loan.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMfTgQoYSXZiJradA2awCTF",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AnamayaSaraogi/Loan-Prediction-Machine-Learning/blob/main/loan.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cFMt0rSO5Io3"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "plt.style.use('fivethirtyeight')\n",
        "%matplotlib inline\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CjcxTjSJ5-H1",
        "outputId": "c18d56e3-bff9-4d3e-e95f-e5e3eeb1f34f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#load the file\n",
        "path ='/content/drive/My Drive/loan_data_set.csv'\n",
        "data= pd.read_csv(path)"
      ],
      "metadata": {
        "id": "QcipSYbd5_pT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NxKJ6-4Q6S73",
        "outputId": "74f34dee-0b39-4545-b008-e5f783e22a10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Loan_ID  Gender Married Dependents     Education Self_Employed  \\\n",
              "0   LP001002    Male      No          0      Graduate            No   \n",
              "1   LP001003    Male     Yes          1      Graduate            No   \n",
              "2   LP001005    Male     Yes          0      Graduate           Yes   \n",
              "3   LP001006    Male     Yes          0  Not Graduate            No   \n",
              "4   LP001008    Male      No          0      Graduate            No   \n",
              "5   LP001011    Male     Yes          2      Graduate           Yes   \n",
              "6   LP001013    Male     Yes          0  Not Graduate            No   \n",
              "7   LP001014    Male     Yes         3+      Graduate            No   \n",
              "8   LP001018    Male     Yes          2      Graduate            No   \n",
              "9   LP001020    Male     Yes          1      Graduate            No   \n",
              "10  LP001024    Male     Yes          2      Graduate            No   \n",
              "11  LP001027    Male     Yes          2      Graduate           NaN   \n",
              "12  LP001028    Male     Yes          2      Graduate            No   \n",
              "13  LP001029    Male      No          0      Graduate            No   \n",
              "14  LP001030    Male     Yes          2      Graduate            No   \n",
              "15  LP001032    Male      No          0      Graduate            No   \n",
              "16  LP001034    Male      No          1  Not Graduate            No   \n",
              "17  LP001036  Female      No          0      Graduate            No   \n",
              "18  LP001038    Male     Yes          0  Not Graduate            No   \n",
              "19  LP001041    Male     Yes          0      Graduate           NaN   \n",
              "20  LP001043    Male     Yes          0  Not Graduate            No   \n",
              "21  LP001046    Male     Yes          1      Graduate            No   \n",
              "22  LP001047    Male     Yes          0  Not Graduate            No   \n",
              "23  LP001050     NaN     Yes          2  Not Graduate            No   \n",
              "24  LP001052    Male     Yes          1      Graduate           NaN   \n",
              "25  LP001066    Male     Yes          0      Graduate           Yes   \n",
              "26  LP001068    Male     Yes          0      Graduate            No   \n",
              "27  LP001073    Male     Yes          2  Not Graduate            No   \n",
              "28  LP001086    Male      No          0  Not Graduate            No   \n",
              "29  LP001087  Female      No          2      Graduate           NaN   \n",
              "30  LP001091    Male     Yes          1      Graduate           NaN   \n",
              "31  LP001095    Male      No          0      Graduate            No   \n",
              "32  LP001097    Male      No          1      Graduate           Yes   \n",
              "33  LP001098    Male     Yes          0      Graduate            No   \n",
              "34  LP001100    Male      No         3+      Graduate            No   \n",
              "35  LP001106    Male     Yes          0      Graduate            No   \n",
              "36  LP001109    Male     Yes          0      Graduate            No   \n",
              "37  LP001112  Female     Yes          0      Graduate            No   \n",
              "38  LP001114    Male      No          0      Graduate            No   \n",
              "39  LP001116    Male      No          0  Not Graduate            No   \n",
              "40  LP001119    Male      No          0      Graduate            No   \n",
              "41  LP001120    Male      No          0      Graduate            No   \n",
              "42  LP001123    Male     Yes          0      Graduate            No   \n",
              "43  LP001131    Male     Yes          0      Graduate            No   \n",
              "44  LP001136    Male     Yes          0  Not Graduate           Yes   \n",
              "45  LP001137  Female      No          0      Graduate            No   \n",
              "46  LP001138    Male     Yes          1      Graduate            No   \n",
              "47  LP001144    Male     Yes          0      Graduate            No   \n",
              "48  LP001146  Female     Yes          0      Graduate            No   \n",
              "49  LP001151  Female      No          0      Graduate            No   \n",
              "\n",
              "    ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "0              5849                0.0         NaN             360.0   \n",
              "1              4583             1508.0       128.0             360.0   \n",
              "2              3000                0.0        66.0             360.0   \n",
              "3              2583             2358.0       120.0             360.0   \n",
              "4              6000                0.0       141.0             360.0   \n",
              "5              5417             4196.0       267.0             360.0   \n",
              "6              2333             1516.0        95.0             360.0   \n",
              "7              3036             2504.0       158.0             360.0   \n",
              "8              4006             1526.0       168.0             360.0   \n",
              "9             12841            10968.0       349.0             360.0   \n",
              "10             3200              700.0        70.0             360.0   \n",
              "11             2500             1840.0       109.0             360.0   \n",
              "12             3073             8106.0       200.0             360.0   \n",
              "13             1853             2840.0       114.0             360.0   \n",
              "14             1299             1086.0        17.0             120.0   \n",
              "15             4950                0.0       125.0             360.0   \n",
              "16             3596                0.0       100.0             240.0   \n",
              "17             3510                0.0        76.0             360.0   \n",
              "18             4887                0.0       133.0             360.0   \n",
              "19             2600             3500.0       115.0               NaN   \n",
              "20             7660                0.0       104.0             360.0   \n",
              "21             5955             5625.0       315.0             360.0   \n",
              "22             2600             1911.0       116.0             360.0   \n",
              "23             3365             1917.0       112.0             360.0   \n",
              "24             3717             2925.0       151.0             360.0   \n",
              "25             9560                0.0       191.0             360.0   \n",
              "26             2799             2253.0       122.0             360.0   \n",
              "27             4226             1040.0       110.0             360.0   \n",
              "28             1442                0.0        35.0             360.0   \n",
              "29             3750             2083.0       120.0             360.0   \n",
              "30             4166             3369.0       201.0             360.0   \n",
              "31             3167                0.0        74.0             360.0   \n",
              "32             4692                0.0       106.0             360.0   \n",
              "33             3500             1667.0       114.0             360.0   \n",
              "34            12500             3000.0       320.0             360.0   \n",
              "35             2275             2067.0         NaN             360.0   \n",
              "36             1828             1330.0       100.0               NaN   \n",
              "37             3667             1459.0       144.0             360.0   \n",
              "38             4166             7210.0       184.0             360.0   \n",
              "39             3748             1668.0       110.0             360.0   \n",
              "40             3600                0.0        80.0             360.0   \n",
              "41             1800             1213.0        47.0             360.0   \n",
              "42             2400                0.0        75.0             360.0   \n",
              "43             3941             2336.0       134.0             360.0   \n",
              "44             4695                0.0        96.0               NaN   \n",
              "45             3410                0.0        88.0               NaN   \n",
              "46             5649                0.0        44.0             360.0   \n",
              "47             5821                0.0       144.0             360.0   \n",
              "48             2645             3440.0       120.0             360.0   \n",
              "49             4000             2275.0       144.0             360.0   \n",
              "\n",
              "    Credit_History Property_Area Loan_Status  \n",
              "0              1.0         Urban           Y  \n",
              "1              1.0         Rural           N  \n",
