQw1oFffvnF4pyeMWOGEEKIjz/+WHTr1k0EBweL6OhoMXz4cJGZmWks37CuoS+p/r46e3EIIa8elZWVieeff16EhoaKBg0aiFdeeUWMHDnSuO+jRo2y2J+0tDRjbD///LPo1q2b0Gq1IjExUZw+fdpY9oIFC4SPj484dOiQReylpaUiPz9fCFHVDkycOFG88MILIiwsTERFRYnVq1eL/Px8MXr0aBEcHCxatmwpvv/+exmfHNVH7lBfp02bJtq0aWOcf/LkSREaGirmzp0rRo0aZZy+Zs0aERAQIIqKioQQVX3UkiVLjPPPnj0r+vTpIwICAkS7du3Ejz/+aNY/V4+3X79+Zsdg0aJFolGjRqJBgwbib3/7mygtLRVCCFFZWSk6dOggunXrJioqKiyOoWE/DNfaX3zxhejdu7fQaDSie/fu4syZM2L//v2iW7duIigoSNx7773i+vXrDnxK9RMH2l6orKxMBAcHi8mTJ4vi4mKrywwYMEA8+OCD4sCBA+Ls2bPixRdfFBERESI7O1sIUXXxHhQUJO68805x+PBhsWPHDhEXFyeeeOIJYxnbtm0Tn3zyiTh16pQ4efKkePrpp0V0dLTQ6/XGZRwZaP/rX/8SjRs3Fhs2bBAXLlwQGzZsEA0aNBDr1q0TQtyq/G3bthXffvutOHPmjHjkkUdEs2bNjBfVu3fvFj4+PmLRokXizJkzYsWKFaJBgwbGgXZhYaF48cUXRYcOHURGRobIyMgQhYWFxtiaNGki1q9fL86dOycmTZokgoODjcfE8N7QaNkyY8YMERwcLB555BFx4sQJ8fXXXwu1Wi0GDhwonn/+eXH69GmxZs0aAUD89ttvdssi7+audTU/P1/4+/uL9evXCyGEOHHihNDpdKK4uFgEBweLCxcuCCGE+PDDD4VGozHGXv1i57nnnhOxsbHi559/FkePHhUPPPCACAkJMQ60s7OzRZMmTcTbb79trI9CVA0Q/P39xYABA8SBAwfEwYMHRbt27Yz7VFFRIcLDw8Wzzz4reYybNWsmGjRoIFauXCnOnj0rnnvuOaHT6cS9994rvvzyS3HmzBkxePBg0a5dO1FZWSlZHtUP1i7cH3roIdG1a1fxzDPPiF69eomdO3eK1NRUsWjRIhEQECDOnj0rSkpKxNKlS4VOpzOe03l5eUKIqvry/fffi/Pnz4u9e/eKxMREMWjQIGP5jg607cUhhHQ9EkKI2bNniwYNGoiNGzeKU6dOifHjxwudTmfc95ycHJGYmCjGjRtn3J/y8nJjbD179hQpKSnixIkTok+fPqJXr17GsuPj48U999wjeaz79esnQkJCxKxZs8TZs2fFrFmzhK+vrxg0aJBYvXq1sd5GRESIgoICyfKo/nGH+moYEBv+kLVixQpx//33i99++83sD9RPPfWU6N+/v/G96UC7oqJCdOzYUdx1113iyJEjYseOHSIhIcGsf96/f7/xj1wZGRnGa4FRo0YJnU4nxo8fL06dOiW++eYbERgYKFavXi2EEOLQoUMCgLFft8X0Wnvr1q3i5MmT4o477hDdunUT/fv3F7t37xaHDh0ScXFxYvz48Q59TvURB9pe6j//+Y8IDw8XGo1G9OrVS0ybNk388ccfQgghdu3aZbxoNtWyZUuxatUqIUTVxbuvr6/473//a5y/ZcsW4ePjY/Ytl6mKigoREhIivvnmG+M0RwbaLVu2tGgAZs2aJRITE4UQtyr/Bx98YJx/4sQJAUCcOnVKCCHEsGHDxP33329WxpNPPmkcaBv2rXPnzhbbByBef/114/v8/HwBQGzZskUIIcSgQYNEfHy81dhNzZgxQwQGBpoNYgYOHCiaN29u9lfENm3aiHnz5kmWR97NXetqUlKScSC7YsUKcd999wkhhLjnnnvEmjVrhBBVFwzJycnGdUwvdvLy8oRarRZffvmlcX52drbQarXGgbYQln/NF+LWnSepqanGaStWrBDR0dFCCCEyMzMFALF48WKr+2eqWbNmYsSIEcb3GRkZAoB44403jNP27t1r8Q0+1W+m53JlZaX46aefREBAgBg9erTw9fW1uJPirrvuEtOmTRNC3PomWcqBAwcEAOOFvSMD7UuXLsmKw149EqLqTpVFixYZ35eXl4vY2FizQYvpXSgGpt9oG3z33XcCgPGbOq1WKyZNmiR5HPr16yd69+5tFkNQUJB46qmnjNMM9Xbv3r2S5VH94w71taCgQKjVauN17KOPPioWLlwoysrKRFBQkPEP1LGxsWLmzJnGck37wB9++EH4+fmZxbtlyxaz/tlwLXz48GGLY9CsWTNRXl5unPboo4+KYcOGCSGE+OKLLwQAq3eYmLJ2rf3ZZ58JAGLbtm3GafPmzTP7Bp+s42+0vdTQoUNx9epVfP3117j33nuRkpKCrl27Yt26dfjjjz+Qn5+PiIgIBAcHG19paWk4f/68sYzY2FjcdtttxveJiYmorKzEmTNnAACZmZkYN24cWrVqhdDQUOh0OuTn5yM9Pd3heAsKCnD+/Hk8/fTTZjHNnj3bLCYAiI+PN/6/cePGAIDr168DAM6cOYMePXqYLV/9vT2mZQcFBUGn0xnLFg48XKl58+YICQkxvo+Ojkb79u3h4+NjNs1QNtVf7lpX+/fvb/xdWUpKCvr37w8A6Nevn9n05ORkq+ufP38epaWl6Nmzp3FagwYN0KZNG1nHJTAwEC1btjS+b9y4cY3qImBer6OjowEAnTp1spjG+kimvv32WwQHB0Oj0WDQoEEYNmwYHnnkEVRUVKB169ZmdXLHjh0WfVV1Bw8exIMPPojY2FiEhISgX79+AFCjPvPYsWOy4rBXj3Jzc5GZmWnWR/r6+qJbt26y47DXHztST03L8fX1RUREBOsoOcTV9TUwMBC33367sX/csWMH+vfvDz8/P/Tq1QspKSm4cOEC0tPTbfabp06dQtOmTRETE2OclpiYKPsYdOjQAb6+vsb3dd1vsj5K83N1AFR3NBoN7r77btx9991444038Mwzz2DGjBn429/+hsaNG1s8tAGAQw8yGjVqFLKzs/Huu++iWbNmCAgIQGJiIkpLSx2ONT8/HwDw/vvvm12YAzBrNADA39/f+H+VSgUAqKysdHib1piWbSjfUHbr1q2xe/dulJWVWSwnpxx7ZVP95o51NTk5GXPmzMGVK1eQkpKCl156CUDVQHvVqlU4f/48Ll++bHygi9Ks1RfDhULDhg0RFhaG06dPO1yWoc2oy3aEvENycjL++c9/Qq1WIyYmBn5+fvjiiy/g6+uLgwcPWvRNwcHBNssqKCjAwIEDMXDgQHz66ado2LAh0tPTMXDgwBr3mXLisFePlGCvHrVu3bpGddRQFusoOcId6mtycjK++OILnDhxAkVFRejatSuAqn7zl19+QWVlJQIDAy2uc5UidQ0LAKdPn0ZCQoJDZdnqN1kfpfEb7Xqkffv2KCgoQNeuXXHt2jX4+fkhLi7O7BUZGWlcPj09HVevXjW+/+233+Dj42P8RmrPnj2YNGkS7rvvPnTo0AEBAQHIysqqUWzR0dGIiYnBhQsXLGJq0aKF7HLatGmDAwcOmE2r/l6tVqOiosLhGJ944gnk5+fjH//4h9X5rk7BQt7DHepqr169oFar8Y9//APFxcXGb7luv/123LhxA2vWrEFQUJDNO0ZatmwJf39/7Nu3zzjt5s2bOHv2rNlyNamPPj4+ePzxx/Hpp5+a7bdBfn6+rLQuRPYEBQUhLi4OsbGx8POr+l4iISEBFRUVuH79ukWdbNSoEQDr5/Tp06eRnZ2N+fPno0+fPmjbtm2tvg2SE4eU0NBQREdHm/WRFRUVOHTokNlytekzf/75Zxw+fNhiXllZGQoKChwuk8gWd6ivycnJOHfuHNavX4/evXsbB/d9+/bFjh07kJKSgqSkJKjVaqvrt2vXDpcvX0ZGRoZx2m+//Wa2jGFdR+tkly5d0L59e7zzzjtWB8i8hq0bHGh7oezsbNx5553417/+haNHjyItLQ3//ve/sXDhQjz88MMYMGAAEhMTMXjwYPz444+4ePEifv31V0yfPt0sj55Go8GoUaPwxx9/YNeuXZg0aRIee+wxY+PUqlUrfPLJJzh16hT27duHJ598ElqtVjK+tLQ0HDlyxOxVUFCAmTNnYt68eVi2bBnOnj2LY8eOYe3atVi8eLHsfX/++efx/fffY/HixTh37hxWrVqFLVu2GP8aB1Td1m2IISsrCyUlJbLK7tmzJ1555RW8+OKLeOWVV7B3715cunQJ27Ztw6OPPoqPPvpIdpxEgHvXVa1WizvuuAPLly9HUlKS8YJBrVabTbd1d0dwcDCefvppvPzyy9i+fTuOHz+O0aNHm/18Aqiqjzt37sSVK1cc+kPdnDlz0LRpU/Ts2RMff/wxTp48iXPnzmHNmjVISEgw3iVDpKTWrVvjySefxMiRI7Fx40akpaVh//79mDdvHr777jsAVed0fn4+tm3bhqysLBQWFiI2NhZqtRrLly/HhQsX8PXXX2PWrFmytnns2DGz/vKPP/6QFYcczz//PObNm4evvvoKZ86cwQsvvICbN29a9Jn79u3DxYsXkZWVJftbrMmTJyMpKQl33XUXVqxYgT/++AMXLlzAl19+iTvuuAPnzp2THSdRTTi7vvbq1QsBAQFYvny58VZzoOonjNevX8dXX31l87ZxABgwYABat25t1p9Pnz7dbJmoqChotVps3boVmZmZslNZqlQqrF27FmfPnkWfPn3w/fff48KFCzh69CjmzJmDhx9+WFY55BgOtL1QcHAwevbsiSVLlqBv377o2LEj3njjDYwbNw7vvfceVCoVvv/+e/Tt2xdjxoxB69at8fjjj+PSpUvG32EAQFxcHP7617/ivvvuwz333IP4+Hizb3M//PBD3Lx5E127dsVTTz2FSZMmISoqSjK+KVOmICEhwex1+PBhPPPMM/jggw+wdu1adOrUCf369cO6desc+kY7KSkJK1euxOLFi9G5c2ds3boV//d//weNRmNcZujQobj33nuRnJyMhg0b4rPPPpNd/oIFC7B+/Xrs27cPAwcORIcOHTBlyhTEx8dj1KhRssshAty/riYnJyMvL8/4+2yDfv36IS8vz+4FAwAsWrQIffr0wYMPPogBAwagd+/eFr//fPvtt3Hx4kW0bNkSDRs2lHHUqjRo0AC//fYbRowYgdmzZyMhIQF9+vTBZ599hkWLFiE0NFR2WUSOWLt2LUaOHIkXX3wRbdq0weDBg3HgwAHExsYCqLrYHj9+PIYNG4aGDRti4cKFaNiwIdatW4d///vfaN++PebPn4+///3vsrbXt29fs/7SUIek4pDj1VdfxfDhwzFy5EgkJiYiODgYAwcONOszX3rpJfj6+qJ9+/bGW2jlCAgIwE8//YRXXnkFq1atwh133IHbb78dy5Ytw6RJk9CxY0fZcRLVlDPrq0ajwR133GHRbwYEBBin2+s3fXx8sGnTJhQVFaFHjx545plnMGfOHLNl/Pz8sGzZMqxatQoxMTEODZB79OiB33//HXFxcRg3bhzatWuHhx56CCdOnMDSpUtll0PyqYSSP9Yhr/HWW29h8+bNOHLkiKtDqbVx48bh9OnT2LVrl6tDIVKcN9VVInKtyspKtGvXDo899pjsb9yJiMg6PgyNvM7f//533H333QgKCsKWLVvw0Ucf2fxdNRERUX116dIl/Pjjj+jXrx9KSkrw3nvvIS0tDU888YSrQyMi8ngcaJPX2b9/PxYuXIi8vDz85S9/wbJly/DMM8+4OiwiIiK34uPjg3Xr1uGll16CEAIdO3bEzz//jHbt2rk6NCIij8dbx4mIiIiIiIgUxIehERERERERESmIA20iIiIiIiIiBXGgTURERERERKQgDrSJiIiIiIiIFMSBNhEREREREZGCONAmIiIiIiIiUhAH2kREREREREQK4kCbiIiIiIiISEEcaBMREREREREp6P8Dbdfhlro3bl8AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x1000 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.pairplot(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 445,
+   "id": "b19e5d1d",
+   "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>SepalLengthCm</th>\n",
+       "      <th>SepalWidthCm</th>\n",
+       "      <th>PetalLengthCm</th>\n",
+       "      <th>PetalWidthCm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
+       "0              5.1           3.5            1.4           0.2\n",
+       "1              4.9           3.0            1.4           0.2\n",
+       "2              4.7           3.2            1.3           0.2\n",
+       "3              4.6           3.1            1.5           0.2\n",
+       "4              5.0           3.6            1.4           0.2\n",
+       "..             ...           ...            ...           ...\n",
+       "145            6.7           3.0            5.2           2.3\n",
+       "146            6.3           2.5            5.0           1.9\n",
+       "147            6.5           3.0            5.2           2.0\n",
+       "148            6.2           3.4            5.4           2.3\n",
+       "149            5.9           3.0            5.1           1.8\n",
+       "\n",
+       "[150 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 445,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 446,
+   "id": "4f5f0cf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         Iris-setosa\n",
+       "1         Iris-setosa\n",
