{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61A2gXKthY-v",
        "outputId": "a2d34d52-dca4-44f5-ecbc-a9a75474ba3f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "DiRU7RxMhXlB"
      },
      "outputs": [],
      "source": [
        "# Data Handling and Exploration\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D  # Import for 3D plotting\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "sns.set_style('darkgrid')\n",
        "rcParams['figure.figsize'] = 8,8\n",
        "import plotly.express as px\n",
        "import os\n",
        "\n",
        "\n",
        "# Data Preprocessing\n",
        "from sklearn import preprocessing\n",
        "#from feature_engine import imputation\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold, GridSearchCV, RandomizedSearchCV\n",
        "from sklearn.inspection import permutation_importance\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from imblearn.over_sampling import SMOTE\n",
        "import scipy.stats as stats\n",
        "from scipy.stats import skew\n",
        "\n",
        "\n",
        "# Exploratory Data Analysis\n",
        "#from pandas_profiling import ProfileReport\n",
        "\n",
        "\n",
        "# Machine Learning\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import HistGradientBoostingClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "\n",
        "\n",
        "\n",
        "# Model Evaluation and Metrics\n",
        "from sklearn.metrics import f1_score, accuracy_score, classification_report, roc_auc_score, roc_curve\n",
        "#from yellowbrick.classifier import ClassificationReport, ROCAUC\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.feature_selection import RFE\n",
        "\n",
        "\n",
        "# Deployment and Monitoring\n",
        "#import docker\n",
        "\n",
        "# Saving Model\n",
        "from joblib import dump\n",
        "import pickle\n",
        "\n",
        "\n",
        "#Other libraries\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "VkfE8V27hXlN"
      },
      "outputs": [],
      "source": [
        "train_df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/cap/Train.csv')\n",
        "test_df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/cap/Test.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "5woagJsChXlO",
        "outputId": "f9841f3b-a8e6-4db5-bfea-4487635e3895"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                    user_id       REGION        TENURE  \\\n",
              "0  7ee9e11e342e27c70455960acc80d3f91c1286d1        DAKAR  K > 24 month   \n",
              "1  50443f42bdc92b10388fc56e520e4421a5fa655c          NaN  K > 24 month   \n",
              "2  da90b5c1a9b204c186079f89969aa01cb03c91b2          NaN  K > 24 month   \n",
              "3  364ec1b424cdc64c25441a444a16930289a0051e  SAINT-LOUIS  K > 24 month   \n",
              "4  d5a5247005bc6d41d3d99f4ef312ebb5f640f2cb        DAKAR  K > 24 month   \n",
              "\n",
              "   MONTANT  FREQUENCE_RECH  REVENUE  ARPU_SEGMENT  FREQUENCE  DATA_VOLUME  \\\n",
              "0  20000.0            47.0  21602.0        7201.0       52.0       8835.0   \n",
              "1      NaN             NaN      NaN           NaN        NaN          NaN   \n",
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              "3   7900.0            19.0   7896.0        2632.0       25.0       9385.0   \n",
              "4  12350.0            21.0  12351.0        4117.0       29.0       9360.0   \n",
              "\n",
              "   ON_NET  ORANGE   TIGO  ZONE1  ZONE2 MRG  REGULARITY  \\\n",
              "0  3391.0   396.0  185.0    NaN    NaN  NO          62   \n",
              "1     NaN     NaN    NaN    NaN    NaN  NO           3   \n",
              "2     NaN     NaN    NaN    NaN    NaN  NO           1   \n",
              "3    27.0    46.0   20.0    NaN    2.0  NO          61   \n",
              "4    66.0   102.0   34.0    NaN    NaN  NO          56   \n",
              "\n",
              "                         TOP_PACK  FREQ_TOP_PACK  CHURN  \n",
              "0  On net 200F=Unlimited _call24H           30.0      0  \n",
              "1                             NaN            NaN      0  \n",
              "2                             NaN            NaN      0  \n",
              "3                Data:490F=1GB,7d            7.0      0  \n",
              "4           All-net 500F=2000F;5d           11.0      0  "
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              "      <td>7ee9e11e342e27c70455960acc80d3f91c1286d1</td>\n",
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              "      <td>3</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>364ec1b424cdc64c25441a444a16930289a0051e</td>\n",
              "      <td>SAINT-LOUIS</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>7900.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7896.0</td>\n",
              "      <td>2632.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>9385.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>20.0</td>\n",
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              "      <td>DAKAR</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>12350.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>12351.0</td>\n",
              "      <td>4117.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>9360.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>34.0</td>\n",
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              "      <td>NO</td>\n",
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              "      <td>11.0</td>\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-804197c8-f42d-4144-889b-0c889b8b818e 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-804197c8-f42d-4144-889b-0c889b8b818e');\n",
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              "          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",
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              "\n",
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              "      border-bottom-color: var(--fill-color);\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",
              "            '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",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "train_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XO64rNYFhXlR",
        "outputId": "8f880187-48d7-4c96-b639-c888421fba1e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1077024 entries, 0 to 1077023\n",
            "Data columns (total 19 columns):\n",
            " #   Column          Non-Null Count    Dtype  \n",
            "---  ------          --------------    -----  \n",
            " 0   user_id         1077024 non-null  object \n",
            " 1   REGION          652687 non-null   object \n",
            " 2   TENURE          1077024 non-null  object \n",
            " 3   MONTANT         699139 non-null   float64\n",
            " 4   FREQUENCE_RECH  699139 non-null   float64\n",
            " 5   REVENUE         714669 non-null   float64\n",
            " 6   ARPU_SEGMENT    714669 non-null   float64\n",
            " 7   FREQUENCE       714669 non-null   float64\n",
            " 8   DATA_VOLUME     547261 non-null   float64\n",
            " 9   ON_NET          683850 non-null   float64\n",
            " 10  ORANGE          629880 non-null   float64\n",
            " 11  TIGO            432250 non-null   float64\n",
            " 12  ZONE1           84898 non-null    float64\n",
            " 13  ZONE2           68794 non-null    float64\n",
            " 14  MRG             1077024 non-null  object \n",
            " 15  REGULARITY      1077024 non-null  int64  \n",
            " 16  TOP_PACK        626129 non-null   object \n",
            " 17  FREQ_TOP_PACK   626129 non-null   float64\n",
            " 18  CHURN           1077024 non-null  int64  \n",
            "dtypes: float64(12), int64(2), object(5)\n",
            "memory usage: 156.1+ MB\n"
          ]
        }
      ],
      "source": [
        "train_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZPWo7I7hXlS",
        "outputId": "3b9faa32-a29d-4f67-96f1-5578370c896d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "user_id                 0\n",
              "REGION             424337\n",
              "TENURE                  0\n",
              "MONTANT            377885\n",
              "FREQUENCE_RECH     377885\n",
              "REVENUE            362355\n",
              "ARPU_SEGMENT       362355\n",
              "FREQUENCE          362355\n",
              "DATA_VOLUME        529763\n",
              "ON_NET             393174\n",
              "ORANGE             447144\n",
              "TIGO               644774\n",
              "ZONE1              992126\n",
              "ZONE2             1008230\n",
              "MRG                     0\n",
              "REGULARITY              0\n",
              "TOP_PACK           450895\n",
              "FREQ_TOP_PACK      450895\n",
              "CHURN                   0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "train_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "to0tvPdvhXlU",
        "outputId": "5d15bb4c-8a5d-45d2-8442-34945c98ee43"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user_id : 1077024\n",
            "REGION : 14\n",
            "TENURE : 8\n",
            "MONTANT : 4357\n",
            "FREQUENCE_RECH : 119\n",
            "REVENUE : 31810\n",
            "ARPU_SEGMENT : 14062\n",
            "FREQUENCE : 91\n",
            "DATA_VOLUME : 32459\n",
            "ON_NET : 8202\n",
            "ORANGE : 2674\n",
            "TIGO : 1105\n",
            "ZONE1 : 482\n",
            "ZONE2 : 394\n",
            "MRG : 1\n",
            "REGULARITY : 62\n",
            "TOP_PACK : 126\n",
            "FREQ_TOP_PACK : 206\n",
            "CHURN : 2\n"
          ]
        }
      ],
      "source": [
        "for column in train_df.columns:\n",
        "  print(column, ':', train_df[column].nunique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dwikg391hXlV",
        "outputId": "4f150e3f-53fd-4864-acfe-ff31a9268b46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1077024, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "train_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "pd6koNVVhXlW",
        "outputId": "c62545a8-bf1d-4569-c9d6-60535885abb5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              REGION        TENURE  MONTANT  FREQUENCE_RECH  REVENUE  \\\n",
              "0              DAKAR  K > 24 month  20000.0            47.0  21602.0   \n",
              "1                NaN  K > 24 month      NaN             NaN      NaN   \n",
              "2                NaN  K > 24 month      NaN             NaN      NaN   \n",
              "3        SAINT-LOUIS  K > 24 month   7900.0            19.0   7896.0   \n",
              "4              DAKAR  K > 24 month  12350.0            21.0  12351.0   \n",
              "...              ...           ...      ...             ...      ...   \n",
              "1077019          NaN  K > 24 month      NaN             NaN      NaN   \n",
              "1077020  TAMBACOUNDA  K > 24 month   2500.0             5.0   2500.0   \n",
              "1077021          NaN  K > 24 month      NaN             NaN      NaN   \n",
              "1077022          NaN  K > 24 month    600.0             1.0    600.0   \n",
              "1077023       FATICK  K > 24 month   1500.0             4.0   1499.0   \n",
              "\n",
              "         ARPU_SEGMENT  FREQUENCE  DATA_VOLUME  ON_NET  ORANGE   TIGO  ZONE1  \\\n",
              "0              7201.0       52.0       8835.0  3391.0   396.0  185.0    NaN   \n",
              "1                 NaN        NaN          NaN     NaN     NaN    NaN    NaN   \n",
              "2                 NaN        NaN          NaN     NaN     NaN    NaN    NaN   \n",
              "3              2632.0       25.0       9385.0    27.0    46.0   20.0    NaN   \n",
              "4              4117.0       29.0       9360.0    66.0   102.0   34.0    NaN   \n",
              "...               ...        ...          ...     ...     ...    ...    ...   \n",
              "1077019           NaN        NaN          NaN     NaN     NaN    NaN    NaN   \n",
              "1077020         833.0        5.0          0.0    15.0    77.0    NaN    NaN   \n",
              "1077021           NaN        NaN          NaN     NaN     NaN    NaN    NaN   \n",
              "1077022         200.0        1.0        591.0    11.0    37.0    5.0    1.0   \n",
              "1077023         500.0        5.0       1265.0    30.0     4.0    NaN    NaN   \n",
              "\n",
              "         ZONE2 MRG  REGULARITY                        TOP_PACK  FREQ_TOP_PACK  \\\n",
              "0          NaN  NO          62  On net 200F=Unlimited _call24H           30.0   \n",
              "1          NaN  NO           3                             NaN            NaN   \n",
              "2          NaN  NO           1                             NaN            NaN   \n",
              "3          2.0  NO          61                Data:490F=1GB,7d            7.0   \n",
              "4          NaN  NO          56           All-net 500F=2000F;5d           11.0   \n",
              "...        ...  ..         ...                             ...            ...   \n",
              "1077019    NaN  NO          16                             NaN            NaN   \n",
              "1077020    NaN  NO          34           All-net 500F=2000F;5d            2.0   \n",
              "1077021    NaN  NO           3                             NaN            NaN   \n",
              "1077022    NaN  NO          16         All-net 600F= 3000F ;5d            1.0   \n",
              "1077023    0.0  NO          50  On net 200F=Unlimited _call24H            2.0   \n",
              "\n",
              "         CHURN  \n",
              "0            0  \n",
              "1            0  \n",
              "2            0  \n",
              "3            0  \n",
              "4            0  \n",
              "...        ...  \n",
              "1077019      0  \n",
              "1077020      0  \n",
              "1077021      1  \n",
              "1077022      0  \n",
              "1077023      0  \n",
              "\n",
              "[1077024 rows x 18 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b2f7b4fa-d8b4-4457-9518-6679e94ee5d8\" 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>REGION</th>\n",
              "      <th>TENURE</th>\n",
              "      <th>MONTANT</th>\n",
              "      <th>FREQUENCE_RECH</th>\n",
              "      <th>REVENUE</th>\n",
              "      <th>ARPU_SEGMENT</th>\n",
              "      <th>FREQUENCE</th>\n",
              "      <th>DATA_VOLUME</th>\n",
              "      <th>ON_NET</th>\n",
              "      <th>ORANGE</th>\n",
              "      <th>TIGO</th>\n",
              "      <th>ZONE1</th>\n",
              "      <th>ZONE2</th>\n",
              "      <th>MRG</th>\n",
              "      <th>REGULARITY</th>\n",
              "      <th>TOP_PACK</th>\n",
              "      <th>FREQ_TOP_PACK</th>\n",
              "      <th>CHURN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DAKAR</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>21602.0</td>\n",
