predicting_default_risk.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NyGkolWDEsJe"
   },
   "source": [
    "## Predict Default or Non-default\n",
    "\n",
    "### Overview \n",
    "\n",
    "In this project, I will be using Logistic Regression, Tree Based models, and Ensemble models to predict whether a borrower will default based only on economic/financial capability features. These may or may not include amount income, amount credit, etc.\n",
    "\n",
    "### Data\n",
    "I will be using the [Home Credit dataset available on Kaggle](https://www.kaggle.com/c/home-credit-default-risk) in this exercise. [Home Credit](http://www.homecredit.net/about-us.aspx) is a financing company dedicated to providing loans to those with little or no credit history. \n",
    "\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AW3tG51QEsJo"
   },
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0yYDwoccEsKA"
   },
   "outputs": [],
   "source": [
    "# data processing and visualization \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import linear_model as lm, metrics, model_selection as ms\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "reg = LogisticRegression(solver = 'lbfgs')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('always')  # \"error\", \"ignore\", \"always\", \"default\", \"module\" or \"once\"\n",
    "\n",
    "from warnings import simplefilter\n",
    "simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "pd.options.mode.chained_assignment = None \n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option('display.max_columns', None)  \n",
    "pd.set_option('display.expand_frame_repr', False)\n",
    "pd.set_option('max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "92N5aFUIJRyg",
    "outputId": "30d26cf9-606c-4fdc-b5e1-c1772c2a3859"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "application_test.csv.zip     HomeCredit_columns_description.csv\n",
      "application_train.csv\t     installments_payments.csv.zip\n",
      "application_train.csv.zip    POS_CASH_balance.csv.zip\n",
      "bureau_balance.csv.zip\t     previous_application.csv.zip\n",
      "bureau.csv.zip\t\t     sample_data\n",
      "credit_card_balance.csv.zip  sample_submission.csv.zip\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "Fpa1AGmVJVFa",
    "outputId": "4267603d-652c-497d-c8d3-c531235f36da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  application_train.csv.zip\n",
      "replace application_train.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n"
     ]
    }
   ],
   "source": [
    "!unzip application_train.csv.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wUaJ7B4oJd5p"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('application_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "rb6R1i4uJ2VS",
    "outputId": "82ce6c30-9cd5-46aa-de37-a5e59e7d1716"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 307511 entries, 0 to 307510\n",
      "Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR\n",
      "dtypes: float64(65), int64(41), object(16)\n",
      "memory usage: 286.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 292
    },
    "colab_type": "code",
    "id": "7EuRvGIBJ0RD",
    "outputId": "403283b1-fad6-43d6-874c-9273ae98c61a"
   },
   "outputs": [
    {
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       "      <th>ELEVATORS_MODE</th>\n",
       "      <th>ENTRANCES_MODE</th>\n",
       "      <th>FLOORSMAX_MODE</th>\n",
       "      <th>FLOORSMIN_MODE</th>\n",
       "      <th>LANDAREA_MODE</th>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <th>LIVINGAREA_MODE</th>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <th>APARTMENTS_MEDI</th>\n",
       "      <th>BASEMENTAREA_MEDI</th>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
       "      <th>YEARS_BUILD_MEDI</th>\n",
       "      <th>COMMONAREA_MEDI</th>\n",
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       "      <th>ENTRANCES_MEDI</th>\n",
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       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <th>FONDKAPREMONT_MODE</th>\n",
       "      <th>HOUSETYPE_MODE</th>\n",
       "      <th>TOTALAREA_MODE</th>\n",
       "      <th>WALLSMATERIAL_MODE</th>\n",
       "      <th>EMERGENCYSTATE_MODE</th>\n",
       "      <th>OBS_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
       "      <th>FLAG_DOCUMENT_2</th>\n",
       "      <th>FLAG_DOCUMENT_3</th>\n",
       "      <th>FLAG_DOCUMENT_4</th>\n",
       "      <th>FLAG_DOCUMENT_5</th>\n",
       "      <th>FLAG_DOCUMENT_6</th>\n",
       "      <th>FLAG_DOCUMENT_7</th>\n",
       "      <th>FLAG_DOCUMENT_8</th>\n",
       "      <th>FLAG_DOCUMENT_9</th>\n",
       "      <th>FLAG_DOCUMENT_10</th>\n",
       "      <th>FLAG_DOCUMENT_11</th>\n",
       "      <th>FLAG_DOCUMENT_12</th>\n",
       "      <th>FLAG_DOCUMENT_13</th>\n",
       "      <th>FLAG_DOCUMENT_14</th>\n",
       "      <th>FLAG_DOCUMENT_15</th>\n",
       "      <th>FLAG_DOCUMENT_16</th>\n",
       "      <th>FLAG_DOCUMENT_17</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
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       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
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       "      <td>Unaccompanied</td>\n",
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       "      <td>Single / not married</td>\n",
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       "      <td>Laborers</td>\n",
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       "      <td>Business Entity Type 3</td>\n",
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       "      <td>0.0247</td>\n",
       "      <td>0.0369</td>\n",
       "      <td>0.9722</td>\n",
       "      <td>0.6192</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.0377</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.0198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0250</td>\n",
       "      <td>0.0369</td>\n",
       "      <td>0.9722</td>\n",
       "      <td>0.6243</td>\n",
       "      <td>0.0144</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0690</td>\n",
       "      <td>0.0833</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.0375</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0193</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>reg oper account</td>\n",
       "      <td>block of flats</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>Stone, brick</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1134.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>1129500.0</td>\n",
       "      <td>Family</td>\n",
       "      <td>State servant</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>0.003541</td>\n",
       "      <td>-16765</td>\n",
       "      <td>-1188</td>\n",
       "      <td>-1186.0</td>\n",
       "      <td>-291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Core staff</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>MONDAY</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>School</td>\n",
       "      <td>0.311267</td>\n",
       "      <td>0.622246</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0959</td>\n",
       "      <td>0.0529</td>\n",
       "      <td>0.9851</td>\n",
       "      <td>0.7960</td>\n",
       "      <td>0.0605</td>\n",
       "      <td>0.08</td>\n",
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       "      <td>0.2917</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.0773</td>\n",
       "      <td>0.0549</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0924</td>\n",
       "      <td>0.0538</td>\n",
       "      <td>0.9851</td>\n",
       "      <td>0.8040</td>\n",
       "      <td>0.0497</td>\n",
       "      <td>0.0806</td>\n",
       "      <td>0.0345</td>\n",
       "      <td>0.2917</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.0554</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0968</td>\n",
       "      <td>0.0529</td>\n",
       "      <td>0.9851</td>\n",
       "      <td>0.7987</td>\n",
       "      <td>0.0608</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.0345</td>\n",
       "      <td>0.2917</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>0.0787</td>\n",
       "      <td>0.0558</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.01</td>\n",
       "      <td>reg oper account</td>\n",
       "      <td>block of flats</td>\n",
       "      <td>0.0714</td>\n",
       "      <td>Block</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-828.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>Unaccompanied</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>0.010032</td>\n",
       "      <td>-19046</td>\n",
       "      <td>-225</td>\n",
       "      <td>-4260.0</td>\n",
       "      <td>-2531</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>MONDAY</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Government</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.555912</td>\n",
       "      <td>0.729567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-815.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>297000.0</td>\n",
       "      <td>Unaccompanied</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>0.008019</td>\n",
       "      <td>-19005</td>\n",
       "      <td>-3039</td>\n",
       "      <td>-9833.0</td>\n",
       "      <td>-2437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>WEDNESDAY</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Business Entity Type 3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.650442</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-617.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>Unaccompanied</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>0.028663</td>\n",
       "      <td>-19932</td>\n",
       "      <td>-3038</td>\n",
       "      <td>-4311.0</td>\n",