+       "2         Iris-setosa\n",
+       "3         Iris-setosa\n",
+       "4         Iris-setosa\n",
+       "            ...      \n",
+       "145    Iris-virginica\n",
+       "146    Iris-virginica\n",
+       "147    Iris-virginica\n",
+       "148    Iris-virginica\n",
+       "149    Iris-virginica\n",
+       "Name: Species, Length: 150, dtype: object"
+      ]
+     },
+     "execution_count": 446,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a2db139-706b-494e-a05c-4e1d81c8b037",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 447,
+   "id": "b5c963df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # print(df.columns)\n",
+    "# # print(df.head())\n",
+    "# r=Y1.columns\n",
+    "# r=np.array(r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 448,
+   "id": "7016a62f-0288-4ed6-814e-9922666288f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 449,
+   "id": "6db4e16d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df.iloc[0]  # Accessing the first row\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 450,
+   "id": "d174c533",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df.iloc[1] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 451,
+   "id": "c07179a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_values=X.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 452,
+   "id": "ea55a2e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[5.1, 3.5, 1.4, 0.2],\n",
+       "       [4.9, 3. , 1.4, 0.2],\n",
+       "       [4.7, 3.2, 1.3, 0.2],\n",
+       "       [4.6, 3.1, 1.5, 0.2],\n",
+       "       [5. , 3.6, 1.4, 0.2],\n",
+       "       [5.4, 3.9, 1.7, 0.4],\n",
+       "       [4.6, 3.4, 1.4, 0.3],\n",
+       "       [5. , 3.4, 1.5, 0.2],\n",
+       "       [4.4, 2.9, 1.4, 0.2],\n",
+       "       [4.9, 3.1, 1.5, 0.1],\n",
+       "       [5.4, 3.7, 1.5, 0.2],\n",
+       "       [4.8, 3.4, 1.6, 0.2],\n",
+       "       [4.8, 3. , 1.4, 0.1],\n",
+       "       [4.3, 3. , 1.1, 0.1],\n",
+       "       [5.8, 4. , 1.2, 0.2],\n",
+       "       [5.7, 4.4, 1.5, 0.4],\n",
+       "       [5.4, 3.9, 1.3, 0.4],\n",
+       "       [5.1, 3.5, 1.4, 0.3],\n",
+       "       [5.7, 3.8, 1.7, 0.3],\n",
+       "       [5.1, 3.8, 1.5, 0.3],\n",
+       "       [5.4, 3.4, 1.7, 0.2],\n",
+       "       [5.1, 3.7, 1.5, 0.4],\n",
+       "       [4.6, 3.6, 1. , 0.2],\n",
+       "       [5.1, 3.3, 1.7, 0.5],\n",
+       "       [4.8, 3.4, 1.9, 0.2],\n",
+       "       [5. , 3. , 1.6, 0.2],\n",
+       "       [5. , 3.4, 1.6, 0.4],\n",
+       "       [5.2, 3.5, 1.5, 0.2],\n",
+       "       [5.2, 3.4, 1.4, 0.2],\n",
+       "       [4.7, 3.2, 1.6, 0.2],\n",
+       "       [4.8, 3.1, 1.6, 0.2],\n",
+       "       [5.4, 3.4, 1.5, 0.4],\n",
+       "       [5.2, 4.1, 1.5, 0.1],\n",
+       "       [5.5, 4.2, 1.4, 0.2],\n",
+       "       [4.9, 3.1, 1.5, 0.1],\n",
+       "       [5. , 3.2, 1.2, 0.2],\n",
+       "       [5.5, 3.5, 1.3, 0.2],\n",
+       "       [4.9, 3.1, 1.5, 0.1],\n",
+       "       [4.4, 3. , 1.3, 0.2],\n",
+       "       [5.1, 3.4, 1.5, 0.2],\n",
+       "       [5. , 3.5, 1.3, 0.3],\n",
+       "       [4.5, 2.3, 1.3, 0.3],\n",
+       "       [4.4, 3.2, 1.3, 0.2],\n",
+       "       [5. , 3.5, 1.6, 0.6],\n",
+       "       [5.1, 3.8, 1.9, 0.4],\n",
+       "       [4.8, 3. , 1.4, 0.3],\n",
+       "       [5.1, 3.8, 1.6, 0.2],\n",
+       "       [4.6, 3.2, 1.4, 0.2],\n",
+       "       [5.3, 3.7, 1.5, 0.2],\n",
+       "       [5. , 3.3, 1.4, 0.2],\n",
+       "       [7. , 3.2, 4.7, 1.4],\n",
+       "       [6.4, 3.2, 4.5, 1.5],\n",
+       "       [6.9, 3.1, 4.9, 1.5],\n",
+       "       [5.5, 2.3, 4. , 1.3],\n",
+       "       [6.5, 2.8, 4.6, 1.5],\n",
+       "       [5.7, 2.8, 4.5, 1.3],\n",
+       "       [6.3, 3.3, 4.7, 1.6],\n",
+       "       [4.9, 2.4, 3.3, 1. ],\n",
+       "       [6.6, 2.9, 4.6, 1.3],\n",
+       "       [5.2, 2.7, 3.9, 1.4],\n",
+       "       [5. , 2. , 3.5, 1. ],\n",
+       "       [5.9, 3. , 4.2, 1.5],\n",
+       "       [6. , 2.2, 4. , 1. ],\n",
+       "       [6.1, 2.9, 4.7, 1.4],\n",
+       "       [5.6, 2.9, 3.6, 1.3],\n",
+       "       [6.7, 3.1, 4.4, 1.4],\n",
+       "       [5.6, 3. , 4.5, 1.5],\n",
+       "       [5.8, 2.7, 4.1, 1. ],\n",
+       "       [6.2, 2.2, 4.5, 1.5],\n",
+       "       [5.6, 2.5, 3.9, 1.1],\n",
+       "       [5.9, 3.2, 4.8, 1.8],\n",
+       "       [6.1, 2.8, 4. , 1.3],\n",
+       "       [6.3, 2.5, 4.9, 1.5],\n",
+       "       [6.1, 2.8, 4.7, 1.2],\n",
+       "       [6.4, 2.9, 4.3, 1.3],\n",
+       "       [6.6, 3. , 4.4, 1.4],\n",
+       "       [6.8, 2.8, 4.8, 1.4],\n",
+       "       [6.7, 3. , 5. , 1.7],\n",
+       "       [6. , 2.9, 4.5, 1.5],\n",
+       "       [5.7, 2.6, 3.5, 1. ],\n",
+       "       [5.5, 2.4, 3.8, 1.1],\n",
+       "       [5.5, 2.4, 3.7, 1. ],\n",
+       "       [5.8, 2.7, 3.9, 1.2],\n",
+       "       [6. , 2.7, 5.1, 1.6],\n",
+       "       [5.4, 3. , 4.5, 1.5],\n",
+       "       [6. , 3.4, 4.5, 1.6],\n",
+       "       [6.7, 3.1, 4.7, 1.5],\n",
+       "       [6.3, 2.3, 4.4, 1.3],\n",
+       "       [5.6, 3. , 4.1, 1.3],\n",
+       "       [5.5, 2.5, 4. , 1.3],\n",
+       "       [5.5, 2.6, 4.4, 1.2],\n",
+       "       [6.1, 3. , 4.6, 1.4],\n",
+       "       [5.8, 2.6, 4. , 1.2],\n",
+       "       [5. , 2.3, 3.3, 1. ],\n",
+       "       [5.6, 2.7, 4.2, 1.3],\n",
+       "       [5.7, 3. , 4.2, 1.2],\n",
+       "       [5.7, 2.9, 4.2, 1.3],\n",
+       "       [6.2, 2.9, 4.3, 1.3],\n",
+       "       [5.1, 2.5, 3. , 1.1],\n",
+       "       [5.7, 2.8, 4.1, 1.3],\n",
+       "       [6.3, 3.3, 6. , 2.5],\n",
+       "       [5.8, 2.7, 5.1, 1.9],\n",
+       "       [7.1, 3. , 5.9, 2.1],\n",
+       "       [6.3, 2.9, 5.6, 1.8],\n",
+       "       [6.5, 3. , 5.8, 2.2],\n",
+       "       [7.6, 3. , 6.6, 2.1],\n",
+       "       [4.9, 2.5, 4.5, 1.7],\n",
+       "       [7.3, 2.9, 6.3, 1.8],\n",
+       "       [6.7, 2.5, 5.8, 1.8],\n",
+       "       [7.2, 3.6, 6.1, 2.5],\n",
+       "       [6.5, 3.2, 5.1, 2. ],\n",
+       "       [6.4, 2.7, 5.3, 1.9],\n",
+       "       [6.8, 3. , 5.5, 2.1],\n",
+       "       [5.7, 2.5, 5. , 2. ],\n",
+       "       [5.8, 2.8, 5.1, 2.4],\n",
+       "       [6.4, 3.2, 5.3, 2.3],\n",
+       "       [6.5, 3. , 5.5, 1.8],\n",
+       "       [7.7, 3.8, 6.7, 2.2],\n",
+       "       [7.7, 2.6, 6.9, 2.3],\n",
+       "       [6. , 2.2, 5. , 1.5],\n",
+       "       [6.9, 3.2, 5.7, 2.3],\n",
+       "       [5.6, 2.8, 4.9, 2. ],\n",
+       "       [7.7, 2.8, 6.7, 2. ],\n",
+       "       [6.3, 2.7, 4.9, 1.8],\n",
+       "       [6.7, 3.3, 5.7, 2.1],\n",
+       "       [7.2, 3.2, 6. , 1.8],\n",
+       "       [6.2, 2.8, 4.8, 1.8],\n",
+       "       [6.1, 3. , 4.9, 1.8],\n",
+       "       [6.4, 2.8, 5.6, 2.1],\n",
+       "       [7.2, 3. , 5.8, 1.6],\n",
+       "       [7.4, 2.8, 6.1, 1.9],\n",
+       "       [7.9, 3.8, 6.4, 2. ],\n",
+       "       [6.4, 2.8, 5.6, 2.2],\n",
+       "       [6.3, 2.8, 5.1, 1.5],\n",
+       "       [6.1, 2.6, 5.6, 1.4],\n",
+       "       [7.7, 3. , 6.1, 2.3],\n",
+       "       [6.3, 3.4, 5.6, 2.4],\n",
+       "       [6.4, 3.1, 5.5, 1.8],\n",
+       "       [6. , 3. , 4.8, 1.8],\n",
+       "       [6.9, 3.1, 5.4, 2.1],\n",
+       "       [6.7, 3.1, 5.6, 2.4],\n",
+       "       [6.9, 3.1, 5.1, 2.3],\n",
+       "       [5.8, 2.7, 5.1, 1.9],\n",
+       "       [6.8, 3.2, 5.9, 2.3],\n",
+       "       [6.7, 3.3, 5.7, 2.5],\n",
+       "       [6.7, 3. , 5.2, 2.3],\n",
