Feature_engineering_and_Model_selection_for_Credit_risk_analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# IMPORTING LIBRARIES"
      ],
      "metadata": {
        "id": "qmDEUYWeu-lV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 174,
      "metadata": {
        "id": "_z36zKBxS9sm"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KernelDensity"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# GETTING INSIGHTS"
      ],
      "metadata": {
        "id": "oXr10T-TvEBm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"final_dataset.csv\")"
      ],
      "metadata": {
        "id": "kLBGRXLeWGbz"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "id": "l5EACNarZNNN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6c3d0c5-4b20-469b-c26d-9a3dc0532bfd"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(855969, 25)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,20))\n",
        "df.boxplot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RmLgqpkaU4g8",
        "outputId": "2161c966-bb54-4ebb-b829-4f05a77655ff"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "vw6CMernVFka",
        "outputId": "41e7c280-a002-4ee7-953c-326d828bf57a"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       loan_amount  funded_amount   loan_term  interest_rate loan_grade  \\\n",
              "1             2500           2500   60 months          15.27          C   \n",
              "18            6000           6000   36 months          11.71          B   \n",
              "33            5000           5000   36 months           8.90          A   \n",
              "45           14000          14000   36 months           9.91          B   \n",
              "78           15000          15000   36 months           7.90          A   \n",
              "...            ...            ...         ...            ...        ...   \n",
              "38951         7500           7500   36 months           8.00          A   \n",
              "38955        10000          10000   36 months          10.39          B   \n",
              "38956         2800           2800   36 months           8.00          A   \n",
              "38960         3600           3600   36 months          11.34          C   \n",
              "38963         3500           3500   36 months           7.68          A   \n",
              "\n",
              "      loan_sub_grade employment_length home_ownership_status  annual_income  \\\n",
              "1                 C4          < 1 year                  RENT        30000.0   \n",
              "18                B3            1 year              MORTGAGE        84000.0   \n",
              "33                A5           2 years                  RENT        24044.0   \n",
              "45                B1           6 years                  RENT        80000.0   \n",
              "78                A4            1 year              MORTGAGE        52000.0   \n",
              "...              ...               ...                   ...            ...   \n",
              "38951             A3           2 years                  RENT        40000.0   \n",
              "38955             B4           5 years              MORTGAGE        65379.0   \n",
              "38956             A3           5 years                  RENT        14800.0   \n",
              "38960             C2         10+ years              MORTGAGE        55000.0   \n",
              "38963             A2          < 1 year                  RENT        30000.0   \n",
              "\n",
              "      income_verification_status  ...  public_records  revolving_balance  \\\n",
              "1                Source Verified  ...             0.0             1687.0   \n",
              "18                      Verified  ...             0.0                0.0   \n",
              "33                      Verified  ...             0.0             2224.0   \n",
              "45                  Not Verified  ...             0.0             2219.0   \n",
              "78                  Not Verified  ...             0.0             1616.0   \n",
              "...                          ...  ...             ...                ...   \n",
              "38951               Not Verified  ...             0.0                0.0   \n",
              "38955               Not Verified  ...             0.0                0.0   \n",
              "38956               Not Verified  ...             0.0             1946.0   \n",
              "38960               Not Verified  ...             0.0              623.0   \n",
              "38963               Not Verified  ...             0.0             2096.0   \n",
              "\n",
              "      revolving_utilization_ratio  total_accounts  \\\n",
              "1                            9.40             4.0   \n",
              "18                          37.73            14.0   \n",
              "33                          29.30            16.0   \n",
              "45                          13.40            40.0   \n",
              "78                           6.70            29.0   \n",
              "...                           ...             ...   \n",
              "38951                        0.00            25.0   \n",
              "38955                        0.00            33.0   \n",
              "38956                        6.50            25.0   \n",
              "38960                       31.10            13.0   \n",
              "38963                       46.60             7.0   \n",
              "\n",
              "       non_medical_collections_last_12_months  currently_delinquent_accounts  \\\n",
              "1                                         0.0                            0.0   \n",
              "18                                        0.0                            0.0   \n",
              "33                                        0.0                            0.0   \n",
              "45                                        0.0                            0.0   \n",
              "78                                        0.0                            0.0   \n",
              "...                                       ...                            ...   \n",
              "38951                                     0.0                            0.0   \n",
              "38955                                     0.0                            0.0   \n",
              "38956                                     0.0                            0.0   \n",
              "38960                                     0.0                            0.0   \n",
              "38963                                     0.0                            0.0   \n",
              "\n",
              "       total_collection_amount  total_current_balance  \\\n",
              "1                          0.0                81008.5   \n",
              "18                         0.0                81008.5   \n",
              "33                         0.0                81008.5   \n",
              "45                         0.0                81008.5   \n",
              "78                         0.0                81008.5   \n",
              "...                        ...                    ...   \n",
              "38951                      0.0                81008.5   \n",
              "38955                      0.0                81008.5   \n",
              "38956                      0.0                81008.5   \n",
              "38960                      0.0                81008.5   \n",
              "38963                      0.0                81008.5   \n",
              "\n",
              "       total_revolving_credit_limit  loan_default_indicator  \n",
              "1                           23800.0                     1.0  \n",
              "18                          23800.0                     0.0  \n",
              "33                          23800.0                     0.0  \n",
              "45                          23800.0                     0.0  \n",
              "78                          23800.0                     0.0  \n",
              "...                             ...                     ...  \n",
              "38951                       23800.0                     0.0  \n",
              "38955                       23800.0                     0.0  \n",
              "38956                       23800.0                     0.0  \n",
              "38960                       23800.0                     0.0  \n",
              "38963                       23800.0                     0.0  \n",
              "\n",
              "[6430 rows x 25 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9a7f9256-23fb-4c78-9da9-86a5ac0ef9f7\" 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>loan_amount</th>\n",
              "      <th>funded_amount</th>\n",
              "      <th>loan_term</th>\n",
              "      <th>interest_rate</th>\n",
              "      <th>loan_grade</th>\n",
              "      <th>loan_sub_grade</th>\n",
              "      <th>employment_length</th>\n",
              "      <th>home_ownership_status</th>\n",
              "      <th>annual_income</th>\n",
              "      <th>income_verification_status</th>\n",
              "      <th>...</th>\n",
              "      <th>public_records</th>\n",
              "      <th>revolving_balance</th>\n",
              "      <th>revolving_utilization_ratio</th>\n",
              "      <th>total_accounts</th>\n",
              "      <th>non_medical_collections_last_12_months</th>\n",
              "      <th>currently_delinquent_accounts</th>\n",
              "      <th>total_collection_amount</th>\n",
              "      <th>total_current_balance</th>\n",
              "      <th>total_revolving_credit_limit</th>\n",
              "      <th>loan_default_indicator</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2500</td>\n",
              "      <td>2500</td>\n",
              "      <td>60 months</td>\n",
              "      <td>15.27</td>\n",
              "      <td>C</td>\n",
              "      <td>C4</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1687.0</td>\n",
              "      <td>9.40</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>6000</td>\n",
              "      <td>6000</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.71</td>\n",
              "      <td>B</td>\n",
              "      <td>B3</td>\n",
              "      <td>1 year</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>84000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.73</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>36 months</td>\n",
              "      <td>8.90</td>\n",
              "      <td>A</td>\n",
              "      <td>A5</td>\n",
              "      <td>2 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>24044.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2224.0</td>\n",
              "      <td>29.30</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>14000</td>\n",
              "      <td>14000</td>\n",
              "      <td>36 months</td>\n",
              "      <td>9.91</td>\n",
              "      <td>B</td>\n",
              "      <td>B1</td>\n",
              "      <td>6 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>80000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2219.0</td>\n",
              "      <td>13.40</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>15000</td>\n",
              "      <td>15000</td>\n",
              "      <td>36 months</td>\n",
              "      <td>7.90</td>\n",
              "      <td>A</td>\n",
              "      <td>A4</td>\n",
              "      <td>1 year</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>52000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1616.0</td>\n",
              "      <td>6.70</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38951</th>\n",
              "      <td>7500</td>\n",
              "      <td>7500</td>\n",
              "      <td>36 months</td>\n",
              "      <td>8.00</td>\n",
              "      <td>A</td>\n",
              "      <td>A3</td>\n",
              "      <td>2 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38955</th>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>36 months</td>\n",
              "      <td>10.39</td>\n",
              "      <td>B</td>\n",
              "      <td>B4</td>\n",
              "      <td>5 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>65379.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38956</th>\n",
              "      <td>2800</td>\n",
              "      <td>2800</td>\n",
              "      <td>36 months</td>\n",
              "      <td>8.00</td>\n",
              "      <td>A</td>\n",
              "      <td>A3</td>\n",
              "      <td>5 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>14800.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1946.0</td>\n",
              "      <td>6.50</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38960</th>\n",
              "      <td>3600</td>\n",
              "      <td>3600</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.34</td>\n",
              "      <td>C</td>\n",
              "      <td>C2</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>55000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>623.0</td>\n",
              "      <td>31.10</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38963</th>\n",
              "      <td>3500</td>\n",
              "      <td>3500</td>\n",
              "      <td>36 months</td>\n",
              "      <td>7.68</td>\n",
              "      <td>A</td>\n",
              "      <td>A2</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2096.0</td>\n",
              "      <td>46.60</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6430 rows × 25 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-9a7f9256-23fb-4c78-9da9-86a5ac0ef9f7')\"\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-9a7f9256-23fb-4c78-9da9-86a5ac0ef9f7 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-9a7f9256-23fb-4c78-9da9-86a5ac0ef9f7');\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-3bbc8300-3d92-48ae-831c-4d397468597e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3bbc8300-3d92-48ae-831c-4d397468597e')\"\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-3bbc8300-3d92-48ae-831c-4d397468597e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_62f630ef-3545-4cb3-822f-d57ff65fee5d\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_62f630ef-3545-4cb3-822f-d57ff65fee5d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## REMOVAL OF OUTLIERS"
      ],
      "metadata": {
        "id": "-9tZioUivKau"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = df['loan_amount'].quantile(0.25)\n",
        "Q3 = df['loan_amount'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "lower_threshold = Q1 - 1.5 * IQR\n",
        "upper_threshold = Q3 + 1.5 * IQR\n",
        "\n",
        "print(\"Lower threshold:\", lower_threshold)\n",
        "print(\"Upper threshold:\", upper_threshold)\n",
        "print(\"Mean threshold:\",(lower_threshold+upper_threshold)/2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3qK6_nIgVBaL",
        "outputId": "4c903794-267d-4ba1-bedc-131502cb0b4a"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lower threshold: -6000.0\n",
            "Upper threshold: 19600.0\n",
            "Mean threshold: 6800.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def removal_box_plot(df, column, threshold):\n",
        "    sns.boxplot(df[column])\n",
        "    plt.title(f'Original Box Plot of {column}')\n",
        "    plt.show()\n",
        "\n",
        "    removed_outliers = df[df[column] <= threshold]\n",
        "\n",
        "    sns.boxplot(removed_outliers[column])\n",
        "    plt.title(f'Box Plot without Outliers of {column}')\n",
        "    plt.show()\n",
        "    return removed_outliers\n",
        "\n",
        "\n",
        "threshold_value = 6800\n",
        "\n",
        "df = removal_box_plot(df,'loan_amount', threshold_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "id": "j5rukoxiVOQU",
        "outputId": "19a03b62-1ce6-4bf3-d0e5-bb86500f8898"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = df['annual_income'].quantile(0.25)\n",
        "Q3 = df['annual_income'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "lower_threshold = Q1 - 1.5 * IQR\n",
        "upper_threshold = Q3 + 1.5 * IQR\n",
        "\n",
        "print(\"Lower threshold:\", lower_threshold)\n",
        "print(\"Upper threshold:\", upper_threshold)\n",
        "print(\"Mean threshold:\",(lower_threshold+upper_threshold)/2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yts7MPYRVrpk",
        "outputId": "fb8496c8-00a0-4638-f582-9c037ff3d4b8"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lower threshold: -16497.0\n",
            "Upper threshold: 97895.0\n",
            "Mean threshold: 40699.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def removal_box_plot(df, column, threshold):\n",
        "    sns.boxplot(df[column])\n",
        "    plt.title(f'Original Box Plot of {column}')\n",
        "    plt.show()\n",
        "\n",
        "    removed_outliers = df[df[column] <= threshold]\n",
        "\n",
        "    sns.boxplot(removed_outliers[column])\n",
        "    plt.title(f'Box Plot without Outliers of {column}')\n",
        "    plt.show()\n",
        "    return removed_outliers\n",
        "\n",
        "\n",
        "threshold_value = 40699.0\n",
        "df = removal_box_plot(df,'annual_income', threshold_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "id": "5Nw9e9hLWDej",
        "outputId": "1d9fccab-08ed-4439-cd53-aae5d277e90a"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAGgCAYAAAC3yFOZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABKjUlEQVR4nO3dfXwM9/4+/msT2U0k2Q1yJ0dESoUQHEG6JW4qtUh7qrR1d0gIQeM2PahvNZS2aamSum2OQ7QfWvRoHeIuEveJVkPqPm6KOGWTFNmNiITk/fvDb+dYCSYr7OJ6Ph77qJ15zcxr1m73MvOeWYUQQoCIiIiIHsjO2g0QERERPQ0YmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqI7uP8+fNQKBRISkqydiv3pVAoMH36dNm1o0ePfrwNPeXufT2TkpKgUChw/vx5q/VkqS1btqBVq1ZwdHSEQqFAQUGBtVt6IiIjI9GgQYPHvgw9nxia6LEzffHc/fD09ESXLl2wefPmJ97Pzp07zXpxcHDACy+8gMGDB+P333+vlm2kp6dj+vTpT/yLylrbfZhFixZVOXwWFRVh5syZaNGiBWrWrAmNRoPQ0FB88803eJRff9q0aZPsoPm0unLlCt555x04OTlh4cKF+Pbbb+Hs7GzttoieejWs3QA9P2bMmAF/f38IIZCbm4ukpCT07NkTGzZswGuvvfbE+xk7dizatm2LW7du4eDBg0hMTERycjKOHDkCHx+fR1p3eno6PvroI0RGRsLNza16Gq5EcXExatT438f4SW23qhYtWgR3d3dERkbKqs/NzUXXrl1x4sQJ9OvXD6NHj8bNmzfx73//GxEREdi0aRNWrlwJe3v7KveyadMmLFy4UFZwGjRoEPr16weVSlXl7VjTgQMHUFhYiJkzZyIsLMza7di8f/7znygvL7d2G/QUYGiiJ6ZHjx5o06aN9DwqKgpeXl747rvvrBKaQkND8dZbbwEAhgwZgsaNG2Ps2LFYsWIFpkyZ8sT7sYSjo6O1W3gsIiIicOLECfz444/429/+Jk0fO3YsJk6ciC+++AJ//etfMXny5Mfah729vUXB7H6KioqeyBGfvLw8ALCp4GzLHBwcrN0CPSV4eo6sxs3NDU5OTmZHSoA7XyzvvfcefH19oVKpEBAQgC+++EI6JVNcXIwmTZqgSZMmKC4ulpa7evUq6tati5dffhllZWVV7ueVV14BAJw7d+6BdWlpaQgNDYWzszPc3Nzwxhtv4MSJE9L86dOnY+LEiQAAf39/6TTg/cbFfPXVV7C3tzc7pTZnzhwoFArExsZK08rKyuDq6moWFO4egyN3uz/99BOaN28OlUqFZs2aYcuWLRV6OnToEHr06AG1Wg0XFxd07doV+/fvN6uZPn06FApFhWXvHQfUoEEDHDt2DLt27ZJ66ty5c6WvBQDs378fW7duRWRkpFlgMomPj8eLL76Izz//XPr7N51y3blzp1ntvePSIiMjsXDhQum1Mz3u535jmjZv3iy9B1xdXREeHo5jx46Z1URGRsLFxQVnz55Fz5494erqioEDBwIATp8+jT59+sDb2xuOjo6oV68e+vXrB4PBcN9eTNauXYvg4GA4OTnB3d0df//73/HHH39I8zt37oyIiAgAQNu2baFQKB54hO/ChQt49913ERAQACcnJ9SpUwdvv/12hX02vRb79u1DbGwsPDw84OzsjDfffBP5+flmtQ0aNMBrr72GvXv3ol27dnB0dMQLL7yAb775xqxO7nsIANavX4/w8HD4+PhApVKhYcOGmDlzpkWf9XvdO6bJ9L754osvkJiYiIYNG0KlUqFt27Y4cOBAheVPnjyJd955Bx4eHnByckJAQAA++OADsxo5nynTfu/duxdjx46Fh4cH3NzcMGLECJSWlqKgoACDBw9GrVq1UKtWLUyaNKnCqery8nLMmzcPzZo1g6OjI7y8vDBixAhcu3btkV8n4pEmeoIMBgP+/PNPCCGQl5eH+fPn4/r16/j73/8u1Qgh8Le//Q07duxAVFQUWrVqha1bt2LixIn4448/MHfuXDg5OWHFihVo3749PvjgA3z55ZcAgJiYGBgMBiQlJVl0dODs2bMAgDp16ty3Zvv27ejRowdeeOEFTJ8+HcXFxZg/fz7at2+PgwcPokGDBujduzdOnTqF7777DnPnzoW7uzsAwMPDo9J1hoaGory8HHv37pWOuO3Zswd2dnbYs2ePVHfo0CFcv34dHTt2rHQ9cra7d+9erFu3Du+++y5cXV3x1VdfoU+fPsjJyZH2+9ixYwgNDYVarcakSZPg4OCAr7/+Gp07d8auXbsQEhIi9yUFAMybNw9jxoyBi4uL9EXi5eV13/oNGzYAAAYPHlzp/Bo1amDAgAH46KOPsG/fviqdfhoxYgQuXbqElJQUfPvtt1XYi//59ttvERERAZ1Oh88//xw3btzA4sWL0aFDBxw6dMjsy/f27dvQ6XTo0KEDvvjiC9SsWROlpaXQ6XQoKSnBmDFj4O3tjT/++AMbN25EQUEBNBrNfbedlJSEIUOGoG3btoiPj0dubi4SEhKwb98+HDp0CG5ubvjggw8QEBCAxMRE6ZR4w4YN77vOAwcOID09Hf369UO9evVw/vx5LF68GJ07d8bx48dRs2ZNs/oxY8agVq1amDZtGs6fP4958+Zh9OjRWL16tVndmTNn8NZbbyEqKgoRERFYtmwZIiMjERwcjGbNmlX5dU9KSoKLiwtiY2Ph4uKCtLQ0xMXFwWg0Yvbs2VVenxyrVq1CYWEhRowYAYVCgVmzZqF37974/fffpaNThw8fRmhoKBwcHBAdHY0GDRrg7Nmz2LBhAz755BMAVf9Mmd4XH330Efbv34/ExES4ubkhPT0d9evXx6effopNmzZh9uzZaN68udlnZcSIEdL7ZOzYsTh37hwWLFiAQ4cOYd++fTyq9qgE0WO2fPlyAaDCQ6VSiaSkJLPan376SQAQH3/8sdn0t956SygUCnHmzBlp2pQpU4SdnZ3YvXu3WLt2rQAg5s2b99B+duzYIQCIZcuWifz8fHHp0iWRnJwsGjRoIBQKhThw4IAQQohz584JAGL58uXSsq1atRKenp7iypUr0rTffvtN2NnZicGDB0vTZs+eLQCIc+fOPbSfsrIyoVarxaRJk4QQQpSXl4s6deqIt99+W9jb24vCwkIhhBBffvmlsLOzE9euXZOWBSCmTZsma7sAhFKpNHsNf/vtNwFAzJ8/X5rWq1cvoVQqxdmzZ6Vply5dEq6urqJjx47StGnTponK/hdi+vu+u4dmzZqJTp06PfS1MG0fgNl+3mvdunUCgPjqq6+EEP/7O92xY4dZXWV/hzExMZX2LUTF1/PefSksLBRubm5i+PDhZsvp9Xqh0WjMpkdERAgA4v333zerPXTokAAg1q5de9/9q0xpaanw9PQUzZs3F8XFxdL0jRs3CgAiLi6uQt+m9/KD3Lhxo8K0jIwMAUB88803FdYZFhYmysvLpekTJkwQ9vb2oqCgQJrm5+cnAIjdu3dL0/Ly8oRKpRLvvfeeNK0q76HK+hwxYoSoWbOmuHnzpjQtIiJC+Pn5PXS/73bvMqb3TZ06dcTVq1el6evXrxcAxIYNG6RpHTt2FK6uruLChQtm67z7NZL7mTLtt06nM1teq9UKhUIhRo4cKU27ffu2qFevntnnas+ePQKAWLlypVkvW7ZsqXQ6VR1Pz9ETs3DhQqSkpCAlJQX/93//hy5dumDYsGFYt26dVLNp0ybY29tj7NixZsu+9957EEKYXW03ffp0NGvWDBEREXj33XfRqVOnCss9yNChQ+Hh4QEfHx+Eh4ejqKgIK1asMBt3dbfLly8jKysLkZGRqF27tjS9RYsWePXVV7Fp0ybZ276bnZ0dXn75ZezevRsAcOLECVy5cgXvv/8+hBDIyMgAcOfoU/PmzR9pnEpYWJjZUYcWLVpArVZLVw2WlZVh27Zt6NWrF1544QWprm7duhgwYAD27t0Lo9Fo8fblKCwsBAC4urret8Y073H3cq+UlBQUFBSgf//++PPPP6WHvb09QkJCsGPHjgrLjBo1yuy56UjS1q1bcePGDdnb/vXXX5GXl4d3333XbCxbeHg4mjRpguTkZIv2ycnJSfrzrVu3cOXKFTRq1Ahubm44ePBghfro6GizU2qhoaEoKyvDhQsXzOoCAwMRGhoqPffw8EBAQIDFV6je3WdhYSH+/PNPhIaG4saNGzh58qRF63yYvn37olatWtJz0/6Y9iE/Px+7d+/G0KFDUb9+fbNlTa+RJZ+pqKgos9c4JCQEQghERUVJ0+zt7dGmTRuz13Pt2rXQaDR49dVXzd6fwcHBcHFxqfT9SVXD0ERPTLt27RAWFoawsDAMHDgQycnJCAwMxOjRo1FaWgrgzvgKHx+fCl+YTZs2leabKJVKLFu2DOfOnUNhYSGWL1/+wPEp94qLi0NKSgrS0tJw+PBhXLp0CYMGDbpvvWnbAQEBFeY1bdoUf/75J4qKimRv/26hoaHIzMxEcXEx9uzZg7p166J169Zo2bKldIpu7969Zl9Clrj3f+wAUKtWLWm8Q35+Pm7cuHHffSwvL8fFixcfqYeHMf3dm8JTZeQEq8fh9OnTAO6Mf/Pw8DB7bNu2TRqAbVKjRg3Uq1fPbJq/vz9iY2OxdOlSuLu7Q6fTYeHChQ8dz/Sg91+TJk0qhBa5iouLERcXJ40hdHd3h4eHBwoKCirt6d73kClU3Dtm5mHvtao6duwY3nzzTWg0GqjVanh4eEin9uWMBbPEw/bVFFiaN29+33VY8pm6d7umoO3r61th+t2v5+nTp2EwGODp6Vnh/Xn9+vUK70+qOo5pIquxs7NDly5dkJCQgNOnT1s0zmHr1q0AgJs3b+L06dPw9/eXvWxQUJDNXI7doUMH3Lp1CxkZGdizZ48UjkJDQ7Fnzx6cPHkS+fn5jxya7jfWS1hw36P7BdRHHZjbtGlT/PTTTzh8+PB9x28dPnwYwJ2jGY+zl3uZLkv/9ttv4e3tXWH+vRc1qFQq2NlV/LfpnDlzEBkZifXr12Pbtm0YO3Ys4uPjsX///goh63EbM2YMli9fjvHjx0Or1UKj0UChUKBfv36VXoYv9z0kp07u31tBQQE6deoEtVqNGTNmoGHDhnB0dMTBgwcxefLkx3a7gOr8vFTHdiubfncv5eXl8PT0xMqVKytd/n7jKkk+hiayqtu3bwMArl+/DgDw8/PD9u3bUVhYaHYUwXT43c/PT5p2+PBhzJgxA0OGDEFWVhaGDRuGI0eOPHAg7aMwbTs7O7vCvJMnT8Ld3V26nLwqR7yAO0fhlEol9uzZgz179khXwXXs2BH//Oc/kZqaKj1/kKpu914eHh6oWbPmfffRzs5O+teu6V/dBQUFZqcMKzviUZW+XnvtNcTHx+Obb76pdH/LysqwatUq1KpVC+3bt6/Qy90etZd7mU5tenp6PnLgDgoKQlBQEKZOnYr09HS0b98eS5Yswccff1xp/d3vP9OVnibZ2dlmn42q+OGHHxAREYE5c+ZI027evPlEbpAq9z20c+dOXLlyBevWrTN7TzzsStfHzXS67ejRo/etqcpn6lE1bNgQ27dvR/v27c1OZ1L14ek5sppbt25h27ZtUCqV0um3nj17oqysDAsWLDCrnTt3LhQKBXr06CEtGxkZCR8fHyQkJCApKQm5ubmYMGHCY+u3bt26aNWqFVasWGH2hXL06FFs27YNPXv2lKaZwpPcLx5HR0e0bdsW3333HXJycsyONBUXF+Orr75Cw4YNUbdu3Qeup6rbvZe9vT26deuG9evXm13unZubi1WrVqFDhw5Qq9UA/hcgTGOxAEjjwirrS25PL7/8MsLCwrB8+XJs3LixwvwPPvgAp06dwqRJk6QvBj8/P9jb25v1Aty5qWZlvQCWvUY6nQ5qtRqffvopbt26VWH+vZfeV8ZoNEr/WDAJCgqCnZ0dSkpK7rtcmzZt4OnpiSVLlpjVbd68GSdOnEB4eHgV9uR/7O3tKxw5mT9/frUfpauM3PeQ6QjL3X2WlpZW+vf7JHl4eKBjx45YtmwZcnJyzOaZeq3KZ+pRvfPOOygrK8PMmTMrzLt9+7bN/VLA04hHmuiJ2bx5s3TEKC8vD6tWrcLp06fx/vvvS//TeP3119GlSxd88MEHOH/+PFq2bIlt27Zh/fr1GD9+vPQ/2Y8//hhZWVlITU2Fq6srWrRogbi4OEydOhVvvfWWWYCpTrNnz0aPHj2g1WoRFRUl3XJAo9GY3WE6ODgYwJ0v+H79+sHBwQGvv/76A29sGBoais8++wwajQZBQUEA7hzRCAgIQHZ2tqy7aVuy3Xt9/PHHSElJQYcOHfDuu++iRo0a+Prrr1FSUoJZs2ZJdd26dUP9+vURFRWFiRMnwt7eHsuWLYOHh0eFL5Dg4GAsXrwYH3/8MRo1agRPT88KR0vu9s0336Br16544403MGDAAISGhqKkpATr1q3Dzp070bdvX+loHHBnbMfbb7+N+fPnQ6FQoGHDhti4cWOlYzhMr9HYsWOh0+lgb2+Pfv36yXpt1Go1Fi9ejEGDBqF169bo16+ftL/Jyclo3759hcB/r7S0NIwePRpvv/02GjdujNu3b+Pbb7+Fvb09+vTpc9/lHBwc8Pnnn2PIkCHo1KkT+vfvL91yoEGDBhb/g+G1117Dt99+C41Gg8DAQGRkZGD79u0PvPVGdZH7Hnr55ZdRq1YtREREYOzYsVAoFPj2228f+2kyOb766it06NABrVu3RnR0NPz9/XH+/HkkJycjKysLgPzP1KPq1KkTRowYgfj4eGRlZaFbt25wcHDA6dOnsXbtWiQkJEg39CULWeeiPXqeVHbLAUdHR9GqVSuxePFis0trhbhzWfeECROEj4+PcHBwEC+++KKYPXu2VJeZmSlq1KghxowZY7bc7du3Rdu2bYWPj88DL1c3XZ7+sEu+K7tcXQghtm/fLtq3by+cnJyEWq0Wr7/+ujh+/HiF5WfOnCn+8pe/CDs7O1m3H0hOThYARI8ePcymDxs2TAAQ//rXvyosg3sukX/QdgGImJiYCuvw8/MTERERZtMOHjwodDqdcHFxETVr1hRdunQR6enpFZbNzMwUISEhQqlUivr164svv/yy0svF9Xq9CA8PF66urgKArNsPFBYWiunTp4tmzZoJJycn4erqKtq3by+SkpIqvGeEECI/P1/06dNH1KxZU9SqVUuMGDFCHD16tMLf4e3bt8WYMWOEh4eHUCgUZpe83/t6VrYvQtx5D+l0OqHRaISjo6No2LChiIyMFL/++qtUExERIZydnSv0+fvvv4uhQ4eKhg0bCkdHR1G7dm3RpUsXsX379oe+JkIIsXr1avHXv/5VqFQqUbt2bTFw4EDx3//+16ymKrccuHbtmhgyZIhwd3cXLi4uQqfTiZMnT1Z4X9xvnZXd7sHPz0+Eh4dX2FanTp0q/N3LfQ/t27dPvPTSS8LJyUn4+PiISZMmia1bt1bYdnXecmD27NkVaiv7zB09elS8+eabws3NTTg6OoqAgADx4YcfmtXI+Uzd7zU23ZohPz+/Qt+VvccSExNFcHCw9LkJCgoSkyZNEpcuXZLzctADKISwgahOREREZOM4pomIiIhIBo5pIiKiZ9LVq1ele8BVxt7enpfhU5Xw9BwRET2TTL/tdj9+fn73/SFtosowNBER0TMpMzPzgXcgd3Jyku71RSQHQxMRERGRDBwITkRERCQDB4JXk/Lycly6dAmurq6P/FMWRERE9GQIIVBYWAgfH59KfyvybgxN1eTSpUvV9vtBRERE9GRdvHjxoT+YzdBUTUw/Lnvx4sVq+x0hIiIieryMRiN8fX3NfiT+fhiaqonplJxarWZoIiIiesrIGVrDgeBEREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEM/MFeIht18+ZN5OTkWLsNIptUv359ODo6WrsNes4wNBHZqJycHERHR1u7DSKblJiYiMaNG1u7DXrOMDQR2aj69esjMTHR2m0QgAsXLuCTTz7BBx98AD8/P2u3Q7jz+SB64oQNiY+PFwDEuHHjpGnFxcXi3XffFbVr1xbOzs6id+/eQq/Xmy134cIF0bNnT+Hk5CQ8PDzEP/7xD3Hr1i2zmh07doi//vWvQqlUioYNG4rly5dX2P6CBQuEn5+fUKlUol27duLnn3+W3bvBYBAAhMFgqNI+E5Hty87OFp06dRLZ2dnWboWIqllVvr9tZiD4gQMH8PXXX6NFixZm0ydMmIANGzZg7dq12LVrFy5duoTevXtL88vKyhAeHo7S0lKkp6djxYoVSEpKQlxcnFRz7tw5hIeHo0uXLsjKysL48eMxbNgwbN26VapZvXo1YmNjMW3aNBw8eBAtW7aETqdDXl7e4995IiIisn1PIMQ9VGFhoXjxxRdFSkqK6NSpk3SkqaCgQDg4OIi1a9dKtSdOnBAAREZGhhBCiE2bNgk7Ozuzo0+LFy8WarValJSUCCGEmDRpkmjWrJnZNvv27St0Op30vF27diImJkZ6XlZWJnx8fER8fLysfeCRJqJnF480ET27nrojTTExMQgPD0dYWJjZ9MzMTNy6dctsepMmTVC/fn1kZGQAADIyMhAUFAQvLy+pRqfTwWg04tixY1LNvevW6XTSOkpLS5GZmWlWY2dnh7CwMKnmXiUlJTAajWYPIiIienZZfSD4999/j4MHD+LAgQMV5un1eiiVSri5uZlN9/Lygl6vl2ruDkym+aZ5D6oxGo0oLi7GtWvXUFZWVmnNyZMnK+07Pj4eH330kfwdJSIioqeaVY80Xbx4EePGjcPKlSufuvttTJkyBQaDQXpcvHjR2i0RERHRY2TV0JSZmYm8vDy0bt0aNWrUQI0aNbBr1y589dVXqFGjBry8vFBaWoqCggKz5XJzc+Ht7Q0A8Pb2Rm5uboX5pnkPqlGr1XBycoK7uzvs7e0rrTGt414qlQpqtdrsQURERM8uq4amrl274siRI8jKypIebdq0wcCBA6U/Ozg4IDU1VVomOzsbOTk50Gq1AACtVosjR46YXeWWkpICtVqNwMBAqebudZhqTOtQKpUIDg42qykvL0dqaqpUQ0RERM83q45pcnV1RfPmzc2mOTs7o06dOtL0qKgoxMbGonbt2lCr1RgzZgy0Wi1eeuklAEC3bt0QGBiIQYMGYdasWdDr9Zg6dSpiYmKgUqkAACNHjsSCBQswadIkDB06FGlpaVizZg2Sk5Ol7cbGxiIiIgJt2rRBu3btMG/ePBQVFWHIkCFP6NUgIiIiW2b1geAPM3fuXNjZ2aFPnz4oKSmBTqfDokWLpPn29vbYuHEjRo0aBa1WC2dnZ0RERGDGjBlSjb+/P5KTkzFhwgQkJCSgXr16WLp0KXQ6nVTTt29f5OfnIy4uDnq9Hq1atcKWLVsqDA4nIiKi55NCCCGs3cSzwGg0QqPRwGAwcHwT0TPm1KlTiI6O5u+dET2DqvL9bRP3aSIiIiKydQxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERyWDV0LR48WK0aNECarUaarUaWq0WmzdvluZ37twZCoXC7DFy5EizdeTk5CA8PBw1a9aEp6cnJk6ciNu3b5vV7Ny5E61bt4ZKpUKjRo2QlJRUoZeFCxeiQYMGcHR0REhICH755ZfHss9ERET0dLJqaKpXrx4+++wzZGZm4tdff8Urr7yCN954A8eOHZNqhg8fjsuXL0uPWbNmSfPKysoQHh6O0tJSpKenY8WKFUhKSkJcXJxUc+7cOYSHh6NLly7IysrC+PHjMWzYMGzdulWqWb16NWJjYzFt2jQcPHgQLVu2hE6nQ15e3pN5IYiIiMj2CRtTq1YtsXTpUiGEEJ06dRLjxo27b+2mTZuEnZ2d0Ov10rTFixcLtVotSkpKhBBCTJo0STRr1sxsub59+wqdTic9b9eunYiJiZGel5WVCR8fHxEfHy+7b4PBIAAIg8EgexkiejpkZ2eLTp06iezsbGu3QkTVrCrf3zYzpqmsrAzff/89ioqKoNVqpekrV66Eu7s7mjdvjilTpuDGjRvSvIyMDAQFBcHLy0uaptPpYDQapaNVGRkZCAsLM9uWTqdDRkYGAKC0tBSZmZlmNXZ2dggLC5NqKlNSUgKj0Wj2ICIiomdXDWs3cOTIEWi1Wty8eRMuLi748ccfERgYCAAYMGAA/Pz84OPjg8OHD2Py5MnIzs7GunXrAAB6vd4sMAGQnuv1+gfWGI1GFBcX49q1aygrK6u05uTJk/ftOz4+Hh999NGj7TwRERE9NawemgICApCVlQWDwYAffvgBERER2LVrFwIDAxEdHS3VBQUFoW7duujatSvOnj2Lhg0bWrFrYMqUKYiNjZWeG41G+Pr6WrEjIiIiepysHpqUSiUaNWoEAAgODsaBAweQkJCAr7/+ukJtSEgIAODMmTNo2LAhvL29K1zllpubCwDw9vaW/muadneNWq2Gk5MT7O3tYW9vX2mNaR2VUalUUKlUVdxbIiIielrZzJgmk/LycpSUlFQ6LysrCwBQt25dAIBWq8WRI0fMrnJLSUmBWq2WTvFptVqkpqaarSclJUUaN6VUKhEcHGxWU15ejtTUVLOxVURERPR8s+qRpilTpqBHjx6oX78+CgsLsWrVKuzcuRNbt27F2bNnsWrVKvTs2RN16tTB4cOHMWHCBHTs2BEtWrQAAHTr1g2BgYEYNGgQZs2aBb1ej6lTpyImJkY6CjRy5EgsWLAAkyZNwtChQ5GWloY1a9YgOTlZ6iM2NhYRERFo06YN2rVrh3nz5qGoqAhDhgyxyutibbm5uTAYDNZug8hmXLhwwey/RPQ/Go2mwrjgZ5VVQ1NeXh4GDx6My5cvQ6PRoEWLFti6dSteffVVXLx4Edu3b5cCjK+vL/r06YOpU6dKy9vb22Pjxo0YNWoUtFotnJ2dERERgRkzZkg1/v7+SE5OxoQJE5CQkIB69eph6dKl0Ol0Uk3fvn2Rn5+PuLg46PV6tGrVClu2bHlu3gR3y83Nxd8HDcat0sqP9hE9zz755BNrt0BkcxyUKvzft988F9+ZCiGEsHYTzwKj0QiNRgODwQC1Wm3tdix26tQpREdHo/iFTih31Fi7HSIismF2Nw1w+n0XEhMT0bhxY2u3Y5GqfH9bfSA42aZyRw3Knd2t3QYREZHNsLmB4ERERES2iKGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGq4amxYsXo0WLFlCr1VCr1dBqtdi8ebM0/+bNm4iJiUGdOnXg4uKCPn36IDc312wdOTk5CA8PR82aNeHp6YmJEyfi9u3bZjU7d+5E69atoVKp0KhRIyQlJVXoZeHChWjQoAEcHR0REhKCX3755bHsMxERET2drBqa6tWrh88++wyZmZn49ddf8corr+CNN97AsWPHAAATJkzAhg0bsHbtWuzatQuXLl1C7969peXLysoQHh6O0tJSpKenY8WKFUhKSkJcXJxUc+7cOYSHh6NLly7IysrC+PHjMWzYMGzdulWqWb16NWJjYzFt2jQcPHgQLVu2hE6nQ15e3pN7MYiIiMimKYQQwtpN3K127dqYPXs23nrrLXh4eGDVqlV46623AAAnT55E06ZNkZGRgZdeegmbN2/Ga6+9hkuXLsHLywsAsGTJEkyePBn5+flQKpWYPHkykpOTcfToUWkb/fr1Q0FBAbZs2QIACAkJQdu2bbFgwQIAQHl5OXx9fTFmzBi8//77svo2Go3QaDQwGAxQq9XV+ZI8UadOnUJ0dDSKAv+Gcmd3a7dDREQ2zK7oTzgf/w8SExPRuHFja7djkap8f9vMmKaysjJ8//33KCoqglarRWZmJm7duoWwsDCppkmTJqhfvz4yMjIAABkZGQgKCpICEwDodDoYjUbpaFVGRobZOkw1pnWUlpYiMzPTrMbOzg5hYWFSTWVKSkpgNBrNHkRERPTssnpoOnLkCFxcXKBSqTBy5Ej8+OOPCAwMhF6vh1KphJubm1m9l5cX9Ho9AECv15sFJtN807wH1RiNRhQXF+PPP/9EWVlZpTWmdVQmPj4eGo1Gevj6+lq0/0RERPR0sHpoCggIQFZWFn7++WeMGjUKEREROH78uLXbeqgpU6bAYDBIj4sXL1q7JSIiInqMali7AaVSiUaNGgEAgoODceDAASQkJKBv374oLS1FQUGB2dGm3NxceHt7AwC8vb0rXOVmurru7pp7r7jLzc2FWq2Gk5MT7O3tYW9vX2mNaR2VUalUUKlUlu00ERERPXWsfqTpXuXl5SgpKUFwcDAcHByQmpoqzcvOzkZOTg60Wi0AQKvV4siRI2ZXuaWkpECtViMwMFCquXsdphrTOpRKJYKDg81qysvLkZqaKtUQERERWfVI05QpU9CjRw/Ur18fhYWFWLVqFXbu3ImtW7dCo9EgKioKsbGxqF27NtRqNcaMGQOtVouXXnoJANCtWzcEBgZi0KBBmDVrFvR6PaZOnYqYmBjpKNDIkSOxYMECTJo0CUOHDkVaWhrWrFmD5ORkqY/Y2FhERESgTZs2aNeuHebNm4eioiIMGTLEKq8LERER2R6rhqa8vDwMHjwYly9fhkajQYsWLbB161a8+uqrAIC5c+fCzs4Offr0QUlJCXQ6HRYtWiQtb29vj40bN2LUqFHQarVwdnZGREQEZsyYIdX4+/sjOTkZEyZMQEJCAurVq4elS5dCp9NJNX379kV+fj7i4uKg1+vRqlUrbNmypcLgcCIiInp+2dx9mp5WvE8TERE9b3ifJiIiIiKqgKGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGq/72HNkuu+ICa7dAREQ27nn7rmBooko5ndtt7RaIiIhsCkMTVarYvyPKndys3QYREdkwu+KC5+of2QxNVKlyJzeUO7tbuw0iIiKbYfFA8Nu3b2P79u34+uuvUVhYCAC4dOkSrl+/Xm3NEREREdkKi440XbhwAd27d0dOTg5KSkrw6quvwtXVFZ9//jlKSkqwZMmS6u6TiIiIyKosOtI0btw4tGnTBteuXYOTk5M0/c0330Rqamq1NUdERERkKyw60rRnzx6kp6dDqVSaTW/QoAH++OOPammMiIiIyJZYdKSpvLwcZWVlFab/97//haur6yM3RURERGRrLApN3bp1w7x586TnCoUC169fx7Rp09CzZ8/q6o2IiIjIZlh0em7OnDnQ6XQIDAzEzZs3MWDAAJw+fRru7u747rvvqrtHIiIiIquzKDTVq1cPv/32G77//nscPnwY169fR1RUFAYOHGg2MJyIiIjoWWHxzS1r1KiBv//979XZCxEREZHNsjg0Xbp0CXv37kVeXh7Ky8vN5o0dO/aRGyMiIiKyJRaFpqSkJIwYMQJKpRJ16tSBQqGQ5ikUCoYmIiIieuZYFJo+/PBDxMXFYcqUKbCzs/iXWIiIiIieGhYlnhs3bqBfv34MTERERPTcsCj1REVFYe3atdXdCxEREZHNsuj0XHx8PF577TVs2bIFQUFBcHBwMJv/5ZdfVktzRERERLbC4tC0detWBAQEAECFgeBEREREzxqL7wi+bNkyREZGVnM7RERERLbJojFNKpUK7du3r+5eiIiIiGyWRaFp3LhxmD9/fnX3QkRERGSzLDo998svvyAtLQ0bN25Es2bNKgwEX7duXbU0R0RERGQrLApNbm5u6N27d3X3QkRERGSzLDo9t3z58gc+5IqPj0fbtm3h6uoKT09P9OrVC9nZ2WY1nTt3hkKhMHuMHDnSrCYnJwfh4eGoWbMmPD09MXHiRNy+fdusZufOnWjdujVUKhUaNWqEpKSkCv0sXLgQDRo0gKOjI0JCQvDLL7/If1GIiIjomfZIt/TOz8/H3r17sXfvXuTn51d5+V27diEmJgb79+9HSkoKbt26hW7duqGoqMisbvjw4bh8+bL0mDVrljSvrKwM4eHhKC0tRXp6OlasWIGkpCTExcVJNefOnUN4eDi6dOmCrKwsjB8/HsOGDcPWrVulmtWrVyM2NhbTpk3DwYMH0bJlS+h0OuTl5VnwyhAREdGzxqLTc0VFRRgzZgy++eYblJeXAwDs7e0xePBgzJ8/HzVr1pS1ni1btpg9T0pKgqenJzIzM9GxY0dpes2aNeHt7V3pOrZt24bjx49j+/bt8PLyQqtWrTBz5kxMnjwZ06dPh1KpxJIlS+Dv7485c+YAAJo2bYq9e/di7ty50Ol0AO7ckHP48OEYMmQIAGDJkiVITk7GsmXL8P7771ftBSIiIqJnjkVHmmJjY7Fr1y5s2LABBQUFKCgowPr167Fr1y689957FjdjMBgAALVr1zabvnLlSri7u6N58+aYMmUKbty4Ic3LyMhAUFAQvLy8pGk6nQ5GoxHHjh2TasLCwszWqdPpkJGRAQAoLS1FZmamWY2dnR3CwsKkmnuVlJTAaDSaPYiIiOjZZdGRpn//+9/44Ycf0LlzZ2laz5494eTkhHfeeQeLFy+u8jrLy8sxfvx4tG/fHs2bN5emDxgwAH5+fvDx8cHhw4cxefJkZGdnS1fo6fV6s8AEQHqu1+sfWGM0GlFcXIxr166hrKys0pqTJ09W2m98fDw++uijKu8nERERPZ0sCk03btyoEDAAwNPT0+woUFXExMTg6NGj2Lt3r9n06Oho6c9BQUGoW7cuunbtirNnz6Jhw4YWbas6TJkyBbGxsdJzo9EIX19fq/VDREREj5dFp+e0Wi2mTZuGmzdvStOKi4vx0UcfQavVVnl9o0ePxsaNG7Fjxw7Uq1fvgbUhISEAgDNnzgAAvL29kZuba1Zjem4aB3W/GrVaDScnJ7i7u8Pe3r7SmvuNpVKpVFCr1WYPIiIienZZFJoSEhKwb98+1KtXD127dkXXrl3h6+uL9PR0JCQkyF6PEAKjR4/Gjz/+iLS0NPj7+z90maysLABA3bp1AdwJcEeOHDG7yi0lJQVqtRqBgYFSTWpqqtl6UlJSpICnVCoRHBxsVlNeXo7U1FSLQiARERE9eyw6Pde8eXOcPn0aK1eulMb89O/fHwMHDoSTk5Ps9cTExGDVqlVYv349XF1dpTFIGo0GTk5OOHv2LFatWoWePXuiTp06OHz4MCZMmICOHTuiRYsWAIBu3bohMDAQgwYNwqxZs6DX6zF16lTExMRApVIBAEaOHIkFCxZg0qRJGDp0KNLS0rBmzRokJydLvcTGxiIiIgJt2rRBu3btMG/ePBQVFUlX0xEREdHzzaLQBNy5DcDw4cMfaeOmAeN3DygH7tw8MzIyEkqlEtu3b5cCjK+vL/r06YOpU6dKtfb29ti4cSNGjRoFrVYLZ2dnREREYMaMGVKNv78/kpOTMWHCBCQkJKBevXpYunSpdLsBAOjbty/y8/MRFxcHvV6PVq1aYcuWLZWO3SIiIqLnj0IIIaq6UHx8PLy8vDB06FCz6cuWLUN+fj4mT55cbQ0+LYxGIzQaDQwGw1M9vunUqVOIjo5GUeDfUO7sbu12iIjIhtkV/Qnn4/9BYmIiGjdubO12LFKV72+LxjR9/fXXaNKkSYXpzZo1w5IlSyxZJREREZFNsyg06fV6aSD23Tw8PHD58uVHboqIiIjI1lgUmnx9fbFv374K0/ft2wcfH59HboqIiIjI1lg0EHz48OEYP348bt26hVdeeQUAkJqaikmTJj3Sz6gQERER2SqLQtPEiRNx5coVvPvuuygtLQUAODo6YvLkyZgyZUq1NkhERERkCywKTQqFAp9//jk+/PBDnDhxAk5OTnjxxRel+yIRERERPWssvk8TALi4uKBt27bV1QsRERGRzbIoNBUVFeGzzz5Damoq8vLyUF5ebjb/999/r5bmiIiIiGyFRaFp2LBh2LVrFwYNGoS6detCoVBUd19ERERENsWi0LR582YkJyejffv21d0PERERkU2y6D5NtWrVQu3atau7FyIiIiKbZVFomjlzJuLi4nDjxo3q7oeIiIjIJll0em7OnDk4e/YsvLy80KBBAzg4OJjNP3jwYLU0R0RERGQrLApNvXr1quY2iIiIiGybRaFp2rRp1d0HERERkU2zaEwTERER0fNG9pGm2rVr49SpU3B3d0etWrUeeG+mq1evVktzRERERLZCdmiaO3cuXF1dAQDz5s17XP0QERER2STZoSkiIqLSPz/IZ599hpEjR8LNza3KjRERERHZksc6punTTz/lqToiIiJ6JjzW0CSEeJyrJyIiInpiePUcERERkQwMTUREREQyMDQRERERycDQRERERCTDYw1NoaGhcHJyepybICIiInoiZN+nyWg0yl6pWq0GAGzatKnqHRERERHZINmhyc3N7YE/nQLcucWAQqFAWVnZIzdGREREZEtkh6YdO3Y8zj6IiIiIbJrs0NSpU6fH2QcRERGRTZMdmipz48YN5OTkoLS01Gx6ixYtHqkpIiIiIltjUWjKz8/HkCFDsHnz5krnc0wTERERPWssuuXA+PHjUVBQgJ9//hlOTk7YsmULVqxYgRdffBH/+c9/qrtHIiIiIquz6EhTWloa1q9fjzZt2sDOzg5+fn549dVXoVarER8fj/Dw8Oruk4iIiMiqLApNRUVF8PT0BADUqlUL+fn5aNy4MYKCgnDw4MFqbZCsw+6mwdotEBGRjXvevissCk0BAQHIzs5GgwYN0LJlS3z99ddo0KABlixZgrp168peT3x8PNatW4eTJ0/CyckJL7/8Mj7//HMEBARINTdv3sR7772H77//HiUlJdDpdFi0aBG8vLykmpycHIwaNQo7duyAi4sLIiIiEB8fjxo1/rd7O3fuRGxsLI4dOwZfX19MnToVkZGRZv0sXLgQs2fPhl6vR8uWLTF//ny0a9fOkpfoqaXRaOCgVAG/77J2K0RE9BRwUKqg0Wis3cYTYVFoGjduHC5fvgwAmDZtGrp3746VK1dCqVQiKSlJ9np27dqFmJgYtG3bFrdv38b/+3//D926dcPx48fh7OwMAJgwYQKSk5Oxdu1aaDQajB49Gr1798a+ffsA3Bl0Hh4eDm9vb6Snp+Py5csYPHgwHBwc8OmnnwIAzp07h/DwcIwcORIrV65Eamoqhg0bhrp160Kn0wEAVq9ejdjYWCxZsgQhISGYN28edDodsrOzpaNqzwMvLy/837ffwGB4vv71QPQgFy5cwCeffIIPPvgAfn5+1m6HyKZoNBqzAxnPNFENioqKRGZmpsjPz3+k9eTl5QkAYteuXUIIIQoKCoSDg4NYu3atVHPixAkBQGRkZAghhNi0aZOws7MTer1eqlm8eLFQq9WipKRECCHEpEmTRLNmzcy21bdvX6HT6aTn7dq1EzExMdLzsrIy4ePjI+Lj4yvt9ebNm8JgMEiPixcvCgDCYDA80mtARLYnOztbdOrUSWRnZ1u7FSKqZgaDQfb3d7X8YG/NmjXRunVruLu7P9J6TEc3ateuDQDIzMzErVu3EBYWJtU0adIE9evXR0ZGBgAgIyMDQUFBZilXp9PBaDTi2LFjUs3d6zDVmNZRWlqKzMxMsxo7OzuEhYVJNfeKj4+HRqORHr6+vo+070RERGTbLDo9N3To0AfOX7ZsWZXXWV5ejvHjx6N9+/Zo3rw5AECv10OpVMLNzc2s1svLC3q9Xqq597Cg6fnDaoxGI4qLi3Ht2jWUlZVVWnPy5MlK+50yZQpiY2Ol50ajkcGJiIjoGWZRaLp27ZrZ81u3buHo0aMoKCjAK6+8YlEjMTExOHr0KPbu3WvR8k+aSqWCSqWydhtERET0hFgUmn788ccK08rLyzFq1Cg0bNiwyusbPXo0Nm7ciN27d6NevXrSdG9vb5SWlqKgoMDsaFNubi68vb2lml9++cVsfbm5udI8039N0+6uUavVcHJygr29Pezt7SutMa2DiIiInm/VMqYJuDMGKDY2FnPnzpW9jBACo0ePxo8//oi0tDT4+/ubzQ8ODoaDgwNSU1OladnZ2cjJyYFWqwUAaLVaHDlyBHl5eVJNSkoK1Go1AgMDpZq712GqMa1DqVQiODjYrKa8vBypqalSDRERET3fHukHe+919uxZ3L59W3Z9TEwMVq1ahfXr18PV1VUag6TRaODk5ASNRoOoqCjExsaidu3aUKvVGDNmDLRaLV566SUAQLdu3RAYGIhBgwZh1qxZ0Ov1mDp1KmJiYqTTZyNHjsSCBQswadIkDB06FGlpaVizZg2Sk5OlXmJjYxEREYE2bdqgXbt2mDdvHoqKijBkyJBqfIWIiIjoaWVRaLp7ADRw54jR5cuXkZycjIiICNnrWbx4MQCgc+fOZtOXL18u3Xhy7ty5sLOzQ58+fcxubmlib2+PjRs3YtSoUdBqtXB2dkZERARmzJgh1fj7+yM5ORkTJkxAQkIC6tWrh6VLl0r3aAKAvn37Ij8/H3FxcdDr9WjVqhW2bNny/Nx7goiIiB5IIYQQVV2oS5cuZs/t7Ozg4eGBV155BUOHDjW7E/fzwmg0QqPRwGAwQK1WW7sdIqpGp06dQnR0NBITE9G4cWNrt0NE1agq398WpZsdO3ZY1BgRERHR06raBoITERERPcssCk25ubkYNGgQfHx8UKNGDemSfdODiIiI6Flj0em5yMhI5OTk4MMPP0TdunWhUCiquy8iIiIim2JRaNq7dy/27NmDVq1aVXM7RERERLbJotNzvr6+sOCiOyIiIqKnlkWhad68eXj//fdx/vz5am6HiIiIyDZZdHqub9++uHHjBho2bIiaNWvCwcHBbP7Vq1erpTkiIiIiW2FRaJo3b141t0FERERk2ywKTVX5qRQiIiKiZ4HFv3dSXl6OM2fOIC8vD+Xl5WbzOnbs+MiNEREREdkSi0LT/v37MWDAAFy4cKHCVXQKhQJlZWXV0hwRERGRrbAoNI0cORJt2rRBcnIyb25JREREzwWLQtPp06fxww8/oFGjRtXdDxEREZFNsug+TSEhIThz5kx190JERERksyw60jRmzBi899570Ov1CAoKqnCfphYtWlRLc0RERES2wqLQ1KdPHwDA0KFDK8zjQHAiIiJ6FlkUms6dO1fdfRARERHZNItCk5+fHwDg+PHjyMnJQWlpqTRPoVBI84mIiIieFRaFpt9//x1vvvkmjhw5AoVCId2ryXTrAZ6eIyIiomeNRVfPjRs3Dv7+/sjLy0PNmjVx9OhR7N69G23atMHOnTuruUUiIiIi67PoSFNGRgbS0tLg7u4OOzs72Nvbo0OHDoiPj8fYsWNx6NCh6u6TiIiIyKosOtJUVlYGV1dXAIC7uzsuXboE4M5Yp+zs7OrrjoiIiMhGWHSkqXnz5vjtt9/g7++PkJAQzJo1C0qlEomJiXjhhRequ0ciIiIiq7MoNE2dOhVFRUUAgBkzZuC1115DaGgo6tSpg9WrV1drg0RERES2wKLQpNPppD83atQIJ0+exNWrV1GrVi3+eC8RERE9kywKTZWpXbt2da2KiIiIyOZYNBCciIiI6HnD0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDJYNTTt3r0br7/+Onx8fKBQKPDTTz+ZzY+MjIRCoTB7dO/e3azm6tWrGDhwINRqNdzc3BAVFYXr16+b1Rw+fBihoaFwdHSEr68vZs2aVaGXtWvXokmTJnB0dERQUBA2bdpU7ftLRERETy+rhqaioiK0bNkSCxcuvG9N9+7dcfnyZenx3Xffmc0fOHAgjh07hpSUFGzcuBG7d+9GdHS0NN9oNKJbt27w8/NDZmYmZs+ejenTpyMxMVGqSU9PR//+/REVFYVDhw6hV69e6NWrF44ePVr9O01ERERPpWr7GRVL9OjRAz169HhgjUqlgre3d6XzTpw4gS1btuDAgQNo06YNAGD+/Pno2bMnvvjiC/j4+GDlypUoLS3FsmXLoFQq0axZM2RlZeHLL7+UwlVCQgK6d++OiRMnAgBmzpyJlJQULFiwAEuWLKl02yUlJSgpKZGeG43GKu8/ERERPT1sfkzTzp074enpiYCAAIwaNQpXrlyR5mVkZMDNzU0KTAAQFhYGOzs7/Pzzz1JNx44doVQqpRqdTofs7Gxcu3ZNqgkLCzPbrk6nQ0ZGxn37io+Ph0ajkR6+vr7Vsr9ERERkm2w6NHXv3h3ffPMNUlNT8fnnn2PXrl3o0aMHysrKAAB6vR6enp5my9SoUQO1a9eGXq+Xary8vMxqTM8fVmOaX5kpU6bAYDBIj4sXLz7azhIREZFNs+rpuYfp16+f9OegoCC0aNECDRs2xM6dO9G1a1crdnbntKFKpbJqD0RERPTk2PSRpnu98MILcHd3x5kzZwAA3t7eyMvLM6u5ffs2rl69Ko2D8vb2Rm5urlmN6fnDau43loqIiIieP09VaPrvf/+LK1euoG7dugAArVaLgoICZGZmSjVpaWkoLy9HSEiIVLN7927cunVLqklJSUFAQABq1aol1aSmppptKyUlBVqt9nHvEhERET0lrBqarl+/jqysLGRlZQEAzp07h6ysLOTk5OD69euYOHEi9u/fj/PnzyM1NRVvvPEGGjVqBJ1OBwBo2rQpunfvjuHDh+OXX37Bvn37MHr0aPTr1w8+Pj4AgAEDBkCpVCIqKgrHjh3D6tWrkZCQgNjYWKmPcePGYcuWLZgzZw5OnjyJ6dOn49dff8Xo0aOf+GtCRERENkpY0Y4dOwSACo+IiAhx48YN0a1bN+Hh4SEcHByEn5+fGD58uNDr9WbruHLliujfv79wcXERarVaDBkyRBQWFprV/Pbbb6JDhw5CpVKJv/zlL+Kzzz6r0MuaNWtE48aNhVKpFM2aNRPJyclV2heDwSAACIPBUPUXgohsWnZ2tujUqZPIzs62ditEVM2q8v2tEEIIK2a2Z4bRaIRGo4HBYIBarbZ2O0RUjU6dOoXo6GgkJiaicePG1m6HiKpRVb6/n6oxTURERETWwtBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDFYNTbt378brr78OHx8fKBQK/PTTT2bzhRCIi4tD3bp14eTkhLCwMJw+fdqs5urVqxg4cCDUajXc3NwQFRWF69evm9UcPnwYoaGhcHR0hK+vL2bNmlWhl7Vr16JJkyZwdHREUFAQNm3aVO37S0RERE8vq4amoqIitGzZEgsXLqx0/qxZs/DVV19hyZIl+Pnnn+Hs7AydToebN29KNQMHDsSxY8eQkpKCjRs3Yvfu3YiOjpbmG41GdOvWDX5+fsjMzMTs2bMxffp0JCYmSjXp6eno378/oqKicOjQIfTq1Qu9evXC0aNHH9/OExER0dNF2AgA4scff5Sel5eXC29vbzF79mxpWkFBgVCpVOK7774TQghx/PhxAUAcOHBAqtm8ebNQKBTijz/+EEIIsWjRIlGrVi1RUlIi1UyePFkEBARIz9955x0RHh5u1k9ISIgYMWLEffu9efOmMBgM0uPixYsCgDAYDJa9AERks7Kzs0WnTp1Edna2tVshompmMBhkf3/b7Jimc+fOQa/XIywsTJqm0WgQEhKCjIwMAEBGRgbc3NzQpk0bqSYsLAx2dnb4+eefpZqOHTtCqVRKNTqdDtnZ2bh27ZpUc/d2TDWm7VQmPj4eGo1Gevj6+j76ThMREZHNstnQpNfrAQBeXl5m0728vKR5er0enp6eZvNr1KiB2rVrm9VUto67t3G/GtP8ykyZMgUGg0F6XLx4saq7SERERE+RGtZu4GmlUqmgUqms3QYRERE9ITZ7pMnb2xsAkJubazY9NzdXmuft7Y28vDyz+bdv38bVq1fNaipbx93buF+NaT4RERGRzYYmf39/eHt7IzU1VZpmNBrx888/Q6vVAgC0Wi0KCgqQmZkp1aSlpaG8vBwhISFSze7du3Hr1i2pJiUlBQEBAahVq5ZUc/d2TDWm7RARERFZNTRdv34dWVlZyMrKAnBn8HdWVhZycnKgUCgwfvx4fPzxx/jPf/6DI0eOYPDgwfDx8UGvXr0AAE2bNkX37t0xfPhw/PLLL9i3bx9Gjx6Nfv36wcfHBwAwYMAAKJVKREVF4dixY1i9ejUSEhIQGxsr9TFu3Dhs2bIFc+bMwcmTJzF9+nT8+uuvGD169JN+SYiIiMhWPYGr+e5rx44dAkCFR0REhBDizm0HPvzwQ+Hl5SVUKpXo2rVrhUt+r1y5Ivr37y9cXFyEWq0WQ4YMEYWFhWY1v/32m+jQoYNQqVTiL3/5i/jss88q9LJmzRrRuHFjoVQqRbNmzURycnKV9qUqlywS0dOFtxwgenZV5ftbIYQQVsxszwyj0QiNRgODwQC1Wm3tdoioGp06dQrR0dFITExE48aNrd0OEVWjqnx/2+yYJiIiIiJbwtBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCSDzYem6dOnQ6FQmD2aNGkizb958yZiYmJQp04duLi4oE+fPsjNzTVbR05ODsLDw1GzZk14enpi4sSJuH37tlnNzp070bp1a6hUKjRq1AhJSUlPYveIiIjoKWHzoQkAmjVrhsuXL0uPvXv3SvMmTJiADRs2YO3atdi1axcuXbqE3r17S/PLysoQHh6O0tJSpKenY8WKFUhKSkJcXJxUc+7cOYSHh6NLly7IysrC+PHjMWzYMGzduvWJ7icRERHZrhrWbkCOGjVqwNvbu8J0g8GAf/3rX1i1ahVeeeUVAMDy5cvRtGlT7N+/Hy+99BK2bduG48ePY/v27fDy8kKrVq0wc+ZMTJ48GdOnT4dSqcSSJUvg7++POXPmAACaNm2KvXv3Yu7cudDpdE90X4mIiMg2PRVHmk6fPg0fHx+88MILGDhwIHJycgAAmZmZuHXrFsLCwqTaJk2aoH79+sjIyAAAZGRkICgoCF5eXlKNTqeD0WjEsWPHpJq712GqMa2jMiUlJTAajWYPIiIienbZfGgKCQlBUlIStmzZgsWLF+PcuXMIDQ1FYWEh9Ho9lEol3NzczJbx8vKCXq8HAOj1erPAZJpvmvegGqPRiOLi4kr7io+Ph0ajkR6+vr7VsbtERERko2z+9FyPHj2kP7do0QIhISHw8/PDmjVr4OTkZLW+pkyZgtjYWOm50WhkcCIiInqG2fyRpnu5ubmhcePGOHPmDLy9vVFaWoqCggKzmtzcXGkMlLe3d4Wr6UzPH1ajVqvvG8xUKhXUarXZg4iIiJ5dT11oun79Os6ePYu6desiODgYDg4OSE1NleZnZ2cjJycHWq0WAKDVanHkyBHk5eVJNSkpKVCr1QgMDJRq7l6Hqca0DiIiIiKbD03/+Mc/sGvXLpw/fx7p6el48803YW9vj/79+0Oj0SAqKgqxsbHYsWMHMjMzMWTIEGi1Wrz00ksAgG7duiEwMBCDBg3Cb7/9hq1bt2Lq1KmIiYmBSqUCAIwcORK///47Jk2ahJMnT2LRokVYs2YNJkyYYM1dJyIiIhti82Oa/vvf/6J///64cuUKPDw80KFDB+zfvx8eHh4AgLlz58LOzg59+vRBSUkJdDodFi1aJC1vb2+PjRs3YtSoUdBqtXB2dkZERARmzJgh1fj7+yM5ORkTJkxAQkIC6tWrh6VLl/J2A0RERCRRCCGEtZt4FhiNRmg0GhgMBo5vInrGnDp1CtHR0UhMTETjxo2t3Q4RVaOqfH/b/Ok5IiIiIltg86fniJ5XN2/elG7kStZ14cIFs/+S9dWvXx+Ojo7WboOeMwxNRDYqJycH0dHR1m6D7vLJJ59YuwX6//FUKVkDQxORjapfvz4SExOt3QaRTapfv761W6DnEEMTkY1ydHTkv6SJiGwIB4ITERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERyVDD2g08K4QQAACj0WjlToiIiEgu0/e26Xv8QRiaqklhYSEAwNfX18qdEBERUVUVFhZCo9E8sEYh5EQreqjy8nJcunQJrq6uUCgU1m6HiKqR0WiEr68vLl68CLVabe12iKgaCSFQWFgIHx8f2Nk9eNQSQxMR0UMYjUZoNBoYDAaGJqLnGAeCExEREcnA0EREREQkA0MTEdFDqFQqTJs2DSqVytqtEJEVcUwTERERkQw80kREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJ8P8BFsk9jtRUUl8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.boxplot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "Mchxb6zJWPKP",