              "2              1.0         Urban           Y  \n",
              "3              1.0         Urban           Y  \n",
              "4              1.0         Urban           Y  \n",
              "5              1.0         Urban           Y  \n",
              "6              1.0         Urban           Y  \n",
              "7              0.0     Semiurban           N  \n",
              "8              1.0         Urban           Y  \n",
              "9              1.0     Semiurban           N  \n",
              "10             1.0         Urban           Y  \n",
              "11             1.0         Urban           Y  \n",
              "12             1.0         Urban           Y  \n",
              "13             1.0         Rural           N  \n",
              "14             1.0         Urban           Y  \n",
              "15             1.0         Urban           Y  \n",
              "16             NaN         Urban           Y  \n",
              "17             0.0         Urban           N  \n",
              "18             1.0         Rural           N  \n",
              "19             1.0         Urban           Y  \n",
              "20             0.0         Urban           N  \n",
              "21             1.0         Urban           Y  \n",
              "22             0.0     Semiurban           N  \n",
              "23             0.0         Rural           N  \n",
              "24             NaN     Semiurban           N  \n",
              "25             1.0     Semiurban           Y  \n",
              "26             1.0     Semiurban           Y  \n",
              "27             1.0         Urban           Y  \n",
              "28             1.0         Urban           N  \n",
              "29             1.0     Semiurban           Y  \n",
              "30             NaN         Urban           N  \n",
              "31             1.0         Urban           N  \n",
              "32             1.0         Rural           N  \n",
              "33             1.0     Semiurban           Y  \n",
              "34             1.0         Rural           N  \n",
              "35             1.0         Urban           Y  \n",
              "36             0.0         Urban           N  \n",
              "37             1.0     Semiurban           Y  \n",
              "38             1.0         Urban           Y  \n",
              "39             1.0     Semiurban           Y  \n",
              "40             1.0         Urban           N  \n",
              "41             1.0         Urban           Y  \n",
              "42             NaN         Urban           Y  \n",
              "43             1.0     Semiurban           Y  \n",
              "44             1.0         Urban           Y  \n",
              "45             1.0         Urban           Y  \n",
              "46             1.0         Urban           Y  \n",
              "47             1.0         Urban           Y  \n",
              "48             0.0         Urban           N  \n",
              "49             1.0     Semiurban           Y  "
            ],
            "text/html": [
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4583</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>LP001011</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5417</td>\n",
              "      <td>4196.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>LP001013</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2333</td>\n",
              "      <td>1516.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>LP001014</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3+</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3036</td>\n",
              "      <td>2504.0</td>\n",
              "      <td>158.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>LP001018</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4006</td>\n",
              "      <td>1526.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>LP001020</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>12841</td>\n",
              "      <td>10968.0</td>\n",
              "      <td>349.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>LP001024</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3200</td>\n",
              "      <td>700.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>LP001027</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2500</td>\n",
              "      <td>1840.0</td>\n",
              "      <td>109.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>LP001028</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3073</td>\n",
              "      <td>8106.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>LP001029</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>1853</td>\n",
              "      <td>2840.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>LP001030</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>1299</td>\n",
              "      <td>1086.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>LP001032</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4950</td>\n",
              "      <td>0.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>LP001034</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3596</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>240.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>LP001036</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3510</td>\n",
              "      <td>0.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>LP001038</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4887</td>\n",
              "      <td>0.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>LP001041</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2600</td>\n",
              "      <td>3500.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>LP001043</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>7660</td>\n",
              "      <td>0.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>LP001046</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5955</td>\n",
              "      <td>5625.0</td>\n",
              "      <td>315.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>LP001047</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2600</td>\n",
              "      <td>1911.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>LP001050</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3365</td>\n",
              "      <td>1917.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>LP001052</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3717</td>\n",
              "      <td>2925.0</td>\n",
              "      <td>151.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>LP001066</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>9560</td>\n",
              "      <td>0.0</td>\n",
              "      <td>191.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>LP001068</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2799</td>\n",
              "      <td>2253.0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>LP001073</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4226</td>\n",
              "      <td>1040.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>LP001086</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>1442</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>LP001087</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3750</td>\n",
              "      <td>2083.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>LP001091</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4166</td>\n",
              "      <td>3369.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Urban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>LP001095</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3167</td>\n",
              "      <td>0.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>LP001097</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4692</td>\n",
              "      <td>0.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>LP001098</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3500</td>\n",
              "      <td>1667.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>LP001100</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>3+</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>12500</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>LP001106</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2275</td>\n",
              "      <td>2067.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>LP001109</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>1828</td>\n",
              "      <td>1330.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>LP001112</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3667</td>\n",
              "      <td>1459.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>LP001114</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4166</td>\n",
              "      <td>7210.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>LP001116</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3748</td>\n",
              "      <td>1668.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>LP001119</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3600</td>\n",
              "      <td>0.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>LP001120</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>1800</td>\n",
              "      <td>1213.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>LP001123</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2400</td>\n",
              "      <td>0.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>LP001131</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3941</td>\n",
              "      <td>2336.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>LP001136</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4695</td>\n",
              "      <td>0.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>LP001137</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3410</td>\n",