+       "       [6.3, 2.5, 5. , 1.9],\n",
+       "       [6.5, 3. , 5.2, 2. ],\n",
+       "       [6.2, 3.4, 5.4, 2.3],\n",
+       "       [5.9, 3. , 5.1, 1.8]])"
+      ]
+     },
+     "execution_count": 452,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 453,
+   "id": "575cd357",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y_values=Y.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 454,
+   "id": "f3d8e023",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-virginica'], dtype=object)"
+      ]
+     },
+     "execution_count": 454,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Y_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 455,
+   "id": "96a38588",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Y_Data = np.array([np.average(X[:, i][Y==j].astype('float32')) for i in range (X.shape[1]) for j in (np.unique(Y))])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 456,
+   "id": "a32bef39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Y_Data_reshaped = Y_Data.reshape(4, 3)\n",
+    "# X_axis = np.arange(len(columns)-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 457,
+   "id": "05578917",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# width = 0.25"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 458,
+   "id": "7b91ffaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# x1=df.drop(columns=['Species_Iris-setosa', 'Species_Iris-versicolor','Species_Iris-virginica'])\n",
+    "# y1=df[['Species_Iris-setosa', 'Species_Iris-versicolor','Species_Iris-virginica']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 459,
+   "id": "8c50265d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 460,
+   "id": "28f91083",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acff4f2b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59dc3c62",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 461,
+   "id": "284fa5b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "accuracy_scores={}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 462,
+   "id": "b4d310b6-bdd7-45a1-84fc-3d009a47bca0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Catagory_kmeans=['Iris-Versicolor', 'Iris-Setosa', 'Iris-Virginica']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 463,
+   "id": "92933948",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 464,
+   "id": "40305390",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-19 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-19 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-19 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-19 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-19 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-19 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-19 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-19 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "SVC()"
+      ]
+     },
+     "execution_count": 464,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "svn = SVC()\n",
+    "# Assuming y_train is one-hot encoded\n",
+    "# y_train_labels = np.argmax(y_train, axis=1)\n",
+    "\n",
+    "svn.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 465,
+   "id": "5eaaa67c-4139-4f79-919a-a8398172c06f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# y_train_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 466,
+   "id": "1bd7b538-0db3-4a5b-828a-2d101f094b14",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10         Iris-setosa\n",
+       "29         Iris-setosa\n",
+       "105     Iris-virginica\n",
+       "79     Iris-versicolor\n",
+       "25         Iris-setosa\n",
+       "            ...       \n",
+       "63     Iris-versicolor\n",
+       "37         Iris-setosa\n",
+       "72     Iris-versicolor\n",
+       "73     Iris-versicolor\n",
+       "7          Iris-setosa\n",
+       "Name: Species, Length: 120, dtype: object"
+      ]
+     },
+     "execution_count": 466,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 467,
+   "id": "93d6b654-17ab-4576-94a7-0a44d0b85d93",
+   "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>SepalLengthCm</th>\n",
+       "      <th>SepalWidthCm</th>\n",
+       "      <th>PetalLengthCm</th>\n",
+       "      <th>PetalWidthCm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>1.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>5.5</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>5.7</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>1.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>5.8</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>5.7</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>1.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>6.4</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>1.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>5.5</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>1.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>1.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>6.8</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>1.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>1.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>1.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>1.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>1.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>4.8</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>7.2</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>2.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>5.4</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>5.4</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>7.2</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>1.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>6.4</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>5.8</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>5.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>5.2</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>6.6</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>1.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>7.1</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>2.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
+       "65             6.7           3.1            4.4           1.4\n",
+       "110            6.5           3.2            5.1           2.0\n",
+       "53             5.5           2.3            4.0           1.3\n",
+       "99             5.7           2.8            4.1           1.3\n",
+       "67             5.8           2.7            4.1           1.0\n",
+       "55             5.7           2.8            4.5           1.3\n",
+       "60             5.0           2.0            3.5           1.0\n",
+       "46             5.1           3.8            1.6           0.2\n",
+       "23             5.1           3.3            1.7           0.5\n",
+       "111            6.4           2.7            5.3           1.9\n",
+       "80             5.5           2.4            3.8           1.1\n",
+       "85             6.0           3.4            4.5           1.6\n",
+       "76             6.8           2.8            4.8           1.4\n",
+       "56             6.3           3.3            4.7           1.6\n",
+       "59             5.2           2.7            3.9           1.4\n",
+       "106            4.9           2.5            4.5           1.7\n",
+       "116            6.5           3.0            5.5           1.8\n",
+       "11             4.8           3.4            1.6           0.2\n",
