{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6697,
     "status": "ok",
     "timestamp": 1752482361690,
     "user": {
      "displayName": "Ahmed Foly",
      "userId": "06032925035970721628"
     },
     "user_tz": -180
    },
    "id": "OSsiwHoVnuC5",
    "outputId": "adaa2e50-9f27-487f-a150-4c3d7abff350"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data source import complete.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import kagglehub\n",
    "atharvaingle_crop_recommendation_dataset_path = kagglehub.dataset_download('atharvaingle/crop-recommendation-dataset')\n",
    "\n",
    "print('Data source import complete.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gz8aFsIY6Rzo"
   },
   "source": [
    "## **Import Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "executionInfo": {
     "elapsed": 83,
     "status": "ok",
     "timestamp": 1752482361777,
     "user": {
      "displayName": "Ahmed Foly",
      "userId": "06032925035970721628"
     },
     "user_tz": -180
    },
    "id": "3OzZa5uUnuC8",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "c7181223-99fe-4973-812b-55b94652eff0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/crop-recommendation-dataset/Crop_recommendation.csv\n"
     ]
    }
   ],
   "source": [
    "# Importing necessary libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score,recall_score,precision_score,f1_score,confusion_matrix, silhouette_score, davies_bouldin_score, pairwise_distances\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn import metrics\n",
    "from sklearn import tree\n",
    "\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qm3uDBHk6auL"
   },
   "source": [
    "## **Read Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1752482361799,
     "user": {
      "displayName": "Ahmed Foly",
      "userId": "06032925035970721628"
     },
     "user_tz": -180
    },
    "id": "H_UZ7MP4nuDA",
    "outputId": "6f99a84f-505e-43f8-e20e-28a1b2bd6a59"
   },
   "outputs": [
    {
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       "type": "dataframe",
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       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
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       "      <td>20.879744</td>\n",
       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "      <td>rice</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
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       "      <td>21.770462</td>\n",
       "      <td>80.319644</td>\n",
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       "      <td>226.655537</td>\n",
       "      <td>rice</td>\n",
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       "    <tr>\n",
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       "      <td>82.320763</td>\n",
       "      <td>7.840207</td>\n",
       "      <td>263.964248</td>\n",
       "      <td>rice</td>\n",
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       "      <td>242.864034</td>\n",
       "      <td>rice</td>\n",
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       "      <td>20.130175</td>\n",
       "      <td>81.604873</td>\n",
       "      <td>7.628473</td>\n",
       "      <td>262.717340</td>\n",
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       "\n",
       "  <style>\n",
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       "      display:flex;\n",
       "      gap: 12px;\n",
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       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-289bab43-34a5-49d2-b43c-51e7f917b123 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-289bab43-34a5-49d2-b43c-51e7f917b123');\n",
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       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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       "    </g>\n",
       "</svg>\n",
       "      </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
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       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "      <script>\n",
       "        async function quickchart(key) {\n",
       "          const quickchartButtonEl =\n",
       "            document.querySelector('#' + key + ' button');\n",
       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "          try {\n",
       "            const charts = await google.colab.kernel.invokeFunction(\n",
       "                'suggestCharts', [key], {});\n",
       "          } catch (error) {\n",
       "            console.error('Error during call to suggestCharts:', error);\n",
       "          }\n",
       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "        }\n",
       "        (() => {\n",
       "          let quickchartButtonEl =\n",
       "            document.querySelector('#df-a1aad7b1-fde5-40f0-8ca5-bc073365ef34 button');\n",
       "          quickchartButtonEl.style.display =\n",
       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "        })();\n",
       "      </script>\n",
       "    </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "    N   P   K  temperature   humidity        ph    rainfall label\n",
       "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
       "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
       "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
       "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
       "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/kaggle/input/crop-recommendation-dataset/Crop_recommendation.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C7NKoBNZ6emH"
   },
   "source": [
    "## **Explore the Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 162,
     "status": "ok",
     "timestamp": 1752482361955,
     "user": {
      "displayName": "Ahmed Foly",
      "userId": "06032925035970721628"
     },
     "user_tz": -180
    },
    "id": "oQORmmr9n2iS",
    "outputId": "8b296644-e4db-4e72-8538-6ba86c3b1e5a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2200 entries, 0 to 2199\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   N            2200 non-null   int64  \n",
      " 1   P            2200 non-null   int64  \n",
      " 2   K            2200 non-null   int64  \n",
      " 3   temperature  2200 non-null   float64\n",
      " 4   humidity     2200 non-null   float64\n",
      " 5   ph           2200 non-null   float64\n",
      " 6   rainfall     2200 non-null   float64\n",
      " 7   label        2200 non-null   object \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 137.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Dataset information\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1752482361972,
     "user": {
      "displayName": "Ahmed Foly",
      "userId": "06032925035970721628"
     },
     "user_tz": -180
    },
    "id": "pwrIZbqfoFhq",
    "outputId": "710bed4d-4b70-4ebd-8315-ea86adb4316f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"N\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 760.3565304071341,\n        \"min\": 0.0,\n        \"max\": 2200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          50.551818181818184,\n          37.0,\n          2200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 759.584792033131,\n        \"min\": 5.0,\n        \"max\": 2200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          53.36272727272727,\n          51.0,\n          2200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 759.6407502585978,\n        \"min\": 5.0,\n        \"max\": 2200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          48.14909090909091,\n          32.0,\n          2200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 769.8229256870055,\n        \"min\": 5.063748599958843,\n        \"max\": 2200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          25.616243851779544,\n          25.5986932,\n          2200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 756.2767059184814,\n        \"min\": 14.25803981,\n        \"max\": 2200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          71.48177921778637,\n          80.473145665,\n          2200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ph\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 775.8015470082165,\n        \"min\": 0.7739376880298721,\n        \"max\": 2200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.469480065256364,\n          6.42504527,\n          2200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rainfall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 744.1226388679374,\n        \"min\": 20.21126747,\n        \"max\": 2200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          103.46365541576817,\n          94.86762427,\n          2200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-1be137b4-c90b-429b-964e-d545ad6d29aa\" class=\"colab-df-container\">\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.551818</td>\n",
       "      <td>53.362727</td>\n",
       "      <td>48.149091</td>\n",
       "      <td>25.616244</td>\n",
       "      <td>71.481779</td>\n",
       "      <td>6.469480</td>\n",
       "      <td>103.463655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>36.917334</td>\n",
       "      <td>32.985883</td>\n",
       "      <td>50.647931</td>\n",
       "      <td>5.063749</td>\n",
       "      <td>22.263812</td>\n",
       "      <td>0.773938</td>\n",
       "      <td>54.958389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.825675</td>\n",
       "      <td>14.258040</td>\n",
       "      <td>3.504752</td>\n",
       "      <td>20.211267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.769375</td>\n",
       "      <td>60.261953</td>\n",
       "      <td>5.971693</td>\n",
       "      <td>64.551686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>25.598693</td>\n",
       "      <td>80.473146</td>\n",
       "      <td>6.425045</td>\n",
       "      <td>94.867624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>84.250000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>28.561654</td>\n",
       "      <td>89.948771</td>\n",
       "      <td>6.923643</td>\n",
       "      <td>124.267508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>43.675493</td>\n",
       "      <td>99.981876</td>\n",
       "      <td>9.935091</td>\n",
       "      <td>298.560117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1be137b4-c90b-429b-964e-d545ad6d29aa')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
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       "\n",
       "    .colab-df-buttons div {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-1be137b4-c90b-429b-964e-d545ad6d29aa button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-1be137b4-c90b-429b-964e-d545ad6d29aa');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
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       "\n",
       "\n",
       "    <div id=\"df-5281c67f-2e35-4708-b50d-fef5c3889d7f\">\n",
       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5281c67f-2e35-4708-b50d-fef5c3889d7f')\"\n",
       "                title=\"Suggest charts\"\n",
       "                style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "      </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "      <script>\n",
       "        async function quickchart(key) {\n",
       "          const quickchartButtonEl =\n",
       "            document.querySelector('#' + key + ' button');\n",
       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "          try {\n",
       "            const charts = await google.colab.kernel.invokeFunction(\n",
       "                'suggestCharts', [key], {});\n",
       "          } catch (error) {\n",
       "            console.error('Error during call to suggestCharts:', error);\n",
       "          }\n",
       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "        }\n",
       "        (() => {\n",
       "          let quickchartButtonEl =\n",