       "      <td>-3458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Core staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>THURSDAY</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Religion</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.322738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1106.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE NAME_TYPE_SUITE NAME_INCOME_TYPE            NAME_EDUCATION_TYPE    NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  REGION_POPULATION_RELATIVE  DAYS_BIRTH  DAYS_EMPLOYED  DAYS_REGISTRATION  DAYS_ID_PUBLISH  OWN_CAR_AGE  FLAG_MOBIL  FLAG_EMP_PHONE  FLAG_WORK_PHONE  FLAG_CONT_MOBILE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  CNT_FAM_MEMBERS  REGION_RATING_CLIENT  REGION_RATING_CLIENT_W_CITY WEEKDAY_APPR_PROCESS_START  HOUR_APPR_PROCESS_START  REG_REGION_NOT_LIVE_REGION  REG_REGION_NOT_WORK_REGION  LIVE_REGION_NOT_WORK_REGION  REG_CITY_NOT_LIVE_CITY  REG_CITY_NOT_WORK_CITY  LIVE_CITY_NOT_WORK_CITY       ORGANIZATION_TYPE  EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  APARTMENTS_AVG  BASEMENTAREA_AVG  YEARS_BEGINEXPLUATATION_AVG  YEARS_BUILD_AVG  COMMONAREA_AVG  ELEVATORS_AVG  ENTRANCES_AVG  FLOORSMAX_AVG  FLOORSMIN_AVG  LANDAREA_AVG  LIVINGAPARTMENTS_AVG  LIVINGAREA_AVG  NONLIVINGAPARTMENTS_AVG  NONLIVINGAREA_AVG  APARTMENTS_MODE  BASEMENTAREA_MODE  YEARS_BEGINEXPLUATATION_MODE  YEARS_BUILD_MODE  COMMONAREA_MODE  ELEVATORS_MODE  ENTRANCES_MODE  FLOORSMAX_MODE  FLOORSMIN_MODE  LANDAREA_MODE  LIVINGAPARTMENTS_MODE  LIVINGAREA_MODE  NONLIVINGAPARTMENTS_MODE  NONLIVINGAREA_MODE  APARTMENTS_MEDI  BASEMENTAREA_MEDI  YEARS_BEGINEXPLUATATION_MEDI  YEARS_BUILD_MEDI  COMMONAREA_MEDI  ELEVATORS_MEDI  ENTRANCES_MEDI  FLOORSMAX_MEDI  FLOORSMIN_MEDI  LANDAREA_MEDI  LIVINGAPARTMENTS_MEDI  LIVINGAREA_MEDI  NONLIVINGAPARTMENTS_MEDI  NONLIVINGAREA_MEDI FONDKAPREMONT_MODE  HOUSETYPE_MODE  TOTALAREA_MODE WALLSMATERIAL_MODE EMERGENCYSTATE_MODE  OBS_30_CNT_SOCIAL_CIRCLE  DEF_30_CNT_SOCIAL_CIRCLE  OBS_60_CNT_SOCIAL_CIRCLE  DEF_60_CNT_SOCIAL_CIRCLE  DAYS_LAST_PHONE_CHANGE  FLAG_DOCUMENT_2  FLAG_DOCUMENT_3  FLAG_DOCUMENT_4  FLAG_DOCUMENT_5  FLAG_DOCUMENT_6  FLAG_DOCUMENT_7  FLAG_DOCUMENT_8  FLAG_DOCUMENT_9  FLAG_DOCUMENT_10  FLAG_DOCUMENT_11  FLAG_DOCUMENT_12  FLAG_DOCUMENT_13  FLAG_DOCUMENT_14  FLAG_DOCUMENT_15  FLAG_DOCUMENT_16  FLAG_DOCUMENT_17  FLAG_DOCUMENT_18  FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR\n",
       "0  100002      1       Cash loans         M           N            Y               0             202500.0          406597.5    24700.5      351000.0         Unaccompanied   Working          Secondary / secondary special  Single / not married  House / apartment  0.018801                   -9461       -637           -3648.0            -2120            NaN           1           1               0                1                 1           0           Laborers        1.0              2                     2                            WEDNESDAY                  10                       0                           0                           0                            0                       0                       0                        Business Entity Type 3  0.083037      0.262949      0.139376      0.0247          0.0369            0.9722                       0.6192           0.0143          0.00           0.0690         0.0833         0.1250         0.0369        0.0202                0.0190          0.0000                   0.0000             0.0252           0.0383             0.9722                        0.6341            0.0144           0.0000          0.0690          0.0833          0.1250          0.0377         0.022                  0.0198           0.0                       0.0                 0.0250           0.0369             0.9722                        0.6243            0.0144           0.00            0.0690          0.0833          0.1250          0.0375         0.0205                 0.0193           0.0000                    0.00                reg oper account   block of flats  0.0149          Stone, brick       No                  2.0                       2.0                       2.0                       2.0                      -1134.0                  0                1                0                0                0                0                0                0                0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0.0                         0.0                        0.0                         0.0                        0.0                        1.0                       \n",
       "1  100003      0       Cash loans         F           N            N               0             270000.0          1293502.5   35698.5      1129500.0        Family          State servant    Higher education               Married               House / apartment  0.003541                   -16765      -1188          -1186.0            -291             NaN           1           1               0                1                 1           0           Core staff      2.0              1                     1                            MONDAY                     11                       0                           0                           0                            0                       0                       0                        School                  0.311267      0.622246     NaN            0.0959          0.0529            0.9851                       0.7960           0.0605          0.08           0.0345         0.2917         0.3333         0.0130        0.0773                0.0549          0.0039                   0.0098             0.0924           0.0538             0.9851                        0.8040            0.0497           0.0806          0.0345          0.2917          0.3333          0.0128         0.079                  0.0554           0.0                       0.0                 0.0968           0.0529             0.9851                        0.7987            0.0608           0.08            0.0345          0.2917          0.3333          0.0132         0.0787                 0.0558           0.0039                    0.01                reg oper account   block of flats  0.0714          Block              No                  1.0                       0.0                       1.0                       0.0                      -828.0                   0                1                0                0                0                0                0                0                0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0.0                         0.0                        0.0                         0.0                        0.0                        0.0                       \n",
       "2  100004      0       Revolving loans    M           Y            Y               0             67500.0           135000.0    6750.0       135000.0         Unaccompanied   Working          Secondary / secondary special  Single / not married  House / apartment  0.010032                   -19046      -225           -4260.0            -2531             26.0         1           1               1                1                 1           0           Laborers        1.0              2                     2                            MONDAY                     9                        0                           0                           0                            0                       0                       0                        Government             NaN            0.555912      0.729567     NaN             NaN               NaN                          NaN              NaN             NaN            NaN            NaN            NaN            NaN           NaN                   NaN             NaN                      NaN                NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN             NaN             NaN            NaN                    NaN              NaN                       NaN                 NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN             NaN             NaN            NaN                    NaN              NaN                       NaN                  NaN                NaN            NaN              NaN                NaN                 0.0                       0.0                       0.0                       0.0                      -815.0                   0                0                0                0                0                0                0                0                0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0.0                         0.0                        0.0                         0.0                        0.0                        0.0                       \n",
       "3  100006      0       Cash loans         F           N            Y               0             135000.0          312682.5    29686.5      297000.0         Unaccompanied   Working          Secondary / secondary special  Civil marriage        House / apartment  0.008019                   -19005      -3039          -9833.0            -2437            NaN           1           1               0                1                 0           0           Laborers        2.0              2                     2                            WEDNESDAY                  17                       0                           0                           0                            0                       0                       0                        Business Entity Type 3 NaN            0.650442     NaN           NaN             NaN               NaN                          NaN              NaN             NaN            NaN            NaN            NaN            NaN           NaN                   NaN             NaN                      NaN                NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN             NaN             NaN            NaN                    NaN              NaN                       NaN                 NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN             NaN             NaN            NaN                    NaN              NaN                       NaN                  NaN                NaN            NaN              NaN                NaN                 2.0                       0.0                       2.0                       0.0                      -617.0                   0                1                0                0                0                0                0                0                0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                NaN                         NaN                        NaN                         NaN                        NaN                        NaN                        \n",
       "4  100007      0       Cash loans         M           N            Y               0             121500.0          513000.0    21865.5      513000.0         Unaccompanied   Working          Secondary / secondary special  Single / not married  House / apartment  0.028663                   -19932      -3038          -4311.0            -3458            NaN           1           1               0                1                 0           0           Core staff      1.0              2                     2                            THURSDAY                   11                       0                           0                           0                            0                       1                       1                        Religion               NaN            0.322738     NaN           NaN             NaN               NaN                          NaN              NaN             NaN            NaN            NaN            NaN            NaN           NaN                   NaN             NaN                      NaN                NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN             NaN             NaN            NaN                    NaN              NaN                       NaN                 NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN             NaN             NaN            NaN                    NaN              NaN                       NaN                  NaN                NaN            NaN              NaN                NaN                 0.0                       0.0                       0.0                       0.0                      -1106.0                  0                0                0                0                0                0                1                0                0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0.0                         0.0                        0.0                         0.0                        0.0                        0.0                       "