+       "109            7.2           3.6            6.1           2.5\n",
+       "84             5.4           3.0            4.5           1.5\n",
+       "16             5.4           3.9            1.3           0.4\n",
+       "100            6.3           3.3            6.0           2.5\n",
+       "129            7.2           3.0            5.8           1.6\n",
+       "51             6.4           3.2            4.5           1.5\n",
+       "14             5.8           4.0            1.2           0.2\n",
+       "22             4.6           3.6            1.0           0.2\n",
+       "28             5.2           3.4            1.4           0.2\n",
+       "27             5.2           3.5            1.5           0.2\n",
+       "58             6.6           2.9            4.6           1.3\n",
+       "102            7.1           3.0            5.9           2.1"
+      ]
+     },
+     "execution_count": 467,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 468,
+   "id": "edfaab67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = svn.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 469,
+   "id": "9f2ba41c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa', 'Iris-virginica',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
+       "       'Iris-versicolor', 'Iris-setosa', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 469,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 470,
+   "id": "31027b30-f2b7-4bfa-882b-9a72ca2d7acb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for i in predictions:\n",
+    "#     print(r[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 471,
+   "id": "f3d12090-ee5e-4009-b00a-e861e6c908d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# y_test_labels = np.argmax(y_test, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 472,
+   "id": "1bb27b9f-ecf4-40ab-8ae2-b32d0d74dd66",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "65     Iris-versicolor\n",
+       "110     Iris-virginica\n",
+       "53     Iris-versicolor\n",
+       "99     Iris-versicolor\n",
+       "67     Iris-versicolor\n",
+       "55     Iris-versicolor\n",
+       "60     Iris-versicolor\n",
+       "46         Iris-setosa\n",
+       "23         Iris-setosa\n",
+       "111     Iris-virginica\n",
+       "80     Iris-versicolor\n",
+       "85     Iris-versicolor\n",
+       "76     Iris-versicolor\n",
+       "56     Iris-versicolor\n",
+       "59     Iris-versicolor\n",
+       "106     Iris-virginica\n",
+       "116     Iris-virginica\n",
+       "11         Iris-setosa\n",
+       "109     Iris-virginica\n",
+       "84     Iris-versicolor\n",
+       "16         Iris-setosa\n",
+       "100     Iris-virginica\n",
+       "129     Iris-virginica\n",
+       "51     Iris-versicolor\n",
+       "14         Iris-setosa\n",
+       "22         Iris-setosa\n",
+       "28         Iris-setosa\n",
+       "27         Iris-setosa\n",
+       "58     Iris-versicolor\n",
+       "102     Iris-virginica\n",
+       "Name: Species, dtype: object"
+      ]
+     },
+     "execution_count": 472,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 473,
+   "id": "500f0f91-cc0c-419d-84a0-56d17feb7d76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# y_test_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 474,
+   "id": "2c4199d1-8c9d-4fb1-bc60-088951cc4535",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print([r[i]  for i in y_test_labels])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 475,
+   "id": "312a5b9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "96.66666666666667"
+      ]
+     },
+     "execution_count": 475,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "accuracy_score(y_test,s)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 476,
+   "id": "7505e30f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "accuracy_scores[\"svm\"]=accuracy_score(y_test, s)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 477,
+   "id": "6dacb2f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "    Iris-setosa       1.00      1.00      1.00         8\n",
+      "Iris-versicolor       0.93      1.00      0.97        14\n",
+      " Iris-virginica       1.00      0.88      0.93         8\n",
+      "\n",
+      "       accuracy                           0.97        30\n",
+      "      macro avg       0.98      0.96      0.97        30\n",
+      "   weighted avg       0.97      0.97      0.97        30\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_test,s))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 478,
+   "id": "beeeb022",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# X_new = np.array([[3, 2, 1, 0.2], [  4.9, 2.2, 3.8, 1.1 ], [  5.3, 2.5, 4.6, 1.9 ],[5.2, 3.4, 1.4, 0.2]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 479,
+   "id": "75410094-e775-4948-b4fd-aef78e90db8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# X_new"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 480,
+   "id": "144d0504-7237-4795-96d1-80c548eefc84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
+      "0            6.7           6.5            3.4           2.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create the DataFrame with correct column names\n",
+    "new_data = pd.DataFrame(np.array([[6.7, 6.5, 3.4, 2.9]]), columns=['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'])\n",
+    "\n",
+    "# Check the DataFrame\n",
+    "print(new_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 481,
+   "id": "7a343ef7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = svn.predict(new_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 482,
+   "id": "4785a3b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from sklearn.metrics import accuracy_score\n",
+    "# accuracy_score(y_test, prediction)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 483,
+   "id": "2e43e20a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction of Species: ['Iris-setosa']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Prediction of Species: {}\".format(prediction))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 484,
+   "id": "cf6640d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# accuracy_scores={\"DecisionTreeClassifier\":accuracy_score(y_test, predictions)*100}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 485,
+   "id": "15bfc836-0203-4124-b988-417a95644329",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# r[[i for i in prediction]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 486,
+   "id": "29e75051",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "with open('SVM.pickle', 'wb') as f:\n",
+    "    pickle.dump(svn, f)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 487,
+   "id": "faa3feae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-setosa'], dtype=object)"
+      ]
+     },
+     "execution_count": 487,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with open('SVM.pickle', 'rb') as f:\n",
+    "    model = pickle.load(f)\n",
+    "model.predict(new_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 488,
+   "id": "60168c9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-20 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-20 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-20 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-20 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-20 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-20 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-20 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-20 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeClassifier()"