              "      <td>0.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>LP001138</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5649</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>LP001144</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5821</td>\n",
              "      <td>0.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>LP001146</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2645</td>\n",
              "      <td>3440.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>LP001151</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4000</td>\n",
              "      <td>2275.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d0c0195-c0da-4db4-ae18-d5d355b08501')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      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-4d0c0195-c0da-4db4-ae18-d5d355b08501 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-4d0c0195-c0da-4db4-ae18-d5d355b08501');\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-1d92f48a-0c60-476a-be77-783ca341c5e3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1d92f48a-0c60-476a-be77-783ca341c5e3')\"\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-1d92f48a-0c60-476a-be77-783ca341c5e3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 614,\n  \"fields\": [\n    {\n      \"column\": \"Loan_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 614,\n        \"samples\": [\n          \"LP002139\",\n          \"LP002223\",\n          \"LP001570\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1\",\n          \"3+\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Graduate\",\n          \"Graduate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6109,\n        \"min\": 150,\n        \"max\": 81000,\n        \"num_unique_values\": 505,\n        \"samples\": [\n          8333,\n          4342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2926.2483692241917,\n        \"min\": 0.0,\n        \"max\": 41667.0,\n        \"num_unique_values\": 287,\n        \"samples\": [\n          1840.0,\n          2042.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.58732523570545,\n        \"min\": 9.0,\n        \"max\": 700.0,\n        \"num_unique_values\": 203,\n        \"samples\": [\n          100.0,\n          70.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.12040985461256,\n        \"min\": 12.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          84.0,\n          120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3648783192364049,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Urban\",\n          \"Rural\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"N\",\n          \"Y\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# About the dataset"
      ],
      "metadata": {
        "id": "FlpXpnMy7e2V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#rows and columns\n",
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Abv64EZN7kfP",
        "outputId": "221d01ba-1db5-4224-b95f-6241c3a21ffe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(614, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#inforrmation\n",
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dwp-ds7Z7m1F",
        "outputId": "c0dea882-f047-4170-a2f4-65e55d2b8487"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 614 entries, 0 to 613\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Loan_ID            614 non-null    object \n",
            " 1   Gender             601 non-null    object \n",
            " 2   Married            611 non-null    object \n",
            " 3   Dependents         599 non-null    object \n",
            " 4   Education          614 non-null    object \n",
            " 5   Self_Employed      582 non-null    object \n",
            " 6   ApplicantIncome    614 non-null    int64  \n",
            " 7   CoapplicantIncome  614 non-null    float64\n",
            " 8   LoanAmount         592 non-null    float64\n",
            " 9   Loan_Amount_Term   600 non-null    float64\n",
            " 10  Credit_History     564 non-null    float64\n",
            " 11  Property_Area      614 non-null    object \n",
            " 12  Loan_Status        614 non-null    object \n",
            "dtypes: float64(4), int64(1), object(8)\n",
            "memory usage: 62.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#null values of each column\n",
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "IgTd_-rO7pW5",
        "outputId": "6ea9c9dc-081d-42c7-c533-c67645e4780a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID               0\n",
              "Gender               13\n",
              "Married               3\n",
              "Dependents           15\n",
              "Education             0\n",
              "Self_Employed        32\n",
              "ApplicantIncome       0\n",
              "CoapplicantIncome     0\n",
              "LoanAmount           22\n",
              "Loan_Amount_Term     14\n",
              "Credit_History       50\n",
              "Property_Area         0\n",
              "Loan_Status           0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Loan_ID</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Married</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dependents</th>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Education</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Self_Employed</th>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LoanAmount</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Credit_History</th>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Property_Area</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loan_Status</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#removing Loan_ID as it doesn't comtribute to the analysis\n",
        "data.drop(columns=['Loan_ID'],inplace=True)"
      ],
      "metadata": {
        "id": "QML2fwki7rDh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Missing Values"
      ],
      "metadata": {
        "id": "arJOHGNO8mpZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#gender - filling missing places with mode\n",
        "data['Gender'].fillna(data['Gender'].mode()[0], inplace=True)"
      ],
      "metadata": {
        "id": "7--BTOaP8rdj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#married - filling missing values with mode\n",
        "data['Married'].fillna(data['Married'].mode()[0],inplace=True)"
      ],
      "metadata": {
        "id": "11HSPVhk91y6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#dependents - filling missig places with -0.5\n",
        "data.dropna(subset=['Dependents'], inplace=True)"
      ],
      "metadata": {
        "id": "B2J7QLIQ94g9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check summary statistics for ApplicantIncome by Self_Employed\n",
        "print(data.groupby('Self_Employed')['ApplicantIncome'].describe())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gWGtsvEhAHRY",
        "outputId": "f3fd7bef-efa1-46c1-f425-db5bd0e749b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               count         mean          std    min      25%     50%  \\\n",
            "Self_Employed                                                            \n",
            "No             487.0  5045.786448  5736.430141  150.0  2776.50  3692.0   \n",
            "Yes             80.0  7437.362500  5946.109008  674.0  3420.75  5909.0   \n",
            "\n",
            "                   75%      max  \n",
            "Self_Employed                    \n",
            "No             5267.50  81000.0  \n",
            "Yes            9407.75  39147.0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Self_employed\n",
        "def impute_self_employed(row):\n",
        "    if pd.isnull(row['Self_Employed']):\n",
        "        if row['ApplicantIncome'] > 5000:  # Adjust threshold based on your analysis\n",
        "            return 'No'  # Likely not self-employed\n",
        "        else:\n",
        "            return 'Yes'  # Likely self-employed\n",
        "    else:\n",
        "        return row['Self_Employed']\n",
        "\n",
        "# Apply the conditional imputation\n",
        "data['Self_Employed'] = data.apply(impute_self_employed, axis=1)"
      ],
      "metadata": {
        "id": "auQi0dtiCJL1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Loan Amount - dropping rows without loan amount as it is crucial for analysis\n",
        "data.dropna(subset=['LoanAmount'], inplace=True)"
      ],
      "metadata": {
        "id": "5uBKiEFCDiaD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#loan amount term - filling missig places with -0.5\n",
        "data.dropna(subset=['Loan_Amount_Term'], inplace=True)"
      ],
      "metadata": {
        "id": "-QqKH2O1ElZZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#credit history - filling missing values with mode\n",
        "data['Credit_History'].fillna(data['Credit_History'].mode()[0],inplace=True)"
      ],
      "metadata": {
        "id": "FWjnyVhcJOfK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#null values of each column\n",
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "tz4hgoTjJ5eS",
        "outputId": "d3449cd3-181e-4c02-a94b-040315e3c455"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender               0\n",
              "Married              0\n",
              "Dependents           0\n",
              "Education            0\n",
              "Self_Employed        0\n",
              "ApplicantIncome      0\n",
              "CoapplicantIncome    0\n",
              "LoanAmount           0\n",
              "Loan_Amount_Term     0\n",
              "Credit_History       0\n",
              "Property_Area        0\n",
              "Loan_Status          0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Married</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dependents</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Education</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Self_Employed</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LoanAmount</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Credit_History</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Property_Area</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loan_Status</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualiasation"