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "Kn5HZimFKQzd",
    "outputId": "b193f71a-c0d2-4fbd-998e-b7bcc9f44caa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SK_ID_CURR', 'TARGET', 'NAME_CONTRACT_TYPE', 'CODE_GENDER',\n",
       "       'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL',\n",
       "       'AMT_CREDIT', 'AMT_ANNUITY',\n",
       "       ...\n",
       "       'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20',\n",
       "       'FLAG_DOCUMENT_21', 'AMT_REQ_CREDIT_BUREAU_HOUR',\n",
       "       'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK',\n",
       "       'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT',\n",
       "       'AMT_REQ_CREDIT_BUREAU_YEAR'],\n",
       "      dtype='object', length=122)"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "TCSYL41QKXmA",
    "outputId": "b022274f-b81b-4951-b3c3-cba3ff54f7a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SK_ID_CURR                    0    \n",
       "TARGET                        0    \n",
       "NAME_CONTRACT_TYPE            0    \n",
       "CODE_GENDER                   0    \n",
       "FLAG_OWN_CAR                  0    \n",
       "                             ..    \n",
       "AMT_REQ_CREDIT_BUREAU_DAY     41519\n",
       "AMT_REQ_CREDIT_BUREAU_WEEK    41519\n",
       "AMT_REQ_CREDIT_BUREAU_MON     41519\n",
       "AMT_REQ_CREDIT_BUREAU_QRT     41519\n",
       "AMT_REQ_CREDIT_BUREAU_YEAR    41519\n",
       "Length: 122, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "QOaAPiDWLF5i",
    "outputId": "97ca57f1-c6c7-48f8-f9b2-9852577cb9d2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>DAYS_REGISTRATION</th>\n",
       "      <th>DAYS_ID_PUBLISH</th>\n",
       "      <th>OWN_CAR_AGE</th>\n",
       "      <th>FLAG_MOBIL</th>\n",
       "      <th>FLAG_EMP_PHONE</th>\n",
       "      <th>FLAG_WORK_PHONE</th>\n",
       "      <th>FLAG_CONT_MOBILE</th>\n",
       "      <th>FLAG_PHONE</th>\n",
       "      <th>FLAG_EMAIL</th>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <th>REGION_RATING_CLIENT</th>\n",
       "      <th>REGION_RATING_CLIENT_W_CITY</th>\n",
       "      <th>HOUR_APPR_PROCESS_START</th>\n",
       "      <th>REG_REGION_NOT_LIVE_REGION</th>\n",
       "      <th>REG_REGION_NOT_WORK_REGION</th>\n",
       "      <th>LIVE_REGION_NOT_WORK_REGION</th>\n",
       "      <th>REG_CITY_NOT_LIVE_CITY</th>\n",
       "      <th>REG_CITY_NOT_WORK_CITY</th>\n",
       "      <th>LIVE_CITY_NOT_WORK_CITY</th>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <th>APARTMENTS_AVG</th>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "      <th>YEARS_BEGINEXPLUATATION_AVG</th>\n",
       "      <th>YEARS_BUILD_AVG</th>\n",
       "      <th>COMMONAREA_AVG</th>\n",
       "      <th>ELEVATORS_AVG</th>\n",
       "      <th>ENTRANCES_AVG</th>\n",
       "      <th>FLOORSMAX_AVG</th>\n",
       "      <th>FLOORSMIN_AVG</th>\n",
       "      <th>LANDAREA_AVG</th>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <th>LIVINGAREA_AVG</th>\n",
       "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <th>APARTMENTS_MODE</th>\n",
       "      <th>BASEMENTAREA_MODE</th>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MODE</th>\n",
       "      <th>YEARS_BUILD_MODE</th>\n",
       "      <th>COMMONAREA_MODE</th>\n",
       "      <th>ELEVATORS_MODE</th>\n",
       "      <th>ENTRANCES_MODE</th>\n",
       "      <th>FLOORSMAX_MODE</th>\n",
       "      <th>FLOORSMIN_MODE</th>\n",
       "      <th>LANDAREA_MODE</th>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <th>LIVINGAREA_MODE</th>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <th>APARTMENTS_MEDI</th>\n",
       "      <th>BASEMENTAREA_MEDI</th>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
       "      <th>YEARS_BUILD_MEDI</th>\n",
       "      <th>COMMONAREA_MEDI</th>\n",
       "      <th>ELEVATORS_MEDI</th>\n",
       "      <th>ENTRANCES_MEDI</th>\n",
       "      <th>FLOORSMAX_MEDI</th>\n",
       "      <th>FLOORSMIN_MEDI</th>\n",
       "      <th>LANDAREA_MEDI</th>\n",
       "      <th>LIVINGAPARTMENTS_MEDI</th>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <th>TOTALAREA_MODE</th>\n",
       "      <th>OBS_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
       "      <th>FLAG_DOCUMENT_2</th>\n",
       "      <th>FLAG_DOCUMENT_3</th>\n",
       "      <th>FLAG_DOCUMENT_4</th>\n",
       "      <th>FLAG_DOCUMENT_5</th>\n",
       "      <th>FLAG_DOCUMENT_6</th>\n",
       "      <th>FLAG_DOCUMENT_7</th>\n",
       "      <th>FLAG_DOCUMENT_8</th>\n",
       "      <th>FLAG_DOCUMENT_9</th>\n",
       "      <th>FLAG_DOCUMENT_10</th>\n",
       "      <th>FLAG_DOCUMENT_11</th>\n",
       "      <th>FLAG_DOCUMENT_12</th>\n",
       "      <th>FLAG_DOCUMENT_13</th>\n",
       "      <th>FLAG_DOCUMENT_14</th>\n",
       "      <th>FLAG_DOCUMENT_15</th>\n",
       "      <th>FLAG_DOCUMENT_16</th>\n",
       "      <th>FLAG_DOCUMENT_17</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>3.075110e+05</td>\n",
       "      <td>3.075110e+05</td>\n",
       "      <td>307499.000000</td>\n",
       "      <td>3.072330e+05</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>104582.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307509.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>134133.000000</td>\n",
       "      <td>3.068510e+05</td>\n",
       "      <td>246546.000000</td>\n",
       "      <td>151450.00000</td>\n",
       "      <td>127568.000000</td>\n",
       "      <td>157504.000000</td>\n",
       "      <td>103023.000000</td>\n",
       "      <td>92646.000000</td>\n",
       "      <td>143620.000000</td>\n",
       "      <td>152683.000000</td>\n",
       "      <td>154491.000000</td>\n",
       "      <td>98869.000000</td>\n",
       "      <td>124921.000000</td>\n",
       "      <td>97312.000000</td>\n",
       "      <td>153161.000000</td>\n",
       "      <td>93997.000000</td>\n",
       "      <td>137829.000000</td>\n",
       "      <td>151450.000000</td>\n",
       "      <td>127568.000000</td>\n",
       "      <td>157504.000000</td>\n",
       "      <td>103023.000000</td>\n",
       "      <td>92646.000000</td>\n",
       "      <td>143620.000000</td>\n",
       "      <td>152683.000000</td>\n",
       "      <td>154491.000000</td>\n",
       "      <td>98869.000000</td>\n",
       "      <td>124921.000000</td>\n",
       "      <td>97312.000000</td>\n",
       "      <td>153161.000000</td>\n",
       "      <td>93997.000000</td>\n",
       "      <td>137829.000000</td>\n",
       "      <td>151450.000000</td>\n",
       "      <td>127568.000000</td>\n",
       "      <td>157504.000000</td>\n",
       "      <td>103023.000000</td>\n",
       "      <td>92646.000000</td>\n",
       "      <td>143620.000000</td>\n",
       "      <td>152683.000000</td>\n",
       "      <td>154491.000000</td>\n",
       "      <td>98869.000000</td>\n",
       "      <td>124921.000000</td>\n",
       "      <td>97312.000000</td>\n",
       "      <td>153161.000000</td>\n",
       "      <td>93997.000000</td>\n",
       "      <td>137829.000000</td>\n",
       "      <td>159080.000000</td>\n",
       "      <td>306490.000000</td>\n",
       "      <td>306490.000000</td>\n",
       "      <td>306490.000000</td>\n",
       "      <td>306490.000000</td>\n",
       "      <td>307510.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.00000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>278180.518577</td>\n",
       "      <td>0.080729</td>\n",
       "      <td>0.417052</td>\n",
       "      <td>1.687979e+05</td>\n",
       "      <td>5.990260e+05</td>\n",
       "      <td>27108.573909</td>\n",
       "      <td>5.383962e+05</td>\n",
       "      <td>0.020868</td>\n",
       "      <td>-16036.995067</td>\n",
       "      <td>63815.045904</td>\n",
       "      <td>-4986.120328</td>\n",
       "      <td>-2994.202373</td>\n",
       "      <td>12.061091</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.819889</td>\n",
       "      <td>0.199368</td>\n",
       "      <td>0.998133</td>\n",
       "      <td>0.281066</td>\n",
       "      <td>0.056720</td>\n",
       "      <td>2.152665</td>\n",
       "      <td>2.052463</td>\n",
       "      <td>2.031521</td>\n",
       "      <td>12.063419</td>\n",
       "      <td>0.015144</td>\n",
       "      <td>0.050769</td>\n",
       "      <td>0.040659</td>\n",
       "      <td>0.078173</td>\n",
       "      <td>0.230454</td>\n",
       "      <td>0.179555</td>\n",
       "      <td>0.502130</td>\n",
       "      <td>5.143927e-01</td>\n",
       "      <td>0.510853</td>\n",
       "      <td>0.11744</td>\n",
       "      <td>0.088442</td>\n",
       "      <td>0.977735</td>\n",
       "      <td>0.752471</td>\n",
       "      <td>0.044621</td>\n",
       "      <td>0.078942</td>\n",
       "      <td>0.149725</td>\n",
       "      <td>0.226282</td>\n",
       "      <td>0.231894</td>\n",
       "      <td>0.066333</td>\n",
       "      <td>0.100775</td>\n",
       "      <td>0.107399</td>\n",
       "      <td>0.008809</td>\n",
       "      <td>0.028358</td>\n",
       "      <td>0.114231</td>\n",
       "      <td>0.087543</td>\n",
       "      <td>0.977065</td>\n",
       "      <td>0.759637</td>\n",
       "      <td>0.042553</td>\n",
       "      <td>0.074490</td>\n",
       "      <td>0.145193</td>\n",
       "      <td>0.222315</td>\n",
       "      <td>0.228058</td>\n",
       "      <td>0.064958</td>\n",
       "      <td>0.105645</td>\n",
       "      <td>0.105975</td>\n",
       "      <td>0.008076</td>\n",
       "      <td>0.027022</td>\n",
       "      <td>0.117850</td>\n",
       "      <td>0.087955</td>\n",
       "      <td>0.977752</td>\n",
       "      <td>0.755746</td>\n",
       "      <td>0.044595</td>\n",
       "      <td>0.078078</td>\n",
       "      <td>0.149213</td>\n",
       "      <td>0.225897</td>\n",
       "      <td>0.231625</td>\n",
       "      <td>0.067169</td>\n",
       "      <td>0.101954</td>\n",
       "      <td>0.108607</td>\n",
       "      <td>0.008651</td>\n",
       "      <td>0.028236</td>\n",
       "      <td>0.102547</td>\n",
       "      <td>1.422245</td>\n",
       "      <td>0.143421</td>\n",
       "      <td>1.405292</td>\n",
       "      <td>0.100049</td>\n",
       "      <td>-962.858788</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.710023</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.015115</td>\n",