+      ]
+     },
+     "execution_count": 488,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import tree\n",
+    "\n",
+    "DT=tree.DecisionTreeClassifier()\n",
+    "DT.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 489,
+   "id": "d84f667a-72dc-4be3-9bf0-40ce8095e98c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from sklearn.tree import export_graphviz\n",
+    "# import graphviz\n",
+    "\n",
+    "# export_graphviz(DT, out_file=\"mytree.dot\")\n",
+    "# with open(\"mytree.dot\") as f:\n",
+    "#     dot_graph = f.read()\n",
+    "# graphviz.Source(dot_graph)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 490,
+   "id": "b081b360-be62-4cbd-b465-a9e667abefe9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DT.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 491,
+   "id": "02929677-920b-49e0-9fd0-15f8b3d43d72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DT.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 492,
+   "id": "fbe94ffc-a863-4fc2-b99b-c27f54a8821a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method BaseEstimator.get_params of DecisionTreeClassifier()>"
+      ]
+     },
+     "execution_count": 492,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "DT.get_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b727736a-7c19-4dd4-adb6-6004a195a10c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 493,
+   "id": "1af3aa90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = DT.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 494,
+   "id": "e92c09ca-17a5-49ed-a2bc-b0d79ba30542",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa', 'Iris-virginica',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
+       "       'Iris-versicolor', 'Iris-setosa', 'Iris-virginica',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 494,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 495,
+   "id": "a2d1a89d-b294-4ee5-be01-51cf72c0ae03",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x1daa6bb8eb0>"
+      ]
+     },
+     "execution_count": 495,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(y_test,d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 496,
+   "id": "dd893606-2e07-4dfd-b51d-cd20e49e10c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# residuals=y_test-predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 497,
+   "id": "97620957-6921-442a-b318-09e4d2f07a0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# residuals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 498,
+   "id": "73adb9fe-aa87-4d63-beb4-f5fb9a1900bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sns.displot(residuals,kind=\"kde\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 499,
+   "id": "16a9a0cf-ea63-4be2-b5c9-75eec5a66752",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plt.scatter(predictions,residuals)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2a800a3-861c-441a-a993-01455a4a6be5",
+   "metadata": {},
+   "source": [
+    "R square and adjusted R square\n",
+    "Formula\n",
+    "\n",
+    "R^2 = 1 - SSR/SST\n",
+    "\n",
+    "R^2 = coefficient of determination SSR = sum of squares of residuals SST = total sum of squares"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f34d7e40-2866-41b4-aefd-6b394c438ce5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 500,
+   "id": "b925ccbe-16f4-4af7-959b-3dce4cc6630b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from sklearn.metrics import r2_score\n",
+    "# score=r2_score(y_test_labels,predictions)\n",
+    "# print(score)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac93eb6d-85d5-480a-89c7-d26b7c0518c4",
+   "metadata": {},
+   "source": [
+    "Adjusted R2 = 1 – [(1-R2)*(n-1)/(n-k-1)]\n",
+    "\n",
+    "where:\n",
+    "\n",
+    "R2: The R2 of the model n: The number of observations k: The number of predictor variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 501,
+   "id": "3f2bb6c2-37a2-4b0f-9127-7f409720735b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# #display adjusted R-squared\n",
+    "# 1 - (1-score)*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 502,
+   "id": "99626cec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "93.33333333333333"
+      ]
+     },
+     "execution_count": 502,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "accuracy_score(y_test, d)*100\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 503,
+   "id": "62d18258",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "accuracy_scores[\"DecisionTreeClassifier\"]=accuracy_score(y_test, d)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 504,
+   "id": "d3079af4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "    Iris-setosa       1.00      1.00      1.00         8\n",
+      "Iris-versicolor       0.88      1.00      0.93        14\n",
+      " Iris-virginica       1.00      0.75      0.86         8\n",
+      "\n",
+      "       accuracy                           0.93        30\n",
+      "      macro avg       0.96      0.92      0.93        30\n",
+      "   weighted avg       0.94      0.93      0.93        30\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_test, d))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 505,
+   "id": "8948cfb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# X_new = np.array([[3, 2, 1, 0.2], [  4.9, 2.2, 3.8, 1.1 ], [  5.3, 2.5, 4.6, 1.9 ]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 506,
+   "id": "62aeea86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = DT.predict(new_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 507,
+   "id": "51b0e3f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction of Species: ['Iris-virginica']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Prediction of Species: {}\".format(prediction))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 508,
+   "id": "375dcab0-41c6-4b27-8c47-a00b5d7123c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# r[[i for i in prediction]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 509,
+   "id": "46e6394e-5c56-4400-9918-d8329abe97a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 510,
+   "id": "7941fe78-3270-4ebf-8f61-3c1f312dc157",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"PATH\"]+= os.pathsep+(r'C:\\Users\\Gnaneshwari\\.conda\\envs\\myenv\\Lib\\site-packages\\graphviz')\n",
+    "# import graphviz\n",
+    "\n",
+    "# vis_data=tree.export_graphviz(DT,out_file=None, feature_names=df.drop(['Species'],axis=1).keys(),class_names=r,filled=True,rounded=True,special_characters=True)\n",
+    "# graphviz.Source(vis_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 511,
+   "id": "e1aee1fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-setosa'], dtype=object)"
+      ]
+     },
+     "execution_count": 511,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "with open('DT.pickle', 'wb') as f:\n",
+    "    pickle.dump(svn, f)\n",
+    "with open('DT.pickle', 'rb') as f:\n",
+    "    model = pickle.load(f)\n",
+    "model.predict(new_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 512,