       "            document.querySelector('#df-5281c67f-2e35-4708-b50d-fef5c3889d7f button');\n",
       "          quickchartButtonEl.style.display =\n",
       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "        })();\n",
       "      </script>\n",
       "    </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                 N            P            K  temperature     humidity  \\\n",
       "count  2200.000000  2200.000000  2200.000000  2200.000000  2200.000000   \n",
       "mean     50.551818    53.362727    48.149091    25.616244    71.481779   \n",
       "std      36.917334    32.985883    50.647931     5.063749    22.263812   \n",
       "min       0.000000     5.000000     5.000000     8.825675    14.258040   \n",
       "25%      21.000000    28.000000    20.000000    22.769375    60.261953   \n",
       "50%      37.000000    51.000000    32.000000    25.598693    80.473146   \n",
       "75%      84.250000    68.000000    49.000000    28.561654    89.948771   \n",
       "max     140.000000   145.000000   205.000000    43.675493    99.981876   \n",
       "\n",
       "                ph     rainfall  \n",
       "count  2200.000000  2200.000000  \n",
       "mean      6.469480   103.463655  \n",
       "std       0.773938    54.958389  \n",
       "min       3.504752    20.211267  \n",
       "25%       5.971693    64.551686  \n",
       "50%       6.425045    94.867624  \n",
       "75%       6.923643   124.267508  \n",
       "max       9.935091   298.560117  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 335
    },
    "executionInfo": {
     "elapsed": 37,
     "status": "ok",
     "timestamp": 1752482362015,
     "user": {
      "displayName": "Ahmed Foly",
      "userId": "06032925035970721628"
     },
     "user_tz": -180
    },
    "id": "D8-dR7iioRVu",
    "outputId": "4973277d-7c3f-46f2-87cf-ded8051a061c"
   },
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperature</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ph</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rainfall</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br><label><b>dtype:</b> int64</label>"
      ],
      "text/plain": [
       "N              0\n",
       "P              0\n",
       "K              0\n",
       "temperature    0\n",
       "humidity       0\n",
       "ph             0\n",
       "rainfall       0\n",
       "label          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null Count\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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       "\n",
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      ],
      "text/plain": [
       "        N   P   K  temperature   humidity        ph    rainfall   label\n",
       "0      90  42  43    20.879744  82.002744  6.502985  202.935536    rice\n",
       "1      85  58  41    21.770462  80.319644  7.038096  226.655537    rice\n",
       "2      60  55  44    23.004459  82.320763  7.840207  263.964248    rice\n",
       "3      74  35  40    26.491096  80.158363  6.980401  242.864034    rice\n",
       "4      78  42  42    20.130175  81.604873  7.628473  262.717340    rice\n",
       "...   ...  ..  ..          ...        ...       ...         ...     ...\n",
       "2195  107  34  32    26.774637  66.413269  6.780064  177.774507  coffee\n",
       "2196   99  15  27    27.417112  56.636362  6.086922  127.924610  coffee\n",
       "2197  118  33  30    24.131797  67.225123  6.362608  173.322839  coffee\n",
       "2198  117  32  34    26.272418  52.127394  6.758793  127.175293  coffee\n",
       "2199  104  18  30    23.603016  60.396475  6.779833  140.937041  coffee\n",
       "\n",
       "[2200 rows x 8 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()]\n",
    "df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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     "height": 805
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1752482362062,
     "user": {
      "displayName": "Ahmed Foly",
      "userId": "06032925035970721628"
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     "user_tz": -180
    },
    "id": "10aZd8zloYSy",
    "outputId": "b7f78adc-4f33-4261-da32-f1cfd5f60c8f"
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   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>pigeonpeas</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mothbeans</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mungbean</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blackgram</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lentil</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pomegranate</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grapes</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watermelon</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>muskmelon</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>papaya</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coconut</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cotton</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jute</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coffee</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br><label><b>dtype:</b> int64</label>"
      ],
      "text/plain": [
       "label\n",
       "rice           100\n",
       "maize          100\n",
       "chickpea       100\n",
       "kidneybeans    100\n",
       "pigeonpeas     100\n",
       "mothbeans      100\n",
       "mungbean       100\n",
       "blackgram      100\n",
       "lentil         100\n",
       "pomegranate    100\n",
       "banana         100\n",
       "mango          100\n",
       "grapes         100\n",
       "watermelon     100\n",
       "muskmelon      100\n",
       "apple          100\n",
       "orange         100\n",
       "papaya         100\n",
       "coconut        100\n",
       "cotton         100\n",
       "jute           100\n",
       "coffee         100\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WwFka_sB6lVB"
   },
   "source": [
    "## **Encoding and Scaling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7Vm8YNLioeyG"
   },
   "outputs": [],
   "source": [
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
    "\n",
    "# creating column transformer (this will help us normalize/preprocess our data)\n",
    "\n",
    "ct = make_column_transformer(\n",
    "                            (MinMaxScaler(), ['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WsJoAvBqo47L"
   },
   "outputs": [],
   "source": [
    "# creating X and y (features and labels)\n",
    "\n",
    "X = df.drop('label', axis = 1)\n",
    "y = df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "y5c6Gbmoo8Kk"
   },
   "outputs": [],
   "source": [
    "# splitting data into training set and test set\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WwCNh24ao-iG"
   },
   "outputs": [],
   "source": [
    "# fitting column transformer on training data\n",
    "ct.fit(X_train)\n",
    "\n",
    "# transforming training and test data with normalizing (MinMaxScaler) and one hot encodig (OneHotEncoder)\n",
    "X_train = ct.transform(X_train)\n",
    "X_test = ct.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FnxS_dk16zXA"
   },
   "source": [
    "## **Helper Functions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Zr2yk3SxpAZi"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,recall_score,precision_score,f1_score,confusion_matrix\n",
    "def train_model(model, X_train, y_train, X_test, y_test):\n",
    "\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred, average='weighted') # Added average='weighted'\n",
    "    recall = recall_score(y_test, y_pred, average='weighted') # Added average='weighted'\n",
    "    f1 = f1_score(y_test, y_pred, average='weighted') # Added average='weighted'\n",
    "\n",
    "    matrix = confusion_matrix(y_test, y_pred)\n",
    "    print(\"Confusion Matrix is:\")\n",
    "    # print(matrix)\n",
    "\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(matrix, annot=True, fmt=\"d\", cmap=\"Blues\",\n",
    "                xticklabels=[\"Predicted No\", \"Predicted Yes\"],\n",
    "                yticklabels=[\"Actual No\", \"Actual Yes\"])\n",
    "    plt.title(\"Confusion Matrix\")\n",
    "    plt.xlabel(\"Predicted Label\")\n",
    "    plt.ylabel(\"True Label\")\n",
    "    plt.show()\n",
    "\n",
    "    print(\"\\n\\nScore Table is: \")\n",
    "    score_df = pd.DataFrame([[accuracy, precision, recall, f1]],\n",
    "                            columns=[\"accuracy\", \"precision\", \"recall\", \"f1\"])\n",
    "\n",
    "    return score_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-dWcHLrz64N2"
   },
   "source": [
    "## **Logistic Regression Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 785
    },
    "executionInfo": {
     "elapsed": 1060,
     "status": "ok",
     "timestamp": 1752482363188,
     "user": {
      "displayName": "Ahmed Foly",
      "userId": "06032925035970721628"
     },
     "user_tz": -180
    },
    "id": "LmvlPt9ppr36",
    "outputId": "e435f12c-61d1-4495-ea9b-6fbf157a6804"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix is:\n"
     ]
    },
    {
     "data": {
      "image/png": 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lC06cOIHdu3ejadOmAIBNmzahevXqmDp1KhITE5GYmFjs501PT0d0dDQOHjyII0eOoF69eujSpQvS09PN6m9nZ4cZM2bg888/x40bNx55zh9//IFevXqhT58+OHXqFCZPnoyJEyc+cjBfUq7lHQAAyRm5j3y8vGM59Gvtiyt30nEjKdOs7Py8PJw9cxphzZobj6nVaoSFNUdC/IkSdxaVy87y7ywym53ZmZ2Vmy3HzmQ5ktpFYcWKFejXrx8AoHPnzrh//z7279+P1q1bAwCmT5+OPn36YMqUKcafadiwIQCgYsWKsLOzg4uLC7y8vMx63rZt25rcX7ZsGdzd3bF//3689NJLZmVFRkaiUaNGmDRpElasWFHk8U8//RTt2rXDxIkTAQD169fHmTNn8Mknn2DAgAFFzs/NzUVurulA1aDPh8rO/pHPr1IBs6KbIPbcHZz9O9Xksdc6+mFa32fgrLHHXzfvo9v0ncjXF5r1+lJSU6DX66HVak2Oa7VaXLly2awsa+SKzGZn+Wezs3Wy2dk62XLsLDJbjp2FkuA6WZEk82rPnz+Po0eP4pVXXgEAlCtXDr179zYZJJ48eRLt2rWz+HPfuXMHQ4YMQb169eDm5gZXV1dkZGTg+vXrJcqbNWsWVq9ejbNnzxZ57OzZs2jRooXJsRYtWuDChQvQ6/VFzo+JiYGbm5vJLe/sT4997k8HhSGghgcGzD9Q5LHvf7uM58dtRefJO3AxMQ2r32oFR3vJ/AoQERERWYRkRjcrVqxAQUEBvL29Ua5cOZQrVw6LFy/Gxo0bcf/+fQCAk5OT2blqtdpkmQMA5Ofnm9yPjo7GyZMnMX/+fBw+fBgnT56EVqtFXl5eiV5Ly5Yt0alTJ3zwwQcl+vl/++CDD3D//n2Tm0PAo2eV5wx8Dp0bV8eLU3/BreSsIo+nZefj0u10HDp7B/0+3Yf63q7o2sTHrD4e7h6ws7Mrsohep9PB09PTrCxr5IrMZmf5Z7OzdbLZ2TrZcuwsMluOnYXiGlzrKygowNdff425c+fi5MmTxlt8fDy8vb3x7bffAgAaNGiA3bt3PzbHwcGhyCxopUqVcPv2bZNB7smTJ03OOXToEEaNGoUuXboYP/yVlJRUqtc0c+ZMbN26FbGxsSbHAwICcOjQoSLPX79+fdjZ2RXJcXR0hKurq8ntUcsT5gx8Dl2b1sRL037BtXsZT+334PdRZfYMrr2DAwICgxB35J/XVVhYiLi4WDRoGGpWljVy2Vn+nUVmszM7s7Nys+XYWagytk2YJNbg/vTTT0hJScHgwYPh5uZm8lj37t2xYsUKvP7665g0aRLatWuHunXrok+fPigoKMC2bdswbtw4AA/2wT1w4AD69OkDR0dHeHp6onXr1rh37x5mz56NHj16YMeOHdi+fTtcXf/ZA7ZevXpYs2YNnn32WaSlpeHdd98t0Wzxv4WEhKBv375YsGCByfF33nkHTZo0wbRp09C7d2/ExsZi4cKFWLRoUYmf69PBz6Fnizro88kepGfno7KbBgCQlpWPnHw9alV2RvfmtbA7/haS0nJRTVseY7qFICevAL+cuGn28/WPHoiJ48chKCgYwSEN8M2a1cjOzkZEZFSJX4PIXHaWf2eR2ezMzuys3Gw5dibLkMQAd8WKFWjfvn2RwS3wYIA7e/ZsJCQkoHXr1tiwYQOmTZuGmTNnwtXVFS1btjSeO3XqVAwbNgx169ZFbm4uDAYDAgICsGjRIsyYMQPTpk1D9+7dMXbsWCxbtszk+YcOHYrGjRujRo0amDFjBsaOHVvq1zV16lR89913JscaN26M77//Hh999BGmTZuGqlWrYurUqY/8gFlxDenoDwDYMbmzyfHXFx3E2v2XkJOvRzP/KhgRHgh3ZwfcTc3BoXN30H7idiSl5Zj9fJ3DuyAlORmLFi5AUtI9+PkHYNHS5dCW8m0ZUbnsLP/OIrPZmZ3ZWbnZcuwsjASXEYikMvx3gSpJnkvv1cKy762NFpZNRERUlmlsOK3o1Ox9ofnZsTOffpIVSWIGl4iIiIgEkuA6WZHK1qslIiIiIsXjDC4RERGR0pWxNbicwSUiIiIiReEMLhEREZHSlbE1uBzgypDInQ58R20RkntxQYSQXCIiIqL/4gCXiIiISOnK2BpcDnCJiIiIlK6MLVEoW6+WiIiIiBSPA9wnUKlU2LJli61rEBEREZWOSi32JjGSaBQbGws7Ozu8+OKLZv9srVq1MG/ePMuXegKDwYD27dujU6dORR5btGgR3N3dcePGDat2epT169YivENbNAkNQd8+PXEqIcGsnx/ZqR5+GtcK5z59ESdnhWP5sOdQp7KzyTl9W/hgw1vP4+zcF3FjUQRcnext2tkW2ews/2x2tk42O1snW46dRWbLsTOVniQGuCtWrMCbb76JAwcO4NatW7au81QqlQqrVq1CXFwcli5dajx+5coVvPfee/j8889RvXp1GzYEdmzfhjmzYzBsxEis37AZfn7+GD5sMHQ6XbEzmvl6YvX+K3j5kwN4ZcEh2NupsO7N5nBysDOeo3Eoh31n7mDhL39JorO1s9lZ/tnszM7srNxsOXYWRq0Se5MYmw9wMzIy8N1332H48OF48cUX8dVXXxU5Z+vWrWjSpAk0Gg08PT0RGRkJAGjdujWuXbuGt99+GyqVCqr//wnByZMno1GjRiYZ8+bNQ61atYz3jx07hg4dOsDT0xNubm5o1aoVjh8/XuzeNWrUwPz58zF27FhcuXIFBoMBgwcPRseOHdG/f3/8+eefCA8Ph7OzM6pUqYL+/fsjKSnJ+PM//PADQkJC4OTkBK1Wi/bt2yMzM7P4F+4p1qxehagevRAR2R11fX0xYdIUaDQabNm0sdgZ/b6IxYYj1/FXYjrO3kzD218fR3VteTSo6W48Z8XeS/ji1ws4fiVFEp2tnc3O8s9mZ3ZmZ+Vmy7EzWYbNB7jff/89/P394efnh379+mHlypUwGAzGx3/++WdERkaiS5cuOHHiBHbv3o2mTZsCADZt2oTq1atj6tSpSExMRGJiYrGfNz09HdHR0Th48CCOHDmCevXqoUuXLkhPTy92RnR0NNq1a4dBgwZh4cKF+PPPP7F06VKkpqaibdu2CA0Nxe+//44dO3bgzp076NWrFwAgMTERr7zyCgYNGoSzZ89i3759iIqKMnndpZGfl4ezZ04jrFlz4zG1Wo2wsOZIiD9R4tyHyw9SM/NK3fG/RHUWmc3O8s9mZ3ZmZ+Vmy7GzUFyDa10rVqxAv379AACdO3fG/fv3sX//fuPj06dPR58+fTBlyhQEBASgYcOG+OCDDwAAFStWhJ2dHVxcXODl5QUvL69iP2/btm3Rr18/+Pv7IyAgAMuWLUNWVpbJcxfHsmXL8Oeff+Ktt97CsmXLUKlSJSxcuBChoaGYMWMG/P39ERoaipUrV2Lv3r3466+/kJiYiIKCAkRFRaFWrVoICQnBiBEj4OzsXCQ/NzcXaWlpJrfc3NwndkpJTYFer4dWqzU5rtVqTWaRzaFSAZN7hODoRR3OJxb/LwHFJaKz6Gx2ln82O1snm52tky3HziKz5di5LIiJiUGTJk3g4uKCypUrIyIiAufPnzc5p3Xr1sZ35h/eXn/9dbOex6YD3PPnz+Po0aN45ZVXAADlypVD7969sWLFCuM5J0+eRLt27Sz+3Hfu3MGQIUNQr149uLm5wdXVFRkZGbh+/bpZOZUrV8awYcMQEBCAiIgIAEB8fDz27t0LZ2dn483f3x8AcOnSJTRs2BDt2rVDSEgIevbsiS+//BIpKY9+iz8mJgZubm4mt09mxZTqtZfE9N4N4eftipErj1n9uYmIiKiUVCqxt2Lav38/Ro4ciSNHjmDnzp3Iz89Hx44diyzTHDJkiPHd+cTERMyePdusl2vTL3pYsWIFCgoK4O3tbTxmMBjg6OiIhQsXws3NDU5OTmbnqtXqIm/35+fnm9yPjo6GTqfD/Pnz4ePjA0dHRzRr1gx5eea//V6uXDmUK/fPpczIyEDXrl0xa9asIudWrVoVdnZ22LlzJw4fPoxff/0Vn3/+OT788EPExcWhdu3aJud/8MEHGDNmjMkxg53jE/t4uHvAzs6uyEJ3nU4HT09Pc18ePu7VAO1DqqD7pweRmJpj9s8Xh6U7WyObneWfzc7WyWZn62TLsbPIbDl2Fkoiywh27Nhhcv+rr75C5cqV8ccff6Bly5bG4+XLlzfrnfn/stmrLSgowNdff425c+fi5MmTxlt8fDy8vb3x7bffAgAaNGiA3bt3PzbHwcEBer3e5FilSpVw+/Ztk0HuyZMnTc45dOgQRo0ahS5duiAoKAiOjo4We1uhcePGOH36NGrVqgVfX1+TW4UKFQA82ImhRYsWmDJlCk6cOAEHBwds3ry5SJajoyNcXV1Nbo6OTx7g2js4ICAwCHFHYo3HCgsLERcXiwYNQ816LR/3aoDOjaqi97xD+FuXZdbPmsOSna2Vzc7yz2ZndmZn5WbLsbOclWRJJQDcv38fwINlp/+2du1aeHp6Ijg4GB988AGysswbg9hsBvenn35CSkoKBg8eDDc3N5PHunfvjhUrVuD111/HpEmT0K5dO9StWxd9+vRBQUEBtm3bhnHjxgF4sA/ugQMH0KdPHzg6OsLT0xOtW7fGvXv3MHv2bPTo0QM7duzA9u3b4erqanyOevXqYc2aNXj22WeRlpaGd999t0SzxY8ycuRIfPnll3jllVfw3nvvoWLFirh48SLWr1+P5cuX4/fff8fu3bvRsWNHVK5cGXFxcbh37x4CAgIs8vwA0D96ICaOH4egoGAEhzTAN2tWIzs7GxGRUcXOmN6nASKerYHBS48gI7cAlVwfDKzTs/ORk18IAKjk6ohKrhrUqvRg4O7v7YqM3ALcSs5Calb+Y7NFdbZ2NjvLP5ud2ZmdlZstx87CmLGMoCRiYmIwZcoUk2OTJk3C5MmTH/szhYWFeOutt9CiRQsEBwcbj7/66qvw8fGBt7c3EhISMG7cOJw/fx6bNm0qdh+bDXBXrFiB9u3bFxncAg8GuLNnz0ZCQgJat26NDRs2YNq0aZg5cyZcXV1NprCnTp2KYcOGoW7dusjNzYXBYEBAQAAWLVqEGTNmYNq0aejevTvGjh2LZcuWmTz/0KFD0bhxY9SoUQMzZszA2LFjLfLavL29cejQIYwbNw4dO3ZEbm4ufHx80LlzZ6jVari6uuLAgQOYN28e0tLS4OPjg7lz5yI8PNwizw8AncO7ICU5GYsWLkBS0j34+Qdg0dLl0Jrx1kl0yzoAgB/efsHk+NtfH8eGIw/WKvd/oTbGvOhvfGzTOy8UOceana2dzc7yz2ZndmZn5WbLsbNcPWpJ5dPecR45ciT+/PNPHDx40OT40KFDjf8eEhKCqlWrol27drh06RLq1q1brD4qg6X2piKrySkQl+07aouQ3IsLIoTkEhERyYXGhp98cur4idD87F/fNev8N954Az/++CMOHDhQ5PNH/5WZmQlnZ2fs2LHjkd8i+yg2/ZAZEREREZUdBoMBb775JjZv3ox9+/Y9dXAL/PM5qqpVqxb7eTjAJSIiIlI6wWtwi2vkyJFYt24dfvzxR7i4uOD27dsAYNw569KlS1i3bh26dOkCrVaLhIQEvP3222jZsiUaNGhQ7OfhAJeIiIiIrGLx4sUAHnyZw7+tWrUKAwYMgIODA3bt2oV58+YhMzMTNWrUQPfu3TFhwgSznocDXCIiIiKlk8g+uE/76FeNGjXM/lbZR+EAl4iIiEjpJLJEwVo4wCUTonY7CP/isJBcANg+srmwbKLHuZv29A3MS6Ky65O31SEioqfjAJeIiIhI6SSyRMFaytarJSIiIiLF4wwuERERkdKVsTW4nMElIiIiIkXhAFfB1q9bi/AObdEkNAR9+/TEqYQESWU38HbF9K7+2DD4Wewd3Rwt6lQ0eXzv6OaPvPVu7G2zztbMFZktx84is0Xkbt30HYb2645u7ZqhW7tmGDWkH47G/maBtg/wOlsnm53lny3HzkKo1GJvEiO9RmQRO7Zvw5zZMRg2YiTWb9gMPz9/DB82GDqdTjLZGns1LiVlYv6+y498POrLYya3WTsvotBgwIGL5r8GUddDDtdZCZ1FZovK9axUBYNHvIUvvlqPL1Z9i0bPNMWk90bj6uWLpcoV2VlkNjuzsy2y5diZLIMDXIVas3oVonr0QkRkd9T19cWESVOg0WiwZdNGyWQfvZaKlbF/4+Cl5Ec+npKVb3JrUccDJ2/cR2IJtmcSdT3kcJ2V0FlktqjcZi+0xnPNX0D1Gj6oXrMWBr0+Ck5O5XH2z9LP8PA6s7OSOovMlmNnYTiDS3KXn5eHs2dOI6zZP/vDqtVqhIU1R0L8CclmP4lHeXuE1fLAttN3zf5ZUZ3leJ3l2FlktrV+n/V6Pfbu3I6cnGwEhjQsVRavMzsrqbPIbDl2FkqlEnuTGA5wFSglNQV6vR5ardbkuFarRVJSkmSzn6RTQCVk5etLtDxBVGc5Xmc5dhaZLfr3+crFv9C17XPo0upZzJ/9MSbNnAef2nVLlcnrbJ1sdpZ/thw7k+VwmzCShfDAyth1Lgn5+id/hzWRlFT3qY0lqzcgMzMDv+3ZiU+mTcDcRStLPcglIjKbBJcRiFS2Xm0Z4eHuATs7uyIL3XU6HTw9PSWb/Tgh3i6oWbE8tp2+U6KfF9VZjtdZjp1FZov+fba3t0e1GjVR3z8Qg0eMRh3f+tj83dpSZfI6WyebneWfLcfOZDkc4CqQvYMDAgKDEHck1nissLAQcXGxaNAwVLLZj9MlqArO38nApaSsEv28qM5yvM5y7Cwy29q/zwZDIfLy80qVwevMzkrqLDJbjp2FKmNrcLlEQaH6Rw/ExPHjEBQUjOCQBvhmzWpkZ2cjIjJKMtkaezWquWmM96u6OaKuZ3mk5xbgbvqDQUB5Bzu0qqfF4t+uSqKztXLZ2XrZonJXLJqPJs1aoLJXVWRnZmLPr9sRf/x3xMxbUqpckZ1FZrMzO9siW46dyTI4wFWozuFdkJKcjEULFyAp6R78/AOwaOlyaC3w1omlsv0qO2Nej2Dj/ZEtawMAdpy5i1k7H+wV2ra+J1QA9pwv3aJ9UddDDtdZCZ1FZovKTU1JxuypE5Csu4cKzs6oXbc+YuYtwTNNm5UqV2RnkdnszM62yJZjZ2HK2BpclcFg4Kd2ZCanwNYNzBf+xWFh2dtHNn/6SUQWdrcE+zEXR2VXRyG5RGR7GhtOKzpFLhean735NaH55uIMLhEREZHSSXCdrEgc4BIREREpnKqMDXDL1oIMIiIiIlI8zuASERERKRxncImIiIiIZIwzuGQVInc68Gg5Xlh2yoEZwrJJ3rjbARHJStmawOUMLhEREREpC2dwiYiIiBSOa3CJiIiIiGSMM7hERERECscZXFKM9evWIrxDWzQJDUHfPj1xKiFB8tmlzR3bvxUOrhiBuzsn4drP4/H9zH6oV7Po94I/F1wD2z8fjKTdk3Fn50fYuWgINA4l+/teWbzOSstmZ+tks7N1suXYWWS2HDuLoFKphN6khgNchdqxfRvmzI7BsBEjsX7DZvj5+WP4sMHQ6XSSzbZE7guhtbFk4xG0GroYL41eiXLl1Php3kCU19gbz3kuuAZ+/HQgdh+9gBdeW4TnBy/Ckh+OoNBgsElna2fLsbPIbHZmZ3ZWbrYcO5NlcICrUGtWr0JUj16IiOyOur6+mDBpCjQaDbZs2ijZbEvkdhvzFb7Zdhxnr9zFqYu3MfTjjajp5YFQ/2rGc2aPehGLNhzGnDUHcPbKXVy4noSNe04hL19vk87WzpZjZ5HZ7MzO7KzcbDl2FoUzuCR7+Xl5OHvmNMKa/bP3rFqtRlhYcyTEn5Bktqhc1woP9ipNScsGAFTyqICmwTVxLyUTe5cOw9WfxuPXL4ageQMfyXQWmS3HziKz2Zmd2Vm52XLsTJbDAa4CpaSmQK/XQ6vVmhzXarVISkqSZLaIXJVKhU/eegmH46/izOU7AIDa3hUBAB8OboeV/3cM3caswsnzN7FtwWDUra59UpxVOovOlmNnkdnsbJ1sdrZOthw7i8yWY2ehVIJvEsMBLinWvHdeRlCdKvjfR+uNx9T//22UFVuOYs3PxxH/VyLeW7ANf12/h+iXnrFVVSIiIrIgbhOmQB7uHrCzsyuy0F2n08HTs+iOAlLItnTuZ2O6oksLP7Qf8SVu3kszHk/UpQMAzl69a3L++av3UKOKu007WyNbjp1FZrOzdbLZ2TrZcuwsMluOnUWS4jpZkTiDq0D2Dg4ICAxC3JFY47HCwkLExcWiQcNQSWZbMvezMV3xcqtAdH5zBa4lppg8di0xBbfu3Uf9/2wd5lvTE9dvp9qss7Wy5dhZZDY7szM7Kzdbjp3JcjiDq1D9owdi4vhxCAoKRnBIA3yzZjWys7MRERkl2WxL5M4b+zJ6d2iInuO+QUZWLqpUdAYA3M/IQU5eAQDgs7W/YcJr7XHq4m3E/3UL/bo0hp9PJbz64TqbdLZ2thw7i8xmZ3ZmZ+Vmy7GzKGVtBpcDXIXqHN4FKcnJWLRwAZKS7sHPPwCLli6H1gJvnYjKtkTusKgwAMDORUNMjg/5+Ad8s+04AGDh94ehcSyH2aO6wMO1PE5dTMRLo1fiys1km3S2drYcO4vMZmd2ZmflZsuxsyhlbYCrMhhKsLs92VROga0bSItHy/HCslMOzBCWTUREZYvGhtOKFfub/y6lOZLXvCo031ycwSUiIiJSuLI2g8sPmRERERGRonAGl4iIiEjpytYELmdwiYiIiEhZOINLREREpHBlbQ0uB7gkeyJ3OvBo8oaQ3JRjC4XkEhEREQe4RERERIrHGVwiIiIiUpSyNsDlh8yIiIiISFE4g0tERESkdGVrApczuEq2ft1ahHdoiyahIejbpydOJSRIPlvKnccO6oiD37yLuwfn4NruGHz/6RDU86lsfLxm1YrIPrHwkbeo9qE26WztXLlms7N1stnZOtly7CwyW46dqfQ4wFWoHdu3Yc7sGAwbMRLrN2yGn58/hg8bDJ1OJ9lsqXd+obEvlnx3AK3+NwcvDV+IcuXs8NPiN1Be4wAAuHEnBbXaf2Bym7r4J6Rn5uCXQ6dt0tmauXLNZmd2ZmflZsuxsygqlUroTWo4wFWoNatXIapHL0REdkddX19MmDQFGo0GWzZtlGy21Dt3e2MRvtkah7OXb+PUXzcxdNI3qFm1IkIDawAACgsNuKNLN7m93KYhNu48jszsPJt0tmauXLPZmZ3ZWbnZcuxMlsEBrgLl5+Xh7JnTCGvW3HhMrVYjLKw5EuJPSDJbjp1dnTUAgJT7WY98PDSgBhr518DqLbFmZ/M6WyebndmZnZWbLcfOInEGl2QvJTUFer0eWq3W5LhWq0VSUpIks+XWWaVS4ZOxPXD4xCWcuZT4yHOiI5rh7OVEHIm/IonOInPlms3O1slmZ+tky7GzyGw5dibL4S4KRCUw74NeCPKtinYDP3vk4xpHe/QOfxYzv9xh5WZERERFSXGWVSQOcBXIw90DdnZ2RRa663Q6eHp6SjJbTp0/G9cTXV4IRvvB83Dzbuojz4ls3wjlNQ5Y+9PRklTmdbZSNjtbJ5udrZMtx84is+XYWaSyNsDlEgUFsndwQEBgEOKO/LP2s7CwEHFxsWjQsGTbVYnOlkvnz8b1xMttG6LzsAW4duvxn5QdENEcP+8/haSUDJt3tkauXLPZmZ3ZWbnZcuxMlsMZXIXqHz0QE8ePQ1BQMIJDGuCbNauRnZ2NiMgoyWZLvfO8D3qhd/iz6Pn2MmRk5qCK1gUAcD8jBzm5+cbz6tTwxPON6yLizcU272zNXLlmszM7s7Nys+XYWZiyNYHLAa5SdQ7vgpTkZCxauABJSffg5x+ARUuXQ2uBt05EZUu987BeLQEAO5e/ZXJ8yEdr8M3WOOP96G7NcPNOKnbFnrN5Z2vmyjWbndmZnZWbLcfOZBkqg8FgsHUJMk9Oga0blB0eTd4QkptybKGQXCIiki6NDacVqw3fLDT/5uJIofnm4hpcIiIiIlIULlEgIiIiUjjuokBEREREJGOcwSUiIiJSOM7gEhERERHJGGdwiZ5A1G4H4V8cFpILANtHNheWTUREMlW2JnA5wCUiIiJSOi5RICIiIiKSMc7gEhERESkcZ3CJiIiIiGSMA1wFW79uLcI7tEWT0BD07dMTpxISJJ9dVjs38HbF9K7+2DD4Wewd3Rwt6lQ0eXzv6OaPvPVu7G2zzkrKZmfrZLOzdbLl2Flkthw7i6BSqYTepIYDXIXasX0b5syOwbARI7F+w2b4+flj+LDB0Ol0ks0uy5019mpcSsrE/H2XH/l41JfHTG6zdl5EocGAAxfN7y/H6ywym53ZmZ2Vmy3HzmQZHOAq1JrVqxDVoxciIrujrq8vJkyaAo1Ggy2bNko2uyx3PnotFStj/8bBS8mPfDwlK9/k1qKOB07euI/EtFybdVZKNjuzMzsrN1uOnUXhDC7JXn5eHs6eOY2wZv/sh6pWqxEW1hwJ8Sckmc3OxedR3h5htTyw7fRds39WjtdZZDY7szM7Kzdbjp3JcjjAVaCU1BTo9XpotVqT41qtFklJSZLMZufi6xRQCVn5+hItT5DjdRaZzc7WyWZn62TLsbPIbDl2Fkol+CYx3CaMSGbCAytj17kk5OsNtq5CREQyIcVlBCJxBleBPNw9YGdnV2Shu06ng6enpySz2bl4QrxdULNieWw7fadEPy/H6ywym52tk83O1smWY2eR2XLsXBbExMSgSZMmcHFxQeXKlREREYHz58+bnJOTk4ORI0dCq9XC2dkZ3bt3x5075v3/Hge4CmTv4ICAwCDEHYk1HissLERcXCwaNAyVZDY7F0+XoCo4fycDl5KySvTzcrzOIrPZmZ3ZWbnZcuwsklQ+ZLZ//36MHDkSR44cwc6dO5Gfn4+OHTsiMzPTeM7bb7+NrVu3YsOGDdi/fz9u3bqFqKgos14vlygoVP/ogZg4fhyCgoIRHNIA36xZjezsbEREmvcLYs3sstxZY69GNTeN8X5VN0fU9SyP9NwC3E3PAwCUd7BDq3paLP7tqiQ6KyWbndmZnZWbLcfOSrdjxw6T+1999RUqV66MP/74Ay1btsT9+/exYsUKrFu3Dm3btgUArFq1CgEBAThy5AjCwsKK9Twc4CpU5/AuSElOxqKFC5CUdA9+/gFYtHQ5tBZ460RUdlnu7FfZGfN6BBvvj2xZGwCw48xdzNp5EQDQtr4nVAD2nC/dBxjkeJ1FZrMzO7OzcrPl2FkU0Utwc3NzkZtrunWlo6MjHB0dn/hz9+/fBwBUrPjgC47++OMP5Ofno3379sZz/P39UbNmTcTGxhZ7gKsyGAz8pIrM5BTYugGVVvgXh4Vlbx/Z/OknERGR1WlsOK3oO3a70Px+znGYMmWKybFJkyZh8uTJj/2ZwsJCvPzyy0hNTcXBgwcBAOvWrcPAgQOLDJabNm2KNm3aYNasWcXqwxlcIiIiIoUTvYvCBx98gDFjxpgce9rs7ciRI/Hnn38aB7eWxAEuEREREZVKcZYj/Nsbb7yBn376CQcOHED16tWNx728vJCXl4fU1FS4u7sbj9+5cwdeXl7FzucuCkREREQKp1KJvRWXwWDAG2+8gc2bN2PPnj2oXbu2yePPPPMM7O3tsXv3buOx8+fP4/r162jWrFmxn4czuEREREQKJ5Uvehg5ciTWrVuHH3/8ES4uLrh9+zYAwM3NDU5OTnBzc8PgwYMxZswYVKxYEa6urnjzzTfRrFmzYn/ADOAAl4iIiIisZPHixQCA1q1bmxxftWoVBgwYAAD47LPPoFar0b17d+Tm5qJTp05YtGiRWc/DXRRkiLso0JOI2qGBuzMQEZWOLXdR8H//F6H552Z2EppvLq7BJSIiIiJF4RIFIiIiIoVTq6WxBtdaOINLRERERIrCGVwiIiIihZPIJgpWwxlcBVu/bi3CO7RFk9AQ9O3TE6cSEiSfzc6Wz27g7YrpXf2xYfCz2Du6OVrUqWjy+N7RzR95693Y22adrZ3NztbJZmfrZMuxs8hsOXam0uMAV6F2bN+GObNjMGzESKzfsBl+fv4YPmwwdDqdZLPZWUy2xl6NS0mZmL/v8iMfj/rymMlt1s6LKDQYcOCi+a9BDteDndnZ1rnsbL1sOXYWRaVSCb1JDQe4CrVm9SpE9eiFiMjuqOvriwmTpkCj0WDLpo2SzWZnMdlHr6ViZezfOHgp+ZGPp2Tlm9xa1PHAyRv3kZiWa7PO1sxmZ3ZmZ+Vmy7GzKFL5JjNr4QBXgfLz8nD2zGmENftn31K1Wo2wsOZIiD8hyWx2tl72k3iUt0dYLQ9sO33X7J+V4/VgZ3ZmZ+Vmy7EzWQ4HuAqUkpoCvV4PrVZrclyr1SIpKUmS2exsvewn6RRQCVn5+hItT5Dj9WBn62Szs3Wy5dhZZLYcO4vEJQpEVGaFB1bGrnNJyNfzCw6JiEi+OMBVIA93D9jZ2RVZ6K7T6eDp6SnJbHa2XvbjhHi7oGbF8th2+k6Jfl6O14OdrZPNztbJlmNnkdly7CwSZ3BJ9uwdHBAQGIS4I7HGY4WFhYiLi0WDhqGSzGZn62U/TpegKjh/JwOXkrJK9PNyvB7szM7srNxsOXYmy+EXPShU/+iBmDh+HIKCghEc0gDfrFmN7OxsRERGSTabncVka+zVqOamMd6v6uaIup7lkZ5bgLvpeQCA8g52aFVPi8W/XZVEZ2tmszM7s7Nys+XYWRQJTrIKxQGuQnUO74KU5GQsWrgASUn34OcfgEVLl0NrgbdORGWzs5hsv8rOmNcj2Hh/ZMvaAIAdZ+5i1s6LAIC29T2hArDnfOk+HCGH68HO7GzrXHa2XrYcO5NlqAwGAz9NIjM5BbZuQFIW/sVhIbnbRzZ/+klERPRYGhtOK4ZO2SM0/8SktkLzzcUZXCIiIiKFK2tLFPghMyIiIiJSFM7gEhERESmcFLfyEokzuERERESkKJzBJSIiIlK4MjaBywEukdKI2u1g1ObTQnIBYEFkkLBsIiIqezjAJSIiIlI4rsElIiIiIpIxzuASERERKVwZm8DlAJeIiIhI6bhEgRRj/bq1CO/QFk1CQ9C3T0+cSkiQfDY7WyfbErn1PMtjZIuamP1SfSzrGYRG3i6PPbdv46pY1jMI7epVLE1tSV8Pa2ezs3Wy2Vn+2XLsTKXHAa5C7di+DXNmx2DYiJFYv2Ez/Pz8MXzYYOh0Oslms7O8OjuWU+NGag7WHU984nmNvF1QR+uElOz80tSW/PVgZ3aWQrYcO4vMlmNnUVQqsTep4QBXodasXoWoHr0QEdkddX19MWHSFGg0GmzZtFGy2ewsr85/3s7Aj6fv4uSt9Mee464ph1dCq2J53E3oCw2S6G2tXHZmZ3a2fbYcO5NlcICrQPl5eTh75jTCmv2zH6parUZYWHMkxJ+QZDY7y7/zf6kADHquGn45n4TEtNxSZcnxerAzO7OzbbPl2FkklUol9CY1HOAqUEpqCvR6PbRarclxrVaLpKQkSWazs3WyRXb+r07+nigsBPZcTC51lhyvBzuLzxWZzc7yz5ZjZ7IcDnCJyOJqumvQrl5FrDp209ZViIgIZW8NLrcJUyAPdw/Y2dkVWeiu0+ng6ekpyWx2tk62yM7/Vq9Sebg4lsPMF+sbj9mpVejZ0Avt6mkxftsFs/LkeD3YWXyuyGx2ln+2HDuT5XAGV4HsHRwQEBiEuCOxxmOFhYWIi4tFg4ahksxmZ/l3/rcj1+5j6q+XMG3nP7eU7Hz8cj4J8w9ck0xv/jdkZ3ZWbrYcO4tU1tbgcgZXofpHD8TE8eMQFBSM4JAG+GbNamRnZyMiMkqy2ewsr86OdmpUcnYw3ves4IDqbhpk5emRnJ2PzDy9yfn6QgPScgpwJyPPpr2tlcvO7MzOts+WY2dRJDgGFYoDXIXqHN4FKcnJWLRwAZKS7sHPPwCLli6H1gJvnYjKZmd5dfapqMHY1rWN93s18gIAHL6agq+O3SpVx0eR+vVgZ3aWQrYcO4vMlmNnsgyVwWAo3eaUZHU5BbZuQGXRqM2nhWUviAwSlk1EJBUaG04rvjD3oND83955Xmi+ubgGl4iIiIgUhUsUiIiIiBROih8EE4kzuERERESkKJzBJSIiIlK4MjaByxlcIiIiIlIWzuAS2UBufqGwbEd7MX9vFbnTQa+Vx4Tkfj+oiZBcIrK90zfShGUHVXcVlm0rZW0NLge4RERERApXxsa3XKJARERERMrCGVwiIiIihStrSxQ4g0tEREREisIBroKtX7cW4R3aokloCPr26YlTCQmSz2bnfxz/4xjGjBqOLh1aommjAOzbs8siuYD0r3OQlzMmdKqHVX0b4v+GNsFzPu5FzqnursGHnXzx7YBQfD+wMeZGBMKzgoPNOls7m52tk83O8s5OTrqLL2ZNxNAe7RHd9XmMG9YHl/86Y4G2D4i8HpamUom9SQ0HuAq1Y/s2zJkdg2EjRmL9hs3w8/PH8GGDodPpJJvNzqZysrNRr74f3v1gYqmz/k0O19nR3g5XdFlYeujaIx/3cnHEzJcDcDM1Bx9uPY9RP5zGdyduIV9v/u4Ucrge7MzOts6VY3ZGehomj3kNdnbl8N7H8/HJl9+h79C3UMHZMjskiLweVHoc4CrUmtWrENWjFyIiu6Oury8mTJoCjUaDLZs2SjabnU01f74lhr/xFtq07VDqrH+Tw3U+/vd9rP39Jo5cTX3k4/2aVsMff6fiq7gbuKzLwu30XBy9lor7OQU262zNbHZmZyV1FpW99fvV0HpWwetjJ8HXPwiVvaqhwTNhqOJdvdR9RXUWSa1SCb1JDQe4CpSfl4ezZ04jrFlz4zG1Wo2wsOZIiD8hyWx2tg45Xuf/UgF4toY7bqXmYHJ4fXzdvxE+iQh45DIGW3aW4+8dO7OzkrKPH/kNdeoHYN7H7+P1Xh3xwYi+2LNtc6m6PiTHP//LGg5wFSglNQV6vR5ardbkuFarRVJSkiSz2dk65Hid/8vNqRzKO9ihe6OqOH7jPiZtO48jV1LwQUdfBFV1kUxnOf7esbP4XJHZcuwsMvtu4k3s+mkjvLxr4P0Zn6P9S92xevFcHNj5U6n6AvL887+srcHlNmFEJCsP3wqLu5aK/zt1BwBwRZcNfy9nhAdUwunEdFvWIyKJKDQUok69APQZNBIAUMvXDzeuXsaunzehZYeXbNyOROMMrgJ5uHvAzs6uyEJ3nU4HT09PSWazs3XI8Tr/V1pOAQoKC/F3SrbJ8RspOajkbN4uCnK8HuxsnWx2ln+2R0VPVPOpY3LMu0Yt6O7eLnGmMVuGf/6rVCqhN6nhAFeB7B0cEBAYhLgjscZjhYWFiIuLRYOGoZLMZmfrkON1/q+CQgMu3M1CNXeNyXFvNw3uZuSZlSXH68HO7KykziKz6wc2ROLfpjux3L55HZ6VvUqc+ZAc//xXq8TepIZLFBSqf/RATBw/DkFBwQgOaYBv1qxGdnY2IiKjJJvNzqaysjJx4/p14/1bN2/gr3Nn4ermBq+q3pLsbKlsTTk1qro5Gu9XcXVEba0T0nP0SMrMw+aERLzbri5OJ6bj1K10NK7hhqY+7hi/9ZzNOlszm53ZWUmdRWWHR72CyW8PxpZvVyGsZXtcOn8ae7ZtxuC3xpe6r6jOZDkc4CpU5/AuSElOxqKFC5CUdA9+/gFYtHQ5tBZ460RUNjubOnv6NIYPiTbenzd3FgDgxa4RmDQtRpKdLZXtW6kCZnT1N95/rVlNAMDu80mYv/8KjlxNxeKD19CjUVUMae6Dm6k5mLnzIs7eybBZZ2tmszM7K6mzqOy6fkF4+6NP8N2qL7B57XJU8vJG/9fH4Pm24aXuK6qzSFJcRiCSymAwGGxdgsxTgq0+SWJy883/QoLicrSX38qjXiuPCcn9flATIblEZHunb6QJyw6qbpkvg/gvjQ2nFbssOSo0f9vrTYXmm4szuEREREQKV8YmcPkhMyIiIiJSFs7gEhERESmcCmVrCpczuERERESkKJzBJSIiIlI4Ke5VKxIHuEQ2IMedDkQStdtB8PvbheQCwJ8zLbPVEBGVjKidDpSqrG0Txv+XJSIiIiJF4QwuERERkcKVsQlczuASERERkbJwBpeIiIhI4dRlbAqXM7gKtn7dWoR3aIsmoSHo26cnTiUkSD6bna2TLcfOlsh+vW0dbBrdDCc/7oC4yW2xeEBj1K5UweQch3JqTI4MxLEp7RA/vQMW/i8UWmcHm3W2dq7IbHa2TrYcO4vMlmNnKj0OcBVqx/ZtmDM7BsNGjMT6DZvh5+eP4cMGQ6fTSTabndlZdHbTOhXxzaHr6Pl5LKKXHkM5OxW+GtoETg52xnM+fDkAbQMr4801J/DqojhUcXXEoujGNutszVx2ZmelZcuxsygqldib1HCAq1BrVq9CVI9eiIjsjrq+vpgwaQo0Gg22bNoo2Wx2ZmfR2YOW/45Nv9/EhTsZOJeYjnHrT6GahxOC//92Q86acujZtDpmbD2HIxeTcfpmGsZ9dwrP1PZAo5ruNulszVx2ZmelZcuxM1lGsQa4CQkJxb6R7eXn5eHsmdMIa9bceEytViMsrDkS4k9IMpud2dkW2S6aBx9DSM3KBwAEV3eFQzk1Dv2VZDzn8r1M3EzJRqiPuyQ6y/E6szM72yJbjp1FUqlUQm9SU6wPmTVq1AgqlQoGg+GRjz98TKVSQa/XW7QgmS8lNQV6vR5ardbkuFarxZUrlyWZzc7WyZZjZ1HZKhXwYbcA/H4lGRduZwAAKrk4Iq+gEOk5BSbnJqXnwtPV0eadReaKzGZn62TLsbPIbDl2FkmCY1ChijXAvXLliugeRERWNTkyCPW9nNHnizhbVyEiIgsr1gDXx8dHdA+yIA93D9jZ2RVZ6K7T6eDp6SnJbHa2TrYcO4vInhQZiLaBlfDKojjcvp9jPH4vPRcO5dRw0ZQzmcX1dHFEUlquTTuLzhWZzc7WyZZjZ5HZcuwsErcJK4Y1a9agRYsW8Pb2xrVr1wAA8+bNw48//mjRclQy9g4OCAgMQtyRWOOxwsJCxMXFokHDUElmszM7Wyt7UmQgOgRXQb8lR3EjOdvksT9vpCGvoBDN6/3ztmPtShVQzcMJJ66l2qyzNXLZmZ2Vli3HzmXFgQMH0LVrV3h7e0OlUmHLli0mjw8YMKDIGt/OnTub9Rxmf9HD4sWL8dFHH+Gtt97C9OnTjWtu3d3dMW/ePHTr1s3cSBKgf/RATBw/DkFBwQgOaYBv1qxGdnY2IiKjJJvNzuwsOntKVCC6hnrj9VXHkZlbAE+XB/vbpmcXILegEBk5Bdhw9AbGvxyA+1n5SM8pwKTIQBy/moKT11Nt0tmauezMzkrLlmNnUaQ0f5uZmYmGDRti0KBBiIp69PXq3LkzVq1aZbzv6Gje5yDMHuB+/vnn+PLLLxEREYGZM2cajz/77LMYO3asuXEkSOfwLkhJTsaihQuQlHQPfv4BWLR0ObQWeOtEVDY7s7Po7L7NHyy3WjfiOZPj761PwKbfbwIApv/fWRgMBiyMDoVDOTV+O5+ESZtO26yzNXPZmZ2Vli3HzmVBeHg4wsPDn3iOo6MjvLy8SvwcKsPjtkZ4DCcnJ5w7dw4+Pj5wcXFBfHw86tSpgwsXLqBBgwbIzs5+egiVyn8+4E1EjxH8/nZh2X/OfPIfzkRE/6Uxe1rRcl75+qTQ/K96ByA31/SzCo6Ojk+deVWpVNi8eTMiIiKMxwYMGIAtW7bAwcEBHh4eaNu2LT7++OMiu1Y8idlrcGvXro2TJ08WOb5jxw4EBASYG0dEREREMhcTEwM3NzeTW0xMTImyOnfujK+//hq7d+/GrFmzsH//foSHh5u1Fa3Zf5cYM2YMRo4ciZycHBgMBhw9ehTffvstYmJisHz5cnPjiIiIiEgwteBFuB988AHGjBljcszcdbMP9enTx/jvISEhaNCgAerWrYt9+/ahXbt2xcowe4D72muvwcnJCRMmTEBWVhZeffVVeHt7Y/78+SaFiIiIiEgaRH/bWHGWI5RUnTp14OnpiYsXL4ob4AJA37590bdvX2RlZSEjIwOVK1cuSQwRERER0RPduHEDOp0OVatWLfbPlHi58927d3H+/HkAD/5WUKlSpZJGEREREZFAUvqeh4yMDFy8eNF4/8qVKzh58iQqVqyIihUrYsqUKejevTu8vLxw6dIlvPfee/D19UWnTp2K/Rxm76KQnp6OESNG4Ntvv0VhYSEAwM7ODr1798YXX3wBNzc3c+KoBLiLApHtLTsi5ivMh4bVFpJLRLZny10U+q+NF5q/pm/DYp+7b98+tGnTpsjx6OhoLF68GBEREThx4gRSU1Ph7e2Njh07Ytq0aahSpUqxn6NEa3BPnDiBn3/+Gc2aNQMAxMbGYvTo0Rg2bBjWr19vbiQRERERCSR6Da45WrdujSfNr/7yyy+lfg6zB7g//fQTfvnlFzz//PPGY506dcKXX35p9teoERERERFZmtkDXK1W+8hlCG5ubvDw8LBIKSIiIiKyHNHbhEmN2V/0MGHCBIwZMwa3b982Hrt9+zbeffddTJw40aLliIiIiIjMVawBbmhoKBo3bozGjRtjyZIlOHLkCGrWrAlfX1/4+vqiZs2aOHz4MJYuXSq6L5lh/bq1CO/QFk1CQ9C3T0+cSkiQfDY7Wydbjp1FZlsi99b5U/h5wSSsGvMqvhjcGZePHzZ5/NIfB/F/c8dj+aie+GJwZ9y7fsnmna2dzc7WyZZjZ5HZcuwsgkqlEnqTmmINcCMiItCtWzfjbezYsfjwww/Rv39/9O/fHx9++CHGjh2Lbt26ie5LxbRj+zbMmR2DYSNGYv2GzfDz88fwYYOh0+kkm83O7GyLbEvl5uflQFu9Nlr1G/nIxwtyc1C1XhCa9xhUqr6W7GzNbHZmZ1tky7GzKCrBN6kxe5swsr3ibBPWt09PBAWHYPyEjwAAhYWF6NiuFV55tT8GDxlaqucXlc3O7GyL7JLmPmmbsC8Gd0b4yI9Qp3HzIo+lJd3GmnED0GvSF6hUs26Rx4uzTVhZus7sLI1cuWZLrbMttwkbtP6U0PyVfUKE5pvL7DW4JH35eXk4e+Y0wpr983+uarUaYWHNkRB/QpLZ7MzOtsgW2VkUXmd2VlJnkdly7CySWqUSepMaswe4er0ec+bMQdOmTeHl5WX81omHN7K9lNQU6PV6aLVak+NarRZJSUmSzGZn62TLsbPIbJGdReF1tk42O8s/W46dyXLMHuBOmTIFn376KXr37o379+9jzJgxiIqKglqtxuTJkwVUJCIiIqLSUKnE3qTG7AHu2rVr8eWXX+Kdd95BuXLl8Morr2D58uX46KOPcOTIEREdyUwe7h6ws7MrstBdp9PB09NTktnsbJ1sOXYWmS2ysyi8ztbJZmf5Z8uxM1mO2QPc27dvIyTkwUJiZ2dn3L9/HwDw0ksv4eeff7ZsOyoRewcHBAQGIe5IrPFYYWEh4uJi0aBhqCSz2ZmdbZEtsrMovM7srKTOIrPl2FmksrZNmNmf56tevToSExNRs2ZN1K1bF7/++isaN26MY8eOwdHRUURHKoH+0QMxcfw4BAUFIzikAb5ZsxrZ2dmIiIySbDY7s7Mtsi2Vm5eTjft3bxnvpyXdxr3rl6Cp4AIXbWXkZKQjPfkuMlMfzPik3r4BACjv5oEKbuZ9fqEsX2d2Vl5nkdly7EyWYfYANzIyErt378Zzzz2HN998E/369cOKFStw/fp1vP322yI6Ugl0Du+ClORkLFq4AElJ9+DnH4BFS5dDa4G3TkRlszM72yLbUrn3rv6FLZ+MM94/9N0yAIB/8/ZoN3gsrpyMxZ5Vnxof/3VpDACgyct90bRbf5t0tmY2O7OzLbLl2FkUCU6yClXqfXCPHDmCw4cPo169eujatauletETFGcfXCIS60n74JZGcfbBJSJ5suU+uMM3nhGav7h7oNB8c5V6H9ywsDCMGTMGzz33HGbMmGGJTkREREREJWaxL3pITEzExIkTLRVHRERERBbCbcKIiIiIiGTMhqtBiIiIiMgapLiVl0icwSUiIiIiRSn2DO6YMWOe+Pi9e/dKXYaISi83v1BIrqM9/z78b6J2Owh+f7uQXAD4c2a4sGwikray9id4sQe4J06ceOo5LVu2LFUZIiIiIqLSKvYAd+/evSJ7EBEREZEgZW0NLj9kRkRERKRw6rI1vi1zSzKIiIiISOE4wFWw9evWIrxDWzQJDUHfPj1xKiFB8tnsLD77+B/HMGbUcHTp0BJNGwVg355dFmj6D7ldD5G5lsh+vW0dbBrdDCc/7oC4yW2xeEBj1K5UweQch3JqTI4MxLEp7RA/vQMW/i8UWmcHm3W2dq7IbHaWf7YcO4ugVom9SQ0HuAq1Y/s2zJkdg2EjRmL9hs3w8/PH8GGDodPpJJvNztbJzsnORr36fnj3A8t/86Acr4fUOzetUxHfHLqOnp/HInrpMZSzU+GroU3g5GBnPOfDlwPQNrAy3lxzAq8uikMVV0csim5ss87WzGVn+XcWmS3HzmQZHOAq1JrVqxDVoxciIrujrq8vJkyaAo1Ggy2bNko2m52tk938+ZYY/sZbaNO2Q6k7/pccr4fUOw9a/js2/X4TF+5k4FxiOsatP4VqHk4Iru4KAHDWlEPPptUxY+s5HLmYjNM30zDuu1N4prYHGtV0t0lna+ays/w7i8yWY2dRVCqV0JvUlGiA+9tvv6Ffv35o1qwZbt68CQBYs2YNDh48aNFyVDL5eXk4e+Y0wpo1Nx5Tq9UIC2uOhPinb/dmi2x2tl62KHK8HnLs7KJ58Nng1Kx8AEBwdVc4lFPj0F9JxnMu38vEzZRshPq4S6KzHK8zO8s/W46dyXLMHuBu3LgRnTp1gpOTE06cOIHc3FwAwP379zFjxgyLFyTzpaSmQK/XQ6vVmhzXarVISkp6zE/ZNpudrZctihyvh9w6q1TAh90C8PuVZFy4nQEAqOTiiLyCQqTnFJicm5SeC09XR5t3FpkrMpud5Z8tx84icQ3uU3z88cdYsmQJvvzyS9jb2xuPt2jRAsePH7doOSIi+sfkyCDU93LGW9/E27oKEZGkmb0P7vnz5x/5jWVubm5ITU21RCcqJQ93D9jZ2RVZ6K7T6eDp6SnJbHa2XrYocrwecuo8KTIQbQMr4ZVFcbh9P8d4/F56LhzKqeGiKWcyi+vp4oiktFybdhadKzKbneWfLcfOIklwmaxQZs/genl54eLFi0WOHzx4EHXq1LFIKSodewcHBAQGIe5IrPFYYWEh4uJi0aBhqCSz2dl62aLI8XrIpfOkyEB0CK6CfkuO4kZytsljf95IQ15BIZrX++et0tqVKqCahxNOXEu1WWdr5LKz/DuLzJZjZ5HUKpXQm9SYPYM7ZMgQjB49GitXroRKpcKtW7cQGxuLsWPHYuJEy287RCXTP3ogJo4fh6CgYASHNMA3a1YjOzsbEZFRks1mZ+tkZ2Vl4sb168b7t27ewF/nzsLVzQ1eVb0l2VlkttQ7T4kKRNdQb7y+6jgycwvg6fJgf9v07ALkFhQiI6cAG47ewPiXA3A/Kx/pOQWYFBmI41dTcPJ6qk06WzOXneXfWWS2HDuTZZg9wH3//fdRWFiIdu3aISsrCy1btoSjoyPGjh2LN998U0RHKoHO4V2QkpyMRQsXICnpHvz8A7Bo6XJoLfDWiahsdrZO9tnTpzF8SLTx/ry5swAAL3aNwKRpMZLsLDJb6p37NvcBAKwb8ZzJ8ffWJ2DT7w92sZn+f2dhMBiwMDoUDuXU+O18EiZtOm2zztbMZWf5dxaZLcfOopS1fWFVBoPBUJIfzMvLw8WLF5GRkYHAwEA4Oztbuhs9xn8+LE1kIje/UEiuo31Z++PRNoLf3y4s+8+Z4cKyiejpNGZPK1rO+G1/Cc2f0aW+0HxzlfhSOzg4IDAw0JJdiIiIiEgACS6TFcrsAW6bNm2e+I0Ve/bsKVUhIiIiIqLSMHuA26hRI5P7+fn5OHnyJP78809ER0c/+oeIiIiIyGakuNOBSGYPcD/77LNHHp88eTIyMjJKXYiIiIiIqDQs9qmRfv36YeXKlZaKIyIiIiILUanE3qTGYp/ni42NhUajsVQcEZUQdzuQN5E7HTSduktI7tGP2gvJJSIqKbMHuFFRphsYGwwGJCYm4vfff+cXPRARERFJkFqCs6wimT3AdXNzM7mvVqvh5+eHqVOnomPHjhYrRkRERESWwQ+ZPYFer8fAgQMREhICDw8PUZ2IiIiIiErMrMV6dnZ26NixI1JTUwXVISIiIiJLK2sfMjP70yjBwcG4fPmyiC5kYevXrUV4h7ZoEhqCvn164lRCguSz2dk62XLsLDK7rHZ+xscdn/dtiF1jX0DC1PZo41/J5PGKFRwwLTIQu8a+gLgJbbC4fyPUrOhk087WzmZn+WfLsTOVntkD3I8//hhjx47FTz/9hMTERKSlpZncSBp2bN+GObNjMGzESKzfsBl+fv4YPmwwdDqdZLPZmZ1tkV2WOzs52OH87QzM+PncIx+f/2oDVPdwwuh18ei9OA63UnOwbEBjOJVgp46yfJ3Z2XbZcuwsilol9iY1xf5TaurUqcjMzESXLl0QHx+Pl19+GdWrV4eHhwc8PDzg7u7OdbkSsmb1KkT16IWIyO6o6+uLCZOmQKPRYMumjZLNZmd2tkV2We588IIOC3dfwp6z94o85qMtj4Y13PHx1nM4fSsNV3VZ+Pinc9CUs0N4iJfNOlszm53lny3HzmQZxR7gTpkyBZmZmdi7d6/xtmfPHuPt4X2yvfy8PJw9cxphzZobj6nVaoSFNUdC/AlJZrMzO9sim50fz8HuwZRMbkGh8ZjBAOTpCxHq425WFq8zO9siW46dRVIJ/kdqir2LgsFgAAC0atVKWBmyjJTUFOj1emi1WpPjWq0WV66Ubv20qGx2tk62HDuLzGbnx7uSlIVbqdkY3cEXU//vLLLz9ejfrCa83DTwdHGUTGc5Xmd2tk62HDuT5Zi1TZhKih+TIyIiiysoNODtbxMwJSIQh8a3RoG+EHGXk/HbX0mS/MQ0ET2ZFNfJimTWALd+/fpPHeQmJyeXqhCVnoe7B+zs7IosdNfpdPD09JRkNjtbJ1uOnUVms/OTnU1MR6/FcXB2tIO9nRopWflYO7QJTt807wPFvM7WyZZjZ5HZcuwsUlkb4Jr1UdgpU6bgs88+e+KNbM/ewQEBgUGIOxJrPFZYWIi4uFg0aBgqyWx2ZmdbZLNz8WTk6pGSlY+aFZ0Q6O2KveeKfijtSXid2dkW2XLsTJZj1gxunz59ULlyZVFdyIL6Rw/ExPHjEBQUjOCQBvhmzWpkZ2cjIjJKstnszM62yC7LnZ0c7Ez2ta3m4QQ/L2fcz87H7fu56BBUGSmZ+Ui8n4N6VZwxLrw+9p69h9hL5r9TV5avMzvbLluOnUUpa8tMiz3ALWsXRu46h3dBSnIyFi1cgKSke/DzD8CipcuhtcBbJ6Ky2ZmdbZFdljsHebti5aBnjPffC68PAPjxxC1M3HwGlZwd8W7n+tBWcMC9jFxsPZmIpfuv2LSzNbPZWf7ZcuxMlqEyPNwe4SnUajVu377NGVwJyCmwdQMikqOmU3cJyT36UXshuURKozHrfXPLmrtf7O4O77SqIzTfXMW+1IWFhU8/iYiIiIjIxmz4dwkiIiIisoayttLU/C8UJyIiIiKSMM7gEhERESmcuoxN4XKAS0RERKRwZe2LHjjAJSIqgdx8MR+8dbQXt3LsnZf9hOR+uP28kFwAmB4upjMRKRsHuEREREQKV8ZWKPBDZkRERESkLJzBJSIiIlI4NcrWFC5ncImIiIhIUTjAVbD169YivENbNAkNQd8+PXEqIUHy2exsnWw5dhaZLSL3+B/HMGbUcHTp0BJNGwVg3x7Lfk2uJTpfP5uA7+dMwIKRvTGjb3uc//2QyeMGgwH7f/gK80f2wuwBXbBuxrtIvn2jWNl1KjphUJNq+KhDXczt6odgL2eTxzvW12Jcm1qYEV4P0zr5YlhYddR015j9Gh6S0++G6Gw5dhaZLcfOIqhUYm9SwwGuQu3Yvg1zZsdg2IiRWL9hM/z8/DF82GDodDrJZrMzO9siW1RuTnY26tX3w7sfTCxVzqNYqnN+bg4q16yDTgPefOTjR376Dr//shnhA0djwNSFsHfUYP3M91GQl/fUbIdyatxKy8WmU3ce+fi9zDxsOnUXc/ZfxcJD15GSlY+hYdVRwcHOrNcAyO93g52tly3HzmQZHOAq1JrVqxDVoxciIrujrq8vJkyaAo1Ggy2bNko2m53Z2RbZonKbP98Sw994C23adihVzqNYqnPdRk3Rutcg+DV5vshjBoMBR3dsQouIvqj/bAtUrlkHXYePQ3qqDuf/OPSINFPn7mZix/kk/Hk745GPn7iZjgtJWUjOysedjDz8eOYenOzt4O3qaNZrAOT3u8HO1suWY2dR1CqxN6nhAFeB8vPycPbMaYQ1a248plarERbWHAnxJySZzc7sbItskZ1FsVbn1HuJyExNRu2gxsZjmvLO8K4bgJsXzljseQDATgU0q+mG7Hw9bqXlmvWzcvzdYGfrZMuxs0hqlUroTWo4wFWglNQU6PV6aLVak+NarRZJSUmSzGZn62TLsbPIbJGdRbFW58zUFABABTcPk+MV3NyRmZpskecIqFwBM8LrYeaL9dGyjgeWxt5AZp7erAw5/m6ws3Wy5diZLIcDXCIisolLuizM3X8Vnx+8jnP3MtH/2apwLsEaXCJ6On7IjGTPw90DdnZ2RRa663Q6eHp6SjKbna2TLcfOIrNFdhbFWp0ruD+Yuc28n2JyPPN+Kiq4V7TIc+TpDdBl5eN6ag6+j7+DwkKgaU03szLk+LvBztbJlmNnshwOcBXI3sEBAYFBiDsSazxWWFiIuLhYNGgYKslsdmZnW2SL7CyKtTq7V6qKCu4VcfX0P+sJc7MycevSWVSrF2ix5/k3lQooZ+anVeT4u8HO1smWY2eRytoaXH6TmUL1jx6IiePHISgoGMEhDfDNmtXIzs5GRGSUZLPZmZ1tkS0qNysrEzeuXzfev3XzBv46dxaubm7wquotic55OdlIuX3TeP/+vUTcuXoRGmcXuHlWQdPOUTi0ZS08vKrBvZIXDvzwFVzctfB7psVTsx3sVPCs4GC8X7G8PbxdHZGVr0dWnh7t6mlx+nYG0nMLUMHBDi1qecBNUw7xt9LNeg2A/H432Nl62XLsTJbBAa5CdQ7vgpTkZCxauABJSffg5x+ARUuXQ2uBt05EZbMzO9siW1Tu2dOnMXxItPH+vLmzAAAvdo3ApGkxkuicePk81k4fa7y/65slAICQFzqi6+vvIeyl3sjLzcH2FZ8hJysDNeoHo/e4mSjn4PC4SKMa7hqMaF7TeL9bUGUAwLG/7+OHhDuo7OyAJs96o4KDHTLzC/F3aja+OPQ37mQ8fY/d/5Lb7wY7Wy9bjp1FkdIk64EDB/DJJ5/gjz/+QGJiIjZv3oyIiAjj4waDAZMmTcKXX36J1NRUtGjRAosXL0a9evWK/Rwqg8FgENCdBMopsHUDIsrNLxSS62gvbuXYdyf/FpKbkJglJBcApof7CcsmsjaNDacVVx67/vSTSmFQk5pPP+n/2759Ow4dOoRnnnkGUVFRRQa4s2bNQkxMDFavXo3atWtj4sSJOHXqFM6cOQONpnjfeMgZXCIiIiKFk9KHrsLDwxEeHv7IxwwGA+bNm4cJEyagW7duAICvv/4aVapUwZYtW9CnT59iPYeUXi8RERERCaBSqYTecnNzkZaWZnLLzTXvi1sA4MqVK7h9+zbat29vPObm5obnnnsOsbGxT/hJUxzgEhEREVGpxMTEwM3NzeQWE2P+5w1u374NAKhSpYrJ8SpVqhgfKw4uUSAiIiJSONGfMfvggw8wZswYk2OOjo6Cn/XxOMAlIiIiolJxdHS0yIDWy8sLAHDnzh1UrVrVePzOnTto1KhRsXM4wCUiKgGRux2I0rtRDUG5QmIBAE2n7hKWffSj9k8/iUghpPhlDI9Su3ZteHl5Yffu3cYBbVpaGuLi4jB8+PBi53CAS0RERERWk5GRgYsXLxrvX7lyBSdPnkTFihVRs2ZNvPXWW/j4449Rr1494zZh3t7eJluJPQ0HuEREREQKJ6X5299//x1t2rQx3n+4djc6OhpfffUV3nvvPWRmZmLo0KFITU3F888/jx07dhR7D1yAX/QgS/yiByIqK7hEgZTEll/0sPaPG0Lz+z5TXWi+uTiDS0RERKRwMlmCazHy+5QEFdv6dWsR3qEtmoSGoG+fnjiVkCD5bHa2TrYcO4vMZmfrZFsi9xkfd3zetyF2jX0BCVPbo41/JZPHK1ZwwLTIQOwa+wLiJrTB4v6NULOik007Wztbjp1FZsuxswiiv+hBajjAVagd27dhzuwYDBsxEus3bIafnz+GDxsMnU4n2Wx2ZmdbZLOzvDo7Odjh/O0MzPj53CMfn/9qA1T3cMLodfHovTgOt1JzsGxAYziVYNeLsnydlZItx85kGRzgKtSa1asQ1aMXIiK7o66vLyZMmgKNRoMtmzZKNpud2dkW2ewsr84HL+iwcPcl7Dl7r8hjPtryaFjDHR9vPYfTt9JwVZeFj386B005O4SHeNmsszWz5dhZZLYcO4uiFnyTGil2olLKz8vD2TOnEdasufGYWq1GWFhzJMSfkGQ2O7OzLbLZWf6d/83B7sHbpLkFhcZjBgOQpy9EqI+7WVm8zvLPlmNnshwOcBUoJTUFer0eWq3W5LhWq0VSUpIks9nZOtly7Cwym52tky2y879dScrCrdRsjO7gCxdNOZSzU2Hg8z7wctPA08W8b1jidZZ/thw7i1TW1uByFwUiIlKEgkID3v42AVMiAnFofGsU6AsRdzkZv/2VVOY+QU5U1nGAq0Ae7h6ws7MrstBdp9PB09NTktnsbJ1sOXYWmc3O1skW2fm/ziamo9fiODg72sHeTo2UrHysHdoEp2+mmZXD6yz/bDl2Fqms/R2PSxQUyN7BAQGBQYg7Ems8VlhYiLi4WDRoGCrJbHZmZ1tks7P8Oz9ORq4eKVn5qFnRCYHerth7ruiH0p6E11n+2XLsTJbDGVyF6h89EBPHj0NQUDCCQxrgmzWrkZ2djYjIKMlmszM72yKbneXV2cnBzmRf22oeTvDzcsb97Hzcvp+LDkGVkZKZj8T7OahXxRnjwutj79l7iL2UbLPO1syWY2eR2XLsLIoU18mKxAGuQnUO74KU5GQsWrgASUn34OcfgEVLl0NrgbdORGWzMzvbIpud5dU5yNsVKwc9Y7z/Xnh9AMCPJ25h4uYzqOTsiHc714e2ggPuZeRi68lELN1/xaadrZktx84is+XYWZSy9pa9ymAwGGxdgsyTU2DrBkRE1tF06i5h2Uc/ai8sm+hRNDacVtwUnyg0P6phVaH55uIMLhEREZHClbUlCmVtxpqIiIiIFI4zuEREREQKV7bmbzmDS0REREQKwxlcIiIiIoUrY0twOcAlIiLpErnTQfgXh4Xkbh/ZXEguERUfB7hERERECqcuY6twOcAlIiIiUriytkSBHzIjIiIiIkXhDC4RERGRwqnK2BIFzuAq2Pp1axHeoS2ahIagb5+eOJWQIPlsdrZOthw7i8xmZ+tkS71zA29XTO/qjw2Dn8Xe0c3Rok5Fk8f3jm7+yFvvxt4262zNXLlmy7EzlR4HuAq1Y/s2zJkdg2EjRmL9hs3w8/PH8GGDodPpJJvNzuxsi2x2ZueHNPZqXErKxPx9lx/5eNSXx0xus3ZeRKHBgAMXzX8NZfk6WzNbjp1FUanE3qSGA1yFWrN6FaJ69EJEZHfU9fXFhElToNFosGXTRslmszM72yKbndn5oaPXUrEy9m8cvJT8yMdTsvJNbi3qeODkjftITMu1WWdr5co1W46dyTI4wFWg/Lw8nD1zGmHN/tmLUa1WIyysORLiT0gym53Z2RbZ7MzOJeVR3h5htTyw7fRds3+W19k62XLsLJIaKqE3qeEAV4FSUlOg1+uh1WpNjmu1WiQlJUkym52tky3HziKz2dk62XLs/DSdAiohK19fouUJvM7WyZZjZ7Ic7qJARERkpvDAyth1Lgn5eoOtqxAVixTXyYrEGVwF8nD3gJ2dXZGF7jqdDp6enpLMZmfrZMuxs8hsdrZOthw7P0mItwtqViyPbafvlOjneZ2tky3HziLxQ2Yke/YODggIDELckVjjscLCQsTFxaJBw1BJZrMzO9sim53ZuSS6BFXB+TsZuJSUVaKf53W2TrYcO5PlcImCQvWPHoiJ48chKCgYwSEN8M2a1cjOzkZEZJRks9mZnW2Rzc7s/JDGXo1qbhrj/apujqjrWR7puQW4m54HACjvYIdW9bRY/NtVSXS2Vq5cs+XYWZSy9kUPHOAqVOfwLkhJTsaihQuQlHQPfv4BWLR0ObQWeOtEVDY7s7MtstmZnR/yq+yMeT2CjfdHtqwNANhx5i5m7bwIAGhb3xMqAHvOl+6DRGX5OlszW46dyTJUBoOBK+RlJqfA1g2IiOQv/IvDQnK3j2z+9JOoTNLYcFpx9zmxuzu085fWwJ5rcImIiIhIUbhEgYiIiEjhytoaXM7gEhEREZGicAaXiIiISOGkuFetSJzBJSIiIiJF4QwuERGVSaJ2Oxi1+bSQXABYEBkkLJuUraytweUAl4iIiEjh1GVrfMslCkRERESkLJzBJSIiIlK4srZEgTO4RERERKQoHOAq2Pp1axHeoS2ahIagb5+eOJWQIPlsdrZOthw7i8xmZ+tkl9XO9TzLY2SLmpj9Un0s6xmERt4ujz23b+OqWNYzCO3qVbRpZyVly7GzCCqV2JvUcICrUDu2b8Oc2TEYNmIk1m/YDD8/fwwfNhg6nU6y2ezMzrbIZmd2Fp3rWE6NG6k5WHc88YnnNfJ2QR2tE1Ky823eWSnZcuxMlsEBrkKtWb0KUT16ISKyO+r6+mLCpCnQaDTYsmmjZLPZmZ1tkc3O7Cw698/bGfjx9F2cvJX+2HPcNeXwSmhVLI+7CX2hweadlZItx86iqATfpIYDXAXKz8vD2TOnEdbsnz0e1Wo1wsKaIyH+hCSz2ZmdbZHNzuxsi87/pQIw6Llq+OV8EhLTckucI8frLDJbjp3JcjjAVaCU1BTo9XpotVqT41qtFklJSZLMZmfrZMuxs8hsdrZONjs/WSd/TxQWAnsuJpcqR47XWWS2HDuLpFaphN6khgNcIiIiG6nprkG7ehWx6thNW1chhStrSxS4D64Cebh7wM7OrshCd51OB09PT0lms7N1suXYWWQ2O1snm50fr16l8nBxLIeZL9Y3HrNTq9CzoRfa1dNi/LYLkugsx2w5dibL4QyuAtk7OCAgMAhxR2KNxwoLCxEXF4sGDUMlmc3O7GyLbHZmZ1t0/rcj1+5j6q+XMG3nP7eU7Hz8cj4J8w9ck0xnOWbLsbNQZWwKlzO4CtU/eiAmjh+HoKBgBIc0wDdrViM7OxsRkVGSzWZndrZFNjuzs+hcRzs1Kjk7GO97VnBAdTcNsvL0SM7OR2ae3uR8faEBaTkFuJORZ7POSsmWY2eyDA5wFapzeBekJCdj0cIFSEq6Bz//ACxauhxaC7x1IiqbndnZFtnszM6ic30qajC2dW3j/V6NvAAAh6+m4Ktjt0rVUVRnpWTLsbMoZe2relUGg6HkG+6RTeQU2LoBERE9zqjNp4VlL4gMEpZN4mlsOK0Yd+m+0Pzn6roJzTcXZ3CJiIiIFE6CO3kJxQ+ZEREREZGicAaXiIiISOHK2AQuB7hEREREilfGRrhcokBEREREisJdFGSIuygQUVlxNy1XWHZlV0dh2aKEf3FYSO72kc2F5Iokx98NW+6i8PuVNKH5z9Z2FZpvLs7gEhEREZGicA0uERERkcJxmzAiIiIiIhnjDC4RERGRwpWxCVzO4CrZ+nVrEd6hLZqEhqBvn544lZAg+Wx2tk62HDuLzGZn62SLyN266TsM7dcd3do1Q7d2zTBqSD8cjf3NAm0fkPp1buDtiuld/bFh8LPYO7o5WtSpaPL43tHNH3nr3djbZp2tlS36dwMQez2odDjAVagd27dhzuwYDBsxEus3bIafnz+GDxsMnU4n2Wx2ZmdbZLOzvDt7VqqCwSPewhdfrccXq75Fo2eaYtJ7o3H18sVS5YrsbMlsjb0al5IyMX/f5Uc+HvXlMZPbrJ0XUWgw4MBF81+DHK7Hv4n83RDVWSiV4JvEcICrUGtWr0JUj16IiOyOur6+mDBpCjQaDbZs2ijZbHZmZ1tks7O8Ozd7oTWea/4CqtfwQfWatTDo9VFwciqPs3+WfiZNDtf56LVUrIz9GwcvJT/y8ZSsfJNbizoeOHnjPhJLsMWWHK7Hv4n83RDVWSSV4H+khgNcBcrPy8PZM6cR1uyffQ3VajXCwpojIf6EJLPZmZ1tkc3O8u/8b3q9Hnt3bkdOTjYCQxqWKkuO1/lpPMrbI6yWB7advmv2z8r9eljydwOw3X9DKj5+yEyBUlJToNfrodVqTY5rtVpcufLot7Fsnc3O1smWY2eR2exsnWyRnQHgysW/MGpof+Tl5cHJqTwmzZwHn9p1S5Upx+v8NJ0CKiErX1+i5QlyvR4ifjcA2/03LA1uE0ZERCQj1X1qY8nqDfh8+Vp0jeyFT6ZNwLUrl2xdS3LCAytj17kk5OvLzheY8nej7OIAV4E83D1gZ2dXZKG7TqeDp6enJLPZ2TrZcuwsMpudrZMtsjMA2Nvbo1qNmqjvH4jBI0ajjm99bP5ubaky5XidnyTE2wU1K5bHttN3SvTzcr0eIn43ANv8NyytMvYZMw5wlcjewQEBgUGIOxJrPFZYWIi4uFg0aBgqyWx2ZmdbZLOz/Ds/isFQiLz8vFJlyPE6P0mXoCo4fycDl5KySvTzSrkelvjdAGzz35DMwzW4CtU/eiAmjh+HoKBgBIc0wDdrViM7OxsRkVGSzWZndrZFNjvLu/OKRfPRpFkLVPaqiuzMTOz5dTvij/+OmHlLSpUrsrMlszX2alRz0xjvV3VzRF3P8kjPLcDd9AcDufIOdmhVT4vFv12VRGdrZYv83RDVWSgpTrMKxAGuQnUO74KU5GQsWrgASUn34OcfgEVLl0NrgbdORGWzMzvbIpud5d05NSUZs6dOQLLuHio4O6N23fqImbcEzzRtVqpckZ0tme1X2RnzegQb749sWRsAsOPMXcza+WC/17b1PaECsOd8kiQ6Wytb5O+GqM5kOSqDwVB2VpsrRE6BrRsQEVnH3RLs11pclV0dhWWLEv7FYSG520c2f/pJEiPH3w2NDacVE/7OEJrfoIZzsc6bPHkypkyZYnLMz88P586ds2gfzuASERERKZyUtgkLCgrCrl27jPfLlbP8cJQDXCIiIiKymnLlysHLy0voc3AXBSIiIiKFE71NWG5uLtLS0kxuubmPXkZy4cIFeHt7o06dOujbty+uX79u8dfLAS4RERERlUpMTAzc3NxMbjExMUXOe+655/DVV19hx44dWLx4Ma5cuYIXXngB6enpFu3DD5nJED9kRkRlhRw/SCQSP2T2Dzn+btjyQ2Z/3hT7IbN6nvZFZmwdHR3h6Pjka5mamgofHx98+umnGDx4sMX6cA0uWUVadr6wbFcne2HZJG8if+8cy9mJybXnG2v/JsdBqEiiBqKt5+wXkgsA+8a2EpLL3w1pKc5g9lHc3d1Rv359XLx40aJ9+CcpERERkcKpBP9TUhkZGbh06RKqVq1qwVfLAS4RERERWcnYsWOxf/9+XL16FYcPH0ZkZCTs7OzwyiuvWPR5uESBiIiISOGksg/ujRs38Morr0Cn06FSpUp4/vnnceTIEVSqVMmiz8MBLhEREZHCSWR8i/Xr11vlebhEQcHWr1uL8A5t0SQ0BH379MSphATJZq9Z9SWG/K83OrZsiq4dWuKDd0bh+tUrFmr7gKjrIafrLDpXbtkif++O/3EMY0YNR5cOLdG0UQD27dn19B8yg5yus+hckdlltXOjGm6Y0yMYW0eG4cj7rdCyntbkcSd7Nd7p4Iv/GxGGfe88j29fexaRjUq3hlLK18MW2VQ6HOAq1I7t2zBndgyGjRiJ9Rs2w8/PH8OHDYZOp5Nk9snjvyOy5ytYumodPvtiGQoK8jHmjaHIzs4qdV9RnUXmsrN1skX+3uVkZ6NefT+8+8HEUmf9l9yuMzvLr7OTvR0u3MnAnJ0XHvn46HZ1EVanIib/dBavLD+G9cdu4p2O9fCCr/aR51urt7VyRWcLIfqbHiSGA1yFWrN6FaJ69EJEZHfU9fXFhElToNFosGXTRklmz/18Kbp0jUDtur7wre+P8ZOn487tRJw/e6bUfUV1FpnLztbJFvl71/z5lhj+xlto07ZDqbP+S27XmZ3l1zn2cjKW/nYV+/969GAtpJobtp26jePX7yPxfi5+jE/ExbsZCKzqYtPe1soVnU2lxwGuAuXn5eHsmdMIa/bPfolqtRphYc2REH9Cstn/lpnxYENqV1e3UmeJ6izH6yzHzqKz/82Sv3eiyPE6s7P8O//XqZv38UI9LSo5OwAAGtd0Rw0PJ8RdTTE7S47Xw5rX2lKkuk2YKBzgKlBKagr0ej20WtO3irRaLZKSkiSb/VBhYSEWzJ2JkIahqONbr9R5ojrL8TrLsbPo7Ics/XsnihyvMztbJ9sa/zt5aO7Oi7iSlIWtbzTDwXdfwLxeIZiz8yJO/n3f7Cw5Xg9rXmsqGe6iQJLz6ayPceXSRXyx/GtbV6EyhL93RMXX85lqCPZ2xdgf/sTt+zloVMMNYzv4Iik9F8eupdq6Hj2CVLYJsxYOcBXIw90DdnZ2RRa663Q6eHp6SjYbAD6bNR2xB/fj82WrUbmKV6nzAHGd5Xid5dhZdDYg5vdOFDleZ3a2Trbo/5085FhOjeGtamPcptM4fCkZAHDxXibqV3HGq8/VMHuAK8frYa1rTSXHJQoKZO/ggIDAIMQdiTUeKywsRFxcLBo0DJVktsFgwGezpuPAvt2Yt3glvKtVL1XPfxPVWY7XWY6dRWaL/L0TRY7XmZ3l3/nf7NQq2NupYTCYHtcXGqAuwSyhHK+Hta61JZWxTRQ4g6tU/aMHYuL4cQgKCkZwSAN8s2Y1srOzEREZJcnsT2d9jF07tmHG3AUoX74CdP9/DZOzszMcNRpJdhaZy87WyRb5e5eVlYkb168b79+6eQN/nTsLVzc3eFX1LlW23K4zO8uvs5O9GtU9nIz3vd01qFe5AtJyCnAnLRfHr6fijTZ1kFugR+L9XDSu6Ybw4CpYsOeSTXtbK1d0thBSHIUKxAGuQnUO74KU5GQsWrgASUn34OcfgEVLl0NrgbdORGRv+eE7AMCoYQNNjn8w6WN06RpRmroAxF0PuV1nuXYWlS3y9+7s6dMYPiTaeH/e3FkAgBe7RmDStJhSZcvtOrOz/DoHVHXBolcbGe+/1c4XAPDzqduY9vN5TPjxDEa0qoPJXQPgqimH22m5WHrgKjadSLRpb2vlis6m0lMZDP99k4GkLqfA1g3Ml5adLyzb1cleWDbJm8jfO8dydmJy7blyjKyv9Zz9wrL3jW0lLFtuNDacVrxwJ1tofr0qTk8/yYr4JykRERERKQqXKBAREREpXFnbJowzuERERESkKJzBJSIiIlK4MjaByxlcIiIiIlIW7qIgQ3LcRYGIiMqmSn1XC8m9tzb66SdJjC13Ubh0T+wuCnUrSWsXBS5RICIiIlI4VRlbpMAlCkRERESkKJzBJSIiIlI4bhNGRERERCRjHOAq2Pp1axHeoS2ahIagb5+eOJWQIPlsdrZOthw7i8xmZ+tks7N1sqXc+Z2IYOyb8SJuffUqLi/rhW/HtkG9qq4m58wfEob4+VG4u6YvrnzZG+vHtkF9b9fHJIrvbKtsS1MJvkkNB7gKtWP7NsyZHYNhI0Zi/YbN8PPzx/Bhg6HT6SSbzc7sbItsdmZndrZedosAL3z5yzm0nbANL0/fCXs7NbZ82AHlHf9ZMXnysg4jlhzCs2O2IGLGTqhUKmz5sAPUJXiPXerXg8ThAFeh1qxehagevRAR2R11fX0xYdIUaDQabNm0UbLZ7MzOtshmZ3ZmZ+tlR8Xswtr9l3DuRir+vJaC1xcdRM1KzgitozWes2r3BRw6ewfX72Ui/koypn53AjU8neFT2dkmnW2RLUQZm8LlAFeB8vPycPbMaYQ1a248plarERbWHAnxJySZzc7sbItsdmZndrZttmt5BwBAckbuIx8v71gO/Vr74sqddNxIypREZ9HZZBkc4CpQSmoK9Ho9tFqtyXGtVoukpCRJZrOzdbLl2FlkNjtbJ5udrZMtt84qFTArugliz93B2b9TTR57raMfEle/ijtf90XHRtXQbfpO5OsLbd7ZGtmiqAT/IzUc4BIREZHVfTooDAE1PDBg/oEij33/22U8P24rOk/egYuJaVj9Vis42nPIQsXH3xYF8nD3gJ2dXZGF7jqdDp6enpLMZmfrZMuxs8hsdrZONjtbJ1tOnecMfA6dG1fHi1N/wa3krCKPp2Xn49LtdBw6ewf9Pt2H+t6u6NrEx6adrZUtikol9iY1HOAqkL2DAwICgxB3JNZ4rLCwEHFxsWjQMFSS2ezMzrbIZmd2ZmfrZ88Z+By6Nq2Jl6b9gmv3Mp56/oMBlMrsGVy5XA9rKWOfMeM3mSlV/+iBmDh+HIKCghEc0gDfrFmN7OxsRERGSTabndnZFtnszM7sbL3sTwc/h54t6qDPJ3uQnp2Pym4aAEBaVj5y8vWoVdkZ3ZvXwu74W0hKy0U1bXmM6RaCnLwC/HLipk062yKbSo8DXIXqHN4FKcnJWLRwAZKS7sHPPwCLli6H1gJvnYjKZmd2tkU2O7MzO1sve0hHfwDAjsmdTY6/vugg1u6/hJx8PZr5V8GI8EC4OzvgbmoODp27g/YTtyMpLccmnW2RLYIUlxGIpDIYDAZblyDz5BTYugEREVHxVOq7WkjuvbXRQnJF0thwWvFGyqO3YrOU6h6OQvPNxRlcIiIiIsUrW1O4/JAZERERESkKZ3CJiIiIFK6srcHlDC4RERERKQpncImIiIgUroxN4HIXBTniLgqkNHfTxHy6t7KrtD7VS0SW02vlMWHZ3w9qIiTXlrsoJN7PE5pf1c1BaL65uESBiIiIiBSFSxSIiIiIFE5VxhYpcAaXiIiIiBSFM7hERERESle2JnA5g6tk69etRXiHtmgSGoK+fXriVEKC5LPZ2TrZcuu8ddN3GNqvO7q1a4Zu7Zph1JB+OBr7mwXaPiC36yEyV2Q2O1snW46dLZEd5OWMCZ3qYVXfhvi/oU3wnI97kXOqu2vwYSdffDsgFN8PbIy5EYHwrFDyD0eJvB5UOhzgKtSO7dswZ3YMho0YifUbNsPPzx/Dhw2GTqeTbDY7s/PjeFaqgsEj3sIXX63HF6u+RaNnmmLSe6Nx9fJFyXYWmc3O7KykzpbKdrS3wxVdFpYeuvbIx71cHDHz5QDcTM3Bh1vPY9QPp/HdiVvI1xfarLM1qQTfpIYDXIVas3oVonr0QkRkd9T19cWESVOg0WiwZdNGyWazMzs/TrMXWuO55i+geg0fVK9ZC4NeHwUnp/I4+2fpZ0vkeD3YmZ2V1NlS2cf/vo+1v9/Ekaupj3y8X9Nq+OPvVHwVdwOXdVm4nZ6Lo9dScb+Ee2+KvB5UehzgKlB+Xh7OnjmNsGbNjcfUajXCwpojIf6EJLPZmZ2LS6/XY+/O7cjJyUZgSMNSZcnxerAzOyups+jsh1QAnq3hjlupOZgcXh9f92+ETyICHrmMQSqdLU2lEnuTGg5wFSglNQV6vR5ardbkuFarRVJSkiSz2dk62XLs/NCVi3+ha9vn0KXVs5g/+2NMmjkPPrXrlipTjteDna2Tzc7KyH7IzakcyjvYoXujqjh+4z4mbTuPI1dS8EFHXwRVdZFkZ0tTCf5HariLAhHJQnWf2liyegMyMzPw256d+GTaBMxdtLLUg1wiUj71/59ijLuWiv87dQcAcEWXDX8vZ4QHVMLpxHRb1iMBOIOrQB7uHrCzsyuy0F2n08HT01OS2exsnWw5dn7I3t4e1WrURH3/QAweMRp1fOtj83drS5Upx+vBztbJZmdlZD+UllOAgsJC/J2SbXL8RkoOKjmbv4uCNTpbXBn7lBkHuApk7+CAgMAgxB2JNR4rLCxEXFwsGjQMlWQ2O7OzuQyGQuTll+671eV4PdiZnZXUWXT2QwWFBly4m4Vq7hqT495uGtzNMP/PEWv/eUfm4xIFheofPRATx49DUFAwgkMa4Js1q5GdnY2IyCjJZrMzOz/OikXz0aRZC1T2qorszEzs+XU74o//jph5SyTbWWQ2O7OzkjpbKltTTo2qbo7G+1VcHVFb64T0HD2SMvOwOSER77ari9OJ6Th1Kx2Na7ihqY87xm89Z7PO1iTBSVahOMBVqM7hXZCSnIxFCxcgKeke/PwDsGjpcmgt8NaJqGx2ZufHSU1JxuypE5Csu4cKzs6oXbc+YuYtwTNNm0m2s8hsdmZnJXW2VLZvpQqY0dXfeP+1ZjUBALvPJ2H+/is4cjUViw9eQ49GVTGkuQ9upuZg5s6LOHsnw2adSRyVwWAw2LoEmaeEW/YRSdbdtFwhuZVdHZ9+EhHJUq+Vx4Rlfz+oiZBcjQ2nFXWZYgcP2grSmjPlGlwiIiIiUhRpDbeJiIiIyOKkuFetSBzgEhERESmcFL9tTCQuUSAiIiIiReEAl4iIiIgUhbsoyBB3USAiIhKn6dRdQnITprYXklscKVl6ofke5e2E5puLa3CJiIiIFI5rcImIiIiIZIwzuEREREQKV9a2CeMMLhEREREpCge4CrZ+3VqEd2iLJqEh6NunJ04lJEg+m52tky3HziKz2dk62exsnWw5dhaZbYncZ3zc8Xnfhtg19gUkTG2PNv6VTB6vWMEB0yIDsWvsC4ib0AaL+zdCzYpOFulvKSqV2JvUcICrUDu2b8Oc2TEYNmIk1m/YDD8/fwwfNhg6nU6y2ezMzrbIZmd2ZmflZlsq18nBDudvZ2DGz+ce+fj8VxuguocTRq+LR+/FcbiVmoNlAxrDyV46wyyV4JvUSOfKk0WtWb0KUT16ISKyO+r6+mLCpCnQaDTYsmmjZLPZmZ1tkc3O7MzOys22VO7BCzos3H0Je87eK/KYj7Y8GtZwx8dbz+H0rTRc1WXh45/OQVPODuEhXqXqTyXHAa4C5efl4eyZ0whr1tx4TK1WIyysORLiT0gym53Z2RbZ7MzO7KzcbJGd/83B7sH8ZW5BofGYwQDk6QsR6uNusecptTI2hcsBrgKlpKZAr9dDq9WaHNdqtUhKSpJkNjtbJ1uOnUVms7N1stnZOtly7CwyW2Tnf7uSlIVbqdkY3cEXLppyKGenwsDnfeDlpoGni6PFnofMw23CiIiIiEqooNCAt79NwJSIQBwa3xoF+kLEXU7Gb38lSerDV2VtmzAOcBXIw90DdnZ2RRbR63Q6eHp6SjKbna2TLcfOIrPZ2TrZ7GydbDl2FpktsvN/nU1MR6/FcXB2tIO9nRopWflYO7QJTt9Ms+jzUPFxiYIC2Ts4ICAwCHFHYo3HCgsLERcXiwYNQyWZzc7sbItsdmZndlZutsjOj5ORq0dKVj5qVnRCoLcr9p4r+qE0Wylr24RxBleh+kcPxMTx4xAUFIzgkAb4Zs1qZGdnIyIySrLZ7MzOtshmZ3ZmZ+VmWyrXycHOZF/bah5O8PNyxv3sfNy+n4sOQZWRkpmPxPs5qFfFGePC62Pv2XuIvZRcqv5UchzgKlTn8C5ISU7GooULkJR0D37+AVi0dDm0FnhbRlQ2O7OzLbLZmZ3ZWbnZlsoN8nbFykHPGO+/F14fAPDjiVuYuPkMKjk74t3O9aGt4IB7GbnYejIRS/dfKVV3S5PgJKtQKoPBYLB1CTJPToGtGxARESlX06m7hOQmTG0vJLc4svLFDvfK20trCM01uERERESkKBzgEhERESmcSvA/5vriiy9Qq1YtaDQaPPfcczh69KhFXy8HuERERERkNd999x3GjBmDSZMm4fjx42jYsCE6deqEu3fvWuw5OMAlIiIiUjgpbRP26aefYsiQIRg4cCACAwOxZMkSlC9fHitXrrTY6+UAl4iIiIhKJTc3F2lpaSa33NzcIufl5eXhjz/+QPv2/3zgTq1Wo3379oiNjS1yfklxmzAZ0pjxXy03NxcxMTH44IMP4Ohoue/EFpVLRERka7bc7UAUc8YOJTH54xhMmTLF5NikSZMwefJkk2NJSUnQ6/WoUqWKyfEqVarg3LlzFuvDbcIULi0tDW5ubrh//z5cXV0ln0tERETyk5ubW2TG1tHRscgk2K1bt1CtWjUcPnwYzZo1Mx5/7733sH//fsTFxVmkD2dwiYiIiKhUHjWYfRRPT0/Y2dnhzp07Jsfv3LkDLy8vi/XhGlwiIiIisgoHBwc888wz2L17t/FYYWEhdu/ebTKjW1qcwSUiIiIiqxkzZgyio6Px7LPPomnTppg3bx4yMzMxcOBAiz0HB7gK5+joiEmTJln8g2CicomIiEjZevfujXv37uGjjz7C7du30ahRI+zYsaPIB89Kgx8yIyIiIiJF4RpcIiIiIlIUDnCJiIiISFE4wCUiIiIiReEAl4iIiIgUhQNcKhG9Xo+TJ08iJSXF1lWIiIiITHCAq0CXLl3Cm2++ifbt26N9+/YYNWoULl26VKrMt956CytWrADwYHDbqlUrNG7cGDVq1MC+ffss0JqIiIjIMjjAVZhffvkFgYGBOHr0KBo0aIAGDRogLi4OQUFB2LlzZ4lzf/jhBzRs2BAAsHXrVly5cgXnzp3D22+/jQ8//NBS9YmIiIhKjfvgKkxoaCg6deqEmTNnmhx///338euvv+L48eMlytVoNLh48SKqV6+OoUOHonz58pg3bx6uXLmChg0bIi0tzRL1iYiIiEqNM7gKc/bsWQwePLjI8UGDBuHMmTMlzq1SpQrOnDkDvV6PHTt2oEOHDgCArKws2NnZlTiXiIiIyNI4wFWYSpUq4eTJk0WOnzx5EpUrVy5x7sCBA9GrVy8EBwdDpVKhffv2AIC4uDj4+/uXOJeIiIjI0srZugBZ1pAhQzB06FBcvnwZzZs3BwAcOnQIs2bNwpgxY0qcO3nyZAQHB+Pvv/9Gz5494ejoCACws7PD+++/b5HuRERERJbANbgKYzAYMG/ePMydOxe3bt0CAHh7e+Pdd9/FqFGjoFKpSv0cOTk50Gg0pc4hIiIiEoEDXAVLT08HALi4uJQ6S6/XY8aMGViyZAnu3LmDv/76C3Xq1MHEiRNRq1atR677JSIiIrIFrsFVMBcXF4sMbgFg+vTp+OqrrzB79mw4ODgYjwcHB2P58uUWeQ4iIiIiS+AMrkK0adPmqcsPVCoVdu/eXaJ8X19fLF26FO3atYOLiwvi4+NRp04dnDt3Ds2aNeM3mhEREZFk8ENmCtGoUaPHPpaeno5169YhNze3xPk3b96Er69vkeOFhYXIz88vcS4RERGRpXGAqxCfffZZkWMFBQX44osvMH36dFSrVg3Tpk0rcX5gYCB+++03+Pj4mBz/4YcfEBoaWuJcIiIiIkvjAFeh1q5di48++gjZ2dmYPHkyhg4dinLlzP/PbTAYoFKp8NFHHyE6Oho3b95EYWEhNm3ahPPnz+Prr7/GTz/9JOAVEBEREZUMP2SmMDt27ECjRo0wYsQIDBgwABcuXMCIESNKNLgFgBYtWuDixYvo1q0btm7dil27dqFChQr46KOPcPbsWWzdutX4rWZEREREUsAZXIU4evQoxo0bhyNHjuD111/Hrl274OnpWerc6tWro1GjRpg1axZGjhyJnTt3WqAtERERkTjcRUEh1Go1nJycMHToUNSuXfux540aNcrs7A0bNuCNN95AgwYNsGrVKlSvXr00VYmIiIiE4gBXIWrVqlWsbcIuX75covx79+4ZZ3D79+9fZMnDp59+WqJcIiIiIkvjEgWFuHr1qtD8ihUrIiAgAJs3b8aJEydMBriW+PpfIiIiIkvhAJee6vTp0/jf//6H5ORk/Prrr2jTpo2tKxERERE9FndRoCeaOXMmnnnmGTRs2BAJCQkc3BIREZHkcQ0uPVHVqlWxbNkydO3a1dZViIiIiIqFA1x6Ip1OB61Wa+saRERERMXGAS4RERERKQo/ZKYAaWlpxT7X1dVVYBMiIiIi2+MMrgKo1epib9Wl1+sFtyEiIiKyLc7gKsDevXuN/3716lW8//77GDBgAJo1awYAiI2NxerVqxETE2OrikRERERWwxlchWnXrh1ee+01vPLKKybH161bh2XLlmHfvn3FzuLSByIiIpIjDnAVpnz58oiPj0e9evVMjv/1119o1KgRsrKyip3FpQ9EREQkR1yioDA1atTAl19+idmzZ5scX758OWrUqGFWFpc+EBERkRxxBldhtm3bhu7du8PX1xfPPfccAODo0aO4cOECNm7ciC5dupQo15JLH4iIiIhE4gBXgf7++28sXrwY586dAwAEBATg9ddfN3sG998sufSBiIiISCQOcKlY/Pz80K1btyJLH9577z38+OOPOH/+vI2aEREREZniAFeBfvvtNyxduhSXL1/Ghg0bUK1aNaxZswa1a9fG888/X6JMUUsfiIiIiCxNbesCZFkbN25Ep06d4OTkhOPHjyM3NxcAcP/+fcyYMaPEuV26dMFff/2Frl27Ijk5GcnJyejatSv++usvDm6JiIhIUjiDqzChoaF4++238b///Q8uLi6Ij49HnTp1cOLECYSHh+P27du2rkhEREQkFGdwFeb8+fNo2bJlkeNubm5ITU0tVfZvv/2Gfv36oXnz5rh58yYAYM2aNTh48GCpcomIiIgsiQNchfHy8sLFixeLHD948CDq1KlT4lxRSx+IiIiILI0DXIUZMmQIRo8ejbi4OKhUKty6dQtr167F2LFjMXz48BLnfvzxx1iyZAm+/PJL2NvbG4+3aNECx48ft0R1IiIiIovgN5kpzPvvv4/CwkK0a9cOWVlZaNmyJRwdHTF27Fi8+eabJc4VufSBiIiIyJI4wFUYlUqFDz/8EO+++y4uXryIjIwMBAYGwtnZuVS5D5c+1KpVy+R4aZc+EBEREVkalygozKBBg5Ceng4HBwcEBgaiadOmcHZ2RmZmJgYNGlTiXFFLH4iIiIgsjduEKYydnR0SExNRuXJlk+NJSUnw8vJCQUFBiXINBgNmzJiBmJgY49fyPlz6MG3atFL3JiIiIrIUDnAVIi0tDQaDAR4eHrhw4QIqVapkfEyv12Pr1q14//33cevWrVI9T15enkWXPhARERFZGtfgKoS7uztUKhVUKhXq169f5HGVSoUpU6aUOH/QoEGYP38+XFxcEBgYaDyemZmJN998EytXrixxNhEREZElcQZXIfbv3w+DwYC2bdti48aNqFixovExBwcH+Pj4wNvbu8T5opY+EBEREVkaZ3AVolWrVgCAK1euoGbNmlCpVBbJfbj0wWAwID09HRqNxviYXq/Htm3bigx6iYiIiGyJA1yF2bNnD5ydndGzZ0+T4xs2bEBWVhaio6PNyhO99IGIiIjI0rhEQWHq16+PpUuXok2bNibH9+/fj6FDh+L8+fNm5Yle+kBERERkaRzgKoxGo8G5c+eKfCHD1atXERAQgOzs7BLlXrt2zaJLH4iIiIhE4Rc9KEzlypWRkJBQ5Hh8fDy0Wm2Jc/fs2YMffvihyPENGzZg9erVJc4lIiIisjQOcBXmlVdewahRo7B3717o9Xro9Xrs2bMHo0ePRp8+fUqcGxMTA09PzyLHK1eujBkzZpSmMhEREZFFcYmCwuTl5aF///7YsGEDypV78BnCwsJC/O9//8OSJUvg4OBQolxRSx+IiIiILI27KCiMg4MDvvvuO0ybNg3x8fFwcnJCSEgIfHx8SpX7cOnDfwe4pV36QERERGRpHOAqVP369R+5rVdJPVz64OLigpYtWwJ4sMNCaZc+EBEREVkalygowJgxYzBt2jRUqFABY8aMeeK5n376aYmeQ9TSByIiIiJL4wyuApw4cQL5+fnGf3+c0mzxJWrpAxEREZGlcQaXiIiIiBSFM7j0WNZY+kBERERkaRzgKkBUVFSxz920aVOxz7XG0gciIiIiS+MAVwHc3NyM/24wGLB582a4ubnh2WefBQD88ccfSE1NNWsgDAB79+595L8TERERSRnX4CrMuHHjkJycjCVLlsDOzg4AoNfrMWLECLi6uuKTTz6xcUMiIiIisTjAVZhKlSrh4MGD8PPzMzl+/vx5NG/eHDqdrthZopY+EBEREYmktnUBsqyCggKcO3euyPFz586hsLDQrCw3NzfjzdXVFbt378bvv/9ufPyPP/7A7t27TZZIEBEREdka1+AqzMCBAzF48GBcunQJTZs2BQDExcVh5syZGDhwoFlZq1atMv77uHHj0KtXr8cufSAiIiKSCi5RUJjCwkLMmTMH8+fPR2JiIgCgatWqGD16NN555x3j4NRcllz6QERERCQSZ3AVRq1W47333sN7772HtLQ0ALDIDOvDpQ//HeCWZOkDERERkUgc4CpQQUEB9u3bh0uXLuHVV18FANy6dQuurq5wdnYuUaYllz4QERERicQlCgpz7do1dO7cGdevX0dubi7++usv1KlTB6NHj0Zubi6WLFlSolxRSx+IiIiILI0DXIWJiIiAi4sLVqxYAa1Wi/j4eNSpUwf79u3DkCFDcOHChVI/hyWXPhARERFZGpcoKMxvv/2Gw4cPw8HBweR4rVq1cPPmzVJli1j6QERERGRpHOAqTGFhIfR6fZHjN27cgIuLS4lz/7v0oUOHDnBxccGsWbNKtfSBiIiIyNL4RQ8K07FjR8ybN894X6VSISMjA5MmTUKXLl1KnDt69Gg8++yzSElJgZOTk/F4ZGQkdu/eXZrKRERERBbFNbgK8/fff6Nz584wGAy4cOECnn32WVy4cAGenp44cOAAKleuXKJcrVaLw4cPw8/PDy4uLsa1vVevXkVgYCCysrIs/EqIiIiISoZLFBSmRo0aiI+Px3fffYf4+HhkZGRg8ODB6Nu3r8nMq7lELX0gIiIisjTO4CpIfn4+/P398dNPPyEgIMCi2b1794abmxuWLVsGFxcXJCQkoFKlSujWrRtq1qxp8rW+RERERLbEAa7CVKtWDbt27bL4AFfU0gciIiIiS+MAV2FmzJiBv/76C8uXL0e5cpZdgVJQUGCy9KFx48alXvpAREREZGkc4CrMw10NnJ2dERISggoVKpg8vmnTJrMzRS59ICIiIrI0fshMYdzd3dG9e3eLZtrb2yMnJ8eimURERESicAaXikXk0gciIiIiS+IAVyEKCwvxySef4P/+7/+Ql5eHdu3aYdKkSRZbHyti6QMRERGRCJyKU4jp06dj8uTJaN++PZycnDB//nzcvXsXK1eutEi+iKUPRERERCJwBlch6tWrh7Fjx2LYsGEAgF27duHFF19EdnY21Gp+IzMRERGVHRzgKoSjoyMuXryIGjVqGI9pNBpcvHgR1atXL3Gu6KUPRERERJbGqT2FKCgogEajMTlmb2+P/Pz8UuVOnz4d48ePh7OzM6pVq4b58+dj5MiRpcokov/Xzr2ERNn9cQD/jtlM42WwMTPvCpopyKQFYgtNM7JNloZSWTMmmalkpWUupMzMqKQboZu80IVulpQKIpEpmZKFtqgsRdGiRYkFY+qoc95V82fSaurN/Dvv9wOzeM45nvN7ZiFfzpznISKi6cQdXDNhYWGBtWvXQiaTGdru3buHiIgIowfCfvVhMB59ICIiotmGAddMJCYmmjSurKzsl+adrqMPRERERNOFb1EwE78aXE01XUcfiIiIiKYLAy79kBACGo3G6OjDyMgIUlJS/tXRByIiIqLpwoBLP6RWqye1JSQkzEAlRERERKbhGVwiIiIiMit8DJ6IiIiIzAoDLhERERGZFQZcIiIiIjIrDLhEREREZFYYcImITKTRaLB+/XrD9cqVK7Fnz56/XkdDQwMkEgk+ffo0bWt8e6+/42/USUQ0FQZcIprVNBoNJBIJJBIJpFIpvL29ceTIEYyPj0/72rdv30Z+fr5JY/922PP09MSZM2f+ylpERP9v+B5cIpr1oqKiUFZWhtHRUdTW1iItLQ1z585FTk7OpLE6nQ5SqfSPrKtUKv/IPERE9GdxB5eIZj2ZTIZFixbBw8MDu3btQmRkJO7evQvgfz+1FxQUwNnZGb6+vgCA/v5+xMXFwc7ODkqlEtHR0ejt7TXMOTExgX379sHOzg729vY4cOAAvn1t+LdHFEZHR5GdnQ03NzfIZDJ4e3vj4sWL6O3tRXh4OABg/vz5kEgk0Gg0AAC9Xo/CwkJ4eXlBLpdDpVLh1q1bRuvU1tZi8eLFkMvlCA8PN6rzd0xMTCApKcmwpq+vL86ePTvl2Ly8PDg4OEChUCAlJQU6nc7QZ0rtREQzgTu4RGR25HI5BgYGDNf379+HQqFAfX09AGBsbAxr1qxBSEgImpqaYGlpiaNHjyIqKgrPnz+HVCpFUVERysvLUVpaCj8/PxQVFeHOnTuIiIj47rrbtm3D48ePce7cOahUKvT09ODjx49wc3NDZWUlYmNj0dnZCYVCAblcDgAoLCzE5cuXUVJSAh8fHzQ2NiIhIQEODg4ICwtDf38/YmJikJaWhuTkZLS1tSEzM/NffT96vR6urq64efMm7O3t0dzcjOTkZDg5OSEuLs7oe5s3bx4aGhrQ29uLxMRE2Nvbo6CgwKTaiYhmjCAimsXUarWIjo4WQgih1+tFfX29kMlkIisry9Dv6OgoRkdHDX9z6dIl4evrK/R6vaFtdHRUyOVyUVdXJ4QQwsnJSZw4ccLQPzY2JlxdXQ1rCSFEWFiYyMjIEEII0dnZKQCI+vr6Ket88OCBACAGBwcNbSMjI8LKyko0NzcbjU1KShKbNm0SQgiRk5Mj/P39jfqzs7MnzfUtDw8Pcfr06e/2fystLU3ExsYartVqtVAqlWJoaMjQVlxcLGxsbMTExIRJtU91z0REfwN3cIlo1quuroaNjQ3Gxsag1+uxefNmHD582NAfEBBgdO62o6MDXV1dsLW1NZpnZGQE3d3d+Pz5M96/f4/g4GBDn6WlJZYvXz7pmMJX7e3tmDNnzi/tXHZ1deHLly9YvXq1UbtOp0NgYCAA4OXLl0Z1AEBISIjJa3zPhQsXUFpair6+PgwPD0On02Hp0qVGY1QqFaysrIzW1Wq16O/vh1ar/WntREQzhQGXiGa98PBwFBcXQyqVwtnZGZaWxv/arK2tja61Wi2WLVuGK1euTJrLwcHht2r4euTgV2i1WgBATU0NXFxcjPpkMtlv1WGKa9euISsrC0VFRQgJCYGtrS1OnjyJ1tZWk+eYqdqJiEzBgEtEs561tTW8vb1NHh8UFITr169j4cKFUCgUU45xcnJCa2srQkNDAQDj4+N4+vQpgoKCphwfEBAAvV6Phw8fIjIyclL/1x3kiYkJQ5u/vz9kMhn6+vq+u/Pr5+dneGDuq5aWlp/f5A88evQIK1asQGpqqqGtu7t70riOjg4MDw8bwntLSwtsbGzg5uYGpVL509qJiGYK36JARP85W7ZswYIFCxAdHY2mpib09PSgoaEBu3fvxtu3bwEAGRkZOH78OKqqqvDq1Sukpqb+8B22np6eUKvV2L59O6qqqgxz3rhxAwDg4eEBiUSC6upqfPjwAVqtFra2tsjKysLevXtRUVGB7u5uPHv2DOfPn0dFRQUAICUlBW/evMH+/fvR2dmJq1evory83KT7fPfuHdrb240+g4OD8PHxQVtbG+rq6vD69Wvk5ubiyZMnk/5ep9MhKSkJL168QG1tLQ4dOoT09HRYWFiYVDsR0UxhwCWi/xwrKys0NjbC3d0dMTEx8PPzQ1JSEkZGRgw7upmZmdi6dSvUarXhZ/wNGzb8cN7i4mJs3LgRqampWLJkCXbs2IGhoSEAgIuLC/Ly8nDw4EE4OjoiPT0dAJCfn4/c3FwUFhbCz88PUVFRqKmpgZeXFwDA3d0dlZWVqKqqgkqlQklJCY4dO2bSfZ46dQqBgYFGn5qaGuzcuRMxMTGIj49HcHAwBgYGjHZzv1q1ahV8fHwQGhqK+Ph4rFu3zuhs889qJyKaKRLxvScmiIiIiIhmIe7gEhEREZFZYcAlIiIiIrPCgEtEREREZoUBl4iIiIjMCgMuEREREZkVBlwiIiIiMisMuERERERkVhhwiYiIiMisMOASERERkVlhwCUiIiIis8KAS0RERERmhQGXiIiIiMzKP9h6OlNSbQn3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Score Table is: \n"
     ]
    },
    {
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       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_f7b2b5e6-44bd-44e8-af8e-eef8e59fac79 button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('result');\n",
       "      }\n",
       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                     accuracy  precision    recall        f1\n",
       "Logistic Regression  0.918182     0.9344  0.918182  0.917196"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "model=LogisticRegression()\n",
    "result=train_model(model,X_train, y_train, X_test, y_test)\n",
    "result.index=[\"Logistic Regression\"]\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XPxZ685l6-k3"
   },
   "source": [
    "## **Decision Tree Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 821
    },
    "executionInfo": {
     "elapsed": 889,
     "status": "ok",
     "timestamp": 1752482364080,
     "user": {
      "displayName": "Ahmed Foly",
      "userId": "06032925035970721628"
     },
     "user_tz": -180
    },
    "id": "a0753530",
    "outputId": "699d84aa-b395-46b3-b5ab-895bffb7debc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix is:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Score Table is: \n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"result\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04821182598999183,\n        \"min\": 0.9181818181818182,\n        \"max\": 0.9863636363636363,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9863636363636363,\n          0.9181818181818182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.037056397696150656,\n        \"min\": 0.9343998953609682,\n        \"max\": 0.9868055555555556,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9868055555555556,\n          0.9343998953609682\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04821182598999183,\n        \"min\": 0.9181818181818182,\n        \"max\": 0.9863636363636363,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9863636363636363,\n          0.9181818181818182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.048874689511816935,\n        \"min\": 0.9171960378986431,\n        \"max\": 0.9863152866630287,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9863152866630287,\n          0.9171960378986431\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "result"
      },
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.918182</td>\n",
       "      <td>0.934400</td>\n",
       "      <td>0.918182</td>\n",
       "      <td>0.917196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.986364</td>\n",
       "      <td>0.986806</td>\n",
       "      <td>0.986364</td>\n",
       "      <td>0.986315</td>\n",
       "    </tr>\n",
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       "\n",
       "      <script>\n",
       "        async function quickchart(key) {\n",
       "          const quickchartButtonEl =\n",
       "            document.querySelector('#' + key + ' button');\n",
       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "          try {\n",
       "            const charts = await google.colab.kernel.invokeFunction(\n",
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       "      .colab-df-generate:hover {\n",
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       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('result')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
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       "\n",
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       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('result');\n",
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       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                     accuracy  precision    recall        f1\n",
       "Logistic Regression  0.918182   0.934400  0.918182  0.917196\n",
       "Decision Tree        0.986364   0.986806  0.986364  0.986315"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "dt_result = train_model(model, X_train, y_train, X_test, y_test)\n",
    "dt_result.index = [\"Decision Tree\"]\n",
    "result = pd.concat([result, dt_result])\n",
    "display(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-GCyQPEJ7D0J"
   },
   "source": [
    "## **Random Forest Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 839
    },
    "executionInfo": {
     "elapsed": 1351,
     "status": "ok",
     "timestamp": 1752482365436,
     "user": {
      "displayName": "Ahmed Foly",
      "userId": "06032925035970721628"
     },
     "user_tz": -180
    },
    "id": "e82dfcdc",