       "      <td>0.088055</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.081376</td>\n",
       "      <td>0.003896</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.003912</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.003525</td>\n",
       "      <td>0.002936</td>\n",
       "      <td>0.00121</td>\n",
       "      <td>0.009928</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.008130</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.000507</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>0.006402</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.034362</td>\n",
       "      <td>0.267395</td>\n",
       "      <td>0.265474</td>\n",
       "      <td>1.899974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>102790.175348</td>\n",
       "      <td>0.272419</td>\n",
       "      <td>0.722121</td>\n",
       "      <td>2.371231e+05</td>\n",
       "      <td>4.024908e+05</td>\n",
       "      <td>14493.737315</td>\n",
       "      <td>3.694465e+05</td>\n",
       "      <td>0.013831</td>\n",
       "      <td>4363.988632</td>\n",
       "      <td>141275.766519</td>\n",
       "      <td>3522.886321</td>\n",
       "      <td>1509.450419</td>\n",
       "      <td>11.944812</td>\n",
       "      <td>0.001803</td>\n",
       "      <td>0.384280</td>\n",
       "      <td>0.399526</td>\n",
       "      <td>0.043164</td>\n",
       "      <td>0.449521</td>\n",
       "      <td>0.231307</td>\n",
       "      <td>0.910682</td>\n",
       "      <td>0.509034</td>\n",
       "      <td>0.502737</td>\n",
       "      <td>3.265832</td>\n",
       "      <td>0.122126</td>\n",
       "      <td>0.219526</td>\n",
       "      <td>0.197499</td>\n",
       "      <td>0.268444</td>\n",
       "      <td>0.421124</td>\n",
       "      <td>0.383817</td>\n",
       "      <td>0.211062</td>\n",
       "      <td>1.910602e-01</td>\n",
       "      <td>0.194844</td>\n",
       "      <td>0.10824</td>\n",
       "      <td>0.082438</td>\n",
       "      <td>0.059223</td>\n",
       "      <td>0.113280</td>\n",
       "      <td>0.076036</td>\n",
       "      <td>0.134576</td>\n",
       "      <td>0.100049</td>\n",
       "      <td>0.144641</td>\n",
       "      <td>0.161380</td>\n",
       "      <td>0.081184</td>\n",
       "      <td>0.092576</td>\n",
       "      <td>0.110565</td>\n",
       "      <td>0.047732</td>\n",
       "      <td>0.069523</td>\n",
       "      <td>0.107936</td>\n",
       "      <td>0.084307</td>\n",
       "      <td>0.064575</td>\n",
       "      <td>0.110111</td>\n",
       "      <td>0.074445</td>\n",
       "      <td>0.132256</td>\n",
       "      <td>0.100977</td>\n",
       "      <td>0.143709</td>\n",
       "      <td>0.161160</td>\n",
       "      <td>0.081750</td>\n",
       "      <td>0.097880</td>\n",
       "      <td>0.111845</td>\n",
       "      <td>0.046276</td>\n",
       "      <td>0.070254</td>\n",
       "      <td>0.109076</td>\n",
       "      <td>0.082179</td>\n",
       "      <td>0.059897</td>\n",
       "      <td>0.112066</td>\n",
       "      <td>0.076144</td>\n",
       "      <td>0.134467</td>\n",
       "      <td>0.100368</td>\n",
       "      <td>0.145067</td>\n",
       "      <td>0.161934</td>\n",
       "      <td>0.082167</td>\n",
       "      <td>0.093642</td>\n",
       "      <td>0.112260</td>\n",
       "      <td>0.047415</td>\n",
       "      <td>0.070166</td>\n",
       "      <td>0.107462</td>\n",
       "      <td>2.400989</td>\n",
       "      <td>0.446698</td>\n",
       "      <td>2.379803</td>\n",
       "      <td>0.362291</td>\n",
       "      <td>826.808487</td>\n",
       "      <td>0.006502</td>\n",
       "      <td>0.453752</td>\n",
       "      <td>0.009016</td>\n",
       "      <td>0.122010</td>\n",
       "      <td>0.283376</td>\n",
       "      <td>0.013850</td>\n",
       "      <td>0.273412</td>\n",
       "      <td>0.062295</td>\n",
       "      <td>0.004771</td>\n",
       "      <td>0.062424</td>\n",
       "      <td>0.002550</td>\n",
       "      <td>0.059268</td>\n",
       "      <td>0.054110</td>\n",
       "      <td>0.03476</td>\n",
       "      <td>0.099144</td>\n",
       "      <td>0.016327</td>\n",
       "      <td>0.089798</td>\n",
       "      <td>0.024387</td>\n",
       "      <td>0.022518</td>\n",
       "      <td>0.018299</td>\n",
       "      <td>0.083849</td>\n",
       "      <td>0.110757</td>\n",
       "      <td>0.204685</td>\n",
       "      <td>0.916002</td>\n",
       "      <td>0.794056</td>\n",
       "      <td>1.869295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>100002.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.565000e+04</td>\n",
       "      <td>4.500000e+04</td>\n",
       "      <td>1615.500000</td>\n",
       "      <td>4.050000e+04</td>\n",
       "      <td>0.000290</td>\n",
       "      <td>-25229.000000</td>\n",
       "      <td>-17912.000000</td>\n",
       "      <td>-24672.000000</td>\n",
       "      <td>-7197.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014568</td>\n",
       "      <td>8.173617e-08</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4292.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>189145.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.125000e+05</td>\n",
       "      <td>2.700000e+05</td>\n",
       "      <td>16524.000000</td>\n",
       "      <td>2.385000e+05</td>\n",
       "      <td>0.010006</td>\n",
       "      <td>-19682.000000</td>\n",
       "      <td>-2760.000000</td>\n",
       "      <td>-7479.500000</td>\n",
       "      <td>-4299.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.334007</td>\n",
       "      <td>3.924574e-01</td>\n",
       "      <td>0.370650</td>\n",
       "      <td>0.05770</td>\n",
       "      <td>0.044200</td>\n",
       "      <td>0.976700</td>\n",
       "      <td>0.687200</td>\n",
       "      <td>0.007800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.069000</td>\n",
       "      <td>0.166700</td>\n",
       "      <td>0.083300</td>\n",
       "      <td>0.018700</td>\n",
       "      <td>0.050400</td>\n",
       "      <td>0.045300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052500</td>\n",
       "      <td>0.040700</td>\n",
       "      <td>0.976700</td>\n",
       "      <td>0.699400</td>\n",
       "      <td>0.007200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.069000</td>\n",
       "      <td>0.166700</td>\n",
       "      <td>0.083300</td>\n",
       "      <td>0.016600</td>\n",
       "      <td>0.054200</td>\n",
       "      <td>0.042700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058300</td>\n",
       "      <td>0.043700</td>\n",
       "      <td>0.976700</td>\n",
       "      <td>0.691400</td>\n",
       "      <td>0.007900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.069000</td>\n",
       "      <td>0.166700</td>\n",
       "      <td>0.083300</td>\n",
       "      <td>0.018700</td>\n",
       "      <td>0.051300</td>\n",
       "      <td>0.045700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1570.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>278202.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.471500e+05</td>\n",
       "      <td>5.135310e+05</td>\n",
       "      <td>24903.000000</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>0.018850</td>\n",
       "      <td>-15750.000000</td>\n",
       "      <td>-1213.000000</td>\n",
       "      <td>-4504.000000</td>\n",
       "      <td>-3254.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.505998</td>\n",
       "      <td>5.659614e-01</td>\n",
       "      <td>0.535276</td>\n",
       "      <td>0.08760</td>\n",
       "      <td>0.076300</td>\n",
       "      <td>0.981600</td>\n",
       "      <td>0.755200</td>\n",
       "      <td>0.021100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.137900</td>\n",
       "      <td>0.166700</td>\n",
       "      <td>0.208300</td>\n",
       "      <td>0.048100</td>\n",
       "      <td>0.075600</td>\n",
       "      <td>0.074500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>0.084000</td>\n",
       "      <td>0.074600</td>\n",
       "      <td>0.981600</td>\n",
       "      <td>0.764800</td>\n",
       "      <td>0.019000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.137900</td>\n",
       "      <td>0.166700</td>\n",
       "      <td>0.208300</td>\n",
       "      <td>0.045800</td>\n",
       "      <td>0.077100</td>\n",
       "      <td>0.073100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001100</td>\n",
       "      <td>0.086400</td>\n",
       "      <td>0.075800</td>\n",
       "      <td>0.981600</td>\n",
       "      <td>0.758500</td>\n",
       "      <td>0.020800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.137900</td>\n",
       "      <td>0.166700</td>\n",
       "      <td>0.208300</td>\n",
       "      <td>0.048700</td>\n",
       "      <td>0.076100</td>\n",
       "      <td>0.074900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003100</td>\n",
       "      <td>0.068800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-757.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>367142.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.025000e+05</td>\n",
       "      <td>8.086500e+05</td>\n",
       "      <td>34596.000000</td>\n",
       "      <td>6.795000e+05</td>\n",
       "      <td>0.028663</td>\n",
       "      <td>-12413.000000</td>\n",
       "      <td>-289.000000</td>\n",
       "      <td>-2010.000000</td>\n",
       "      <td>-1720.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.675053</td>\n",
       "      <td>6.636171e-01</td>\n",
       "      <td>0.669057</td>\n",
       "      <td>0.14850</td>\n",
       "      <td>0.112200</td>\n",
       "      <td>0.986600</td>\n",
       "      <td>0.823200</td>\n",
       "      <td>0.051500</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.206900</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.085600</td>\n",
       "      <td>0.121000</td>\n",
       "      <td>0.129900</td>\n",
       "      <td>0.003900</td>\n",
       "      <td>0.027700</td>\n",
       "      <td>0.143900</td>\n",
       "      <td>0.112400</td>\n",
       "      <td>0.986600</td>\n",
       "      <td>0.823600</td>\n",
       "      <td>0.049000</td>\n",
       "      <td>0.120800</td>\n",
       "      <td>0.206900</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.084100</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.125200</td>\n",
       "      <td>0.003900</td>\n",
       "      <td>0.023100</td>\n",
       "      <td>0.148900</td>\n",
       "      <td>0.111600</td>\n",
       "      <td>0.986600</td>\n",
       "      <td>0.825600</td>\n",
       "      <td>0.051300</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.206900</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.086800</td>\n",
       "      <td>0.123100</td>\n",
       "      <td>0.130300</td>\n",
       "      <td>0.003900</td>\n",