+   "id": "7605713e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-21 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-21 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-21 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-21 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-21 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-21 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-21 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-21 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-21 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-21 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-21 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-21 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-21 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-21 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-21 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-21 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-21\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 512,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "knn=KNeighborsClassifier(n_neighbors=5)\n",
+    "\n",
+    "knn.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 513,
+   "id": "057abe64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "k = knn.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 514,
+   "id": "5abb46ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "96.66666666666667"
+      ]
+     },
+     "execution_count": 514,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "accuracy_score(y_test, k)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 515,
+   "id": "25113e18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "accuracy_scores[\"KNeighborsClassifier\"]=accuracy_score(y_test, k)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 516,
+   "id": "69713808",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "    Iris-setosa       1.00      1.00      1.00         8\n",
+      "Iris-versicolor       0.93      1.00      0.97        14\n",
+      " Iris-virginica       1.00      0.88      0.93         8\n",
+      "\n",
+      "       accuracy                           0.97        30\n",
+      "      macro avg       0.98      0.96      0.97        30\n",
+      "   weighted avg       0.97      0.97      0.97        30\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_test, k))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 517,
+   "id": "7752db89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# X_new = np.array([[3, 1, 0.2,1], [  4.9, 3.8, 1.1,0 ], [  5.3,4.6, 1.9,7 ]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 518,
+   "id": "3597d69d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction of Species: ['Iris-virginica']\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = knn.predict(new_data)\n",
+    "print(\"Prediction of Species: {}\".format(prediction))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 519,
+   "id": "8602eb71-4267-4e01-85ed-86ce85560997",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# r[[i for i in prediction]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 520,
+   "id": "5649e84a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-setosa'], dtype=object)"
+      ]
+     },
+     "execution_count": 520,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "with open('knn.pickle', 'wb') as f:\n",
+    "    pickle.dump(svn, f)\n",
+    "with open('knn.pickle', 'rb') as f:\n",
+    "    model = pickle.load(f)\n",
+    "model.predict(new_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 521,
+   "id": "eb884b87",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-22 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-22 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-22 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-22 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-22 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-22 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-22 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-22 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-22 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-22 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-22 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-22 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-22 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-22 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-22 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-22 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-22\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3, random_state=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=3, random_state=2)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=3, random_state=2)"
+      ]
+     },
+     "execution_count": 521,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "km=KMeans(n_clusters=3,random_state=2)\n",
+    "km.fit(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 522,
+   "id": "91b04bdd-7332-4b51-a7f4-074ffe335584",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colormap=np.array(['Red','green','blue'])\n",
+    "fig=plt.scatter(df['PetalLengthCm'],df['PetalWidthCm'],c=colormap[km.labels_],s=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 523,
+   "id": "500e53ff-a3c1-454f-a4ba-c1d9f1f5e8d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "150"
+      ]
+     },
+     "execution_count": 523,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "km.labels_.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 524,
+   "id": "57e4cc1a-8eec-4003-861d-7a12507c37f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      1.4\n",
+       "1      1.4\n",
+       "2      1.3\n",
+       "3      1.5\n",
+       "4      1.4\n",
+       "      ... \n",
+       "145    5.2\n",
+       "146    5.0\n",
+       "147    5.2\n",
+       "148    5.4\n",
+       "149    5.1\n",
+       "Name: PetalLengthCm, Length: 150, dtype: float64"
+      ]
+     },
+     "execution_count": 524,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['PetalLengthCm']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 525,
+   "id": "447e4cb5-7009-43b6-8d84-47b10f9323e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      0.2\n",
+       "1      0.2\n",
+       "2      0.2\n",
+       "3      0.2\n",
+       "4      0.2\n",
+       "      ... \n",
+       "145    2.3\n",
+       "146    1.9\n",
+       "147    2.0\n",
+       "148    2.3\n",
+       "149    1.8\n",
+       "Name: PetalWidthCm, Length: 150, dtype: float64"
+      ]
+     },
+     "execution_count": 525,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['PetalWidthCm']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 526,
+   "id": "a012079e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[5.9016129  2.7483871  4.39354839 1.43387097]\n",
+      " [5.006      3.418      1.464      0.244     ]\n",
+      " [6.85       3.07368421 5.74210526 2.07105263]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "centers=km.cluster_centers_\n",
+    "print(centers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 527,
+   "id": "433c0698",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2,\n",
+       "       2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2,\n",
+       "       2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0])"
+      ]
+     },
+     "execution_count": 527,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "km.labels_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 528,
+   "id": "48751858",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Iris-Versicolor', 'Iris-Setosa', 'Iris-Virginica']"
+      ]
+     },
+     "execution_count": 528,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Catagory_kmeans=['Iris-Versicolor', 'Iris-Setosa', 'Iris-Virginica']\n",