      ],
      "metadata": {
        "id": "nsZpLWUhxici"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.histplot(data['LoanAmount'], bins=30, kde=True)\n",
        "plt.title('Loan Amount Distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "ukg2aJeayhmZ",
        "outputId": "c0895423-d22a-4917-ec87-6ace3862dd7c"
      },
      "execution_count": null,
      "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": [
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "# 1. Boxplot for Loan Amount by Gender\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x='Gender', y='LoanAmount', data=data)\n",
        "plt.title('Loan Amount by Gender')\n",
        "plt.xlabel('Gender')\n",
        "plt.ylabel('Loan Amount')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "z3fb-cXDzEGA",
        "outputId": "b87b9426-4e29-412a-9e83-0fee331680dd"
      },
      "execution_count": null,
      "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": [
        "\n",
        "# 2. Boxplot for Loan Amount by Marital Status\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x='Married', y='LoanAmount', data=data)\n",
        "plt.title('Loan Amount by Marital Status')\n",
        "plt.xlabel('Marital Status')\n",
        "plt.ylabel('Loan Amount')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "EIsnXhJhzHTN",
        "outputId": "64f3d204-292c-4f02-f22c-8c19d9ee0248",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        }
      },
      "execution_count": null,
      "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": [
        "\n",
        "# 3. Boxplot for Loan Amount by Employment Status\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x='Self_Employed', y='LoanAmount', data=data)\n",
        "plt.title('Loan Amount by Employment Status')\n",
        "plt.xlabel('Self Employed')\n",
        "plt.ylabel('Loan Amount')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "uGlgdOPXzKta",
        "outputId": "a2549284-fe48-4d1f-eca1-70aedfe5e612",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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Xp5SUFH388cc6fvy4PD09nabi1hQFBQWOUdhyv/zyiySdMZiV/7+d6eP2yiuvdOrvUrwXgYGBKikpqdJoeFW58vFRmXbt2unrr7/Wr7/+qmbNmp2x36+//lqHDh3SzJkzFR0d7Tj+55WVz1Zb+QyGP48Cn2mWRPlqzg8//LC+/fZb3XHHHXrvvff02GOPVenaAOBS4J5dAKiFPDw81L17d61atcrpB9rffvtNH3/8sa677jrHtFQPD48Ko04rV66s8r2AVXH8+HF9+umn+stf/qJbbrmlwp+77rpLR48e1erVq6vl9bp16yZPT0+9++67Tte2dOlSHT58WDfeeKPjWIsWLfT999+rtLTUceyLL77Q/v37XX59b2/v854aumnTJqd7Wffv369Vq1ape/fuTqOFfn5+uuGGG7R8+XLHaPifp+jWBCdPntT777/veFxaWqr3339ffn5+atu2baXP8ff3V5s2bfThhx86/QJix44dWr9+vdP/m3Rp3ovY2Fht2rRJX375ZYVzxcXFOnny5Hn3Wb6iclWmNv/666/Kzc2tcLy0tFQbNmyQ1WpVYGCgU79//tgrn41w+udCaWlppdOfz/SxW/4ap9+jbLPZtGTJEqd2R44cqfCehIaGymq1On2OAUBNwMguANRgy5Ytq/SH8OHDh2vs2LHKysrSnXfeqTvvvFMeHh56//33VVpaqnHjxjna/uUvf9GsWbP01FNPKSoqSjt27FB6errLo5qVWb16tY4ePXrG/WY7duwoPz8/LV++XHFxcRf8en5+fho5cqRmzpyp+++/X71799bOnTu1aNEiRUZGOi1gFB8fr08++UT333+/YmNjtXv3bsf2Na5q27atMjIyNHXqVEVGRqpevXrn3Gs3NDRUiYmJTlsPSdKYMWMqtL311lv16KOPSpJjb9Wq+t///qcTJ05UOB4WFqbw8PDz6uts/P39NXfuXO3du1dBQUHKyMjQtm3b9O9///us2z49+eSTeuCBBzR06FANGTLEsfVQw4YNNXr06ArtL+S9qIrExEStXr1ao0aN0l//+le1bdtWx44d044dO/TJJ59o1apV5x2wy8P+pEmT1KNHD3l4eKh///6Vtj1w4IDi4+PVpUsXde3aVVdffbV+//13rVixQtu3b9c999zjeP02bdrIw8NDc+fO1eHDh+Xl5aUuXbooKipKvr6+SkpKUkJCgiwWiz766KNKp1af6WP32muvVceOHfXyyy+rqKhIvr6+jgXBTrdx40Y999xzuuWWWxQUFCSbzaaPPvpIHh4e6tev33m9TwBwsRF2AaAGKw9Ef/a3v/1N1157rRYuXKiXXnpJs2fPlmEYat++vaZNm+ZYWVc6tbfmsWPHlJ6eroyMDEVERGj27Nl66aWXqq3O5cuXq27duurevXul561Wq/7yl78oPT1df/zxR7W85pgxY+Tn56cFCxZo6tSp8vX11e23367HH3/cKWzdcMMNSkpK0ltvvaUpU6aoXbt2euONN/T888+7/Np33nmntm3bpg8++EBvv/22mjVrds6wGx0drY4dO2rWrFnat2+fWrduralTp1YaQHv16iVfX1/Z7XbddNNN51Xbu+++W+nx0aNHV2vY9fX1VUpKiiZNmqQlS5bo6quv1sSJE3X77bef9XndunXTm2++qRkzZmjGjBmqU6eOoqOjNW7cuEp/AXMh70VVeHt7691339Xs2bOVmZmpDz/8UA0aNFBQUJDGjBnj0qJLffv2VUJCglasWKHly5fLMIwzht3g4GBNmDBBa9as0aJFi/T777/Ly8tLoaGhmjRpktOexY0bN9azzz6r2bNnKzk5WTabTfPnz1dMTIzjY/qVV16Rj4+PBg0apK5duyoxMdHp9c72sfviiy9q4sSJmjNnjnx8fDRkyBDFxMTovvvuczw/LCxMPXr00BdffKGDBw/K29tbYWFhmjt3bqWLkgGAO1kMd6w4AQAAzujkyZO64YYb1KtXrwr3WtcECQkJ+uOPP/Txxx9f9Neq6e8FAKDm4p5dAABqmM8//1yFhYW69dZb3V2K2/FeAABcxTRmAABqiO+//14//vijXnvtNUVERKhz587uLslteC8AABeKsAsAQA3x3nvvafny5QoPD1dKSoq7y3Er3gsAwIXinl0AAAAAgOlwzy4AAAAAwHQIuwAAAAAA0+Ge3fOwadMmGYbhtH8jAAAAAODSKSsrk8ViUVRU1FnbEXbPg2EY4hZnAAAAAHCfqmYywu55KB/RjYyMdHMlAAAAAHB52rJlS5Xacc8uAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANOpUWE3ISFBYWFhlf5ZsWKFo11aWpr69eunyMhIDRo0SF988UWFvg4fPqwJEyaoc+fOioqK0qOPPqqCgoJLeTkAAAAAADexGIZhuLuIcrm5uTpy5IjTsXfeeUeffvqpvvzyS/n5+WnFihX6xz/+oVGjRqlLly7KyMjQsmXLtHDhQnXs2NHxvMTEROXm5mr8+PGqW7euXnnlFVmtVi1btkx16tRxqb4tW7ZIkiIjI12+RgCVs9lsysnJUWFhofz8/BQRESEPDw93lwUAAIAapqq5zLXUd5G0bt26wrF//OMf6t69u/z8/CRJM2bMUP/+/TV27FhJUpcuXbRjxw7NmjVLc+fOlSRt2rRJ69atU2pqqnr06CFJCg4OVlxcnD799FPFxcVdmgsCUCVZWVlKTU11mn3h7++vxMREdevWzY2VAQAAoLaqUdOY/+zbb7/Vnj17NHDgQElSfn6+fvnlF8XGxjq1i4uL04YNG1RaWipJWrt2rXx8fNS9e3dHm5CQELVp00Zr1669dBcA4JyysrKUkpKioKAgTZs2TUuWLNG0adMUFBSklJQUZWVlubtEAAAA1EI1Oux+/PHHqlevnm666SZJUl5enqRTo7Sna9WqlcrKypSfn+9oFxwcLIvF4tQuJCTE0QcA97PZbEpNTVV0dLSSk5MVHh4ub29vhYeHKzk5WdHR0Zo3b55sNpu7SwUAAEAtU6OmMZ/u5MmTWrlypXr37q169epJkoqKiiRJPj4+Tm3LH5efLy4uVsOGDSv06evrqx9++OGC6jIMQyUlJRfUB4BTtm7dqoKCAo0ZM0bHjx+vcH7gwIF65pln9O2336pt27ZuqBAAAAA1jWEYFQY2K1Njw+769etVWFioAQMGuLsUJ2VlZdq2bZu7ywBMYevWrZKkkpKSSj+vTpw44WhntdboiSgAAAC4hLy8vM7ZpsaG3Y8//liNGjVyLDAlnRqZlU5tK9S4cWPH8eLiYqfzPj4+OnDgQIU+i4qKHG1c5enpWelCWgDOn91u17Jly1SvXj2FhoZWOL9jxw5JUtu2bdWmTZtLXR4AAABqoNzc3Cq1q5Fh9/jx4/r88881aNAgeXp6Oo6HhIRIOnVPbvm/yx97enqqRYsWjnYbNmyoMLy9c+fOSn+gPh8Wi8UxrRrAhenUqZP8/f2Vnp6u5ORkp9Fbu92u9PR0BQQEqFOnTmxDBAAAAEmq0hRmqYYuULV69WqVlJQ4VmEu16JFCwUFBSkzM9PpeEZGhrp27eoYyu7Zs6eKioq0YcMGR5udO3cqJydHPXv2vPgXAKBKPDw8lJiYqOzsbE2ePFnbt29XSUmJtm/frsmTJys7O1sjRowg6AIAAOC81ciR3fT0dDVt2lTXXXddhXNjxozRE088ocDAQMXExCgjI0ObN2/WggULHG2ioqLUo0cPTZgwQePHj1fdunU1ffp0hYWFqW/fvpfyUgCcQ7du3ZSUlKTU1FSNGzfOcTwgIEBJSUnsswsAAACXWAzDMNxdxOmKiorUvXt33XPPPU4/+J4uLS1Nc+fO1b59+xQcHKzHH39cvXr1cmpz+PBhTZ06VZ999plOnjypHj166Omnn1ZAQIDLtW3ZskWSFBkZ6XIfACpns9mUk5OjwsJC+fn5KSIighFdAAAAVFDVXFbjwm5NRtgFAAAAAPeqai6rkffsAgAAAABwIQi7AAAAAADTIewCAAAAAEyHsAsAAAAAMB3CLgAAAADAdAi7AAAAAADTIewCAAAAAEyHsAsAAAAAMB3CLgAAAADAdAi7AAAAAADTIewCAAAAAEyHsAsAAAAAMB3CLgAAAADAdAi7AAAAAADTIewCAAAAAEyHsAsAAAAAMB3CLgAAAADAdAi7AAAAAADTIewCAAAAAEyHsAsAAAAAMJ067i4AAAAAMAObzaacnBwVFhbKz89PERER8vDwcHdZwGWLsAsAAABcoKysLKWmpqqgoMBxzN/fX4mJierWrZsbKwMuX0xjBgAAAC5AVlaWUlJSFBQUpGnTpmnJkiWaNm2agoKClJKSoqysLHeXCFyWCLsAAACAi2w2m1JTUxUdHa3k5GSFh4fL29tb4eHhSk5OVnR0tObNmyebzebuUoHLDmEXAAAAcFFOTo4KCgoUHx8vq9X5R2ur1ar4+HgdPHhQOTk5bqoQuHwRdgEAAAAXFRYWSpJatmxZ6fnAwECndgAuHcIuAAAA4CI/Pz9J0q5duyo9v3v3bqd2AC4dwi4AAADgooiICPn7+ystLU12u93pnN1uV1pamgICAhQREeGmCoHLF2EXAAAAcJGHh4cSExOVnZ2tyZMna/v27SopKdH27ds1efJkZWdna8SIEey3C7iBxTAMw91F1BZbtmyRJEVGRrq5EgAAANQkle2zGxAQoBEjRrDPLlDNqprL6lyKYgAAAAAz69atm2JiYpSTk6PCwkL5+fkpIiKCEV3AjQi7AAAAQDXw8PBgBiBQg3DPLgAAAADAdAi7AAAAAADTIewCAAAAAEyHsAsAAAAAMB3CLgAAAADAdAi7AAAAAADTIewCAAAAAEyHsAsAAAAAMB3CLgAAAADAdAi7AAAAAADTIewCAAAAAEyHsAsAAAAAMB3CLgAAAADAdAi7AAAAAADTIewCAAAAAEyHsAsAAAAAMB3CLgAAAADAdAi7AAAAAADTIewCAAAAAEynRobd//znP7r11lsVGRmpmJgY3X///Tp+/Ljj/OrVqzVo0CBFRkaqX79+WrZsWYU+SktL9fzzz6t79+7q2LGj7rvvPuXl5V3KywAAAAAAuEkddxfwZ6+//rrmzp2rUaNGqWPHjvrjjz+0YcMG2Ww2SdL//vc/jR49WkOGDNGECRO0ceNGJScnq379+rrlllsc/UyaNEkZGRlKSkpSQECA3njjDd17771asWKFGjZs6K7LA3AGNptNOTk5KiwslJ+fnyIiIuTh4eHusgAAqDK+lwE1i8UwDMPdRZTLy8vTwIED9dprr+nGG2+stE1iYqKOHj2qxYsXO4794x//0LZt25SRkSFJOnDggHr37q1//vOfGjp0qCTp0KFD6tWrlx5++GE98MADLtW3ZcsWSVJkZKRLzwdQuaysLKWmpqqgoMBxzN/fX4mJierWrZsbKwMAoGr4XgZcOlXNZTVqGvMHH3yg5s2bnzHolpaW6quvvnIawZWkuLg4/fzzz9qzZ48kad26dbLb7U7tGjVqpO7du2vt2rUX7wIAnLesrCylpKQoKChI06ZN05IlSzRt2jQFBQUpJSVFWVlZ7i4RAICz4nsZUDPVqLD7/fffKzQ0VK+99pq6du2qdu3aadiwYfr+++8lSbt371ZZWZlCQkKcnteqVStJctyTm5eXp6uuukq+vr4V2nHfLlBz2Gw2paamKjo6WsnJyQoPD5e3t7fCw8OVnJys6OhozZs3z3EbAwAANQ3fy4Caq0bds/vrr7/qhx9+0I4dO/TPf/5T3t7eeuONNzRixAh9+umnKioqkiT5+Pg4Pa/8cfn54uLiSu/L9fHxcbRxlWEYKikpuaA+AJyydetWFRQUaMyYMU6L0JUbOHCgnnnmGX377bdq27atGyoEAODs+F4GXHqGYchisZyzXY0Ku+VB8v/9v/+n8PBwSVKHDh3Uu3dvLViwQD169HBzhVJZWZm2bdvm7jIAU9i6daskqaSkpNLPqxMnTjjaWa01aiIKAACS+F4GuIuXl9c529SosOvj46NGjRo5gq506l7biIgI5ebmqn///pKkw4cPOz2vuLhYkhzTln18fHTkyJEK/RcXF1eY2ny+PD091bp16wvqA8Apdrtdy5YtU7169RQaGlrh/I4dOyRJbdu2VZs2bS51eQAAnBPfy4BLLzc3t0rtalTYbd26tXbv3l3puRMnTigwMFCenp7Ky8vTDTfc4DhXfh9u+b28ISEh+u2331RUVOQUbvPy8irc73u+LBaL6tWrd0F9ADilU6dO8vf3V3p6upKTk51+422325Wenq6AgAB16tSJrRsAADUS38uAS68qU5ilGrZAVa9evXTo0CGnKSB//PGHtm7dqrZt28rLy0sxMTH65JNPnJ6XkZGhVq1aqXnz5pKkHj16yGq16tNPP3W0KSoq0rp169SzZ89LczEAzsnDw0OJiYnKzs7W5MmTtX37dpWUlGj79u2aPHmysrOzNWLECH44AADUWHwvA2quGrXPrt1u1+23366ioiI99thjqlu3rubMmaNffvlFH3/8sRo3bqz//e9/Gj58uG6//XbFxsbqq6++0muvvabp06crNjbW0dfEiRO1cuVKJSUlKSAgQLNnz9auXbu0YsWKShevqgr22QUujsr2JgwICNCIESPYmxAAUCvwvQy4dKqay2pU2JWkwsJCTZ06VV988YXKysp0/fXX66mnnnK6T3bVqlV65ZVXtHPnTjVt2lQPPvighgwZ4tRPaWmppk+fro8++khHjx5Vp06d9PTTTzu2KXIFYRe4eGw2m3JyclRYWCg/Pz9FRETwW3AAQK3C9zLg0qi1YbcmI+wCAAAAgHtVNZfVqHt2AQAAAACoDoRdAAAAAIDpEHYBAAAAAKZD2AUAAAAAmA5hFwAAAABgOoRdAAAAAIDpEHYBAAAAAKZTx90FAIAk2Ww25eTkqLCwUH5+foqIiJCHh4e7ywIAAEAtRdgF4HZZWVlKTU1VQUGB45i/v78SExPVrVs3N1YGAACA2oppzADcKisrSykpKQoKCtK0adO0ZMkSTZs2TUFBQUpJSVFWVpa7SwQAAEAtRNgF4DY2m02pqamKjo5WcnKywsPD5e3trfDwcCUnJys6Olrz5s2TzWZzd6kAAACoZQi7ANwmJydHBQUFio+Pl9Xq/OXIarUqPj5eBw8eVE5OjpsqBAAAQG1F2AXgNoWFhZKkli1bVno+MDDQqR0AAABQVYRdAG7j5+cnSdq1a1el53fv3u3UDgAAAKgqwi4At4mIiJC/v7/S0tJkt9udztntdqWlpSkgIEARERFuqhAAAAC1FWEXgNt4eHgoMTFR2dnZmjx5srZv366SkhJt375dkydPVnZ2tkaMGMF+uwAAADhvFsMwDHcXUVts2bJFkhQZGenmSgBzqWyf3YCAAI0YMYJ9dgEAAOCkqrmszqUoBgDOplu3boqJiVFOTo4KCwvl5+eniIgIRnQBAADgMsIugBrBw8ODWRMAAACoNtyzCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATKdGhd0PPvhAYWFhFf68+OKLTu3S0tLUr18/RUZGatCgQfriiy8q9HX48GFNmDBBnTt3VlRUlB599FEVFBRcqksBAAAAALhRHXcXUJk333xTDRs2dDwOCAhw/HvFihV65plnNGrUKHXp0kUZGRkaPXq0Fi5cqI4dOzrajR07Vrm5ufrXv/6lunXr6pVXXtEDDzygZcuWqU6dGnnZwGXNZrMpJydHhYWF8vPzU0REhDw8PNxdFgAAAGqpGpn62rZtKz8/v0rPzZgxQ/3799fYsWMlSV26dNGOHTs0a9YszZ07V5K0adMmrVu3TqmpqerRo4ckKTg4WHFxcfr0008VFxd3Sa4DQNVkZWUpNTXVafaFv7+/EhMT1a1bNzdWBgAAgNqqRk1jPpf8/Hz98ssvio2NdToeFxenDRs2qLS0VJK0du1a+fj4qHv37o42ISEhatOmjdauXXtJawZwdllZWUpJSVFQUJCmTZumJUuWaNq0aQoKClJKSoqysrLcXSIAAABqoRoZdgcMGKA2bdropptu0uzZs2Wz2SRJeXl5kk6N0p6uVatWKisrU35+vqNdcHCwLBaLU7uQkBBHHwDcz2azKTU1VdHR0Ro3bpx+/PFHvfPOO/rxxx81btw4RUdHa968eY6vAQAAAEBV1ahpzI0bN9aYMWPUoUMHWSwWrV69Wq+88ooOHjyoiRMnqqioSJLk4+Pj9Lzyx+Xni4uLne75Lefr66sffvjhgmo0DEMlJSUX1AeAU7Zu3aqCggJde+21Gjp0qOx2u+PcvHnz1LVrVx08eFDffvut2rZt68ZKAQAAUFMYhlFhYLMyNSrs3nDDDbrhhhscj3v06KG6devqnXfe0ahRo9xY2f8pKyvTtm3b3F0GYApbt26VJK1fv17169dX7969FRoaqh07dmj16tVav369o53VWiMnogAAAMANvLy8ztmmRoXdysTGxmrevHnatm2bfH19JZ3aVqhx48aONsXFxZLkOO/j46MDBw5U6KuoqMjRxlWenp5q3br1BfUB4JSysjJJUv369TVnzhzHSumdO3fWsGHD9OCDD+ro0aMKDw9XmzZt3FkqAAAAaojc3NwqtavxYfd0ISEhkk7dk1v+7/LHnp6eatGihaPdhg0bKgxv79y5U6GhoRdUg8ViUb169S6oDwCn7N+/X9KpWxi8vb21fft2x9ZD4eHhaty4sY4ePar9+/erS5cubq4WAAAANUFVpjBLtSDsZmRkyMPDQxEREWrcuLGCgoKUmZmpPn36OLXp2rWrYyi7Z8+eeu2117RhwwbHtiU7d+5UTk6O7r//frdcB4CKDh48KEn65ZdfNGzYMMeK6tKpqSnlj8vbAQAAAFVVo8JuYmKiYmJiFBYWJklatWqVlixZouHDhzumLY8ZM0ZPPPGEAgMDFRMTo4yMDG3evFkLFixw9BMVFaUePXpowoQJGj9+vOrWravp06crLCxMffv2dcu1AajommuuOeO5039jd7Z2AAAAQGUshmEY7i6i3KRJk/Tll1/qwIEDstvtCgoKUnx8vBISEpx+8E1LS9PcuXO1b98+BQcH6/HHH1evXr2c+jp8+LCmTp2qzz77TCdPnlSPHj309NNPKyAgwOX6tmzZIkmKjIx0uQ8A/+fYsWO6/fbbVadOHS1atEi5ubmOacytW7fWnXfeqZMnT2rJkiXy9vZ2d7kAAACoAaqay2pU2K3pCLtA9dqyZYsmTJggSWrUqJHuuusuRUdHKzs7WwsXLtShQ4ckSVOmTOHzDgAAAJKqnstq1DRmAJeXwsJCSdKgQYP08ccfa9asWY5zHh4eGjhwoNLT0x3tAAAAgKpi40oAbuPn5yfp1B7bixYtUkxMjFq2bKmYmBgtXLhQPXv2dGoHAEBNZrPZtGXLFq1Zs0ZbtmyRzWZzd0nAZY2RXQBuExERIX9/f7300ksqKCiQ3W6XJO3atUt33nmn/P39FRAQoIiICDdXCgDA2WVlZSk1NVUFBQWOY/7+/kpMTHTsDgLg0mJkF4DbeHh4KDg4WAcOHJDVatVtt92m2bNn67bbbpPVatWBAwcUFBQkDw8Pd5cKAMAZZWVlKSUlRUFBQZo2bZqWLFmiadOmKSgoSCkpKcrKynJ3icBliQWqzgMLVAHVq7S0VPHx8briiitUv359/frrr45z/v7+OnLkiE6cOKElS5Y49tEGAKAmsdlsevDBBxUUFKTk5GRZrf83lmS32zV58mTt2rVLs2fP5pe3QDVhgSoANd7KlStlt9s1YsQI9enTRzk5OY6thyIiIvTZZ59p1qxZWrlypQYPHuzucgEAqCAnJ0cFBQUaN26cU9CVJKvVqvj4eI0bN045OTkMmACXGGEXgNvs379fktS5c2d5eHhU+CEgOjraqR0AADVN+Y4BLVu2rPR8YGCgUzsAlw737AJwm2uuuUaS9PXXX1d6Pjs726kdAAA1TfmOAbt27ar0/O7du53aAbh0CLsA3CY2NlZWq1ULFizQyZMnnc6dPHlSCxculIeHh2JjY91UIQAAZ1e+s0BaWppjV4FydrtdaWlp7CwAuAlhF4DbeHl56dZbb9WhQ4d03333KTMzU7///rsyMzN133336dChQxo8eDCLUwEAaiwPDw8lJiYqOztbkydP1vbt21VSUqLt27dr8uTJys7O1ogRI1icCnADVmM+D6zGDFwcb731lj788EOn34h7eHho8ODBuu+++9xYGQAAVVPZPrsBAQEaMWIE++wC1ayquYywex4Iu8DFU1paqpUrV2r//v265pprFBsby4guAKBWsdlsFXYWYEQXqH5sPQSgVvHy8mJ7IQBArVbZzgIA3Id7dgEAAAAApkPYBQAAAACYDmEXAAAAAGA6hF0AAAAAgOkQdgEAAAAApkPYBQAAAACYDmEXAAAAAGA6hF0AAAAAgOkQdgEAAAAApkPYBQAAAACYDmEXAAAAAGA6hF0AAAAAgOkQdgEAAAAApkPYBQAAAACYDmEXAAAAAGA6hF0AAAAAgOnUcXcBACBJNptNOTk5KiwslJ+fnyIiIuTh4eHusgAAAFBLEXYBuF1WVpZSU1NVUFDgOObv76/ExER169bNjZUBAACgtmIaMwC3ysrKUkpKioKCgjRt2jQtWbJE06ZNU1BQkFJSUpSVleXuEgEAAFALuRR2b7rpJq1ateqM57/44gvddNNNLhcF4PJgs9mUmpqq6OhoJScnKzw8XN7e3goPD1dycrKio6M1b9482Ww2d5cKAACAWsalsLt3716VlJSc8XxJSYn27dvnclEALg85OTkqKChQfHy8rFbnL0dWq1Xx8fE6ePCgcnJy3FQhAAAAaiuXpzFbLJYzntuyZYt8fHxc7RrAZaKwsFCS1LJly0rPBwYGOrUDAAAAqqrKC1S98847mj9/vqRTQXfKlCmaPn16hXZHjhxRcXGxBgwYUH1VAjAlPz8/SdKuXbsUHh5e4fzu3bud2gEAAABVVeWwe9VVV+naa6+VdGoac0BAgAICAiq0q1evntq2bas777yz+qoEYEoRERHy9/dXWlqakpOTnaYy2+12paWlKSAgQBEREW6sEgAAALWRxTAM43yflJCQoIcfflhdu3a9GDXVWFu2bJEkRUZGurkSwDzKV2OOjo5WfHy8AgMDtXv3bqWlpSk7O1tJSUlsPwQAAACHquYyl8Lu5YqwC1wcle2zGxAQoBEjRhB0AQAA4KSquazK05grk5ubq/z8fBUVFVV6/tZbb72Q7gFcJrp166aYmBjl5OSosLBQfn5+ioiIkIeHh7tLAwAAQC3lUtjdvXu3xo0bp82bN+tMA8MWi4WwC6DKPDw8mDUBAACAauNS2J04caJ27NihCRMm6Prrr2ebIQAAAABAjeJS2P322281cuRIJSQkVHc9AAAAAABcMOu5m1R05ZVXqmHDhtVdCwAAAAAA1cKlsDts2DAtX75cNputuusBAAAAAOCCuTSNOSgoSHa7XYMHD9Ztt92mJk2aVLpqat++fS+4QAAAAAAAzpdL++yGh4efu2OLRdu2bXOpqJqKfXYBAAAAwL0u6j678+fPd+VpAAAAAABcEi6F3c6dO1d3HQAAAAAAVBuXwi4AVDebzaacnBwVFhbKz89PERERla4FAAAAAFSFS2F3+PDh52xjsVj0zjvvuNI9gMtMVlaWUlNTVVBQ4Djm7++vxMREdevWzY2VAQAAoLZyaeshwzAq/LHZbMrPz9fXX3+tgwcPym63X1BhR48eVc+ePRUWFua4AblcWlqa+vXrp8jISA0aNEhffPFFhecfPnxYEyZMUOfOnRUVFaVHH33U6QdpADVDVlaWUlJSFBQUpGnTpmnJkiWaNm2agoKClJKSoqysLHeXCAAAgFrIpdWYz+aLL77QM888ozlz5igiIsLlfqZNm6YPP/xQv/32m5YuXepYaWvFihX6xz/+oVGjRqlLly7KyMjQsmXLtHDhQnXs2NHx/MTEROXm5mr8+PGqW7euXnnlFVmtVi1btkx16rg2e5vVmIHqZbPZ9OCDDyooKEjJycmyWv/v9292u12TJ0/Wrl27NHv2bKY0AwBqPG7JAS6Ni7oa89n06tVLgwYN0pQpU7RgwQKX+vj555+1aNEijR8/Xv/85z+dzs2YMUP9+/fX2LFjJUldunTRjh07NGvWLM2dO1eStGnTJq1bt06pqanq0aOHJCk4OFhxcXH69NNPFRcX5/oFAqg2OTk5Kigo0Lhx45yCriRZrVbFx8dr3LhxysnJ4ZdMAIAajVtygJrHpWnM5xIYGFhh6vH5mDRpkoYNG6bg4GCn4/n5+frll18UGxvrdDwuLk4bNmxQaWmpJGnt2rXy8fFR9+7dHW1CQkLUpk0brV271uW6AFSvwsJCSVLLli0rPR8YGOjUDgCAmohbcoCaqdrD7smTJ7Vy5UpdeeWVLj0/MzNTO3bs0COPPFLhXF5eniRVCMGtWrVSWVmZ8vPzHe2Cg4NlsVic2oWEhDj6AOB+fn5+kqRdu3ZVen737t1O7QAAqGlsNptSU1MVHR2t5ORkhYeHy9vbW+Hh4UpOTlZ0dLTmzZsnm83m7lKBy45L05ifeuqpSo8fPnxY3333nX777TclJSWdd7/Hjh1TSkqKHnvsMTVo0KDC+aKiIkmSj4+P0/Hyx+Xni4uL1bBhwwrP9/X11Q8//HDedZ3OMAyVlJRcUB8ATgkODlbjxo21ePFiPfHEExXu2V28eLH8/f0VHBzM5x0AoEbaunWrCgoKNGbMGB0/frzC+YEDB+qZZ57Rt99+q7Zt27qhQsB8DMOoMLBZGZfC7ldffVXhmMVika+vr6677jrFx8c77pU9H6+//rquuuoq3Xbbba6UdUmUlZVp27Zt7i4DMI1evXppyZIl+uc//6kbbrhB/v7+Kigo0JdffqkdO3bo9ttv148//ujuMgEAqNTWrVslSSUlJZX+jHjixAlHuz+vTwHAdV5eXuds41LYXb16tStPO6u9e/dq3rx5mjVrlg4fPixJjpGckpISHT16VL6+vpJOjSA3btzY8dzi4mJJcpz38fHRgQMHKrxGUVGRo42rPD091bp16wvqA8D/adOmjZo3b653331XqampjuP+/v56/PHHFRMT48bqAAA4O7vdrmXLlqlevXoKDQ2tcH7Hjh2SpLZt26pNmzaXujzAlHJzc6vUrtpXY3bVnj17VFZWpgcffLDCueHDh6tDhw566aWXJJ26JzckJMRxPi8vT56enmrRooWkU/fmbtiwocLw9s6dOyv9InQ+LBaL6tWrd0F9AHDWq1cv9ezZk+0aAAC1TqdOneTv76/09PRKt9FLT09XQECAOnXqxPc1oJpUZQqzdAFh12azafny5frvf/+rffv2SZKaNm2qXr16aeDAgef9ydymTRvNnz/f6di2bds0depUPfvss4qMjFSLFi0UFBSkzMxM9enTx9EuIyNDXbt2dQxl9+zZU6+99po2bNjgWOp9586dysnJ0f333+/qJQO4iDw8PNheCABQ63h4eCgxMVEpKSmaPHmy4uPjFRgYqN27dystLU3Z2dlKSkoi6AJuYDEMwzjfJx0+fFiJiYnasmWL6tev7xhR3bNnj44cOaL27dsrNTW10kWmzsdXX32l4cOHa+nSpY4fgj/++GM98cQTevjhhxUTE6OMjAwtXbpUCxYsUFRUlOO5iYmJ+vnnnzV+/HjVrVtX06dPl9Vq1bJly1SnjmsZv6qbFwMAAODyUtk+uwEBARoxYgT77ALVrKq5zKXUN336dG3dulVPP/20br/9dnl6eko6tXhTWlqaJk+erOnTp+uZZ55xpfuzGjBggI4dO6a5c+dqzpw5Cg4O1syZM52CriS98sormjp1qiZOnKiTJ0+qR48eevrpp10OugAAAMCZdOvWTTExMdySA9QgLo3s3nDDDerXr5+efvrpSs9PmjRJmZmZWrdu3QUXWJMwsgsAAAAA7lXVXObS+ueHDh1ScHDwGc8HBwc79rwFAAAAAOBScynstmzZ8qzbD61evVqBgYEuFwUAAADUNjabTVu2bNGaNWu0ZcsW2Ww2d5cEXNZcuoH1jjvu0L///W898MADuueeexQUFCTp1IrH7777rrKysi7K/boAAABATVTZAlX+/v5KTExkgSrATVwKu3fddZcKCws1Z86cCvfl1qlTR4888ojuvPPOaikQAAAAqMmysrKUkpKi6OhojRs3Ti1bttSuXbuUlpamlJQUJSUlEXgBN3BpgapyhYWF2rBhg/bu3StJatasmbp27So/P79qK7AmYYEqAAAAnM5ms+nBBx9UUFCQkpKStH37dsdqzOHh4UpJSdGuXbs0e/ZsVmYGqslF3XqonJ+fn/r3738hXQAAAAC1Vk5OjgoKCnTLLbdo1KhRFaYx9+vXT19//bVycnIYMAEusQsKu2VlZTp48KCKi4tV2QBx27ZtL6R7AAAAoEYrLCyUJM2fP1+dO3euMI353XffdWoH4NJxKewWFxfr+eefV3p6usrKyiqcNwxDFotF27Ztu+ACAVwebDabcnJyHFO/IiIimO4FAKjxfH19JUkRERFKTk6W1Xpqs5Pw8HAlJycrKSlJ27Ztc7QDcOm4FHaTkpL0xRdfKC4uTh06dFDDhg2ruy4AlxFWsAQA1FYWi6VK58/VDkD1cynsrl+/XgkJCZowYUJ11wPgMsMKlgCA2uzQoUOSpG3btmny5MmKj49XYGCgdu/erbS0NMdMx/J2AC4dqytPatSokVq2bFndtQC4zNhsNqWmpio6OlrJyckKDw+Xt7e3Y+pXdHS05s2bJ5vN5u5SAQCoVPkuJAkJCfrll180btw4DR06VOPGjdOuXbuUkJDg1A7ApePSyO7tt9+uFStW6I477nDclwAA56t8Bctx48ZV+FpitVoVHx+vcePGsYIlAKDGioiIkL+/v7Zv36433nij0q2HAgICFBER4e5SgcuOS2H3kUceUWlpqW677TYNHjxYAQEBlS4k07dv3wsuEIB5la9MeaaZIoGBgU7tAACoaTw8PJSYmKiUlBSlpKQoPj5e0dHR2r17t1JSUpSdna2kpCQWXQTcwKWwe/DgQX311Vfatm3bGVdcZjVmAOdSPqVr165dCg8Pr3B+9+7dTu0AAKiJunXrpqSkJKWmpmrcuHGO4wEBAaw9AbiRS2F3woQJ2rp1q0aOHKn27duzGjMAl5RP/UpLS1NSUlKFqV9paWlM/QIA1ArdunVTTEwM2+gBNYhLYfebb77RAw88oEcffbS66wFwGSmf+jV16lQNGzZMpaWljnNeXl4qLS3VU089xQ8KAIBawcPDgzUmgBrEpdWlrr76ajbGBgAAAADUWC6N7N53331avHixhgwZovr161d3TQAuE+VbD3Xu3LnSacwpKSmaN2+eYmJiGN0FAADAeXEp7JaWlqpOnTrq27evYmNj1aRJkwo/iFosFt17773VUSMAkzp96yFPT88KU7/YeggAAACucinsPv/8845/L1iwoNI2hF0A58LWQwAAALhYXAq7q1atqu46AFyG2HoIAGAmNpuN1ZiBGsSlsNusWbNztikqKnKlawCXEbYeAgCYRVZWllJTU1VQUOA45u/vr8TERPbZBdzEpbB7JqWlpVq1apXS09P15ZdfasuWLdXZPQCTYeshAIAZZGVlKSUlRdHR0Ro3bpxatmypXbt2KS0tTSkpKUpKSiLwAm5wwWHXMAxt2LBB6enp+uyzz3TkyBH5+flpwIAB1VEfgMuUxWJxdwkAAJxT+c4C0dHRSk5OltV6amfP8PBwJScna/LkyewsALiJy2H3hx9+UHp6ulasWKHffvtNFotFcXFxuvvuu9WxY0d+UAVwTmw9BACo7U7fWaA86JazWq3sLAC40XmF3fz8fC1fvlzp6enatWuXAgICNHDgQLVv316PPfaY+vXrp6ioqItVKwCTYeshAEBtx84CQM1V5bA7dOhQbd68WVdeeaX69eunSZMm6frrr5f0fyumAsD54AcEAEBtx84CQM1lPXeTU77//ns1a9ZMzz33nJKTkx1BFwBcdfoPCJXhBwQAQE13+s4Cdrvd6ZzdbmdnAcCNqhx2n3nmGTVu3FijR49W9+7dNXHiRG3cuFGGYVzM+gCYGD8gAABqu/KdBbKzszV58mRt375dJSUl2r59uyZPnqzs7GyNGDGCtScAN7AY55lW8/PzlZ6ero8//lh5eXm6+uqrFRMTo4yMDM2YMUM333zzxarV7cq3UuLeQaD6nL5dQ3x8vAIDA7V7926lpaUpOzub7RoAALVCZfvsBgQEaMSIEXwfA6pZVXPZeYfd05WvyJyRkaFff/1VV199tXr16qXevXurW7duqlu3rqtd10iEXeDi4AcEAIAZ2Gw25eTkOHYWiIiIYEQXuAguSdgtZ7fbtXHjRi1fvlyfffaZjh49Km9vb23atOlCu65RCLvAxcMPCAAAAKiKquYyl/fZPZ3ValW3bt3UrVs3Pfvss1q1apXS09Oro2sAlwkPDw9+kQQAAIBqUy1h93R169ZVXFyc4uLiqrtrACbGyC4AAACqU7WHXQA4X5Xds+vv76/ExETu2QUAAIBLqrz1EABcDOWrMQcFBWnatGlasmSJpk2bpqCgIKWkpCgrK8vdJQIAAKAWIuwCcBubzabU1FRFR0crOTlZ4eHh8vb2Vnh4uJKTkxUdHa158+bJZrO5u1QAAADUMoRdAG6Tk5OjgoICxcfHyzAMbdmyRWvWrNGWLVtkGIbi4+N18OBB5eTkuLtUAAAA1DLcswvAbQoLCyVJBw4c0LRp0yrcs3v33Xc7tQMAAACq6oLCbm5urvLz81VUVFTp+VtvvfVCugdgcn5+fpKkl19+Wddff73+9re/ycvLS6Wlpfrmm280ffp0p3YAAABAVbkUdnfv3q1x48Zp8+bNMgyj0jYWi4WwC+CswsLCZLVadcUVV2jnzp3Kzs52nLv66qvl7e2tEydOKCwszI1VAgAAoDZyKexOnDhRO3bs0IQJE3T99dfLx8enuusCcBn48ccfZbfbVVJSIi8vLz3yyCPq3Lmzvv76ay1cuFAlJSWOdpGRkW6uFgAAALWJS2H322+/1ciRI5WQkFDd9QC4jPz222+SpJCQEB05ckSzZs3SrFmzJEkBAQEKCQlRXl6eox0AAABQVS6F3SuvvFINGzas7loAXGaKi4slSXFxcerVq5dWrlyp/fv365prrlFsbKxWr16tWbNmOdoBAAAAVeVS2B02bJiWL1+uu+66Sx4eHtVdE4DLRPktEBkZGVq8eLHTCO6HH37oOM+tEgAAADhfLoXdoKAg2e12DR48WLfddpuaNGlSaejt27fvBRcIwLyuvvpqSVJeXp4sFovTud9//90RfsvbAQAAAFXlUth97LHHHP9+/vnnK21jsVi0bds216oCcFkICwuTxWKRYRgVVnYvf2y1WlmNGQAAAOfNpbA7f/786q4DwGVo27ZtjlDr4+Oj9u3bq27dujpx4oQ2b96s4uJi2e12bdu2TR06dHBztQAAAKhNXAq7nTt3ru46AFyGvvvuO0mSn5+f/vjjD61bt85xzmKx6Morr9Qff/yh7777jrALAACA8+JS2AWA6vD7779LkgoLCxUdHa3rrrtOXl5eKi0t1TfffKPs7GyndgAAAEBVuRx2f/31Vy1dulQ5OTk6fPiw7Ha703mLxaJ33nnnggsEYF5XXXWVJKl+/foaP368duzYocLCQjVp0kR9+vTRPffco6NHjzraAQAAAFXlUtjdvn27hg8fruPHjys4OFg7duxQ69atVVxcrIMHDyowMFBNmjQ5737XrFmjuXPnKjc3V0eOHFFAQID69Omj0aNHO+3ru3r1ar3yyivauXOnmjZtqgcffFC33XabU1+lpaWaPn26li9frqNHjyoqKkrPPPOMQkJCXLlkABdB+ZZCR48e1Z133qnS0lLHufIR3tPbAQAAAFVldeVJL730kurVq6fMzEy99dZbMgxDEyZM0Jo1azR9+nQVFRXpiSeeOO9+Dx06pPbt2+vZZ59Vamqq7rvvPn344Yf6+9//7mjzv//9T6NHj1bHjh01d+5cxcbGKjk5WZmZmU59TZo0SWlpaXrsscf06quvqrS0VPfee68OHz7syiUDuAgaNWrk+PfpQffPj09vBwAAAFSFSyO73377re6//341bdpUhw4dkvR/24TExsbqm2++0QsvvKAFCxacV7+DBw92ehwTEyMvLy8988wzOnjwoAICAvT666+rffv2eu655yRJXbp0UX5+vmbMmKFbbrlFknTgwAEtXbpU//znPzVkyBBJUmRkpHr16qXFixfrgQcecOWyAVSzK