       "      <td>0.026600</td>\n",
       "      <td>0.127600</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-274.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>456255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.170000e+08</td>\n",
       "      <td>4.050000e+06</td>\n",
       "      <td>258025.500000</td>\n",
       "      <td>4.050000e+06</td>\n",
       "      <td>0.072508</td>\n",
       "      <td>-7489.000000</td>\n",
       "      <td>365243.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962693</td>\n",
       "      <td>8.549997e-01</td>\n",
       "      <td>0.896010</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          SK_ID_CURR         TARGET   CNT_CHILDREN  AMT_INCOME_TOTAL    AMT_CREDIT    AMT_ANNUITY  AMT_GOODS_PRICE  REGION_POPULATION_RELATIVE     DAYS_BIRTH  DAYS_EMPLOYED  DAYS_REGISTRATION  DAYS_ID_PUBLISH    OWN_CAR_AGE     FLAG_MOBIL  FLAG_EMP_PHONE  FLAG_WORK_PHONE  FLAG_CONT_MOBILE     FLAG_PHONE     FLAG_EMAIL  CNT_FAM_MEMBERS  REGION_RATING_CLIENT  REGION_RATING_CLIENT_W_CITY  HOUR_APPR_PROCESS_START  REG_REGION_NOT_LIVE_REGION  REG_REGION_NOT_WORK_REGION  LIVE_REGION_NOT_WORK_REGION  REG_CITY_NOT_LIVE_CITY  REG_CITY_NOT_WORK_CITY  LIVE_CITY_NOT_WORK_CITY   EXT_SOURCE_1  EXT_SOURCE_2   EXT_SOURCE_3  APARTMENTS_AVG  BASEMENTAREA_AVG  YEARS_BEGINEXPLUATATION_AVG  YEARS_BUILD_AVG  COMMONAREA_AVG  ELEVATORS_AVG  ENTRANCES_AVG  FLOORSMAX_AVG  FLOORSMIN_AVG   LANDAREA_AVG  LIVINGAPARTMENTS_AVG  LIVINGAREA_AVG  NONLIVINGAPARTMENTS_AVG  NONLIVINGAREA_AVG  APARTMENTS_MODE  BASEMENTAREA_MODE  YEARS_BEGINEXPLUATATION_MODE  YEARS_BUILD_MODE  COMMONAREA_MODE  ELEVATORS_MODE  ENTRANCES_MODE  FLOORSMAX_MODE  FLOORSMIN_MODE  LANDAREA_MODE  LIVINGAPARTMENTS_MODE  LIVINGAREA_MODE  NONLIVINGAPARTMENTS_MODE  NONLIVINGAREA_MODE  APARTMENTS_MEDI  BASEMENTAREA_MEDI  YEARS_BEGINEXPLUATATION_MEDI  YEARS_BUILD_MEDI  COMMONAREA_MEDI  ELEVATORS_MEDI  ENTRANCES_MEDI  FLOORSMAX_MEDI  FLOORSMIN_MEDI  LANDAREA_MEDI  LIVINGAPARTMENTS_MEDI  LIVINGAREA_MEDI  NONLIVINGAPARTMENTS_MEDI  NONLIVINGAREA_MEDI  TOTALAREA_MODE  OBS_30_CNT_SOCIAL_CIRCLE  DEF_30_CNT_SOCIAL_CIRCLE  OBS_60_CNT_SOCIAL_CIRCLE  DEF_60_CNT_SOCIAL_CIRCLE  DAYS_LAST_PHONE_CHANGE  FLAG_DOCUMENT_2  FLAG_DOCUMENT_3  FLAG_DOCUMENT_4  FLAG_DOCUMENT_5  FLAG_DOCUMENT_6  FLAG_DOCUMENT_7  FLAG_DOCUMENT_8  FLAG_DOCUMENT_9  FLAG_DOCUMENT_10  FLAG_DOCUMENT_11  FLAG_DOCUMENT_12  FLAG_DOCUMENT_13  FLAG_DOCUMENT_14  FLAG_DOCUMENT_15  FLAG_DOCUMENT_16  FLAG_DOCUMENT_17  FLAG_DOCUMENT_18  FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR\n",
       "count  307511.000000  307511.000000  307511.000000  3.075110e+05      3.075110e+05  307499.000000  3.072330e+05     307511.000000               307511.000000  307511.000000  307511.000000      307511.000000    104582.000000  307511.000000  307511.000000   307511.000000    307511.000000     307511.000000  307511.000000  307509.000000    307511.000000         307511.000000                307511.000000            307511.000000               307511.000000               307511.000000                307511.000000           307511.000000           307511.000000            134133.000000  3.068510e+05  246546.000000  151450.00000    127568.000000     157504.000000                103023.000000    92646.000000    143620.000000  152683.000000  154491.000000  98869.000000   124921.000000  97312.000000          153161.000000   93997.000000             137829.000000      151450.000000    127568.000000      157504.000000                 103023.000000     92646.000000     143620.000000   152683.000000   154491.000000   98869.000000    124921.000000  97312.000000           153161.000000    93997.000000              137829.000000       151450.000000    127568.000000      157504.000000                 103023.000000     92646.000000     143620.000000   152683.000000   154491.000000   98869.000000    124921.000000  97312.000000           153161.000000    93997.000000              137829.000000       159080.000000   306490.000000             306490.000000             306490.000000             306490.000000             307510.000000           307511.000000    307511.000000    307511.000000    307511.000000    307511.000000    307511.000000    307511.000000    307511.000000    307511.000000     307511.000000     307511.000000     307511.000000     307511.000000     307511.00000      307511.000000     307511.000000     307511.000000     307511.000000     307511.000000     307511.000000     265992.000000               265992.000000              265992.000000               265992.000000              265992.000000              265992.000000             \n",
       "mean   278180.518577  0.080729       0.417052       1.687979e+05      5.990260e+05  27108.573909   5.383962e+05     0.020868                   -16036.995067   63815.045904  -4986.120328       -2994.202373      12.061091      0.999997       0.819889        0.199368         0.998133          0.281066       0.056720       2.152665         2.052463              2.031521                     12.063419                0.015144                    0.050769                    0.040659                     0.078173                0.230454                0.179555                 0.502130       5.143927e-01  0.510853       0.11744         0.088442          0.977735                     0.752471         0.044621        0.078942       0.149725       0.226282       0.231894       0.066333       0.100775              0.107399        0.008809                 0.028358           0.114231         0.087543           0.977065                      0.759637          0.042553         0.074490        0.145193        0.222315        0.228058        0.064958       0.105645               0.105975         0.008076                  0.027022            0.117850         0.087955           0.977752                      0.755746          0.044595         0.078078        0.149213        0.225897        0.231625        0.067169       0.101954               0.108607         0.008651                  0.028236            0.102547        1.422245                  0.143421                  1.405292                  0.100049                 -962.858788              0.000042         0.710023         0.000081         0.015115         0.088055         0.000192         0.081376         0.003896         0.000023          0.003912          0.000007          0.003525          0.002936          0.00121           0.009928          0.000267          0.008130          0.000595          0.000507          0.000335          0.006402                    0.007000                   0.034362                    0.267395                   0.265474                   1.899974                  \n",
       "std    102790.175348  0.272419       0.722121       2.371231e+05      4.024908e+05  14493.737315   3.694465e+05     0.013831                    4363.988632    141275.766519  3522.886321        1509.450419      11.944812      0.001803       0.384280        0.399526         0.043164          0.449521       0.231307       0.910682         0.509034              0.502737                     3.265832                 0.122126                    0.219526                    0.197499                     0.268444                0.421124                0.383817                 0.211062       1.910602e-01  0.194844       0.10824         0.082438          0.059223                     0.113280         0.076036        0.134576       0.100049       0.144641       0.161380       0.081184       0.092576              0.110565        0.047732                 0.069523           0.107936         0.084307           0.064575                      0.110111          0.074445         0.132256        0.100977        0.143709        0.161160        0.081750       0.097880               0.111845         0.046276                  0.070254            0.109076         0.082179           0.059897                      0.112066          0.076144         0.134467        0.100368        0.145067        0.161934        0.082167       0.093642               0.112260         0.047415                  0.070166            0.107462        2.400989                  0.446698                  2.379803                  0.362291                  826.808487              0.006502         0.453752         0.009016         0.122010         0.283376         0.013850         0.273412         0.062295         0.004771          0.062424          0.002550          0.059268          0.054110          0.03476           0.099144          0.016327          0.089798          0.024387          0.022518          0.018299          0.083849                    0.110757                   0.204685                    0.916002                   0.794056                   1.869295                  \n",
       "min    100002.000000  0.000000       0.000000       2.565000e+04      4.500000e+04  1615.500000    4.050000e+04     0.000290                   -25229.000000  -17912.000000  -24672.000000      -7197.000000      0.000000       0.000000       0.000000        0.000000         0.000000          0.000000       0.000000       1.000000         1.000000              1.000000                     0.000000                 0.000000                    0.000000                    0.000000                     0.000000                0.000000                0.000000                 0.014568       8.173617e-08  0.000527       0.00000         0.000000          0.000000                     0.000000         0.000000        0.000000       0.000000       0.000000       0.000000       0.000000       0.000000              0.000000        0.000000                 0.000000           0.000000         0.000000           0.000000                      0.000000          0.000000         0.000000        0.000000        0.000000        0.000000        0.000000       0.000000               0.000000         0.000000                  0.000000            0.000000         0.000000           0.000000                      0.000000          0.000000         0.000000        0.000000        0.000000        0.000000        0.000000       0.000000               0.000000         0.000000                  0.000000            0.000000        0.000000                  0.000000                  0.000000                  0.000000                 -4292.000000             0.000000         0.000000         0.000000         0.000000         0.000000         0.000000         0.000000         0.000000         0.000000          0.000000          0.000000          0.000000          0.000000          0.00000           0.000000          0.000000          0.000000          0.000000          0.000000          0.000000          0.000000                    0.000000                   0.000000                    0.000000                   0.000000                   0.000000                  \n",