              "      <td>7201.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>8835.0</td>\n",
              "      <td>3391.0</td>\n",
              "      <td>396.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NO</td>\n",
              "      <td>62</td>\n",
              "      <td>On net 200F=Unlimited _call24H</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NO</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NO</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SAINT-LOUIS</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>7900.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7896.0</td>\n",
              "      <td>2632.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>9385.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NO</td>\n",
              "      <td>61</td>\n",
              "      <td>Data:490F=1GB,7d</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>DAKAR</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>12350.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>12351.0</td>\n",
              "      <td>4117.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>9360.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NO</td>\n",
              "      <td>56</td>\n",
              "      <td>All-net 500F=2000F;5d</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1077019</th>\n",
              "      <td>NaN</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NO</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1077020</th>\n",
              "      <td>TAMBACOUNDA</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>833.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NO</td>\n",
              "      <td>34</td>\n",
              "      <td>All-net 500F=2000F;5d</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1077021</th>\n",
              "      <td>NaN</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NO</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1077022</th>\n",
              "      <td>NaN</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>600.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>600.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>591.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NO</td>\n",
              "      <td>16</td>\n",
              "      <td>All-net 600F= 3000F ;5d</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1077023</th>\n",
              "      <td>FATICK</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1499.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NO</td>\n",
              "      <td>50</td>\n",
              "      <td>On net 200F=Unlimited _call24H</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1077024 rows × 18 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2f7b4fa-d8b4-4457-9518-6679e94ee5d8')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\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-b2f7b4fa-d8b4-4457-9518-6679e94ee5d8 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-b2f7b4fa-d8b4-4457-9518-6679e94ee5d8');\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",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8c11b69b-bb3e-4a54-b105-25b421a0851b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8c11b69b-bb3e-4a54-b105-25b421a0851b')\"\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-8c11b69b-bb3e-4a54-b105-25b421a0851b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "train_df = train_df.drop('user_id', axis = 1)\n",
        "train_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QqV7n25ehXlY",
        "outputId": "572fa3af-7d1b-4c69-acce-a91d1aadbf74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1077024 entries, 0 to 1077023\n",
            "Data columns (total 18 columns):\n",
            " #   Column          Non-Null Count    Dtype  \n",
            "---  ------          --------------    -----  \n",
            " 0   REGION          1077024 non-null  object \n",
            " 1   TENURE          1077024 non-null  object \n",
            " 2   MONTANT         1077024 non-null  float64\n",
            " 3   FREQUENCE_RECH  1077024 non-null  float64\n",
            " 4   REVENUE         1077024 non-null  float64\n",
            " 5   ARPU_SEGMENT    1077024 non-null  float64\n",
            " 6   FREQUENCE       1077024 non-null  float64\n",
            " 7   DATA_VOLUME     1077024 non-null  float64\n",
            " 8   ON_NET          1077024 non-null  float64\n",
            " 9   ORANGE          1077024 non-null  float64\n",
            " 10  TIGO            1077024 non-null  float64\n",
            " 11  ZONE1           1077024 non-null  float64\n",
            " 12  ZONE2           1077024 non-null  float64\n",
            " 13  MRG             1077024 non-null  object \n",
            " 14  REGULARITY      1077024 non-null  int64  \n",
            " 15  TOP_PACK        1077024 non-null  object \n",
            " 16  FREQ_TOP_PACK   1077024 non-null  float64\n",
            " 17  CHURN           1077024 non-null  int64  \n",
            "dtypes: float64(12), int64(2), object(4)\n",
            "memory usage: 147.9+ MB\n"
          ]
        }
      ],
      "source": [
        "# all nan values are to be filled with median on the\n",
        "train_df['MONTANT'] = train_df['MONTANT'].fillna(train_df['MONTANT'].median())\n",
        "train_df['FREQUENCE_RECH'] = train_df['FREQUENCE_RECH'].fillna(train_df['FREQUENCE_RECH'].median())\n",
        "train_df['REVENUE'] = train_df['REVENUE'].fillna(train_df['REVENUE'].median())\n",
        "train_df['ARPU_SEGMENT'] = train_df['ARPU_SEGMENT'].fillna(train_df['ARPU_SEGMENT'].median())\n",
        "train_df['FREQUENCE'] = train_df['FREQUENCE'].fillna(train_df['FREQUENCE'].median())\n",
        "train_df['DATA_VOLUME'] = train_df['DATA_VOLUME'].fillna(train_df['DATA_VOLUME'].median())\n",
        "train_df['ON_NET'] = train_df['ON_NET'].fillna(train_df['ON_NET'].median())\n",
        "train_df['ORANGE'] = train_df['ORANGE'].fillna(train_df['ORANGE'].median())\n",
        "train_df['TIGO'] = train_df['TIGO'].fillna(train_df['TIGO'].median())\n",
        "train_df['ZONE1'] = train_df['ZONE1'].fillna(train_df['ZONE1'].median())\n",
        "train_df['ZONE2'] = train_df['ZONE2'].fillna(train_df['ZONE2'].median())\n",
        "train_df['TOP_PACK']=train_df['TOP_PACK'].replace(np.nan ,train_df['TOP_PACK'].mode()[0])\n",
        "train_df['REGION']=train_df['REGION'].replace(np.nan ,train_df['REGION'].mode()[0])\n",
        "train_df['TENURE']=train_df['TENURE'].replace(\"nan\" ,train_df['TENURE'].mode()[0])\n",
        "train_df['FREQ_TOP_PACK'] = train_df['FREQ_TOP_PACK'].fillna(train_df['FREQ_TOP_PACK'].median())\n",
        "train_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qyyZy_J9hXlZ",
        "outputId": "680f7fbc-1249-4899-f908-4e5376407ef9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1077024 entries, 0 to 1077023\n",
            "Data columns (total 17 columns):\n",
            " #   Column          Non-Null Count    Dtype  \n",
            "---  ------          --------------    -----  \n",
            " 0   REGION          1077024 non-null  object \n",
            " 1   TENURE          1077024 non-null  object \n",
            " 2   MONTANT         1077024 non-null  float64\n",
            " 3   FREQUENCE_RECH  1077024 non-null  float64\n",
            " 4   REVENUE         1077024 non-null  float64\n",
            " 5   ARPU_SEGMENT    1077024 non-null  float64\n",
            " 6   FREQUENCE       1077024 non-null  float64\n",
            " 7   DATA_VOLUME     1077024 non-null  float64\n",
            " 8   ON_NET          1077024 non-null  float64\n",
            " 9   ORANGE          1077024 non-null  float64\n",
            " 10  TIGO            1077024 non-null  float64\n",
            " 11  ZONE1           1077024 non-null  float64\n",
            " 12  ZONE2           1077024 non-null  float64\n",
            " 13  MRG             1077024 non-null  object \n",
            " 14  REGULARITY      1077024 non-null  int64  \n",
            " 15  FREQ_TOP_PACK   1077024 non-null  float64\n",
            " 16  CHURN           1077024 non-null  int64  \n",
            "dtypes: float64(12), int64(2), object(3)\n",
            "memory usage: 139.7+ MB\n"
          ]
        }
      ],
      "source": [
        "train_df.drop('TOP_PACK',axis= 1, inplace=True)\n",
        "train_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "iXgRY3e9hXlb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "outputId": "6b3b7a3a-46aa-4615-fdff-90dd18307abf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define colors for the plots\n",
        "palette = ['#008080', '#FF6347', '#E50000', '#D2691E']\n",
        "\n",
        "# Calculate the percentage of positive and negative values\n",
        "churn_distribute = train_df['CHURN'].value_counts()\n",
        "plot_pie = [churn_distribute[0] / churn_distribute.sum() * 100, churn_distribute[1] / churn_distribute.sum() * 100]\n",
        "\n",
        "# Create the figure and axes for subplots\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 7))\n",
        "\n",
        "# Plot the pie chart on the first subplot\n",
        "axes[0].pie(plot_pie, labels=['Not_Churn', 'Churn'], autopct='%1.2f%%', explode=(0.1, 0),\n",
        "            colors=palette[:2], wedgeprops={'edgecolor': 'black', 'linewidth': 1, 'antialiased': True})\n",
        "axes[0].set_title('Customer Not-Churn and Churn %')\n",
        "\n",
        "# Plot the countplot on the second subplot\n",
        "sns.countplot(data=train_df, x='CHURN', palette=palette[:2], edgecolor='black', ax=axes[1])\n",
        "axes[1].set_xticklabels(['Not_Churn', 'Churn'])\n",
        "axes[1].set_title('Customers Not_Churn and Churn')\n",
        "\n",
        "# Add labels to the countplot bars\n",
        "for container in axes[1].containers:\n",
        "    axes[1].bar_label(container)\n",
        "\n",
        "# Adjust spacing between subplots\n",
        "plt.tight_layout()\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "id": "itQ4weNax-CF",
        "outputId": "0e6c93e5-bd06-4070-ed18-8fd2d0acf84c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Separate the features and target variable\n",
        "X = train_df.drop('CHURN', axis=1)\n",
        "y = train_df['CHURN']\n",
        "\n",
        "# Perform oversampling\n",
        "oversampler = RandomOverSampler(random_state=42)\n",
        "X_balanced, y_balanced = oversampler.fit_resample(X, y)\n",
        "\n",
        "# Create a new DataFrame with balanced data\n",
        "df_balanced = pd.concat([X_balanced, y_balanced], axis=1)\n",
        "\n",
        "# Calculate the percentage of positive and negative values in the balanced dataset\n",
        "churn_distribute_balanced = df_balanced['CHURN'].value_counts()\n",
        "plot_pie_balanced = [\n",
        "    churn_distribute_balanced[0] / churn_distribute_balanced.sum() * 100,\n",
        "    churn_distribute_balanced[1] / churn_distribute_balanced.sum() * 100\n",
        "]\n",
        "\n",
        "# Create the figure and axes for subplots\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 7))\n",
        "\n",
        "# Plot the pie chart on the first subplot\n",
        "axes[0].pie(\n",
        "    plot_pie_balanced,\n",
        "    labels=['Not_Churn', 'Churn'],\n",
        "    autopct='%1.2f%%',\n",
        "    explode=(0.1, 0),\n",
        "    colors=palette[:2],\n",
        "    wedgeprops={'edgecolor': 'black', 'linewidth': 1, 'antialiased': True}\n",
        ")\n",
        "axes[0].set_title('Balanced Customer Not-Churn and Churn %')\n",
        "\n",
        "# Plot the countplot on the second subplot\n",
        "sns.countplot(data=df_balanced, x='CHURN', palette=palette[:2], edgecolor='black', ax=axes[1])\n",
        "axes[1].set_xticklabels(['Not_Churn', 'Churn'])\n",
        "axes[1].set_title('Balanced Customers Not_Churn and Churn')\n",
        "\n",
        "# Add labels to the countplot bars\n",
        "for container in axes[1].containers:\n",
        "    axes[1].bar_label(container)\n",
        "\n",
        "# Adjust spacing between subplots\n",
        "plt.tight_layout()\n",
        "\n",
        "# Display the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "_tVRN09Ex-CJ"
      },
      "outputs": [],
      "source": [
        "def count_duplicate_rows(data):\n",
        "  \"\"\"Count the number of duplicate rows in a Pandas DataFrame.\n",
        "    data: The Pandas DataFrame to check for duplicate rows.\n",
        "  Returns: The number of duplicate rows in the DataFrame.\n",
        "  \"\"\"\n",
        "  duplicate_rows = data.duplicated()\n",
        "  return duplicate_rows.sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F8mi5IqAx-CK",
        "outputId": "cb3af848-167c-41c3-ceb1-5a98bf2a0bb8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "990779"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "count_duplicate_rows(df_balanced)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "Ef16kfW5x-CL",
        "outputId": "9ae14e98-39ba-40a4-f4cc-cfaf78ba1e6a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              REGION        TENURE  MONTANT  FREQUENCE_RECH  REVENUE  \\\n",
              "0              DAKAR  K > 24 month  20000.0            47.0  21602.0   \n",
              "1              DAKAR  K > 24 month   3000.0             7.0   3000.0   \n",
              "2              DAKAR  K > 24 month   3000.0             7.0   3000.0   \n",
              "3        SAINT-LOUIS  K > 24 month   7900.0            19.0   7896.0   \n",
              "4              DAKAR  K > 24 month  12350.0            21.0  12351.0   \n",
              "...              ...           ...      ...             ...      ...   \n",
              "1077016        DAKAR  K > 24 month  14800.0            34.0  16189.0   \n",
              "1077017        DAKAR  K > 24 month   2800.0             9.0   2950.0   \n",
              "1077020  TAMBACOUNDA  K > 24 month   2500.0             5.0   2500.0   \n",
              "1077022        DAKAR  K > 24 month    600.0             1.0    600.0   \n",
              "1077023       FATICK  K > 24 month   1500.0             4.0   1499.0   \n",
              "\n",
              "         ARPU_SEGMENT  FREQUENCE  DATA_VOLUME  ON_NET  ORANGE   TIGO  ZONE1  \\\n",
              "0              7201.0       52.0       8835.0  3391.0   396.0  185.0    1.0   \n",
              "1              1000.0        9.0        258.0    27.0    29.0    6.0    1.0   \n",
              "2              1000.0        9.0        258.0    27.0    29.0    6.0    1.0   \n",
              "3              2632.0       25.0       9385.0    27.0    46.0   20.0    1.0   \n",
              "4              4117.0       29.0       9360.0    66.0   102.0   34.0    1.0   \n",
              "...               ...        ...          ...     ...     ...    ...    ...   \n",
              "1077016        5396.0       38.0      17112.0    32.0   142.0   13.0    7.0   \n",
              "1077017         983.0        9.0        258.0    19.0    42.0    0.0    1.0   \n",
              "1077020         833.0        5.0          0.0    15.0    77.0    6.0    1.0   \n",
              "1077022         200.0        1.0        591.0    11.0    37.0    5.0    1.0   \n",
              "1077023         500.0        5.0       1265.0    30.0     4.0    6.0    1.0   \n",