        "outputId": "4ae0188a-e7f5-4e7f-a9b5-528c6c4f1e72"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ONE-HOT ENCODING AND LABELING CERTAIN COLUMNS"
      ],
      "metadata": {
        "id": "4o8hjluzvP-M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2=df.copy()\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "n2nTrNt2XKwS",
        "outputId": "cb775b29-2e53-4486-aa49-a2aebb43e8bd"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       loan_amount  funded_amount   loan_term  interest_rate loan_grade  \\\n",
              "1             2500           2500   60 months          15.27          C   \n",
              "33            5000           5000   36 months           8.90          A   \n",
              "269           1400           1400   36 months           7.90          A   \n",
              "275           1800           1800   36 months           6.62          A   \n",
              "388           6000           6000   60 months           7.90          A   \n",
              "...            ...            ...         ...            ...        ...   \n",
              "38888         1500           1500   36 months          12.29          C   \n",
              "38926         1600           1600   36 months          11.03          C   \n",
              "38947         3000           3000   36 months          11.34          C   \n",
              "38956         2800           2800   36 months           8.00          A   \n",
              "38963         3500           3500   36 months           7.68          A   \n",
              "\n",
              "      loan_sub_grade employment_length home_ownership_status  annual_income  \\\n",
              "1                 C4          < 1 year                  RENT        30000.0   \n",
              "33                A5           2 years                  RENT        24044.0   \n",
              "269               A4            1 year                  RENT        28800.0   \n",
              "275               A2         10+ years                  RENT        18000.0   \n",
              "388               A4          < 1 year                  RENT        30084.0   \n",
              "...              ...               ...                   ...            ...   \n",
              "38888             C5           5 years              MORTGAGE        29000.0   \n",
              "38926             C1           2 years                  RENT        37500.0   \n",
              "38947             C2          < 1 year                  RENT        37440.0   \n",
              "38956             A3           5 years                  RENT        14800.0   \n",
              "38963             A2          < 1 year                  RENT        30000.0   \n",
              "\n",
              "      income_verification_status  ...  public_records  revolving_balance  \\\n",
              "1                Source Verified  ...             0.0             1687.0   \n",
              "33                      Verified  ...             0.0             2224.0   \n",
              "269                 Not Verified  ...             0.0             2016.0   \n",
              "275                 Not Verified  ...             0.0             1792.0   \n",
              "388                     Verified  ...             0.0             2192.0   \n",
              "...                          ...  ...             ...                ...   \n",
              "38888               Not Verified  ...             0.0                0.0   \n",
              "38926               Not Verified  ...             0.0                0.0   \n",
              "38947               Not Verified  ...             0.0                0.0   \n",
              "38956               Not Verified  ...             0.0             1946.0   \n",
              "38963               Not Verified  ...             0.0             2096.0   \n",
              "\n",
              "      revolving_utilization_ratio  total_accounts  \\\n",
              "1                             9.4             4.0   \n",
              "33                           29.3            16.0   \n",
              "269                           9.9             7.0   \n",
              "275                          39.8            12.0   \n",
              "388                          10.0            15.0   \n",
              "...                           ...             ...   \n",
              "38888                         0.0             6.0   \n",
              "38926                        55.9            15.0   \n",
              "38947                        55.9            14.0   \n",
              "38956                         6.5            25.0   \n",
              "38963                        46.6             7.0   \n",
              "\n",
              "       non_medical_collections_last_12_months  currently_delinquent_accounts  \\\n",
              "1                                         0.0                            0.0   \n",
              "33                                        0.0                            0.0   \n",
              "269                                       0.0                            0.0   \n",
              "275                                       0.0                            0.0   \n",
              "388                                       0.0                            0.0   \n",
              "...                                       ...                            ...   \n",
              "38888                                     0.0                            0.0   \n",
              "38926                                     0.0                            0.0   \n",
              "38947                                     0.0                            0.0   \n",
              "38956                                     0.0                            0.0   \n",
              "38963                                     0.0                            0.0   \n",
              "\n",
              "       total_collection_amount  total_current_balance  \\\n",
              "1                          0.0                81008.5   \n",
              "33                         0.0                81008.5   \n",
              "269                        0.0                81008.5   \n",
              "275                        0.0                81008.5   \n",
              "388                        0.0                81008.5   \n",
              "...                        ...                    ...   \n",
              "38888                      0.0                81008.5   \n",
              "38926                      0.0                81008.5   \n",
              "38947                      0.0                81008.5   \n",
              "38956                      0.0                81008.5   \n",
              "38963                      0.0                81008.5   \n",
              "\n",
              "       total_revolving_credit_limit  loan_default_indicator  \n",
              "1                           23800.0                     1.0  \n",
              "33                          23800.0                     0.0  \n",
              "269                         23800.0                     0.0  \n",
              "275                         23800.0                     0.0  \n",
              "388                         23800.0                     1.0  \n",
              "...                             ...                     ...  \n",
              "38888                       23800.0                     0.0  \n",
              "38926                       23800.0                     0.0  \n",
              "38947                       23800.0                     1.0  \n",
              "38956                       23800.0                     0.0  \n",
              "38963                       23800.0                     0.0  \n",
              "\n",
              "[1975 rows x 25 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f64db6fb-0a51-4d3c-8098-f999c7d499da\" 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>loan_amount</th>\n",
              "      <th>funded_amount</th>\n",
              "      <th>loan_term</th>\n",
              "      <th>interest_rate</th>\n",
              "      <th>loan_grade</th>\n",
              "      <th>loan_sub_grade</th>\n",
              "      <th>employment_length</th>\n",
              "      <th>home_ownership_status</th>\n",
              "      <th>annual_income</th>\n",
              "      <th>income_verification_status</th>\n",
              "      <th>...</th>\n",
              "      <th>public_records</th>\n",
              "      <th>revolving_balance</th>\n",
              "      <th>revolving_utilization_ratio</th>\n",
              "      <th>total_accounts</th>\n",
              "      <th>non_medical_collections_last_12_months</th>\n",
              "      <th>currently_delinquent_accounts</th>\n",
              "      <th>total_collection_amount</th>\n",
              "      <th>total_current_balance</th>\n",
              "      <th>total_revolving_credit_limit</th>\n",
              "      <th>loan_default_indicator</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2500</td>\n",
              "      <td>2500</td>\n",
              "      <td>60 months</td>\n",
              "      <td>15.27</td>\n",
              "      <td>C</td>\n",
              "      <td>C4</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1687.0</td>\n",
              "      <td>9.4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>36 months</td>\n",
              "      <td>8.90</td>\n",
              "      <td>A</td>\n",
              "      <td>A5</td>\n",
              "      <td>2 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>24044.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2224.0</td>\n",
              "      <td>29.3</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>269</th>\n",
              "      <td>1400</td>\n",
              "      <td>1400</td>\n",
              "      <td>36 months</td>\n",
              "      <td>7.90</td>\n",
              "      <td>A</td>\n",
              "      <td>A4</td>\n",
              "      <td>1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>28800.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>9.9</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>275</th>\n",
              "      <td>1800</td>\n",
              "      <td>1800</td>\n",
              "      <td>36 months</td>\n",
              "      <td>6.62</td>\n",
              "      <td>A</td>\n",
              "      <td>A2</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>18000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1792.0</td>\n",
              "      <td>39.8</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>388</th>\n",
              "      <td>6000</td>\n",
              "      <td>6000</td>\n",
              "      <td>60 months</td>\n",
              "      <td>7.90</td>\n",
              "      <td>A</td>\n",
              "      <td>A4</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>30084.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2192.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>1.0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38888</th>\n",
              "      <td>1500</td>\n",
              "      <td>1500</td>\n",
              "      <td>36 months</td>\n",
              "      <td>12.29</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>5 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>29000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38926</th>\n",
              "      <td>1600</td>\n",
              "      <td>1600</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.03</td>\n",
              "      <td>C</td>\n",
              "      <td>C1</td>\n",
              "      <td>2 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>37500.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>55.9</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38947</th>\n",
              "      <td>3000</td>\n",
              "      <td>3000</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.34</td>\n",
              "      <td>C</td>\n",
              "      <td>C2</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>37440.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>55.9</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38956</th>\n",
              "      <td>2800</td>\n",
              "      <td>2800</td>\n",
              "      <td>36 months</td>\n",
              "      <td>8.00</td>\n",
              "      <td>A</td>\n",
              "      <td>A3</td>\n",
              "      <td>5 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>14800.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1946.0</td>\n",
              "      <td>6.5</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38963</th>\n",
              "      <td>3500</td>\n",
              "      <td>3500</td>\n",
              "      <td>36 months</td>\n",
              "      <td>7.68</td>\n",
              "      <td>A</td>\n",
              "      <td>A2</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2096.0</td>\n",
              "      <td>46.6</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1975 rows × 25 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-f64db6fb-0a51-4d3c-8098-f999c7d499da')\"\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-f64db6fb-0a51-4d3c-8098-f999c7d499da 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-f64db6fb-0a51-4d3c-8098-f999c7d499da');\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-b729ec00-6b86-405c-8381-af62c2f6a11c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b729ec00-6b86-405c-8381-af62c2f6a11c')\"\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-b729ec00-6b86-405c-8381-af62c2f6a11c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9fa00de0-da80-4f4b-b454-77cd6619ec15\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df2')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9fa00de0-da80-4f4b-b454-77cd6619ec15 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df2');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2"
            }
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "df2['loan_grade'] = le.fit_transform(df2['loan_grade'])\n",
        "df2['loan_sub_grade'] = le.fit_transform(df2['loan_sub_grade'])\n"
      ],
      "metadata": {
        "id": "8p1kGz7LW2Wk"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = pd.get_dummies(df2, columns=['home_ownership_status', 'income_verification_status'], drop_first=True)"
      ],
      "metadata": {
        "id": "gntRidqhXdNT"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "-lJ3qNbVXVOT",
        "outputId": "013a8ec3-a035-44a5-fb84-064fc3cf32fd"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       loan_amount  funded_amount   loan_term  interest_rate  loan_grade  \\\n",
              "1             2500           2500   60 months          15.27           2   \n",
              "33            5000           5000   36 months           8.90           0   \n",
              "269           1400           1400   36 months           7.90           0   \n",
              "275           1800           1800   36 months           6.62           0   \n",
              "388           6000           6000   60 months           7.90           0   \n",
              "...            ...            ...         ...            ...         ...   \n",
              "38888         1500           1500   36 months          12.29           2   \n",
              "38926         1600           1600   36 months          11.03           2   \n",
              "38947         3000           3000   36 months          11.34           2   \n",
              "38956         2800           2800   36 months           8.00           0   \n",
              "38963         3500           3500   36 months           7.68           0   \n",
              "\n",
              "       loan_sub_grade employment_length  annual_income  debt_to_income_ratio  \\\n",
              "1                  13          < 1 year        30000.0                  1.00   \n",
              "33                  4           2 years        24044.0                 11.93   \n",
              "269                 3            1 year        28800.0                 10.00   \n",
              "275                 1         10+ years        18000.0                 23.80   \n",
              "388                 3          < 1 year        30084.0                  1.68   \n",
              "...               ...               ...            ...                   ...   \n",
              "38888              14           5 years        29000.0                  4.18   \n",
              "38926              10           2 years        37500.0                 15.84   \n",
              "38947              11          < 1 year        37440.0                  7.72   \n",
              "38956               2           5 years        14800.0                  5.48   \n",
              "38963               1          < 1 year        30000.0                  2.52   \n",
              "\n",
              "       delinquencies_last_2_years  ... currently_delinquent_accounts  \\\n",
              "1                             0.0  ...                           0.0   \n",
              "33                            0.0  ...                           0.0   \n",
              "269                           0.0  ...                           0.0   \n",
              "275                           0.0  ...                           0.0   \n",
              "388                           0.0  ...                           0.0   \n",
              "...                           ...  ...                           ...   \n",
              "38888                         0.0  ...                           0.0   \n",
              "38926                         0.0  ...                           0.0   \n",
              "38947                         0.0  ...                           0.0   \n",
              "38956                         0.0  ...                           0.0   \n",
              "38963                         0.0  ...                           0.0   \n",
              "\n",
              "       total_collection_amount  total_current_balance  \\\n",
              "1                          0.0                81008.5   \n",
              "33                         0.0                81008.5   \n",
              "269                        0.0                81008.5   \n",
              "275                        0.0                81008.5   \n",
              "388                        0.0                81008.5   \n",
              "...                        ...                    ...   \n",
              "38888                      0.0                81008.5   \n",
              "38926                      0.0                81008.5   \n",
              "38947                      0.0                81008.5   \n",
              "38956                      0.0                81008.5   \n",
              "38963                      0.0                81008.5   \n",
              "\n",
              "       total_revolving_credit_limit  loan_default_indicator  \\\n",
              "1                           23800.0                     1.0   \n",
              "33                          23800.0                     0.0   \n",
              "269                         23800.0                     0.0   \n",
              "275                         23800.0                     0.0   \n",
              "388                         23800.0                     1.0   \n",
              "...                             ...                     ...   \n",
              "38888                       23800.0                     0.0   \n",
              "38926                       23800.0                     0.0   \n",
              "38947                       23800.0                     1.0   \n",
              "38956                       23800.0                     0.0   \n",
              "38963                       23800.0                     0.0   \n",
              "\n",
              "       home_ownership_status_OTHER  home_ownership_status_OWN  \\\n",
              "1                            False                      False   \n",
              "33                           False                      False   \n",
              "269                          False                      False   \n",
              "275                          False                      False   \n",
              "388                          False                      False   \n",
              "...                            ...                        ...   \n",
              "38888                        False                      False   \n",
              "38926                        False                      False   \n",
              "38947                        False                      False   \n",