       "25%    189145.500000  0.000000       0.000000       1.125000e+05      2.700000e+05  16524.000000   2.385000e+05     0.010006                   -19682.000000  -2760.000000   -7479.500000       -4299.000000      5.000000       1.000000       1.000000        0.000000         1.000000          0.000000       0.000000       2.000000         2.000000              2.000000                     10.000000                0.000000                    0.000000                    0.000000                     0.000000                0.000000                0.000000                 0.334007       3.924574e-01  0.370650       0.05770         0.044200          0.976700                     0.687200         0.007800        0.000000       0.069000       0.166700       0.083300       0.018700       0.050400              0.045300        0.000000                 0.000000           0.052500         0.040700           0.976700                      0.699400          0.007200         0.000000        0.069000        0.166700        0.083300        0.016600       0.054200               0.042700         0.000000                  0.000000            0.058300         0.043700           0.976700                      0.691400          0.007900         0.000000        0.069000        0.166700        0.083300        0.018700       0.051300               0.045700         0.000000                  0.000000            0.041200        0.000000                  0.000000                  0.000000                  0.000000                 -1570.000000             0.000000         0.000000         0.000000         0.000000         0.000000         0.000000         0.000000         0.000000         0.000000          0.000000          0.000000          0.000000          0.000000          0.00000           0.000000          0.000000          0.000000          0.000000          0.000000          0.000000          0.000000                    0.000000                   0.000000                    0.000000                   0.000000                   0.000000                  \n",
       "50%    278202.000000  0.000000       0.000000       1.471500e+05      5.135310e+05  24903.000000   4.500000e+05     0.018850                   -15750.000000  -1213.000000   -4504.000000       -3254.000000      9.000000       1.000000       1.000000        0.000000         1.000000          0.000000       0.000000       2.000000         2.000000              2.000000                     12.000000                0.000000                    0.000000                    0.000000                     0.000000                0.000000                0.000000                 0.505998       5.659614e-01  0.535276       0.08760         0.076300          0.981600                     0.755200         0.021100        0.000000       0.137900       0.166700       0.208300       0.048100       0.075600              0.074500        0.000000                 0.003600           0.084000         0.074600           0.981600                      0.764800          0.019000         0.000000        0.137900        0.166700        0.208300        0.045800       0.077100               0.073100         0.000000                  0.001100            0.086400         0.075800           0.981600                      0.758500          0.020800         0.000000        0.137900        0.166700        0.208300        0.048700       0.076100               0.074900         0.000000                  0.003100            0.068800        0.000000                  0.000000                  0.000000                  0.000000                 -757.000000              0.000000         1.000000         0.000000         0.000000         0.000000         0.000000         0.000000         0.000000         0.000000          0.000000          0.000000          0.000000          0.000000          0.00000           0.000000          0.000000          0.000000          0.000000          0.000000          0.000000          0.000000                    0.000000                   0.000000                    0.000000                   0.000000                   1.000000                  \n",
       "75%    367142.500000  0.000000       1.000000       2.025000e+05      8.086500e+05  34596.000000   6.795000e+05     0.028663                   -12413.000000  -289.000000    -2010.000000       -1720.000000      15.000000      1.000000       1.000000        0.000000         1.000000          1.000000       0.000000       3.000000         2.000000              2.000000                     14.000000                0.000000                    0.000000                    0.000000                     0.000000                0.000000                0.000000                 0.675053       6.636171e-01  0.669057       0.14850         0.112200          0.986600                     0.823200         0.051500        0.120000       0.206900       0.333300       0.375000       0.085600       0.121000              0.129900        0.003900                 0.027700           0.143900         0.112400           0.986600                      0.823600          0.049000         0.120800        0.206900        0.333300        0.375000        0.084100       0.131300               0.125200         0.003900                  0.023100            0.148900         0.111600           0.986600                      0.825600          0.051300         0.120000        0.206900        0.333300        0.375000        0.086800       0.123100               0.130300         0.003900                  0.026600            0.127600        2.000000                  0.000000                  2.000000                  0.000000                 -274.000000              0.000000         1.000000         0.000000         0.000000         0.000000         0.000000         0.000000         0.000000         0.000000          0.000000          0.000000          0.000000          0.000000          0.00000           0.000000          0.000000          0.000000          0.000000          0.000000          0.000000          0.000000                    0.000000                   0.000000                    0.000000                   0.000000                   3.000000                  \n",
       "max    456255.000000  1.000000       19.000000      1.170000e+08      4.050000e+06  258025.500000  4.050000e+06     0.072508                   -7489.000000    365243.000000  0.000000           0.000000         91.000000      1.000000       1.000000        1.000000         1.000000          1.000000       1.000000       20.000000        3.000000              3.000000                     23.000000                1.000000                    1.000000                    1.000000                     1.000000                1.000000                1.000000                 0.962693       8.549997e-01  0.896010       1.00000         1.000000          1.000000                     1.000000         1.000000        1.000000       1.000000       1.000000       1.000000       1.000000       1.000000              1.000000        1.000000                 1.000000           1.000000         1.000000           1.000000                      1.000000          1.000000         1.000000        1.000000        1.000000        1.000000        1.000000       1.000000               1.000000         1.000000                  1.000000            1.000000         1.000000           1.000000                      1.000000          1.000000         1.000000        1.000000        1.000000        1.000000        1.000000       1.000000               1.000000         1.000000                  1.000000            1.000000        348.000000                34.000000                 344.000000                24.000000                 0.000000                1.000000         1.000000         1.000000         1.000000         1.000000         1.000000         1.000000         1.000000         1.000000          1.000000          1.000000          1.000000          1.000000          1.00000           1.000000          1.000000          1.000000          1.000000          1.000000          1.000000          4.000000                    9.000000                   8.000000                    27.000000                  261.000000                 25.000000                 "
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MIDvxOOzLiFp"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "kyP3f4HzLnuG",
    "outputId": "70e2912a-3c39-44ee-9b6d-916bbce7173f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "307511"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QVe1SvnmEsKW"
   },
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "colab_type": "code",
    "id": "ZrU2FL8lEsKZ",
    "outputId": "2355e94b-54a9-4b80-d91d-bcdc5acfd41a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME_CONTRACT_TYPE             0     \n",
       "CODE_GENDER                    0     \n",
       "FLAG_OWN_CAR                   0     \n",
       "FLAG_OWN_REALTY                0     \n",
       "CNT_CHILDREN                   0     \n",
       "AMT_INCOME_TOTAL               0     \n",
       "AMT_CREDIT                     0     \n",
       "AMT_ANNUITY                    12    \n",
       "AMT_GOODS_PRICE                278   \n",
       "NAME_INCOME_TYPE               0     \n",
       "NAME_EDUCATION_TYPE            0     \n",
       "NAME_FAMILY_STATUS             0     \n",
       "DAYS_BIRTH                     0     \n",
       "DAYS_EMPLOYED                  0     \n",
       "OCCUPATION_TYPE                96391 \n",
       "CNT_FAM_MEMBERS                2     \n",
       "REGION_RATING_CLIENT           0     \n",
       "REGION_RATING_CLIENT_W_CITY    0     \n",
       "AMT_REQ_CREDIT_BUREAU_HOUR     41519 \n",
       "AMT_REQ_CREDIT_BUREAU_DAY      41519 \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK     41519 \n",
       "AMT_REQ_CREDIT_BUREAU_MON      41519 \n",
       "AMT_REQ_CREDIT_BUREAU_QRT      41519 \n",
       "AMT_REQ_CREDIT_BUREAU_YEAR     41519 \n",
       "EXT_SOURCE_1                   173378\n",
       "EXT_SOURCE_2                   660   \n",
       "EXT_SOURCE_3                   60965 \n",
       "TARGET                         0     \n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine and isolate features and target\n",
    "df = data[['NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY',\n",
    "               'CNT_CHILDREN', 'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY',\n",
    "               'AMT_GOODS_PRICE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE',\n",
    "               'NAME_FAMILY_STATUS', 'DAYS_BIRTH', 'DAYS_EMPLOYED',\n",
    "               'OCCUPATION_TYPE', 'CNT_FAM_MEMBERS', 'REGION_RATING_CLIENT',\n",
    "               'REGION_RATING_CLIENT_W_CITY', 'AMT_REQ_CREDIT_BUREAU_HOUR',\n",
    "               'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK',\n",
    "               'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT',\n",
    "               'AMT_REQ_CREDIT_BUREAU_YEAR', 'EXT_SOURCE_1',\t'EXT_SOURCE_2',\t'EXT_SOURCE_3', 'TARGET']]\n",
    "               \n",
    "\n",
    "# Check no of null values per field\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "cxaa9jvFg8KM",
    "outputId": "acd7d271-dd71-401f-b656-dd6140db5433"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EXT_SOURCE_1                  0.563811\n",