6+8slrbAQAAAOVcGtm12+26+uqrJZ2aXujh4eEIvdKpvTO3bt1aLQWWj+iUlZWptLRUX331lSPUlouLi9PPP/+sPXv2SJLWrVsnu93u1K5Ro0bq3r271q5dWy11AbhwFovF8W8vLy+nc6c/Pr0dAAAAUBUujew2b97cESytVquaN2+uDRs2KC4uTtKpkd/T77E9XzabTSdPnlRubq5mzZql3r17q3nz5srNzVVZWVmF+25btWolScrLy1Pz5s2Vl5enq666Sr6+vhXaLV261OW6AFSvwsJCx78jIyPVtGlTlZaWysvLS/v27dM333xToR0AAABQFS6F3R49eigzM1OPPfaYJOmOO+5QSkqK8vPzZRiGvv76a913330uF9WrVy8dPHhQknTDDTfopZdekiQVFRVJqrhYTfnj8vPFxcWVhm0fHx9HG1cZhqGSkpIL6gPAKb/99pskqX379tq0aZMj3EqnfpHWvn17bd68Wb/99hufdwAAAJB0KpNVZeafS2F31KhR6t+/v8rKyuTp6al77rlHJSUl+vTTT2W1WvXwww9r5MiRrnQtSZozZ46OHTum3Nxcvf766xo1apTeeustl/urTmVlZdq2bZu7ywBM4ciRI5KkzZs369prr9W1114rT09PlZWV6aefftLmzZsd7fi8AwAAQLk/3wJXGZfCrq+vr9MUYYvFoocfflgPP/ywK91VEB4eLkmKiopSZGSkBg8erM8++0ytW7eWpAorKhcXFzvqkk6N4Jb/EP3ndn+e2ny+PD09HXUAuDAnT57UBx98IElq0KCBAgICVLduXZ04cUL79+93tIuIiFCbNm3cVSYAAABqkNzc3Cq1cynsnu7o0aM6cOCAJKlJkyaqX7/+hXbpJCwsTJ6entq9e7d69+4tT09P5eXl6YYbbnC0ycvLkyTHvbwhISH67bffVFRU5BRu8/LyLnifXYvFonr16l1QHwBOqVu3riTJz89P3333nTZt2uQ4Z7FY5Ofnp8LCQtWtW5fPOwAAAEiq+uKlLq3GLJ2adpiQkKDOnTtrwIABGjBggDp37qzhw4dry5YtrnZbwffff6+ysjI1b95cXl5eiomJ0SeffOLUJiMjQ61atVLz5s0lnbqn2Gq16tNPP3W0KSoq0rp169SzZ89qqw3AhSm/h76wsFC+vr669dZbNWrUKN16663y9fV1LEx1offaAwAA4PLj0sju999/r4SEBHl6emrIkCGO1ZB//vlnrVixQnfffbfeffddtW/f/rz6HT16tNq1a6ewsDBdccUV2r59u1JTUxUWFqY+ffpIkh566CENHz5c//rXvxQbG6uvvvpKH3/8saZPn+7op0mTJhoyZIheeOEFWa1WBQQEaPbs2WrYsKGGDRvmyiUDuAjKZ140b95cpaWl+vDDDx3nAgIC1KxZM+3du/eCbz8AAFR04MCBSm/7AmqyBg0aqEmTJu4uA7WES2F3+vTpCggI0KJFi9S4cWOnc2PGjNEdd9yh6dOnn/eiUu3bt1dGRobmzJkjwzDUrFkzxcfHKzEx0XED8vXXX69XX31Vr7zyipYuXaqmTZtq0qRJio2Nderr6aefVv369fXSSy/p6NGj6tSpk956660L2hIJQPUqn4Li4+OjSZMmafv27SosLJSfn5/Cw8P19NNPa+/eveyzCwDVrKioSCNHjpTdbnd3KcB5sVqtmj9/Pr8IR5VYDMMwzvdJUVFReuSRR3T//fdXen7u3Ll67bXXnO6/M4Py6dmRkZFurgQwhzVr1ujFF1+UxWJRdHS04uPjFRgYqN27dystLU3Z2dkyDENPPPGEbrzxRneXCwCmwsjuxZGfn6+XX35Zjz/+uFq0aOHuckyHkV1IVc9lLo3sWq1W2Wy2M5632+2yWl2+HRjAZcLPz0+SlJCQoMzMTI0bN85xLiAgQAkJCZo/f76jHQCg+hAYLq4WLVqwgwfgZi6F3aioKC1cuFADBgxQs2bNnM7t27dPixYtUqdOnaqlQADmFRERIX9/f23fvl2zZs3SJ598ov379+uaa65Rv379NG3aNAUEBCgiIsLdpQIAAKCWcSnsPv7447rrrrsUGxurm2++WUFBQZKknTt3atWqVfLw8NA//vGP6qwTgAl5eHgoMTFRU6dO1Z133qmysjLHuXfeeUdlZWV66qmn5OHh4cYqAQAAUBu5FHYjIiKUlpam6dOna/Xq1Tp27JgkydvbWzfccIPGjh3LtA0A5+X0oFvZYwAAAOB8uBR2Jal169aaNWuW7Ha7Yy9MPz8/Wa1WlZSU6ODBgwoICKi2QgGYj81m06xZsySduj2itLRUxcXF8vHxkZeXlzZt2qTXXntNMTExjO4CAADgvLgcdstZrVZdffXVTsfeeecdzZgxQ9u2bbvQ7gGY2A8//KDi4mI1aNCg0tXbGzRooKKiIv3www/q0KGDGyoEAABAbcWSyQDcZvPmzZKkI0eOqE6dOhoyZIjmzJmjIUOGqE6dOo4tMcrbAQAAAFV1wSO7AOCq8i3MPDw89P7778vLy0uSdM899+iOO+7Q7bffLpvNdtatzgAAAIDKMLILwG3y8/MlSb6+vqpTx/l3b3Xq1JGvr69TOwAAAKCqCLsA3Ka0tFSSVFhYqEmTJmn79u0qKSnR9u3bNWnSJMfid+XtAAAAgKqq8jTmrVu3VrnTgoICl4oBcHlp2rSpvvvuO0nS999/r+zsbMe58inN5e0AAACA81HlsHvbbbfJYrFUqa1hGFVuC+Dyde+99yojI0NWq7XC6G1paamsVqvsdrvuvfde9xQIAACAWqvKYXfq1KkXsw4AlyFvb29de+21+umnn2SxWNShQwdFRkZqy5Yt+v7772W323XttdfK29vb3aUCAACglqly2P3rX/96MesAcBmy2WwqKipSo0aNdOjQIX333XeOac2S1KhRIxUXF8tms8nDw8N9hQIAAKDWYeshAG6Tk5OjgoICTZs2TS1bttTbb7+tffv2qWnTprr33nu1a9cujRs3Tjk5OYqMjHR3uQAAAKhFCLsA3KZ8teWWLVvK29tbDz30kNP5wMBAp3YAAABAVbH1EAC38fPzkyTt2rWr0vO7d+92agcAAABUFWEXgNtERETI399faWlpstvtTufsdrvS0tIUEBCgiIgIN1UIAACA2oppzIALDhw4oCNHjri7DFPo37+/3n77bU2YMEF9+vRRkyZNdODAAX3++efKycnRvffeq507d7q7TFNo0KCBmjRp4u4yAAAALgnCLnCeioqKNHLkyAojkbgwW7du1datWyscf+utt9xQjTlZrVbNnz9fvr6+7i4FAADgoiPsAufJ19dXs2fPZmS3mtntdm3cuFFpaWmKj49Xly5dZLVyp0V1atCgAUEXAABcNgi7gAuYCnpxWK1WpaWlqVu3bmrdurW7ywEAAEAtxrAJAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0alTYXblypR566CH17NlTHTt21ODBg7V06VIZhuHULi0tTf369VNkZKQGDRqkL774okJfhw8f1oQJE9S5c2dFRUXp0UcfVUFBwaW6FAAAAACAG9WosPv222/L29tbSUlJev3119WzZ08988wzmjVrlqPNihUr9Mwzzyg2NlZz585Vx44dNXr0aH333XdOfY0dO1br16/Xv/71L7344ovauXOnHnjgAZ08efISXxUAAAAA4FKr4+4CTvf666/Lz8/P8bhr1646dOiQ3nrrLT388MOyWq2aMWOG+vfvr7Fjx0qSunTpoh07dmjWrFmaO3euJGnTpk1at26dUlNT1aNHD0lScHCw4uLi9OmnnyouLu6SXxsAAAAA4NKpUSO7pwfdcm3atNGRI0dUUlKi/Px8/fLLL4qNjXVqExcXpw0bNqi0tFSStHbtWvn4+Kh79+6ONiEhIWrTpo3Wrl17cS8CAAAAAOB2NSrsVuabb75RQECAGjRooLy8PEmnRmlP16pVK5WVlSk/P1+SlJeXp+DgYFksFqd2ISEhjj4AAAAAAOZVo6Yx/9n//vc/ZWRkaPz48ZKkoqIiSZKPj49Tu/LH5eeLi4vVsGHDCv35+vrqhx9+uKCaDMNQSUnJBfUBoHLHjx93/M3nGQCgNuJ7GXDxGYZRYWCzMjU27B44cECPPfaYYmJiNHz4cHeX41BWVqZt27a5uwzAlPbt2ydJ2rlzp06cOOHmagAAOH98LwMuDS8vr3O2qZFht7i4WA888IAaNWqkV199VVbrqdnWvr6+kk5tK9S4cWOn9qef9/Hx0YEDByr0W1RU5GjjKk9PT7Vu3fqC+gBQubp160o6datCSEiIm6sBAOD88b0MuPhyc3Or1K7Ghd3jx49r5MiROnz4sN5//32n6cjlXzDy8vKcvnjk5eXJ09NTLVq0cLTbsGFDheHtnTt3KjQ09ILqs1gsqlev3gX1AaByV1xxheNvPs8AALUR38uAi68qU5ilGrZA1cmTJzV27Fjl5eXpzTffVEBAgNP5Fi1aKCgoSJmZmU7HMzIy1LVrV8dQds+ePVVUVKQNGzY42uzcuVM5OTnq2bPnxb8QAAAAAIBb1aiR3WeffVZffPGFkpKSdOTIEX333XeOcxEREfLy8tKYMWP0xBNPKDAwUDExMcrIyNDmzZu1YMECR9uoqCj16NFDEyZM0Pjx41W3bl1Nnz5dYWFh6tu3rxuuDAAAAABwKdWosLt+/XpJUkpKSoVzq1atUvPmzTVgwAAdO3ZMc+fO1Zw5cxQcHKyZM2cqKirKqf0rr7yiqVOnauLEiTp58qR69Oihp59+WnXq1KhLBgAAAABcBDUq+a1evbpK7eLj4xUfH3/WNg0bNtSUKVM0ZcqU6igNAAAAAFCL1KiwCwAAcLqCggLHrgtAbZCfn+/0N1Bb+Pj4yN/f391lVCvCLgAAqJEKCgo06qGHVFZa6u5SgPP28ssvu7sE4Lx4ennpjddfN1XgJewCAIAaqbi4WGWlpbqiaRdZvXzcXQ4AmJa9tFjH921UcXExYRcAAOBSsXr5yMPbz91lAABqmRq1zy4AAAAAANWBsAsAAAAAMB3CLgAAAADAdAi7AAAAAADTIewCAAAAAEyHsAsAAAAAMB3CLgAAAADAdAi7AAAAAADTIewCAAAAAEyHsAsAAAAAMB3CLgAAAADAdOq4uwBcPAUFBSouLnZ3GUCV5efnO/0N1BY+Pj7y9/d3dxkAAOA0hF2TKigo0KiHHlJZaam7SwHO28svv+zuEoDz4unlpTdef53ACwBADULYNani4mKVlZbqiqZdZPXycXc5AGBa9tJiHd+3UcXFxYRdAABqEMKuyVm9fOTh7efuMgAAAADgkmKBKgAAAACA6RB2AQAAAACmQ9gFAAAAAJgOYRcAAAAAYDqEXQAAAACA6RB2AQAAAACmQ9gFAAAAAJgOYRcAAAAAYDqEXQAAAACA6RB2AQAAAACmQ9gFAAAAAJgOYRcAAAAAYDqEXQAAAACA6RB2AQAAAACmU8fdBQAAAJyN/USxu0sAAFMz69dZwi4AAKjRju/f6O4SAAC1EGEXAADUaFdc00XWuj7uLgMATMt+otiUv1gk7AIAgBrNWtdHHt5+7i4DAFDLsEAVAAAAAMB0CLsAAAAAANNhGrPJmXVlNQCoKfg6CwBAzUTYNTkz3mgOAAAAAOdC2DU5VrAEgIvLrCtYAgBQ2xF2TY4VLAEAAABcjligCgAAAABgOoRdAAAAAIDpEHYBAAAAAKZD2AUAAAAAmA5hFwAAAABgOoRdAAAAAIDpEHYBAAAAAKZD2AUAAAAAmE6NCru7du3SxIkTNXjwYEVERGjAgAGVtktLS1O/fv0UGRmpQYMG6YsvvqjQ5vDhw5owYYI6d+6sqKgoPfrooyooKLjYlwAAAAAAqAHquLuA0/30009as2aNOnToILvdLsMwKrRZsWKFnnnmGY0aNUpdunRRRkaGRo8erYULF6pjx46OdmPHjlVubq7+9a9/qW7dunrllVf0wAMPaNmyZapTp0ZdNgAAOAt7abG7SwAAUzPr19kalfp69+6tPn36SJKSkpL0ww8/VGgzY8YM9e/fX2PHjpUkdenSRTt27NCsWbM0d+5cSdKmTZu0bt06paamqkePHpKk4OBgxcXF6dNPP1VcXNyluSAAAOAyHx8feXp56fi+je4uBQBMz9PLSz4+Pu4uo1rVqLBrtZ59VnV+fr5++eUXjRs3zul4XFycXnjhBZWWlsrLy0tr166Vj4+Punfv7mgTEhKiNm3aaO3atYRdAABqAX9/f73x+usqLjbniAPMKT8/Xy+//LIef/xxtWjRwt3lAFXm4+Mjf39/d5dRrWpU2D2XvLw8SadGaU/XqlUrlZWVKT8/X61atVJeXp6Cg4NlsVic2oWEhDj6AAAANZ+/v7/pfvjC5aFFixZq3bq1u8sALmu1KuwWFRVJUoXh9fLH5eeLi4vVsGHDCs/39fWtdGr0+TAMQyUlJRfUx6Vw/PhxSeadfw8ANUX519njx4/Xiu8PAC6u8p/B+JoAXDyGYVQY2KxMrQq7NUFZWZm2bdvm7jLO6dChQ6pTx5P7nADgEqhTx1MHDx7UiRMn3F0KADfbt2+fJGnnzp18TQAuIi8vr3O2qVVh19fXV9KpbYUaN27sOF5+L0/5eR8fHx04cKDC84uKihxtXOXp6VlrpqRce+213OeEWmXv3r2aOXOmRo8erWbNmrm7HKDKfHx8dPXVV7u7DAA1QN26dSWduu0uJCTEzdUA5pSbm1uldrUq7JZ/wcjLy3P64pGXlydPT0/HIgAhISHasGFDheHtnTt3KjQ09IJqsFgsqlev3gX1cakEBga6uwTgvFxxxRWSTt2HX1t+qQQAwOnKv5ddccUVteZnRqC2qcoUZkk6+/LHNUyLFi0UFBSkzMxMp+MZGRnq2rWrYyi7Z8+eKioq0oYNGxxtdu7cqZycHPXs2fOS1gwAAAAAuPRq1MjusWPHtGbNGkmnpjMeOXLEEWw7d+4sPz8/jRkzRk888YQCAwMVExOjjIwMbd68WQsWLHD0ExUVpR49emjChAkaP3686tatq+nTpyssLEx9+/Z1y7UBAAAAAC6dGhV2f//9d/397393Olb+eP78+YqJidGAAQN07NgxzZ07V3PmzFFwcLBmzpypqKgop+e98sormjp1qiZOnKiTJ0+qR48eevrpp1WnTo26ZAAAAADARVCjkl/z5s31448/nrNdfHy84uPjz9qmYcOGmjJliqZMmVJd5QEAAAAAaoladc8uAAAAAABVQdgFAAAAAJgOYRcAAAAAYDqEXQAAAACA6RB2AQAAAACmQ9gFAAAAAJgOYRcAAAAAYDqEXQAAAACA6RB2AQAAAACmQ9gFAAAAAJgOYRcAAAAAYDqEXQAAAACA6RB2AQAAAACmQ9gFAAAAAJgOYRcAAAAAYDqEXQAAAACA6RB2AQAAAACmQ9gFAAAAAJgOYRcAAAAAYDqEXQAAAACA6RB2AQAAAACmQ9gFAAAAAJgOYRcAAAAAYDqEXQAAAACA6RB2AQAAAACmQ9gFAAAAAJgOYRcAAAAAYDqEXQAAAACA6RB2AQAAAACmQ9gFAAAAAJgOYRcAAAAAYDqEXQAAAACA6RB2AQAAAACmQ9gFAAAAAJgOYRcAAAAAYDqEXQAAAACA6RB2AQAAAACmQ9gFAAAAAJgOYRcAAAAAYDqEXQAAAACA6dRxdwEAAAC49A4cOKAjR464uwzTyc/Pd/ob1atBgwZq0qSJu8tALUHYBQAAuMwUFRVp5MiRstvt7i7FtF5++WV3l2BKVqtV8+fPl6+vr7tLQS1A2AUAALjM+Pr6avbs2YzsotZp0KABQRdVRtgFAAC4DDEVFIDZsUAVAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANNh6yHABQcOHGBvwosgPz/f6W9UrwYNGrDVCAAAuGyYOuz+/PPPmjRpkjZt2qT69etr8ODBGjt2rLy8vNxdGmqxoqIijRw5Una73d2lmNbLL7/s7hJMyWq1av78+fL19XV3KQAAABedacNuUVGR7rnnHgUFBenVV1/VwYMHlZKSouPHj2vixInuLg+1mK+vr2bPns3ILmqdBg0aEHQBAMBlw7Rhd/HixTp69KhmzpypRo0aSZJsNpueffZZjRw5UgEBAe4tELUaU0EBAACAms20C1StXbtWXbt2dQRdSYqNjZXdbtf69evdVxgAAAAA4KIzbdjNy8tTSEiI0zEfHx81btxYeXl5bqoKAAAAAHApmHYac3FxsXx8fCoc9/X1VVFRkcv9GoahkpKSCykNAAAAAOAiwzBksVjO2c60YfdiKSsr07Zt29xdBgAAAABctqqyw45pw66Pj48OHz5c4XhRUdEFrUbq6emp1q1bX0hpAAAAAAAX5ebmVqmdacNuSEhIhXtzDx8+rF9//bXCvbznw2KxqF69ehdaHgAAAADABVWZwiyZeIGqnj17KisrS8XFxY5jmZmZslqt6t69uxsrAwAAAABcbKYNu8OGDVP9+vX1yCOPaN26dVq2bJleeOEFDRs2jD12AQAAAMDkTBt2fX199c4778jDw0OPPPKIXnrpJQ0ZMkRJSUnuLg0AAAAAcJGZ9p5dSWrVqpXefvttd5cBAAAAALjETDuyCwAAAAC4fBF2AQAAAACmQ9gFAAAAAJgOYRcAAAAAYDqEXQAAAACA6RB2AQAAAACmQ9gFAAAAAJgOYRcAAAAAYDp13F1AbVJWVibDMLRlyxZ3lwIAAAAAl6XS0lJZLJZztiPsnoeqvKEAAAAAgIvHYrFUKZtZDMMwLkE9AAAAAABcMtyzCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewC8Dtfv75Z913333q2LGjunfvrhdeeEGlpaXuLgsAgCrbtWuXJk6cqMGDBysiIkIDBgxwd0nAZa+OuwsAcHkrKirSPffco6CgIL366qs6ePCgUlJSdPz4cU2cONHd5QEAUCU//fST1qxZow4dOshut8swDHeXBFz2CLsA3Grx4sU6evSoZs6cqUaNGkmSbDabnn32WY0cOVIBAQHuLRAAgCro3bu3+vTpI0lKSkrSDz/84OaKADCNGYBbrV27Vl27dnUEXUmKjY2V3W7X+vXr3VcYAADnwWrlx2qgpuGzEoBb5eXlKSQkxOmYj4+PGjdurLy8PDdVBQAAgNqOsAvArYqLi+Xj41PhuK+vr4qKitxQEQAAAMyAsAsAAAAAMB3CLgC38vHx0eHDhyscLyoqkq+vrxsqAgAAgBkQdgG4VUhISIV7cw8fPqxff/21wr28AAAAQFURdgG4Vc+ePZWVlaXi4mLHsczMTFmtVnXv3t2NlQEAAKA2Y59dAG41bNgwvfvuu3rkkUc0cuRIHTx4UC+88IKGDRvGHrsAgFrj2LFjWrNmjSRp7969OnLkiDIzMyVJnTt3lp+fnzvLAy5LFsMwDHcXAeDy9vPPP+vf//63Nm3apPr162vw4MF67LHH5OXl5e7SAACokj179uimm26q9Nz8+fMVExNziSsCQNgFAAAAAJgO9+wCAAAAAEyHsAsAAAAAMB3CLgAAAADAdAi7AAAAAADTIewCAAAAAEyHsAsAAAAAMB3CLgAAAADAdAi7AABcJElJSerdu7fTsaNHjyo5OVndu3dXWFiYJk+e7KbqXJOQkKCEhAR3l3FWYWFhevXVV91dBgDAzeq4uwAAAGqKH3/8UbNmzdKWLVv022+/qVGjRmrdurV69+5dbQFv9uzZ+s9//qOHH35YLVq0UKtWrc7Ytnfv3tq7d2+l53r06KHU1NRqqQkAADMi7AIAIOnbb7/V8OHD1bRpU8XHx6tx48bav3+/vv/+e82fP7/awu7GjRvVoUMHjR49ukrt27Rpo/vuu6/CcX9//2qpBwAAsyLsAgAg6Y033lDDhg21dOlS+fj4OJ37/fffq+11fv/9d7Vu3brK7QMCAjR48OBqe30AAC4X3LMLAICk3bt3q3Xr1hWCriRdddVVFY599NFH+tvf/qb27durc+fOeuyxx7R///4z9v/VV18pLCxMe/bs0X//+1+FhYU5Hl+opKQkRUVFad++fRo5cqSioqJ0ww03aOHChZJOTc8ePny4OnbsqF69eik9Pd3p+R988IHCwsKUnZ2tiRMnKiYmRp06ddKTTz6poqKic77+77//rgkTJqhbt26KjIzUoEGD9J///Mdx3jAM9e7dWw899FCF5544cULXXXedJk6c6DhWWlqqGTNm6Oabb1a7du1044036oUXXlBpaanTc0tLSzVlyhR16dJFUVFRGjVqlA4cOHBe7x0AwLwY2QUAQFKzZs20adMm7dixQ6GhoWdt+/rrr+v//b//p9jYWA0ZMkSFhYVasGCB7rrrLn344YeVBuZWrVrphRde0NSpU9WkSRPH1GQ/P7+zvtbJkydVWFhY4Xi9evV0xRVXOB7bbDY98MADuv766/XEE08oPT1dzz33nLy9vTV9+nQNHDhQffv21eLFizV+/Hh17NhRLVq0cOrzueeek4+Pj0aPHq2dO3fqvffe0759+/Tuu+/KYrFUWt/x48eVkJCg3bt366677lLz5s2VmZmppKQkFRcX65577pHFYtHAgQOVmpqqQ4cOqVGjRo7nr169WkeOHNGgQYMkSXa7XQ899JC++eYb3X777WrVqpV27Nihd955R7/88otee+01x3OTk5O1fPlyDRgwQJ06ddLGjRv14IMPnvX9BABcRgwAAGCsW7fOaNOmjdGmTRtj6NChxgsvvGB8+eWXRmlpqVO7PXv2GG3atDFef/11p+M//vijERER4XR8/PjxRq9evZza9erVy3jwwQerVFOvXr2M0NDQSv/Mnj3b6XVCQ0ONN954w3GsqKjIaN++vREWFmasWLHCcfznn382QkNDjRkzZjiOLVu2zAgNDTX++te/Ol3v3LlzjdDQUOPzzz93HLv77ruNu+++2/H47bffNkJDQ42PPvrIcay0tNQYOnSo0bFjR+Pw4cOGYRhGXl6eERoaaixatMjpGkeNGmX06tXLsNvthmEYxocffmiEh4cb2dnZTu3ee+89IzQ01Pjmm28MwzCMbdu2GaGhoca//vUvp3aPP/54hesDAFyemMYMAICk7t27a/Hixerdu7e2b9+uN998U4mJierZs6dWrVrlaPfZZ5/JbrcrNjZWhYWFjj9XX321WrZsqa+++qpa6+rQoYPeeuutCn/69+9foW18fLzj3z4+PgoODpa3t7diY2Mdx0NCQuTj46P8/PwKzx86dKg8PT0dj++44w7VqVNHa9asOWN9a9euVePGjTVgwADHMU9PTyUkJKikpETZ2dmSpODgYHXo0MFpCvWhQ4f05ZdfauDAgY6R48zMTLVq1UohISFO72+XLl0kyfH+ltf054XD7rnnnjPWCgC4vDCNGQCA/1/79u01c+ZMlZaWavv27fr888/19ttv6+9//7s+/PBDtW7dWr/88osMw1Dfvn0r7aNOner91nrllVeqW7du52xXt27dClOiGzZsqCZNmlSYgtywYUMVFxdX6KNly5ZOj+vXr6/GjRufcfsjSdq7d69atmwpq9X59+flWyrt27fPcWzw4MH697//rb1796pZs2bKzMxUWVmZ0wJcu3bt0s8//6yuXbtW+nrli4Xt3btXVqtVgYGBTudDQkLOWCsA4PJC2AUA4E+8vLzUvn17tW/fXkFBQXrqqaeUmZmp0aNHy263y2KxaO7cufLw8Kjw3Hr16rmhYlVay9mOG4ZxMcupVP/+/TV16lSlp6dr1KhRWr58udq1a+cUUO12u0JDQ/XUU09V2keTJk0uVbkAgFqOsAsAwFm0a9dOklRQUCBJCgwMlGEYat68uYKDg91ZWrXbtWuXY7qwJB09elS//vqrevbsecbnNGvWTD/++KPsdrvT6G5eXp4kqWnTpo5jjRo10l/+8help6dr4MCB+vbbbzVhwgSn/gIDA7V9+3Z17dr1jItilb+u3W7X7t27ncJy+esCAMA9uwAASNq4cWOlo53l94aWB6q+ffvKw8NDM2fOrNDeMAz98ccfF7/Yi+T9999XWVmZ4/F7772nkydPnjXs9uzZU7/++qsyMjIcx06ePKl3331X9erVU3R0tFP7wYMHKzc3Vy+88II8PDwq3HscGxurgwcPasmSJRVe6/jx4yopKXG8riS9++67Tm3eeeedKl4tAMDsGNkFAEDSpEmTdOzYMd18880KCQlRWVmZvv32W61cuVLNmjXT3/72N0mnRh7Hjh2rl156SXv37lWfPn1Uv3597dmzR59//rluv/12JSYmVltdBw8e1EcffVTheP369dWnT59qex1JKisr07333qvY2Fjt3LlTixYt0nXXXaebbrrpjM8ZOnSo3n//fSUlJWnr1q1q1qyZPvnkE8eobYMGDZza33jjjWrUqJEyMzPVs2fPCnsYDx48WCtXrtQ///lPffXVV+rUqZNsNpvy8vKUmZmpN998U5GRkWrTpo0GDBigRYsW6fDhw4qKitLGjRu1a9euan1PAAC1F2EXAABJTz75pDIzM7VmzRrHCGfTpk1155136qGHHnLaO/fBBx9UUFCQ3n77bc2aNUvSqXtJu3fvrt69e1drXdu2bdOTTz5Z4XizZs2qPexOnDhR6enpmjFjhsrKytS/f389/fTTZ51OfMUVV+jdd9/Viy++qP/85z86cuSIgoODNXXqVMcvCE7n5eWluLg4LVq0yGlhqnJWq1WzZs3S22+/rY8++kifffaZvL291bx5cyUkJDhNHZ8yZYquvPJKpaena9WqVYqJidGcOXN04403Vs8bAgCo1SyGO1aoAAAANcYHH3ygp556SkuXLlVkZORFf70pU6Zo6dKlWr9+vby9vS/66wEALk/cswsAAC6ZEydOaPny5erXrx9BFwBwUTGNGQAAXHS///67srKy9Mknn+jQoUMaPny4u0sCAJgcYRcAAFx0ubm5euKJJ3TVVVfp6aefVps2bdxdEgDA5LhnFwAAAABgOtyzCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwHcIuAAAAAMB0CLsAAAAAANMh7AIAAAAATIewCwAAAAAwnf8Pb0LISuEQs5UAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Countplot for Loan Status by Gender\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(x='Gender', hue='Loan_Status', data=data)\n",
        "plt.title('Loan Status by Gender')\n",
        "plt.xlabel('Gender')\n",
        "plt.ylabel('Count')\n",
        "plt.legend(title='Loan Status')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "sX9mAzlzx9tx",
        "outputId": "36dab7f3-da42-4d47-cb45-dc8304c52c60"
      },
      "execution_count": null,
      "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": [
        "# 6. Countplot for Loan Status by Employment Status\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(x='Self_Employed', hue='Loan_Status', data=data)\n",
        "plt.title('Loan Status by Employment Status')\n",
        "plt.xlabel('Self Employed')\n",
        "plt.ylabel('Count')\n",
        "plt.legend(title='Loan Status')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "35tMby78yVc-",
        "outputId": "b378535b-7fb5-41b3-f89c-dffe24465252"
      },
      "execution_count": null,
      "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": [
        "# 5. Countplot for Loan Status by Marital Status\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(x='Married', hue='Loan_Status', data=data)\n",
        "plt.title('Loan Status by Marital Status')\n",
        "plt.xlabel('Marital Status')\n",
        "plt.ylabel('Count')\n",
        "plt.legend(title='Loan Status')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "_UgHjmFTyOSr",
        "outputId": "c956e550-3c6e-4a86-89af-4fcdd80603f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Engineering"
      ],
      "metadata": {
        "id": "A43UK0oWK9xb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
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        "id": "Vh3eJE-NLC1N",
        "outputId": "c3d17b49-a9e5-4118-90ac-66aa98da5808"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Gender Married Dependents     Education Self_Employed  ApplicantIncome  \\\n",
              "1   Male     Yes          1      Graduate            No             4583   \n",
              "2   Male     Yes          0      Graduate           Yes             3000   \n",
              "3   Male     Yes          0  Not Graduate            No             2583   \n",
              "4   Male      No          0      Graduate            No             6000   \n",
              "5   Male     Yes          2      Graduate           Yes             5417   \n",
              "\n",
              "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              "1             1508.0       128.0             360.0             1.0   \n",
              "2                0.0        66.0             360.0             1.0   \n",
              "3             2358.0       120.0             360.0             1.0   \n",
              "4                0.0       141.0             360.0             1.0   \n",
              "5             4196.0       267.0             360.0             1.0   \n",
              "\n",
              "  Property_Area Loan_Status  \n",
              "1         Rural           N  \n",
              "2         Urban           Y  \n",
              "3         Urban           Y  \n",
              "4         Urban           Y  \n",
              "5         Urban           Y  "
            ],
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              "      <th>Gender</th>\n",
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              "      <th>Loan_Status</th>\n",
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              "      <th>1</th>\n",
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              "      <td>Graduate</td>\n",
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              "      <td>128.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
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              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
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              "      <td>2358.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
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              "      <td>Graduate</td>\n",
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              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5417</td>\n",
              "      <td>4196.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "          '<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",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "</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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 566,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"0\",\n          \"3+\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Graduate\",\n          \"Graduate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6290,\n        \"min\": 150,\n        \"max\": 81000,\n        \"num_unique_values\": 464,\n        \"samples\": [\n          3200,\n          4692\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3018.8284945989312,\n        \"min\": 0.0,\n        \"max\": 41667.0,\n        \"num_unique_values\": 269,\n        \"samples\": [\n          1644.0,\n          4417.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 87.10763696225924,\n        \"min\": 9.0,\n        \"max\": 700.0,\n        \"num_unique_values\": 203,\n        \"samples\": [\n          100.0,\n          70.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.22661475979643,\n        \"min\": 12.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          84.0,\n          120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3374162323818029,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Rural\",\n          \"Urban\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Y\",\n          \"N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# map each of the gender groups to a numerical value\n",
        "gender_mapping = {\"Male\": 0, \"Female\": 1}\n",
        "data['Gender'] = data['Gender'].map(gender_mapping)\n"
      ],
      "metadata": {
        "id": "oAYkCEhhLFif"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Married'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FSILjEqLO6_",
        "outputId": "735f2a05-525e-4452-ad72-3031bd814c66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Yes', 'No'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# map each of the married groups to a numerical value\n",
        "married_mapping = {\"No\": 0, \"Yes\": 1}\n",
        "data['Married'] = data['Married'].map(married_mapping)"
      ],
      "metadata": {
        "id": "JKv6XCtjOR25"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Dependents'] = data['Dependents'].replace({'0': 0, '1': 1, '2': 2, '3+': 3})"
      ],
      "metadata": {
        "id": "_vKm5eSkO8y0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Education'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qhwxTcylPOD3",
        "outputId": "0d966cd5-3088-41c4-c201-fe0c2ab1ddc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Graduate', 'Not Graduate'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Education']=data['Education'].replace({'Graduate':1, 'Not Graduate':0})"
      ],
      "metadata": {