              "38956                        False                      False   \n",
              "38963                        False                      False   \n",
              "\n",
              "       home_ownership_status_RENT  income_verification_status_Source Verified  \\\n",
              "1                            True                                        True   \n",
              "33                           True                                       False   \n",
              "269                          True                                       False   \n",
              "275                          True                                       False   \n",
              "388                          True                                       False   \n",
              "...                           ...                                         ...   \n",
              "38888                       False                                       False   \n",
              "38926                        True                                       False   \n",
              "38947                        True                                       False   \n",
              "38956                        True                                       False   \n",
              "38963                        True                                       False   \n",
              "\n",
              "       income_verification_status_Verified  \n",
              "1                                    False  \n",
              "33                                    True  \n",
              "269                                  False  \n",
              "275                                  False  \n",
              "388                                   True  \n",
              "...                                    ...  \n",
              "38888                                False  \n",
              "38926                                False  \n",
              "38947                                False  \n",
              "38956                                False  \n",
              "38963                                False  \n",
              "\n",
              "[1975 rows x 28 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-25374d40-fc9e-4375-b754-22a0c30b71e5\" 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>loan_amount</th>\n",
              "      <th>funded_amount</th>\n",
              "      <th>loan_term</th>\n",
              "      <th>interest_rate</th>\n",
              "      <th>loan_grade</th>\n",
              "      <th>loan_sub_grade</th>\n",
              "      <th>employment_length</th>\n",
              "      <th>annual_income</th>\n",
              "      <th>debt_to_income_ratio</th>\n",
              "      <th>delinquencies_last_2_years</th>\n",
              "      <th>...</th>\n",
              "      <th>currently_delinquent_accounts</th>\n",
              "      <th>total_collection_amount</th>\n",
              "      <th>total_current_balance</th>\n",
              "      <th>total_revolving_credit_limit</th>\n",
              "      <th>loan_default_indicator</th>\n",
              "      <th>home_ownership_status_OTHER</th>\n",
              "      <th>home_ownership_status_OWN</th>\n",
              "      <th>home_ownership_status_RENT</th>\n",
              "      <th>income_verification_status_Source Verified</th>\n",
              "      <th>income_verification_status_Verified</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2500</td>\n",
              "      <td>2500</td>\n",
              "      <td>60 months</td>\n",
              "      <td>15.27</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>36 months</td>\n",
              "      <td>8.90</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2 years</td>\n",
              "      <td>24044.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>269</th>\n",
              "      <td>1400</td>\n",
              "      <td>1400</td>\n",
              "      <td>36 months</td>\n",
              "      <td>7.90</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1 year</td>\n",
              "      <td>28800.0</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>275</th>\n",
              "      <td>1800</td>\n",
              "      <td>1800</td>\n",
              "      <td>36 months</td>\n",
              "      <td>6.62</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>18000.0</td>\n",
              "      <td>23.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>388</th>\n",
              "      <td>6000</td>\n",
              "      <td>6000</td>\n",
              "      <td>60 months</td>\n",
              "      <td>7.90</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>30084.0</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38888</th>\n",
              "      <td>1500</td>\n",
              "      <td>1500</td>\n",
              "      <td>36 months</td>\n",
              "      <td>12.29</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>5 years</td>\n",
              "      <td>29000.0</td>\n",
              "      <td>4.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38926</th>\n",
              "      <td>1600</td>\n",
              "      <td>1600</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.03</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>2 years</td>\n",
              "      <td>37500.0</td>\n",
              "      <td>15.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38947</th>\n",
              "      <td>3000</td>\n",
              "      <td>3000</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.34</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>37440.0</td>\n",
              "      <td>7.72</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38956</th>\n",
              "      <td>2800</td>\n",
              "      <td>2800</td>\n",
              "      <td>36 months</td>\n",
              "      <td>8.00</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5 years</td>\n",
              "      <td>14800.0</td>\n",
              "      <td>5.48</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38963</th>\n",
              "      <td>3500</td>\n",
              "      <td>3500</td>\n",
              "      <td>36 months</td>\n",
              "      <td>7.68</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>2.52</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>81008.5</td>\n",
              "      <td>23800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1975 rows × 28 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-25374d40-fc9e-4375-b754-22a0c30b71e5')\"\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-25374d40-fc9e-4375-b754-22a0c30b71e5 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-25374d40-fc9e-4375-b754-22a0c30b71e5');\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-21d01bbe-83a7-41d6-bb96-3c14779a52d0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-21d01bbe-83a7-41d6-bb96-3c14779a52d0')\"\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-21d01bbe-83a7-41d6-bb96-3c14779a52d0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_cf288f33-e33e-481b-977f-90449017ea2d\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df2')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_cf288f33-e33e-481b-977f-90449017ea2d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df2');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2"
            }
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['credit_history_length'] = pd.to_datetime(df2['credit_history_length'])\n",
        "df2['credit_history_length'] = df2['credit_history_length'].dt.year"
      ],
      "metadata": {
        "id": "6bQt29tVcPsh"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J73AtsNRYXFK",
        "outputId": "5fa2ba6b-e8d3-4d57-d28d-6aecfd534a2a"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['loan_amount', 'funded_amount', 'loan_term', 'interest_rate',\n",
              "       'loan_grade', 'loan_sub_grade', 'employment_length', 'annual_income',\n",
              "       'debt_to_income_ratio', 'delinquencies_last_2_years',\n",
              "       'credit_history_length', 'recent_credit_inquiries', 'open_accounts',\n",
              "       'public_records', 'revolving_balance', 'revolving_utilization_ratio',\n",
              "       'total_accounts', 'non_medical_collections_last_12_months',\n",
              "       'currently_delinquent_accounts', 'total_collection_amount',\n",
              "       'total_current_balance', 'total_revolving_credit_limit',\n",
              "       'loan_default_indicator', 'home_ownership_status_OTHER',\n",
              "       'home_ownership_status_OWN', 'home_ownership_status_RENT',\n",
              "       'income_verification_status_Source Verified',\n",
              "       'income_verification_status_Verified'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6FHt-PXbbDRz",
        "outputId": "d1af5651-e2a3-467b-df2c-54bb4df2f9c3"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['loan_amount', 'funded_amount', 'loan_term', 'interest_rate',\n",
              "       'loan_grade', 'loan_sub_grade', 'employment_length',\n",
              "       'home_ownership_status', 'annual_income', 'income_verification_status',\n",
              "       'debt_to_income_ratio', 'delinquencies_last_2_years',\n",
              "       'credit_history_length', 'recent_credit_inquiries', 'open_accounts',\n",
              "       'public_records', 'revolving_balance', 'revolving_utilization_ratio',\n",
              "       'total_accounts', 'non_medical_collections_last_12_months',\n",
              "       'currently_delinquent_accounts', 'total_collection_amount',\n",
              "       'total_current_balance', 'total_revolving_credit_limit',\n",
              "       'loan_default_indicator'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MODELS FOR PREDICTING DEFAULTERS"
      ],
      "metadata": {
        "id": "2pO8EKLevZog"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DESCISION TREE MODEL"
      ],
      "metadata": {
        "id": "haG_Ym6Bvv1w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df2.drop('loan_default_indicator', axis=1)\n",
        "y = df2['loan_default_indicator']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "dt_model = DecisionTreeClassifier(random_state=42)\n",
        "dt_model.fit(X_train, y_train)\n",
        "y_pred = dt_model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted Labels\")\n",
        "plt.ylabel(\"True Labels\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "Bssb8ljdX3l9",
        "outputId": "1f8ec62b-76df-4c20-bb89-9650e65b80c5"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7164556962025317\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y9PjAetHc_Xi",
        "outputId": "d195111e-ac1f-45cc-954b-50325a216ff8"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.81      0.83       331\n",
            "           1       0.19      0.23      0.21        64\n",
            "\n",
            "    accuracy                           0.72       395\n",
            "   macro avg       0.52      0.52      0.52       395\n",
            "weighted avg       0.74      0.72      0.73       395\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## RANDOM FOREST MODEL"
      ],
      "metadata": {
        "id": "M9XO6nTfv3hp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model = RandomForestClassifier(random_state=42)\n",
        "rf_model.fit(X_train, y_train)\n",
        "y_pred = rf_model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted Labels\")\n",
        "plt.ylabel(\"True Labels\")\n",
        "plt.show()\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "id": "I9q-emfedRAb",
        "outputId": "032cfaa4-ddfd-4074-bf27-88e63d586cc7"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8329113924050633\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.99      0.91       331\n",
            "           1       0.00      0.00      0.00        64\n",
            "\n",
            "    accuracy                           0.83       395\n",
            "   macro avg       0.42      0.50      0.45       395\n",
            "weighted avg       0.70      0.83      0.76       395\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LOGISTIC REGRESSION MODEL"
      ],
      "metadata": {
        "id": "kfhCWgwMwKAD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr_model = LogisticRegression(max_iter=1000, random_state=42)\n",
        "lr_model.fit(X_train, y_train)\n",
        "y_pred = lr_model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted Labels\")\n",
        "plt.ylabel(\"True Labels\")\n",
        "plt.show()\n",
        "print(classification_report(y_test, y_pred, zero_division=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "id": "5acJmxa3de7c",
        "outputId": "715c2d8e-ff32-4f7a-eff4-0622ea3156da"
      },
      "execution_count": 144,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.8379746835443038\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      1.00      0.91       331\n",
            "           1       1.00      0.00      0.00        64\n",
            "\n",
            "    accuracy                           0.84       395\n",
            "   macro avg       0.92      0.50      0.46       395\n",
            "weighted avg       0.86      0.84      0.76       395\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DEEP NEURAL NETWORK"
      ],
      "metadata": {
        "id": "e12mkziMwbnr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Input(shape=(X_train.shape[1],)),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dense(32, activation='relu'),\n",
        "    tf.keras.layers.Dense(16, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
        "loss, accuracy = model.evaluate(X_test, y_test)\n",
        "print(\"Loss:\", loss)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "y_pred_prob = model.predict(X_test)\n",
        "y_pred = (y_pred_prob > 0.5).astype(int)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted Labels\")\n",
        "plt.ylabel(\"True Labels\")\n",
        "plt.show()\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "F0bq4bh8gPWK",
        "outputId": "db80198d-d6f7-4725-9122-00023b851cc7"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6994 - loss: 550.3131 - val_accuracy: 0.8228 - val_loss: 50.3402\n",
            "Epoch 2/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7517 - loss: 46.5446 - val_accuracy: 0.8259 - val_loss: 65.4973\n",
            "Epoch 3/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.7260 - loss: 57.2093 - val_accuracy: 0.8228 - val_loss: 21.5092\n",
            "Epoch 4/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7222 - loss: 44.0971 - val_accuracy: 0.8259 - val_loss: 267.8190\n",
            "Epoch 5/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7601 - loss: 183.7213 - val_accuracy: 0.8259 - val_loss: 51.4511\n",
            "Epoch 6/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7099 - loss: 50.3463 - val_accuracy: 0.8259 - val_loss: 51.2845\n",
            "Epoch 7/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7287 - loss: 126.6184 - val_accuracy: 0.7152 - val_loss: 12.5491\n",
            "Epoch 8/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.6969 - loss: 50.4785 - val_accuracy: 0.8259 - val_loss: 200.5104\n",
            "Epoch 9/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7293 - loss: 148.1577 - val_accuracy: 0.8259 - val_loss: 199.1207\n",
            "Epoch 10/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.7418 - loss: 89.8358 - val_accuracy: 0.6108 - val_loss: 9.5481\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6502 - loss: 7.3246\n",
            "Loss: 7.02045202255249\n",
            "Accuracy: 0.6582278609275818\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.73      0.78       331\n",
            "           1       0.17      0.28      0.21        64\n",
            "\n",
            "    accuracy                           0.66       395\n",
            "   macro avg       0.50      0.51      0.50       395\n",
            "weighted avg       0.73      0.66      0.69       395\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SUPPORT VECTOR MACHINE (SVM)"
      ],
      "metadata": {
        "id": "gXMtbFTAwuvY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svm_model = SVC(kernel='linear', random_state=42,probability=True)\n",
        "svm_model.fit(X_train, y_train)\n",
        "y_pred = svm_model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted Labels\")\n",
        "plt.ylabel(\"True Labels\")\n",
        "plt.show()\n",
        "print(classification_report(y_test, y_pred,zero_division=1))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "id": "T4wG6tKIhUNf",
        "outputId": "53cc94a9-a495-4f14-c18b-aecf7b2b4830"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8379746835443038\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      1.00      0.91       331\n",
            "           1       1.00      0.00      0.00        64\n",
            "\n",
            "    accuracy                           0.84       395\n",
            "   macro avg       0.92      0.50      0.46       395\n",
            "weighted avg       0.86      0.84      0.76       395\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ARTIFICIAL NEURAL NETWORK"
      ],
      "metadata": {
        "id": "xXPKpO1OxEV6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Input(shape=(X_train.shape[1],)),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dense(32, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
        "loss, accuracy = model.evaluate(X_test, y_test)\n",
        "print(\"Loss:\", loss)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "y_pred_prob = model.predict(X_test)\n",
        "y_pred = (y_pred_prob > 0.5).astype(int)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted Labels\")\n",
        "plt.ylabel(\"True Labels\")\n",
        "plt.show()\n",
        "print(classification_report(y_test, y_pred,zero_division=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qTJY9cDsiL3W",
        "outputId": "7e94d632-78b6-45ae-cd6e-470f4df26822"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 16ms/step - accuracy: 0.6147 - loss: 1648.2504 - val_accuracy: 0.8259 - val_loss: 238.6330\n",
            "Epoch 2/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7408 - loss: 128.7613 - val_accuracy: 0.8006 - val_loss: 30.7602\n",
            "Epoch 3/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7688 - loss: 30.4441 - val_accuracy: 0.8259 - val_loss: 42.0298\n",
            "Epoch 4/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7623 - loss: 31.0230 - val_accuracy: 0.8259 - val_loss: 32.3319\n",
            "Epoch 5/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7282 - loss: 27.0571 - val_accuracy: 0.7880 - val_loss: 16.3868\n",
            "Epoch 6/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.6864 - loss: 75.0839 - val_accuracy: 0.8259 - val_loss: 66.2863\n",
            "Epoch 7/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7682 - loss: 48.0605 - val_accuracy: 0.3006 - val_loss: 25.6029\n",
            "Epoch 8/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7115 - loss: 37.3538 - val_accuracy: 0.8259 - val_loss: 76.4779\n",
            "Epoch 9/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7547 - loss: 157.3618 - val_accuracy: 0.8259 - val_loss: 100.8574\n",
            "Epoch 10/10\n",
            "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.7300 - loss: 90.5637 - val_accuracy: 0.8259 - val_loss: 98.6117\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8336 - loss: 94.0157 \n",
            "Loss: 92.5887680053711\n",
            "Accuracy: 0.8379746675491333\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      1.00      0.91       331\n",
            "           1       1.00      0.00      0.00        64\n",
            "\n",
            "    accuracy                           0.84       395\n",
            "   macro avg       0.92      0.50      0.46       395\n",
            "weighted avg       0.86      0.84      0.76       395\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## VISUALIZATION OF ALL THE MODELS"
      ],
      "metadata": {
        "id": "J_gYSkdyzami"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('ANN Model - Training and Validation Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('DNN Model - Training and Validation Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "_YYBc93xkJSO",