       "OCCUPATION_TYPE               0.313455\n",
       "EXT_SOURCE_3                  0.198253\n",
       "AMT_REQ_CREDIT_BUREAU_YEAR    0.135016\n",
       "AMT_REQ_CREDIT_BUREAU_QRT     0.135016\n",
       "AMT_REQ_CREDIT_BUREAU_MON     0.135016\n",
       "AMT_REQ_CREDIT_BUREAU_WEEK    0.135016\n",
       "AMT_REQ_CREDIT_BUREAU_DAY     0.135016\n",
       "AMT_REQ_CREDIT_BUREAU_HOUR    0.135016\n",
       "EXT_SOURCE_2                  0.002146\n",
       "AMT_GOODS_PRICE               0.000904\n",
       "AMT_ANNUITY                   0.000039\n",
       "CNT_FAM_MEMBERS               0.000007\n",
       "CNT_CHILDREN                  0.000000\n",
       "AMT_CREDIT                    0.000000\n",
       "AMT_INCOME_TOTAL              0.000000\n",
       "TARGET                        0.000000\n",
       "FLAG_OWN_REALTY               0.000000\n",
       "FLAG_OWN_CAR                  0.000000\n",
       "CODE_GENDER                   0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top missing columns\n",
    "train_missing = (df.isnull().sum() / len(df)).sort_values(ascending = False)\n",
    "train_missing.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r4DXk5L9Oxqh"
   },
   "outputs": [],
   "source": [
    "#train_missing = train_missing.index[train_missing > 0.50]\n",
    "#df1 = pd.get_dummies(df.drop(columns = train_missing))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eaueiO9VO-qP"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 292
    },
    "colab_type": "code",
    "id": "_FKXBTMBPI1z",
    "outputId": "d718277f-04b2-45ff-c4d3-3147882bc6b9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>NAME_INCOME_TYPE</th>\n",
       "      <th>NAME_EDUCATION_TYPE</th>\n",
       "      <th>NAME_FAMILY_STATUS</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <th>REGION_RATING_CLIENT</th>\n",
       "      <th>REGION_RATING_CLIENT_W_CITY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>351000.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>-9461</td>\n",
       "      <td>-637</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.083037</td>\n",
       "      <td>0.262949</td>\n",
       "      <td>0.139376</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>1129500.0</td>\n",
       "      <td>State servant</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>-16765</td>\n",
       "      <td>-1188</td>\n",
       "      <td>Core staff</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.311267</td>\n",
       "      <td>0.622246</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>-19046</td>\n",
       "      <td>-225</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.555912</td>\n",
       "      <td>0.729567</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>297000.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>-19005</td>\n",
       "      <td>-3039</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.650442</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>-19932</td>\n",
       "      <td>-3038</td>\n",
       "      <td>Core staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.322738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE NAME_INCOME_TYPE            NAME_EDUCATION_TYPE    NAME_FAMILY_STATUS  DAYS_BIRTH  DAYS_EMPLOYED OCCUPATION_TYPE  CNT_FAM_MEMBERS  REGION_RATING_CLIENT  REGION_RATING_CLIENT_W_CITY  AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  TARGET\n",
       "0  Cash loans         M           N            Y               0             202500.0          406597.5    24700.5      351000.0         Working          Secondary / secondary special  Single / not married -9461       -637            Laborers        1.0              2                     2                            0.0                         0.0                        0.0                         0.0                        0.0                        1.0                         0.083037      0.262949      0.139376      1     \n",
       "1  Cash loans         F           N            N               0             270000.0          1293502.5   35698.5      1129500.0        State servant    Higher education               Married              -16765      -1188           Core staff      2.0              1                     1                            0.0                         0.0                        0.0                         0.0                        0.0                        0.0                         0.311267      0.622246     NaN            0     \n",
       "2  Revolving loans    M           Y            Y               0             67500.0           135000.0    6750.0       135000.0         Working          Secondary / secondary special  Single / not married -19046      -225            Laborers        1.0              2                     2                            0.0                         0.0                        0.0                         0.0                        0.0                        0.0                        NaN            0.555912      0.729567      0     \n",
       "3  Cash loans         F           N            Y               0             135000.0          312682.5    29686.5      297000.0         Working          Secondary / secondary special  Civil marriage       -19005      -3039           Laborers        2.0              2                     2                           NaN                         NaN                        NaN                         NaN                        NaN                        NaN                         NaN            0.650442     NaN            0     \n",
       "4  Cash loans         M           N            Y               0             121500.0          513000.0    21865.5      513000.0         Working          Secondary / secondary special  Single / not married -19932      -3038           Core staff      1.0              2                     2                            0.0                         0.0                        0.0                         0.0                        0.0                        0.0                        NaN            0.322738     NaN            0     "
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jtbypYlNTdNn"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 578
    },
    "colab_type": "code",
    "id": "m-SRm4SRPGn-",
    "outputId": "84e4bffd-3c4c-4358-b82e-13a8290df158"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 307511 entries, 0 to 307510\n",
      "Data columns (total 28 columns):\n",
      "NAME_CONTRACT_TYPE             307511 non-null object\n",
      "CODE_GENDER                    307511 non-null object\n",
      "FLAG_OWN_CAR                   307511 non-null object\n",
      "FLAG_OWN_REALTY                307511 non-null object\n",
      "CNT_CHILDREN                   307511 non-null int64\n",
      "AMT_INCOME_TOTAL               307511 non-null float64\n",
      "AMT_CREDIT                     307511 non-null float64\n",
      "AMT_ANNUITY                    307499 non-null float64\n",
      "AMT_GOODS_PRICE                307233 non-null float64\n",
      "NAME_INCOME_TYPE               307511 non-null object\n",
      "NAME_EDUCATION_TYPE            307511 non-null object\n",
      "NAME_FAMILY_STATUS             307511 non-null object\n",
      "DAYS_BIRTH                     307511 non-null int64\n",
      "DAYS_EMPLOYED                  307511 non-null int64\n",
      "OCCUPATION_TYPE                211120 non-null object\n",
      "CNT_FAM_MEMBERS                307509 non-null float64\n",
      "REGION_RATING_CLIENT           307511 non-null int64\n",
      "REGION_RATING_CLIENT_W_CITY    307511 non-null int64\n",
      "AMT_REQ_CREDIT_BUREAU_HOUR     265992 non-null float64\n",
      "AMT_REQ_CREDIT_BUREAU_DAY      265992 non-null float64\n",
      "AMT_REQ_CREDIT_BUREAU_WEEK     265992 non-null float64\n",
      "AMT_REQ_CREDIT_BUREAU_MON      265992 non-null float64\n",
      "AMT_REQ_CREDIT_BUREAU_QRT      265992 non-null float64\n",
      "AMT_REQ_CREDIT_BUREAU_YEAR     265992 non-null float64\n",
      "EXT_SOURCE_1                   134133 non-null float64\n",
      "EXT_SOURCE_2                   306851 non-null float64\n",
      "EXT_SOURCE_3                   246546 non-null float64\n",
      "TARGET                         307511 non-null int64\n",
      "dtypes: float64(14), int64(6), object(8)\n",
      "memory usage: 65.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QflSR1iE3gZq"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 292
    },
    "colab_type": "code",
    "id": "JMykLNjvWVkp",
    "outputId": "187dd512-fdd9-4fb5-b071-4f5bd5b845e9"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>NAME_INCOME_TYPE</th>\n",
       "      <th>NAME_EDUCATION_TYPE</th>\n",
       "      <th>NAME_FAMILY_STATUS</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <th>REGION_RATING_CLIENT</th>\n",
       "      <th>REGION_RATING_CLIENT_W_CITY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>351000.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>-9461</td>\n",
       "      <td>-637</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.083037</td>\n",
       "      <td>0.262949</td>\n",
       "      <td>0.139376</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>1129500.0</td>\n",
       "      <td>State servant</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>-16765</td>\n",
       "      <td>-1188</td>\n",
       "      <td>Core staff</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.311267</td>\n",
       "      <td>0.622246</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>-19046</td>\n",
       "      <td>-225</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.555912</td>\n",
       "      <td>0.729567</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>297000.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>-19005</td>\n",
       "      <td>-3039</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.650442</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>-19932</td>\n",
       "      <td>-3038</td>\n",
       "      <td>Core staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.322738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE NAME_INCOME_TYPE            NAME_EDUCATION_TYPE    NAME_FAMILY_STATUS  DAYS_BIRTH  DAYS_EMPLOYED OCCUPATION_TYPE  CNT_FAM_MEMBERS  REGION_RATING_CLIENT  REGION_RATING_CLIENT_W_CITY  AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  TARGET\n",
       "0  Cash loans         M           N            Y               0             202500.0          406597.5    24700.5      351000.0         Working          Secondary / secondary special  Single / not married -9461       -637            Laborers        1.0              2                     2                            0.0                         0.0                        0.0                         0.0                        0.0                        1.0                         0.083037      0.262949      0.139376      1     \n",