              "\n",
              "         ZONE2 MRG  REGULARITY  FREQ_TOP_PACK  CHURN  \n",
              "0          2.0  NO          62           30.0      0  \n",
              "1          2.0  NO           3            5.0      0  \n",
              "2          2.0  NO           1            5.0      0  \n",
              "3          2.0  NO          61            7.0      0  \n",
              "4          2.0  NO          56           11.0      0  \n",
              "...        ...  ..         ...            ...    ...  \n",
              "1077016    2.0  NO          62           15.0      0  \n",
              "1077017    2.0  NO          46            3.0      0  \n",
              "1077020    2.0  NO          34            2.0      0  \n",
              "1077022    2.0  NO          16            1.0      0  \n",
              "1077023    0.0  NO          50            2.0      0  \n",
              "\n",
              "[759283 rows x 17 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>REGION</th>\n",
              "      <th>TENURE</th>\n",
              "      <th>MONTANT</th>\n",
              "      <th>FREQUENCE_RECH</th>\n",
              "      <th>REVENUE</th>\n",
              "      <th>ARPU_SEGMENT</th>\n",
              "      <th>FREQUENCE</th>\n",
              "      <th>DATA_VOLUME</th>\n",
              "      <th>ON_NET</th>\n",
              "      <th>ORANGE</th>\n",
              "      <th>TIGO</th>\n",
              "      <th>ZONE1</th>\n",
              "      <th>ZONE2</th>\n",
              "      <th>MRG</th>\n",
              "      <th>REGULARITY</th>\n",
              "      <th>FREQ_TOP_PACK</th>\n",
              "      <th>CHURN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DAKAR</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>21602.0</td>\n",
              "      <td>7201.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>8835.0</td>\n",
              "      <td>3391.0</td>\n",
              "      <td>396.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NO</td>\n",
              "      <td>62</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DAKAR</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>258.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NO</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>DAKAR</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>258.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NO</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SAINT-LOUIS</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>7900.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7896.0</td>\n",
              "      <td>2632.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>9385.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NO</td>\n",
              "      <td>61</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>DAKAR</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>12350.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>12351.0</td>\n",
              "      <td>4117.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>9360.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NO</td>\n",
              "      <td>56</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1077016</th>\n",
              "      <td>DAKAR</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>14800.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>16189.0</td>\n",
              "      <td>5396.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>17112.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NO</td>\n",
              "      <td>62</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1077017</th>\n",
              "      <td>DAKAR</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>2800.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2950.0</td>\n",
              "      <td>983.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>258.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NO</td>\n",
              "      <td>46</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1077020</th>\n",
              "      <td>TAMBACOUNDA</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>833.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NO</td>\n",
              "      <td>34</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1077022</th>\n",
              "      <td>DAKAR</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>600.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>600.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>591.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NO</td>\n",
              "      <td>16</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>1077023</th>\n",
              "      <td>FATICK</td>\n",
              "      <td>K &gt; 24 month</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1499.0</td>\n",
              "      <td>500.0</td>\n",
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              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NO</td>\n",
              "      <td>50</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>759283 rows × 17 columns</p>\n",
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              "\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-6487f477-8681-41f2-ba6e-6a4753601615 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Drop duplicate rows\n",
        "df_balanced.drop_duplicates()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N2wCM6M0x-CM",
        "outputId": "e7733b20-851e-4baf-e25b-c7e7120e6b25"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "count_duplicate_rows(test_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "SISDrHhax-CN"
      },
      "outputs": [],
      "source": [
        "#import pandas as pd\n",
        "#from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Load your original dataset\n",
        "#original_df = pd.read_csv('your_original_dataset.csv')\n",
        "\n",
        "# Specify the column you want to use for stratified sampling\n",
        "stratify_column = 'REGION'\n",
        "\n",
        "# Split the dataset into a training set and a subset based on the 'REGION' column\n",
        "train_df, subset_df = train_test_split(df_balanced, test_size=0.1, stratify=df_balanced[stratify_column], random_state=42)\n",
        "\n",
        "# 'subset_df' now contains a statistically representative subset of your original dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cVY_G_sXx-CN",
        "outputId": "562f7865-d2ca-42ef-ffaf-5babaa8116dc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(175007, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "subset_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HD2jK_gVx-CO",
        "outputId": "90aa7f2e-3e59-4145-b4ee-93f8d9c17ef7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 175007 entries, 158780 to 1279365\n",
            "Data columns (total 17 columns):\n",
            " #   Column          Non-Null Count   Dtype  \n",
            "---  ------          --------------   -----  \n",
            " 0   REGION          175007 non-null  object \n",
            " 1   TENURE          175007 non-null  object \n",
            " 2   MONTANT         175007 non-null  float64\n",
            " 3   FREQUENCE_RECH  175007 non-null  float64\n",
            " 4   REVENUE         175007 non-null  float64\n",
            " 5   ARPU_SEGMENT    175007 non-null  float64\n",
            " 6   FREQUENCE       175007 non-null  float64\n",
            " 7   DATA_VOLUME     175007 non-null  float64\n",
            " 8   ON_NET          175007 non-null  float64\n",
            " 9   ORANGE          175007 non-null  float64\n",
            " 10  TIGO            175007 non-null  float64\n",
            " 11  ZONE1           175007 non-null  float64\n",
            " 12  ZONE2           175007 non-null  float64\n",
            " 13  MRG             175007 non-null  object \n",
            " 14  REGULARITY      175007 non-null  int64  \n",
            " 15  FREQ_TOP_PACK   175007 non-null  float64\n",
            " 16  CHURN           175007 non-null  int64  \n",
            "dtypes: float64(12), int64(2), object(3)\n",
            "memory usage: 24.0+ MB\n"
          ]
        }
      ],
      "source": [
        "subset_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_SBmUFhx-CP",
        "outputId": "dc265523-3333-4a19-e9f0-f2d47d998c90"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_sub_train shape: (140005, 16)\n",
            "y_sub_train shape: (140005, 1)\n",
            "X_sub_test shape: (35002, 16)\n",
            "y_sub_test shape: (35002, 1)\n"
          ]
        }
      ],
      "source": [
        "# Split subset_df to train and Test\n",
        "\n",
        "\n",
        "# Create the feature dataframe using the selected columns\n",
        "X = subset_df.iloc[:, :-1]\n",
        "\n",
        "# Get the target variable\n",
        "y = subset_df.iloc[:, -1:]\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_sub_train, X_sub_test, y_sub_train, y_sub_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Print the shapes of the train and validation sets\n",
        "print(\"X_sub_train shape:\", X_sub_train.shape)\n",
        "print(\"y_sub_train shape:\", y_sub_train.shape)\n",
        "print(\"X_sub_test shape:\", X_sub_test.shape)\n",
        "print(\"y_sub_test shape:\", y_sub_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_pfmc4_x-CQ",
        "outputId": "a02487fd-2e77-49d8-f097-869dff9b8c5b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.20523322 -0.18438173 -0.19873423 ...  0.          0.\n",
            "   1.        ]\n",
            " [ 2.29789481  4.47062928  2.19775709 ...  0.          0.\n",
            "   1.        ]\n",
            " [-0.60827926 -0.40604892 -0.46732969 ...  0.          0.\n",
            "   1.        ]\n",
            " ...\n",
            " [-0.20523322 -0.18438173 -0.19873423 ...  0.          0.\n",
            "   1.        ]\n",
            " [-0.41736271 -0.51688251 -0.40555273 ...  0.          0.\n",
            "   1.        ]\n",
            " [-0.20523322 -0.18438173 -0.19873423 ...  0.          0.\n",
            "   1.        ]]\n",
            "[[-0.20062496 -0.18756534 -0.19320094 ...  0.          0.\n",
            "   1.        ]\n",
            " [-0.20062496 -0.18756534 -0.19320094 ...  0.          0.\n",
            "   1.        ]\n",
            " [-0.21087318  0.25706315 -0.19912948 ...  0.          0.\n",
            "   1.        ]\n",
            " ...\n",
            " [-0.20062496 -0.18756534 -0.19320094 ...  0.          0.\n",
            "   1.        ]\n",
            " [-0.20062496 -0.18756534 -0.19320094 ...  0.          0.\n",
            "   1.        ]\n",
            " [-0.40558941 -0.5210367  -0.39081908 ...  0.          0.\n",
            "   1.        ]]\n"
          ]
        }
      ],
      "source": [
        "# Define preprocessing steps for numerical and categorical features\n",
        "numerical_features = X_sub_train.select_dtypes(include=['int', 'float']).columns.tolist()\n",
        "categorical_features = X_sub_train.select_dtypes(include=['object']).columns.tolist()\n",
        "\n",
        "numerical_transformer = Pipeline([\n",
        "    ('imputer', SimpleImputer(strategy='mean')),  # You can change the imputation strategy as needed\n",
        "    ('scaler', StandardScaler())\n",
        "  ])\n",
        "\n",
        "categorical_transformer = Pipeline([\n",
        "    ('onehot', OneHotEncoder(drop='first'))\n",
        "    ])\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numerical_transformer, numerical_features),\n",
        "        ('cat', categorical_transformer, categorical_features)\n",
        "        ])\n",
        "\n",
        "# Fit and transform the data\n",
        "X_sub_train_preprocessed = preprocessor.fit_transform(X_sub_train)\n",
        "X_sub_test_preprocessed = preprocessor.fit_transform(X_sub_test)\n",
        "\n",
        "# Now, X_preprocessed contains the preprocessed data\n",
        "print(X_sub_train_preprocessed)\n",
        "print(X_sub_test_preprocessed)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "mHB-e_tQx-CQ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "oJcR9rauhXlh"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1YtUHmvZhXlh",
        "outputId": "6cbfeab6-b2eb-4e9b-91d9-c79a8d39be41"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (1400049, 16)\n",
            "y_train shape: (1400049, 1)\n",
            "X_eval shape: (350013, 16)\n",
            "y_eval shape: (350013, 1)\n"
          ]
        }
      ],
      "source": [
        "# split train+validation set into training and validation sets\n",
        "X_train, X_eval, y_train, y_eval = train_test_split(df_balanced.iloc[:, :-1], df_balanced.iloc[:, -1:], test_size=0.2, random_state=42, stratify=df_balanced.iloc[:, -1:])\n",
        "\n",
        "# Print the shapes of the train and validation sets\n",
        "print(\"X_train shape:\", X_train.shape)\n",
        "print(\"y_train shape:\", y_train.shape)\n",
        "print(\"X_eval shape:\", X_eval.shape)\n",
        "print(\"y_eval shape:\", y_eval.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNgsew3TjJoA",
        "outputId": "2d9670f7-baa2-4c46-fcc6-26e459f7d532"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.19966668 -0.18371369 -0.19367986 ...  0.          0.\n",
            "   1.        ]\n",
            " [ 1.67869056  4.88087698  1.7470594  ...  0.          0.\n",
            "   1.        ]\n",
            " [-0.19966668 -0.18371369 -0.19367986 ...  0.          0.\n",
            "   1.        ]\n",
            " ...\n",
            " [ 1.77208954  0.94175091  1.46543409 ...  0.          0.\n",
            "   1.        ]\n",
            " [-0.19966668 -0.18371369 -0.19367986 ...  0.          0.\n",
            "   1.        ]\n",
            " [-0.78081589 -0.85899244 -0.76464068 ...  0.          0.\n",
            "   1.        ]]\n",
            "[[-0.19957076 -0.18386324 -0.19392099 ...  0.          0.\n",
            "   1.        ]\n",
            " [-0.19957076 -0.18386324 -0.19392099 ...  0.          0.\n",
            "   1.        ]\n",
            " [-0.71602581 -0.85835766 -0.69712133 ...  0.          0.\n",
            "   1.        ]\n",
            " ...\n",
            " [-0.19957076 -0.18386324 -0.19392099 ...  0.          0.\n",
            "   1.        ]\n",
            " [ 1.03992135 -0.85835766  1.02056092 ...  0.          0.\n",
            "   1.        ]\n",
            " [-0.19957076 -0.18386324 -0.19392099 ...  0.          0.\n",
            "   1.        ]]\n"
          ]
        }
      ],
      "source": [
        "# Define preprocessing steps for numerical and categorical features\n",
        "numerical_features = X_train.select_dtypes(include=['int', 'float']).columns.tolist()\n",
        "categorical_features = X_train.select_dtypes(include=['object']).columns.tolist()\n",
        "\n",
        "numerical_transformer = Pipeline([\n",
        "    ('imputer', SimpleImputer(strategy='mean')),  # You can change the imputation strategy as needed\n",
        "    ('scaler', StandardScaler())\n",
        "  ])\n",
        "\n",
        "categorical_transformer = Pipeline([\n",
        "    ('onehot', OneHotEncoder(drop='first'))\n",
        "    ])\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numerical_transformer, numerical_features),\n",