    "outputId": "b211afb5-8676-4525-8249-c4919f4bdbcd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix is:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Score Table is: \n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"result\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04147338088839692,\n        \"min\": 0.9181818181818182,\n        \"max\": 0.9931818181818182,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9181818181818182,\n          0.9863636363636363,\n          0.9931818181818182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03244226919504752,\n        \"min\": 0.9343998953609682,\n        \"max\": 0.9937348484848485,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9343998953609682,\n          0.9868055555555556,\n          0.9937348484848485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04147338088839692,\n        \"min\": 0.9181818181818182,\n        \"max\": 0.9931818181818182,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9181818181818182,\n          0.9863636363636363,\n          0.9931818181818182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04202659615139117,\n        \"min\": 0.9171960378986431,\n        \"max\": 0.9931754816901672,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9171960378986431,\n          0.9863152866630287,\n          0.9931754816901672\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "result"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-03fbe3c4-86f6-4516-88a4-5dc49b5c5a72\" class=\"colab-df-container\">\n",
       "    <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>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.918182</td>\n",
       "      <td>0.934400</td>\n",
       "      <td>0.918182</td>\n",
       "      <td>0.917196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.986364</td>\n",
       "      <td>0.986806</td>\n",
       "      <td>0.986364</td>\n",
       "      <td>0.986315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.993182</td>\n",
       "      <td>0.993735</td>\n",
       "      <td>0.993182</td>\n",
       "      <td>0.993175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "        document.querySelector('#df-03fbe3c4-86f6-4516-88a4-5dc49b5c5a72 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "        const element = document.querySelector('#df-03fbe3c4-86f6-4516-88a4-5dc49b5c5a72');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "        element.appendChild(docLink);\n",
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       "\n",
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       "    </g>\n",
       "</svg>\n",
       "      </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
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       "      --hover-bg-color: #E2EBFA;\n",
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       "      --disabled-fill-color: #AAA;\n",
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       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
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       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
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       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "      <script>\n",
       "        async function quickchart(key) {\n",
       "          const quickchartButtonEl =\n",
       "            document.querySelector('#' + key + ' button');\n",
       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "          try {\n",
       "            const charts = await google.colab.kernel.invokeFunction(\n",
       "                'suggestCharts', [key], {});\n",
       "          } catch (error) {\n",
       "            console.error('Error during call to suggestCharts:', error);\n",
       "          }\n",
       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "        }\n",
       "        (() => {\n",
       "          let quickchartButtonEl =\n",
       "            document.querySelector('#df-1a40c478-2eec-4c73-acfe-d33fcaa438f5 button');\n",
       "          quickchartButtonEl.style.display =\n",
       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "        })();\n",
       "      </script>\n",
       "    </div>\n",
       "\n",
       "  <div id=\"id_f2674d2e-0efd-4d4b-83b3-095f7ba988ab\">\n",
       "    <style>\n",
       "      .colab-df-generate {\n",
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       "        border: none;\n",
       "        border-radius: 50%;\n",
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       "        display: none;\n",
       "        fill: #1967D2;\n",
       "        height: 32px;\n",
       "        padding: 0 0 0 0;\n",
       "        width: 32px;\n",
       "      }\n",
       "\n",
       "      .colab-df-generate:hover {\n",
       "        background-color: #E2EBFA;\n",
       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "        fill: #174EA6;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate {\n",
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       "        fill: #D2E3FC;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate:hover {\n",
       "        background-color: #434B5C;\n",
       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "        fill: #FFFFFF;\n",
       "      }\n",
       "    </style>\n",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('result')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_f2674d2e-0efd-4d4b-83b3-095f7ba988ab button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('result');\n",
       "      }\n",
       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                     accuracy  precision    recall        f1\n",
       "Logistic Regression  0.918182   0.934400  0.918182  0.917196\n",
       "Decision Tree        0.986364   0.986806  0.986364  0.986315\n",
       "Random Forest        0.993182   0.993735  0.993182  0.993175"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "model = RandomForestClassifier()\n",
    "rf_result = train_model(model, X_train, y_train, X_test, y_test)\n",
    "rf_result.index = [\"Random Forest\"]\n",
    "result = pd.concat([result, rf_result])\n",
    "display(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xwtwAL7J7J28"
   },
   "source": [
    "## **Gradient Boosting Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 870
    },
    "collapsed": true,
    "executionInfo": {
     "elapsed": 18031,
     "status": "ok",
     "timestamp": 1752482383472,
     "user": {
      "displayName": "Ahmed Foly",
      "userId": "06032925035970721628"
     },
     "user_tz": -180
    },
    "id": "3f188c9a",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "238ba50a-0509-4233-8db0-7106e3625282"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix is:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Score Table is: \n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
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       "\n",
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       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.918182</td>\n",
       "      <td>0.934400</td>\n",
       "      <td>0.918182</td>\n",
       "      <td>0.917196</td>\n",
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       "      <th>Decision Tree</th>\n",
       "      <td>0.986364</td>\n",
       "      <td>0.986806</td>\n",
       "      <td>0.986364</td>\n",
       "      <td>0.986315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.993182</td>\n",
       "      <td>0.993735</td>\n",
       "      <td>0.993182</td>\n",
       "      <td>0.993175</td>\n",
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       "      <td>0.981818</td>\n",
       "      <td>0.984271</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.981851</td>\n",
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       "\n",
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       "\n",
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      ],
      "text/plain": [
       "                     accuracy  precision    recall        f1\n",
       "Logistic Regression  0.918182   0.934400  0.918182  0.917196\n",
       "Decision Tree        0.986364   0.986806  0.986364  0.986315\n",
       "Random Forest        0.993182   0.993735  0.993182  0.993175\n",
       "Gradient Boosting    0.981818   0.984271  0.981818  0.981851"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gradient Boosting\n",
    "\n",
    "model = GradientBoostingClassifier()\n",
    "gb_result = train_model(model, X_train, y_train, X_test, y_test)\n",
    "gb_result.index = [\"Gradient Boosting\"]\n",
    "result = pd.concat([result, gb_result])\n",
    "display(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VgrXg0_37Os3"
   },
   "source": [
    "## **KNN Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 902
    },
    "executionInfo": {
     "elapsed": 1246,
     "status": "ok",
     "timestamp": 1752482384739,
     "user": {
      "displayName": "Ahmed Foly",
      "userId": "06032925035970721628"
     },
     "user_tz": -180
    },
    "id": "e48Gl6wpskSP",
    "outputId": "16b3e7f7-c04b-48c8-b911-e14394df74fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix is:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Score Table is: \n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"result\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.030133998261758565,\n        \"min\": 0.9181818181818182,\n        \"max\": 0.9931818181818182,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.9863636363636363,\n          0.9681818181818181,\n          0.9931818181818182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.023619419907471424,\n        \"min\": 0.9343998953609682,\n        \"max\": 0.9937348484848485,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.9868055555555556,\n          0.9718506745779474,\n          0.9937348484848485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.030133998261758565,\n        \"min\": 0.9181818181818182,\n        \"max\": 0.9931818181818182,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.9863636363636363,\n          0.9681818181818181,\n          0.9931818181818182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03054924469305566,\n        \"min\": 0.9171960378986431,\n        \"max\": 0.9931754816901672,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.9863152866630287,\n          0.9682415691923216,\n          0.9931754816901672\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "result"
      },
      "text/html": [
       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.918182</td>\n",
       "      <td>0.934400</td>\n",
       "      <td>0.918182</td>\n",
       "      <td>0.917196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.986364</td>\n",
       "      <td>0.986806</td>\n",
       "      <td>0.986364</td>\n",
       "      <td>0.986315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.993182</td>\n",
       "      <td>0.993735</td>\n",
       "      <td>0.993182</td>\n",
       "      <td>0.993175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gradient Boosting</th>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.984271</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.981851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.968182</td>\n",
       "      <td>0.971851</td>\n",
       "      <td>0.968182</td>\n",
       "      <td>0.968242</td>\n",
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       "\n",
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      ],
      "text/plain": [
       "                     accuracy  precision    recall        f1\n",
       "Logistic Regression  0.918182   0.934400  0.918182  0.917196\n",
       "Decision Tree        0.986364   0.986806  0.986364  0.986315\n",
       "Random Forest        0.993182   0.993735  0.993182  0.993175\n",
       "Gradient Boosting    0.981818   0.984271  0.981818  0.981851\n",
       "KNN                  0.968182   0.971851  0.968182  0.968242"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KNN\n",