        "id": "Ak1bpO-_PfN2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Self_Employed'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "le7RxYrQP3As",
        "outputId": "b2bc5b31-418c-4157-adef-378f225db907"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No', 'Yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Self_Employed']=data['Self_Employed'].replace({'No':0, 'Yes':1})"
      ],
      "metadata": {
        "id": "O8onqEHyQB0Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Loan_Amount_Term'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCyhKG27QTIo",
        "outputId": "f4ef6f95-4087-4191-daec-9b2a08d1076c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([360., 120., 240., 180.,  60., 300., 480.,  36.,  84.,  12.])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Loan_Amount_Term']=data['Loan_Amount_Term'].replace({'12':0, '36':1,'60':2,'84':3,'120':4,'180':5,'240':6,'300':7,'360':8,'480':9})"
      ],
      "metadata": {
        "id": "wZIcfGRyS_Bq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Property_Area'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3_hb5abgdMSy",
        "outputId": "9d6ae265-6e1b-4ba3-d934-4368eea8f5ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Rural', 'Urban', 'Semiurban'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Property_Area'] = data['Property_Area'].replace({'Rural': 0, 'Urban': 1, 'Semiurban': 2})"
      ],
      "metadata": {
        "id": "iD0njMQgdXXD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Loan_Status'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zoJarQC2dtih",
        "outputId": "0b935a97-58aa-4bae-d6c4-d38da47fbb9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['N', 'Y'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Loan_Status']=data['Loan_Status'].replace({'N':0, 'Y':1})"
      ],
      "metadata": {
        "id": "8jQUxEu4d0qO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
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          "height": 423
        },
        "id": "JbWgVwJRd8fH",
        "outputId": "dfe59ffd-6325-48e2-92bd-93bc14d2bd10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
              "1         0        1           1          1              0             4583   \n",
              "2         0        1           0          1              1             3000   \n",
              "3         0        1           0          0              0             2583   \n",
              "4         0        0           0          1              0             6000   \n",
              "5         0        1           2          1              1             5417   \n",
              "..      ...      ...         ...        ...            ...              ...   \n",
              "609       1        0           0          1              0             2900   \n",
              "610       0        1           3          1              0             4106   \n",
              "611       0        1           1          1              0             8072   \n",
              "612       0        1           2          1              0             7583   \n",
              "613       1        0           0          1              1             4583   \n",
              "\n",
              "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              "1               1508.0       128.0             360.0             1.0   \n",
              "2                  0.0        66.0             360.0             1.0   \n",
              "3               2358.0       120.0             360.0             1.0   \n",
              "4                  0.0       141.0             360.0             1.0   \n",
              "5               4196.0       267.0             360.0             1.0   \n",
              "..                 ...         ...               ...             ...   \n",
              "609                0.0        71.0             360.0             1.0   \n",
              "610                0.0        40.0             180.0             1.0   \n",
              "611              240.0       253.0             360.0             1.0   \n",
              "612                0.0       187.0             360.0             1.0   \n",
              "613                0.0       133.0             360.0             0.0   \n",
              "\n",
              "     Property_Area  Loan_Status  \n",
              "1                0            0  \n",
              "2                1            1  \n",
              "3                1            1  \n",
              "4                1            1  \n",
              "5                1            1  \n",
              "..             ...          ...  \n",
              "609              0            1  \n",
              "610              0            1  \n",
              "611              1            1  \n",
              "612              1            1  \n",
              "613              2            0  \n",
              "\n",
              "[566 rows x 12 columns]"
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              "<p>566 rows × 12 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 566,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6290,\n        \"min\": 150,\n        \"max\": 81000,\n        \"num_unique_values\": 464,\n        \"samples\": [\n          3200,\n          4692\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3018.8284945989312,\n        \"min\": 0.0,\n        \"max\": 41667.0,\n        \"num_unique_values\": 269,\n        \"samples\": [\n          1644.0,\n          4417.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 87.10763696225924,\n        \"min\": 9.0,\n        \"max\": 700.0,\n        \"num_unique_values\": 203,\n        \"samples\": [\n          100.0,\n          70.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.22661475979643,\n        \"min\": 12.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          84.0,\n          120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3374162323818029,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Training"
      ],
      "metadata": {
        "id": "gRKMBkH0e46f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Drop the Survived and PassengerId\n",
        "# column from the trainset\n",
        "predictors = data.drop(['Loan_Status'], axis=1)\n",
        "target = data[\"Loan_Status\"]\n",
        "x_train, x_val, y_train, y_val = train_test_split(\n",
        "    predictors, target, test_size=0.2, random_state=0)\n"
      ],
      "metadata": {
        "id": "2K5KekIoeEcQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Forest"
      ],
      "metadata": {
        "id": "Q21Bm7SYfBVb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "randomforest = RandomForestClassifier()\n",
        "\n",
        "# Fit the training data along with its output\n",
        "randomforest.fit(x_train, y_train)\n",
        "y_pred = randomforest.predict(x_val)\n",
        "\n",
        "# Find the accuracy score of the model\n",
        "acc_randomforest = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
        "print(acc_randomforest)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8_g3kTqNes4r",
        "outputId": "898c9a98-9bb6-461f-bc4f-991e10dc45dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "78.95\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Logistic Regression"
      ],
      "metadata": {
        "id": "dHAIsXxixZxB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "\n",
        "# Initialize the Logistic Regression model\n",
        "logreg = LogisticRegression(max_iter=1000)\n",
        "\n",
        "# Fit the model on the training data\n",
        "logreg.fit(x_train, y_train)\n",
        "\n",
        "# Predict on the validation set\n",
        "y_pred = logreg.predict(x_val)\n",
        "\n",
        "# Evaluate the model\n",
        "# 1. Accuracy score\n",
        "accuracy = accuracy_score(y_val, y_pred)\n",
        "print(f'Accuracy: {accuracy:.4f}')\n",
        "\n",
        "# 2. Confusion matrix\n",
        "conf_matrix = confusion_matrix(y_val, y_pred)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n"
      ],
      "metadata": {
        "id": "2mb7zS-HexxD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0dabe045-7ef7-4b3e-9f6a-de4ea6494c70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8509\n",
            "Confusion Matrix:\n",
            "[[15 16]\n",
            " [ 1 82]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Decision Tree"
      ],
      "metadata": {
        "id": "AK6eL7Tsxdit"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "\n",
        "# Initialize the Decision Tree model\n",
        "decision_tree = DecisionTreeClassifier(random_state=0)\n",
        "\n",
        "# Fit the model on the training data\n",
        "decision_tree.fit(x_train, y_train)\n",
        "\n",
        "# Predict on the validation set\n",
        "y_pred = decision_tree.predict(x_val)\n",
        "\n",
        "# Evaluate the model\n",
        "# 1. Accuracy score\n",
        "accuracy = accuracy_score(y_val, y_pred)\n",
        "print(f'Accuracy: {accuracy:.4f}')\n",
        "\n",
        "# 2. Confusion matrix\n",
        "conf_matrix = confusion_matrix(y_val, y_pred)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "# 3. Classification report\n",
        "class_report = classification_report(y_val, y_pred)\n",
        "print(\"Classification Report:\")\n",
        "print(class_report)\n"
      ],
      "metadata": {
        "id": "r7AdvGafvhxK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6bf951ee-de0a-4e10-b10f-e06c7acf28c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6404\n",
            "Confusion Matrix:\n",
            "[[15 16]\n",
            " [25 58]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.38      0.48      0.42        31\n",
            "           1       0.78      0.70      0.74        83\n",
            "\n",
            "    accuracy                           0.64       114\n",
            "   macro avg       0.58      0.59      0.58       114\n",
            "weighted avg       0.67      0.64      0.65       114\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tm9MM523wkgj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}