+    "Catagory_kmeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73e6f74f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 529,
+   "id": "c51caf19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# new_labels=km.labels_\n",
+    "# fig,axes=plt.subplots(1,2,figsize=(16,8))\n",
+    "# axes[0].scatter(X[:,2],X[:,3],c=y,cmap='gist_rainbow',edgecolor='k',s=150)\n",
+    "# axes[1].scatter(X[:,2],X[:,3],c=y,cmap='jet',edgecolor='k',s=150)\n",
+    "# axes[0].set_title('Actual',fontsize=18)\n",
+    "# axes[1].set_title('Predicted',fontsize=18)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 530,
+   "id": "4961d1a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iris-Versicolor\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Lets predict on custom input value\n",
+    "# X_km=np.array([[1, 1, 1,9]])\n",
+    "X_km_prediction=km.predict(new_data)\n",
+    "X_km_prediction[0]\n",
+    "print(Catagory_kmeans[int(X_km_prediction[0])])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 531,
+   "id": "17bcf06c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# logistic regression \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "model = LogisticRegression()\n",
+    "logreg = LogisticRegression(max_iter=1000, solver='liblinear')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 532,
+   "id": "d0d140c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-23 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-23 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-23 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-23 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-23 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-23 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-23 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-23 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-23 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-23 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-23 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-23 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-23 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-23 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-23 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-23 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-23\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" checked><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression(max_iter=1000, solver='liblinear')"
+      ]
+     },
+     "execution_count": 532,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# model training\n",
+    "logreg.fit(X_train, y_train)\n",
+    "# y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 533,
+   "id": "347fad17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "l = logreg.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 534,
+   "id": "890fdd9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa', 'Iris-virginica',\n",
+       "       'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
+       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 534,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "l"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 535,
+   "id": "f8ec21a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "86.66666666666667"
+      ]
+     },
+     "execution_count": 535,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "accuracy_score(y_test, l)*100\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 536,
+   "id": "215b870d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "accuracy_scores[\"LogisticRegression\"]=accuracy_score(y_test, l)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 537,
+   "id": "c951aa9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "    Iris-setosa       1.00      1.00      1.00         8\n",
+      "Iris-versicolor       1.00      0.71      0.83        14\n",
+      " Iris-virginica       0.67      1.00      0.80         8\n",
+      "\n",
+      "       accuracy                           0.87        30\n",
+      "      macro avg       0.89      0.90      0.88        30\n",
+      "   weighted avg       0.91      0.87      0.87        30\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_test, l))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 538,
+   "id": "8b0424ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# X_new = np.array([[ 2, 1, 0.2,1], [2.2, 3.8, 1.1,0 ], [ 2.5, 4.6, 1.9,7 ]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 539,
+   "id": "11889390",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# prediction = svn.predict(new_data)\n",
+    "# print(\"Prediction of Species: {}\".format(prediction))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 541,
+   "id": "c9f357ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = logreg.predict(new_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 542,
+   "id": "9a2f5288",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 543,
+   "id": "d154694a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction of Species: ['Iris-setosa']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Prediction of Species: {}\".format(prediction))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 544,
+   "id": "192e3f58-cf93-4f48-b89b-a658898c4b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# r[[i for i in prediction]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 545,
+   "id": "07479401",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-setosa'], dtype=object)"
+      ]
+     },
+     "execution_count": 545,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "with open('Logistic.pickle', 'wb') as f:\n",
+    "    pickle.dump(svn, f)\n",
+    "with open('Logistic.pickle', 'rb') as f:\n",
+    "    model = pickle.load(f)\n",
+    "model.predict(new_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 546,
+   "id": "a0de0e6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-24 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-24 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-24 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-24 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-24 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-24 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-24 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-24 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-24 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-24 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-24 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-24 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-24 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-24 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-24 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-24 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-24\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" checked><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GaussianNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.naive_bayes.GaussianNB.html\">?<span>Documentation for GaussianNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GaussianNB()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "GaussianNB()"
+      ]
+     },
+     "execution_count": 546,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fitting Naive Bayes to the Training set  \n",
+    "from sklearn.naive_bayes import GaussianNB  \n",
+    "classifier = GaussianNB()  \n",
+    "classifier.fit(X_train, y_train)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 547,