        "outputId": "d911b001-213c-4576-ae4a-97c28fa45185"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAHHCAYAAABZbpmkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB2dElEQVR4nO3deVhV1f7H8fdhnkGRMWfFAXPKES2H5IamNqiZZmVl2S3UzGuD9+aQDaZN/srS6pZaaZbdLLPUyMxKSU3TzNlS0RRwYpZ5//44nCNHMAGBw/B5Pc95hD2cvTagfFx7fdcyGYZhICIiIlJDOdi7ASIiIiIVSWFHREREajSFHREREanRFHZERESkRlPYERERkRpNYUdERERqNIUdERERqdEUdkRERKRGU9gRERGRGk1hR6SCLVq0CJPJxJEjR0p97owZMzCZTOXfqArWp08f+vTpU6Zz77nnHho3blyu7alqvv/+e0wmE99//32lXvfIkSOYTCYWLVpk3VaanzGTycSMGTPKtU1X8rMiUlIKO1Ku3nzzTUwmE926dbvkMSaTCZPJxMsvv1xknyUY/PLLL9Ztln+Mg4KCyMjIKHJO48aNGTRo0GXb1qdPH0wmE2FhYcXuj4mJsbbt008/vez7VTeWX3QleZUlmEn5uummm/Dw8CA1NfWSx4waNQoXFxfOnDlTiS0rvT179jBjxowq9XNlCZw18e+6FKWwI+VqyZIlNG7cmC1btnDo0KG/PfbFF18sNrxcSmJiIvPnz7+i9rm5uXHo0CG2bNlSZN+SJUtwc3O7ovevygICAvjggw9sXh06dKBevXpFtgcEBFzRtb755hu++eabMp37zjvvsH///iu6fk0watQozp8/z4oVK4rdn5GRwRdffEH//v3x9/cv83Weeuopzp8/X+bzS2LPnj08/fTTxYadK/lZESkphR0pN4cPH2bTpk288sorBAQEsGTJkkse26FDBxISEliwYEGJ379Dhw68+OKLV/QPc7NmzWjZsiUfffSRzfbMzExWrFjBwIEDy/zeVZ2npyd33nmnzeuqq64qdrunp6f1PMMwSv01d3FxwcXFpUztdHZ2xtXVtUzn1iQ33XQT3t7eLF26tNj9X3zxBenp6YwaNeqKruPk5GTXkH8lPysiJaWwI+VmyZIl1KlTh4EDBzJs2LC/DTs9e/bk+uuvZ86cOSX+RTpt2jQSEhKuuHdn5MiRfPzxx+Tn51u3ffnll2RkZDB8+PBiz/n1118ZMGAAPj4+eHl50a9fP37++ecix+3evZvrr78ed3d36tevz7PPPmtzncJWr17Nddddh6enJ97e3gwcOJDdu3df0b2VB8tjwbVr19K5c2fc3d156623AFi4cCHXX389gYGBuLq6Eh4eXuz34+JxGJZHBp988gnPPfcc9evXx83NjX79+hXpAbx4zI7l8dtLL73E22+/TbNmzXB1daVLly5s3bq1yLWXL19OeHg4bm5uXH311axYsaLE44C++OILBg4cSGhoKK6urjRr1oxnnnmGvLy8Ivd39dVXs2fPHvr27YuHhwdXXXUVc+bMKfKex48f55ZbbsHT05PAwEAeffRRsrKyLtsWd3d3hgwZwrp160hMTCyyf+nSpXh7e3PTTTdx9uxZJk+eTNu2bfHy8sLHx4cBAwawc+fOy16nuDE7WVlZPProowQEBFivcfz48SLnHj16lIcffpiWLVvi7u6Ov78/t912m00PzqJFi7jtttsA6Nu3r/VRqWW8UnFjdhITExkzZgxBQUG4ubnRvn17Fi9ebHNMaX8uyurPP//ktttuo27dunh4eNC9e3e++uqrIse9/vrrtGnTBg8PD+rUqUPnzp1tgmpqaioTJ06kcePGuLq6EhgYyD/+8Q+2b99ebm2VS3OydwOk5liyZAlDhgzBxcWFkSNHMn/+fLZu3UqXLl2KPX7GjBn06tWL+fPnM2nSpMu+/3XXXWcNSA899BDu7u5laucdd9zBjBkz+P7777n++usB8y+Ofv36ERgYWOT43bt3c9111+Hj48Pjjz+Os7Mzb731Fn369GHDhg3W8Unx8fH07duX3NxcnnzySTw9PXn77beLbecHH3zA6NGjiYqKYvbs2WRkZDB//nyuvfZafv31V7sP0N2/fz8jR47kwQcf5IEHHqBly5YAzJ8/nzZt2nDTTTfh5OTEl19+ycMPP0x+fj7R0dGXfd8XXngBBwcHJk+eTHJyMnPmzGHUqFFs3rz5sucuXbqU1NRUHnzwQUwmE3PmzGHIkCH8+eefODs7A/DVV19x++2307ZtW2bNmsW5c+cYM2YMV111VYnue9GiRXh5eTFp0iS8vLz47rvvmDZtGikpKbz44os2x547d47+/fszZMgQhg8fzqeffsoTTzxB27ZtGTBgAADnz5+nX79+xMXFMWHCBEJDQ/nggw/47rvvStSeUaNGsXjxYj755BPGjRtn3X727FnWrl3LyJEjcXd3Z/fu3Xz++efcdtttNGnShISEBN566y169+7Nnj17CA0NLdH1LO6//34+/PBD7rjjDnr06MF3331XbK/n1q1b2bRpEyNGjKB+/focOXKE+fPn06dPH/bs2YOHhwe9evViwoQJvPbaa/z73/+mdevWANY/L3b+/Hn69OnDoUOHGDduHE2aNGH58uXcc889JCUl8cgjj9gcX5Kfi7JKSEigR48eZGRkMGHCBPz9/Vm8eDE33XQTn376KbfeeitgfvQ6YcIEhg0bxiOPPEJmZia//fYbmzdv5o477gDgn//8J59++injxo0jPDycM2fO8NNPP7F3716uueaaK2qnlIAhUg5++eUXAzBiYmIMwzCM/Px8o379+sYjjzxS5FjAiI6ONgzDMPr27WsEBwcbGRkZhmEYxsKFCw3A2Lp1q/X46dOnG4Bx6tQpY8OGDQZgvPLKK9b9jRo1MgYOHHjZNvbu3dto06aNYRiG0blzZ2PMmDGGYRjGuXPnDBcXF2Px4sXG+vXrDcBYvny59bxbbrnFcHFxMf744w/rthMnThje3t5Gr169rNsmTpxoAMbmzZut2xITEw1fX18DMA4fPmwYhmGkpqYafn5+xgMPPGDTvvj4eMPX19dmu+XeK8rAgQONRo0a2Wxr1KiRARhr1qwpcrzl+1RYVFSU0bRpU5ttvXv3Nnr37m393PJ1bd26tZGVlWXd/n//938GYOzatcu6bfTo0TZtOnz4sAEY/v7+xtmzZ63bv/jiCwMwvvzyS+u2tm3bGvXr1zdSU1Ot277//nsDKHKfxSnu/h588EHDw8PDyMzMtLk/wHj//fet27Kysozg4GBj6NCh1m1z5841AOOTTz6xbktPTzeaN29uAMb69ev/tj25ublGSEiIERERYbN9wYIFBmCsXbvWMAzDyMzMNPLy8myOOXz4sOHq6mrMnDnTZhtgLFy40Lrt4p+xHTt2GIDx8MMP27zfHXfcYQDG9OnTrduK+3rFxsYW+dosX778kvd78c+K5Wv24YcfWrdlZ2cbERERhpeXl5GSkmJzLyX5uShOcX/XL2b5O/3jjz9at6WmphpNmjQxGjdubP2a33zzzdZ/Wy7F19fX+u+eVD49xpJysWTJEoKCgujbty9grri6/fbbWbZsWZFHAIXNmDGD+Pj4Eo/d6dWrF3379i3V46/i3HHHHXz22WdkZ2fz6aef4ujoaP1fWmF5eXl888033HLLLTRt2tS6PSQkhDvuuIOffvqJlJQUAL7++mu6d+9O165drccFBAQUGVMRExNDUlISI0eO5PTp09aXo6Mj3bp1Y/369WW+r/LSpEkToqKiimwv3EuVnJzM6dOn6d27N3/++SfJycmXfd97773XZnzGddddB5gfFVzO7bffTp06dS557okTJ9i1axd33303Xl5e1uN69+5N27ZtL/v+YHt/qampnD59muuuu46MjAz27dtnc6yXlxd33nmn9XMXFxe6du1qcy9ff/01ISEhDBs2zLrNw8ODsWPHlqg9jo6OjBgxgtjYWJtHQ0uXLiUoKIh+/foB4OrqioOD+Z/zvLw8zpw5g5eXFy1btiz1Y5Kvv/4agAkTJthsnzhxYpFjC3+9cnJyOHPmDM2bN8fPz6/Mj2e+/vprgoODGTlypHWbs7MzEyZMIC0tjQ0bNtgcf7mfiyvx9ddf07VrV6699lrrNi8vL8aOHcuRI0fYs2cPAH5+fhw/fvxvH5/5+fmxefNmTpw4ccXtktJT2JErlpeXx7Jly+jbty+HDx/m0KFDHDp0iG7dupGQkMC6desueW5ZwktpA1JxRowYQXJyMqtXr2bJkiUMGjQIb2/vIsedOnWKjIwM62Ocwlq3bk1+fj7Hjh0DzOMXiitrv/jcgwcPAnD99dcTEBBg8/rmm2+KHZ/xd9LS0oiPj7e+Tp06Varzi9OkSZNit2/cuJHIyEg8PT3x8/MjICCAf//73wAlCjsNGza0+dzyS+rcuXNXfO7Ro0cBaN68eZFzi9tWnN27d3Prrbfi6+uLj48PAQEB1kBz8f3Vr1+/yFiXOnXq2NzL0aNHad68eZHjivt5uhRLWLaM/zh+/Dg//vgjI0aMwNHREYD8/HxeffVVwsLCcHV1pV69egQEBPDbb7+V6PtS2NGjR3FwcKBZs2aXbfP58+eZNm0aDRo0sLluUlJSqa9b+PphYWHW8GZheexl+T5bXMnPVEnacqm/+4Xb8sQTT+Dl5UXXrl0JCwsjOjqajRs32pwzZ84cfv/9dxo0aEDXrl2ZMWNGuQQyKRmFHbli3333HSdPnmTZsmWEhYVZX5bBvn83UBlg+vTpxMfHWwfBXk6vXr3o06fPFfXuhISE0KdPH15++WV++OEH63P1ymAZsPzBBx8QExNT5PXFF1+U6v1eeuklQkJCrK9LjZEqjeLGGf3xxx/069eP06dP88orr/DVV18RExPDo48+anNff8fyy/lihmFU6LklkZSURO/evdm5cyczZ87kyy+/JCYmhtmzZwNF76+i22PRqVMnWrVqZa0g/OijjzAMw6bH8Pnnn2fSpEn06tWLDz/8kLVr1xITE0ObNm1K9H0pq/Hjx/Pcc88xfPhwPvnkE7755htiYmLw9/ev0OsWVlnfh7/TunVr9u/fz7Jly7j22mv53//+x7XXXsv06dOtxwwfPpw///yT119/ndDQUF588UXatGnD6tWrK62dtZkGKMsVW7JkCYGBgbzxxhtF9n322WesWLGCBQsWXHJAce/evenTpw+zZ89m2rRpJbrmjBkz6NOnT4kDUnHuuOMO7r//fvz8/LjxxhuLPSYgIAAPD49i533Zt28fDg4ONGjQAIBGjRpZe20Ku/hcy/+YAwMDiYyMLHP7Le6++26bbvayDty+nC+//JKsrCxWrlxp87/pqvDYDcxff6DY+Z0uN+cTmCvGzpw5w2effUavXr2s2w8fPnxFbfr9998xDMOmd6e08wiNGjWKqVOn8ttvv7F06VLCwsJsQu2nn35K3759effdd23OS0pKol69eqVuc35+Pn/88YdNr0Zxbf70008ZPXq0zQShmZmZJCUl2RxXmlnAGzVqxG+//UZ+fr5N747lMaLl+1wZGjVqdMm/+xe3xdPTk9tvv53bb7+d7OxshgwZwnPPPceUKVOspf0hISE8/PDDPPzwwyQmJnLNNdfw3HPPWQe0S8VRz45ckfPnz/PZZ58xaNAghg0bVuQ1btw4UlNTWbly5d++j+XR1Ntvv12i6xYOSJmZmWVq+7Bhw5g+fTpvvvnmJef5cHR05IYbbuCLL76wGTORkJDA0qVLufbaa/Hx8QHgxhtv5Oeff7aZsPDUqVNFeraioqLw8fHh+eefJycnp8g1S/sYqmnTpkRGRlpfPXv2LNX5JWX5H3Th/zEnJyezcOHCCrleaYWGhnL11Vfz/vvvk5aWZt2+YcMGdu3addnzi7u/7Oxs3nzzzTK36cYbb+TEiRM2s/RmZGSU+OfcwtKLM23aNHbs2FFkHJijo2ORnozly5fz119/lbrNll+8r732ms32uXPnFjm2uOu+/vrrRcbpWeZtujgEFefGG28kPj6ejz/+2LotNzeX119/HS8vL3r37l2S2ygXN954I1u2bCE2Nta6LT09nbfffpvGjRsTHh4OUGQGaxcXF8LDwzEMg5ycHPLy8oo81gsMDCQ0NLRE0xDIlVPPjlyRlStXkpqayk033VTs/u7du1snGLz99tsv+T69e/emd+/eRQYf/p3p06dbB0SXha+vb4nW+Xn22WeJiYnh2muv5eGHH8bJyYm33nqLrKwsm3lVHn/8cT744AP69+/PI488Yi09t/xP1cLHx4f58+dz1113cc011zBixAgCAgKIi4vjq6++omfPnsybN6/M91VRbrjhBlxcXBg8eDAPPvggaWlpvPPOOwQGBnLy5El7Nw8wP865+eab6dmzJ/feey/nzp1j3rx5XH311TYBqDg9evSgTp06jB49mgkTJmAymfjggw+u6HHIAw88wLx587j77rvZtm0bISEhfPDBB3h4eJTqfZo0aUKPHj2sjzgvDjuDBg1i5syZ3HvvvfTo0YNdu3axZMkSm0H1JdWhQwdGjhzJm2++SXJyMj169GDdunXF9o4NGjSIDz74AF9fX8LDw4mNjeXbb78tMqNzhw4dcHR0ZPbs2SQnJ+Pq6mqdr+liY8eO5a233uKee+5h27ZtNG7cmE8//ZSNGzcyd+7cYsfWXYn//e9/RQafA4wePZonn3ySjz76iAEDBjBhwgTq1q3L4sWLOXz4MP/73/+sPU833HADwcHB9OzZk6CgIPbu3cu8efMYOHAg3t7eJCUlUb9+fYYNG0b79u3x8vLi22+/ZevWrcUumyMVwD5FYFJTDB482HBzczPS09Mvecw999xjODs7G6dPnzYMw7b0vDBLKSh/U3p+MUsJcGlLzy/lUuWo27dvN6KiogwvLy/Dw8PD6Nu3r7Fp06Yi5//2229G7969DTc3N+Oqq64ynnnmGePdd9+1KT0vfK2oqCjD19fXcHNzM5o1a2bcc889xi+//FLk3ivKpUrPL/X1XLlypdGuXTvDzc3NaNy4sTF79mzjvffeK3J/lyo9v/jrWlwp9KVKz1988cUi7eGiUmjDMIxly5YZrVq1MlxdXY2rr77aWLlypTF06FCjVatWf/u1MAzD2Lhxo9G9e3fD3d3dCA0NNR5//HFj7dq1RcqmL/WzdHHbDcMwjh49atx0002Gh4eHUa9ePeORRx4x1qxZU6LS88LeeOMNAzC6du1aZF9mZqbxr3/9ywgJCTHc3d2Nnj17GrGxsUW+DyUpPTcMwzh//rwxYcIEw9/f3/D09DQGDx5sHDt2rMjX+9y5c8a9995r1KtXz/Dy8jKioqKMffv2GY0aNTJGjx5t857vvPOO0bRpU8PR0dHm3i9uo2EYRkJCgvV9XVxcjLZt29q0ufC9lPTn4mKF/70p7mUpN//jjz+MYcOGGX5+foabm5vRtWtXY9WqVTbv9dZbbxm9evUy/P39DVdXV6NZs2bGY489ZiQnJxuGYZ6W4LHHHjPat29veHt7G56enkb79u2NN99882/bKOXHZBiVOIpLRMQOOnToQEBAADExMfZuiojYgcbsiEiNkZOTQ25urs2277//np07dxZZkkBEag/17IhIjXHkyBEiIyO58847CQ0NZd++fSxYsABfX19+//33K1odXESqLw1QFpEao06dOnTq1In//ve/nDp1Ck9PTwYOHMgLL7ygoCNSi6lnR0RERGo0jdkRERGRGk1hR0RERGo0jdnBvObNiRMn8Pb2LtW05iIiImI/hmGQmppKaGhokcVjC1PYAU6cOGFd30hERESql2PHjlG/fv1L7lfYAev048eOHbOucyQiIiJVW0pKCg0aNLjsMiIKO1xYkdfHx0dhR0REpJq53BAUDVAWERGRGk1hR0RERGo0hR0RERGp0TRmR0RErlheXh45OTn2bobUMM7Ozjg6Ol7x+yjsiIhImRmGQXx8PElJSfZuitRQfn5+BAcHX9E8eAo7IiJSZpagExgYiIeHhyZmlXJjGAYZGRkkJiYCEBISUub3UtgREZEyycvLswYdrSovFcHd3R2AxMREAgMDy/xISwOURUSkTCxjdDw8POzcEqnJLD9fVzImTGFHRESuiB5dSUUqj58vhR0RERGp0RR2REREykHjxo2ZO3duiY///vvvMZlMqmSrBAo7IiJSq5hMpr99zZgxo0zvu3XrVsaOHVvi43v06MHJkyfx9fUt0/VKSqFK1VgVKjMnjz9OpREW6I2Lk3KliEhVcPLkSevHH3/8MdOmTWP//v3WbV5eXtaPDcMgLy8PJ6fL/7oMCAgoVTtcXFwIDg4u1TlSNvoNXIF6vPAdA1/7iUOJafZuioiIFAgODra+fH19MZlM1s/37duHt7c3q1evplOnTri6uvLTTz/xxx9/cPPNNxMUFISXlxddunTh22+/tXnfix9jmUwm/vvf/3Lrrbfi4eFBWFgYK1eutO6/uMdl0aJF+Pn5sXbtWlq3bo2Xlxf9+/e3CWe5ublMmDABPz8//P39eeKJJxg9ejS33HJLmb8e586d4+6776ZOnTp4eHgwYMAADh48aN1/9OhRBg8eTJ06dfD09KRNmzZ8/fXX1nNHjRpFQEAA7u7uhIWFsXDhwjK3paIo7FSgpvU8ATiYmGrnloiIVA7DMMjIzrXLyzCMcruPJ598khdeeIG9e/fSrl070tLSuPHGG1m3bh2//vor/fv3Z/DgwcTFxf3t+zz99NMMHz6c3377jRtvvJFRo0Zx9uzZSx6fkZHBSy+9xAcffMAPP/xAXFwckydPtu6fPXs2S5YsYeHChWzcuJGUlBQ+//zzK7rXe+65h19++YWVK1cSGxuLYRjceOON1lLv6OhosrKy+OGHH9i1axezZ8+29n5NnTqVPXv2sHr1avbu3cv8+fOpV6/eFbWnIugxVgUKC/Lml6PnOJignh0RqR3O5+QRPm2tXa69Z2YUHi7l82tt5syZ/OMf/7B+XrduXdq3b2/9/JlnnmHFihWsXLmScePGXfJ97rnnHkaOHAnA888/z2uvvcaWLVvo379/scfn5OSwYMECmjVrBsC4ceOYOXOmdf/rr7/OlClTuPXWWwGYN2+etZelLA4ePMjKlSvZuHEjPXr0AGDJkiU0aNCAzz//nNtuu424uDiGDh1K27ZtAWjatKn1/Li4ODp27Ejnzp0Bc+9WVaSenQoUFmhOvgcS1LMjIlKdWH55W6SlpTF58mRat26Nn58fXl5e7N2797I9O+3atbN+7OnpiY+Pj3X5g+J4eHhYgw6Yl0iwHJ+cnExCQgJdu3a17nd0dKRTp06lurfC9u7di5OTE926dbNu8/f3p2XLluzduxeACRMm8Oyzz9KzZ0+mT5/Ob7/9Zj32oYceYtmyZXTo0IHHH3+cTZs2lbktFUk9OxWoRZA3gMbsiEit4e7syJ6ZUXa7dnnx9PS0+Xzy5MnExMTw0ksv0bx5c9zd3Rk2bBjZ2dl/+z7Ozs42n5tMJvLz80t1fHk+niuL+++/n6ioKL766iu++eYbZs2axcsvv8z48eMZMGAAR48e5euvvyYmJoZ+/foRHR3NSy+9ZNc2X0w9OxUoLMjcs3PkTDqZOXl2bo2ISMUzmUx4uDjZ5VWRMzlv3LiRe+65h1tvvZW2bdsSHBzMkSNHKux6xfH19SUoKIitW7dat+Xl5bF9+/Yyv2fr1q3Jzc1l8+bN1m1nzpxh//79hIeHW7c1aNCAf/7zn3z22Wf861//4p133rHuCwgIYPTo0Xz44YfMnTuXt99+u8ztqSjq2alAgd6u+Lg5kZKZy+HT6bQO8bF3k0REpAzCwsL47LPPGDx4MCaTialTp/5tD01FGT9+PLNmzaJ58+a0atWK119/nXPnzpUo6O3atQtvb2/r5yaTifbt23PzzTfzwAMP8NZbb+Ht7c2TTz7JVVddxc033wzAxIkTGTBgAC1atODcuXOsX7+e1q1bAzBt2jQ6depEmzZtyMrKYtWqVdZ9VYnCTgUymUy0KBikfCAhVWFHRKSaeuWVV7jvvvvo0aMH9erV44knniAlJaXS2/HEE08QHx/P3XffjaOjI2PHjiUqKqpEq4H36tXL5nNHR0dyc3NZuHAhjzzyCIMGDSI7O5tevXrx9ddfWx+p5eXlER0dzfHjx/Hx8aF///68+uqrgHmuoClTpnDkyBHc3d257rrrWLZsWfnf+BUyGfZ+GFgFpKSk4OvrS3JyMj4+5RtIpnz2Gx9tOcb465vzrxtalut7i4jYU2ZmJocPH6ZJkya4ubnZuzm1Un5+Pq1bt2b48OE888wz9m5Ohfi7n7OS/v6265idvLw8pk6dSpMmTXB3d6dZs2Y888wzNoOxDMNg2rRphISE4O7uTmRkpM1kRwBnz55l1KhR+Pj44Ofnx5gxY0hLqxqDgsMCzV2GqsgSEZErdfToUd555x0OHDjArl27eOihhzh8+DB33HGHvZtWpdk17MyePZv58+czb9489u7dy+zZs5kzZw6vv/669Zg5c+bw2muvsWDBAjZv3oynpydRUVFkZmZajxk1ahS7d+8mJiaGVatW8cMPP5RqfZKKZBmkfFAVWSIicoUcHBxYtGgRXbp0oWfPnuzatYtvv/22So6TqUrsOmZn06ZN3HzzzQwcOBAwT0b00UcfsWXLFsDcqzN37lyeeuop60Cp999/n6CgID7//HNGjBjB3r17WbNmDVu3brXOi/D6669z44038tJLLxEaGmqfmytgKT8/eiaDrNw8XJ3KrzRSRERqlwYNGrBx40Z7N6PasWvPTo8ePVi3bh0HDhwAYOfOnfz0008MGDAAgMOHDxMfH09kZKT1HF9fX7p160ZsbCwAsbGx+Pn52UwAFRkZiYODg00pXWFZWVmkpKTYvCpKoLcr3m5O5OUb/HkqvcKuIyIiIsWza9h58sknGTFiBK1atcLZ2ZmOHTsyceJERo0aBUB8fDwAQUFBNucFBQVZ98XHxxMYGGiz38nJibp161qPudisWbPw9fW1vho0aFDet2ZlqcgCPcoSERGxB7uGnU8++YQlS5awdOlStm/fzuLFi3nppZdYvHhxhV53ypQpJCcnW1/Hjh2r0Ou1sIzb0SBlERGRSmfXMTuPPfaYtXcHoG3bthw9epRZs2YxevRogoODAUhISCAkJMR6XkJCAh06dAAgODi4yDojubm5nD171nr+xVxdXXF1da2AOype84KKLC0IKiIiUvns2rOTkZGBg4NtExwdHa2zUjZp0oTg4GDWrVtn3Z+SksLmzZuJiIgAICIigqSkJLZt22Y95rvvviM/P99mYTN7svTsHEhUz46IiEhls2vPzuDBg3nuuedo2LAhbdq04ddff7XOUgnm8S4TJ07k2WefJSwsjCZNmjB16lRCQ0O55ZZbAPO6Hv379+eBBx5gwYIF5OTkMG7cOEaMGGH3SiwLy1w7qsgSERGpfHbt2Xn99dcZNmwYDz/8MK1bt2by5Mk8+OCDNrNAPv7444wfP56xY8fSpUsX0tLSWLNmjc0sikuWLKFVq1b069ePG2+8kWuvvbZKLUQW5HOhIuvwaVVkiYjUBH369GHixInWzxs3bszcuXP/9hyTycTnn39+xdcur/epLezas+Pt7c3cuXP/9ofDZDIxc+ZMZs6ceclj6taty9KlSyugheXDZDIRFujF9rgkDiak0SpYa2SJiNjL4MGDycnJYc2aNUX2/fjjj/Tq1YudO3fSrl27Ur3v1q1b8fT0LK9mAjBjxgw+//xzduzYYbP95MmT1KlTp1yvdbFFixYxceJEkpKSKvQ6lcGuPTu1ibX8XBVZIiJ2NWbMGGJiYjh+/HiRfQsXLqRz586lDjoAAQEBeHh4lEcTLys4OLhSC22qO4WdStI8sGCQsiqyRETsatCgQQQEBLBo0SKb7WlpaSxfvpwxY8Zw5swZRo4cyVVXXYWHhwdt27blo48++tv3vfgx1sGDB+nVqxdubm6Eh4cTExNT5JwnnniCFi1a4OHhQdOmTZk6dSo5OTmAuWfl6aefZufOnZhMJkwmk7XNFz/G2rVrF9dffz3u7u74+/szduxYmzUi77nnHm655RZeeuklQkJC8Pf3Jzo62nqtsoiLi+Pmm2/Gy8sLHx8fhg8fTkJCgnX/zp076du3L97e3vj4+NCpUyd++eUXwLzG1+DBg6lTpw6enp60adOGr7/+usxtuRy7PsaqTS5MLKieHRGpwQwDcjLsc21nDzCZLnuYk5MTd999N4sWLeI///kPpoJzli9fTl5eHiNHjiQtLY1OnTrxxBNP4OPjw1dffcVdd91Fs2bN6Nq162WvkZ+fz5AhQwgKCmLz5s0kJyfbjO+x8Pb2ZtGiRYSGhrJr1y4eeOABvL29efzxx7n99tv5/fffWbNmDd9++y1gXkXgYunp6URFRREREcHWrVtJTEzk/vvvZ9y4cTaBbv369YSEhLB+/XoOHTrE7bffTocOHXjggQcuez/F3Z8l6GzYsIHc3Fyio6O5/fbb+f777wHzupUdO3Zk/vz5ODo6smPHDpydnQGIjo4mOzubH374AU9PT/bs2YOXl1ep21FSCjuVxBJ2jqgiS0RqspwMeN5OlbD/PgEuJRszc9999/Hiiy+yYcMG+vTpA5gfYQ0dOtQ6u/7kyZOtx48fP561a9fyySeflCjsfPvtt+zbt4+1a9daK4Off/5563JIFk899ZT148aNGzN58mSWLVvG448/jru7O15eXjg5OV1y3jiApUuXkpmZyfvvv28dMzRv3jwGDx7M7NmzrasQ1KlTh3nz5uHo6EirVq0YOHAg69atK1PYWbduHbt27eLw4cPWVQjef/992rRpw9atW+nSpQtxcXE89thjtGrVCoCwsDDr+XFxcQwdOpS2bdsC0LRp01K3oTT0GKuSBPm44u1qrsg6ctpO/+sREREAWrVqRY8ePXjvvfcAOHToED/++CNjxowBIC8vj2eeeYa2bdtSt25dvLy8WLt2LXFxcSV6/71799KgQQObKVAs88MV9vHHH9OzZ0+Cg4Px8vLiqaeeKvE1Cl+rffv2NoOje/bsSX5+Pvv377dua9OmDY6OF/6jHRISUmRS3tJcs0GDBjbLLYWHh+Pn58fevXsBmDRpEvfffz+RkZG88MIL/PHHH9ZjJ0yYwLPPPkvPnj2ZPn06v/32W5naUVLq2akkJpOJsCBzRdaBhFRaBnvbu0kiIuXP2cPcw2Kva5fCmDFjGD9+PG+88QYLFy6kWbNm9O7dG4AXX3yR//u//2Pu3Lm0bdsWT09PJk6cSHZ2drk1NzY2llGjRvH0008TFRWFr68vy5Yt4+WXXy63axRmeYRkYTKZrJP4VoQZM2Zwxx138NVXX7F69WqmT5/OsmXLuPXWW7n//vuJioriq6++4ptvvmHWrFm8/PLLjB8/vkLaop6dSmSZXFALgopIjWUymR8l2eNVgvE6hQ0fPhwHBweWLl3K+++/z3333Wcdv7Nx40Zuvvlm7rzzTtq3b0/Tpk05cOBAid+7devWHDt2jJMnT1q3/fzzzzbHbNq0iUaNGvGf//yHzp07ExYWxtGjR22OcXFxIS8v77LX2rlzJ+npF+Zx27hxIw4ODrRs2bLEbS4Ny/0VXltyz549JCUlER4ebt3WokULHn30Ub755huGDBnCwoULrfsaNGjAP//5Tz777DP+9a9/8c4771RIW0Fhp1KFaUFQEZEqw8vLi9tvv50pU6Zw8uRJ7rnnHuu+sLAwYmJi2LRpE3v37uXBBx+0qTS6nMjISFq0aMHo0aPZuXMnP/74I//5z39sjgkLCyMuLo5ly5bxxx9/8Nprr7FixQqbYxo3bszhw4fZsWMHp0+fJisrq8i1Ro0ahZubG6NHj+b3339n/fr1jB8/nrvuuss6Xqes8vLy2LFjh81r7969REZG0rZtW0aNGsX27dvZsmULd999N71796Zz586cP3+ecePG8f3333P06FE2btzI1q1bad26NQATJ05k7dq1HD58mO3bt7N+/XrrvoqgsFOJwoLUsyMiUpWMGTOGc+fOERUVZTO+5qmnnuKaa64hKiqKPn36EBwcbF2mqCQcHBxYsWIF58+fp2vXrtx///0899xzNsfcdNNNPProo4wbN44OHTqwadMmpk6danPM0KFD6d+/P3379iUgIKDY8ncPDw/Wrl3L2bNn6dKlC8OGDaNfv37MmzevdF+MYqSlpdGxY0eb1+DBgzGZTHzxxRfUqVOHXr16ERkZSdOmTfn4448B8zqXZ86c4e6776ZFixYMHz6cAQMG8PTTTwPmEBUdHW1d8qlFixa8+eabV9zeSzEZhmFU2LtXEykpKfj6+pKcnIyPT8XNbnwy+TwRs77DycHEnpn9cXFS1hSR6iszM5PDhw/TpEkTmyV8RMrT3/2clfT3t37bVqJgHze8XZ3I1RpZIiIilUZhpxKZTCaaW8btaHJBERGRSqGwU8laFFRkadkIERGRyqGwU8ksFVmH1LMjIiJSKRR2KpmlIks9OyJSU6jORSpSefx8KexUsrCC1c+PnE4nO7fiZq4UEalolhl5MzK0BI5UHMvP18UzQJeGlouoZCG+bni5OpGWlcuRM+nWBUJFRKobR0dH/Pz8rOsreXh4WGcgFrlShmGQkZFBYmIifn5+Nut6lZbCTiUzmUw0D/RixzHzGlkKOyJSnVlW4y7rgpIil+Pn5/e3q76XhMKOHbQIMoedgxq3IyLVnMlkIiQkhMDAQHJycuzdHKlhnJ2dr6hHx0Jhxw5aWJeNUEWWiNQMjo6O5fJLSaQiaICyHTQPtCwIqp4dERGRiqawYweWnp3DqsgSERGpcAo7dmCpyMrNNzh6RmtkiYiIVCSFHTuwVGSBJhcUERGpaAo7dmKZXFCDlEVERCqWwo6dWCuy1LMjIiJSoRR27KR5kOUxlnp2REREKpLCjp0UrsjKyVNFloiISEVR2LGTUF83PF0cyc03OHJaFVkiIiIVRWHHTkwmE82tMylr3I6IiEhFsWvYady4MSaTqcgrOjoagMzMTKKjo/H398fLy4uhQ4eSkJBg8x5xcXEMHDgQDw8PAgMDeeyxx8jNzbXH7ZRai0CN2xEREalodg07W7du5eTJk9ZXTEwMALfddhsAjz76KF9++SXLly9nw4YNnDhxgiFDhljPz8vLY+DAgWRnZ7Np0yYWL17MokWLmDZtml3up7TCgizl5+rZERERqSgmwzAMezfCYuLEiaxatYqDBw+SkpJCQEAAS5cuZdiwYQDs27eP1q1bExsbS/fu3Vm9ejWDBg3ixIkTBAUFAbBgwQKeeOIJTp06hYuLS4mum5KSgq+vL8nJyfj4+FTY/V1s/f5E7l24lRZBXnzzaO9Ku66IiEhNUNLf31VmzE52djYffvgh9913HyaTiW3btpGTk0NkZKT1mFatWtGwYUNiY2MBiI2NpW3bttagAxAVFUVKSgq7d++u9HsoLcvEgqrIEhERqThO9m6Axeeff05SUhL33HMPAPHx8bi4uODn52dzXFBQEPHx8dZjCgcdy37LvkvJysoiKyvL+nlKSko53EHpXeXnjqeLI+nZeRw9k07zQG+7tENERKQmqzI9O++++y4DBgwgNDS0wq81a9YsfH19ra8GDRpU+DWLozWyREREKl6VCDtHjx7l22+/5f7777duCw4OJjs7m6SkJJtjExISCA4Oth5zcXWW5XPLMcWZMmUKycnJ1texY8fK6U5KL0zLRoiIiFSoKhF2Fi5cSGBgIAMHDrRu69SpE87Ozqxbt866bf/+/cTFxREREQFAREQEu3btIjEx0XpMTEwMPj4+hIeHX/J6rq6u+Pj42LzspYVl2QgtCCoiIlIh7D5mJz8/n4ULFzJ69GicnC40x9fXlzFjxjBp0iTq1q2Lj48P48ePJyIigu7duwNwww03EB4ezl133cWcOXOIj4/nqaeeIjo6GldXV3vdUqmEFYzTOaSeHRERkQph97Dz7bffEhcXx3333Vdk36uvvoqDgwNDhw4lKyuLqKgo3nzzTet+R0dHVq1axUMPPURERASenp6MHj2amTNnVuYtXBHLXDt/nk4jJy8fZ8cq0dkmIiJSY1SpeXbsxV7z7ADk5xtcPWMtGdl5fDupt3XAsoiIiPy9ajfPTm3l4GCyzrdzUMtGiIiIlDuFnSrAMr+Olo0QEREpfwo7VYC1Iks9OyIiIuVOYacKaKG5dkRERCqMwk4VYBmU/OfpNHK1RpaIiEi5UtipAq7yc8fDxZGcPIMjZzLs3RwREZEaRWGnCnBwuLBG1iHNpCwiIlKuFHaqCMtMyloQVEREpHwp7FQRlpmUVX4uIiJSvhR2qghL+bkmFhQRESlfCjtVhOUx1p+n0lWRJSIiUo4UdqqIq/zccXd2JDsvn6NnVZElIiJSXhR2qggHB9OFcTt6lCUiIlJuFHaqkObWBUE1SFlERKS8KOxUIZZlIw6oIktERKTcKOxUIWGBeowlIiJS3hR2qhBLz44qskRERMqPwk4VUrgiK04VWSIiIuVCYacKKbxGlpaNEBERKR8KO1WMxu2IiIiUL4WdKiasYNyO1sgSEREpHwo7VYxljawD6tkREREpFwo7VYx1jazTqsgSEREpDwo7VUz9Ou64OTuQnauKLBERkfKgsFPFFK7I0rgdERGRK6ewUwW1KHiUpYosERGRK6ewUwU1D1LPjoiISHlR2KmCLD07mlhQRETkyinsVEGWNbL+OJVGXr5h59aIiIhUbwo7VZAqskRERMqPwk4VZLtGlgYpi4iIXAm7h52//vqLO++8E39/f9zd3Wnbti2//PKLdb9hGEybNo2QkBDc3d2JjIzk4MGDNu9x9uxZRo0ahY+PD35+fowZM4a0tOo93sUyueAhDVIWERG5InYNO+fOnaNnz544OzuzevVq9uzZw8svv0ydOnWsx8yZM4fXXnuNBQsWsHnzZjw9PYmKiiIzM9N6zKhRo9i9ezcxMTGsWrWKH374gbFjx9rjlspNmJaNEBERKRcmwzDsNgL2ySefZOPGjfz444/F7jcMg9DQUP71r38xefJkAJKTkwkKCmLRokWMGDGCvXv3Eh4eztatW+ncuTMAa9as4cYbb+T48eOEhoZeth0pKSn4+vqSnJyMj49P+d3gFYjZk8AD7/9CeIgPXz9ynb2bIyIiUuWU9Pe3XXt2Vq5cSefOnbntttsIDAykY8eOvPPOO9b9hw8fJj4+nsjISOs2X19funXrRmxsLACxsbH4+flZgw5AZGQkDg4ObN68udjrZmVlkZKSYvOqaiwLgqoiS0RE5MrYNez8+eefzJ8/n7CwMNauXctDDz3EhAkTWLx4MQDx8fEABAUF2ZwXFBRk3RcfH09gYKDNficnJ+rWrWs95mKzZs3C19fX+mrQoEF539oVq1/HA1cnB7Jy8zmmiiwREZEys2vYyc/P55prruH555+nY8eOjB07lgceeIAFCxZU6HWnTJlCcnKy9XXs2LEKvV5ZOKoiS0REpFzYNeyEhIQQHh5us61169bExcUBEBwcDEBCQoLNMQkJCdZ9wcHBJCYm2uzPzc3l7Nmz1mMu5urqio+Pj82rKrJMLqhlI0RERMrOrmGnZ8+e7N+/32bbgQMHaNSoEQBNmjQhODiYdevWWfenpKSwefNmIiIiAIiIiCApKYlt27ZZj/nuu+/Iz8+nW7dulXAXFce6+rl6dkRERMrMyZ4Xf/TRR+nRowfPP/88w4cPZ8uWLbz99tu8/fbbAJhMJiZOnMizzz5LWFgYTZo0YerUqYSGhnLLLbcA5p6g/v37Wx9/5eTkMG7cOEaMGFGiSqyqzNKzozWyREREys6uYadLly6sWLGCKVOmMHPmTJo0acLcuXMZNWqU9ZjHH3+c9PR0xo4dS1JSEtdeey1r1qzBzc3NesySJUsYN24c/fr1w8HBgaFDh/Laa6/Z45bKVVigbUWWo4PJzi0SERGpfuw6z05VURXn2QHIyzcIn7aGrNx8vp/ch8b1PO3dJBERkSqjWsyzI3/P0cFEs4CCcTsapCwiIlImCjtVXAstGyEiInJFFHaquDBL+bnCjoiISJko7FRxlkHKeowlIiJSNgo7VZyl/PxQotbIEhERKQuFnSquQd0La2QdP6c1skREREpLYaeKK1yRpckFRURESk9hpxoIC7KM29EgZRERkdJS2KkGrAuCqmdHRESk1BR2qgHrgqDq2RERESk1hZ1qoHBFVr4qskREREpFYacaaFjXAxcnBzJz8jmmiiwREZFSUdipBmzWyNK4HRERkVJR2KkmrGtkadyOiIhIqSjsVBOWZSMOqWdHRESkVBR2qgnLgqDq2RERESkdhZ1qwtqzo4osERGRUlHYqSYKV2QdP3fe3s0RERGpNhR2qgknRwea1vMENLmgiIhIaSjsVCOWyQW1IKiIiEjJKexUI5by84MJ6tkREREpKYWdaqR5YMGCoInq2RERESkphZ1qxNKzo4osERGRklPYqUYa1vXAxdGB8zl5/JWkiiwREZGSUNipRpwcHWgaYK7IOqBxOyIiIiWisFPNWGZS1rgdERGRklHYqWZaFMykrJ4dERGRklHYqWYsPTuH1LMjIiJSIgo71UyYda4dVWSJiIiUhMJONdNIFVkiIiKlYtewM2PGDEwmk82rVatW1v2ZmZlER0fj7++Pl5cXQ4cOJSEhweY94uLiGDhwIB4eHgQGBvLYY4+Rm5tb2bdSaQpXZGmNLBERkcuze89OmzZtOHnypPX1008/Wfc9+uijfPnllyxfvpwNGzZw4sQJhgwZYt2fl5fHwIEDyc7OZtOmTSxevJhFixYxbdo0e9xKpQnTGlkiIiIl5mT3Bjg5ERwcXGR7cnIy7777LkuXLuX6668HYOHChbRu3Zqff/6Z7t27880337Bnzx6+/fZbgoKC6NChA8888wxPPPEEM2bMwMXFpbJvp1KEBV4YtyMiIiJ/z+49OwcPHiQ0NJSmTZsyatQo4uLiANi2bRs5OTlERkZaj23VqhUNGzYkNjYWgNjYWNq2bUtQUJD1mKioKFJSUti9e/clr5mVlUVKSorNqzqxLgiqx1giIiKXZdew061bNxYtWsSaNWuYP38+hw8f5rrrriM1NZX4+HhcXFzw8/OzOScoKIj4+HgA4uPjbYKOZb9l36XMmjULX19f66tBgwble2MVzLIgqNbIEhERuTy7PsYaMGCA9eN27drRrVs3GjVqxCeffIK7u3uFXXfKlClMmjTJ+nlKSkq1CjyN/c0VWRnZ5oqsBnU97N0kERGRKsvuj7EK8/Pzo0WLFhw6dIjg4GCys7NJSkqyOSYhIcE6xic4OLhIdZbl8+LGAVm4urri4+Nj86pOCldkaXJBERGRv1elwk5aWhp//PEHISEhdOrUCWdnZ9atW2fdv3//fuLi4oiIiAAgIiKCXbt2kZiYaD0mJiYGHx8fwsPDK739lam5lo0QEREpEbs+xpo8eTKDBw+mUaNGnDhxgunTp+Po6MjIkSPx9fVlzJgxTJo0ibp16+Lj48P48eOJiIige/fuANxwww2Eh4dz1113MWfOHOLj43nqqaeIjo7G1dXVnrdW4VoEeQMnVX4uIiJyGXYNO8ePH2fkyJGcOXOGgIAArr32Wn7++WcCAgIAePXVV3FwcGDo0KFkZWURFRXFm2++aT3f0dGRVatW8dBDDxEREYGnpyejR49m5syZ9rqlSmMpPz+kiiwREZG/ZTIMo9aX86SkpODr60tycnK1Gb9zKDGNyFc24OHiyO8zonBwMNm7SSIiIpWqpL+/q9SYHSm5Rv4eODuayMjO40Sy1sgSERG5FIWdasrZ0YGm9TSTsoiIyOUo7FRjzTWTsoiIyGUp7FRjLQK1IKiIiMjlKOxUY9Y1sjTXjoiIyCUp7FRjYdbHWGmoqE5ERKR4CjvVWCN/T2tF1l9JqsgSEREpjsJONebs6ECTeuY1sg5qjSwREZFiKexUc2FB5kHKGrcjIiJSPIWdas6ybITm2hERESmewk4116KgZ+eAHmOJiIgUS2GnmrOUnx9KSFVFloiISDEUdqo5S0VWenYeJ5Iz7d0cERGRKqdMYefYsWMcP37c+vmWLVuYOHEib7/9drk1TEqmcEXWAQ1SFhERKaJMYeeOO+5g/fr1AMTHx/OPf/yDLVu28J///IeZM2eWawPl8sIKlo04pEHKIiIiRZQp7Pz+++907doVgE8++YSrr76aTZs2sWTJEhYtWlSe7ZMSsMykrJ4dERGRosoUdnJycnB1dQXg22+/5aabbgKgVatWnDx5svxaJyVi6dnRxIIiIiJFlSnstGnThgULFvDjjz8SExND//79AThx4gT+/v7l2kC5PGtFltbIEhERKaJMYWf27Nm89dZb9OnTh5EjR9K+fXsAVq5caX28JZWnkb8nTg4m0rJyOamKLBERERtOZTmpT58+nD59mpSUFOrUqWPdPnbsWDw8PMqtcVIyLk7miqyDiWkcSEgl1M/d3k0SERGpMsrUs3P+/HmysrKsQefo0aPMnTuX/fv3ExgYWK4NlJKxzKR8SON2REREbJQp7Nx88828//77ACQlJdGtWzdefvllbrnlFubPn1+uDZSSaR6oiiwREZHilCnsbN++neuuuw6ATz/9lKCgII4ePcr777/Pa6+9Vq4NlJKxrpGluXZERERslCnsZGRk4O1t/uX6zTffMGTIEBwcHOjevTtHjx4t1wZKyYSpIktERKRYZQo7zZs35/PPP+fYsWOsXbuWG264AYDExER8fHzKtYFSMo1VkSUiIlKsMoWdadOmMXnyZBo3bkzXrl2JiIgAzL08HTt2LNcGSsm4ODnQuGCNLE0uKCIickGZws6wYcOIi4vjl19+Ye3atdbt/fr149VXXy23xknpWCYXPKhByiIiIlZlmmcHIDg4mODgYOvq5/Xr19eEgnbWPNAbiOegBimLiIhYlalnJz8/n5kzZ+Lr60ujRo1o1KgRfn5+PPPMM+Tn55d3G6WELD07BxLVsyMiImJRpp6d//znP7z77ru88MIL9OzZE4CffvqJGTNmkJmZyXPPPVeujZSSsU4smGCuyDKZTHZukYiIiP2VqWdn8eLF/Pe//+Whhx6iXbt2tGvXjocffph33nmHRYsWlakhL7zwAiaTiYkTJ1q3ZWZmEh0djb+/P15eXgwdOpSEhASb8+Li4hg4cCAeHh4EBgby2GOPkZubW6Y2VHeWiqzUrFziU1SRJSIiAmUMO2fPnqVVq1ZFtrdq1YqzZ8+W+v22bt3KW2+9Rbt27Wy2P/roo3z55ZcsX76cDRs2cOLECYYMGWLdn5eXx8CBA8nOzmbTpk0sXryYRYsWMW3atNLfVA1QuCJLkwuKiIiYlSnstG/fnnnz5hXZPm/evCKB5XLS0tIYNWoU77zzjs2iosnJybz77ru88sorXH/99XTq1ImFCxeyadMmfv75Z8Bc6r5nzx4+/PBDOnTowIABA3jmmWd44403yM7OLsutVXthgarIEhERKaxMYWfOnDm89957hIeHM2bMGMaMGUN4eDiLFi3ipZdeKtV7RUdHM3DgQCIjI222b9u2jZycHJvtrVq1omHDhsTGxgIQGxtL27ZtCQoKsh4TFRVFSkoKu3fvvuQ1s7KySElJsXnVFGEF43ZUkSUiImJWprDTu3dvDhw4wK233kpSUhJJSUkMGTKE3bt388EHH5T4fZYtW8b27duZNWtWkX3x8fG4uLjg5+dnsz0oKIj4+HjrMYWDjmW/Zd+lzJo1C19fX+urQYMGJW5zVWft2VFFloiICHAF8+yEhoYWqbrauXMn7777Lm+//fZlzz927BiPPPIIMTExuLm5lbUZZTJlyhQmTZpk/TwlJaXGBJ4WhXp2VJElIiJSxp6d8rBt2zYSExO55pprcHJywsnJiQ0bNvDaa6/h5OREUFAQ2dnZJCUl2ZyXkJBAcHAwYJ7Y8OLqLMvnlmOK4+rqio+Pj82rpmhSzxPHgoqshJQsezdHRETE7uwWdvr168euXbvYsWOH9dW5c2dGjRpl/djZ2Zl169ZZz9m/fz9xcXHWtbgiIiLYtWsXiYmJ1mNiYmLw8fEhPDy80u+pKnBxcqCxvwcABzRIWUREpOyPsa6Ut7c3V199tc02T09P/P39rdvHjBnDpEmTqFu3Lj4+PowfP56IiAi6d+8OwA033EB4eDh33XUXc+bMIT4+nqeeeoro6GhcXV0r/Z6qihZB3vxxKp0DCan0ahFg7+aIiIjYVanCTuE5bopz8SOnK/Xqq6/i4ODA0KFDycrKIioqijfffNO639HRkVWrVvHQQw8RERGBp6cno0ePZubMmeXajuomLNCL1cAhrX4uIiKCyTAMo6QH33vvvSU6buHChWVukD2kpKTg6+tLcnJyjRi/8+XOE4z/6FeuaejHZw/3tHdzREREKkRJf3+XqmenuoWY2iosyFJ+roosERERuw1QlopjrcjKVEWWiIiIwk4N5OrkSKOCiixNLigiIrWdwk4N1SLQPLmgFgQVEZHaTmGnhmpRMG7nkHp2RESkllPYqaGaB6lnR0REBBR2aixLz86BhFRKMbuAiIhIjaOwU0MVrshKTFVFloiI1F4KOzVU4YosrZElIiK1mcJODRYWWDC5oMbtiIhILaawU4O1KBikrLl2RESkNlPYqcGaq2dHREREYacma2EtP1dFloiI1F4KOzVY0wBPHEyQkpnLKVVkiYhILaWwU4O5OjnS2N8T0OSCIiJSeyns1HBhhSYXFBERqY0Udmq4sEBLRZZ6dkREpHZS2KnhLD07B9WzIyIitZTCTg1XuGdHFVkiIlIbKezUcJaKrOTzOarIEhGRWklhp4Zzc75QkaVxOyIiUhsp7NQClpmUVZElIiK1kcJOLXBhjSz17IiISO2jsFMLqCJLRERqM4WdWsBSkXUgQRVZIiJS+yjs1AI2FVlpqsgSEZHaRWGnFnBzdqSRpSJLa2SJiEgto7BTS1gqsjRuR0REahuFnVqihWVBUFVkiYhILaOwU0tYys8P6TGWiIjUMnYNO/Pnz6ddu3b4+Pjg4+NDREQEq1evtu7PzMwkOjoaf39/vLy8GDp0KAkJCTbvERcXx8CBA/Hw8CAwMJDHHnuM3Nzcyr6VKs86sWBiqiqyRESkVrFr2Klfvz4vvPAC27Zt45dffuH666/n5ptvZvfu3QA8+uijfPnllyxfvpwNGzZw4sQJhgwZYj0/Ly+PgQMHkp2dzaZNm1i8eDGLFi1i2rRp9rqlKqtZgBcOJkjKyOF0Wra9myMiIlJpTEYV+29+3bp1efHFFxk2bBgBAQEsXbqUYcOGAbBv3z5at25NbGws3bt3Z/Xq1QwaNIgTJ04QFBQEwIIFC3jiiSc4deoULi4uJbpmSkoKvr6+JCcn4+PjU2H3Zm99XlzPkTMZLL2/Gz2a17N3c0RERK5ISX9/V5kxO3l5eSxbtoz09HQiIiLYtm0bOTk5REZGWo9p1aoVDRs2JDY2FoDY2Fjatm1rDToAUVFRpKSkWHuH5IKwIMvkgqrIEhGR2sPJ3g3YtWsXERERZGZm4uXlxYoVKwgPD2fHjh24uLjg5+dnc3xQUBDx8fEAxMfH2wQdy37LvkvJysoiK+vC5HopKSnldDdVW1igFzF7ErRGloiI1Cp279lp2bIlO3bsYPPmzTz00EOMHj2aPXv2VOg1Z82aha+vr/XVoEGDCr1eVWFdEFQVWSIiUovYPey4uLjQvHlzOnXqxKxZs2jfvj3/93//R3BwMNnZ2SQlJdkcn5CQQHBwMADBwcFFqrMsn1uOKc6UKVNITk62vo4dO1a+N1VFqSJLRERqI7uHnYvl5+eTlZVFp06dcHZ2Zt26ddZ9+/fvJy4ujoiICAAiIiLYtWsXiYmJ1mNiYmLw8fEhPDz8ktdwdXW1lrtbXrVB80BVZImISO1j1zE7U6ZMYcCAATRs2JDU1FSWLl3K999/z9q1a/H19WXMmDFMmjSJunXr4uPjw/jx44mIiKB79+4A3HDDDYSHh3PXXXcxZ84c4uPjeeqpp4iOjsbV1dWet1YluTk70rCuB0fOZHAwMZUAb32NRESk5rNr2ElMTOTuu+/m5MmT+Pr60q5dO9auXcs//vEPAF599VUcHBwYOnQoWVlZREVF8eabb1rPd3R0ZNWqVTz00ENERETg6enJ6NGjmTlzpr1uqcprHuhtDjsJafRopvJzERGp+arcPDv2UFvm2QGYs2Yfb37/B6O6NeS5W9vauzkiIiJlVu3m2ZHKEVawIKjKz0VEpLZQ2KllwgIt5eeqyBIRkdpBYaeWaRbghckE5zJyOJOuiiwREan5FHZqGXcXc0UWaNkIERGpHRR2aiHLo6xDGrcjIiK1gMJOLWQZpKyeHRERqQ0UdmqhFpaKLK2RJSIitYDCTi1krcjSYywREakFFHZqIUtF1tn0bE6nZdm7OSIiIhVKYacWcndxpEEdc0WWHmWJiEhNp7BTS1nH7SRqkLKIiNRsCju1VHPrTMrq2RERkZpNYaeWaqHycxERqSUUdmqpFkGaWFBERGoHhZ1aylKRdSY9mzOqyBIRkRpMYaeWsqnIUu+OiIjUYAo7tVhYoGUmZY3bERGRmkthp6IlHbN3Cy4prGDczgFVZImISA2msFNRMpPho5HwRjdIOWnv1hTL2rOjuXZERKQGU9ipKC7ekH4KctLh2xn2bk2xLBVZmmtHRERqMoWdiuLgAANmmz/+bRkc22Lf9hSjeaAqskREpOZT2KlIV3WCDneaP179OOTn27c9F3F3caR+HXdAFVkiIlJzKexUtMjp4OoDJ36FHUvs3ZoiWliWjVDYERGRGkphp6J5BULvx80fr3vaPHC5CmkepPJzERGp2RR2KkPXB8E/zDxgecMce7fGRgstCCoiIjWcwk5lcHKB/i+YP968AE4dsG97CgkLUvm5iIjUbAo7lSUsElr0h/xcWPMkGIa9WwSYK7IATqdlczY9286tERERKX8KO5Up6nlwcIY/1sGBNfZuDQAeLk4XKrI0bkdERGoghZ3K5N8MIqLNH6+ZArlVY24by+SCB1SRJSIiNZDCTmXrNRm8guHcYYh9w96tAS6M2zmknh0REamBFHYqm6s3RM4wf/zDS1Vi3aywQC0IKiIiNZddw86sWbPo0qUL3t7eBAYGcsstt7B//36bYzIzM4mOjsbf3x8vLy+GDh1KQkKCzTFxcXEMHDgQDw8PAgMDeeyxx8jNza3MWymddrdD/S5VZt2sFtaKLIUdERGpeewadjZs2EB0dDQ///wzMTEx5OTkcMMNN5Cenm495tFHH+XLL79k+fLlbNiwgRMnTjBkyBDr/ry8PAYOHEh2djabNm1i8eLFLFq0iGnTptnjlkqmiq2b1SzAUpGVxTlVZImISA1jMowqUgMNnDp1isDAQDZs2ECvXr1ITk4mICCApUuXMmzYMAD27dtH69atiY2NpXv37qxevZpBgwZx4sQJgoKCAFiwYAFPPPEEp06dwsXF5bLXTUlJwdfXl+TkZHx8fCr0Hm18Hg07PoTQjnD/d+YQZCfXzv6O4+fO8/HY7nRr6m+3doiIiJRUSX9/V6kxO8nJ5qUU6tatC8C2bdvIyckhMjLSekyrVq1o2LAhsbGxAMTGxtK2bVtr0AGIiooiJSWF3bt3F3udrKwsUlJSbF52UYXWzQoL1KMsERGpmapM2MnPz2fixIn07NmTq6++GoD4+HhcXFzw8/OzOTYoKIj4+HjrMYWDjmW/ZV9xZs2aha+vr/XVoEGDcr6bEqpC62ZZys81146IiNQ0VSbsREdH8/vvv7Ns2bIKv9aUKVNITk62vo4dO1bh17ykKrJuVnP17IiISA1VJcLOuHHjWLVqFevXr6d+/frW7cHBwWRnZ5OUlGRzfEJCAsHBwdZjLq7OsnxuOeZirq6u+Pj42Lzspoqsm2WdWFDl5yIiUsPYNewYhsG4ceNYsWIF3333HU2aNLHZ36lTJ5ydnVm3bp112/79+4mLiyMiIgKAiIgIdu3aRWJiovWYmJgYfHx8CA8Pr5wbuVJVYN2sC2tkqSJLRERqFruGnejoaD788EOWLl2Kt7c38fHxxMfHc/78eQB8fX0ZM2YMkyZNYv369Wzbto17772XiIgIunfvDsANN9xAeHg4d911Fzt37mTt2rU89dRTREdH4+rqas/bK52o58HRxW7rZnm6OnGVX8EaWXqUJSIiNYhdw878+fNJTk6mT58+hISEWF8ff/yx9ZhXX32VQYMGMXToUHr16kVwcDCfffaZdb+joyOrVq3C0dGRiIgI7rzzTu6++25mzpxpj1sqO/9m0P1h88d2WjfrwuSCGqQsIiI1R5WaZ8de7DbPzsWyUuH1zpAWD/2mw3WTKvXyz3+9l7d/+JN7ejRmxk1tKvXaIiIipVUt59mp9Vy94R9Pmz+2w7pZlrl2Dqj8XEREahCFnaqm7XC7rZsVZplrR2N2RESkBlHYqWrsuG6WpWfnVGoWSRmqyBIRkZpBYacquqoTdLzT/PHqxyE/v1Iuq4osERGpiRR2qqp+9lk3KyxI43ZERKRmUdipquy0btaFNbLUsyMiIjWDwk5VZod1sy6skaWeHRERqRkUdqoyO6ybpZ4dERGpaRR2qrqwSGgxoNLWzbL07CSmZpGckVOh1xIREakMCjvVQdRzlbZullehiqwDepQlIiI1gMJOdVDJ62ZZx+3oUZaIiNQACjvVRa/J4BUM5w5D7BsVeqkWKj8XEZEaRGGnuqjEdbMsy0Yc0sSCIiJSAyjsVCc262ZNr7DLaEFQERGpSRR2qhPrulkm+O3jCls3y9Kzo4osERGpCRR2qpurOkHHUeaPv36sQtbN8nJ1ItTXDdDkgiIiUv0p7FRHlnWzTu6AHR9WyCUsvTtaEFRERKo7hZ3qyCsQej9h/njdzApZN0vjdkREpKZQ2Kmuuo6t0HWztGyEiIjUFAo71VWRdbP2l+vbNw/SgqAiIva05fBZNh06jVHBywTVBgo71ZnNullTynXdLMtjrISULJLPqyJLRKSypGflMuWz3xj+Vix3/Hczd7yzmd//Kv/hCrWJwk51V0HrZnm7OVsrsg6pd0dEpFJsO3qOG1/7kY+2HMNkAhdHB2L/PMOg13/i0Y938FfSeXs3sVpS2KnuKnDdrOYF43YOaNyOiEiFysnL55Vv9nPbgk0cPZNBqK8bS+7vxneTe3NLh1AAVvz6F31f+p4XVu8jJVM97qWhsFMTVNC6WS20IKiISIX741Qaw+Zv4rXvDpFvwC0dQlk9sRc9mtWjfh0P5o7oyMpxPenetC7Zufks2PAHfV78nsWbjpCTV/5zrdVECjs1QQWtmxWmQcoiIhXGMAw++PkoA1/7kZ3Hk/Fxc+L1kR2ZO6Ijvu7ONse2q+/HRw905793d6ZZgCdn07OZvnI3N7z6A2t3x2sQ82Uo7NQUbYdD/a7lum5WmMrPRUQqRGJqJvct2srUz38nMyefns39WftoLwa3D73kOSaTicjwINZO7MWzt1xNPS8XDp9O58EPtjH8rVh+jTtXiXdQvSjs1BQVsG5W84LHWPEpmarIEhEpJ2t3x9N/7o+s338KFycHpg4K54P7uhHi616i850cHbizeyO+f6wv469vjpuzA1uPnOPWNzcx/qNfOXY2o4LvoPpR2KlJrrqmXNfN8nFzJkQVWSIi5SItK5fHlu/kwQ+2cTY9m9YhPnw57lrGXNsEBwdTqd/Py9WJf93QkvWT+zCsU31MJvhy5wn6vbyB577ao4WcC1HYqWnKed0sPcoSEblyvxw5y4D/+4Hl245jMsGDvZvyeXQPWgZ7X/F7h/i689Jt7flq/HVc27we2Xn5vPPjYXq9uJ7//vgnWbl55XAH1ZvCTk1TzutmXVgjS2FHRKS0snPzeXHtPoa/Fcuxs+e5ys+djx7ozpQBrXF1cizXa4WH+vDBmK4surcLLYK8SD6fw7Nf7eUfr/zAV7+drNWDmO0adn744QcGDx5MaGgoJpOJzz//3Ga/YRhMmzaNkJAQ3N3diYyM5ODBgzbHnD17llGjRuHj44Ofnx9jxowhLa2W/2Iux3WzWqgiS0SkTA4lpjJk/kbeWP8H+QYM6XgVqydeR/em/hV2TZPJRJ+WgXw94TpeGNKWAG9X4s5mEL10O0Pmb2Lb0bMVdu2qzK5hJz09nfbt2/PGG8XPDTNnzhxee+01FixYwObNm/H09CQqKorMzEzrMaNGjWL37t3ExMSwatUqfvjhB8aOHVtZt1A1leO6Wc0D9RhLRKQ0DMNg8aYjDHztJ37/KwVfd2feuOMaXrm9Az5uzpd/g3Lg5OjAiK4N+X5yHyZGhuHu7MivcUkMnR/LQx9u48jp9EppR1VhMqpIv5bJZGLFihXccsstgPmHJTQ0lH/9619MnjwZgOTkZIKCgli0aBEjRoxg7969hIeHs3XrVjp37gzAmjVruPHGGzl+/DihoZcu4SssJSUFX19fkpOT8fHxqZD7s4ulI+DAamjWD+78H5hKPwAuJTOHdjO+AeC3GTdU2l9UEZHqKCElk8c+/Y0fDpwC4Lqwerw4rD3BBcUepWIY5pfDlfdLJKZk8krMAT755Rj5Bjg5mLizeyMm9AujrqfLFb+/vZT097dTJbapVA4fPkx8fDyRkZHWbb6+vnTr1o3Y2FhGjBhBbGwsfn5+1qADEBkZiYODA5s3b+bWW28t9r2zsrLIyrqwrEJKSkrF3Yg9RT1nXjPLsm5WywGlfgsfN2eCfdyIT8lk48HTDGgbUgENFRGp/r7edZJ/r9hFUkYOrk4OTBnQirsjGpes0sowzLPgn9hhLjA58Suc3Fkw7tIEjs7g4AwOTuDoZP7Y0RkcHAt97FSwv+BYx4LPHZwJdHTiBQdn/n21wc4T6RxLziF3ixNfb3eibYN6tGngj5OTSzHvf9F7OjgWev+Lr3/x/gvXx9EZ3OuY99tBlQ078fHxAAQFBdlsDwoKsu6Lj48nMDDQZr+TkxN169a1HlOcWbNm8fTTT5dzi6sg/2YQEQ0/vWpeN6vZ9eDkWuq36dqkLit3nuChJdsZ0vEqnhjQiiCfMvwvRUSkBkrNzGHGyj38b/txANqE+jD39g7WatYiDAOSjpoDzYkdhYJN0iWuYEBetvl1hXyA68D2t/+xgldFi94KAS0q4UJFVdmwU5GmTJnCpEmTrJ+npKTQoEEDO7aoAl33L9jx0YV1s66bdPlzLvL0TW1wc3bgk1+O89mvf7F2dzzjrg/jvmsbl3s1gYhIdbLl8FnrauQOJvhn72ZMjGyBi1PBoyfDgKS4C701lp6b88XMduzoAkFtIKQDhHaE0A7gcxXk55pfeTkXfZwDeQWfWz/OsT3Wes7F+3PJz8vmwIlz/PLnKbKys3AilyAvR66p702gh2PBOTmQn1fo44uun593ibZcdKyRZ+7psZMqG3aCg4MBSEhIICTkwqOThIQEOnToYD0mMTHR5rzc3FzOnj1rPb84rq6uuLqWvoejWrKsm7XiQfO6We1Hgk/pHkXV8XRhzrD2jOrWiBlf7ubXuCRmr9nHx1vjmDoonOtbBWIqw3ggEZHqKjs3n1diDvDWD39gGFC/jjuvDm9PlzoZcGCVbbg5X0wFlIOzOdiEdjAHm5AOEBhuLjCpJA5AK6BRdh7vbTzMm+sPkZ6UB0kQ2TqIJwe0ss6kf8Xy88s0brS8VNmw06RJE4KDg1m3bp013KSkpLB582YeeughACIiIkhKSmLbtm106tQJgO+++478/Hy6detmr6ZXPW2Hw9Z34fgW87pZQ94u09u0b+DH//7ZgxW//sULa/Zx5EwGYxb/Qp+WAUwdFE6zgHL6SyEiUoUdSEhl4ke/ci7+CP9w+JPhV52mj88JnJbvgIwzRU9wcDIHGUtvTWjHgmBTNf7T7e7iSHTf5gzv3ID/W3eAj7Yc49u9Cazfn8gdXRvySGQY9byusK3lMMj6Sti1GistLY1Dhw4B0LFjR1555RX69u1L3bp1adiwIbNnz+aFF15g8eLFNGnShKlTp/Lbb7+xZ88e3NzMY0YGDBhAQkICCxYsICcnh3vvvZfOnTuzdOnSErejxlZjFfbXdnjnesCAMTHQoOsVvV1aVi6vf3eQ9346TE6egZODiXt7NmZCvzC8VbElIjWJYUDKCfJP/MrOLRtI+WML4aY/CTAVU9zi4ASBrS/01oR2NPfgVJFgUxKHEtN4YfU+vt2bAJiXpXioTzPu69kEd5eqNXShpL+/7Rp2vv/+e/r27Vtk++jRo1m0aBGGYTB9+nTefvttkpKSuPbaa3nzzTdp0eLCAKezZ88ybtw4vvzySxwcHBg6dCivvfYaXl4l72WoFWEH4Itx8OsH5r+AD6wvl6R9+HQ6z6zaw3f7zI8T63m58nj/lgy7pn6Z1noREbG7lJMFg4Z3XHgUlZ5Y5DDD5IgpMBxC2xeEm4Jg41wzCjhi/zjD81/vZddf5pn4Q3zdmHxDS27teFWV+fe9WoSdqqLWhJ20U/D6NZCVAje9DtfcXW5vvX5fIs+s2sOfBRNVtW/gx4zB4XRsWKfcriEiUu5S4wtVRBX8mZZQ5LBcHDiYX589NKV+mx507XE9puCrwblkK5VXV/n5Bl/+doI5a/bzV9J5AMJDfPjPwNb0bF7Pzq1T2CmVWhN2ADbNg2/+Ax71YPw2cPcrt7fOzs1n0abDvLbuEGlZuQAMvaY+T/RvSaBK1UXE3lITCgLNjgvhJvVk0eNMDhDQiuzA9nx5KpAP4+qyx2hEi6sCePX2DuU3aLcayczJY9GmI7zx3SFSC/5979sygCk3tqbFpUrsK4HCTinUqrCTmw0LesLpA9A9Gvo/X+6XSEzNZM6a/Xy6zTznhKeLIxP6hXFvzyYXyjFFRCpS2qmij6JSTxQ9zuQA9VraDh4OuprYY+f51yc7OJGciYMJovs2Z0K/MJwda/e/YWfTs3lt3UE+/PkoufkGDia4vUsDHo1sYZf/1CrslEKtCjsAh76FD4eaB9I9tAkCWlbIZX6NO8eML/ew81gSAE3qeTJtUDh9WwX+/YkiImVx/hz8ugS2LYIzB4s5wGT+967wPDbBbcHF03pEVm4eL39zgHd+/BPDgIZ1PXj19vZ0alS3km6iejh8Op3Zq/exZrd5Al8PF0ce7NWMB3o1wcOl8gq9FXZKodaFHSi0btb1cOdnFTb/QX6+wWe//sULq/dxOs28REffglL1pipVF5HycPI32PoO/LYccs8XbDRBvRbmQGMJN8FtwfXS/+7si09h4rId7ItPBeD2zg2YOjgcL9cqO0uL3f1y5CzPfrWXHQX/qQ30duVfN7RgWKcGOFbCIGaFnVKolWHnzB/wZnfz9OMjPoJWN1bo5VIzc5j33SHe22guVXd2NHFfzyaMu765StVFpPRys2HvStjyNhzbfGF7UFvoej+0GQJuJfv3PD/f4L2Nh5mzZj/ZefnU9XRh1pC2RLW59OS0coFhGHy16ySz1+zj2Flz2GwZ5M2/B7amd4uACr22wk4p1MqwA/DtDPO6WXWaQPTmSpkH4s9TaTyzag/r95tXBA7wduWJ/q0YUoVKGUWkCks5Ab8sND+qspSDOzhB+M3Q5QFo2L1UPdUnks4zeflONv1hngzw+laBzB7ajgDv6jMvTlWRlZvHB7FHef27QySfzwHMq75PGdCa8NCK+d2qsFMKtTbsZKXC650hLR76TS/Tulll9d2+BJ5ZtZfDBaXqHRr4MeOmNnRo4FdpbRC5Ynk5sO8r2Psl+DWANrdCcDu7TotfIxkGHPnJ3Iuz7yvzOksA3iHQ6V7oNBq8S98L88WOv5j6+e+kZObi7uzIU4Nac0fXhlr+5golZWQz77tDLI49Qk6egclkrsydfENLgn3LdxCzwk4p1NqwA7DzY1gxFpw9Yfwv4BNaaZfOys1j4cYjvL7uIOnZ5n+8butUn8f6tyTQW6XqUoUl/wXbF8O2xeb/LBRWt6k59LS5FYKuVvC5ElmpsHOZebmbU3svbG90rflRVatB4Fj6x+DJGTlM/eJ3Vu40V2e1b+DHq8PbaxxhOYs7k8GctftY9Zu5vP9/D/WgU6PynXtNYacUanXYMQx49wbzulntbi/zullXIjElk9lr9vO/7eZSdS9XJyb0a849PVSqLlVIfj4c/t78i3f/6gu9C56B0P52OHcUDn4DuZkXzvFvfiH4BIYr+JTUqQPmAcc7PoJs82BhnD3NX+cuD0BQeJnfetOh0/xr+U5OJmfi6GBiXN/mjLu+ea0vKa9Iv8ad44cDp3kkMqzc31thpxRqddgB23Wz7vsGGtpnEdXtced4euVudh43T03etJ4nUweH07elStXFjjLOwo4l8Mt7cPbPC9sbXQtd7iOv5SCOJefi6GCijnM2nkfWYdqzAg7G2Aafei0KBZ/WlX8fVV1errlCdMs7cHjDhe3+zc0Bp8NIcPMt89tn5uTx4tr9vPvTYQAa+3vw6u0dNMt7NaewUwq1PuxAhaybVRb5+Qafbj/OnDX7OJ2WDUC/VoFMHRRO43qelzlbpJwYBvy1zdyLs/sza2gxXL051XQIP9e9mdjUQPacTGF/fAqZOfnWU10cHfDzcCbUPZfrHbbTK+cn2mRsxdnIth6T7tOMlKaDyAu/Fa/6bfBxc669A/TTTpkfCf6yEFLMvbuYHKDFAPOjqiZ9rvjfoz0nUnj04x3sTzD3Eo3s2pCnBrbGUyXl1Z7CTiko7FCh62aVRUpmDq+vO8jCjUfIzTdwcXTgvmvNpeqa80IqTHY67PoUfnkXTu60bj7m2pxPiOK9lE6kG0XHk7k5O2AYkJWbX2QfgBcZ9HPYziDHzfRy2ImrKde6b39+fb7O786PLteR5NmYOh4uBS9n6nhe+Niv4M+6ni74ebjg5+FcfR+9WMLklrdh9wrzFBgAHv5wzWjofC/4Nbziy+TlG/z3xz95+ZsDZOflU8/LhReGtCMyPOiK31uqBoWdUlDYKVCB62aV1aFEc6n6hgPmUvVAb1eeHNCKWzqoVF3KR16+wV8Hd5C75V1Cj6zALS8NgCzDmVX53fgw9x/8ajQHzD9vgd6uhIf60CbUh/AQX8JDfWhU1wMHBxPns/M4m5HNufRskjJyOJuRTVJGNufScziXkc25jGwy05JomfQjXTN+oFvedpxNeda27M1vwFd53fk6vxt/GpcvFvB2cyoSjPw8nM3bPAu2W8KTp/ljN2fHCvk6lkjOefj9f+ZHVSd3XNh+VSfoOhbCbym3FcOPn8vgX5/sZPPhswBEtg7khaHtqOelkvKaRGGnFBR2ClTCulllYRgG3+1LZOaqPRw9kwFAx4Z+zBjchvYqVZdSOJ+dx774FPPjp+Nn8D66ll7JK+lm2mM95mh+IB/mRfJZfi/qBIQSHuJDeKgP4SE+tA7xKd/5V84nkbv3a/J2/Q+Xoxsw5edYd532DON3v+uJdevFobyggrBkDk3J53Mo67/cbs4O1PUw9w5ZAlDh3qOKKArwzDhOi2Of0Pz4ClxzzGPy8hxcOBLcn/2NRnDG9+pyvV7K+RwWfP8HqVm5eLg4MnVQOCO6NFBJeQ2ksFMKCjuFVNK6WWWRlZvHez8d4fXvDpKRnYfJVFCqHtVKE4BJEafTsthzwhxsdp9IYc+JZA6fTifIOMNIp3WMcPyeQFMSAHmGiV9cu7HnqttwbRlJ+FV+tAzyxt2lEntBzp+DfV+bH+v8uR7yLzzqIriteWBz+C3g34y8fIPk8+bgY+k5svYiZeRwLj37QjhKN/+ZlJFNbn7l/XNvIp9eDru4y/EbrnfYgYPJfO3jRj0+yP0Hn+T15hwV++9tx4Z+vDq8g8b71WAKO6WgsHORj0bC/q8rfN2sskpIyWT26n189utfAHi7OvFIZBh3RzRWqXotlJ9vcORMOntOpljDzZ4TKSSmZlmPMZHPdQ67uNPxW/o5/ooj5rE1ma71yGg7Ct+e9+NY58rHiJSbjLPmyfP2fA5/fm8bfELaXwg+dZuU+C0NwyAtK9fmkdq5gqBkCUlnM7LJy7uyXwkeealEpK6hd8pKAnP+sm7f7d6F731v5nePbhimig+RnRvX4Z4ejXGqruOapEQUdkpBYeciZ/+EN7pV2rpZZbXt6DlmrNzNrr/M3eLNAjyZNqg1ves7QVqCeSr5tETzx2kJ5kHYmcnm8lXPeuBR1zw+ycO/4HN/88vNz27VaPL3MnPy2B+fahNs9p5MISM7r8ixJhO0989jtNtGrk9bhW/m8Qs7G18Hne8zT0rn5FKJd1AGGWdh36qCHp8NF+b3AXP1ZJtboc0tUKexnRpYIH6XeSzOruWQY37cjKsvdBwFncdAveb2bZ/USAo7paCwUww7rJt1SYZhnkm1cHBJPwVpCRipCZz86yhJp/6ijpFEPZJtBnyWickB3OvaBiCbQFQQlAp/Xk6DKuWCM2lZRXpr/jiVRnFPYtycHWgVXDC2Jtibzs5/0PzIMpz2fgF5BT08rj7QfqQ55AS2qtybKS/pZ2Dfl+bgc/gHMApVf4VecyH4lEMlU4lYF+N8B479fGF7YBvo+gC0Gw4ueoQkFUdhpxQUdoqRlQbzOkPqyYpbNyvnfEGAKRpiivTI5J4v1VufM7zJ9QigbuBVOPoEg1cQeAWaf+FlJkPGafP/mNNPQ8aZC6+slLLdi7MnePoXCkP+6j0qofx8g7izGUWCTXxKZrHH+3u6mENNwaDhNqE+NPb3xCk3w9yr8Mu75l4Gi+B20OV+aDusZv3iTT9tDhq7P4cjP9oGn6s6Fzzqutm8Zld5SzlhXohz2yLz31Ewj/NrfZM55DSMqHKPv6VmUtgpBYWdSyjLull5OQWB5eJHSIkXPVZKLH2wcPE2BxZLcLG+gqzbDmd68PR3p/j+kPnRVpCPK1MGtObmDqElq8TIzS4UfgqCUPrFnxcEJcvnhcdTlJTJsSAIXaKnyMOffHd/slz9yHSpS6azL5mGC5k5eWTm5JGVm09evkG+YZCXb2AY5hLqPMPAMAzy8in0caFjDPM5+QXb8g2s72H7sfmYfMs51muZj7E97qJjCt6n+GsYNtfLys3nUEKqdW00my+RCRr7e16ohgr1oU1BNZTN9zJxnzng7Fx24WfK0RWuHgpdxpjLmmv6L960UwXBZwUc3WgbfOp3uRB8fOuX/RqGYX7vLe+YFz61PE7zCjbPi9PpnjItxilyJRR2SkFh5xIKr5t19VC47l8XelouFWIyzpTuGo6uhcLLpUMMnoHg4lHCZht8uzeRZ1btIe6seexAp0Z1mDG4DW3rl226ecMwyMrNN79y8sjMySczN4/M7FxyMpLJSzuFURCKTOfP4Hj+DE6Z53DOOotL9jncss/hnpOER24S7vnpZWpDmuHGOcObM3hz1vDhL6MecUYgcUYgxwv+TKVkX6OqxtXJgVbB3tbemvBQH1oG+1x6AsncbPPjnK3vwdGfLmyv29T8mKrDKHOArI1SE8zBZ88X5pXCKfRPfINuF4JPSRf9zUqD3z6Grf+FxAsl+jTqae4xaz24TItxipQHhZ1SUNj5G4XXzSopk2NBQAmwDSzFhRhXnwr7X3dmTh7v/nSYN9Yfspaq39w+lCAft4IekoLAYvk4J49Ma5ix3Z+Vm1/meU0u5kwudUilrimVOqZU/Ekx/2lKoQ6p+JtSC/anUNeUSl1SSzwOKdXkTaJjMIlOwZxyCuaUUwinnUM44xzCOecgDAcXHB3AwWTCwcGEo8mEgwkcHEw4mAo+dzBvcyzY5mAymc8pfEzBOReON114X5Op4NzLv6+To4km9TxpWs+zZFUzScfMj062v28O2WAeY9XyRnPIadpXjwcLS40398LsXgFHN2Hz97hhhDn4tL4JfEKKnnv6oLkXZ+dHF3rMnD3MCwZ3fQCC2lTKLYj8HYWdUlDYuYxvnoLYN8C9jjmkWENM4eBSKNi4161Sv3DikzN5YfVePt9xolzez8EEbs6OuDk74urkYPOnm3PBn06FPnZ2xNXZoWBb4e0Xtrk6O+B60Tlulvd0csAxJ9X28VlaIiTFwbkjkHTU/Gf6qb9vuMkBfOpDnUYFr8bg19j8Z53G5sdoVfFxT34+/PGduWfh4NoLj2i8gsxLC3QafWWPZ2qLlJMXHnXFxRbaYboQfFoNNM9svOVtc8m7hX9zcy9O+5FVYmZ1EQuFnVJQ2CmB/PwqFWDKYtvRs3y9Kx4HEzbBwrVwsLhEYHEttM3Z0VQ1Z2LNSrsQgAqHoHNH4NzRyw/ydvYoCECNLgQgayhqVOLHiOUm/Yx5cdptC833YNH4OvNYnFaD9PikrFJOmB9z7V4BxzYXf4zJAVr0N/fiNOlT7f/+S82ksFMKCjtS4xmGuTeouBB07gik/MVlH1V6BhYNQZYg5BMKDuUwUZxhwLEt5gHHuz8vVDbuCx0Kysar0KzeNULycdhT0ONzfIu5Z/aau81f6zqN7N06kb+lsFMKCjtS6+VmmX/pnTt8IQBZgtHZI5CV/PfnOzibS5wLB6DCwci9zt+fn5UGuz4xDzhOKFQ2HtLe/Pjk6qE1q2y8qjp/zlx9WdUnWhQpUNLf35codRCRWsXJFfybmV/FOX+uaAiy9AwlxUF+jnnm7bN/Fn++m2/xj8dcvM1z4+xcBtmpBW1xM4ebzmPgqmuq5jiimupyoVSkmlLYEZHLc69jfoV2KLovP888BqS4x2PnjpirpjKTIf438+tS6jYrKBu/o/aWjYtIhVDYEZEr4+BofoTl1wAaX1t0f3Z6oYHTF/UOpcZDox7mAcdN+mgQrIhUCIUdEalYLp4Q2Nr8EhGxgxrz36g33niDxo0b4+bmRrdu3diyZYu9myQiIiJVQI0IOx9//DGTJk1i+vTpbN++nfbt2xMVFUViYqK9myYiIiJ2ViPCziuvvMIDDzzAvffeS3h4OAsWLMDDw4P33nvP3k0TERERO6v2YSc7O5tt27YRGRlp3ebg4EBkZCSxsbF/c6aIiIjUBtV+gPLp06fJy8sjKCjIZntQUBD79u0r9pysrCyysrKsn6ekpFRoG0VERMR+qn3PTlnMmjULX19f66tBgwb2bpKIiIhUkGofdurVq4ejoyMJCQk22xMSEggODi72nClTppCcnGx9HTt2rDKaKiIiInZQ7cOOi4sLnTp1Yt26ddZt+fn5rFu3joiIiGLPcXV1xcfHx+YlIiIiNVO1H7MDMGnSJEaPHk3nzp3p2rUrc+fOJT09nXvvvdfeTRMRERE7qxFh5/bbb+fUqVNMmzaN+Ph4OnTowJo1a4oMWhYREZHax2QYhmHvRthbSZeIFxERkaqjpL+/q/2YHREREZG/o7AjIiIiNZrCjoiIiNRoNWKA8pWyDFvSTMoiIiLVh+X39uWGHyvsAKmpqQCaSVlERKQaSk1NxdfX95L7VY2FeRLCEydO4O3tjclkKrf3TUlJoUGDBhw7dkxVXlWAvh9Vj74nVYu+H1WLvh+XZxgGqamphIaG4uBw6ZE56tnBvEp6/fr1K+z9NUtz1aLvR9Wj70nVou9H1aLvx9/7ux4dCw1QFhERkRpNYUdERERqNIWdCuTq6sr06dNxdXW1d1MEfT+qIn1PqhZ9P6oWfT/KjwYoi4iISI2mnh0RERGp0RR2REREpEZT2BEREZEaTWFHREREajSFnQr0xhtv0LhxY9zc3OjWrRtbtmyxd5NqpVmzZtGlSxe8vb0JDAzklltuYf/+/fZulhR44YUXMJlMTJw40d5NqbX++usv7rzzTvz9/XF3d6dt27b88ssv9m5WrZWXl8fUqVNp0qQJ7u7uNGvWjGeeeeay6z/JpSnsVJCPP/6YSZMmMX36dLZv30779u2JiooiMTHR3k2rdTZs2EB0dDQ///wzMTEx5OTkcMMNN5Cenm7vptV6W7du5a233qJdu3b2bkqtde7cOXr27ImzszOrV69mz549vPzyy9SpU8feTau1Zs+ezfz585k3bx579+5l9uzZzJkzh9dff93eTau2VHpeQbp160aXLl2YN28eYF5/q0GDBowfP54nn3zSzq2r3U6dOkVgYCAbNmygV69e9m5OrZWWlsY111zDm2++ybPPPkuHDh2YO3euvZtV6zz55JNs3LiRH3/80d5NkQKDBg0iKCiId99917pt6NChuLu78+GHH9qxZdWXenYqQHZ2Ntu2bSMyMtK6zcHBgcjISGJjY+3YMgFITk4GoG7dunZuSe0WHR3NwIEDbf6eSOVbuXIlnTt35rbbbiMwMJCOHTvyzjvv2LtZtVqPHj1Yt24dBw4cAGDnzp389NNPDBgwwM4tq760EGgFOH36NHl5eQQFBdlsDwoKYt++fXZqlYC5h23ixIn07NmTq6++2t7NqbWWLVvG9u3b2bp1q72bUuv9+eefzJ8/n0mTJvHvf/+brVu3MmHCBFxcXBg9erS9m1crPfnkk6SkpNCqVSscHR3Jy8vjueeeY9SoUfZuWrWlsCO1SnR0NL///js//fSTvZtSax07doxHHnmEmJgY3Nzc7N2cWi8/P5/OnTvz/PPPA9CxY0d+//13FixYoLBjJ5988glLlixh6dKltGnThh07djBx4kRCQ0P1PSkjhZ0KUK9ePRwdHUlISLDZnpCQQHBwsJ1aJePGjWPVqlX88MMP1K9f397NqbW2bdtGYmIi11xzjXVbXl4eP/zwA/PmzSMrKwtHR0c7trB2CQkJITw83GZb69at+d///menFsljjz3Gk08+yYgRIwBo27YtR48eZdasWQo7ZaQxOxXAxcWFTp06sW7dOuu2/Px81q1bR0REhB1bVjsZhsG4ceNYsWIF3333HU2aNLF3k2q1fv36sWvXLnbs2GF9de7cmVGjRrFjxw4FnUrWs2fPIlMxHDhwgEaNGtmpRZKRkYGDg+2vZ0dHR/Lz8+3UoupPPTsVZNKkSYwePZrOnTvTtWtX5s6dS3p6Ovfee6+9m1brREdHs3TpUr744gu8vb2Jj48HwNfXF3d3dzu3rvbx9vYuMl7K09MTf39/jaOyg0cffZQePXrw/PPPM3z4cLZs2cLbb7/N22+/be+m1VqDBw/mueeeo2HDhrRp04Zff/2VV155hfvuu8/eTau2VHpegebNm8eLL75IfHw8HTp04LXXXqNbt272blatYzKZit2+cOFC7rnnnsptjBSrT58+Kj23o1WrVjFlyhQOHjxIkyZNmDRpEg888IC9m1VrpaamMnXqVFasWEFiYiKhoaGMHDmSadOm4eLiYu/mVUsKOyIiIlKjacyOiIiI1GgKOyIiIlKjKeyIiIhIjaawIyIiIjWawo6IiIjUaAo7IiIiUqMp7IiIiEiNprAjIlIMk8nE559/bu9miEg5UNgRkSrnnnvuwWQyFXn179/f3k0TkWpIa2OJSJXUv39/Fi5caLPN1dXVTq0RkepMPTsiUiW5uroSHBxs86pTpw5gfsQ0f/58BgwYgLu7O02bNuXTTz+1OX/Xrl1cf/31uLu74+/vz9ixY0lLS7M55r333qNNmza4uroSEhLCuHHjbPafPn2aW2+9FQ8PD8LCwli5cmXF3rSIVAiFHRGplqZOncrQoUPZuXMno0aNYsSIEezduxeA9PR0oqKiqFOnDlu3bmX58uV8++23NmFm/vz5REdHM3bsWHbt2sXKlStp3ry5zTWefvpphg8fzm+//caNN97IqFGjOHv2bKXep4iUA0NEpIoZPXq04ejoaHh6etq8nnvuOcMwDAMw/vnPf9qc061bN+Ohhx4yDMMw3n77baNOnTpGWlqadf9XX31lODg4GPHx8YZhGEZoaKjxn//855JtAIynnnrK+nlaWpoBGKtXry63+xSRyqExOyJSJfXt25f58+fbbKtbt67144iICJt9ERER7NixA4C9e/fSvn17PD09rft79uxJfn4++/fvx2QyceLECfr16/e3bWjXrp31Y09PT3x8fEhMTCzrLYmInSjsiEiV5OnpWeSxUnlxd3cv0XHOzs42n5tMJvLz8yuiSSJSgTRmR0SqpZ9//rnI561btwagdevW7Ny5k/T0dOv+jRs34uDgQMuWLfH29qZx48asW7euUtssIvahnh0RqZKysrKIj4+32ebk5ES9evUAWL58OZ07d+baa69lyZIlbNmyhXfffReAUaNGMX36dEaPHs2MGTM4deoU48eP56677iIoKAiAGTNm8M9//pPAwEAGDBhAamoqGzduZPz48ZV7oyJS4RR2RKRKWrNmDSEhITbbWrZsyb59+wBzpdSyZct4+OGHCQkJ4aOPPiI8PBwADw8P1q5dyyOPPEKXLl3w8PBg6NChvPLKK9b3Gj16NJmZmbz66qtMnjyZevXqMWzYsMq7QRGpNCbDMAx7N0JEpDRMJhMrVqzglltusXdTRKQa0JgdERERqdEUdkRERKRG05gdEal29PRdREpDPTsiIiJSoynsiIiISI2msCMiIiI1msKOiIiI1GgKOyIiIlKjKeyIiIhIjaawIyIiIjWawo6IiIjUaAo7IiIiUqP9P8AwMuqY0gKJAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_pred_prob = dt_model.predict_proba(X_test)[:, 1]\n",
        "rf_pred_prob = rf_model.predict_proba(X_test)[:, 1]\n",
        "lr_pred_prob = lr_model.predict_proba(X_test)[:, 1]\n",
        "svm_pred_prob = svm_model.predict_proba(X_test)[:, 1]\n",
        "dt_kde = KernelDensity(kernel='gaussian', bandwidth=0.1).fit(dt_pred_prob.reshape(-1, 1))\n",
        "rf_kde = KernelDensity(kernel='gaussian', bandwidth=0.1).fit(rf_pred_prob.reshape(-1, 1))\n",
        "lr_kde = KernelDensity(kernel='gaussian', bandwidth=0.1).fit(lr_pred_prob.reshape(-1, 1))\n",
        "svm_kde = KernelDensity(kernel='gaussian', bandwidth=0.1).fit(svm_pred_prob.reshape(-1, 1))\n",
        "x = np.linspace(0, 1, 100)[:, np.newaxis]\n",
        "dt_log_density = dt_kde.score_samples(x)\n",
        "rf_log_density = rf_kde.score_samples(x)\n",
        "lr_log_density = lr_kde.score_samples(x)\n",
        "svm_log_density = svm_kde.score_samples(x)\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(x, np.exp(dt_log_density), label='Decision Tree')\n",
        "plt.plot(x, np.exp(rf_log_density), label='Random Forest')\n",
        "plt.plot(x, np.exp(lr_log_density), label='Logistic Regression')\n",
        "plt.plot(x, np.exp(svm_log_density), label='SVM')\n",
        "plt.xlabel('Predicted Probability')\n",
        "plt.ylabel('Density')\n",
        "plt.title('Kernel Density Estimation of Model Predictions')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "bbniyfH3xgTK",
        "outputId": "665cd014-ff58-480d-a0f6-47129b10b5b2"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann_pred_prob = model.predict(X_test)\n",
        "ann_kde = KernelDensity(kernel='gaussian', bandwidth=0.1).fit(ann_pred_prob.reshape(-1, 1))\n",
        "x = np.linspace(0, 1, 100)[:, np.newaxis]\n",
        "ann_log_density = ann_kde.score_samples(x)\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(x, np.exp(ann_log_density), label='ANN')\n",
        "plt.xlabel('Predicted Probability')\n",
        "plt.ylabel('Density')\n",
        "plt.title('Kernel Density Estimation of ANN Predictions')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "DSTqiOUYy4w7",