    "model = KNeighborsClassifier()\n",
    "knn_result = train_model(model, X_train, y_train, X_test, y_test)\n",
    "knn_result.index = [\"KNN\"]\n",
    "result = pd.concat([result, knn_result])\n",
    "display(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rdAbsX_Fmjh3"
   },
   "source": [
    "## **Naive Bayes Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 933
    },
    "executionInfo": {
     "elapsed": 1368,
     "status": "ok",
     "timestamp": 1752482386122,
     "user": {
      "displayName": "Ahmed Foly",
      "userId": "06032925035970721628"
     },
     "user_tz": -180
    },
    "id": "O917ZfQlmhWo",
    "outputId": "eb9786f0-d651-4045-97a8-91f3d2a78ebd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix is:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Score Table is: \n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"result\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.028953867553074688,\n        \"min\": 0.9181818181818182,\n        \"max\": 0.9954545454545455,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9181818181818182,\n          0.9863636363636363,\n          0.9954545454545455\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.022893292906326856,\n        \"min\": 0.9343998953609682,\n        \"max\": 0.9958181818181817,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9343998953609682,\n          0.9868055555555556,\n          0.9958181818181817\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.028953867553074688,\n        \"min\": 0.9181818181818182,\n        \"max\": 0.9954545454545455,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9181818181818182,\n          0.9863636363636363,\n          0.9954545454545455\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.029323254613935767,\n        \"min\": 0.9171960378986431,\n        \"max\": 0.9954229797979799,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9171960378986431,\n          0.9863152866630287,\n          0.9954229797979799\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "result"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-5b42aa17-f7b8-422c-9ec8-fb6ef2845706\" class=\"colab-df-container\">\n",
       "    <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>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.918182</td>\n",
       "      <td>0.934400</td>\n",
       "      <td>0.918182</td>\n",
       "      <td>0.917196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.986364</td>\n",
       "      <td>0.986806</td>\n",
       "      <td>0.986364</td>\n",
       "      <td>0.986315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.993182</td>\n",
       "      <td>0.993735</td>\n",
       "      <td>0.993182</td>\n",
       "      <td>0.993175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gradient Boosting</th>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.984271</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.981851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.968182</td>\n",
       "      <td>0.971851</td>\n",
       "      <td>0.968182</td>\n",
       "      <td>0.968242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naive Bayes</th>\n",
       "      <td>0.995455</td>\n",
       "      <td>0.995818</td>\n",
       "      <td>0.995455</td>\n",
       "      <td>0.995423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
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       "  <div class=\"colab-df-container\">\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
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       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
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       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-5b42aa17-f7b8-422c-9ec8-fb6ef2845706 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-5b42aa17-f7b8-422c-9ec8-fb6ef2845706');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
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       "\n",
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       "                title=\"Suggest charts\"\n",
       "                style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "    </g>\n",
       "</svg>\n",
       "      </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
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       "    30% {\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "      <script>\n",
       "        async function quickchart(key) {\n",
       "          const quickchartButtonEl =\n",
       "            document.querySelector('#' + key + ' button');\n",
       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "          try {\n",
       "            const charts = await google.colab.kernel.invokeFunction(\n",
       "                'suggestCharts', [key], {});\n",
       "          } catch (error) {\n",
       "            console.error('Error during call to suggestCharts:', error);\n",
       "          }\n",
       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "        }\n",
       "        (() => {\n",
       "          let quickchartButtonEl =\n",
       "            document.querySelector('#df-d3cb86fc-8f2a-4b7a-874e-98c76f1119b9 button');\n",
       "          quickchartButtonEl.style.display =\n",
       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "        })();\n",
       "      </script>\n",
       "    </div>\n",
       "\n",
       "  <div id=\"id_ffe4ed78-0630-4d90-b9dc-a8195d0b9778\">\n",
       "    <style>\n",
       "      .colab-df-generate {\n",
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       "        border: none;\n",
       "        border-radius: 50%;\n",
       "        cursor: pointer;\n",
       "        display: none;\n",
       "        fill: #1967D2;\n",
       "        height: 32px;\n",
       "        padding: 0 0 0 0;\n",
       "        width: 32px;\n",
       "      }\n",
       "\n",
       "      .colab-df-generate:hover {\n",
       "        background-color: #E2EBFA;\n",
       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "        fill: #174EA6;\n",
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       "\n",
       "      [theme=dark] .colab-df-generate {\n",
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       "        fill: #D2E3FC;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate:hover {\n",
       "        background-color: #434B5C;\n",
       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "        fill: #FFFFFF;\n",
       "      }\n",
       "    </style>\n",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('result')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_ffe4ed78-0630-4d90-b9dc-a8195d0b9778 button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('result');\n",
       "      }\n",
       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                     accuracy  precision    recall        f1\n",
       "Logistic Regression  0.918182   0.934400  0.918182  0.917196\n",
       "Decision Tree        0.986364   0.986806  0.986364  0.986315\n",
       "Random Forest        0.993182   0.993735  0.993182  0.993175\n",
       "Gradient Boosting    0.981818   0.984271  0.981818  0.981851\n",
       "KNN                  0.968182   0.971851  0.968182  0.968242\n",
       "Naive Bayes          0.995455   0.995818  0.995455  0.995423"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the naive bayes Model\n",
    "naive_model = GaussianNB()\n",
    "naive = train_model(naive_model, X_train, y_train, X_test, y_test)\n",
    "naive.index = [\"Naive Bayes\"]\n",
    "result = pd.concat([result, naive])\n",
    "\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lu3zwstPyp1y"
   },
   "source": [
    "## **XGB Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 964
    },
    "executionInfo": {
     "elapsed": 1351,
     "status": "ok",
     "timestamp": 1752482387479,
     "user": {
      "displayName": "Ahmed Foly",
      "userId": "06032925035970721628"
     },
     "user_tz": -180
    },
    "id": "sIQ7nDuGtxNf",
    "outputId": "8df3c21a-680e-4e74-dfc9-bac53b541e3d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix is:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Score Table is: \n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"result\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.026850080083162774,\n        \"min\": 0.9181818181818182,\n        \"max\": 0.9954545454545455,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9181818181818182,\n          0.9863636363636363,\n          0.9954545454545455\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.021179011991538398,\n        \"min\": 0.9343998953609682,\n        \"max\": 0.9958181818181817,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.9343998953609682,\n          0.9868055555555556,\n          0.9958181818181817\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.026850080083162774,\n        \"min\": 0.9181818181818182,\n        \"max\": 0.9954545454545455,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9181818181818182,\n          0.9863636363636363,\n          0.9954545454545455\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.027191778092748628,\n        \"min\": 0.9171960378986431,\n        \"max\": 0.9954229797979799,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.9171960378986431,\n          0.9863152866630287,\n          0.9954229797979799\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "result"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-d2bd03dc-d395-4928-a67a-543cb0580edb\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.918182</td>\n",
       "      <td>0.934400</td>\n",
       "      <td>0.918182</td>\n",
       "      <td>0.917196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.986364</td>\n",
       "      <td>0.986806</td>\n",
       "      <td>0.986364</td>\n",
       "      <td>0.986315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.993182</td>\n",
       "      <td>0.993735</td>\n",
       "      <td>0.993182</td>\n",
       "      <td>0.993175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gradient Boosting</th>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.984271</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.981851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.968182</td>\n",
       "      <td>0.971851</td>\n",
       "      <td>0.968182</td>\n",
       "      <td>0.968242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naive Bayes</th>\n",
       "      <td>0.995455</td>\n",
       "      <td>0.995818</td>\n",
       "      <td>0.995455</td>\n",
       "      <td>0.995423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>0.986364</td>\n",
       "      <td>0.986901</td>\n",
       "      <td>0.986364</td>\n",
       "      <td>0.986347</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                     accuracy  precision    recall        f1\n",
       "Logistic Regression  0.918182   0.934400  0.918182  0.917196\n",
       "Decision Tree        0.986364   0.986806  0.986364  0.986315\n",
       "Random Forest        0.993182   0.993735  0.993182  0.993175\n",
       "Gradient Boosting    0.981818   0.984271  0.981818  0.981851\n",
       "KNN                  0.968182   0.971851  0.968182  0.968242\n",
       "Naive Bayes          0.995455   0.995818  0.995455  0.995423\n",
       "XGB                  0.986364   0.986901  0.986364  0.986347"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the XGB model\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Encode the string labels to numerical labels\n",
    "le = LabelEncoder()\n",
    "y_train_encoded = le.fit_transform(y_train)\n",
    "y_test_encoded = le.transform(y_test)\n",
    "\n",
    "xgb_model = XGBClassifier()\n",
    "xgb = train_model(xgb_model, X_train, y_train_encoded, X_test, y_test_encoded)\n",
    "xgb.index = [\"XGB\"]\n",
    "result = pd.concat([result, xgb])\n",
    "\n",
    "result"
   ]
  }
 ],
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