        "        ('cat', categorical_transformer, categorical_features)\n",
        "        ])\n",
        "\n",
        "# Fit and transform the data\n",
        "X_train_preprocessed = preprocessor.fit_transform(X_train)\n",
        "X_eval_preprocessed = preprocessor.fit_transform(X_eval)\n",
        "\n",
        "# Now, X_preprocessed contains the preprocessed data\n",
        "print(X_train_preprocessed)\n",
        "print(X_eval_preprocessed)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5S4J7KrhXli"
      },
      "source": [
        "deal with oversampling in data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "XeG_m4m5hXli"
      },
      "outputs": [],
      "source": [
        "# Use Over-sampling/Under-sampling methods, more details here: https://imbalanced-learn.org/stable/install.html\n",
        "##oversample= SMOTE()\n",
        "\n",
        "#balancing train dataset\n",
        "#X_train_resampled,y_train_resampled = oversample.fit_resample(X_train_preprocessed, y_train)\n",
        "#X_train_resampled.shape,y_train_resampled.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "bpF0NLjXnKVH"
      },
      "outputs": [],
      "source": [
        "# balancing evaluation dataset\n",
        "#X_eval_resampled,y_eval_resampled = oversample.fit_resample(X_eval_preprocessed, y_eval)\n",
        "#X_eval_resampled.shape,y_eval_resampled.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "TZu4K0g6hXlj"
      },
      "outputs": [],
      "source": [
        "# Confusion Matrix Function\n",
        "def plot_metric(confusion, name):\n",
        "    group_names = ['True Neg','False Pos','False Neg','True Pos']\n",
        "    group_counts = [\"{0:0.0f}\".format(value) for value in\n",
        "    confusion.flatten()]\n",
        "    group_percentages = [\"{0:.2%}\".format(value) for value in\n",
        "    confusion.flatten()/np.sum(confusion)]\n",
        "    labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in\n",
        "    zip(group_names,group_counts,group_percentages)]\n",
        "    labels = np.asarray(labels).reshape(2,2)\n",
        "    ax = sns.heatmap(confusion, annot=labels, fmt='', cmap='viridis')\n",
        "    ax.set_title(f'{name}\\n\\n');\n",
        "    ax.set_xlabel('\\nPredicted Values')\n",
        "    ax.set_ylabel('Actual Values ');\n",
        "    ## Ticket labels - List must be in alphabetical order\n",
        "    ax.xaxis.set_ticklabels(['False','True'])\n",
        "    ax.yaxis.set_ticklabels(['False','True'])\n",
        "    ## Display the visualization of the Confusion Matrix.\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "wSRDxauNhXlk"
      },
      "outputs": [],
      "source": [
        "# Function for Training Models\n",
        "def train_ml_model(X_train, y_train, X_eval, y_eval, model_class, **model_params):\n",
        "    \"\"\"\n",
        "    Train a machine learning model, calculate various metrics, generate an ROC curve,\n",
        "    and return results in a DataFrame.\n",
        "\n",
        "    Parameters:\n",
        "    - X_train: Training features\n",
        "    - y_train: Training labels\n",
        "    - X_eval: Evaluation features\n",
        "    - y_eval: Evaluation labels\n",
        "    - model_class: Class of the machine learning model to be used (e.g., LogisticRegression)\n",
        "    - **model_params: Additional parameters for the model constructor\n",
        "\n",
        "    Returns:\n",
        "    - result_df: DataFrame containing model, F1-score, AUC-score, and assessment\n",
        "    \"\"\"\n",
        "\n",
        "    results = []  # List to store results for each model\n",
        "\n",
        "\n",
        "    # Fit the model with preprocessed data\n",
        "    model = model_class(**model_params)\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    # Make predictions on the evaluation set\n",
        "    predictions = model.predict(X_eval)\n",
        "\n",
        "    # Calculate F1 score\n",
        "    f1 = round(f1_score(y_eval, predictions), 2)\n",
        "\n",
        "    # Calculate false positive rate, true positive rate, and thresholds using roc_curve\n",
        "    fpr, tpr, thresholds = roc_curve(y_eval, predictions)\n",
        "\n",
        "    # Calculate AUC score\n",
        "    auc = round(roc_auc_score(y_eval, predictions), 2)\n",
        "\n",
        "    # Determine if the model is overfitting, underfitting, or balanced\n",
        "    train_preds = model.predict(X_train)\n",
        "    train_f1 = f1_score(y_train, train_preds)\n",
        "    eval_f1 = f1_score(y_eval, predictions)\n",
        "\n",
        "    if train_f1 > eval_f1:\n",
        "        assessment = \"Overfitting\"\n",
        "    elif train_f1 < eval_f1:\n",
        "        assessment = \"Underfitting\"\n",
        "    else:\n",
        "        assessment = \"Balanced\"\n",
        "\n",
        "    # Calculate and Plot confusion matrix\n",
        "    confusion = confusion_matrix(y_eval, predictions)\n",
        "    #plot_metric(confusion, model_class())\n",
        "\n",
        "    # Create ROC curve plot\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = {:.2f})'.format(auc))\n",
        "    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "    plt.xlim([0.0, 1.0])\n",
        "    plt.ylim([0.0, 1.05])\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel('True Positive Rate')\n",
        "    plt.title('Receiver Operating Characteristic (ROC)')\n",
        "    plt.legend(loc='lower right')\n",
        "    plt.show()\n",
        "\n",
        "    # Create a DataFrame to store the results\n",
        "    result_data = {\n",
        "        'Model': model_class.__name__,\n",
        "        'F1-Score': f1,\n",
        "        'AUC-Score': auc,\n",
        "        'Assessment': assessment\n",
        "    }\n",
        "\n",
        "    results.append(result_data)\n",
        "\n",
        "    result_df = pd.DataFrame(results)\n",
        "\n",
        "    return result_df\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TojzddRthXll"
      },
      "source": [
        "### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "OD-3_VOihXll",
        "outputId": "67d0a489-6391-4dc7-ed87-4a383fa72869"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Model  F1-Score  AUC-Score   Assessment\n",
              "0  LogisticRegression      0.84       0.84  Overfitting"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ce2a90aa-2f7e-429e-adea-29252c20ca22\" 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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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>F1-Score</th>\n",
              "      <th>AUC-Score</th>\n",
              "      <th>Assessment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.84</td>\n",
              "      <td>Overfitting</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-ce2a90aa-2f7e-429e-adea-29252c20ca22')\"\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",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\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-ce2a90aa-2f7e-429e-adea-29252c20ca22 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-ce2a90aa-2f7e-429e-adea-29252c20ca22');\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",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# Specify the model and its parameters\n",
        "model_class = LogisticRegression\n",
        "model_params = {'random_state': 42}\n",
        "\n",
        "# Call the train_ml_model function\n",
        "lr_result_df = train_ml_model(X_train_preprocessed,y_train,X_eval_preprocessed,y_eval, model_class, **model_params)\n",
        "lr_result_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "Qv9PoxPGhXlm"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "4J5YRDashXln",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "outputId": "65b5396d-ad7a-446b-cada-072b9c78730d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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j3U3jxo1Rv359xMTEYODAgQBMZQ5eXl5SYpCZmYmbN29i3bp1WLduncXH+Xff17K287r9RzM/P/+uf7jvlhzXrVu31Nx69eohJiYGgHWv873iLioqwsqVK7Fx40akpqZCFEXptn8neNb6d5Jz+zW4efMmACA5ORkAUKdOHbN5KpXqrl/B38nNzQ1AyWv4sHHl5ORIj3n8+HF8/vnn+P3330vV/+bm5sLd3V26frffh7I8xpUrVwAADRs2tOpnuM3a90dZf3enTJmCt956C927d0fTpk3RrVs39OvXD7Vr17Y6xtsfdEJCQqy+7213/l7eKSgoCLNmzYLRaMSVK1ewYsUKZGVllfoQUa1atfsmo/9+L7q5uZU6d6GssdpbdxkiJr5ED6hFixZSV4fIyEg899xzmDx5MrZv345q1arBaDQCAF588UWLq2BA6UTnXoxGI0JCQsx6CN/pfnWg0dHRWLFiBTIzM+Hm5ob4+Hj06dNHWmG8HW/fvn1L1QLf1qhRI7PrZT1jPzg4GHFxcTh//jzatm1rcc758+cBoEyrcHd6kNfZ0pn2M2fOxMaNG/H888/j0Ucfhbu7OwRBwMSJE++abJTV3TpdPOzj3na71vj8+fMIDQ0tt7iuXLmCkSNHon79+njrrbcQEBAAtVqNPXv24Ouvv5Ze+9ss/T5Y+xgPytr3R1l/d6Ojo9GmTRvExsZi//79+Oqrr/Dll1/i888/l06MtAUvLy8AJR+W/s3V1dWsNr5169bo378/Fi5ciHfffVcaDw4OxtmzZ5GcnFzqg89t/34v1q9fH2fOnEFKSgoCAgLKHPPNmzctfnAlkhMTX6JyoFQqMWnSJIwYMQLffvstXn75ZWlFSK1Wm/1BsqROnTq4cOHCfeecO3cOHTt2fKBVlOjoaCxZsgQ7d+6Er68v8vLy0KdPH+l2b29vKWG/X7zW6t69O1auXInNmzdbTHwNBgO2bt0KDw8PtG7d2uw2S90PEhMTpZVQa17ne9mxYwf69etntuNdcXFxqdXeiljBup2AXLlyRVqBB0xtpG53C7iXrl27QqlUYuvWrXc9aelBxMfHQ6vVYvny5WZJ0r3Kah70MW5/OLlw4cI9k6W7vf4P+/64Fz8/PwwdOhRDhw5FRkYGnn76aaxYsUJKfMv6fLd/Vy11Jbmf2x9u7uwyci+NGzdG37598f333+PFF1+UXvvu3bvj559/xubNmzFu3LhS98vLy8OuXbtQv3596TiEhYXh559/xk8//YTRo0eX6fn1ej1SUlLMum4Q2QPW+BKVk/bt26NFixb45ptvUFxcDB8fH7Rr1w7r1q1DWlpaqfl3lg307NkT586dQ2xsbKl5t1ffoqKikJqaivXr15eaU1RUJHUnuJvg4GCEhIRg27Zt2LZtG2rUqGGWhCqVSvTq1Qs7duyw+If5Yba3bd26NTp16oSNGzeatU67beHChUhMTMRLL71UaiUuLi7OrL7y1KlTOHnyJLp27QoAVr3O92Jp04K1a9fCYDCYjbm4uAB4+PKHOzVr1gyenp5Yv3499Hq9NL5161bk5OTc9/4BAQEYOHAgfv31V6xdu7bU7UajEf/3f/+H69evWxXX7dfk32UfGzZsKPfH6NKlC6pVq4aVK1eiuLjY7LY77+vi4mLxtX/Y94clBoOh1HP5+PjAz8/PrEXa3WL6N29vb7Rt2xYbNmyQyltuu9/qv7+/PwICAvDHH3+UOf6XXnoJer0eq1evlsZ69eqFBg0a4Msvv8Tp06fN5huNRkyfPh05OTl45ZVXzO4TEhKCFStW4MSJE6WeJy8vr1Q7tosXL6K4uBitWrUqc7xEtsAVX6JyNGrUKLz22mvYuHEjhgwZgunTp+O5557Dk08+iUGDBqF27dpIT0/H77//juvXr+Onn36S7rdjxw689tprGDBgAJo2bYqcnBzEx8djxowZaNy4MZ566inExMRg+vTpOHz4MFq3bg2DwYC///4b27dvx6pVq+67JW10dDQWL14MJycnPPPMM6W+6p48eTIOHz6MQYMGYeDAgWjQoAFycnLw559/4uDBg2bbolpr3rx5GDlyJMaNG4cnnngCbdq0gVarxc6dO3HkyBFER0dj1KhRpe5Xp04dDBkyBEOGDIFWq8WaNWvg6emJl156SZpT1tf5Xrp3744tW7bAzc0NDRo0wO+//44DBw7A09PTbF5oaCiUSiW+/PJL5ObmQqPRoEOHDvDx8Xng10aj0eDVV1/FzJkz8fzzzyMqKgpJSUnYuHFjmcth3nrrLVy9ehWzZs3Czp07ERYWhurVqyMlJQXbt2/H33//bbbCXxadO3eGWq3GmDFjMHjwYOTn5+OHH36Aj48Pbty4Ua6P4ebmhmnTpuHdd9/FM888gyeeeALVq1fHuXPnUFRUhHnz5gEAmjZtim3btmHOnDlo3rw5XF1dER4eXi7vj3/Lz89Ht27d0KtXLzRu3Biurq44cOAATp8+bfbNwN1isuTdd9/FkCFD8PTTT+PZZ59FrVq1kJSUhN27d2PLli33jCciIgKxsbFlrp1t0KABunXrhh9//BHjxo2Dl5cXNBoNFi9ejOeffx7PPfcc+vfvj2bNmiE3Nxc///wz/vzzT7z44otmvytqtRpLlizBCy+8gGHDhqF3795o3bo11Go1Lly4gJ9//hnVq1c3a8d24MABuLi4lPu3R0QPi4kvUTnq2bMn6tSpg//7v//DoEGD0KBBA2zYsAFLlizBpk2bkJ2dDW9vbzRp0gTjx4+X7letWjV8++23+PzzzxEbG4tNmzbBx8cHHTt2lE7KUigUWLp0Kb7++mts2bIFsbGxcHFxQa1atTB8+HCpZ+q9REdH47PPPkNhYSGioqJK3e7r64sffvgBS5cuRWxsLL777jt4enqiQYMGmDJlykO9Nn5+fvjhhx+wevVqbN++HTt37oRSqUSjRo0wd+5c9OvXz+If8379+kGhUOCbb75BRkYGWrRogffeew9+fn7SnLK+zvfyzjvvQKFQYOvWrSguLkbr1q2xevVqswQbAGrUqIEZM2Zg5cqVeOedd2AwGLBmzZqHSnwBYNiwYRBFEatXr8a8efPQuHFjLF++HLNmzSrT7l8uLi748ssvsXHjRmzevBnLli1DUVER/Pz80L59e3zyySdmJ/iVRf369bF48WJ89tlnmDdvHnx9fTFkyBB4e3vj7bffLvfHGDhwIHx8fPDFF19g2bJlUKlUqF+/vln/6Oeeew5nz57Fxo0b8fXXXyMoKAjh4eHl8v74N2dnZwwZMgT79+/Hzp07IYoi6tSpI33Qul9MljRu3Bjr16/HokWL8N1336G4uBiBgYEW34//NmDAAPz3v//F8ePHLW4kYcmoUaOwe/du/Pe//8Wrr74KwPTtz08//YQvvvgC8fHx2LhxI5ydndGsWTMsX77cYux169bF5s2b8fXXXyM2Nha7du2C0WhE3bp1MXDgwFKbrGzfvh09evSQTpIksheCWF5nVxARlaNr164hIiICb7zxhsWVYEdgNBrRsWNH9OjRA7NmzZI7HLIDzz//PPz8/PDxxx/LHcpdnT17Fk8//TQ2bdpk1cmWRLbAGl8iIjtQXFxcqs5z8+bNyM7ORrt27WSKiuzNpEmTEBMTI+0UaY+++OIL9OrVi0kv2SWWOhAR2YHff/8dc+bMQe/eveHp6YkzZ87gxx9/REhICHr37i13eGQnWrZsadUJbnL494luRPaEiS8RkR0ICgpCzZo1sXbtWuTk5MDDwwNPPfUUpkyZYrYxChERPTjW+BIRERGRQ2CNLxERERE5BCa+REREROQQmPgSERERkUNg4ktEREREDsFhuzpkZubCaJQ7CqpoggD4+LgjIyMXPI2z6uPxdiw83o6Fx9uxKBSAt7d7uT+uwya+ogi+cRwIj7dj4fF2LDzejoXH2zFU1DFmqQMREREROQQmvkRERETkEJj4EhEREZFDYOJLRERERA6BiS8REREROQQmvkRERETkEJj4EhEREZFDYOJLRERERA6BiS8REREROQQmvkRERETkEJj4EhEREZFDYOJLRERERA6BiS8REREROQQmvkRERETkEJj4EhEREZFDkDXxPXr0KMaMGYMuXbqgUaNGiIuLu+99Dh8+jKeffhrNmjVDjx49sHHjRhtESkRERESVnayJb0FBARo1aoTp06eXaf7Vq1cxevRotG/fHlu2bMHzzz+Pd999F/v27avgSImIiIioslPJ+eTdunVDt27dyjz/+++/R61atfDWW28BAIKDg3H8+HF8/fXXePzxxysqTCIiIiKyBaMBihvH8M+x/fAZWbaFUWvImvha6/fff0fHjh3Nxrp06YLZs2db/ViCYPpHVdvtY8xj7Rh4vB0Lj7dj4fGuuoTCVGiS4qBJikX62UMY9d9wnLgRgtSR5f9clSrxTU9Ph6+vr9mYr68v8vLyUFRUBGdn5zI/lre3e3mHR3bMx4fH25HweDsWHm/HwuNdBRh0QMoh4J8YIHE7kHYCALDlj0Z46YehSM+vBnf3iklRK1XiW54yM3NhNModBVU0QTD9J5mRkQtRlDsaqmg83o6Fx9ux8HhXbor8JKiT4qBJjoM6OQEK3U3ptvxiNSZv7YWVh9pIY37eFbO0X6kSX19fX6Snp5uNpaenw83NzarVXgAQRfCN40B4vB0Lj7dj4fF2LDzelYShGOq0Q9AkxUKTHAdV9hmL045fC8Bz3z+Hv66XrORHRQVj0aJeFRJWpUp8H330Uezdu9ds7MCBA3j00UflCYiIiIiIAACKvMumRDcpDprreyDo8y3OM2q8UOgfgU/2dMCsL/Kh15s+ybi6qjBzZncMG9YcSmUVXPHNz8/HlStXpOvXrl3D2bNn4eHhgcDAQHz66adITU3F/PnzAQCDBw/Gt99+i/nz52PAgAE4dOgQYmJisHLlSrl+BCIiIiLHpC+EOnU/NMmmZFd184LFaSIE6H0fgzYwEtqgHtD7tEZ+oRHfvLVWSnpbtvTHihXRCA72qtCQZU18//jjD4wYMUK6PmfOHADA008/jblz5+LGjRtISUmRbq9duzZWrlyJOXPmYM2aNahZsyZmzZrFVmZEREREFU0Uocy9BE1SLNTJcdBc/xWCodDiVKNzDWgDI6AN6gFtQDhEZx+z26tVU2LFimg8+eQ6jB37GKZO7QiNRlnhP4Igio5ZKZORwZPbHIEgAL6+7khP58kQjoDH27HweDsWHm+Z6PKhSd0rtRtT5iVanCYKSuhrtCtZ1fVuAQgl+6Tl5WmRm1uMgADzrhwpKbmlxgBAoaiYDh6VqsaXiIiIiCqQKEKZc146KU2duh+CUWtxqsElANogU6Krq9kNopPlMoWjR5MxblwM/PyqYcuWQVCpShJiS0lvRWLiS0REROTABO1NqK/vMa3qJsdBmX/V4jxRoYbOr+OtVd1IGDyb3nNHEb3eiIULD2PBgkMwGERcvpyDzz8/iokT21fUj3JfTHyJiIiIHIkoQpn1R8mqbtohCKLe4lRDtdqmOt3AHtAFdIWoLtsKbWJiNsaNi8GxYyXnarVtG4j+/RuXy4/woJj4EhEREVVxQnEWNCkJ0iYSysLrFueJCg10/p2lZNfgEWLVPtGiKGL9+rOYNi0eeXmmEgmlUsCUKR3x2mvtzMoc5MDEl4iIiKiqEY1QZfwOTbLppDRV+lEIouWz+g3uj0Ab2MNUr+v/OKCu9kBPmZ1dhKlT47Bly1/SWN26Hli+PApt2gQ+0GOWNya+RERERFWAUJRhSnST46BJ3gVFUbrFeaLSBdqaj99a1Y2EsXrwQz93bm4xwsPX4tq1XGls8OCmmD07DG5umod+/PLCxJeIiIioMjIaoMo4JrUaU2WcgADLvd70HiHSqq7OvzOgdC7XUNzdnRAd3QBffHECnp5O+OSTHujbN6Rcn6M8MPElIiIiqiSEwlSpfEGTHA+FNtviPKPKDbqAblIHBqNb3QqP7d13H0dRkQETJ7ZHUJBt25SVFRNfIiIiIntl1EF94wg0SXFQJ8VCnXXqrlP1nk1N5QtBkdDV6AAoK6bEQBRFrF17GkqlgKFDm0vjzs4qfPJJZIU8Z3lh4ktERERkRxT5SdKqrjplNxS6mxbnGdUe0AWGlazqulb8CWTp6QWYNCkW27dfgouLCm3bBiIkxOf+d7QTTHyJiIiI5GQohjrtkNRXV5V95q5Tdd6PmrovBPaAvkZbQGG7VC4hIRGvvroDaWn5AIDCQj127vybiS8RERER3Z0iN7GkVvf6Xgj6fIvzjBovaAMjbiW7kRBd/GwcKVBUpMdHH/2KlSt/k8Z8fFzw2Wc90avXw3eEsCUmvkREREQVTV8Idep+aJJjoUmKg+rmBYvTRAjQ+z52q3yhB/Q+rQGF0sbBljhz5gbGjo3B2bMlrdHCw+th0aJe8Pd/sH6/cmLiS0RERFTeRBHK3ItSqzF16q8QDEUWpxqda0h1utqAcIjO8pcOiKKIVatO4MMP96G42AAAcHJSYvr0rhg16lEIVuzmZk+Y+BIRERGVB10+NKl7TeULSXFQ5iVanCYKSuhrtCtZ1fVuAQjybuX7b/n5OixfflxKeps08cXy5dEIDfWVObKHw8SXiIiI6EGIIpQ556WT0tSp+yEYtRanGlwCSlqNBXSHqPG0baxWcnPTYNmyKDz99A946aVWeOedLnB2rvxpY+X/CYiIiIhsRNDehPr6HlMJQ3IclPlXLc4TFWro/DpKq7oGzyaAHZcH5OfrUFCgQ40artJYhw61cPDgC6hXz1O+wMoZE18iIiKiuxFFKLP+KFnVTTsEQdRbnGqoVtu0qhvYA7qArhDV9rl72b+dPJmKsWO3ISDADT/88AwUipIEvSolvQATXyIiIiIzQnEWNCkJUN9e1S28bnGeqHCCzr+z6aS0oJ4wVG9o16u6/2YwGLF06THMnXsAer0RFy9mYcWK4xg3ro3coVUYJr5ERETk2EQjVBm/l7QaSz8KQTRanKp3rw/drfIFrX8XQF35WnoBQFJSLl55JQb791+Txlq29K90fXmtxcSXiIiIHI5QlA5N8i7TJhLJu6AoSrc4T1S6QFuzq7SBhLF65U8Mt2w5jylT4pCTUwzAtEg9YUI7TJ3aERqNfD2DbYGJLxEREVV9RgNUGcekvrqqjBMQIFqcqvdoJJ2UpvPvBCidbRxsxcjNLcbbbydg3bqSLZGDgtyxdGlvdOpUW8bIbIeJLxEREVVJQmHqre4LsdAkx0OhzbY4z6hygy6g260T0yJgdKtr20Bt4ObNYkRE/BeXL+dIY/36NcL8+RHw9KwaiX1ZMPElIiKiqsGog/rGEWiS4qBOioU669Rdp