+   "id": "95e3ad15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g = classifier.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 548,
+   "id": "ab8b5d74",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa', 'Iris-virginica',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
+       "       'Iris-versicolor', 'Iris-setosa', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'],\n",
+       "      dtype='<U15')"
+      ]
+     },
+     "execution_count": 548,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 549,
+   "id": "da3c4053",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "96.66666666666667"
+      ]
+     },
+     "execution_count": 549,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "accuracy_score(y_test, g)*100\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 550,
+   "id": "f221ad00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "accuracy_scores[\"GaussianNB\"]=accuracy_score(y_test, g)*100\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 551,
+   "id": "889ad20a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "    Iris-setosa       1.00      1.00      1.00         8\n",
+      "Iris-versicolor       0.93      1.00      0.97        14\n",
+      " Iris-virginica       1.00      0.88      0.93         8\n",
+      "\n",
+      "       accuracy                           0.97        30\n",
+      "      macro avg       0.98      0.96      0.97        30\n",
+      "   weighted avg       0.97      0.97      0.97        30\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_test, g))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 552,
+   "id": "3826c23e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# X_new = np.array([[2, 1, 0.2,1], [  4.9, 3.8, 1.1,0 ], [  5.3, 2.5, 1.9,7 ]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa3a986d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 553,
+   "id": "e91e8cf2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-setosa'], dtype=object)"
+      ]
+     },
+     "execution_count": 553,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "with open('GaussianNB.pickle', 'wb') as f:\n",
+    "    pickle.dump(svn, f)\n",
+    "with open('GaussianNB.pickle', 'rb') as f:\n",
+    "    model = pickle.load(f)\n",
+    "model.predict(new_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2fd1c6e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 554,
+   "id": "2dfbe515-f337-42b2-bf66-094c43675f8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "rf=RandomForestClassifier()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 555,
+   "id": "66c128af-f05a-4c9a-98d9-da5125674723",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-25 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-25 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-25 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-25 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-25 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-25 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-25 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-25 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-25 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-25 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-25 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-25 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-25 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-25 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-25 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-25 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-25\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" checked><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier()"
+      ]
+     },
+     "execution_count": 555,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# model training\n",
+    "rf.fit(X_train, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 556,
+   "id": "b8683574-71f2-429c-bc51-50e1d29abdc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = rf.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 557,
+   "id": "11f836b5-ff9f-401f-b678-487396b12a36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa', 'Iris-virginica',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
+       "       'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
+       "       'Iris-versicolor', 'Iris-setosa', 'Iris-virginica',\n",
+       "       'Iris-virginica', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',\n",
+       "       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 557,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 558,
+   "id": "31a480cc-105b-4a19-ad5d-bdab6e14324c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "96.66666666666667"
+      ]
+     },
+     "execution_count": 558,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_test, r)*100\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 559,
+   "id": "50b2dc54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "accuracy_scores[\"RandomForestClassifier\"]=accuracy_score(y_test, r)*100\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 560,
+   "id": "1a538009-df48-4068-9c47-5376945671df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "    Iris-setosa       1.00      1.00      1.00         8\n",
+      "Iris-versicolor       0.93      1.00      0.97        14\n",
+      " Iris-virginica       1.00      0.88      0.93         8\n",
+      "\n",
+      "       accuracy                           0.97        30\n",
+      "      macro avg       0.98      0.96      0.97        30\n",
+      "   weighted avg       0.97      0.97      0.97        30\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test, r))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 561,
+   "id": "33d5d969-4d24-42ab-a655-0106b3d77da3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# X_new = np.array([[3, 2, 1, 1], [  4.9, 3.8, 1.1,0 ], [  5.3, 2.5, 4.6, 1.9]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 562,
+   "id": "bf081fc5-2739-45c2-8ebc-8153f6200ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction of Species: ['Iris-versicolor']\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = rf.predict(new_data)\n",
+    "print(\"Prediction of Species: {}\".format(prediction))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 563,
+   "id": "8ef07085-8085-4b02-8f62-04339eb70f5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Iris-versicolor'], dtype=object)"
+      ]
+     },
+     "execution_count": 563,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "with open('rf.pickle', 'wb') as f:\n",
+    "    pickle.dump(rf, f)\n",
+    "with open('rf.pickle', 'rb') as f:\n",
+    "    model = pickle.load(f)\n",
+    "model.predict(new_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 564,
+   "id": "ca4c9c86-a53a-487d-b1a4-c363270507f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# r[[i for i in prediction]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 565,
+   "id": "8718f624",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'svm': 96.66666666666667,\n",
+       " 'DecisionTreeClassifier': 93.33333333333333,\n",
+       " 'KNeighborsClassifier': 96.66666666666667,\n",
+       " 'LogisticRegression': 86.66666666666667,\n",
+       " 'GaussianNB': 96.66666666666667,\n",
+       " 'RandomForestClassifier': 96.66666666666667}"
+      ]
+     },
+     "execution_count": 565,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2383f8ce",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d439afae",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}