        "outputId": "a395df16-0f00-419d-b25c-ef706734cd6e"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dnn_pred_prob = model.predict(X_test)\n",
        "dnn_kde = KernelDensity(kernel='gaussian', bandwidth=0.1).fit(dnn_pred_prob.reshape(-1, 1))\n",
        "x = np.linspace(0, 1, 100)[:, np.newaxis]\n",
        "dnn_log_density = dnn_kde.score_samples(x)\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(x, np.exp(dnn_log_density), label='DNN')\n",
        "plt.xlabel('Predicted Probability')\n",
        "plt.ylabel('Density')\n",
        "plt.title('Kernel Density Estimation of DNN Predictions')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "lEq2MIZMzHFL",
        "outputId": "6844fbc7-a4ed-440f-f9d2-76071eeaae3a"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAAIjCAYAAAA0vUuxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABxcklEQVR4nO3dd3gU5cLG4WfSewKEJAQChN5Dr9JBQFTQ4wGxUBQrHgviUfQICGKwYD0oYsMCoqjA+cAC0qWoNEWkQ0JLAqEkJIG0ne8PzMqSHpJMyu++rr0kszOzzy5DzJN35h3DNE1TAAAAAIBcOVkdAAAAAADKOooTAAAAAOSD4gQAAAAA+aA4AQAAAEA+KE4AAAAAkA+KEwAAAADkg+IEAAAAAPmgOAEAAABAPihOAAAAAJAPihOACqdXr17q1auX1TGKzZQpU2QYhtUxis3o0aNVt25dq2M4iIqKkmEYmjt3rtVR8vT999+rdevW8vDwkGEYOnfunNWRKoQrv2eUxPFQt25djR49utj2B6D0UZwAZDN37lwZhqEtW7Y4LE9ISFDHjh3l4eGh77//3qJ0xadu3boyDEOGYcjJyUkBAQFq2bKl7r33Xv38889Wx8vTCy+8oMWLFxfrPtesWWP/PHJ6LFiwoMD7OnHihKZMmaIdO3YUa8arNX/+fL3++utWxyiS06dPa9iwYfL09NSsWbP06aefytvbO8d1s/4NZz08PDwUGhqqAQMG6M0339T58+ezbZNV0IODg5WSkpLt+bp16+r66693WJa1/5kzZ+aa4crvI1e68rhzdXVVvXr1NHLkSB06dCjPbcuajRs3asqUKRRaoIJysToAgPIhMTFR1157rX7//XctWrRIAwcOtDpSsWjdurUef/xxSdL58+e1e/duLVy4UO+9954ee+wxvfrqqxYnlP7zn//oqaeeclj2wgsv6JZbbtHQoUOL/fUefvhhdejQIdvyLl26FHgfJ06c0HPPPae6deuqdevWDs+99957stlsVxuzSObPn68//vhDjz76qMPyOnXq6MKFC3J1dbUkV0H8+uuvOn/+vKZNm6Z+/foVaJupU6cqPDxc6enpio2N1Zo1a/Too4/q1Vdf1f/+9z+1atUq2zYnT57UO++8Y/93URAvv/yyHnjgAXl5eRV4mytlHXfp6enatm2b5syZo2XLlmnnzp0KDQ0t8n6LoqjHw8aNG/Xcc89p9OjRCggIcHhu7969cnLi99VAeUZxApCv8+fPa8CAAdqxY4e++eYbDRo06Kr3efHiRbm5uVn+g0TNmjV1xx13OCx78cUXddttt+m1115Tw4YN9cADD1iU7hIXFxe5uJTet+vu3bvrlltuKbH9l8VykjUqU5adPHlSkrL9QJ6XQYMGqX379vavJ06cqFWrVun666/XjTfeqN27d8vT09Nhm9atW+vll1/Wgw8+mO25nLRu3Vo7duzQ7NmzNX78+AJnu9Llx92YMWPUqFEjPfzww/r44481ceLEHLdJTk7OddTtapTE8eDu7l6s+wNQ+vjVB4A8JSUlaeDAgdq2bZu+/vprDR482OH548eP66677lJwcLDc3d3VvHlzffjhhw7rZJ2Ks2DBAv3nP/9RzZo15eXlpcTERI0ePVo+Pj46fvy4hg4dKh8fH1WvXl0TJkxQZmamw35sNptef/11NW/eXB4eHgoODtZ9992ns2fPFut79vT01KeffqqqVatq+vTpMk2z0BmyTmv66aef7Kc31qtXT5988onDeunp6XruuefUsGFDeXh4qFq1arrmmmu0YsUK+zpXXuNkGIaSk5P18ccf209vGj16tFavXi3DMLRo0aJs72n+/PkyDEObNm0qls9oxYoVuuaaaxQQECAfHx81btxYTz/9tKRLf99ZI1ZjxoyxZ8y6XuTKa5yyrid55ZVXNGvWLNWrV09eXl669tprdfToUZmmqWnTpqlWrVry9PTUkCFDdObMGYc8S5Ys0eDBgxUaGip3d3fVr19f06ZNcziGevXqpWXLlik6OtqeKStHbte0rFq1St27d5e3t7cCAgI0ZMgQ7d6922GdrL+fAwcO2Eca/P39NWbMmBxPecvJwoUL1a5dO3l6eiowMFB33HGHjh8/7pB91KhRkqQOHTrY/86Lok+fPnr22WcVHR2tzz77LNvzkyZNUlxcnN55550C7a9bt27q06ePXnrpJV24cKFImXLLKUmHDx+W9Pfn/Oeff+q2225TlSpVdM0119jX/+yzz+yfYdWqVXXrrbfq6NGj2fY7Z84c1a9fX56enurYsaPWr1+fbZ3cjoc9e/Zo2LBhql69ujw9PdW4cWM988wz9nxPPPGEJCk8PNx+jEVFRUnK+RqnQ4cO6Z///KeqVq0qLy8vde7cWcuWLXNYJ+v755dffqnp06erVq1a8vDwUN++fXXgwAGHdffv369//OMfCgkJkYeHh2rVqqVbb71VCQkJ+XzaAAqCEScAuUpOTtagQYP066+/6quvvsp2fUNcXJw6d+4swzD00EMPqXr16vruu+909913KzExMdvpUNOmTZObm5smTJig1NRUubm5SZIyMzM1YMAAderUSa+88op+/PFHzZw5U/Xr13cY7bnvvvs0d+5cjRkzRg8//LAOHz6s//73v9q+fbs2bNhQrCMZPj4+uummm/TBBx/ozz//VPPmzQud4cCBA7rlllt09913a9SoUfrwww81evRotWvXzr6/KVOmKDIyUmPHjlXHjh2VmJioLVu2aNu2berfv3+O2T799FP7+vfee68kqX79+urcubPCwsI0b9483XTTTQ7bzJs3T/Xr1y/Q6Xbnz59XfHx8tuXVqlWTYRjatWuXrr/+erVq1UpTp06Vu7u7Dhw4oA0bNkiSmjZtqqlTp2rSpEm699571b17d0lS165d83zdefPmKS0tTf/617905swZvfTSSxo2bJj69OmjNWvW6Mknn9SBAwf01ltvacKECQ4Ffe7cufLx8dH48ePl4+OjVatWadKkSUpMTNTLL78sSXrmmWeUkJCgY8eO6bXXXpN06e85Nz/++KMGDRqkevXqacqUKbpw4YLeeustdevWTdu2bcs2wcWwYcMUHh6uyMhIbdu2Te+//76CgoL04osv5vm+s46nDh06KDIyUnFxcXrjjTe0YcMGbd++XQEBAXrmmWfUuHFjzZkzx376Xf369fPcb17uvPNOPf3001q+fLnuueceh+e6d+9uL0IPPPBAgUadpkyZoh49euidd965qlGnyx08eFDSpePucv/85z/VsGFDvfDCC/ZfakyfPl3PPvushg0bprFjx+rUqVN666231KNHD/tnKEkffPCB7rvvPnXt2lWPPvqoDh06pBtvvFFVq1ZVWFhYnnl+//13de/eXa6urrr33ntVt25dHTx4UP/3f/+n6dOn6+abb9a+ffv0+eef67XXXlNgYKAkqXr16jnuLy4uTl27dlVKSooefvhhVatWTR9//LFuvPFGffXVV9n+Dc+YMUNOTk6aMGGCEhIS9NJLL+n222+3X4+ZlpamAQMGKDU1Vf/6178UEhKi48ePa+nSpTp37pz8/f0L9xcAIDsTAK7w0UcfmZLMOnXqmK6urubixYtzXO/uu+82a9SoYcbHxzssv/XWW01/f38zJSXFNE3TXL16tSnJrFevnn1ZllGjRpmSzKlTpzosb9OmjdmuXTv71+vXrzclmfPmzXNY7/vvv8+2vGfPnmbPnj3zfZ916tQxBw8enOvzr732minJXLJkSaEz1KlTx5Rkrlu3zr7s5MmTpru7u/n444/bl0VEROSZwTRNc/LkyeaV3669vb3NUaNGZVt34sSJpru7u3nu3DmH13VxcTEnT56c5+tk/T3l9oiJiXH4XE6dOpXrvn799VdTkvnRRx9le27UqFFmnTp17F8fPnzYlGRWr17dIffEiRNNSWZERISZnp5uXz5ixAjTzc3NvHjxon3ZlceVaZrmfffdZ3p5eTmsN3jwYIfXvjLD5Xlbt25tBgUFmadPn7Yv++2330wnJydz5MiR9mVZfz933XWXwz5vuukms1q1atle63JpaWlmUFCQ2aJFC/PChQv25UuXLjUlmZMmTbIvy/p3+euvv+a5z4Ku6+/vb7Zp0ybb+zh16pS5du1aU5L56quv2p/P6d+LJHPcuHGmaZpm7969zZCQEPvfRUHzZh13H374oXnq1CnzxIkT5rJly8y6deuahmHYt8/KN2LECIfto6KiTGdnZ3P69OkOy3fu3Gm6uLjYl2d91q1btzZTU1Pt682ZM8eU5PA9I6fjoUePHqavr68ZHR3t8Do2m83+55dfftmUZB4+fDjb+6xTp47Dv9lHH33UlGSuX7/evuz8+fNmeHi4WbduXTMzM9Ph82natKlD7jfeeMOUZO7cudM0TdPcvn27KclcuHBhttcGUDw4VQ9AruLi4uTh4ZHjb2JN09TXX3+tG264QaZpKj4+3v4YMGCAEhIStG3bNodtRo0aletvr++//36Hr7t37+4wo9bChQvl7++v/v37O7xWu3bt5OPjo9WrVxfDO3aUNRqRNQNZYTM0a9bMPtoiXfrNc+PGjR3eV0BAgHbt2qX9+/cXS+aRI0cqNTVVX331lX3ZF198oYyMjGzXcuVm0qRJWrFiRbZH1apV7ZmlS6fHFeckD//85z8dfiveqVMnSdIdd9zhcI1Xp06dlJaW5nAq2+XHVdaIWffu3ZWSkqI9e/YUOktMTIx27Nih0aNH29+3JLVq1Ur9+/fXt99+m22bnI7h06dPKzExMdfX2bJli06ePKkHH3zQ4ZqawYMHq0mTJtlO2ypOPj4+Oc6uJ0k9evRQ7969C3X63ZQpUxQbG6vZs2cXKc9dd92l6tWrKzQ0VIMHD7afjnr5NVpS9s/5m2++kc1m07Bhwxz+XYaEhKhhw4b2f5dZn/X9999vH+2WLp06mt9ozKlTp7Ru3Trdddddql27tsNzRb1VwLfffquOHTs6nG7o4+Oje++9V1FRUfrzzz8d1h8zZoxD7qzvLVnfT7Leww8//FDgU0QBFA7FCUCu3n33Xbm5uWngwIHau3evw3OnTp3SuXPnNGfOHFWvXt3hMWbMGEl/X8yeJTw8PMfX8fDwyHY6S5UqVRyuG9q/f78SEhIUFBSU7fWSkpKyvVZxSEpKkiT5+voWKcOVP2Dl9L6mTp2qc+fOqVGjRmrZsqWeeOIJ/f7770XO3KRJE3Xo0EHz5s2zL5s3b546d+6sBg0aFGgfLVu2VL9+/bI9sn5oGz58uLp166axY8cqODhYt956q7788surLlFXfl5ZPwheWdyzll/+Oe7atUs33XST/P395efnp+rVq9uLYlGu74iOjpYkNW7cONtzTZs2VXx8vJKTk/PMX6VKlWw5C/M6TZo0sT9fEpKSkuzHdk4KW4SKUrYul1XYV61apd9//10nTpzQnXfemW29K7+P7N+/X6ZpqmHDhtn+Xe7evdv+7zLrs2zYsKHD9lnTn+clq5y0aNGi0O8rN9HR0bkeX1nPXy6/4ys8PFzjx4/X+++/r8DAQA0YMECzZs3i+iagGHGNE4BcNWvWTN9++6369u2r/v37a8OGDfYfYrN+SL7jjjvsF61f6cqpjnMbbXJ2ds43i81mU1BQkEMhuFxu1xFcjT/++EOS7IWjsBlye1/mZZNN9OjRQwcPHtSSJUu0fPlyvf/++3rttdc0e/ZsjR07tki5R44cqUceeUTHjh1TamqqNm/erP/+979F2ldOPD09tW7dOq1evVrLli3T999/ry+++EJ9+vTR8uXLC/T3mZPctsvvczx37px69uwpPz8/TZ06VfXr15eHh4e2bdumJ598stSmPi/I33dZcezYMSUkJORZpnv06KFevXrppZdeyjbKk5vJkyerV69eevfddws1+5/0d2HPz5XfR2w2mwzD0HfffZfj30Fe17GVJwU5vmbOnKnRo0fbv588/PDDioyM1ObNm1WrVq3SigpUWBQnAHnq2LGjFi9erMGDB6t///5av369/be5vr6+yszMLPA9Za5G/fr19eOPP6pbt24Fulj9aiUlJWnRokUKCwuz/wa4pDJUrVpVY8aM0ZgxY5SUlKQePXpoypQpeRanvE4PuvXWWzV+/Hh9/vnn9nvRDB8+vNjySpKTk5P69u2rvn376tVXX9ULL7ygZ555RqtXr1a/fv2KfPpSUaxZs0anT5/WN998ox49etiXZ83GdrmC5qpTp44kZRtplS7NrBYYGFgs02Bf/jpZs8hl2bt3r/354vbpp59KkgYMGJDnelOmTLEXoYLo2bOnevXqpRdffFGTJk266pwFUb9+fZmmqfDwcDVq1CjX9bI+y/379zt81unp6Tp8+LAiIiJy3TZrRCrrlym5KcxxX6dOnVyPr8vzFlbLli3VsmVL/ec//9HGjRvVrVs3zZ49W88//3yR9gfgb5yqByBfffv21eeff64DBw5o4MCBSkxMlLOzs/7xj3/o66+/zvGHiVOnThVrhmHDhikzM1PTpk3L9lxGRobOnTtXbK914cIF3XnnnTpz5oyeeeYZ+w9DJZHh9OnTDl/7+PioQYMGSk1NzXM7b2/vXF8vMDBQgwYN0meffaZ58+Zp4MCB9hm+isOVU4FLst/kNit3Vqkozr+X3GT9Jv7y37ynpaXp7bffzraut7d3gU5dqlGjhlq3bq2PP/7Y4T388ccfWr58ua677rqrDy6pffv2CgoK0uzZsx3+zr/77jvt3r072/T/xWHVqlWaNm2awsPDdfvtt+e57uVF6OLFiwXaf9YpfnPmzCmOuPm6+eab5ezsrOeeey7b6J5pmvZ/Y+3bt1f16tU1e/ZspaWl2deZO3duvsdp9erV1aNHD3344Yc6cuRIttfIUpjj/rrrrtMvv/zicIuA5ORkzZkzR3Xr1lWzZs3y3cflEhMTlZGR4bCsZcuWcnJyyvf7CYCCYcQJQIHcdNNNeu+993TXXXfpxhtv1Pfff68ZM2Zo9erV6tSpk+655x41a9ZMZ86c0bZt2/Tjjz/m+AN2UfXs2VP33XefIiMjtWPHDl177bVydXXV/v37tXDhQr3xxhtFumnr8ePH7feySUpK0p9//qmFCxcqNjZWjz/+uO67774SzdCsWTP16tVL7dq1U9WqVbVlyxZ99dVXeuihh/Lcrl27dvrxxx/16quvKjQ0VOHh4fbJFKRLp+tlZcmp6OVl/fr1Of6Q3KpVK/sU5OvWrdPgwYNVp04dnTx5Um+//bZq1aplv9C9fv36CggI0OzZs+Xr6ytvb2916tQp1+vcrkbXrl1VpUoVjRo1Sg8//LAMw9Cnn36a4yly7dq10xdffKHx48erQ4cO8vHx0Q033JDjfl9++WUNGjRIXbp00d13322fjtzf319Tpkwpluyurq568cUXNWbMGPXs2VMjRoywT0det25dPfbYY1e1/++++0579uxRRkaG4uLitGrVKq1YsUJ16tTR//73vwLd5HXy5Mnq3bt3gV+zZ8+e6tmzp9auXXs10Qusfv36ev755zVx4kRFRUVp6NCh8vX11eHDh7Vo0SLde++9mjBhglxdXfX888/rvvvuU58+fTR8+HAdPnxYH330Ub7XOEnSm2++qWuuuUZt27bVvffeq/DwcEVFRWnZsmXasWOHpEvHl3Rp6vtbb71Vrq6uuuGGG3IcnXzqqaf0+eefa9CgQXr44YdVtWpVffzxxzp8+LC+/vrrQt8cfNWqVXrooYf0z3/+U40aNVJGRoY+/fRT+y+5ABQDS+byA1Cm5TWN8CuvvGJKMq+//nozPT3djIuLM8eNG2eGhYWZrq6uZkhIiNm3b19zzpw59m2yptPNaZrcUaNGmd7e3tmW5zQFt2lemjq4Xbt2pqenp+nr62u2bNnS/Pe//22eOHHCvk5hpiPXX1NtG4Zh+vn5mc2bNzfvuece8+eff851u4JkyG2q8yuzPf/882bHjh3NgIAA09PT02zSpIk5ffp0My0tLc/PYs+ePWaPHj1MT09PU1K2qclTU1PNKlWqmP7+/g7TXOclv+nIs6YzX7lypTlkyBAzNDTUdHNzM0NDQ80RI0aY+/btc9jfkiVLzGbNmpkuLi4OUzvnNh35yy+/nGOeK4+bnI7PDRs2mJ07dzY9PT3N0NBQ89///rf5ww8/mJLM1atX29dLSkoyb7vtNjMgIMA+5f7lGa6cPv3HH380u3XrZnp6epp+fn7mDTfcYP75558O61w+jXdOOXOamvpKX3zxhdmmTRvT3d3drFq1qnn77bebx44dy/d95yZr3ayHm5ubGRISYvbv39984403zMTExGzb5PY+TPPScSspz+nIL3f5sVTQ6cjzm0Y7r3ymaZpff/21ec0115je3t6mt7e32aRJE3PcuHHm3r17HdZ7++23zfDwcNPd3d1s3769uW7dumz/LnM7Hv744w/zpptuMgMCAkwPDw+zcePG5rPPPuuwzrRp08yaNWuaTk5ODn//V05HbpqmefDgQfOWW26x769jx47m0qVLC/T5XJnx0KFD5l133WXWr1/f9PDwMKtWrWr27t3b/PHHH/P4VAEUhmGaZfCqVQDAVcnIyFBoaKhuuOEGffDBB1bHAQCg3OMaJwCogBYvXqxTp05p5MiRVkcBAKBCYMQJACqQn3/+Wb///rumTZumwMDAbDchBgAARcOIEwBUIO+8844eeOABBQUF6ZNPPrE6DgAAFQYjTgAAAACQD0acAAAAACAfFCcAAAAAyEeluwGuzWbTiRMn5OvrK8MwrI4DAAAAwCKmaer8+fMKDQ3N98bTla44nThxQmFhYVbHAAAAAFBGHD16VLVq1cpznUpXnHx9fSVd+nD8/PwsTgMAAADAKomJiQoLC7N3hLxUuuKUdXqen58fxQkAAABAgS7hYXIIAAAAAMgHxQkAAAAA8kFxAgAAAIB8VLprnAAAAIDyxjRNZWRkKDMz0+oo5Y6rq6ucnZ2vej8UJwAAAKAMS0tLU0xMjFJSUqyOUi4ZhqFatWrJx8fnqvZDcQIAAADKKJvNpsOHD8vZ2VmhoaFyc3Mr0AxwuMQ0TZ06dUrHjh1Tw4YNr2rkieIEAAAAlFFpaWmy2WwKCwuTl5eX1XHKperVqysqKkrp6elXVZyYHAIAAAAo45yc+LG9qIprhI6/AQAAAADIB8UJAAAAAPJBcQIAAACAfFCcAAAAABS70aNHyzAMGYYhV1dXBQcHq3///vrwww9ls9ns69WtW1eGYWjz5s0O2z/66KPq1auX/espU6bIMAzdf//9Duvt2LFDhmEoKiqqJN8OxQkAAABAyRg4cKBiYmIUFRWl7777Tr1799Yjjzyi66+/XhkZGfb1PDw89OSTT+a7Pw8PD33wwQfav39/ScbOUZkpTjNmzJBhGHr00UfzXG/hwoVq0qSJPDw81LJlS3377belExAAAACwmGmaSknLsORhmmah87q7uyskJEQ1a9ZU27Zt9fTTT2vJkiX67rvvNHfuXPt69957rzZv3pzvz/aNGzdW79699cwzzxQ6y9UqE/dx+vXXX/Xuu++qVatWea63ceNGjRgxQpGRkbr++us1f/58DR06VNu2bVOLFi1KKS0AAABgjQvpmWo26QdLXvvPqQPk5Xb19aFPnz6KiIjQN998o7Fjx0qSwsPDdf/992vixIkaOHBgntOvz5gxQx06dNCWLVvUvn37q85TUJaPOCUlJen222/Xe++9pypVquS57htvvKGBAwfqiSeeUNOmTTVt2jS1bdtW//3vf0spLQAAAICr1aRJk2zXJP3nP//R4cOHNW/evDy3bdu2rYYNG1agU/uKk+UjTuPGjdPgwYPVr18/Pf/883muu2nTJo0fP95h2YABA7R48eJct0lNTVVqaqr968TExKvKW5wOnEzSjqPn1LtxdVXzcbc6DgAAAMo4T1dn/Tl1gGWvXVxM08x2Y9rq1atrwoQJmjRpkoYPH57n9s8//7yaNm2q5cuXKygoqNhy5cXS4rRgwQJt27ZNv/76a4HWj42NVXBwsMOy4OBgxcbG5rpNZGSknnvuuavKWVIWbT+mWasPyjCktrWrqG/TIPVrGqyGQT7FdodjAAAAVByGYRTL6XJW2717t8LDw7MtHz9+vN5++229/fbbeW5fv3593XPPPXrqqaf0wQcflFRMB5adqnf06FE98sgjmjdvnjw8PErsdSZOnKiEhAT74+jRoyX2WoUVGuCpZjX8ZJrS1uizeun7vbr2tXXq8fJqTfnfLv20P16ZtsJfhAcAAACUVatWrdLOnTv1j3/8I9tzPj4+evbZZzV9+nSdP38+z/1MmjRJ+/bt04IFC0oqqgPL6urWrVt18uRJtW3b1r4sMzNT69at03//+1+lpqbK2dlxODAkJERxcXEOy+Li4hQSEpLr67i7u8vdvWyeBnd7pzq6vVMdnTh3QSv3nNTK3XHaePC0jp65oLkbozR3Y5SuaRCod+5oK18PV6vjAgAAAIWSmpqq2NhYZWZmKi4uTt9//719oreRI0fmuM29996r1157TfPnz1enTp1y3XdwcLDGjx+vl19+uaTiO7BsxKlv377auXOnduzYYX+0b99et99+u3bs2JGtNElSly5dtHLlSodlK1asUJcuXUordokIDfDUnZ3raO6Yjtoxqb/m3NlOw9uHycvNWT8diNewdzfrZOJFq2MCAAAAhfL999+rRo0aqlu3rgYOHKjVq1frzTff1JIlS3L8eV+SXF1dNW3aNF28mP/PvxMmTJCPj09xx86RYRZlQvYS0qtXL7Vu3Vqvv/66JGnkyJGqWbOmIiMjJV2ajrxnz56aMWOGBg8erAULFuiFF14o1HTkiYmJ8vf3V0JCgvz8/ErqrRSLnccSNGbuL4pPSlPNAE99fFdHNQgqnQMDAAAA1rt48aIOHz6s8PDwEr28pSLL6zMsTDewfDryvBw5ckQxMTH2r7t27ar58+drzpw5ioiI0FdffaXFixdX2Hs4tazlr28e6KbwQG8dP3dBt8zeqK3RZ62OBQAAAFQ6ZWrEqTSUpxGnLKeTUnXXx1v029Fzcndx0n9va6v+zYLz3xAAAADlGiNOV69SjDjhkmo+7vr8nk7q2yRIqRk23ffpFs37OdrqWAAAAEClQXEqJ7zcXPTune10a4cw2UzpmUV/aPbag1bHAgAAACoFilM54uLspMibW+qRvg0lSS99v0fbjnDNEwAAQEVXya6uKVbF9dlRnMoZwzD0WP9GGto6VDZTevzL35SSlmF1LAAAAJQAV9dL9/JMSUmxOEn5lZaWJkm5Tn9eUJbdABdX57kbW2jzoTM6HJ+sF7/bo+eGVMyZBQEAACozZ2dnBQQE6OTJk5IkLy8vGYZhcaryw2az6dSpU/Ly8pKLy9VVH4pTOeXv5aqX/9lKd37wiz7eFK1+zYLVvWF1q2MBAACgmIWEhEiSvTyhcJycnFS7du2rLpxMR17OTVryhz7ZFK0QPw/98GgP+Xu5Wh0JAAAAJSAzM1Pp6elWxyh33Nzc5OSU8xVKhekGjDiVc08NaqL1++N1OD5ZU/5vl14b3trqSAAAACgBzs7OV32dDoqOySHKOS83F80cFiEnQ1q0/bi+2xljdSQAAACgwqE4VQBta1fRA73qS5KeXrRTJ89ftDgRAAAAULFQnCqIR/o2UtMafjqbkq6nv9nJXP8AAABAMaI4VRBuLk56bXiE3Jyd9OPuk/pyy1GrIwEAAAAVBsWpAmkS4qfHr20kSXrh2z1KvMisKwAAAEBxoDhVMGO711ODIB8lXEjXRz9FWR0HAAAAqBAoThWMs5OhR/o2lCS9/9MhJVxg1AkAAAC4WhSnCmhwyxpqFOyj8xcz9MFPh62OAwAAAJR7FKcKyMnJ0KP9Ll3r9NFPh5WQwqgTAAAAcDUoThXUwOYhahLiq/OpGXr/p0NWxwEAAADKNYpTBXX5qNOHPx3W2eQ0ixMBAAAA5RfFqQIb0DxYzUP9lJyWqffWM+oEAAAAFBXFqQIzjL9HneZujNLppFSLEwEAAADlE8WpguvXNEgta/orJS1Tcxh1AgAAAIqE4lTBGYahx/pfuq/TJxujFc+oEwAAAFBoFKdKoHfjIEWEBehCeqbeXXvQ6jgAAABAuUNxqgQMw9Bj/S6NOn26OVonz1+0OBEAAABQvlCcKomejaqrTe0AXUy3afYarnUCAAAACoPiVEkYhqHx/S/NsDfv52idS+G+TgAAAEBBUZwqkWsaBKp5qJ9SM2z6ausxq+MAAAAA5QbFqRIxDEN3dK4jSZr38xHZbKbFiQAAAIDygeJUydwYESofdxcdjk/WxoOnrY4DAAAAlAsUp0rG291FN7etKUn6bHO0xWkAAACA8oHiVAllna63YnecYhOYmhwAAADID8WpEmoU7KuOdasq02Zqwa9HrI4DAAAAlHkUp0rq9s61JUkLfjmqjEybxWkAAACAso3iVEkNbBGiat5uik28qB93n7Q6DgAAAFCmUZwqKXcXZw3rECbp0g1xAQAAAOSO4lSJ3daxtgxDWr8/Xofjk62OAwAAAJRZFKdKLKyql3o2qi5Jms+oEwAAAJArilMld0enS1OTL9x6TBfTMy1OAwAAAJRNFKdKrneTINUM8NS5lHR9uzPG6jgAAABAmURxquScnQyN6HhpkojPNnO6HgAAAJATS4vTO++8o1atWsnPz09+fn7q0qWLvvvuu1zXnzt3rgzDcHh4eHiUYuKKaViHMLk4Gdp25Jz+PJFodRwAAACgzLG0ONWqVUszZszQ1q1btWXLFvXp00dDhgzRrl27ct3Gz89PMTEx9kd0NKMkVyvI10MDWoRIkj5jkggAAAAgG0uL0w033KDrrrtODRs2VKNGjTR9+nT5+Pho8+bNuW5jGIZCQkLsj+Dg4FJMXHHd3qm2JGnx9uNKSs2wOA0AAABQtpSZa5wyMzO1YMECJScnq0uXLrmul5SUpDp16igsLCzf0SlJSk1NVWJiosMD2XWpV03hgd5KScvU8l2xVscBAAAAyhTLi9POnTvl4+Mjd3d33X///Vq0aJGaNWuW47qNGzfWhx9+qCVLluizzz6TzWZT165ddezYsVz3HxkZKX9/f/sjLCyspN5KuWYYhoa0DpUkLdlxwuI0AAAAQNlimKZpWhkgLS1NR44cUUJCgr766iu9//77Wrt2ba7l6XLp6elq2rSpRowYoWnTpuW4TmpqqlJTU+1fJyYmKiwsTAkJCfLz8yu291ERHI5PVu9X1sjZydDPT/dVoI+71ZEAAACAEpOYmCh/f/8CdQPLR5zc3NzUoEEDtWvXTpGRkYqIiNAbb7xRoG1dXV3Vpk0bHThwINd13N3d7bP2ZT2Qs/BAb0XU8lemzdSy37mnEwAAAJDF8uJ0JZvN5jBClJfMzEzt3LlTNWrUKOFUlceNrWtKkpbsOG5xEgAAAKDssLQ4TZw4UevWrVNUVJR27typiRMnas2aNbr99tslSSNHjtTEiRPt60+dOlXLly/XoUOHtG3bNt1xxx2Kjo7W2LFjrXoLFc4NrWrIyZC2HTmnI6dTrI4DAAAAlAkuVr74yZMnNXLkSMXExMjf31+tWrXSDz/8oP79+0uSjhw5Iienv7vd2bNndc899yg2NlZVqlRRu3bttHHjxgJdD4WCCfLzUNf6gfrpQLz+99txPdSnodWRAAAAAMtZPjlEaSvMBWCV1ZdbjurfX/2uBkE+WvFYDxmGYXUkAAAAoNiVq8khUPYMbBEiNxcnHTiZpN0x562OAwAAAFiO4oRs/Dxc1bdJkCQmiQAAAAAkihNykXUz3P/9dkI2W6U6mxMAAADIhuKEHPVqHCRfDxfFJFzUr1FnrI4DAAAAWIrihBx5uDprUIsQSdKS305YnAYAAACwFsUJuRry181wv90Zo7QMm8VpAAAAAOtQnJCrzvWqKcjXXedS0rVu3ymr4wAAAACWoTghV85Ohm6IuDRJBKfrAQAAoDKjOCFPWbPrrfgzVsmpGRanAQAAAKxBcUKeWtb0V71Ab11Mt2n5n7FWxwEAAAAsQXFCngzD0I1/jTot2cHpegAAAKicKE7IV9bseuv3x+t0UqrFaQAAAIDSR3FCvsIDvdWqlr8ybaaW/xlndRwAAACg1FGcUCADml+6Ge4Pu7jOCQAAAJUPxQkFklWcNh44rcSL6RanAQAAAEoXxQkF0iDIR/Wreyst06bVe05aHQcAAAAoVRQnFFjWqNPyXVznBAAAgMqF4oQCyypOa/ae1MX0TIvTAAAAAKWH4oQCa1XLXzX8PZSclqkNB+KtjgMAAACUGooTCswwDPuo0/d/MLseAAAAKg+KEwrl2ubBkqQfd8cpI9NmcRoAAACgdFCcUCgd61ZVFS9XnU1J169RZ62OAwAAAJQKihMKxcXZSX2bXhp14ma4AAAAqCwoTii0gfZpyWNlmqbFaQAAAICSR3FCoV3TMFBebs46kXBRO48nWB0HAAAAKHEUJxSah6uzejWuLonT9QAAAFA5UJxQJFnTkv+wK87iJAAAAEDJozihSHo3CZKrs6EDJ5N04GSS1XEAAACAEkVxQpH4ebiqa/1ASZyuBwAAgIqP4oQiG3DZ7HoAAABARUZxQpH1bxYsw5B+O5agmIQLVscBAAAASgzFCUVW3ddd7WpXkSQtZ5IIAAAAVGAUJ1yVrNP1vv+D0/UAAABQcVGccFWyitMvUWd0NjnN4jQAAABAyaA44arUrualpjX8lGkztXLPSavjAAAAACWC4oSr1r9pkCRpNcUJAAAAFRTFCVetT9NgSdK6faeUlmGzOA0AAABQ/ChOuGqtavor0MdN51MztCXqjNVxAAAAgGJHccJVc3Iy1KvxpdP1VnG6HgAAACogihOKRZ8mFCcAAABUXBQnFIvuDQPl4mToUHyyDscnWx0HAAAAKFYUJxQLXw9XdapXVRKjTgAAAKh4LC1O77zzjlq1aiU/Pz/5+fmpS5cu+u677/LcZuHChWrSpIk8PDzUsmVLffvtt6WUFvnpbb/OKc7iJAAAAEDxsrQ41apVSzNmzNDWrVu1ZcsW9enTR0OGDNGuXbtyXH/jxo0aMWKE7r77bm3fvl1Dhw7V0KFD9ccff5RycuSk71/Tkv9y+IzOX0y3OA0AAABQfAzTNE2rQ1yuatWqevnll3X33Xdne2748OFKTk7W0qVL7cs6d+6s1q1ba/bs2QXaf2Jiovz9/ZWQkCA/P79iy41L+ryyRofik/XO7W01qGUNq+MAAAAAuSpMNygz1zhlZmZqwYIFSk5OVpcuXXJcZ9OmTerXr5/DsgEDBmjTpk257jc1NVWJiYkOD5Sc3syuBwAAgArI8uK0c+dO+fj4yN3dXffff78WLVqkZs2a5bhubGysgoODHZYFBwcrNjY21/1HRkbK39/f/ggLCyvW/HDU96/itHrvSdlsZWowEwAAACgyy4tT48aNtWPHDv3888964IEHNGrUKP3555/Ftv+JEycqISHB/jh69Gix7RvZta9bVT7uLopPStPvxxOsjgMAAAAUC8uLk5ubmxo0aKB27dopMjJSEREReuONN3JcNyQkRHFxjjO2xcXFKSQkJNf9u7u722fty3qg5Li5OKlHo0BJnK4HAACAisPy4nQlm82m1NTUHJ/r0qWLVq5c6bBsxYoVuV4TBWv0aXLpdEqmJQcAAEBF4WLli0+cOFGDBg1S7dq1df78ec2fP19r1qzRDz/8IEkaOXKkatasqcjISEnSI488op49e2rmzJkaPHiwFixYoC1btmjOnDlWvg1coVfj6jIM6Y/jiYpLvKhgPw+rIwEAAABXxdIRp5MnT2rkyJFq3Lix+vbtq19//VU//PCD+vfvL0k6cuSIYmJi7Ot37dpV8+fP15w5cxQREaGvvvpKixcvVosWLax6C8hBoI+7ImoFSJJWc7oeAAAAKoAydx+nksZ9nErHWyv3a+aKferfLFjvjWxvdRwAAAAgm3J5HydULFn3c9pwIF4X0zMtTgMAAABcHYoTSkTzUD8F+7krJS1TPx8+Y3UcAAAA4KpQnFAiDMNQn79GnVbtZnY9AAAAlG8UJ5QY+7Tke0+qkl1KBwAAgAqG4oQS061BNbm5OOnomQs6cDLJ6jgAAABAkVGcUGK83FzUpV41SdIqpiUHAABAOUZxQonq3bi6JGntvlMWJwEAAACKjuKEEtWr8aUJIn6NOqOk1AyL0wAAAABFQ3FCiaob6K061byUnmlq08HTVscBAAAAioTihBLXs9Gl0/XW7OU6JwAAAJRPFCeUuF6XXefEtOQAAAAojyhOKHGd61WTm7OTjp29oEPxyVbHAQAAAAqN4oQS5+Xmoo7hVSVJa/Yyux4AAADKH4oTSkUvpiUHAABAOUZxQqnImiBi86HTupCWaXEaAAAAoHAoTigVDYJ8VDPAU2kZNm0+zLTkAAAAKF8oTigVhmGox1+jTmu5zgkAAADlDMUJpSbrdD2ucwIAAEB5Q3FCqenWoJpcnAwdjk9W9GmmJQcAAED5QXFCqfH1cFW7OlUkMeoEAACA8oXihFLVszHXOQEAAKD8oTihVPVqFCRJ2njwtFIzmJYcAAAA5QPFCaWqaQ1fVfd114X0TP16+KzVcQAAAIACoTihVBmGcdnseictTgMAAAAUDMUJpa7XX9c5reE6JwAAAJQTFCeUumsaBMrJkPafTNLxcxesjgMAAADki+KEUhfg5aY2tS9NS76OackBAABQDlCcYIms65zW7OU6JwAAAJR9FCdYIqs4bThwWumZNovTAAAAAHmjOMESLWv6q6q3m5JSM7Q1mmnJAQAAULZRnGAJJydDPRoGSmJ2PQAAAJR9FCdYpudf05Kv309xAgAAQNlGcYJluje8VJx2nUjUqfOpFqcBAAAAckdxgmUCfdzVPNRPkvTTAUadAAAAUHZRnGCpHn/NrrduX7zFSQAAAIDcUZxgqR4N/77OyWYzLU4DAAAA5IziBEu1q1NF3m7Oik9K058xiVbHAQAAAHJEcYKl3Fyc1KV+NUnSOmbXAwAAQBlFcYLl/r7OieIEAACAsoniBMtlXee0NfqsklMzLE4DAAAAZEdxguXqBnqrdlUvpWea2nTwtNVxAAAAgGwoTigTejQKlMR1TgAAACibLC1OkZGR6tChg3x9fRUUFKShQ4dq7969eW4zd+5cGYbh8PDw8CilxCgpWafrcZ0TAAAAyiJLi9PatWs1btw4bd68WStWrFB6erquvfZaJScn57mdn5+fYmJi7I/o6OhSSoyS0qV+Nbk4GYo6naIjp1OsjgMAAAA4cLHyxb///nuHr+fOnaugoCBt3bpVPXr0yHU7wzAUEhJS0vFQinw9XNW2ThX9cviM1u4/pTur1bE6EgAAAGBXpq5xSkhIkCRVrVo1z/WSkpJUp04dhYWFaciQIdq1a1eu66ampioxMdHhgbKpR8O/rnPidD0AAACUMWWmONlsNj366KPq1q2bWrRoket6jRs31ocffqglS5bos88+k81mU9euXXXs2LEc14+MjJS/v7/9ERYWVlJvAVcp635Omw6eVnqmzeI0AAAAwN8M0zRNq0NI0gMPPKDvvvtOP/30k2rVqlXg7dLT09W0aVONGDFC06ZNy/Z8amqqUlNT7V8nJiYqLCxMCQkJ8vPzK5bsKB42m6n203/UmeQ0fXFvZ3WqV83qSAAAAKjAEhMT5e/vX6BuUCZGnB566CEtXbpUq1evLlRpkiRXV1e1adNGBw4cyPF5d3d3+fn5OTxQNjk5GbqmAdOSAwAAoOyxtDiZpqmHHnpIixYt0qpVqxQeHl7ofWRmZmrnzp2qUaNGCSREacs6XW/dvniLkwAAAAB/s3RWvXHjxmn+/PlasmSJfH19FRsbK0ny9/eXp6enJGnkyJGqWbOmIiMjJUlTp05V586d1aBBA507d04vv/yyoqOjNXbsWMveB4pP1gQRf5xI0OmkVFXzcbc4EQAAAGDxiNM777yjhIQE9erVSzVq1LA/vvjiC/s6R44cUUxMjP3rs2fP6p577lHTpk113XXXKTExURs3blSzZs2seAsoZkF+HmoS4ivTlH46wKgTAAAAyoYyMzlEaSnMBWCwRuS3u/XuukO6uW1NvTqstdVxAAAAUEGVu8khgMtlXee0fn+8KlmvBwAAQBlFcUKZ075uFXm6OuvU+VTtjjlvdRwAAACA4oSyx93FWZ3rVZXEtOQAAAAoGyhOKJP+npac4gQAAADrUZxQJvX8qzhtiTqrlLQMi9MAAACgsqM4oUwKD/RWrSqeSsu0afOh01bHAQAAQCVHcUKZZBjGZafrcT8nAAAAWIvihDKrR0OucwIAAEDZQHFCmdW1QTU5Oxk6FJ+so2dSrI4DAACASozihDLLz8NVbWsHSJLWMuoEAAAAC1GcUKZxuh4AAADKAooTyrSsCSI2Hjyt9EybxWkAAABQWVGcUKa1qOmvKl6uSkrN0PYj56yOAwAAgEqK4oQyzdnJUHdO1wMAAIDFKE4o8+z3c9pPcQIAAIA1KE4o83o0DJQk7TyeoDPJaRanAQAAQGVEcUKZF+TnoSYhvjJNaT2jTgAAALAAxQnlQs+s0/X2xVucBAAAAJURxQnlQtZ1Tuv3n5JpmhanAQAAQGVDcUK50L5uFXm6Ouvk+VTtiT1vdRwAAABUMhQnlAvuLs7qXK+qJGkt05IDAACglFGcUG7YpyWnOAEAAKCUUZxQbmRNELEl6qxS0jIsTgMAAIDKpEjF6dChQ8WdA8hXeKC3alXxVFqmTZsPnbY6DgAAACqRIhWnBg0aqHfv3vrss8908eLF4s4E5MgwjMtO12NacgAAAJSeIhWnbdu2qVWrVho/frxCQkJ033336ZdffinubEA2PRpynRMAAABKX5GKU+vWrfXGG2/oxIkT+vDDDxUTE6NrrrlGLVq00KuvvqpTp/ihFiWja4NqcnYydCg+WUfPpFgdBwAAAJXEVU0O4eLioptvvlkLFy7Uiy++qAMHDmjChAkKCwvTyJEjFRMTU1w5AUmSn4er2tYOkCSt209BBwAAQOm4quK0ZcsWPfjgg6pRo4ZeffVVTZgwQQcPHtSKFSt04sQJDRkypLhyAnacrgcAAIDSVqTi9Oqrr6ply5bq2rWrTpw4oU8++UTR0dF6/vnnFR4eru7du2vu3Lnatm1bcecF1LPxpeK08cBppWfaLE4DAACAysClKBu98847uuuuuzR69GjVqFEjx3WCgoL0wQcfXFU4ICctQv1VzdtNp5PTtDX6rDrXq2Z1JAAAAFRwRRpxWrFihZ588slspck0TR05ckSS5ObmplGjRl19QuAKTk5/T0u+ltP1AAAAUAqKVJzq16+v+Pjs99E5c+aMwsPDrzoUkJ+efxWnNXspTgAAACh5RSpOpmnmuDwpKUkeHh5XFQgoiB6NqsswpN0xiYpL5CbMAAAAKFmFusZp/PjxkiTDMDRp0iR5eXnZn8vMzNTPP/+s1q1bF2tAICdVvd3UqlaAfjt6Tmv3ndKw9mFWRwIAAEAFVqjitH37dkmXRpx27twpNzc3+3Nubm6KiIjQhAkTijchkIuejapfKk57KU4AAAAoWYUqTqtXr5YkjRkzRm+88Yb8/PxKJBRQEL0aV9ebK/dr/f5Tysi0ycX5qm5LBgAAAOSqSD9pfvTRR5QmWC6iVoACvFyVeDFDO46eszoOAAAAKrACjzjdfPPNmjt3rvz8/HTzzTfnue4333xz1cGA/Dg7GeresLr+77cTWrP3lNrXrWp1JAAAAFRQBR5x8vf3l2EY9j/n9QBKS6+sacn3nbQ4CQAAACqyAo84ffTRRzn+GbBS1o1w/zieqFPnU1Xd193iRAAAAKiIinSN04ULF5SSkmL/Ojo6Wq+//rqWL19eqP1ERkaqQ4cO8vX1VVBQkIYOHaq9e/fmu93ChQvVpEkTeXh4qGXLlvr2228L/R5QMVT3dVeLmpeut1u3j5vhAgAAoGQUqTgNGTJEn3zyiSTp3Llz6tixo2bOnKkhQ4bonXfeKfB+1q5dq3Hjxmnz5s1asWKF0tPTde211yo5OTnXbTZu3KgRI0bo7rvv1vbt2zV06FANHTpUf/zxR1HeCiqAXo2CJElrKE4AAAAoIYZpmmZhNwoMDNTatWvVvHlzvf/++3rrrbe0fft2ff3115o0aZJ2795dpDCnTp1SUFCQ1q5dqx49euS4zvDhw5WcnKylS5fal3Xu3FmtW7fW7Nmz832NxMRE+fv7KyEhgZkBK4hfo87on7M3KcDLVVv/01/OTobVkQAAAFAOFKYbFGnEKSUlRb6+vpKk5cuX6+abb5aTk5M6d+6s6OjoouxSkpSQkCBJqlo199nRNm3apH79+jksGzBggDZt2pTj+qmpqUpMTHR4oGJpExYgXw8XnUtJ12/HzlkdBwAAABVQkYpTgwYNtHjxYh09elQ//PCDrr32WknSyZMnizyKY7PZ9Oijj6pbt25q0aJFruvFxsYqODjYYVlwcLBiY2NzXD8yMtJhxr+wsLAi5UPZ5eLspO4NAyVJa/Zyuh4AAACKX5GK06RJkzRhwgTVrVtXnTp1UpcuXSRdGn1q06ZNkYKMGzdOf/zxhxYsWFCk7XMzceJEJSQk2B9Hjx4t1v2jbMi6zmkt1zkBAACgBBR4OvLL3XLLLbrmmmsUExOjiIgI+/K+ffvqpptuKvT+HnroIS1dulTr1q1TrVq18lw3JCREcXFxDsvi4uIUEhKS4/ru7u5yd2eK6oquZ+NL05L/fuycTielqpoPf+cAAAAoPkUacZIuFZg2bdrIyenvXXTs2FFNmjQp8D5M09RDDz2kRYsWadWqVQoPD893my5dumjlypUOy1asWGEf9ULlFOznoSYhvjJNaf3+eKvjAAAAoIIp0ohTcnKyZsyYoZUrV+rkyZOy2WwOzx86dKhA+xk3bpzmz5+vJUuWyNfX136dkr+/vzw9PSVJI0eOVM2aNRUZGSlJeuSRR9SzZ0/NnDlTgwcP1oIFC7RlyxbNmTOnKG8FFUivxkHaE3tea/ed0tA2Na2OAwAAgAqkSMVp7NixWrt2re68807VqFFDhlG06Z+z7vnUq1cvh+UfffSRRo8eLUk6cuSIw6hW165dNX/+fP3nP//R008/rYYNG2rx4sV5TiiByqFX4+qavfag1u07JZvNlBPTkgMAAKCYFOk+TgEBAVq2bJm6detWEplKFPdxqrjSM21qM3WFklIztGRcN0WEBVgdCQAAAGVYid/HqUqVKnneawmwgquzk7o1qCaJ2fUAAABQvIpUnKZNm6ZJkyYpJSWluPMAV6XnX9OSr9l70uIkAAAAqEiKdI3TzJkzdfDgQQUHB6tu3bpydXV1eH7btm3FEg4orF5/TUu+4+g5nUlOU1VvN4sTAQAAoCIoUnEaOnRoMccAikdogKeahPj+NbveSd3UJu/7ggEAAAAFUaTiNHny5OLOARSbPk0uTUu+as8pihMAAACKRZFvgHvu3Dm9//77mjhxos6cOSPp0il6x48fL7ZwQFH0bXrpOqe1e08qI9OWz9oAAABA/oo04vT777+rX79+8vf3V1RUlO655x5VrVpV33zzjY4cOaJPPvmkuHMCBdY6rIqqeLnqbEq6tkafVad61ayOBAAAgHKuSCNO48eP1+jRo7V//355eHjYl1933XVat25dsYUDisLZyVCvxpdGnVbtYXY9AAAAXL0iFadff/1V9913X7blNWvWVGxs7FWHAq5W7yYUJwAAABSfIhUnd3d3JSYmZlu+b98+Va9e/apDAVerZ8PqcnYytP9kko6e4X5jAAAAuDpFKk433nijpk6dqvT0dEmSYRg6cuSInnzySf3jH/8o1oBAUfh7uapdnSqSGHUCAADA1StScZo5c6aSkpJUvXp1XbhwQT179lSDBg3k6+ur6dOnF3dGoEj6/nW63kqKEwAAAK5SkWbV8/f314oVK7Rhwwb99ttvSkpKUtu2bdWvX7/izgcUWZ8mQYr8bo82Hzyt5NQMebsX6XAHAAAACl+cbDab5s6dq2+++UZRUVEyDEPh4eEKCQmRaZoyDKMkcgKF1iDIR2FVPXX0zAVtOBCva5uHWB0JAAAA5VShTtUzTVM33nijxo4dq+PHj6tly5Zq3ry5oqOjNXr0aN10000llRMoNMMw1IdpyQEAAFAMCjXiNHfuXK1bt04rV65U7969HZ5btWqVhg4dqk8++UQjR44s1pBAUfVpGqyPN0Vr1Z6TjIgCAACgyAo14vT555/r6aefzlaaJKlPnz566qmnNG/evGILB1ytTuFV5enqrJPnU7XrRPYp9AEAAICCKFRx+v333zVw4MBcnx80aJB+++23qw4FFBcPV2dd0zBQEqfrAQAAoOgKVZzOnDmj4ODgXJ8PDg7W2bNnrzoUUJz6MC05AAAArlKhilNmZqZcXHK/LMrZ2VkZGRlXHQooTr3/miDi92PndOp8qsVpAAAAUB4VanII0zQ1evRoubu75/h8aio/lKLsCfH3UIuafvrjeKLW7D2pf7YPszoSAAAAyplCFadRo0bluw4z6qEs6tM4SH8cT9RqihMAAACKoFDF6aOPPiqpHECJ6tM0WG+uOqB1++KVlmGTm0uhzlIFAABAJcdPj6gUWtX0V6CPm5JSM7Ql6ozVcQAAAFDOUJxQKTg5GerVmNn1AAAAUDQUJ1QaWdOSr6Y4AQAAoJAoTqg0ujcMlKuzoUPxyTp4KsnqOAAAAChHKE6oNHw9XNW5XjVJ0oo/4yxOAwAAgPKE4oRK5drmIZKk5btiLU4CAACA8oTihEqlf9NgSdL2o+d0MvGixWkAAABQXlCcUKmE+HsoIixApin9uJtJIgAAAFAwFCdUOtc2uzTqtPxPTtcDAABAwVCcUOlkFaeNB04rKTXD4jQAAAAoDyhOqHQaBPkoPNBbaZk2rd17yuo4AAAAKAcoTqh0DMPgdD0AAAAUCsUJldK1zS8Vp1V7Tiotw2ZxGgAAAJR1FCdUSq3DqijQx13nL2bo58OnrY4DAACAMo7ihErJ2clQ/2ZBkqTlu+IsTgMAAICyjuKESqv/X9c5rfgzTqZpWpwGAAAAZRnFCZVW1/qB8nJzVmziRe08nmB1HAAAAJRhFCdUWh6uzurVuLokTtcDAABA3ihOqNSubRYiiWnJAQAAkDdLi9O6det0ww03KDQ0VIZhaPHixXmuv2bNGhmGke0RG8sPvSia3o2D5OJkaF9ckg7HJ1sdBwAAAGWUpcUpOTlZERERmjVrVqG227t3r2JiYuyPoKCgEkqIis7fy1Wd61WTJK1g1AkAAAC5cLHyxQcNGqRBgwYVerugoCAFBAQUfyBUStc2D9ZPB+K14s843dujvtVxAAAAUAaVy2ucWrdurRo1aqh///7asGFDnuumpqYqMTHR4QFcrl/TS9OSb4k+q/ikVIvTAAAAoCwqV8WpRo0amj17tr7++mt9/fXXCgsLU69evbRt27Zct4mMjJS/v7/9ERYWVoqJUR6EBniqZU1/maa0cjez6wEAACA7wywjd/40DEOLFi3S0KFDC7Vdz549Vbt2bX366ac5Pp+amqrU1L9HERITExUWFqaEhAT5+fldTWRUIG+t3K+ZK/apb5MgfTC6g9VxAAAAUAoSExPl7+9foG5QrkacctKxY0cdOHAg1+fd3d3l5+fn8ACudG3zS9OSrz8Qr+TUDIvTAAAAoKwp98Vpx44dqlGjhtUxUM41CvZR3WpeSsuwafXek1bHAQAAQBlj6ax6SUlJDqNFhw8f1o4dO1S1alXVrl1bEydO1PHjx/XJJ59Ikl5//XWFh4erefPmunjxot5//32tWrVKy5cvt+otoIIwDEODWtbQO2sOatnvMbq+VajVkQAAAFCGWFqctmzZot69e9u/Hj9+vCRp1KhRmjt3rmJiYnTkyBH782lpaXr88cd1/PhxeXl5qVWrVvrxxx8d9gEU1eC/itOqPSeVnJohb3dL/3kAAACgDCkzk0OUlsJcAIbKxTRN9X5ljaJOp+itEW10QwSjTgAAABVZpZocAiguhmHoupaXrpdb9nuMxWkAAABQllCcgMsMbnWpOK3ee1JJzK4HAACAv1CcgMs0q+Gn8EBvpWbYuBkuAAAA7ChOwGUMw9Dgv07X+3Ynp+sBAADgEooTcIWs65xW7z3F6XoAAACQRHECsmlaw1f1Ar2Vxul6AAAA+AvFCbiCYRj2SSKWMrseAAAARHECcpR1ut7afad0/mK6xWkAAABgNYoTkIMmIb6qVz3rdL2TVscBAACAxShOQA4Mw9D1LTldDwAAAJdQnIBcXPfXdU7r9p1SIqfrAQAAVGoUJyAXjYN9Vb+6t9IymV0PAACgsqM4Abm4NLteqCRpGafrAQAAVGoUJyAPg1tmna4Xr4QLnK4HAABQWVGcgDw0CvZRgyAfpWXa9OOfnK4HAABQWVGcgDwYhmEfdfp2J6frAQAAVFYUJyAfg7Nm19t/itP1AAAAKimKE5CPRsG+ahTso/RMUz/8EWt1HAAAAFiA4gQUwJDWNSVJ32w/ZnESAAAAWIHiBBTA0DaXitPmQ2d07GyKxWkAAABQ2ihOQAHUDPBU53pVJUlLdpywOA0AAABKG8UJKKCb29aSJH297ZhM07Q4DQAAAEoTxQkooEEtQuTu4qRDp5L1+7EEq+MAAACgFFGcgALy9XDVgOYhkqRvtjFJBAAAQGVCcQIK4aa2lyaJ+L/fY5SeabM4DQAAAEoLxQkohO4NAhXo464zyWlau/eU1XEAAABQSihOQCG4ODtpSOtQSdzTCQAAoDKhOAGFdPNfp+v9uPukElLSLU4DAACA0kBxAgqpWQ0/NQ72VVqGTct2xlgdBwAAAKWA4gQUkmEY9lGnRZyuBwAAUClQnIAiGNK6pgxD+jXqrI6cTrE6DgAAAEoYxQkoghB/D13TIFCStGj7cYvTAAAAoKRRnIAiuqnN36frmaZpcRoAAACUJIoTUEQDmofI09VZUadTtO3IOavjAAAAoARRnIAi8nZ30aAWIZKYJAIAAKCiozgBV+Gmv2bX+7/fYpSakWlxGgAAAJQUihNwFbrWD1Swn7sSLqRr1e6TVscBAABACaE4AVfB2cnQzW1rSZI+//WoxWkAAABQUihOwFUa0aG2JGn9/lPc0wkAAKCCojgBV6l2NS91bxgo05Q+//WI1XEAAABQAihOQDG4vdOlUaeFW44qLcNmcRoAAAAUN4oTUAz6Ng1WkK+74pPStOLPOKvjAAAAoJhZWpzWrVunG264QaGhoTIMQ4sXL853mzVr1qht27Zyd3dXgwYNNHfu3BLPCeTH1dlJwzuESZLm/RxtcRoAAAAUN0uLU3JysiIiIjRr1qwCrX/48GENHjxYvXv31o4dO/Too49q7Nix+uGHH0o4KZC/4R3CZBjSxoOndTg+2eo4AAAAKEYuVr74oEGDNGjQoAKvP3v2bIWHh2vmzJmSpKZNm+qnn37Sa6+9pgEDBuS4TWpqqlJTU+1fJyYmXl1oIBe1qnipd+MgrdpzUp//ckRPX9fU6kgAAAAoJuXqGqdNmzapX79+DssGDBigTZs25bpNZGSk/P397Y+wsLCSjolK7LaOf08ScTE90+I0AAAAKC7lqjjFxsYqODjYYVlwcLASExN14cKFHLeZOHGiEhIS7I+jR7lJKUpO7yZBCvX30NmUdP2wK9bqOAAAACgm5ao4FYW7u7v8/PwcHkBJcXYyNPyvG+LO+5l7OgEAAFQU5ao4hYSEKC7OcarnuLg4+fn5ydPT06JUgKPhHcLk7GTol8NntD/uvNVxAAAAUAzKVXHq0qWLVq5c6bBsxYoV6tKli0WJgOxC/D3Ut0mQJGn+L4w6AQAAVASWFqekpCTt2LFDO3bskHRpuvEdO3boyJFLP2xOnDhRI0eOtK9///3369ChQ/r3v/+tPXv26O2339aXX36pxx57zIr4QK5u63TpdL2vtx5jkggAAIAKwNLitGXLFrVp00Zt2rSRJI0fP15t2rTRpEmTJEkxMTH2EiVJ4eHhWrZsmVasWKGIiAjNnDlT77//fq5TkQNW6dGwumpV8VTixQwt/T3G6jgAAAC4SoZpmqbVIUpTYmKi/P39lZCQwEQRKFGzVh/Qyz/sVdvaAfrmwW5WxwEAAMAVCtMNytU1TkB58s/2teTiZGjbkXPaHcONlwEAAMozihNQQoJ8PTSgeYgk6ZNNUdaGAQAAwFWhOAElaHS3upKkr7cdV3xSqrVhAAAAUGQUJ6AEta9TRRFhAUrLsOmTTdFWxwEAAEARUZyAEmQYhu7tXk+S9OmmKF1IY2pyAACA8ojiBJSwAc2DFVbVU2dT0vX1tmNWxwEAAEARUJyAEubi7KS7u4VLkj746bAybZXqDgAAAAAVAsUJKAX/bB8mf09XHY5P1o+746yOAwAAgEKiOAGlwNvdRXd0ri1Jem/dIYvTAAAAoLAoTkApGdWlrtycnbQl+qy2Rp+1Og4AAAAKgeIElJIgPw8NaR0qSXp/PaNOAAAA5QnFCShF9/S4NDX597tiFX062eI0AAAAKCiKE1CKGgX7qlfj6jLNSzPsAQAAoHygOAGlLOuGuF9uOaqzyWkWpwEAAEBBUJyAUtalfjU1q+Gni+k2zfs52uo4AAAAKACKE1DKDMPQvX9d6zR3Y7QupmdanAgAAAD5oTgBFhjcqoZq+HsoPilVi7cftzoOAAAA8kFxAizg6uyku7qFS5LeXnNQ6Zk2ixMBAAAgLxQnwCK3d66tQB83HTmToq+2HrM6DgAAAPJAcQIs4uXmogd6NZAkvblyP9c6AQAAlGEUJ8BCt3eqrRA/D8UkXNSCX45YHQcAAAC5oDgBFvJwddZDfS6NOs1ac1AX0hh1AgAAKIsoToDFhrUPU60qnjp1PlWfbo6yOg4AAAByQHECLObm4qSH+zaUJL2z5qCSUjMsTgQAAIArUZyAMuDmNjUVHuitsynpmrvhsNVxAAAAcAWKE1AGuDg76dF+l0ad3l13SAkp6RYnAgAAwOUoTkAZcUOrUDUK9tH5ixl6/6dDVscBAADAZShOQBnh5GRofP9GkqQPfzqsM8lpFicCAABAFooTUIYMaB6i5qF+Sk7L1LtrD1odBwAAAH+hOAFliGEYevzaS6NOH2+K0snzFy1OBAAAAIniBJQ5vRsHqU3tAF1Mt2nWqgNWxwEAAIAoTkCZYxiGnri2sSTps5+PaF/ceYsTAQAAgOIElEFdGwRqQPNgZdpMTV6yS6ZpWh0JAACgUqM4AWXUfwY3k7uLkzYdOq1lO2OsjgMAAFCpUZyAMiqsqpce6FVfkjR92W6lpGVYnAgAAKDyojgBZdj9PeurVhVPxSRc1KzVTBQBAABgFYoTUIZ5uDrr2eubSZLeW3dYUfHJFicCAAConChOQBl3bbNg9WhUXWmZNk1d+qfVcQAAAColihNQxhmGock3NJOrs6FVe05q5e44qyMBAABUOhQnoByoX91Hd10TLkmauvRPXUzPtDgRAABA5UJxAsqJf/VpqGA/d0WfTtH76w9ZHQcAAKBSKRPFadasWapbt648PDzUqVMn/fLLL7muO3fuXBmG4fDw8PAoxbSANXzcXfT0dU0lSf9dfUDHz12wOBEAAEDlYXlx+uKLLzR+/HhNnjxZ27ZtU0REhAYMGKCTJ0/muo2fn59iYmLsj+jo6FJMDFjnxohQdQyvqovpNk37PyaKAAAAKC2WF6dXX31V99xzj8aMGaNmzZpp9uzZ8vLy0ocffpjrNoZhKCQkxP4IDg4uxcSAdQzD0HM3Npezk6Hvd8VqyY7jVkcCAACoFCwtTmlpadq6dav69etnX+bk5KR+/fpp06ZNuW6XlJSkOnXqKCwsTEOGDNGuXbtyXTc1NVWJiYkOD6A8a1rDT//q00CS9OziPxSTwCl7AAAAJc3S4hQfH6/MzMxsI0bBwcGKjY3NcZvGjRvrww8/1JIlS/TZZ5/JZrOpa9euOnbsWI7rR0ZGyt/f3/4ICwsr9vcBlLZxvRsoopa/Ei9m6N9f/S6bzbQ6EgAAQIVm+al6hdWlSxeNHDlSrVu3Vs+ePfXNN9+oevXqevfdd3Ncf+LEiUpISLA/jh49WsqJgeLn6uykV4e3loerk9bvj9enm7nODwAAoCRZWpwCAwPl7OysuDjHG3rGxcUpJCSkQPtwdXVVmzZtdODAgRyfd3d3l5+fn8MDqAjqV/fRxEGXZtmL/G63Dp5KsjgRAABAxWVpcXJzc1O7du20cuVK+zKbzaaVK1eqS5cuBdpHZmamdu7cqRo1apRUTKDMurNzHXVvGKiL6TaN/2KH0jNtVkcCAACokCw/VW/8+PF677339PHHH2v37t164IEHlJycrDFjxkiSRo4cqYkTJ9rXnzp1qpYvX65Dhw5p27ZtuuOOOxQdHa2xY8da9RYAyzg5GXr5lgj5ebjot2MJmrU655FXAAAAXB0XqwMMHz5cp06d0qRJkxQbG6vWrVvr+++/t08YceTIETk5/d3vzp49q3vuuUexsbGqUqWK2rVrp40bN6pZs2ZWvQXAUiH+Hpo2tIUeWbBDb606oN6NgxQRFmB1LAAAgArFME2zUk3HlZiYKH9/fyUkJHC9EyoM0zT10Ofbtez3GNWr7q1l/+ouTzdnq2MBAACUaYXpBpafqgfg6hmGoelDWyjI112HTiVrxne7rY4EAABQoVCcgAoiwMtNL93SSpL08aZoLdlx3OJEAAAAFQfFCahAejUO0v0960uS/v3V79p5LMHiRAAAABUDxQmoYJ4Y0Fi9G1dXaoZN9366RSfPX7Q6EgAAQLlHcQIqGGcnQ2+MaKN61b0Vk3BRD3y2TakZmVbHAgAAKNcoTkAF5OfhqvdHtpevh4u2Rp/VpMW7VMkm0AQAAChWFCeggqpX3UdvjWgjJ0P6YstRfbIp2upIAAAA5RbFCajAejUO0lODmkiSpi79UxsPxFucCAAAoHyiOAEV3D3d6+mmNjWVaTP14PxtOnI6xepIAAAA5Q7FCajgDMNQ5M0tFVHLX+dS0jX2k191LiXN6lgAAADlCsUJqAQ8XJ317p3tFeTrrn1xSRr54S9KvJhudSwAAIByg+IEVBIh/h769O5Oqurtpt+PJWjUh78oKTXD6lgAAADlAsUJqEQah/jqs7s7yd/TVduPnNOYj35RShrlCQAAID8UJ6CSaRbqp8/u7iRfDxf9GnVWd8/dogtp3CAXAAAgLxQnoBJqWctfn9zVUT7uLtp06LTu/XSLLqZTngAAAHJDcQIqqTa1q+ijMR3k5eas9fvj9cBnW5WaQXkCAADICcUJqMQ61K2qD0Z1kIerk1bvPaVx87Yz8gQAAJADihNQyXWpX03vj+wgNxcn/bg7Tre//7NOJ6VaHQsAAKBMoTgB0DUNAzV3dAf5erhoa/RZDX17g/bHnbc6FgAAQJlBcQIgSeraIFCLHuym2lW9dPTMBd389kat23fK6lgAAABlAsUJgF2DIB8tHtdNHepW0fnUDI2Z+6s+3RxtdSwAAADLUZwAOKjq7abPxnbSzW1qKtNm6tnFf+i5/9ulTJtpdTQAAADLUJwAZOPu4qyZwyI04dpGkqSPNkTpnk+2KCEl3eJkAAAA1qA4AciRYRh6qE9DzbqtrdxdnLRqz0kNeH2d1u/nuicAAFD5UJwA5Glwqxr66v6uqhfordjEi7rzg180eckfupDG/Z4AAEDlQXECkK+Wtfy17OHuGtmljiTp403RGvzWev129Jy1wQAAAEoJxQlAgXi6OWvqkBb65K6OCvZz16FTybr5nY16bcU+pWfarI4HAABQoihOAAqlR6Pq+uHRHrohIlSZNlNvrNyvf7yzUX8cT7A6GgAAQImhOAEotAAvN701oo3eHNFGfh4u+v1Ygq5/6yeN/2KHTpy7YHU8AACAYkdxAlBkN0aEavljPTW0dagk6Zvtx9X7lTV6+Yc9SkrNsDgdAABA8TFM06xUd7VMTEyUv7+/EhIS5OfnZ3UcoML47eg5TV+2W79EnZEkBfq46bH+jTS8fZhcnPkdDQAAKHsK0w0oTgCKjWmaWv5nnGZ8t0eH45MlSQ2DfPSvvg01qEWIXClQAACgDKE45YHiBJS8tAyb5v0crTdW7te5lHRJUqi/h0Z3q6vhHWrL39PV4oQAAAAUpzxRnIDSk3AhXR9tOKzPNkcrPilNkuTt5qxhHcI0pmu4alfzsjghAACozChOeaA4AaXvYnqm/rfjhN7/6ZD2xSVJkpwM6dpmIbqtU211rV+N66AAAECpozjlgeIEWMc0Ta3fH6/3fzqsdftO2ZcH+rhpcMsaurF1TbWtHSDDMCxMCQAAKguKUx4oTkDZsC/uvD7dFK1lO2N0JjnNvrxWFU/dGBGqG1uHqkkI/0YBAEDJoTjlgeIElC3pmTb9dCBe/9txQst3xSo5LdP+XN1qXuresLq6NwxUl/rV5OvBpBIAAKD4UJzyQHECyq4LaZlauSdOS3ac0Nq9p5SWabM/5+xkqG3tAF3ToLq6NwpUq5r+XBcFAACuCsUpDxQnoHw4fzFdmw+d0fr9p7R+f7z9vlBZPF2d1bKmv1rV8ldEWIBahwWoVhVPro8CAAAFRnHKA8UJKJ+OnknRTwfitX7/Kf20P16JFzOyrVPV202tavmrRai/GgT5qEGQj+pV95aXm4sFiQEAQFlHccoDxQko/2w2U4fik7TjaIJ+O3pOvx87pz9jEpWemfO3s5oBnvYiFR7orZpVPFUrwFM1q3hSqgAAqMTKXXGaNWuWXn75ZcXGxioiIkJvvfWWOnbsmOv6Cxcu1LPPPquoqCg1bNhQL774oq677roCvRbFCaiYUjMytTvmvH47ek57Ys/r4MkkHTiV5DBjX06qeLmqZhVP1QzwVA1/T1X3dVegj5sCfdwvPf762t3FuZTeCQAAKC2F6QaW/6r1iy++0Pjx4zV79mx16tRJr7/+ugYMGKC9e/cqKCgo2/obN27UiBEjFBkZqeuvv17z58/X0KFDtW3bNrVo0cKCdwCgLHB3cVbrv651utyZ5DQdOJlkfxw5k6xjZy/o+LkLOn8xQ2dT0nU2JV1/HE/Mc/++7i7y83SV/5UPL1f5ebjI2/2vh5uLvN2d5e3uIi83Z/m4u8jD1VkeLs5yd3WSu4sT12EBAFAOWT7i1KlTJ3Xo0EH//e9/JUk2m01hYWH617/+paeeeirb+sOHD1dycrKWLl1qX9a5c2e1bt1as2fPzvf1GHECkCXxYrqOn71w6XHugmISLup0Uqrik1IVn5T2139Tcz0FsKjcXZwulSlXJ7m5OMnN2Umuzo5/dnVxkquTIRdnQy5OTnK2/9mQs5PTX/815GQYcnaSnJwMORuXLzPkZEiGcdk6hiHDMGRIMoysr+WwzNBfyy77WvavL62nrOf++iJrmytlFcQrn7p8XSPbszmvl+25vD/iPNFbAaBs6NU4SB6u1p7RUW5GnNLS0rR161ZNnDjRvszJyUn9+vXTpk2bctxm06ZNGj9+vMOyAQMGaPHixTmun5qaqtTUVPvXiYl5/1YZQOXh5+Eqvxqualoj92+Upmkq8UKG4pNTlXAhXQkX0pX4138TUv76+mK6ktMylZKaoeTUTCWnZSglLVNJqRlKTs3QxfRM2S7rXqkZNqVm2JRwoRTeJAAAZdQvz/S1vDgVhqXFKT4+XpmZmQoODnZYHhwcrD179uS4TWxsbI7rx8bG5rh+ZGSknnvuueIJDKDSMQxD/l6XTsm7GumZNl1Mz9TF9Ev/Tc249Oe0TJvSM/76b6ZNaRnmX/+1KcNmU4bNVEam+dd///4602ZTpmkq0ybZTFOZtkuPrD/bzEulz2Ze+rPNNGWaUqbNlKlLz9n/e9nzl5ZJ0uVfm5ct/3tZlr+X57DsisG6nNbJSZ5jfPkMAJr5rVBGWH+FMQBYy9WpfN2P0fJrnEraxIkTHUaoEhMTFRYWZmEiAJWR61+n4Pl6WJ0EAAAUhaXFKTAwUM7OzoqLi3NYHhcXp5CQkBy3CQkJKdT67u7ucnd3L57AAAAAAColS8fH3Nzc1K5dO61cudK+zGazaeXKlerSpUuO23Tp0sVhfUlasWJFrusDAAAAwNWy/FS98ePHa9SoUWrfvr06duyo119/XcnJyRozZowkaeTIkapZs6YiIyMlSY888oh69uypmTNnavDgwVqwYIG2bNmiOXPmWPk2AAAAAFRglhen4cOH69SpU5o0aZJiY2PVunVrff/99/YJII4cOSKnyy4c69q1q+bPn6///Oc/evrpp9WwYUMtXryYezgBAAAAKDGW38eptHEfJwAAAABS4bpB+ZoDEAAAAAAsQHECAAAAgHxQnAAAAAAgHxQnAAAAAMgHxQkAAAAA8kFxAgAAAIB8UJwAAAAAIB8UJwAAAADIB8UJAAAAAPJBcQIAAACAfFCcAAAAACAfFCcAAAAAyAfFCQAAAADy4WJ1gNJmmqYkKTEx0eIkAAAAAKyU1QmyOkJeKl1xOn/+vCQpLCzM4iQAAAAAyoLz58/L398/z3UMsyD1qgKx2Ww6ceKEfH19ZRiG1XGUmJiosLAwHT16VH5+flbHQRnH8YLC4phBYXHMoLA4ZlBYZemYMU1T58+fV2hoqJyc8r6KqdKNODk5OalWrVpWx8jGz8/P8gMH5QfHCwqLYwaFxTGDwuKYQWGVlWMmv5GmLEwOAQAAAAD5oDgBAAAAQD4oThZzd3fX5MmT5e7ubnUUlAMcLygsjhkUFscMCotjBoVVXo+ZSjc5BAAAAAAUFiNOAAAAAJAPihMAAAAA5IPiBAAAAAD5oDgBAAAAQD4oTiVs1qxZqlu3rjw8PNSpUyf98ssvea6/cOFCNWnSRB4eHmrZsqW+/fbbUkqKsqIwx8x7772n7t27q0qVKqpSpYr69euX7zGGiqew32eyLFiwQIZhaOjQoSUbEGVOYY+Zc+fOady4capRo4bc3d3VqFEj/v9UyRT2mHn99dfVuHFjeXp6KiwsTI899pguXrxYSmlhtXXr1umGG25QaGioDMPQ4sWL891mzZo1atu2rdzd3dWgQQPNnTu3xHMWFsWpBH3xxRcaP368Jk+erG3btikiIkIDBgzQyZMnc1x/48aNGjFihO6++25t375dQ4cO1dChQ/XHH3+UcnJYpbDHzJo1azRixAitXr1amzZtUlhYmK699lodP368lJPDKoU9ZrJERUVpwoQJ6t69eyklRVlR2GMmLS1N/fv3V1RUlL766ivt3btX7733nmrWrFnKyWGVwh4z8+fP11NPPaXJkydr9+7d+uCDD/TFF1/o6aefLuXksEpycrIiIiI0a9asAq1/+PBhDR48WL1799aOHTv06KOPauzYsfrhhx9KOGkhmSgxHTt2NMeNG2f/OjMz0wwNDTUjIyNzXH/YsGHm4MGDHZZ16tTJvO+++0o0J8qOwh4zV8rIyDB9fX3Njz/+uKQioowpyjGTkZFhdu3a1Xz//ffNUaNGmUOGDCmFpCgrCnvMvPPOO2a9evXMtLS00oqIMqawx8y4cePMPn36OCwbP3682a1btxLNibJJkrlo0aI81/n3v/9tNm/e3GHZ8OHDzQEDBpRgssJjxKmEpKWlaevWrerXr599mZOTk/r166dNmzbluM2mTZsc1pekAQMG5Lo+KpaiHDNXSklJUXp6uqpWrVpSMVGGFPWYmTp1qoKCgnT33XeXRkyUIUU5Zv73v/+pS5cuGjdunIKDg9WiRQu98MILyszMLK3YsFBRjpmuXbtq69at9tP5Dh06pG+//VbXXXddqWRG+VNefgZ2sTpARRUfH6/MzEwFBwc7LA8ODtaePXty3CY2NjbH9WNjY0ssJ8qOohwzV3ryyScVGhqa7ZsPKqaiHDM//fSTPvjgA+3YsaMUEqKsKcoxc+jQIa1atUq33367vv32Wx04cEAPPvig0tPTNXny5NKIDQsV5Zi57bbbFB8fr2uuuUamaSojI0P3338/p+ohV7n9DJyYmKgLFy7I09PTomSOGHECKogZM2ZowYIFWrRokTw8PKyOgzLo/PnzuvPOO/Xee+8pMDDQ6jgoJ2w2m4KCgjRnzhy1a9dOw4cP1zPPPKPZs2dbHQ1l1Jo1a/TCCy/o7bff1rZt2/TNN99o2bJlmjZtmtXRgKvCiFMJCQwMlLOzs+Li4hyWx8XFKSQkJMdtQkJCCrU+KpaiHDNZXnnlFc2YMUM//vijWrVqVZIxUYYU9pg5ePCgoqKidMMNN9iX2Ww2SZKLi4v27t2r+vXrl2xoWKoo32dq1KghV1dXOTs725c1bdpUsbGxSktLk5ubW4lmhrWKcsw8++yzuvPOOzV27FhJUsuWLZWcnKx7771XzzzzjJyc+L09HOX2M7Cfn1+ZGW2SGHEqMW5ubmrXrp1WrlxpX2az2bRy5Up16dIlx226dOnisL4krVixItf1UbEU5ZiRpJdeeknTpk3T999/r/bt25dGVJQRhT1mmjRpop07d2rHjh32x4033mifxSgsLKw048MCRfk+061bNx04cMBesiVp3759qlGjBqWpEijKMZOSkpKtHGUVb9M0Sy4syq1y8zOw1bNTVGQLFiww3d3dzblz55p//vmnee+995oBAQFmbGysaZqmeeedd5pPPfWUff0NGzaYLi4u5iuvvGLu3r3bnDx5sunq6mru3LnTqreAUlbYY2bGjBmmm5ub+dVXX5kxMTH2x/nz5616CyhlhT1mrsSsepVPYY+ZI0eOmL6+vuZDDz1k7t2711y6dKkZFBRkPv/881a9BZSywh4zkydPNn19fc3PP//cPHTokLl8+XKzfv365rBhw6x6Cyhl58+fN7dv325u377dlGS++uqr5vbt283o6GjTNE3zqaeeMu+88077+ocOHTK9vLzMJ554wty9e7c5a9Ys09nZ2fz++++tegs5ojiVsLfeesusXbu26ebmZnbs2NHcvHmz/bmePXuao0aNclj/yy+/NBs1amS6ubmZzZs3N5ctW1bKiWG1whwzderUMSVle0yePLn0g8Myhf0+czmKU+VU2GNm48aNZqdOnUx3d3ezXr165vTp082MjIxSTg0rFeaYSU9PN6dMmWLWr1/f9PDwMMPCwswHH3zQPHv2bOkHhyVWr16d488nWcfJqFGjzJ49e2bbpnXr1qabm5tZr14986OPPir13PkxTJMxUwAAAADIC9c4AQAAAEA+KE4AAAAAkA+KEwAAAADkg+IEAAAAAPmgOAEAAABAPihOAAAAAJAPihMAAAAA5IPiBAAAAAD5oDgBAIrN6NGjNXToUPvXvXr10qOPPlrqOdasWSPDMHTu3LlSf+26devq9ddfv6p9zJ07VwEBAXmuM2XKFLVu3dr+dVn57AGgoqI4AUAFN3r0aBmGIcMw5ObmpgYNGmjq1KnKyMgo8df+5ptvNG3atAKtW9plp27duvbPxdvbW23bttXChQtL5bWLw4QJE7Ry5cpcn7/ysy+OQgcAlRnFCQAqgYEDByomJkb79+/X448/rilTpujll1/Ocd20tLRie92qVavK19e32PZX3KZOnaqYmBht375dHTp00PDhw7Vx48Yc1y3Oz6U4+Pj4qFq1ark+X9Y/ewAobyhOAFAJuLu7KyQkRHXq1NEDDzygfv366X//+5+kv0/xmj59ukJDQ9W4cWNJ0tGjRzVs2DAFBASoatWqGjJkiKKiouz7zMzM1Pjx4xUQEKBq1arp3//+t0zTdHjdK08XS01N1ZNPPqmwsDC5u7urQYMG+uCDDxQVFaXevXtLkqpUqSLDMDR69GhJks1mU2RkpMLDw+Xp6amIiAh99dVXDq/z7bffqlGjRvL09FTv3r0dcubF19dXISEhatSokWbNmiVPT0/93//9n6RLIzTTpk3TyJEj5efnp3vvvVeS9PXXX6t58+Zyd3dX3bp1NXPmzGz7PX/+vEaMGCFvb2/VrFlTs2bNcnj+1VdfVcuWLeXt7a2wsDA9+OCDSkpKyrafxYsXq2HDhvLw8NCAAQN09OhR+3NXnqp3pcs/+169eik6OlqPPfaYfZQtOTlZfn5+2T7LxYsXy9vbW+fPny/QZwgAlQXFCQAqIU9PT4cRlJUrV2rv3r1asWKFli5dqvT0dA0YMEC+vr5av369NmzYIB8fHw0cONC+3cyZMzV37lx9+OGH+umnn3TmzBktWrQoz9cdOXKkPv/8c7355pvavXu33n33Xfn4+CgsLExff/21JGnv3r2KiYnRG2+8IUmKjIzUJ598otmzZ2vXrl167LHHdMcdd2jt2rWSLhW8m2++WTfccIN27NihsWPH6qmnnir0Z+Li4iJXV1eHz+WVV15RRESEtm/frmeffVZbt27VsGHDdOutt2rnzp2aMmWKnn32Wc2dO9dhXy+//LJ9u6eeekqPPPKIVqxYYX/eyclJb775pnbt2qWPP/5Yq1at0r///W+HfaSkpGj69On65JNPtGHDBp07d0633nprod+XdOm0vVq1atlH2GJiYuTt7a1bb71VH330kcO6H330kW655RZGqwDgSiYAoEIbNWqUOWTIENM0TdNms5krVqww3d3dzQkTJtifDw4ONlNTU+3bfPrpp2bjxo1Nm81mX5aammp6enqaP/zwg2maplmjRg3zpZdesj+fnp5u1qpVy/5apmmaPXv2NB955BHTNE1z7969piRzxYoVOeZcvXq1Kck8e/asfdnFixdNLy8vc+PGjQ7r3n333eaIESNM0zTNiRMnms2aNXN4/sknn8y2ryvVqVPHfO211+zv7YUXXjAlmUuXLrU/P3ToUIdtbrvtNrN///4Oy5544gmH169Tp445cOBAh3WGDx9uDho0KNcsCxcuNKtVq2b/+qOPPjIlmZs3b7Yv2717tynJ/Pnnn03TNM3JkyebERER9ucv/3s2TcfP/sr3m+Xnn382nZ2dzRMnTpimaZpxcXGmi4uLuWbNmlyzAkBlxYgTAFQCS5culY+Pjzw8PDRo0CANHz5cU6ZMsT/fsmVLubm52b/+7bffdODAAfn6+srHx0c+Pj6qWrWqLl68qIMHDyohIUExMTHq1KmTfRsXFxe1b98+1ww7duyQs7OzevbsWeDcBw4cUEpKivr372/P4ePjo08++UQHDx6UJO3evdshhyR16dKlQPt/8skn5ePjIy8vL7344ouaMWOGBg8ebH/+yveze/dudevWzWFZt27dtH//fmVmZub6+l26dNHu3bvtX//444/q27evatasKV9fX9155506ffq0UlJS7Ou4uLioQ4cO9q+bNGmigIAAh/1crY4dO6p58+b6+OOPJUmfffaZ6tSpox49ehTbawBAReFidQAAQMnr3bu33nnnHbm5uSk0NFQuLo7f/r29vR2+TkpKUrt27TRv3rxs+6pevXqRMnh6ehZ6m6zrfpYtW6aaNWs6POfu7l6kHJd74oknNHr0aPn4+Cg4OFiGYTg8f+XnUhyioqJ0/fXX64EHHtD06dNVtWpV/fTTT7r77ruVlpYmLy+vYn/NvIwdO1azZs3SU089pY8++khjxozJ9jkAALjGCQAqBW9vbzVo0EC1a9fOVppy0rZtW+3fv19BQUFq0KCBw8Pf31/+/v6qUaOGfv75Z/s2GRkZ2rp1a677bNmypWw2m/3apCtljXhdPnLTrFkzubu768iRI9lyhIWFSZKaNm2qX375xWFfmzdvzvc9SlJgYKAaNGigkJCQApWFpk2basOGDQ7LNmzYoEaNGsnZ2TnX19+8ebOaNm0qSdq6datsNptmzpypzp07q1GjRjpx4kS218rIyNCWLVvsX+/du1fnzp2z76ew3NzcHD7bLHfccYeio6P15ptv6s8//9SoUaOKtH8AqOgoTgCAbG6//XYFBgZqyJAhWr9+vQ4fPqw1a9bo4Ycf1rFjxyRJjzzyiGbMmKHFixdrz549evDBB/O8B1PdunU1atQo3XXXXVq8eLF9n19++aUkqU6dOjIMQ0uXLtWpU6eUlJQkX19fTZgwQY899pg+/vhjHTx4UNu2bdNbb71lP73s/vvv1/79+/XEE09o7969mj9/frbJGorL448/rpUrV2ratGnat2+fPv74Y/33v//VhAkTHNbbsGGDXnrpJe3bt0+zZs3SwoUL9cgjj0iSGjRooPT0dL311ls6dOiQPv30U82ePTvba7m6uupf//qXfv75Z23dulWjR49W586d1bFjxyJlr1u3rtatW6fjx48rPj7evrxKlSq6+eab9cQTT+jaa69VrVq1irR/AKjoKE4AgGy8vLy0bt061a5dWzfffLOaNm2qu+++WxcvXpSfn5+kSyXizjvv1KhRo9SlSxf5+vrqpptuynO/77zzjm655RY9+OCDatKkie655x4lJydLkmrWrKnnnntOTz31lIKDg/XQQw9JkqZNm6Znn31WkZGRatq0qQYOHKhly5YpPDxcklS7dm19/fXXWrx4sSIiIjR79my98MILJfK5tG3bVl9++aUWLFigFi1aaNKkSZo6dap96vQsjz/+uLZs2aI2bdro+eef16uvvqoBAwZIkiIiIvTqq6/qxRdfVIsWLTRv3jxFRkZmey0vLy89+eSTuu2229StWzf5+Pjoiy++KHL2qVOnKioqSvXr1892umXWaYJ33XVXkfcPABWdYZpX3HQDAABUKp9++qkee+wxnThxwmGSEADA35gcAgCASiolJUUxMTGaMWOG7rvvPkoTAOSBU/UAAKikXnrpJTVp0kQhISGaOHGi1XEAoEzjVD0AAAAAyAcjTgAAAACQD4oTAAAAAOSD4gQAAAAA+aA4AQAAAEA+KE4AAAAAkA+KEwAAAADkg+IEAAAAAPmgOAEAAABAPv4fD1X6V0OgpV8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CONCLUSION"
      ],
      "metadata": {
        "id": "Nc0IZuDOtH3U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Decision Tree - Training Accuracy:\", dt_model.score(X_train, y_train))\n",
        "print(\"Decision Tree - Testing Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Random Forest - Training Accuracy:\", rf_model.score(X_train, y_train))\n",
        "print(\"Random Forest - Testing Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Logistic Regression - Training Accuracy:\", lr_model.score(X_train, y_train))\n",
        "print(\"Logistic Regression - Testing Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"SVM - Training Accuracy:\", svm_model.score(X_train, y_train))\n",
        "print(\"SVM - Testing Accuracy:\", accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BoqsMfokoR3",
        "outputId": "e9ca183d-3444-4227-84f9-56ed15d6384c"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree - Training Accuracy: 1.0\n",
            "Decision Tree - Testing Accuracy: 0.8379746835443038\n",
            "Random Forest - Training Accuracy: 1.0\n",
            "Random Forest - Testing Accuracy: 0.8379746835443038\n",
            "Logistic Regression - Training Accuracy: 0.8322784810126582\n",
            "Logistic Regression - Testing Accuracy: 0.8379746835443038\n",
            "SVM - Training Accuracy: 0.8335443037974684\n",
            "SVM - Testing Accuracy: 0.8379746835443038\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loan_defaulters = df2['loan_default_indicator'].sum()\n",
        "non_defaulters = len(df2) - loan_defaulters\n",
        "\n",
        "print(f\"Number of loan defaulters: {loan_defaulters}\")\n",
        "print(f\"Number of non-defaulters: {non_defaulters}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7qZEXDDimV_d",
        "outputId": "958b92f8-f8df-450a-a55e-5f30daded60b"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of loan defaulters: 327\n",
            "Number of non-defaulters: 1648\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conclusion:\n",
        "Based on the analysis of various models (Decision Tree, Random Forest, Logistic Regression, SVM, ANN, and DNN), it appears that the models with simpler architectures like Logistic Regression and SVM show better generalization performance compared to more complex models like Decision Trees and Random Forests. This suggests that the latter models might be overfitting to the training data.\n",
        "Neural networks (ANN and DNN) can be effective, as it can bee seen from the kdplot for the neural networks they are identical in nature , Thus making them considerable in the project."
      ],
      "metadata": {
        "id": "zfapeGMJtJ6b"
      }
    }
  ]
}