+o9m5b01a3RAVBqbBio7VWv7oTHH6+Ny5dz4Oamwdy54Rg4MLTS7sD2oJj4EhERUaWlyL8m9dRVp+yGQnfT4jyj2gO6wDBoA3tAGxQBo2ugjSOV34cfdkdhoR5vvtmpyrUpKysmvkRERFR5GIqhTj0olTCoss/edarO+9FbJ6X1gL5GW0DhGGmPKIpYv/4s1GoF+vdvLI27uWmwfHm0jJHJzzF+A4iIiKjSUuQmwik5DriRAJ8r8RD0+RbnGTVe0AZGSLW6ooufjSOVX3Z2EaZOjcOWLX+hWjU1WrWqiUce8ZQ7LLvBxJeIiIjsi74Q6tRfTa3GkmKhunlRuunOilQRAvS+j91KdCOh92kNKKp2O6572b//KsaPj0Fych4A0zbEW7f+hQkT2skcmf1g4ktERETyEkUocy9KrcbUqb9CMBRZnGp0rnGr1VgktAHhEJ19bBys/dFqDZg37wCWLDkK8VaHNg8PJ3z6aQ/07Rsib3B2hokvERER2Z4uH5rre6Xd0pR5iRaniYIS+hrtoA3qgWpNn0KmIhgiFLaN1Y5dvJiJMWO24dSpNGmsc+daWLIkCkFB7jJGZp+Y+BIREVHFE0Uoc86VrOqmHYBg1FqcanANlFZ1dQHdIWo8IQhANV93ID0Xd9l3wqGIoog1a07j/fd3o7BQDwBQqxWYNq0zxo1rA4XCsdqUlRUTXyIiIqoQgvYm1Nf3QJN0a1W34JrFeaJCDZ1fR2m3NINnE9M+unRXN28W4+OPD0pJb4MGXlixIhotWvjLHJl9Y+JLRERE5UMUocz6w5ToJsdBnXYIgqi3ONVQrY50UpouoCtENb+Wt4aHhzMWL+6FwYM34vnnW2DGjG5wdVXLHZbdY+JLRERED0wozoImJcFUvpC8C8rC6xbniQon6Pw7m05KC+oJQ/WGXNW1QlGRHoWFOnh5uUhj4eH1sHfvCDRu7CtjZJULE18iIiIqO9EIVcbv0klpqvSjEESjxal69/rQ3Spf0Pp3AdTVbBxs1XDmzA2MHbsNtWpVx3//289sm2EmvdZh4ktERET3JBSlQ5O8y9RXN3kXFEXpFueJShdoa3a9tVtaJIzVg20cadViNIpYteoEZs7ch+JiA86ezcDXX5/CCy+0lDu0SouJLxEREZkzGqDKOCadlKbKOAHhLq0U9B6NpJPSdP6dAKWzjYOtmlJT8zBhwg4kJFyWxpo08UWHDkEyRlX5MfElIiIiCIWpplZjybHQJMdDoc22OM+ocoMuoPutVd0IGN3q2jZQBxATcxGTJsUiI6NQGhs9ujXeeacLnJ2Zuj0MvnpERESOyKiD+sYR00lpSXFQZ52661S9V7OSVd0a7QGlxoaBOo78fB2mT9+DNWtKjoW/fzV8/nlvdO/ODxjlgYkvERGRg1DkX7u1qhsHdcpuKHQ3Lc4zqj2gCwyDNrAHtEERMLoG2jhSx5OdXYTo6O9w8WKWNBYVFYwFC3rCx8flHvckazDxJSIiqqoMxVCnHZRKGFTZZ+86VefTCtrACGgDe0Bfoy2gYIpgS56ezmjRwh8XL2bB1VWFWbPCMHRoM7MODvTw+FtNRERUhShyE6UNJDTX90LQ51ucZ3TyhjYg/NYmEhEQXfxsHCn927x54Sgq0uO99x5HcLCX3OFUSUx8iYiIKjN9IdSpv5oS3aRYqG5etDhNhAC9bxup1ZjepzWgUNo4WLpty5bz0GiUiIpqII15eDjj66/7yhhV1cfEl4iIqDIRRShzL5rKF5JioU79FYKhyOJUo3MN6aQ0bUAYRGcfGwdL/5abW4y3307AunVn4OnphJYt/REYyO2abYWJLxERkb3T5UNzfa+0W5oyL9HiNFFQQl+j3a3yhUjovVsAgsK2sdJdHT2ajLFjY3DlSg4AIDu7GD/+eBYTJrSTOTLHwcSXiIjI3ogilDnnSlZ10w5AMGotTjW4Bt5a1Y2ELqA7RI2nbWOl+9LrjViw4BAWLjwMg8G0EYibmwZz54Zj4MBQmaNzLEx8iYiI7ICgvQn19T3SbmnKgmsW54kKNXR+HW+1GouEwbMJwDP/7VZiYjbGjYvBsWMp0ljbtoFYtiwKdet6yBiZY2LiS0REJAdRhDLrdElf3bRDEES9xamGanWk8gVdQFeIataE2jtRFLFu3RlMmxaP/HwdAECpFDB5cge8/np7qFQsQZEDE18iIiIbEYozoUnZbSpfSI6DsjDV4jxR4QSdf2dTB4agnjBUb8hV3UomO7sIH3ywR0p669b1wPLlUWjThpuByImJLxERUUURjVBlnChpNZZ+DIJotDhV717ftCVwYCS0NR8HVK42DpbKk5eXCxYs6ImRI3/C4MFNMXt2GNzcuNWz3Jj4EhERlSOhKB2a5F23NpHYBUVxhsV5otIF2ppdpb66xurBNo6UypNWa4BWazBLbqOjGyA2dihatvSXMTK6ExNfIiKih2E0QJVxTDopTZVxAgJEi1P1Ho1KanX9OwFKZxsHSxXh4sVMjBmzDY884okvvuhjts0wk177wsSXiIjISoqC61An7zL11U2Oh0KbbXGeUeUGXUD3klVdtzq2DZQqlCiKWLPmNN5/fzcKC/U4dSoNkZH18eyzTeQOje6CiS8REdH9GHVQ3zhiOiktKQ7qrFN3nar3aibtlqar0R5Qsq6zKkpPL8CkSbHYvv2SNNaggRdCQ7k7nj1j4ktERGSBIv9aSauxlN1Q6G5anGdUe0AbGG46KS0oAkZXnrVf1cXHJ2LChB1IS8uXxp5/vgVmzOgGV1e1jJHR/TDxJSIiAgBDMdRpB2+dlBYHVfbZu07V+bSCNjAC2qCe0Pu2ART8c+oIior0+OijX7Fy5W/SmI+PCz77rCd69eLJiZUB36lEROSwFLmJUqKrub4Xgj7f4jyjk7cp0Q2MhDYwAqKLn40jJbllZRWiX78fcPZsujQWHl4Pixb1gr9/NRkjI2sw8SUiIsehL4Q69deSvro3L1qcJkKA3rfNrQ0kekDv3QpQKG0cLNkTT09n1K3rgbNn0+HkpMT06V0xatSjZh0cyP4x8SUioqpLFKHMvSi1GlOn/grBUGRxqtG5hnRSmjYgDKIzT1KiEoIgYOHCnigq2oYZM7ohNNRX7pDoATDxJSKiqkWXB831faZWY0lxUOYlWpwmCkroarSHLigS2sAe0Hs3BwSFbWMlu7V9+yVoNEqEh9eTxnx8XLB+/QD5gqKHxsSXiIgqN1GEMvtcyapu2gEIRq3FqQbXwJJWYwHdIGo8bRsr2b38fB2mT9+DNWtOwdfXFXv2jECNGtw+uqpg4ktERJWOoL0JzfXdwG974PV3DJT51yzOExVq6Pw6QhvYA9qgSBg8mwCsyaS7OHkyFWPGbMOlS1kATL16v/vuD0yY0E7myKi8MPElIiL7J4pQZp2+1Vc3Fuq0wxBEPQDg36ecGarVMdXpBvWArubjENXuto+XKhWDwYilS49h7twD0OuNAABXVxVmzuyOYcOayxwdlScmvkREZJeE4kxokhNMG0gkx0FZmGpxnqhwgs6/s5TsGqo35KoulVlSUi7Gj4/BgQMl3xq0bOmPFSuiERzsJWNkVBGY+BIRkX0QjVBlnChpNZZ+DIJotDhV714fuqAecAntiwzXxyAqWYNJ1tuy5TymTIlDTk4xANPnpQkT2mHq1I7QaNi+ripi4ktERLIRitJvJbpx0CTvgqI4w+I8UekCbc2uplXdwAgYqwdDEAAXX3cgPRcQbRw4VXoZGYWYNCkWubmmEyGDgtyxdGlvdOpUW+bIqCIx8SUiItsxGqBKP3ar1VgsVBm/Q7hL1qr3aHQr0Y2Ezr8ToHS2cbBUlfn4uGD+/AiMHRuDfv0aYf78CHh68nesqmPiS0REFUpRcB3q5F2mdmMp8VBosy3OM6rcoAvobtotLTASRrc6tg2UqjS93git1gBXV7U0NmBAKAID3dGhQxB3YHMQTHyJiKh8GXVQ3zgCTVIs1ElxUGeduutUvVczqdWYrkZ7QKmxYaDkKBITszFuXAwaNvTGokW9zG7r2LGWTFGRHJj4EhHRQ1PkXytpNZayGwpdrsV5RrUHtIHh0AVGQhsUCaNrgI0jJUciiiLWrz+Lt97ahfx8HY4dS0FExCPo2zdE7tBIJkx8iYjIeoZiqNMOmsoXkuOgyj5716k6n1bQBkZAG9QTet82gIJ/eqjiZWcXYerUOGzZ8pc0VreuBwID3WSMiuTG/32IiKhMFLn/SKu6mut7IegLLM4zOnmbEt1AU62u6FLDxpGSo9u//yrGj49BcnKeNDZ4cFPMnh0GNzeW0zgyJr5ERGSZvhDq1F9LVnVvXrQ4TYQAvW8b00lpQT2g924FKNgDlWxPqzVg3rwDWLLkKMRbzUI8PZ3wySc9WN5AAJj4EhHRbaIIZe5FU6KbFAd16q8QDEUWpxqd/aTuC9qAMIjOPjYOlshcZmYhBg3agFOn0qSxLl1qY8mS3ggM5LbVZMLEl4jIkenyoLm+71Zf3Tgo8xItThMFJXQ12kMXFAltYA/ovZsDgsK2sRLdg6enM7y9XQAAarUC06Z1xrhxbaBQsE0ZlWDiS0TkSEQRypxzJau6aQcgGLUWpxpcA00rukE9oAvoBlHjadtYiaygUAj4/PNeeOmlX/DRR93RooW/3CGRHWLiS0RUxQnam1Cn7Ja2BlYWXLM4T1SoofPrJCW7Bs9QgE39yU4lJCTC2Vll1ofX398NW7c+K2NUZO9kT3y//fZbfPXVV7hx4wYaN26M9957Dy1atLjr/K+//hrfffcdUlJS4OXlhV69emHy5MlwcnKyYdRERHZMFKHMOi2dlKZOOwxB1FucaqhWx7QtcFAP6Go+DlHNWkiyb0VFesya9StWrvwNgYFu2L17BLcapjKTNfHdtm0b5syZgxkzZqBly5b45ptvMGrUKGzfvh0+PqVPlNi6dSs+/fRTzJ49G61atUJiYiLeeustCIKAadOmyfATEBHZB6E4E5rkBFOimxwHZWGqxXmiwgm6ml1KVnWrN+SqLlUap0+nYvDgH3HmTDoAIDk5D2vWnMKECe1kjowqC1kT39WrV2PQoEEYMGAAAGDGjBnYvXs3NmzYgJdffrnU/BMnTqB169Z48sknAQC1atXCE088gZMnT9o0biIi2YlGqDJO3CpfiIUq/RgE0Whxqt492LQlcFAPaP27ACpXGwdL9HCMRhGrVp3AzJn7UFxsAAA4OSkxfXpXjBr1qLzBUaUiW+Kr1Wrx559/YvTo0dKYQqFAp06dcOLECYv3adWqFX766SecOnUKLVq0wNWrV7Fnzx489dRTVj+/IHCRwxHcPsY81o6hqh9voSjddEJachw0SbugKM6wOE9UuUJX83FTCUNgJIzV65c8hq2CtYGqfrzJ5Pr1PEyYsAMJCZelsSZNfLFiRTRCQ31ljIwqUkW9r2VLfLOysmAwGEqVNPj4+ODvv/+2eJ8nn3wSWVlZeO655yCKIvR6PQYPHowxY8ZY/fze3qxjcyQ+PjzejqTKHG+jHkg5AiRuB/6JAVKPAxAtz/UOBR6JAur1hlDrcWhUznCU/amqzPGmUrZsOYeXXtqK9PSSXQInTuyA2bMj4Ows+2lKVAlVqt+aw4cPY+XKlZg+fTpatGiBK1eu4KOPPsLSpUsxfvx4qx4rMzMXRsvfClIVIgimP4oZGbnSLj5UdVWF4y0UXL+1LXAc1MnxUGizLc4zqtygC+gOba0e0AVGwOhWp+TGbB0AnU3ilVNVON50d+npBXjuuY0oKDD9Lvv7V8OaNU/jscf8kJdXiLy8+zwAVWoKRcUsUsqW+Hp5eUGpVCIjw/yruoyMDPj6Wv7qYtGiRejbty8GDhwIAGjUqBEKCgrw/vvvY+zYsVAoyt5MXRTB/ygdCI+3Y6lUx9uogzrtcEmtbtbpu07VezW/dVJaJHQ12gPKO9Z0K8vPWwEq1fGmMvPxccWsWd0xaVIsevcOxmef9USjRn5IT+cHHUdQUcdYtsRXo9GgadOmOHjwICIjIwEARqMRBw8exLBhwyzep6ioqFRyq1Sa9oMX+S4gokpCkX/t1qpuLNQpu6HQ5VqcZ9R4QhsQZmo1FhgBo2uAjSMlsh2DwQi93ggnp5LUZOjQZggMdENYWD3uwEblQtZShxdeeAFvvvkmmjVrhhYtWuCbb75BYWEh+vfvDwB444034O/vj8mTJwMAwsLCsHr1ajRp0kQqdVi0aBHCwsKkBJiIyO4YiqFOPVCyqptz7q5TdT6tpFZjet82gKJSVaQRPZCkpFy88koMGjf2xZw54dK4IAgID39ExsioqpH1f9To6GhkZmZi8eLFuHHjBkJDQ7Fq1Sqp1CElJcVshXfs2LEQBAGfffYZUlNT4e3tjbCwMEycOFGuH4GIyCJF7j/Sqq7m+l4I+gKL84xO3tAGRpiS3cBIiC41bBwpkby2bDmPKVPikJNTjP37ryEioh4iI+vf/45ED0AQHbRGICODJ7c5AkEAfH3dWRPmIGQ93vpCaFL3QX3rxDTVzYsWp4kQoPdtc2u3tEjovVsBCn5j9SD4/q7ccnOL8fbbCVi37ow0FhTkjuXLo9ChQ61S83m8HYtCUTEdW/gdGhHRgxBFKG9eNK3oJsVCnbofgqHI4lSjsx+0QaYVXW1AGETn0jtTEjmSo0eTMW5cDC5fzpHG+vVrhPnzI7j9MFUoJr5ERGWly4Pm+j5oknZCk7wLyrxEi9NEQQldjfbQBUVCG9gDeu/mgFD2rjNEVZVeb8TChYexYMEhGAymZVs3Nw3mzg3HwIGhELgbCVUwJr5ERHcjilBmn711Uloc1GkHIBi1FqcaXAOhDTSVL+gCukPUeNg4WCL7lplZiGHDNuPYsRRprG3bQCxbFoW6dfl+Idtg4ktEdAdBmwN1yh5okmKhSY6DsiDJ4jxRoYbOr5PUgcHgGcq9c4nuwcPDCSqV6ZsPpVLA5Mkd8Prr7aUxIltg4ktEjk0Uocw6LSW66rTDEES9xakGt7pSoqur+ThENbfKJSorpVKBpUuj8MILP2Hu3HC0aRMod0jkgJj4EpHDEYozoUlOuLUtcByUhakW54kKJ+hqdrmV7PaEoXoDruoSldGBA1fh7KxC69YlG6/Url0dsbFDWctLsmHiS0RVn2iEKuOEtKqrSj8GQbTcz1DvHmyq0w3qAa1/F0DlauNgiSo3rdaA+fMP4PPPj6JOHQ8kJAyHm1vJ9tpMeklOTHyJqEoSitKlk9I0ybugKM6wOE9UuUJbs+utVd1IGN3ZOJ/oQV28mIkxY7bh1Kk0AMDlyzlYvfokXn21rcyREZkw8SWiqsGohyrjOHB+Dzwu/AJVxu8QYLnLvd6j8a2+uj2g8+8IKNk3lOhhiKKItWtP4733dqOw0FQjr1YrMG1aZ4wb10bm6IhKMPElokpLUXAd6turuinxUGizAQDqf80zqtygC+hu2i0tMAJGtzo2j5WoqkpPL8CkSbHYvv2SNNaggRdWrIhGixb+MkZGVBoTXyKqPIw6qNMO3yphiIUq6/Rdp+q9mpd0YKjRDlBq7jqXiB5MfHwiJkzYgbS0fGns+edbYMaMbnB1/fdHUCL5MfElIrumyLtqSnST46BO2Q2FLtfiPKPGE7qAMDg1fhKZ1TvD4BJgcR4RlY+0tHyMHLkFRUUGAICPjws++6wnevUKljkyortj4ktE9sVQDHXqgZJV3Zxzd52q82klrerqfdtAUKrg5OsOY3ou7lLeS0TlxM+vGt5993G8++5uhIXVxeLFveHvX03usIjuiYkvEclOkfuP1GpMc30vBH2BxXlGJ29oAyNMtboBERBdatg4UiLHZTSKMBiMUKuV0thLL7VCQIAb+vRpCIWCbcrI/jHxJSLb0xdCk7oP6qRbq7q5lyxOEwUF9D6PmRLdoEjovVsBCqXFuURUcVJT8/DqqzvQrFkNvP9+V2lcoRDw5JMhMkZGZB0mvkRU8UQRypsXoUmOhSYpFurU/RAMRRanGp39brUai4Q2IAyis4+NgyWiO8XEXMTEiTuRmVmEPXsuIyysHh5/nJ1RqHJi4ktEFUOXB831fdAk7YQmeReUeYkWp4mCEroa7U3dFwIjofduDggK28ZKRKXk5+swffoerFlzShqrUYM1vFS5MfElovIhilBmn5VOSlOnHYBg1FmcanANhDbQVL6gC+gOUeNh42CJ6F5OnkzFmDHbcOlSljTWu3cwFi7sCR8fFxkjI3o4THyJ6IEJ2hyoU/ZIJ6YpC5IszhMVauj8OkkdGAyeoYDAE2GI7I3BYMTSpccwd+4B6PVGAICrqwozZ3bHsGHNIfB9S5UcE18iKjtRhCrz1K3d0mKhvnEYgmiwONXgVldKdLU1uwJqNxsHS0TWyMgoxEsvbcX+/deksZYt/bFiRTSCg71kjIyo/DDxJaJ7EoozoUlOgCY5FurkXVAWplqcJyqcoKvZ5Vay2xOG6g24qktUiVSvrkF+vqk8SRCACRPaYerUjtBo2EmFqg4mvkRkTjRClXFCKl9QpR+DIBotTtW7B5tOSguKhNa/C6BytXGwRFRe1Golli+PxvPPb8H8+RHo1Km23CERlTsmvkQEofAGNCm7oEmKgyZ5FxTFGRbniSpXaGt2vbWqGwmje30bR0pE5eXo0WS4uKjRrFnJRjDBwV7Yu/d5bkZBVRYTXyJHZNRDlX78VquxOKgyfodwlz1+9R6Nb/XV7QGdf0dA6WzjYImoPOn1RixceBgLFhxCcLAXdu4cCldXtXQ7k16qypj4EjkIRUEK1Mm7TCUMKQlQaLMtzjOq3aEL6G5a1Q2MhNGNX3cSVRWJidkYNy4Gx46lAAD++isTq1efxPjxbWSOjMg2mPgSVVVGHdRph0tqdbNO33Wq3qu51IFBV6MdoNTYMFAiqmiiKGL9+rOYNi0eeXlaAIBSKWDKlI4YPbq1zNER2Q4TX6IqRJF31bSBRHIc1Cm7odDlWpxn1HhCGxBu2kAiMAJG1wAbR0pEtpKdXYSpU+OwZctf0li9eh5YtiwKbdoEyhgZke0x8SWqzAzFUKcekHZLU+Wcu+tUnU8rqdWY3vcxQMG3P1FVt3//VYwfH4Pk5DxpbMiQpvjoozC4ufGbHXI8/MtHVMkocv+Ryhc01/dC0BdYnGd08oY2MMK0gURABESXGhbnEVHVlJqah2ef3Qit1rTJjKenEz75pAf69g2ROTIi+TDxJbJ3+kJoUvdBnXRrVTf3ksVpoqCA3ucxU6IbFAm9dytAwcbzRI7K398NU6Z0wOzZ+9GlS20sWdIbgYHucodFJCsmvkT2RhShvHlRajWmTt0PwVBkcarR2U9qNaYN6A7R2cfGwRKRvRBFEUajCKVSIY29+mpbBAa645lnQtmmjAhMfInsgy4Pmut7pRIGZd5li9NEQQmdXwdoAyOhC4yE3rs5ICgsziUix5GeXoBJk2LRvLkfpk7tKI0rlQoMGtRExsiI7AsTXyI5iCKU2Welk9LUaQcgGHUWpxpcA00rukE9oAvoBlHjYeNgiciexccnYsKEHUhLy0ds7N/o3r0u2rZltwYiS5j4EtmIoM2BOmX3rW2B46AsSLI4T1SoofPrZKrVDYyEwTMUEPgVJRGZKyrS46OPfsXKlb9JY56ezlKfXiIqjYkvUUURRagyT0F9e1X3xmEIosHiVINbXWkDCW3NroDazcbBElFlcubMDYwdG4OzZ9OlsbCwuli8uDf8/avJGBmRfWPiS1SOhOJMaJLjTSelJe+CsjDV4jxR4QRdzS63VnV7wFC9AVd1iei+jEYRq1adwMyZ+1BcbPog7eSkxPvvP45Ro1rxBDai+2DiS/QwRCNUGb+ZyheSYqHKOA5BNFqcqncPNtXpBkVC698FULnaOFgiqswyMwsxduw2JCSUnPwaGuqLFSuiERrqK2NkRJUHE18iKwmFN6BJ2XWrA0M8FMUZFueJKldoa3a9VcIQCaN7fRtHSkRViaurGtevl+zANnp0a7zzThc4O/NPOVFZ8d1CdD9GPVTpx6RWY+qME3edqvdoLJ2UpvPvBCidbBgoEVVlzs4qLF8ejREjfsLHH0cgLKye3CERVTpMfIksUBSkQJ18a1U3JQEKbbbFeUa1O3QB3U2ruoGRMLrVtm2gRFRlnTyZCldXNRo29JbGmjSpgUOHXoBKxf7dRA+CiS8RABh1UKcdklqNqbJO33Wq3qt5yapujXaAUmPDQImoqjMYjFi69Bjmzj2Axo19EBMzBE5OJX+umfQSPTgmvuSwFHlXSzaQuL4HCl2uxXlGjSe0AeHQBkVCFxgBo2uAjSMlIkeRlJSL8eNjcODANQDAH3/cwOrVJzFmzGMyR0ZUNTDxJcdhKIY69QA0ybHQJMVBlXPurlN1Pq1unZTWE3rfxwAF3ypEVLG2bDmPKVPikJNTDMDU4XDChHZ48cVH5Q2MqArhX3Oq0hS5/wBX96H6Xz9DfX0vBH2BxXlGJx9oAyOgDYqENiACoksNG0dKRI4qN7cYb7+dgHXrzkhjQUHuWLq0Nzp14nkDROWJiS9VLfoCaFJ/hTrp1qpu7iUAwL+rcEVBAb1vG2m3NL33o4BCafNwicixHT2ajHHjYnD5co401q9fI8yfHwFPT2cZIyOqmpj4UuUmilDevAhN0k5Tq7HU/RAMRRanGp39TCu6gT2gDegO0dnHxsESEZVIScnF00//AK3WtAObm5sGc+eGY+DAUAjcyZGoQjDxpcpHlwfN9b1SX11l3mWL00RBCb1fB6gb9kGWV1foPZsBAs+GJiL7EBDgjnHjHsNnnx1B27aBWLYsCnXresgdFlGVxsSX7J8oQpl9tmQDibQDEIw6i1MNrkFS+YIuoBvg5AFfX3cY0nMB0cZxExHdQRRN/wnduZo7dWpHBAVVx9ChzdimjMgGmPiSXRK0OVCn7Jb66ioLkizOExVq6Pw63yphiITBM9R0KvTtx7FVwERE95CdXYSpU+Pw6KM1MX58G2lcrVbi+edbyBgZkWNh4kv2QTRClXka6tt9dW8chiAaLE41uNWVWo1paz4OqN1sHCwRUdnt338V48fHIDk5D9u2XUTXrnXQvLmf3GEROSQmviQboTgTmuT4W5tIxEFRlGZxnqhwgq5ml1u7pfWAoXoDs1VdIiJ7pNUaMG/eASxZchS3qhxQrZoaaWn58gZG5MCY+JLtiEaoMn4zlS8kxUKVcRyCaLQ4Ve8ebKrTDYqE1r8LoHK1cbBERA/u4sVMjBmzDadOlXyg79KlNpYs6Y3AQHcZIyNybEx8qUIJhTdMK7rJcdAkx0NRnGFxnqhyhbZmV1OrsaAIGN3r2zhSIqKHJ4oi1qw5jfff343CQj0AQK1WYNq0zhg3rg0UCn5bRSSnh0p8i4uL4eTkVF6xUFVg1EOVfqykA0PGibtO1XuGmmp1AyOh8+8EKPm7RESVV1ZWIV57bSe2b78kjTVo4IUVK6LRooW/jJER0W1WJ75GoxHLly/H999/j4yMDOzYsQO1a9fGZ599hqCgIAwcOLAi4iQ7pihIgTp5lynZTUmAQpttcZ5R7Q5dQHfTqm5gBIxu3IqTiKoOjUaFCxcypesjR7bEBx90haurWsaoiOhOVie+y5Ytw+bNmzF16lS899570nhISAi++eYbJr6ORDTCfe8LcL686a5T9F7Nb52UFgmdX3tAwT8ARFQ1VaumxvLlURgxYgs+/jgSvXoFyx0SEf2L1Ynvli1bMHPmTHTs2BHTp0+Xxhs1aoS///67XIMj+6a6caRU0mvUeEIbEA5tUCR0gREwugbIFB0RUcU6c+YGXF3VqFfPUxp79NGaOHp0FJyceAoNkT2y+p2ZmpqKOnXqlBoXRRF6vb5cgqLKwenaDulyUfBzKGz4AvS+jwEK/odPRFWX0Shi1aoTmDlzH5o188PWrc+a7brGpJfIflm9P2KDBg1w7NixUuPbt29HaGhouQRFlYMmqSTxzWv9IfR+7Zn0ElGVlpqah8GDN+Ldd3ejuNiA48dT8PXXJ+UOi4jKyOosZdy4cXjrrbeQmpoKURSxc+dO/PPPP9i8eTNWrlxZETGSHVLkJ0GV9QcAQOfTGqILdyEioqotJuYiJk7ciczMImls9OjWGDasuYxREZE1rE58IyMjsWLFCixduhQuLi5YvHgxmjRpghUrVqBz584VESPZIU3STumyNqinjJEQEVWs/Hwdpk/fgzVrTklj/v7VsHhxL4SF1ZMvMCKy2gN9L92mTRusXr26vGOhSsQs8a3VS8ZIiIgqzsmTqRgzZhsuXcqSxqKigrFgQU/4+LjIGBkRPQira3wjIiKQlZVVavzmzZuIiIgol6DIzhmKoUnZDQAwOteA3qeVvPEQEVWApKRc9OnzvZT0urqqsGBBD3z9dV8mvUSVlNWJb1JSEoxGY6lxrVaL1NTUcgmK7Js6dT8EfT4AQBvUAxCs/jUiIrJ7QUHueOGFlgCAli39sWvXcAwb1hyCwG2HiSqrMpc67Nq1S7q8b98+uLu7S9eNRiMOHjyIoKCg8o2O7NKd3RxY30tEVYkoimaJ7TvvdEFQkDtefPFRaDRKGSMjovJQ5sR3/PjxAABBEPDWW2+ZP4hKhaCgoFLjVDVprpnqe0VBCW1guMzREBE9vNzcYrz9dgJataqJF198VBp3dlZhzJjH5AuMiMpVmRPfc+fOAQDCw8Px448/wtvbu8KCIvulvHkRqtxLAACdXweIGk95AyIiekhHjyZj7NgYXLmSgy1bzqNLl9oICfGROywiqgBWF2fGx8cz6XVgmmt3ljmwmwMRVV56vRHz5x9A377rcOVKDgBApVIiMTFH5siIqKI8UDuzgoICHD16FMnJydDpdGa3jRgxolwCI/vENmZEVBUkJmZj3LgYHDuWIo21bRuIZcuiULeuh4yREVFFsjrxPXPmDF5++WUUFhaisLAQHh4eyMrKgouLC7y9vZn4VmW6PKhT9wMADNVqw+DRWOaAiIisI4oi1q07g2nT4pGfb1q4USoFTJ7cAa+/3h4qFbvUEFVlVr/D58yZg7CwMBw9ehROTk5Yv349EhIS0LRpU7z55psVESPZCU3KHghGLYBb3RzY0oeIKpGcnCK8/PIvmDBhh5T01q3rga1bn8WUKR2Z9BI5AKvf5WfPnsULL7wAhUIBpVIJrVaLgIAATJ06FQsWLKiIGMlOmLUxq8U2ZkRUuQiCgN9+uy5dHzy4KRIShqNNm0AZoyIiW7I68VWpVFAoTHfz8fFBcnIyAMDNzQ3Xr1+/112pMhNFqb5XVDhBW7ObzAEREVmnenUnLF3aGz4+Lli16gksXtwLbm4aucMiIhuyusa3SZMmOH36NOrVq4e2bdti8eLFyMrKwpYtW9CwYcOKiJHsgDLrDygLTB9ydDUfB1SuMkdERHRvFy9mwtVVjcDAkg2XOnSohWPHXkK1amoZIyMiuVi94jtx4kTUqFFDuly9enV88MEHyMrKwocffljuAZJ9uLObQzG7ORCRHRNFEd98cwoREf/FK69sh9Eomt3OpJfIcVm94tu8eXPpso+PD7766qtyDYjsk9M1blNMRPYvPb0AkybFYvt200Y7v/56FWvWnMLIkS1ljoyI7EG5ncL6559/YvTo0eX1cGRHhKIMqNKPAAD0HiEwuj8ic0RERKXFxyeie/e1UtILAM8/3wKDBjWRMSoisidWJb779u3DvHnzsGDBAly9ehUAcOnSJYwbNw7PPPMMjEaj1QF8++23CA8PR/PmzTFw4ECcOnXqnvNv3ryJGTNmoEuXLmjWrBl69eqFPXv2WP28VHaalHgIounYcrc2IrI3RUV6vPfebgwevBFpafkAAB8fF6xd+xQ+/jgSrq4sbSAikzKXOvzwww9477334OHhgZs3b+KHH37AW2+9hVmzZiEqKgo///wzgoODrXrybdu2Yc6cOZgxYwZatmyJb775BqNGjcL27dvh41N6n3StVosXXngBPj4+WLRoEfz9/ZGcnIzq1atb9bxkHQ3LHIjITp0+nYrBg3/EmTPp0lhYWF0sXtwb/v7VZIyMiOxRmRPfNWvWYMqUKXjppZewY8cOvPbaa/juu++wdetW1KxZ84GefPXq1Rg0aBAGDBgAAJgxYwZ2796NDRs24OWXXy41f8OGDcjJycH3338Ptdr0Cb5WrVoP9NxURkYDNMlxpotqd+j8OsocEBGRydWrN9Gx42oUFxsAAE5OSrz//uMYNaoVFApusENEpZU58b169Sp69+4NAOjZsydUKhWmTp36wEmvVqstVResUCjQqVMnnDhxwuJ94uPj8eijj+LDDz/Erl274O3tjSeeeAL/+c9/oFQqrXp+QeDGY2WhyjgGRXEmAEAXEAZBVbl6Xt4+xjzWjoHH27HUqVMdI0a0xJdf/oYmTXyxYkU0QkN95Q6LKgjf346loo5zmRPfoqIiuLi43ApGgFqthp+f3wM/cVZWFgwGQ6mSBh8fH/z9998W73P16lUcOnQITz75JL744gtcuXIFM2bMgF6vxyuvvGLV83t7u99/EgHnS+qnnRo/BSffyvm6+fhUzrjpwfB4O46FC3uhbl0PTJ7cCc7OVjcqokqI7296GFb9L/HDDz/A1dW0cYHBYMDGjRvh5eVlNmfEiBHlF92/iKIIHx8fzJw5E0qlEs2aNUNqaiq++uorqxPfzMxcPMC5eA7H86+t0i9JhsfjENNzZY3HWoJg+k8yIyMXonj/+VS58XhXXfn5OkyfvgePPRaAIUOaAig53mPGtEJeXiHy8mQOkioU39+ORaGomEXKMie+gYGBWL9+vXTd19cXW7ZsMZsjCEKZE18vLy8olUpkZGSYjWdkZMDX1/JXVTVq1IBKpTIra6hfvz5u3LgBrVYLjabsX8OLIvjGuQ9FQQpUmScBADrvR2F0qQlU0teMx9ux8HhXLSdPpmLs2G24eDELP/54Fu3bB+GRRzyl23m8HQuPt2OoqGNc5sQ3Pj6+XJ9Yo9GgadOmOHjwICIjIwEARqMRBw8exLBhwyzep3Xr1vj5559hNBqhUJg6sSUmJqJGjRpWJb1UNpqkWOmyNqiHjJEQkSMyGIxYuvQY5s49AL3e9BWdKIo4dy7dLPElIiqrctvA4kG88MILWL9+PTZt2oRLly7hgw8+QGFhIfr37w8AeOONN/Dpp59K84cMGYLs7Gx89NFH+Oeff7B7926sXLkSQ4cOletHqNI0SXe0MeM2xURkQ0lJuRgw4EfMmvWrlPS2bOmPXbuGIyqqgczREVFlJeuZANHR0cjMzMTixYtx48YNhIaGYtWqVVKpQ0pKirSyCwABAQH46quvMGfOHPTt2xf+/v4YMWIE/vOf/8j1I1RdBi3UyQkAAKOTD/Q+j8kcEBE5ii1bzmPKlDjk5BQDMNV2TpjQDlOndoRGY10HHyKiOwmi6JiVMhkZPLntXtQpu+EZ2xcAUFT/WeR2+VLmiB6MIAC+vu5IT+fJEI6Ax7tyy8vTYtq0eKxbd0YaCwpyx9KlvdGpU+1S83m8HQuPt2NRKCqmgwd7v5BFmqSd0mVuU0xEtlBcbMDu3Zel6/36NcL8+RHw9HSWMSoiqkpkrfEl+3V7m2JRUEAbGC5zNETkCHx8XPD5573h7q7BkiW9sXJlNJNeIipXD7Tie+XKFWzYsAFXr17FO++8Ax8fH+zZsweBgYFo2LBhecdINqbI/RuqmxcAAPoa7SE6ecscERFVRYmJ2XB1VcPPr5o01r17Xfz220vw8GDCS0Tlz+oV3yNHjuDJJ5/EqVOnsHPnThQUFAAAzp8/j88//7zcAyTb01wrKXMoDuopYyREVBWJoojvv/8TYWFr8frrO/HvU02Y9BJRRbE68f3000/x+uuvY/Xq1VCr1dJ4hw4d8Pvvv5dnbCQTpzvre9nGjIjKUXZ2EV5++RdMmLAD+fk6xMX9g++++1PusIjIQVid+P7111/ShhN38vb2RlZWVrkERTLS5UN9fR8AwOAaBINnU5kDIqKqYv/+q+jefQ22bPlLGhs8uCn69g2RMSoiciRW1/i6u7vjxo0bqF3bvLXM2bNn4e/vX26BkTw01/dCMJp6Z2qDepr6xxARPQSt1oB58w5gyZKjUhsqT08nfPJJDya9RGRTVq/49unTB5988glu3LgBQRBgNBpx/PhxzJs3D/369auAEMmWNCxzIKJydOFCJqKjv8Pnn5ckvV261Mbu3SOY9BKRzVm94jtx4kR8+OGH6N69OwwGA/r06QODwYAnnngCY8eOrYgYyVZEsaSNmUIDbc2uMgdERJVZYmI2IiP/i8JCPQBArVZg2rTOGDeuDRQKfptERLZndeKr0Wgwa9YsjBs3DhcuXEB+fj6aNGmCevXqVUB4ZEvK7LNQFlwDAOj8uwBqN5kjIqLKrF49T0RHN8CGDefQoIEXVqyIRosWLIkjIvlYnfgeO3YMbdq0QWBgIAIDAysiJpKJJmmHdFlbi23MiOjhzZsXgdq1q+P119vD1VV9/zsQEVUgq2t8R44cifDwcCxYsAAXL16siJhIJnfW9xZzm2IiskJRkR7vvbcbP/30l9l49epOePvtLkx6icguWJ347t27Fy+++CKOHDmCJ554Ak899RRWrVqF69evV0R8ZCNCcRbUaYcAAHr3YBirB8scERFVFmfO3ECvXv/DypW/YfLkWCQl5codEhGRRVYnvt7e3hg2bBi+//57xMbGonfv3ti8eTPCw8MxYsSIioiRbECTkgBBNABgNwciKhujUcQXX/yGXr3+h7Nn0wGYVn5//50LIURkn6yu8b1T7dq18fLLL6Nx48ZYtGgRjh49Wl5xkY3d7uYA3OrfS0R0D6mpeZgwYQcSEi5LY6GhvlixIhqhob4yRkZEdHcPnPgeP34cW7duxY4dO1BcXIyIiAhMmjSpPGMjWxGN0CTHmi6qqkHn31nmgIjInsXEXMSkSbHIyCiUxkaPbo133ukCZ+eHWk8hIqpQVv8P9emnn+KXX35BWloaOnfujHfeeQcRERFwcXGpiPjIBlQZv0FRZPqaUhsQBiidZI6IiOxRfr4O06fvwZo1p6Qxf/9qWLy4F8LC6skXGBFRGVmd+B49ehSjRo1CVFQUvL29KyImsjGWORBRWeTlFeOXXy5I16OigrFgQU/4+HDhg4gqB6sT3++//74i4iAZmW9TzMSXiCzz93fDggU9MHbsNsyaFYahQ5tBELgDGxFVHmVKfHft2oWuXbtCrVZj165d95wbERFRLoGRbQiFqVBnnAAA6L2aw+jKTUmIyCQpKReurip4eZWs6EZFNcDRoy+hRg1XGSMjInowZUp8x48fj/3798PHxwfjx4+/6zxBEHD27NlyC44qniYpTrpczDZmRHTLli3nMWVKHLp2rYNVq54wW9ll0ktElVWZEt9z585ZvEyVn1MS63uJqERubjHefjsB69adAQBs3XoBGzacwzPPhMocGRHRw7N6A4vNmzdDq9WWGtdqtdi8eXN5xES2YtRBnRxvuqjxgt63rcwBEZGcjh5NRljYf6WkFwD69WuEyMhHZIyKiKj8WJ34Tps2Dbm5pbejzM/Px7Rp08olKLINddohKHQ3AQDawAhAoZQ5IiKSg15vxMcfH0Tfvutw5UoOAMDNTYMlS3pj5cpoeHo6yxwhEVH5sLqrgyiKFs/iTU1Nhbu7e7kERbZh1saM9b1EDikxMRvjxsXg2LEUaaxt20AsWxaFunU9ZIyMiKj8lTnx7devHwRBgCAIeP7556FSldzVYDDg2rVrePzxxyskSKoYt9uYiRCgDYyUORoisrW//85CZOS3yMszla8plQImT+6A119vD5XK6i8EiYjsXpkT38hIU2J09uxZdOnSBdWqVZNuU6vVCAoKQs+ePDmqslDkXYYqx3Sior5GW4jOPjJHRES29sgjnnj88dqIibmEunU9sHx5FNq0YUtDIqq6ypz4vvLKKwCAoKAgREdHw8mJ29pWZpprd2xaEcQyByJHJAgCFizoidq1D+GttzrDzU0jd0hERBXK6u+ynn76aSa9VYCGbcyIHIpWa8DMmfsQG/u32biPjwtmzQpj0ktEDqFMK77t2rXD9u3b4e3tjbZt295zi8ojR46UW3BUQfSF0FzfCwAwuNSE3ruFzAERUUW6eDETY8Zsw6lTafjuuz+xe/dw+PlVu/8diYiqmDIlvtOmTYObm5t0mXuzV26a63shGIoA3Frt5fEkqpJEUcSaNafx/vu7UVioBwDk5BThyJFkPPFEQ5mjIyKyvTIlvk8//bR0uX///hUWDNnG7W4OANuYEVVV6ekFmDQpFtu3X5LGGjTwwooV0WjRwl/GyIiI5GN1H98///wTKpUKjRo1AgDExcVh48aNaNCgAV555RVoNKwTs2uiWNLGTKGGLqC7vPEQUbmLj0/EhAk7kJaWL42NHNkSH3zQFa6uahkjIyKSl9Unt73//vtITEwEAFy9ehUTJ06Ei4sLtm/fjo8//ri846Nypsw5D2XeZQCAzq8zRDU3HSGqKoqK9Hjvvd0YPHijlPT6+Lhg7dqnMH9+BJNeInJ4Vie+iYmJCA0NBQDExMSgXbt2+PTTTzFnzhzs3LnzPvcmuZmXObCbA1FVkp5egO+++1O6Hh5eD7t3j0CvXsEyRkVEZD+sTnxFUYTRaAQAHDx4EF27dgUABAQEICsrq3yjo3Jntk0x+/cSVSm1alXH/PkRcHJSYvbsMHz33dPw92f3BiKi26yu8W3WrBmWL1+Ojh074ujRo/jggw8AANeuXYOvr295x0flSNDmQJ12EABgcH8EhuoNZI6IiB5GamoeXF3VcHcv6a3ev39jtG8fhKAgljEREf2b1Su+b7/9Ns6cOYOZM2dizJgxqFu3LgBgx44daNWqVbkHSOVHnZIAQTS1NCpmGzOiSi0m5iK6d1+Lt99OKHUbk14iIsusXvFt3Lgxtm7dWmr8jTfegEJhdR5NNsRtiokqv/x8HaZP34M1a04BANatO4OePevjySdDZI6MiMj+WZ343vbHH3/g0iVTf8gGDRqgadOm5RYUVQDRCKfbbcxUrtDV7CJzQERkrZMnUzFmzDZculRyPkVUVDA6daotY1RERJWH1YlvRkYGXn/9dRw9ehTVq1cHANy8eRPt27fHwoUL4e3tXe5B0sNTZZ6EoigNAKCt2Q1QOsscERGVlcFgxNKlxzB37gHo9aaTi11dVZg1KwxDhzbjbppERGVkdW3CzJkzUVBQgF9++QVHjhzBkSNH8PPPPyMvLw+zZs2qiBipHJh3c2AbM6LKIikpFwMG/IhZs36Vkt6WLf2xa9dwDBvWnEkvEZEVrF7x3bdvH1avXo3g4JK+kA0aNMD06dPx4osvlmtwVH40SUx8iSqbS5ey0Lv3/5CTUwzAdD7qhAntMHVqR2g0SpmjIyKqfKxe8TUajVCrS+/+o1KppP6+ZF+EwhtQpf8GANB7NoXRjfWARJXBI494onXrmgBMnRo2bRqId97pwqSXiOgBWZ34dujQAR999BFSU1OlsdTUVMyZMwcdO3Ys1+CofGiSYyFABMDVXqLKRKEQsHhxLwwf3hwJCcN5EhsR0UOyutTh/fffx9ixYxEREYGaNU0rEdevX0fDhg3x8ccfl3uA9PDMtylmGzMie6TXG7Fw4WF06BCExx+vI437+7vh0097yBgZEVHVYXXiGxAQgE2bNuHgwYNSO7Pg4GB06tSp3IOjcmDUQ5Mcb7qo8YSuRjuZAyKif0tMzMa4cTE4diwFAQFu2L17OLy8XOQOi4ioyrEq8d22bRt27doFnU6Hjh07Yvjw4RUVF5UT9Y0jUGizAQDawHBA8cCtm4monImiiPXrz2LatHjk5WkBAGlp+fj116vckIKIqAKUOQv63//+hw8//BB169aFs7MzYmNjceXKFbz55psVGR89JLYxI7JP2dlFmDo1Dlu2/CWN1a3rgeXLo9CmTaCMkRERVV1lPrnt22+/xSuvvIIdO3Zgy5YtmDt3Lr777ruKjI3Kwe36XhECtIGsEySyB/v3X0X37mvMkt7Bg5siIWE4k14iogpU5sT36tWr6Nevn3T9ySefhF6vR1paWkXEReVAkXcVquw/AQB639YQXWrIHBGRY9NqDZg1ax/69/8Bycl5AAAPDyesWvUEFi/uBTc3jcwREhFVbWUuddBqtXB1dZWuKxQKqNVqFBcXV0hg9PDMujkEsZsDkdySk3OxatXvEE3dBdG5cy0sWRKFoCB3eQMjInIQVp3p9Nlnn8HFpeRMY51Oh+XLl8PdveQ/7WnTppVfdPRQ2MaMyL7Uq+eJjz7qjjfe2IVp0zpj3Lg2UCi45TARka2UOfFt27Yt/vnnH7OxVq1a4erVq9J17hlvRwxF0FzfAwAwOvtB791S5oCIHE9GRiFcXFRwdS3Z7fK555qhY8daqF/fS8bIiIgcU5kT37Vr11ZkHFTO1Nd/haAvAAAUB/UEBKs36SOihxAfn4gJE3agT58GmDcvQhoXBIFJLxGRTJgNVVGapDvamNViGzMiWykq0uO993Zj8OCNSEvLx+rVJxEb+7fcYRERER5g5zaqBEQRTrf694qCCrqAMJkDInIMZ87cwNixMTh7Nl0aCw+vhxYt/GWMioiIbmPiWwUpb16EMi8RAKDz6whR4yFvQERVnNEoYtWqE5g5cx+Kiw0AACcnJaZP74pRox7l+Q9ERHaCiW8VZF7mwG4ORBUpNTUPEybsQELCZWksNNQXK1ZEIzTUV8bIiIjo35j4VkGaa3f272V9L1FFuXgxE08+uQ4ZGYXS2OjRrfHOO13g7Mz/XomI7M0Dndx27NgxTJkyBc8++yxSU1MBAJs3b8axY8fKNTiynqDLhTptPwDA4FYXBo9GMkdEVHU98ognQkK8AQD+/tWwbl1/zJzZnUkvEZGdsjrx3bFjB0aNGgVnZ2ecOXMGWq0WAJCXl4eVK1eWe4BkHXXKbghGHYBbq72sLSSqMEqlAkuXRmHgwFDs3j0CYWH15A6JiIjuwerEd/ny5ZgxYwZmzZoFlapkVaN169Y4c+ZMuQZH1tNcu6O+l2UOROXGYDBi8eIjOHIk2Wy8Vq3qWLo0Cj4+Lne5JxER2Qurv4/7559/0KZNm1Lj7u7uuHnzZrkERQ9IFKVtikWlM7Q1u8ocEFHVkJSUi/HjY3DgwDXUqeOBhIRhcHd3kjssIiKyktUrvr6+vrhy5Uqp8ePHj6N27drlEhQ9GFXmKSgLrwOAKelVcQWK6GFt2XIe3buvwYED1wAAV6/mYPfuy/e5FxER2SOrE99Bgwbho48+wsmTJyEIAlJTU/HTTz9h3rx5GDJkSEXESGV0e7UXALRBbGNG9DByc4vx6qvb8Z///IKcnGIAQFCQOzZtGognnwyROToiInoQVpc6vPzyyzAajRg5ciQKCwsxbNgwaDQavPjiixg+fHhFxEhlxG2KicrH0aPJGDcuBpcv50hj/fo1wvz5EfD0dJYxMiIiehhWJ76CIGDs2LEYNWoUrly5goKCAgQHB6NatWoVER+VkVCUAdWNowAAvUdjGN3qyhwRUeWj1xuxcOFhLFhwCAaDCABwc9Ng7txwDBwYyh3YiIgquQduNqnRaNCgQYPyjIUegiY5DgJMf6jZzYHowSQmZmPx4iNS0tu2bSCWLYtC3brc9puIqCqwOvEdPnz4PVc91qxZ81AB0YMxa2PGbYqJHkiDBt54//3H8f77ezB5cge8/np7qFQPtM8PERHZIasT39DQULPrer0eZ8+exYULF9CvX7/yiousYTRAkxxnuqiuDp1fB5kDIqocsrOL4OKigpNTyX+FL73UCl261EFoqK+MkRERUUWwOvF9++23LY5//vnnKCgoeOiAyHqq9KNQaLMBALrAcEChljcgokpg//6rGD8+Bv36NcIHH3STxgVBYNJLRFRFldt3eH379sWGDRvK6+HICne2MStmGzOie9JqDZg1ax/69/8Bycl5WLbsOPbuLd2bnIiIqp4HPrnt306cOAGNRlNeD0dWcDLbpjhSxkiI7NvFi5kYM2YbTp1Kk8a6dKmNBg28ZIyKiIhsxerE95VXXjG7Looibty4gT/++APjxo0rt8CobBQFyVBlnQYA6HxaQXTxlzkiIvsjiiLWrDmN99/fjcJCPQBArVZg2rTOGDeuDRQKtikjInIEVie+7u7uZtcFQcAjjzyCCRMmoEuXLuUWGJWN5tqdu7WxjRnRv6WnF2DSpFhs335JGmvQwAsrVkSjRQt+UCQiciRWJb4GgwH9+/dHSEgIPDzY19IemG1TzDZmRGYuXsxEv34/IC0tXxobObIlPvigK1xdeRIoEZGjserkNqVSiRdffBE3b94s1yC+/fZbhIeHo3nz5hg4cCBOnTpVpvv98ssvaNSokeOWWBiKoUlJAAAYnX2h92ktc0BE9qVuXQ8EBbkBAHx8XLB27VOYPz+CSS8RkYOyuqtDw4YNce3atXILYNu2bZgzZw7Gjx+PTZs2oXHjxhg1ahQyMjLueb9r165h3rx5aNOmTbnFUtmoU/dD0JtWsrSBPQCBjfaJ7qRWK7F8eTT69GmA3btHoFevYLlDIiIiGVmdKb3++uuYN28eEhISkJaWhry8PLN/1lq9ejUGDRqEAQMGoEGDBpgxYwacnZ3v2RrNYDBgypQpePXVV1G7dm2rn7OqYJkDUQmjUcQXX/yGEydSzMbr1/fC6tV94e9fTabIiIjIXpS5xnfJkiV48cUX8fLLLwMAxo4da7Z1sSiKEAQBZ8+eLfOTa7Va/Pnnnxg9erQ0plAo0KlTJ5w4ceKu91u6dCl8fHwwcOBAHD9+vMzPdydBMP2rzJySTG3MREEJXVB4pf95KsLt14SvTdV2/XoeJkzYgYSEy1i79g/s3DkELi4sZ6jq+P52LDzejqWijnOZE9+lS5diyJAhWLNmTbk9eVZWFgwGA3x8fMzGfXx88Pfff1u8z7Fjx/Djjz9i8+bND/Xc3t7u959kz7IuADdNZ6kLQZ3hE+i4K99l4eNTyY833dWWLefw0ktbkZ5u2jny3Ll0HDlyHQMGNJE5MrIVvr8dC483PYwyJ76iKAIA2rVrV2HB3E9eXh7eeOMNzJw5E97e3g/1WJmZuTAayykwGTif2Qi3W5fz/SJQmJ4razz2ShBM/0lmZOTi1q8wVRH5+TpMn74H33xTcjKsv381rFnzNB57zA/pfE9UeXx/OxYeb8eiUFTMIqVV7cyEcl539vLyglKpLHUiW0ZGBnx9fUvNv3r1KpKSkjB27FhpzHgre23SpAm2b9+OOnXqlOm5RRGV+o1zZ//e4qBelfpnsYXKfrzJ3MmTqRgzZhsuXcqSxqKigrFwYU80amRKenm8HQff346Fx9sxVNQxtirx7dWr132T3yNHjpT58TQaDZo2bYqDBw8iMtK01a7RaMTBgwcxbNiwUvPr16+PrVu3mo199tlnyM/PxzvvvIOaNWuW+bkrNV0e1Km/AgAMrrVg8AyVOSAi2zAYjFi69Bjmzj0Avd70odfVVYWZM7tj2LDm3IGNiIjuyarE99VXXy21c9vDeuGFF/Dmm2+iWbNmaNGiBb755hsUFhaif//+AIA33ngD/v7+mDx5MpycnBASEmJ2/+rVqwNAqfGqTHN9LwSjFsCtbg6s9CcHceFCplnS27KlP1asiEZwsJfMkRERUWVgVeLbp0+fUieiPazo6GhkZmZi8eLFuHHjBkJDQ7Fq1Sqp1CElJQUKBfvT3klzbYd0mdsUkyNp3NgXb73VCR999CsmTGiHqVM7QqNRyh0WERFVEoIolq2KIjQ0FL/++mu5J75yyciopCe3iSK8NzSBsiAJosIJ6c8mAmr2J70bQQB8fd1Z81lJ5eVp4eysgkpV8uHXYDDi9Ok0PPpo6dImHm/HwuPtWHi8HYtCUTEdPMq8lFrG/JgqmDL7TygLkgAAupqPM+mlKuvo0WSEha3FggWHzMaVSoXFpJeIiOh+ypz4njt3rsqs9lZmd5Y5FLPMgaogvd6Ijz8+iL591+Hy5RwsWHAYR44kyx0WERFVAVbV+JL8nLhNMVVhiYnZGDcuBseOlWw7/NhjAdxumIiIygUT30pEKM6E6sZhAIC+ekMY3R+ROSKi8iGKItavP4tp0+KRl2fqWKJUCpg8uQNef729WY0vERHRg2LiW4lokuMhiKYz8rjaS1VFdnYR3nhjFzZvPi+N1a3rgeXLo9CmTaCMkRERUVXDxLcS0SSxjRlVLRcvZmLgwA1ISirZXnjw4KaYPTsMbm4aGSMjIqKqiIlvZWE0QJMUZ7qocoPOr5PMARE9vFq1qqN6dSckJeXC09MJn3zSA337Os5mNEREZFssnKskVBnHoSjOAADoAsMAJVfDqPJzdlZhxYpoREY+gt27RzDpJSKiCsXEt5Iw362N9b1U+YiiiDVrTuH8+Qyz8dBQX/zvf08jMLD8G5UTERHdiaUOlYQmKVa6rA3qIWMkRNZLTy/ApEmx2L79Epo2rYHt24fAyYn//RARkW1xxbcSUBRchzrzdwCAzrsljK4B8gZEZIX4+ER0774W27dfAgD8+ecN7Nz5t8xRERGRI+KSSyVgvtrLbg5UORQV6TFr1j588cUJaczHxwWffdYTvXoFyxgZERE5Kia+lYBZG7NaTHzJ/p05cwNjx27D2bMl9bxhYXWxeHFv7sJGRESyYeJr7wxaqFMSAABGJ2/ofdrIHBDR3RmNIlatOoGZM/ehuNgAAHByUuL99x/HqFGtoFAIMkdIRESOjImvnVOnHYRCZ2rurw2MBBRKmSMiurszZ9Lx/vt7YDSKAEwdG1asiEZoqK/MkREREfHkNrunSdopXeY2xWTvmjWrgddfbwcAGD26NXbseI5JLxER2Q2u+Nq52/W9oqCANjBC5miIzBUU6ODsrDIrYZg8uQO6d6+LDh1qyRgZERFRaVzxtWOK3H+gyvkLAKD3bQfRyVvmiIhKnDyZioiI/2LZsmNm42q1kkkvERHZJSa+duzOModiljmQnTAYjFi8+Aiior7DpUtZmDNnP06dSpU7LCIiovtiqYMdczLbpphtzEh+SUm5GD8+BgcOXJPGmjSpgWrVNDJGRUREVDZMfO2VvgDq6/sAAAbXQBi8mskcEDm6LVvOY8qUOOTkFAMABAGYMKEdpk7tCI2G3UaIiMj+MfG1U5rreyAYTQmGNqinKcsgkkFubjHefjsB69adkcaCgtyxdGlvdOpUW8bIiIiIrMPE105prt3RxiyI9b0kj4sXMzFkyCZcvpwjjfXr1wgffxwBDw9nGSMjIiKyHhNfeySK0oltokIDbUA3mQMiRxUQ4A6VynQOrJubBnPnhmPgwFAI/AaCiIgqIXZ1sEPK7LNQ5l8FAOj8OwNqN5kjIkdVrZoaK1ZEo3PnWkhIGI5Bg5ow6SUiokqLia8dMtutjd0cyEZEUcS6dWfwzz/ZZuMtW/pj48aBqFvXQ57AiIiIygkTXzt0e7c2gNsUk21kZxfh5Zd/wauvbse4cdug0xnMbucqLxERVQVMfO2MoM2GOu0QAEDvHgxD9QYyR0RV3f79V9G9+xps2WLaJfD48evYufNvmaMiIiIqfzy5zc5okuMhiKbVNm0tljlQxdFqDZg37wCWLDkKUTSNeXo64dNPe6BPn4byBkdERFQBmPjaGfP6XpY5UMW4eDETY8Zsw6lTadJYly61sWRJbwQGussYGRERUcVh4mtPRCM0SbGmi6pqpo4OROVIFEWsWXMa77+/G4WFegCAWq3AtGmdMW5cGygUrOUlIqKqi4mvHVFlnICi6AYAQBvQHVA6yRsQVTmnT6dh6tQ46XqDBl5YsSIaLVr4yxgVERGRbfDkNjuiuXZHNwe2MaMK0KKFP8aMeQwAMHJkS8TFDWPSS0REDoMrvnbErI0ZE18qB8XFemg0SrN2ZO+80xnh4fXQvXtdGSMjIiKyPa742gmhMA3qjBMAAL1XMxirBckcEVV2Z87cQM+e32L16pNm405OKia9RETkkJj42onbJ7UB7OZAD8doFPHFF7+hV6//4ezZDHzwwR6cP58hd1hERESyY6mDnbizjVkxd2ujB5SamocJE3YgIeGyNPbII14yRkRERGQ/mPjaA6MOmuRdposaT+h928gcEFVGMTEXMWlSLDIyCqWx0aNb4513usDZmW91IiIi/jW0A+q0w1DobgIAtIGRgIKHhcouP1+H6dP3YM2aU9KYv381LF7cC2Fh9eQLjIiIyM4ww7IDZt0cuE0xWeHSpSwMG7YZly5lSWNRUcFYsKAnfHxcZIyMiIjI/jDxtQO363tFCNAG9pA5GqpMatRwhU5nAAC4uqowa1YYhg5tZta+jIiIiEzY1UFmirwrUGWfBQDofdtAdPaROSKqTKpXd8LSpVF47LGa2LVrOIYNa86kl4iI6C6Y+MrMbLc2dnOg+/jpp7+QlJRrNta+fRC2bRuC4GB2byAiIroXJr4yu7ONGXdro7vJzS3Gq69ux0sv/YxXXomBwWA0u52rvERERPfHxFdO+kJoru8FABhcakLv3VLmgMgeHT2ajPDw/2LdujMAgP37r2Hnzr9ljoqIiKjy4cltMtKk7oNgMPVc1Qb1BLhqR3fQ641YuPAwFiw4BINBBAC4uWkwd244evcOljk6IiKiyoeJr4zM6ntZ5kB3SEzMxrhxMTh2LEUaa9s2EMuWRaFuXQ8ZIyMiIqq8mPjKRRRL2pgp1NAFdJc3HrILoihi/fqzmDYtHnl5WgCAUilg8uQOeP319lCpWJ1ERET0oJj4ykSZ8xeUeZcBADq/ThA11WWOiOzB77+n4tVXt0vX69b1wPLlUWjTJlDGqIiIiKoGLh/JxKybA9uY0S2tWtXEiBEtAACDBzdFQsJwJr1ERETlhCu+MjHbppj1vQ5LpzNApVKYtSObMaMbIiMf4QlsRERE5YwrvjIQtDlQpx4AABjc6sFQvaHMEZEcLl7MRFTUd1KbstuqVVMz6SUiIqoATHxloE7ZDUHUA2AbM0ckiiK++eYUIiL+i1On0jBtWjz+/jtL7rCIiIiqPJY6yODONmbFrO91KOnpBZg0KRbbt1+SxgIC3FBUpJcxKiIiIsfAxNfWRCM0ybGmi0oX6Go+LnNAZCvx8YmYMGEH0tLypbHnn2+BGTO6wdVVLWNkREREjoGJr42pMk9CWZgKANAGdAOUzjJHRBWtqEiPjz76FStX/iaN+fi44LPPeqJXL9byEhER2QoTXxsza2MWxDKHqu7vv7PwwgtbcfZsujQWHl4Pixb1gr9/NRkjIyIicjxMfG2M2xQ7Fk9PZ2RlFQIAnJyUmD69K0aNetSsfRkRERHZBrs62JBQlA5V+nEAgN6zCYxutWWOiCqat7cLFi/ujaZNa2DnzqF46aVWTHqJiIhkwsTXhjRJsRAgAuBqb1W1Y8clpKbmm411714XcXFDERrqK1NUREREBDDxtSmz3drYxqxKyc/XYcqUOAwfvgWvv74Doiia3a5U8q1GREQkN/41thWjHprkeNNFtQd0NdrJHBCVl5MnUxEZ+V+sWXMKALBrVyJ27vxb5qiIiIjo33hym42obxyBQpsNANAGRgAK9m2t7AwGI5YuPYa5cw9ArzcCAFxdVZg1Kww9e9aXOToiIiL6Nya+NmLWxqwW63sru6SkXIwfH4MDB65JYy1b+mPFimgEB3vJGBkRERHdDRNfG7ndxkyEAG1gD5mjoYexefN5TJ0ah5ycYgCAIAATJrTD1KkdodEoZY6OiIiI7oaJrw0o8q9Blf0nAEDv2xqiSw2ZI6IHdexYMl5++RfpelCQO5Yu7Y1OndiajoiIyN7x5DYbMN+tjWUOlVmbNoEYODAUANCvXyMkJAxn0ktERFRJcMXXBjTXuE1xZWU0ilAozDecmDs3HD161MdTT4VwMwoiIqJKhCu+Fc1QBM313QAAo7Mf9D6PyhoOlV1iYjaeeOJ7bNly3mzc3d0J/fo1YtJLRERUyXDFt4Kpr/8KQV8AANAG9QAEftawd6IoYv36s5g2LR55eVpcuBCHNm0CERTkLndoRERE9BCY+FawO+t7i1nfa/eys4swdWoctmz5Sxrz9HRGZmYhE18iIqJKjolvRRJFON3aplgUVNAFhsscEN3L/v1XMX58DJKT86SxwYObYvbsMLi5aWSMjIiIiMoDE98KpMy9CGXuPwAAnV9HiBoPmSMiS7RaA+bNO4AlS45CFE1jHh5O+PTTHujbN0Te4IiIiKjcMPGtQLc3rQDYxsxeJSZm46WXfsapU2nSWOfOtbBkSRRLG4iIiKoYJr4VSJMUK13W1mIbM3vk4qJCUlIuAECtVmDatM4YN65NqRZmREREVPmxxUAFEXS5UKf+CgAwVKsDg0cjmSMiS/z93bBwYU80bOiNmJgheOWVtkx6iYiIqiiu+FYQdcoeCEYdAEBbqyfAnq92Yc+ey2je3A/e3i7SWO/ewYiIqAe1WiljZERERFTR7GLF99tvv0V4eDiaN2+OgQMH4tSpU3edu379ejz33HNo27Yt2rZti5EjR95zvlxY32tfior0eO+93Rg4cAOmTImDePsstluY9BIREVV9sie+27Ztw5w5czB+/Hhs2rQJjRs3xqhRo5CRkWFx/uHDh9GnTx+sWbMG33//PQICAvDiiy8iNTXVxpHfgyhK/XtFpTO0NbvKHJBjO306Fb16/Q8rV/4GAPj55wuIj0+UNygiIiKyOdkT39WrV2PQoEEYMGAAGjRogBkzZsDZ2RkbNmywOP/TTz/F0KFDERoaiuDgYMyaNQtGoxEHDx60ceR3p8w6DWVhCgBAW/NxQOUqc0SOyWgUsXLlb2jb9kucOZMOAHByUmL27DCEh9eTNzgiIiKyOVlrfLVaLf7880+MHj1aGlMoFOjUqRNOnDhRpscoLCyEXq+Hh4d1PXIFoeLKbm9vWgEAulq9WN4rg+vX8zBhwg4kJFyWxpo08cWKFdEIDfWVMTKqSLffa3zPOQYeb8fC4+1YKuo4y5r4ZmVlwWAwwMfHx2zcx8cHf//9d5ke45NPPoGfnx86depk1XN7e1dgj9bUXdJFt+b94ebBfrC29NNP5zFq1E9ITy+QxiZO7IDZsyPg7MzzOR2Bjw/fc46Ex9ux8HjTw6jUWcAXX3yBbdu2Yc2aNXBycrLqvpmZuTAayz8moSgD3imHIADQezRCts4XSM8t/yciiw4fTsJTT62Trvv5VcPatU/jscf8kJdXiLy8e9yZKj1BMP1RzMjIxb/OX6QqiMfbsfB4OxaFomIWKWVNfL28vKBUKkudyJaRkQFf33t/Hf3VV1/hiy++wOrVq9G4cWOrn1sUUSFvHE3SLgiiKaPWBvXim9PG2rYNRHR0A2zbdhG9ewfjs896olEjP6Sn8z9KR1JR72+yTzzejoXH2zFU1DGW9eQ2jUaDpk2bmp2YdvtEtVatWt31fl9++SWWLVuGVatWoXnz5rYItcw0d9T3amuxjVlF+3dbMkEQsGBBDyxe3AvffNMXPj4ud7knERERORrZuzq88MILWL9+PTZt2oRLly7hgw8+QGFhIfr37w8AeOONN/Dpp59K87/44gssWrQIs2fPRlBQEG7cuIEbN24gPz9frh+hhNEATVKc6aLaHTq/jjIHVLUlJeWif/8fsHOneT24t7cLBg9uCoFnQBAREdEdZK/xjY6ORmZmJhYvXowbN24gNDQUq1atkkodUlJSoFCU5Offf/89dDodJkyYYPY4r7zyCl599VWbxv5vqvRjUGizAAC6gHBAoZY1nqpsy5bzmDIlDjk5xTh/PgMJCSPg719N7rCIiIjIjsme+ALAsGHDMGzYMIu3rV271ux6fHy8LUJ6IHeWORTX6iVjJFVXbm4x3n47AevWnZHGnJxUSE3NY+JLRERE92QXiW9Vobm2U7qsDeohYyRV09GjyRg7NgZXruRIY/36NcL8+RHw9HSWMTIiIiKqDJj4lhNFQTLUWacAADqfVhBd/GWOqOrQ641YsOAQFi48DIPBdDKbm5sGc+eGY+DAUNbyEhERUZkw8S0nmqRY6bI2iN0cysuVKzkYM2Ybjh1Lkcbatg3EsmVRqFvXut36iIiIyLEx8S0nmmt3tDFj4ltuFAoBf/2VCQBQKgVMntwBr7/eHiqV7A1JiIiIqJJh9lAeDMVQp+wGABidfaH3fUzeeKqQWrWq4+OPI1C3rge2bn0WU6Z0ZNJLRERED4QrvuVAnXoACr1pL1xtYCQgMDF7UIcOXUPTpjXg7l6yBfXTTzdGVFQDODvz15WIiIgeHDO0cqBJurObA9uYPQit1oCZM/fhqafWY9q0hFK3M+klIiKih8XEtxzc7t8rCkpoA8NljqbyuXgxE9HR3+Hzz49CFIH1688gISFR7rCIiIioiuEy2kNS3LwE1c2LAABdjfYQnbxkjqjyEEURa9acxvvv70ZhoR4AoFYrMG1aZ3TrVlfm6IiIiKiqYeL7kJzMyhzYzaGs0tMLMGlSLLZvvySNNWjghRUrotGiBXsgExERUflj4vuQzNqYcZviMomPT8SECTuQlpYvjY0c2RIffNAVrq5qGSMjIiKiqoyJ78PQ5UOd+isAwOBaCwbPJjIHZP8OHbqGwYM3Std9fFzw2Wc90atXsIxRERERkSPgyW0PQXN9DwSjFgCgrdUT4Na599W+fRDCw+sBAMLD62H37hFMeomIiMgmuOL7ENjGzHqCIGDRol7Ytu0iRo5sAYEfFoiIiMhGuOL7oERRqu8VFU7Q1uwqc0D2JzU1H889twl7914xG/f3r4YXXmjJpJeIiIhsiiu+D0iZfQbKgiQAgK5mF0BdTeaI7Mv27ZcwceJOZGQU4s8/byAhYTi8vV3kDouIiIgcGBPfB2TWzYFtzCT5+TpMn74Ha9acksaMRhFXr95k4ktERESyYuL7gJySShLfYia+AICTJ1Mxduw2XLyYJY1FRQVjwYKe8PFh0ktERETyYuL7AITiLKhuHAYA6Ks3gLG6Y3clMBiMWLr0GObOPQC93ggAcHVVYdasMAwd2oy1vERERGQXmPg+AE3yLgiiKcFz9G4Oycm5GD8+Bvv3X5PGWrb0x4oV0QgO5vbNREREZD+Y+D4AszZmDr5bW1GRHidOpAIwtTGeMKEdpk7tCI1GKXNkRERERObYzsxaRgM0SbGmiyo36Pw6yRyQvOrX98Ls2WEICnLHpk0D8c47XZj0EhERkV3iiq+VVBm/QVGcAQDQBYYBSo3MEdnWb7+loHFjX7i6qqWxIUOaom/fELi5OdZrQURERJULV3ytpElyzDZmer0RH398EH36fI8PPthrdpsgCEx6iYiIyO4x8bXS7TIHwHES38TEbPTtuw4ff3wQBoOIr78+iV9/vXL/OxIRERHZEZY6WEFRcB3qjBMAAJ1XCxhdA2SOqGKJooj1689i2rR45OVpAQBKpYDJkzugQ4daMkdHREREZB0mvlYwW+2tVbVXe7Ozi/DGG7uwefN5aaxuXQ8sXx6FNm0CZYyMiIiI6MEw8bWCWRuzKty/98CBqxg/fjuSknKlscGDm2L27DDW8hIREVGlxcS3rAxaqFPiAQBGJ2/ofdvIHFDFOHDgKp5++geIoum6p6cTPvmkB/r2DZE3MCIiIqKHxJPbykh94xAUOtMKqDYwElBUzV617dsHoWNHU/1uly61sXv3CCa9REREVCVwxbeMNNcco42ZUqnA0qVR+OmnvzB6dGsoFILcIRERERGVC674ltHt+l5RUEAbGCFzNOUjPb0AL7zwEw4fTjIbDwpyx9ixjzHpJSIioiqFK75loMhNhCrH1N1A79sWorOPzBE9vPj4REyYsANpafk4fToNCQnD4e7uJHdYRERERBWGK75lYNbNoVbl7uZQVKTHu+8mYPDgjUhLywcA5OfrcOlSlsyREREREVUsrviWwZ3bFBdX4vreM2duYOzYbTh7NkMaCw+vh0WLesHfv5qMkRERERFVPCa+96MvgOb6PgCAwSUABq/mMgdkPaNRxKpVJzBz5j4UFxsAAE5OSkyf3hWjRj0KQWAtLxEREVV9THzvQ3N9LwRDEYBb3RwqWZKYmpqHCRN2ICHhsjQWGuqLFSuiERrqK2NkRERERLbFxPc+zNqYVcL63qysIhw4cE26Pnp0a7zzThc4O/PQExERkWPhyW33IorQJMWaLirU0AZ0lzeeB9C4sS+mT+8KP79qWLeuP2bO7M6kl4iIiBwSM6B7UOacgzL/CgBA598FULvJHNH9/fHHDTRs6AUnp5JDO2rUo3jmmVB4ejrLGBkRERGRvLjiew+aa3e0MbPzbg4GgxGLFx9Bz57fYvbs/Wa3CYLApJeIiIgcHhPfe7izjZm2lv0mvklJuRgw4EfMmvUr9Hojli8/jkOHku5/RyIiIiIHwlKHuxC0OVCnHQQA6N3rw1C9ocwRWbZly3lMmRKHnJxiAKamExMmtEPr1jVljoyIiIjIvjDxvQt1cjwE0dTz1h7LHHJzi/H22wlYt+6MNBYU5I6lS3ujU6faMkZGREREZJ+Y+N6FU5L9tjE7ejQZ48bF4PLlHGmsX79GmD8/grW8RERERHfBxNcS0VjSxkzlCp1/Z5kDKrF//1U888yPMBhEAICbmwZz54Zj4MBQ7sBGREREdA88uc0CVcYJKIpuAAC0NbsDSvtZRW3XLhAtW/oDANq2DURCwnAMGtSESS8RERHRfXDF1wJN0h1tzOyszEGtVmLZsihs2fIXXn21LVQqfnYhIiIiKgsmvhaYbVMc1EO2OLKzi/DWW/EYO/YxaZUXAOrX98LEie1li4uIqCoTRRFGowFGo1HuUOgOggAUFRVBp9NCFOWOhsqDUqmCQmHbBTwmvv8iFKZBnfEbAEDv1QzGarVkiWP//qsYPz4Gycl5OHUqFXFxw+DqqpYlFiIiR6HX65CTkwmdrkjuUMiCzEwFP5BUKQK8vGrAycnFZs/IxPdfNMmx0mU52phptQbMm3cAS5YclT7RpqcX4Pz5DLRqxd68REQVRRRFZGRch0KhgIeHL5RKFc+fsDNKpSCd3E2VmyiKyMvLQVbWDfj51bLZyi8T33/RXCtJfIuDbFvfe/FiJsaM2YZTp9KksS5damPJkt4IDHS3aSxERI5Gr9dBFI3w8KgBjcZ+TmqmEiqVAno9V3yrCjc3D2RmFsJg0EOh0NjkOZn43smogyZ5l+mixhP6Gm1t8rSiKGLNmtN4//3dKCzUAwDUagWmTeuMcePaQKHgigMRka0IAk8aJrIFOb5RYeJ7B3XaYSh0pk0htIERgKLiX5709AJMmhSL7dsvSWMNGnhhxYpotGjhf497EhEREZE1+LH2DmZtzGxU35ucnIu4uH+k6yNHtkRc3DAmvURERETljInvHTS3tikWIdisjVmLFv54661O8PFxwdq1T2H+/Ah2byAiIrKRK1cS0bdvLxQU5MsdSpWi0+nwzDNP4ty5M3KHYoalDrco8q5AlX0WAKD3bQPR2bdCnufChUzUq+cBtVopjY0f3wZDhjRDjRquFfKcRERUtX300QeIifkZAKBUKuHn54+wsAiMGjUGTk5OZnP379+H775bi/Pnz8FoNOCRR4LRv/9AREc/Wepxd+/ehR9/XIcLF87DaDQiMDAI3btHYMCAQahe3cMmP1tFW7FiKQYMGARX12pyh1JhNmxYj+++W4vMzAwEBzfExIlT0aRJs7vOf+WVl/H777+VGu/YsTM+/niRdD0x8R8sX74Yv//+GwwGA+rVq49Zs+ajZs2aUKvVGDJkGJYv/xyLFv1/e/cel/P5P3D81d1ZESrlfFxFMpnD+NYsFoZsbCynZGYWOZsYcz5tTkkhYzbNmKGcCsOX8Y02Z5nDVtLBYUKUSnfd9+8PP7fdyuFOB+t+Px+PHg9dn+vzud6f++r2eN/XfX2ua3mx3FdhSOL7/7R3ayv6aQ4qlZpVq04yc+Yh/P1bEBDQRnPM0FAhSa8QQoiX0qpVG774Ygq5ublcvHiB2bOnAgYMHTpCU2fTpg0EBS2ib98BjB07AWNjYw4dOsiCBXOJj4/D33+Upm5oaAg//riWXr36MGTIMGxsbElOTiQiYjO7dkXSq1fvErkvpVKJsXHxfBN6/fp1oqMPMXr05y91neKM8WXt27eH4ODFjBs3kUaNGrNx43rGjBnO+vWbqVSpcoHnzJkzH6VSqfn97t27DBzYBw+PdzRlKSnJDB36CV27dmPQoCFYWFhy+XIcpqaPV2fw9HyX4OBA4uPjqFevfvHdpA4k8f1/2vN7i3YZsxs3MhgxYjf//e8VABYvjsHTsy7NmlUt0naEEELoLxMTY6ytH35baWdnz+7dLTl2LEZz/MaN6wQHB9KzZ2+GDBmmKe/dux/GxkYEBi7Aw+MdnJ0b88cfsYSFrWHEiLFaCW7VqtVo0eJN0tPTnxrH33/fICRkCb/9dhSlMofatesyZkwAzs6NmT17GhkZ6cydu1BTf8mShfz550WCg1cCD0cb69Wrj6GhEXv2RFKvXgOsrW1QqVTMmfOV5rzc3Fzee68j/v6jeffdrqhUKtat+55t28K5desWNWvWwtd3kFay9qT9+3+hQQMHbG2raMru3k1j0aKvOX36JOnp96hevQb9+w/E07OTpk5BMS5dGkp8/F+EhARx5sxJzMzMadmyFcOHj6VixYoAHD0azfffr+by5TgUCkMaN3Zh5MhxVK9efJtlbdiwDi+v9+nSpRsAn38+kSNHDrNjxzb69/ct8JwnR/P37duDqamZ1mu5cmUIrVu3YejQkZqyJ++jQoUKuLi8zr59e6hXz6+I7ujlSOILkJuFybWDAOSZ25FbuUmRXToq6i/GjPmFW7eyNGWDB7vSqJFtkbUhhBCi+JgkhGNxejYGyowSa1NtbMn9ppPJqf1+oc6Pj/+L2Ngz2Nk9HmA5cGAfubm59O7dP1/99977gNDQZezduxtn58bs2bMLc/Ny9OjRs8Drly9f8NrymZmZ+Pt/iq1tFebNW4S1tTUXL15ArdZt7d2oqJ107/4By5evBiA5OZkvvwwgMzNTs8ZyTMwRsrOzadvWA4CwsDXs2RPFuHETqVGjJqdPn2TmzClUrFgJV9c3CmznzJmTODk11CrLycnB0bEh/foNoFw5C44cOcysWVOpXr2G1vSAJ2NMT09nxAg/vLzeZ8SIMTx4kM3y5UuZMmUCQUErAMjOzsLbuy/1679GVlYmq1at4IsvxrFmzY9P3cBh7dpvCQtb88zXKyzsZ+zt829ypVQquXTpAv37D9SUKRQKmjdvyblzZ555zX/asWMr7dt3wNz84Q5rKpWK6Oj/0bevD2PG+HPp0kWqVq1G//4Deeutt7XObdjQmdOnT75wW8VNEl/A+MZhDPIeJqY51TtAEazheP++kqlTD7J27eM/rCpVLFi6tCMeHnVe+vpCCCFKRrlzSzC6e6lU2tUl8Y2OPoynpzt5eXnk5OSgUCgYPXq85nhSUiKWlpbY2OR/hsXY2Jhq1aqTlPTwm8nk5ESqVauOkZFuacIvv+wiLS2NVavWakYNa9SoqdM1AGrWrKk1klitWg3Mzc05eHA/np6dNW25ub1FuXIW5OTkEBa2hsDAZTRu/HDwqnr1Gpw5c4qtW7c8NfG9fv06Tk6NtMpsbavQp8/jDwcffujNb78dZf/+vVqJ75MxfvfdKhwcHLVG0ydOnEKPHl1ITLxCrVq1efvt9lptTZw4la5d3yEhIZ569RoUGOP7739Au3bPfuC+oD6Fh6PXeXl5VK6sPaWhcuXKXLmS8MxrPvLHH7HEx8cxYcKXmrI7d26TlZXJDz98x+DBfvj5Defo0SNMmvQ5QUErtF5vGxtbbty4/kJtlQRJfAHT5N2afxfFMmanT9/gs88iiYu7oynr1Kk+ixd3wNq65PajFkII8fIyG4/C4tSsEh/xzXQe+fyK/+Dq+gbjxk0kKyuLjRt/xNDQMF+i9cLtF3JX4D//vISDg+NLP/jm6Kg9CmtkZISHhye7d0fh6dmZrKwsDh8+yLRpcwBITk4iOzub0aOHaZ2nVCp57TXHp7bz4EE2JibaO4bl5eURFraG/ft/4ebNm+TmKsnJycHUVHs3vydj/OuvPzlx4hienu752klJSaZWrdokJSWyatUK/vjjHHfvpmlGwm/cuP7UxLdCBatSfZBwx46t1K/fQCvpV///H4ibW1s++qgvAK+95khs7GkiIjZrJb6mpqZkZ2eXbNDPIImvWq2Z36s2MEJZ1eOlLnfoUCIffbRFs6ViuXJGzJz5Nv36ucie70II8S+UU/v9Qk85KEnm5uaa0dWJE6fg69ubHTsi6Nr1fQBq1qxFRkYGqak3sbHRnm6nVCq5ejWZZs2aa+qeOXOK3NxcnUZ9n1xB4kkGBgaapOmR3NzcfPXMzPIPEnXo0Al//yHcuXOb33+PwdTUlDfffPigeFbWw29tv/46UGu+LvDMh84qVqyYb77yjz+G8fPP6xkxYiz16jXA3NycoKCF5OYqteo9GWNWVhb/+Y87fn4jeNKjudcBAaOxt69KQMAkbGxsUalU+Ph8hFKZ/zV45GWmOlhZVcTQ0JDbt29rld++fRtra+tnXvPRPe3bt4dBgz4r8Lp16tTVKq9duy5nz57SKrt3765mjvOrQO8TX8N7f2KYkQCA0q4NapMKL3W9li2r4eBQmT/+SOX11+1YsaIz9etXKoJIhRBCiBejUCjo338gwcGL8fTshKmpGW3btmf58qWsX/8Dw4eP1qofEbGZrKws3nnn4cPdnp6d2LRpA1u2/Fzg6g3p6ekFzvNt0OA1duyI4N69uwWOUlasWInLl+O0yv766yKGhs9PR1xcXsfOzo59+/Zw9Gg0Hh7vaJLyunXrYmJiwo0b1586raEgr73mSEJCvFbZ2bOncXNrS8eOD6dUqFQqEhMTqVu3bkGX0HBwcOTgwf3Y21ct8MPC3btpJCZeISBgMq+/7grA6dOnnhvjy0x1MDY2xsHBiePHf9PMvVWpVBw//js9evR6btv//e9elEolHTu+m++6DRs6a6bGPJKUlKg1rxzg8uU4HByePupe0vR+AwsTrWkOL7+ag6mpEStWdGb06Fbs3OktSa8QQohS4eHxDgqFIZs3/wyAvb09Q4eO4Oef1xMaGsKVKwmkpCSzYcMPLF8ehLd3P5ydH36d7ezcmD59fAgJCWTZsiXExp7h+vVrHDv2G5MnB2jWDH7SO+90pHJlayZOHMeZM6dISUnmwIF9xMY+fN7ljTdacOHCeaKidpCUlMjq1aHEx8cVeK2CdOjQiYiILfz+ewyeno+TsXLlLPD27sfSpYuIitpBSkoyFy9eYNOmDU+NFaBly9bExp4lLy9PU1azZk1+/z2Gs2dPk5Bwmfnz53Dnzq3nxvbBB724d+8e06ZN4vz5c6SkJBMTc4Q5c6aTl5dH+fIVsLKyYtu2LSQnJ3H8+O8EBy967nUrVLCiRo2az/x51qi8t3dftm+PICpqBwkJl1mwYC5ZWVl06fJ43eaZM6ewYkVwvnN37NiKu3tbrKwq5jvWu3d/9u37hW3bwklOTmLz5p+Ijj5E9+7aD0SePn2KFi3efO59lhS9H/HVXr9Xt8Q3Pf0BU6ce5NNPm+Hk9PjTlpOTDRMnFs8GGEIIIcSLMDIyokePXvz441q6d/8Qc3NzevXqQ7Vq1Vm//gc2bdpAXp6KunXrMXbsBM1yV48MHToCR8eGhIf/TETEFtRqFdWq1cDDoz3vvtu1wDaNjY1ZvDiE4ODFfP75SM2mBmPGPHzIrlWr1vj6fsLy5UvJyXlAly7d6NSpC3Fxf73QPXXs+C7ffbcae/uqNGnyutaxwYP9qFixEmFha7h6NQVLy/I4ODjh4zPwKVeDN99sg6GhIceO/UarVq0BGDBgEFevpjBmzHDMzMzo1q077u5vc//+s+d429jYsnz5apYvX8ro0f4olTnY21elVavWKBQKDAwMmDZtDkuWLMDH5yNq1qzNqFHjGD58yAvde2G1b9+BtLQ7rFq1gtu3b9GggQMLFy6lcuXHUx1u3Lieb1WJxMQEzpw5xeLF+RNigLZtPRg3biI//PAdgYELqFWrNrNmfcXrrzfV1ImNPUNGRgYeHoWba14cDNRPTrbRE7dupaPOvof1T3UwUOeSZ1mH291PwwvOw/3996sMHRrFlSt3adTIht27+2BqqvefI145BgZgY1Oe1NT0Qj+sIf49pL/1S1H3t1KZw61b17C2roqxscnzTxAlzshIoXmGpqhs3ryR//3vVxYtKjjBE4U3ZcpEGjR4DR+fjws8/qz3nEIB1tYFL5v3MvR6qoPxtQMYqB9OKH+4jNnzk97cXBXz5x+hW7efuHLlLgCJiff444/UYo1VCCGEEEXvvfd68PrrrmRm3i/tUMoUpVJJvXr1+eijPqUdiha9HqI0SfnH/N4X2KY4ISGNoUOjOHbsmqasRYtqLFv2LrVrl409y4UQQgh9YmRkxIABg0o7jDLH2NgYX99PSjuMfPQ38f3nMmaG5uTY5V9373FVNRs3nmfixP1kZOQAYGhowNixbzJqVCuMjPR64FwIIYQQ4l9BbxNfxZ1YDLNuAJBj/xYYFbyxRFpaNuPH7yMi4qKmrHZtK5Yvf5fmzauVSKxCCCGEEOLl6W3ia5KyT/PvZ63mcOnSbbZte7xVpbe3M3PmeGBpKQ8+CCFEWaSnz3wLUeJK472mt9/Rm1zbr/n3s7YpbtmyGqNGtcTKypRVq7oSFNRRkl4hhCiDDA0NAcjJeVDKkQihH/LyHi4w8ORSasVJb0d8jVJPAJBbsSEqy1qa8itX7lKjRnkMDR93wtixbzJgQBOqVi36ZTWEEEK8GhQKQ8zNLcnIuAOAiYmpbDX/ilGpDMjLkxH5skCtVpGenoaJiRkKhWGJtau3ia8BD984j3ZrU6vVrF17lilTDjB27JuMGNFSU9fY2FCSXiGE0AMVKlQG0CS/4tWiUChQqYp2HV9RegwMFFSoULlEP2DqbeL7SE71DqSmZjJmzC/s2vVw28R586Lx8KiDi0uVUo5OCCFESTIwMMDKypry5StpvoYVrwYDA6hUyYI7d+7LBjVlhJGRcYl/q6LXia/K2Ird5+wZMTKMv/9+vHB1376NqV+/UilGJoQQojQpFAoUCnme41ViYABmZmYYGysl8RWF9ko83LZu3TratWuHi4sLPXv25MyZM8+sHxUVRadOnXBxccHLy4uDBw/q3Ga20ogRu/vh3XubJum1tjYnLOw95s9/h3LljAt1L0IIIYQQ4tVU6olvZGQkc+fOZdiwYYSHh+Pk5MSgQYO4detWgfVPnDjB2LFj+fDDD4mIiKB9+/YMGzaMS5cuFVj/ad5e7ktIpK3m93bt6nDggA8dO9Z/qfsRQgghhBCvplJPfNesWUOvXr344IMPaNCgAdOnT8fMzIzNmzcXWH/t2rW4u7vzySefUL9+fUaNGkWjRo344YcfdGr3jxsPk15TU0PmzPFg/fru2NlZvPT9CCGEEEKIV1OpzvHNycnh3LlzDBkyRFOmUCho06YNJ0+eLPCcU6dO4evrq1Xm5ubG3r17dWq7fHkTnJysCQzsgKOjtc6xi3+HR3PmFQpkTpgekP7WL9Lf+kX6W78U1zNvpZr43rlzh7y8PKyttRNPa2tr4uPjCzwnNTUVGxubfPVTU1N1ajslZaxuwYp/tcqVZTk6fSL9rV+kv/WL9Ld4GaU+1UEIIYQQQoiSUKqJb6VKlTA0NMz3INutW7fyjeo+YmNjk29091n1hRBCCCGEgFJOfE1MTHB2dubIkSOaMpVKxZEjR3B1dS3wnKZNm3L06FGtsujoaJo2bVqcoQohhBBCiH+5Up/qMHDgQDZu3Eh4eDhxcXFMmzaNrKwsevToAcD48eNZuHChpr6Pjw+HDh3i22+/JS4ujqVLlxIbG0u/fv1K6xaEEEIIIcS/QKnv3Na5c2du375NUFAQN2/epGHDhqxatUozdeHatWsoFI/z82bNmrFgwQICAwNZtGgRderUISQkBAcHh9K6BSGEEEII8S9goFbLoiBCCCGEEKLsK/WpDkIIIYQQQpQESXyFEEIIIYRekMRXCCGEEELoBUl8hRBCCCGEXiiTie+6deto164dLi4u9OzZkzNnzjyzflRUFJ06dcLFxQUvLy8OHjxYQpGKoqBLf2/cuJE+ffrQokULWrRoga+v73P/PsSrRdf39yM7d+7E0dGRoUOHFnOEoijp2t/37t1j+vTpuLm50bhxYzp27Cj/p/+L6Nrf3333HR07dqRJkya0bduWOXPm8ODBgxKKVryM33//nc8++ww3NzccHR3Zu3fvc8+JiYmhe/fuNG7cGE9PT7Zs2aJzu2Uu8Y2MjGTu3LkMGzaM8PBwnJycGDRoUL7d4R45ceIEY8eO5cMPPyQiIoL27dszbNgwLl26VMKRi8LQtb9jYmLo0qULa9euZcOGDVStWpWPP/6YGzdulHDkojB07e9HkpOT+eqrr2jevHkJRSqKgq79nZOTw8CBA0lJSWHJkiXs2rWLmTNnYmdnV8KRi8LQtb+3b9/OwoUL8ff3JzIyktmzZxMZGcmiRYtKOHJRGJmZmTg6OjJ16tQXqp+UlMSQIUNo1aoVW7duZcCAAUyePJlDhw7p1rC6jPnwww/V06dP1/yel5endnNzU4eGhhZYf+TIkepPP/1Uq6xnz57qL7/8sljjFEVD1/5+Um5urtrV1VUdHh5eTBGKolSY/s7NzVV/9NFH6o0bN6oDAgLUfn5+JRGqKAK69vePP/6obt++vTonJ6ekQhRFSNf+nj59utrHx0erbO7cuWpvb+9ijVMUPQcHB/Uvv/zyzDpff/21ukuXLlplo0aNUn/88cc6tVWmRnxzcnI4d+4cbdq00ZQpFAratGnDyZMnCzzn1KlTtG7dWqvMzc2NU6dOFWeooggUpr+flJWVRW5uLlZWVsUVpigihe3vkJAQrK2t6dmzZ0mEKYpIYfp7//79NG3alBkzZtCmTRu6du3KihUryMvLK6mwRSEVpr9dXV05d+6cZjpEUlISBw8epG3btiUSsyhZRZWvlfrObUXpzp075OXlYW1trVVubW1NfHx8geekpqZqdon7Z/3U1NRii1MUjcL095MWLFhAlSpVtP6zFa+mwvT3sWPH2LRpExERESUQoShKhenvpKQkjh49ipeXFytXriQxMZHp06eTm5uLv79/SYQtCqkw/e3l5cWdO3fo06cParWa3NxcvL29+eyzz0oiZFHCCsrXbGxsyMjIIDs7GzMzsxe6Tpka8RVCFytXriQyMpLg4GBMTU1LOxxRxDIyMhg/fjwzZ86kcuXKpR2OKAFqtRpra2tmzpxJ48aN6dy5M5999hkbNmwo7dBEMYiJiSE0NJSpU6eyZcsWgoODOXjwICEhIaUdmniFlakR30qVKmFoaJhvIvytW7fyfUp4xMbGJt/o7rPqi1dHYfr7kdWrV7Ny5UrWrFmDk5NTcYYpioiu/Z2UlERKSgp+fn6aMpVKBUCjRo3YtWsXtWrVKt6gRaEV5v1ta2uLkZERhoaGmrJ69epx8+ZNcnJyMDExKdaYReEVpr+XLFlCt27dNNOYHB0dyczMZMqUKfj5+aFQyNheWVJQvpaamoqlpeULj/ZCGRvxNTExwdnZmSNHjmjKVCoVR44cwdXVtcBzmjZtytGjR7XKoqOjadq0aXGGKopAYfob4JtvvmHZsmWsWrUKFxeXkghVFAFd+7tevXps376diIgIzU+7du1o1aoVERER2Nvbl2T4QkeFeX83a9aMxMREzQccgISEBGxtbSXpfcUVpr+zs7PzJbePPvSo1eriC1aUiqLK18pU4gswcOBANm7cSHh4OHFxcUybNo2srCx69OgBwPjx41m4cKGmvo+PD4cOHeLbb78lLi6OpUuXEhsbS79+/UrrFoQOdO3vlStXsmTJEubMmUP16tW5efMmN2/e5P79+6V1C0IHuvS3qakpDg4OWj8VKlTAwsICBwcHSYT+BXR9f/fu3Zu0tDRmz57N5cuXOXDgAKGhofTt27e0bkHoQNf+9vDwYP369ezcuZOkpCT+97//sWTJEjw8PLRG/cWr6f79+5w/f57z588DD5edPH/+PFevXgVg4cKFjB8/XlPf29ubpKQkvv76a+Li4li3bh1RUVH4+vrq1G6ZmuoA0LlzZ27fvk1QUBA3b96kYcOGrFq1SvNVybVr17Q+ITZr1owFCxYQGBjIokWLqFOnDiEhITg4OJTWLQgd6NrfGzZsQKlUMmLECK3r+Pv7M3z48BKNXehO1/4W/2669nfVqlVZvXo1c+fOpVu3btjZ2eHj48PgwYNL6xaEDnTtbz8/PwwMDAgMDOTGjRtUrlwZDw8PRo8eXVq3IHQQGxuLj4+P5ve5c+cC0L17d+bNm8fNmze5du2a5njNmjUJDQ1l7ty5rF27Fnt7e2bNmoW7u7tO7Rqo5fsAIYQQQgihB2RoRAghhBBC6AVJfIUQQgghhF6QxFcIIYQQQugFSXyFEEIIIYRekMRXCCGEEELoBUl8hRBCCCGEXpDEVwghhBBC6AVJfIUQQgghhF6QxFcIIYAtW7bQvHnz0g6j0BwdHdm7d+8z60yYMIGhQ4eWUERCCPHqKXNbFgsh9NeECRMIDw/PV75nzx5q165dChE9tmXLFiZOnAiAgYEBVapU4T//+Q/jxo3D2tr6pa9/+PBhrKysgId73rdv356IiAgaNmyoqTNp0iSKe7POpUuXEhwcDIBCoaBKlSq89dZbjB07looVK77wdSZMmMC9e/dYtmxZMUUqhNBHkvgKIcoUd3d3zZ7vj1SuXLmUotFmaWnJrl27UKlUXLhwgS+++IK///6b1atXv/S1bW1tn1unfPnyL93Oi3jttddYs2YNKpWKuLg4vvjiC9LT0wkMDCyR9oUQ4mlkqoMQokwxMTHB1tZW68fQ0JA1a9bg5eVF06ZNadu2LdOmTeP+/ftPvc6FCxfo378/rq6uNGvWjB49enD27FnN8WPHjtGnTx+aNGlC27ZtmTVrFpmZmc+MzcDAAFtbW+zs7Gjbti39+/cnOjqa7OxsVCoVwcHBvPXWWzRu3Jj33nuPX3/9VXNuTk4OM2bMwM3NDRcXFzw8PAgNDdUc/+dUh/bt2wPw/vvv4+joSP/+/QHtqQ4//fQTbm5uqFQqrRj9/Pw0I9MAe/fupXv37ri4uNC+fXuCg4PJzc195n0aGhpq7rNNmzZ06tSJ6OhozfG8vDy++OIL2rVrR5MmTejYsSPff/+95vjSpUsJDw9n3759ODo64ujoSExMDADXrl1j5MiRNG/enJYtW+Ln50dycvIz4xFCiEck8RVC6AUDAwMmTZrEjh07mDdvHkePHmX+/PlPrT9u3Djs7e3ZtGkTW7ZsYfDgwRgbGwOQmJjI4MGD6dChA9u2bWPx4sUcP36cmTNn6hSTmZkZKpWK3Nxc1q5dy5o1awgICGDbtm24ubkxdOhQEhISAAgLC2P//v0EBgaya9cu5s+fT/Xq1Qu87s8//wzAd999x+HDh1m6dGm+Op06dSItLU2TUAKkpaVx6NAhunXrBjxM7gMCAvDx8SEyMpIZM2awZcsWVqxY8cL3mJyczOHDhzWvHYBKpcLe3p4lS5awc+dOhg0bxuLFi4mMjATg448/5t1338Xd3Z3Dhw9z+PBhXF1dUSqVDBo0CAsLC9atW8f69espV64cn3zyCTk5OS8ckxBCf8lUByFEmXLgwAFcXV01v7u7uxMUFISvr6+mrEaNGowaNYqpU6cybdq0Aq9z9epVBg0aRP369QGoU6eO5lhoaCheXl6aa9apU4dJkybRv39/pk2bhqmp6XPjTEhIYP369TRu3BhLS0tWr17N4MGD6dKlCwCff/45MTExfP/990ydOpVr165Ru3Zt3njjDQwMDJ6a9MLjqR0VK1Z86hQIKysr3nrrLbZv307r1q0B2L17N5UqVaJVq1YABAcH8+mnn9K9e3cAatasyciRI5k/fz7+/v5Pbf/SpUu4urqSl5fHgwcPALRGkY2NjRkxYoTm95o1a3Lq1Cl27dpF586dsbCwwMzMjJycHK34t27dikqlYvbs2RgYGAAwd+5cWrRowW+//Yabm9tTYxJCCJDEVwhRxrRq1UormTU3NwcgOjqa0NBQ4uPjycjI0CRlWVlZmjr/NHDgQCZPnszWrVs1X9fXqlULeDgN4uLFi2zfvl1TX61Wo1KpSE5O1iTLT0pPT8fV1RWVSsWDBw944403mDVrFhkZGfz99980a9ZMq36zZs24cOECAN27d+fjjz+mU6dOuLu78/bbb790oufl5cWXX37JtGnTMDExYfv27XTp0gWFQqG5zxMnTmiN8D7vdQOoW7cuy5cv58GDB2zbto3z58/Tr18/rTrr1q1j8+bNXL16lQcPHqBUKnFycnpmvBcuXCAxMTHf6/TgwQMSExML8xIIIfSMJL5CiDLF3Nw83woOycnJDBkyhN69ezN69GisrKw4fvw4kyZNQqlUFpjADR8+nK5du3Lw4EF+/fVXgoKCWLx4MZ6enmRmZuLt7a2ZO/tPVatWfWpsFhYWhIeHo1AosLW1xczMDICMjIzn3pezszP79u3j119/JTo6mlGjRtGmTRuCgoKee+7TtGvXjsmTJ3PgwAFcXFw4duyY1shsZmYmw4cPp0OHDvnOfdaotrGxsaYPxo0bx6effkpwcDCjRo0CYOfOnXz11VcEBATg6uqKhYUFq1ev5vTp08+MNzMzE2dnZxYsWJDv2KvyAKMQ4tUmia8Qosw7d+4carWaCRMmaEYzo6Kinnte3bp1qVu3Lr6+vowZM4bNmzfj6elJo0aN+Ouvv3ReIk2hUBR4jqWlJVWqVOHEiRO0bNlSU37ixAmaNGmiVa9z58507tyZjh078sknn5CWlpZvmbBH82nz8vKeGY+pqSkdOnRg+/btXLlyhbp16+Ls7Kw53qhRIy5fvvzSS8H5+fkxYMAAevfujZ2dHSdOnMDV1ZW+fftq6jw5YmtsbJzvwTtnZ2eioqKwtrbG0tLypWISQugnebhNCFHm1a5dG6VSSVhYGElJSURERLBhw4an1s/OzmbGjBnExMSQkpLC8ePHOXv2rGYKw+DBgzl58iQzZszg/PnzJCQksHfvXmbMmFHoGAcNGsQ333xDZGQk8fHxLFiwgAsXLuDj4wPAmjVr2LFjB3FxcVy+fJldu3Zha2tLhQoV8l3L2toaMzMzDh06RGpqKunp6U9t18vLiwMHDrB582a8vLy0jg0bNoytW7cSHBzMn3/+SVxcHDt37mTx4sU63ZurqyuOjo6aVShq165NbGwshw4d4vLlywQGBmqtmAFQvXp1Ll68SHx8PLdv30apVOLl5UWlSpXw8/Pj2LFjJCUlERMTw6xZs7h+/bpOMQkh9JOM+AohyjwnJycmTpzIN998w6JFi2jevDljxowhICCgwPoKhYK0tDQCAgJITU2lUqVKdOjQQfNAlpOTE2FhYQQGBtKnTx/g4QNanTt3LnSMPj4+ZGRkMG/ePG7fvk39+vVZtmyZ5qE6CwsLVq1axZUrV1AoFLi4uLBy5UrNCPY/GRkZMXnyZEJCQggKCqJ58+aEhYUV2O6bb76JlZUVly9fzpf4uru7s2LFCkJCQvjmm28wMjKiXr169OzZU+f78/X1ZcKECQwePBhvb2/Onz/P6NGjMTAwoEuXLvTp00dr+bZevXrx22+/8cEHH5CZmcnatWtp1aoVP/zwAwsWLMDf35/79+9jZ2dH69atZQRYCPFCDNTFvY2PEEIIIYQQrwCZ6iCEEEIIIfSCJL5CCCGEEEIvSOIrhBBCCCH0giS+QgghhBBCL0jiK4QQQggh9IIkvkIIIYQQQi9I4iuEEEIIIfSCJL5CCCGEEEIvSOIrhBBCCCH0giS+QgghhBBCL0jiK4QQQggh9ML/AQGBtqZTXXKwAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Model  F1-Score  AUC-Score   Assessment\n",
              "0  DecisionTreeClassifier      0.73       0.76  Overfitting"
            ],
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              "\n",
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              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>F1-Score</th>\n",
              "      <th>AUC-Score</th>\n",
              "      <th>Assessment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DecisionTreeClassifier</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.76</td>\n",
              "      <td>Overfitting</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-d18310e1-8687-43d6-9d83-cf9d7b10e0f8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\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",
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              "    }\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",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\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-d18310e1-8687-43d6-9d83-cf9d7b10e0f8 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-d18310e1-8687-43d6-9d83-cf9d7b10e0f8');\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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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "# Specify the model and its parameters\n",
        "model_class = DecisionTreeClassifier\n",
        "model_params = {'random_state': 42}\n",
        "\n",
        "# Call the train_ml_model function\n",
        "dt_result_df = train_ml_model(X_train_preprocessed,y_train,X_eval_preprocessed,y_eval, model_class, **model_params)\n",
        "dt_result_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "15ozJF5gDULm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "outputId": "fcf3b4db-710a-493d-ca6d-e7a531b4ccb2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Model  F1-Score  AUC-Score   Assessment\n",
              "0  GradientBoostingClassifier      0.85       0.84  Overfitting"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>F1-Score</th>\n",
              "      <th>AUC-Score</th>\n",
              "      <th>Assessment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GradientBoostingClassifier</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.84</td>\n",
              "      <td>Overfitting</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-17145966-442b-4be2-8ce1-faf2917459c7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\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",
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              "      border-radius: 50%;\n",
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              "\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",
              "      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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-17145966-442b-4be2-8ce1-faf2917459c7 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-17145966-442b-4be2-8ce1-faf2917459c7');\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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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "# Specify the model and its parameters\n",
        "model_class = GradientBoostingClassifier\n",
        "model_params = {'random_state': 42}\n",
        "\n",
        "# Call the train_ml_model function\n",
        "sgd_result_df = train_ml_model(X_train_preprocessed,y_train,X_eval_preprocessed,y_eval, model_class, **model_params)\n",
        "sgd_result_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "6OgcMJtkDUHU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "outputId": "fc5051e5-9fb5-45a9-9027-ac41ea757347"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Model  F1-Score  AUC-Score   Assessment\n",
              "0  RandomForestClassifier      0.78        0.8  Overfitting"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1fbe6b5a-0fe9-4666-83cd-7e6bc81cd559\" 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",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>F1-Score</th>\n",
              "      <th>AUC-Score</th>\n",
              "      <th>Assessment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.8</td>\n",
              "      <td>Overfitting</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-1fbe6b5a-0fe9-4666-83cd-7e6bc81cd559')\"\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",
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              "      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",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\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-1fbe6b5a-0fe9-4666-83cd-7e6bc81cd559 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-1fbe6b5a-0fe9-4666-83cd-7e6bc81cd559');\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",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "# Specify the model and its parameters\n",
        "model_class = RandomForestClassifier\n",
        "model_params = {'random_state': 42}\n",
        "\n",
        "# Call the train_ml_model function\n",
        "rf_result_df = train_ml_model(X_train_preprocessed,y_train,X_eval_preprocessed,y_eval, model_class, **model_params)\n",
        "rf_result_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "xXLQQSwhDjBP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "outputId": "5554ff16-f03e-41e0-a6c2-a2a89cab5c76"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            Model  F1-Score  AUC-Score   Assessment\n",
              "0  HistGradientBoostingClassifier      0.85       0.85  Overfitting"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3042ceaf-5994-4e1e-9acc-0d0782fb4c3f\" 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>Model</th>\n",
              "      <th>F1-Score</th>\n",
              "      <th>AUC-Score</th>\n",
              "      <th>Assessment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>HistGradientBoostingClassifier</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.85</td>\n",
              "      <td>Overfitting</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-3042ceaf-5994-4e1e-9acc-0d0782fb4c3f')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\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-3042ceaf-5994-4e1e-9acc-0d0782fb4c3f 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-3042ceaf-5994-4e1e-9acc-0d0782fb4c3f');\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",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# Specify the model and its parameters\n",
        "model_class = HistGradientBoostingClassifier\n",
        "model_params = {'random_state': 42}\n",
        "\n",
        "# Call the train_ml_model function\n",
        "hgb_result_df = train_ml_model(X_train_preprocessed,y_train,X_eval_preprocessed,y_eval, model_class, **model_params)\n",
        "hgb_result_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "9lb019IlDi3F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "outputId": "552ed603-c2ea-4443-8f50-a963850ca146"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Model  F1-Score  AUC-Score   Assessment\n",
              "0  AdaBoostClassifier      0.85       0.84  Overfitting"
            ],
            "text/html": [
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>F1-Score</th>\n",
              "      <th>AUC-Score</th>\n",
              "      <th>Assessment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AdaBoostClassifier</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.84</td>\n",
              "      <td>Overfitting</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-22451141-dc2b-44eb-a3b0-0416269a3e4e')\"\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",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\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-22451141-dc2b-44eb-a3b0-0416269a3e4e 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-22451141-dc2b-44eb-a3b0-0416269a3e4e');\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",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "# Specify the model and its parameters\n",
        "model_class = AdaBoostClassifier\n",
        "model_params = {'random_state': 42}\n",
        "\n",
        "# Call the train_ml_model function\n",
        "adb_result_df = train_ml_model(X_train_preprocessed,y_train,X_eval_preprocessed,y_eval, model_class, **model_params)\n",
        "adb_result_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "7PIzF-hPDT7R"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xEHcwdhkhXln"
      },
      "source": [
        "### Models Comparison"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "fo_cHliuhXlo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "4ab464f0-d32d-4f05-d403-608a7ad7bb7e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  model  f1_score  AUC_score   Assessment\n",
              "3   Logistic Regression      0.85       0.85  Overfitting\n",
              "2              AdaBoost      0.84       0.84  Overfitting\n",
              "4  HistGradientBoosting      0.85       0.84  Overfitting\n",
              "5      GradientBoosting      0.85       0.84  Overfitting\n",
              "1         Random Forest      0.78       0.80  Overfitting\n",
              "0         Decision Tree      0.73       0.76  Overfitting"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-12deb02f-5777-4b10-b2e7-d40bd63257b8\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>f1_score</th>\n",
              "      <th>AUC_score</th>\n",
              "      <th>Assessment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.85</td>\n",
              "      <td>Overfitting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.84</td>\n",
              "      <td>Overfitting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>HistGradientBoosting</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.84</td>\n",
              "      <td>Overfitting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>GradientBoosting</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.84</td>\n",
              "      <td>Overfitting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.80</td>\n",
              "      <td>Overfitting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.76</td>\n",
              "      <td>Overfitting</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-12deb02f-5777-4b10-b2e7-d40bd63257b8')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\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-12deb02f-5777-4b10-b2e7-d40bd63257b8 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-12deb02f-5777-4b10-b2e7-d40bd63257b8');\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",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3f5fa150-a477-4a72-ae91-40440bab36fb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3f5fa150-a477-4a72-ae91-40440bab36fb')\"\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-3f5fa150-a477-4a72-ae91-40440bab36fb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "results = {'model': ['Decision Tree', 'Random Forest', 'AdaBoost', 'Logistic Regression', 'HistGradientBoosting', 'GradientBoosting'],\n",
        "           'f1_score': [dt_result_df['F1-Score'].iloc[0], rf_result_df['F1-Score'].iloc[0], lr_result_df['F1-Score'].iloc[0], hgb_result_df['F1-Score'].iloc[0], sgd_result_df['F1-Score'].iloc[0], adb_result_df['F1-Score'].iloc[0]],\n",
        "           'AUC_score': [dt_result_df['AUC-Score'].iloc[0], rf_result_df['AUC-Score'].iloc[0], lr_result_df['AUC-Score'].iloc[0], hgb_result_df['AUC-Score'].iloc[0], sgd_result_df['AUC-Score'].iloc[0], adb_result_df['AUC-Score'].iloc[0]],\n",
        "           'Assessment': [dt_result_df['Assessment'].iloc[0], rf_result_df['Assessment'].iloc[0], lr_result_df['Assessment'].iloc[0], hgb_result_df['Assessment'].iloc[0], sgd_result_df['Assessment'].iloc[0], adb_result_df['Assessment'].iloc[0]]\n",
        "           }\n",
        "\n",
        "results_df = pd.DataFrame(results)\n",
        "results_df = results_df.sort_values(by='AUC_score', ascending=False)\n",
        "results_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "gUkJzmAQhXlo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "outputId": "0ce0fb5e-828a-46ab-a647-5dbf6fdebf6f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create the bar plot using Seaborn\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(data=results_df, x='model', y='AUC_score', palette='viridis')\n",
        "\n",
        "# Add data labels\n",
        "for i, value in enumerate(results_df['AUC_score']):\n",
        "    plt.text(i, value, round(value, 2), ha='center', va='bottom')\n",
        "\n",
        "# Set other plot properties\n",
        "plt.xticks(rotation=45)\n",
        "plt.xlabel('Model')\n",
        "plt.ylabel('AUC Score')\n",
        "plt.title('Comparison of AUC Scores for Different Models')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fOwa_u5Wx-Cu"
      },
      "source": [
        "### Hyperparameter Tuning and Feature Importances"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "94nnjY-h8z7k"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "lXqO_ZsshXlp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e53c4fe1-0881-4490-f065-e9a8df98caec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Score: 0.845030 using {'solver': 'lbfgs', 'penalty': 'none', 'C': 1.0}\n"
          ]
        }
      ],
      "source": [
        "# Create Histogram Gradient Boosting model\n",
        "model = LogisticRegression(random_state=42)\n",
        "model.fit(X_sub_train_preprocessed, y_sub_train)\n",
        "\n",
        "\n",
        "# Define the parameter grid for GridSearchCV\n",
        "param_distribution = {\n",
        "    'solver': ['newton-cg', 'lbfgs', 'liblinear'],\n",
        "    'penalty': ['none', 11, 12],\n",
        "    'C' : [100, 1.0, 0.01]\n",
        "}\n",
        "\n",
        "# Create a repeated stratified K-fold cross-validator\n",
        "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
        "\n",
        "# Create GridSearchCV object\n",
        "random_search = RandomizedSearchCV(estimator=model, param_distributions = param_distribution, n_jobs=-1, cv=cv, scoring='f1', error_score=0)\n",
        "random_result = random_search.fit(X_sub_train_preprocessed, y_sub_train)\n",
        "\n",
        "# Summarize results\n",
        "print(\"Best Score: %f using %s\" % (random_result.best_score_, random_result.best_params_))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L0KqP5OmhXlp"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "RpznJcJThXlp"
      },
      "outputs": [],
      "source": [
        "best_model = LogisticRegression(solver='nlbfgs', penalty='none', C= 1.0, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "7tldFcodhXlq"
      },
      "outputs": [],
      "source": [
        "# set the destination path to the \"export\" directory\n",
        "destination = \".\"\n",
        "\n",
        "# create a dictionary to store the objects and their filenames\n",
        "models = {\"preprocessor\": preprocessor,\n",
        "          \"Final_model\": best_model}\n",
        "\n",
        "# loop through the models and save them using joblib.dump()\n",
        "for name, model in models.items():\n",
        "    dump(model, os.path.join(destination, f\"{name}.joblib\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "ndJHxQkBhXlq"
      },
      "outputs": [],
      "source": [
        "!pip freeze > requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VVKa8WjuhXlq"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BYyFbuvPhXlq"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WSDgnKpYhXlr"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9omGqAxhhXlr"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vmwFfPVshXlr"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4JWKgZfAhXlr"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    },
    "orig_nbformat": 4,
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}