       "1  Cash loans         F           N            N               0             270000.0          1293502.5   35698.5      1129500.0        State servant    Higher education               Married              -16765      -1188           Core staff      2.0              1                     1                            0.0                         0.0                        0.0                         0.0                        0.0                        0.0                         0.311267      0.622246     NaN            0     \n",
       "2  Revolving loans    M           Y            Y               0             67500.0           135000.0    6750.0       135000.0         Working          Secondary / secondary special  Single / not married -19046      -225            Laborers        1.0              2                     2                            0.0                         0.0                        0.0                         0.0                        0.0                        0.0                        NaN            0.555912      0.729567      0     \n",
       "3  Cash loans         F           N            Y               0             135000.0          312682.5    29686.5      297000.0         Working          Secondary / secondary special  Civil marriage       -19005      -3039           Laborers        2.0              2                     2                           NaN                         NaN                        NaN                         NaN                        NaN                        NaN                         NaN            0.650442     NaN            0     \n",
       "4  Cash loans         M           N            Y               0             121500.0          513000.0    21865.5      513000.0         Working          Secondary / secondary special  Single / not married -19932      -3038           Core staff      1.0              2                     2                            0.0                         0.0                        0.0                         0.0                        0.0                        0.0                        NaN            0.322738     NaN            0     "
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "colab_type": "code",
    "id": "Dbzb4O9aEsKh",
    "outputId": "ed3677cb-84bc-400a-f71d-f069db182d8b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME_CONTRACT_TYPE             0\n",
       "CODE_GENDER                    0\n",
       "FLAG_OWN_CAR                   0\n",
       "FLAG_OWN_REALTY                0\n",
       "CNT_CHILDREN                   0\n",
       "AMT_INCOME_TOTAL               0\n",
       "AMT_CREDIT                     0\n",
       "AMT_ANNUITY                    0\n",
       "AMT_GOODS_PRICE                0\n",
       "NAME_INCOME_TYPE               0\n",
       "NAME_EDUCATION_TYPE            0\n",
       "NAME_FAMILY_STATUS             0\n",
       "DAYS_BIRTH                     0\n",
       "DAYS_EMPLOYED                  0\n",
       "OCCUPATION_TYPE                0\n",
       "CNT_FAM_MEMBERS                0\n",
       "REGION_RATING_CLIENT           0\n",
       "REGION_RATING_CLIENT_W_CITY    0\n",
       "AMT_REQ_CREDIT_BUREAU_HOUR     0\n",
       "AMT_REQ_CREDIT_BUREAU_DAY      0\n",
       "AMT_REQ_CREDIT_BUREAU_WEEK     0\n",
       "AMT_REQ_CREDIT_BUREAU_MON      0\n",
       "AMT_REQ_CREDIT_BUREAU_QRT      0\n",
       "AMT_REQ_CREDIT_BUREAU_YEAR     0\n",
       "EXT_SOURCE_1                   0\n",
       "EXT_SOURCE_2                   0\n",
       "EXT_SOURCE_3                   0\n",
       "TARGET                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove rows with null values\n",
    "df = df.dropna()\n",
    "\n",
    "# Recheck if all fields contain zero null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "58JG3G0JRE7f",
    "outputId": "0770a455-b24b-4d7e-a085-5a21469a4791"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(82454, 28)"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Q-mOuVSJSXCD"
   },
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 557
    },
    "colab_type": "code",
    "id": "aRpDI3bNEsKq",
    "outputId": "b9bef381-0339-4af4-bcd0-a01c387fed63"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pFbb/VlXPmqmJSJIkSZIeaCZ2iZQkSZIkjcDQgS3JM5OsTvJfSTYn2TLG596Z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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # Visualize data distributions\n",
    "plt.figure(figsize=(14,8))\n",
    "plt.rcParams.update({'font.size': 20})\n",
    "plt.hist(abs(df['DAYS_BIRTH']) / 365, edgecolor = 'k', bins = 25)\n",
    "plt.title('Age of Client')\n",
    "plt.xlabel('Age (years)') \n",
    "plt.ylabel('Count')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 416
    },
    "colab_type": "code",
    "id": "x2oIP6nv1F31",
    "outputId": "e619b720-2447-48af-d5ee-16c820d33561"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1dnhFkdKU2i11j6/VVHJPiO9XO29RMTV2Fc1+v4rxL24gN4R9kzy+yLNN7KvyWA8jJ7ml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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,6))\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "\n",
    "plt.subplot(121)\n",
    "sns.countplot(y=data[\"NAME_INCOME_TYPE\"],\n",
    "              order=data[\"NAME_INCOME_TYPE\"].value_counts().index[:4])\n",
    "plt.title(\"Distribution of client income type\")\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.countplot(y=data[\"NAME_INCOME_TYPE\"],\n",
    "              hue=data[\"CODE_GENDER\"],\n",
    "              order=data[\"NAME_INCOME_TYPE\"].value_counts().index[:4])\n",
    "plt.ylabel(\"\")\n",
    "plt.title(\"Distribution of client income  type by gender\")\n",
    "plt.subplots_adjust(wspace = .4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 310
    },
    "colab_type": "code",
    "id": "rzgLoAuMEsKy",
    "outputId": "0ff53855-a64a-42f0-abcb-e9bb2247b8d7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2ef36bb70>"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JasogkiQ1ZRBJkpoyiCRJTRlEkqSmDCJJUlMGkSSpKYNIktSUQSRJasogkiQ1ZRBJkpoy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\nTRlEkqSmDCJJUlP/H14CEdp5dUQTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # Show count of default and non-default (distribution)\n",
    "sns.countplot(x=\"TARGET\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Mrl1AE2AEsK8",
    "outputId": "51d6dc09-35a0-4ba8-986f-607753871452"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/seaborn/matrix.py:268: PendingDeprecationWarning: \n",
      "The label function will be deprecated in a future version. Use Tick.label1 instead.\n",
      "  fontsize = tick.label.get_size()\n",
      "/usr/local/lib/python3.6/dist-packages/seaborn/matrix.py:268: PendingDeprecationWarning: \n",
      "The label function will be deprecated in a future version. Use Tick.label1 instead.\n",
      "  fontsize = tick.label.get_size()\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ne2TacOfWTQ6dOBMp7oT6npv42Tuc2B/5M0aHhqJ8fChZvS4vvGs62gfevsWN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sXoKin3rqM3OXWpqEurWeFkX462FtxOKOpE4WhnTkJy5Sb4uhGbzpDJO7AwM8bK0pSE5CzGDWzE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/0LaI9nUTHXO79WlX//qJ7nBL/r41i/r9PxXmGxq48+/SrYP/9RMJUQMBTv+fiMpbKS7r37VI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\nfSHdqU2/kp+dQZcXJ2NqZUPM6UMc+HUWVuM/xd6z6pcC/ZPWRXN/BzzsLZi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bTkRM2PGZsiRQtehmu5yxBQvRbBF42XIfjieQKMlCE0j/2QrN0PNtr9kJmZyfnD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\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aOjrrLbdxdCYjOdHQKpr1kpOwMbiO4XNDQX4eO//8kRpNWmPvWn5OSGNu9YW9gX2behvn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j4uhs8Ea7pY6WwDcD4yWbuNScG0xZy8oj5/FcjNV/F1tSlIo4AKuXlFJ4K5efmoKtRyvb2z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\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Hv75mkxUnd0+Cm7dlw68/EPn3aW5dj+DPqV9jZWtPYDPNRGpg01bcvHKBHYvnkBB9ndir\nF9nw80RqOjpT21d3syRFUWjStS8H1yziwqE9xF2/yoYZEzA1tyC0dcmt94u/fJfdi2Zp/9+ka1+u\nnT3OwdULSYi6xoFVC7gefoIm3fpq02SlpxIbcYn4G1cBTRzF2IhLOqv/Tu/cSNSFcJJiozn711ZW\nT/mMpt364eDmedv+LNZj0JPs2rCG7WtWEhVxlbmTJ5KUEEfn3v0BmP75GKZ/PkabvnPvfiTF32Lu\nlElERVxl+5qV7Nqwhh6DS34A6P7EUC6dPc3Kub8Sc+M6B7ZvYeOyRTzap+S2zfTUFCIunufGFc0G\nIDFR14m4eJ7kCmJWGvLogCHs3bSW3ev+JDryKgumfktyfDwdHtecz1lfjWXWV2O16R/q2Zek+DgW\nTvuO6Mir7F73J3s3raXLwCcBsLSuiYevv85hXsMCKxtbPHz9URSF7KxMFv80hUtnTxEfE825E0f5\nYfRb2Ng50KjdQ1VuQ48nnmTn+jVsW72SGxFXmTN5IonxcTzSR9MP0z4fw7RS/fBI734kxt1izuRJ\n3Ii4yrbVK9m5fg2Pl+mHi2dPs6KoH/Zv38KGZYvo0lfTD9mZmcyf9j0Xzpzi1s1ozh47woT33sTW\n3oHm1Rj+YuXuKzzRMYC29VzwcanJu4Mbkp1TwPZjJQPhCS+15JkeJSuxTIwV/N1s8HezwczUGIea\n5vi72eDmVH4zm24tvNh5Iorse3jr6cbjUTzWxIOm/o54OFrywiNBZOcVsP98nDbNB33rMbC1j/b/\nxkYKXk5WeDlZYWpihK2VGV5OVtQqFUv5qYf8aR9am582niPjf+zdd3gUVRfA4d9NCBBKSIOEBEIg\nwAZC771I79KbSrGBiAURwYKonwVRwYKCivTepUpTkN6lD7339EIJyXx/zGaTDckmgYQFct7n2Qd2\nc+fuOTv9zp07d+5RII8TBfI4kcvp4Q6Z6nfswZ51q9ixehnXzp9l6YRxRIQEU6v1swDM+eYz5nzz\nmaV8rdbPEh58kz8nfs+182fZsXoZe9atomGnnpYyd27FcPnUCS6fOoGuxxN64xqXT50g9Lox3ujt\nmGhWT/2V88cOE3r9KhdPHGPe2C8Iv3mDig0yPhRJckoparTpwrYlczi2YxPXz59h2fivyJnbmaB6\nTS3lZn4yhL9n/mZ5X6NNZ84e2svWxbO4eek8WxbP5NzhfdRo09lS5lZkBFfPnOTGBWN7G3L1ElfP\nnLyv9/STFnNUaAhXz5wk5LLRgHrz4lmunjnJrcjUL2YopajRtgtbl8zm6PZNXD9/mqU/GTGXq58Y\n8/RRQ1g/41ermM8c3MuWRTO5efEcmxfN5OyhfdRs2yXd9bp5FaaQXwnLy8OniPlzH1zMPTI9fIpS\nunpd/vrjRy4cO8iNC2f5c/xo8hZwpVTV2ln6u6en3iKlg3DOl59lP4/m2tmTBF++wPppEwi7fpmS\n5vgSHN2+kTsx0VRs3CrV+ZF0vlRp2ZFdy+dyYtdmbl44w1+/jsEpd24CayeuXwu+HMbmuZMs7ys3\nf5YLR/azc9kcQi6fZ+efs7l49D8qt+gIGD2EV/z4GVdOHaXVoBEoFNFhIUSHhXDvbmIDd7XWXdm3\nZgnHd2wi7Noldv45m6unjlKhcRubMddq25Uti2dzZNsmrp07zZIfjd+rfIPE+TB15Nusm564LNVs\n24UzB/fx78KZ3Lh4jn8XzuDsoX3Uatc13fVGhNxk8odvkM/VnZYvDiYmIpzI0GAiQ4OJj7PeVxzZ\nasyHyk1apzkfEr6/YrOO7F05j1O7NxN88Szrf/8Wp9y5KV0rcd+55OvhbJ3/h+V9xWbPcvHofvas\nmEvolQvsXj6HS8f+o2IzY17cvRXN9oVTuHrqGJHB17l+9gTrJ31HVOhNStZoYKknOjyEG+dPEXbV\nuBAacuk8N86f4nZURvp5wOZTN2lUqiBBhV3wyp+LblWKcPdePPsuJo4+93Ld4rQsm9gbe8vpYAI8\n89GoVEEK5stFo1IFCfDMx+ZT1sduTo6KykVd2WXj4XHpKZMRffo2Y8mSLcyfv4lTpy7zxeezuXEj\njO49GgHw3nuTeO+9SVbTHD16nqNHzxMVfYvw8GiOHj3PyZOX76t73rxN1KodSNGiBe/7W2bq/nxX\nVi79iz8XreDs6XOMG/0TN2/cpGPXdgB89sGXfPaB9VAzx4+d5Pixk0RHRxMRHsHxYyc5c+qs5e8z\np8zhl+9/Y8SodylarCjBN0MIvhlCVOTDNcRnhhx58+BaMRDXioEoBwfy+vngWjGQPEUzvxNJRi36\n5xTdm5aiXsXC+BfOz3vPVebW3TjW707sgDDm9Tq82C7xNDyHoyLA14UAXxdyOjng7pKLAF8XfDzv\nH6qjdZ1i/L33YpYeuz4t9Hg9W72edjJ8hMhUSqkyGI3AL+m6fkQp9SKwUim1SNf1LVn41Ucwlufc\nwH6M4RveUUrNTtrIqpTyAXoDk5OMJ6wBVblfFfPfbPkEWKWU+lXX9cvAGuAmMBxon7ywUso1q8YV\nxrjIk+oTljRNm2symTw2zps2PjI0mEJ+xXnuo9G4FvIGIPyG9W1ibl4+PPfR16z+40d2rV5KfncP\nWr/0JkF1GlnKlK/XhFsREWycN42U6gRo9eJgHBwcmffNKO7duUPxClXp/Ob7ODgm3urV+e0PWf3H\nT8z6fDi6ruNXpgJ9Px1LzlxGQ02JClXpMmQkmxfPZsvi2Tg65cSnZBm6vGeccCRXo1137t29w7op\nP3I7OpLCAWXoOuIrcjonNl6FXbtM/iTjCPqWDqLd4A/ZPG8ym+dPxdXLh3aDP8QnSW+RU3u2sWqi\npVM8f/1mjPlYp9Pz1O3SB4CQKxfYNHcSt6MiKVDQi1odelOtdWKjQXrUbtKcyPAwFk+dRFjwTYoW\nD+C9MT9Q0Nyb9OY162cTFvLxZdiYH5j+47esW7IAN8+C9HnrXWo2SrzdN6BMEEO+/Ja5E8ezeOrv\neBTypttLA2nWqaulzJ7NG5nwxSeW97+NNsZL7NzvFbq8+GqGcqjxTDOiIsJZPn0y4SE38fUP4M2v\nxlp6xIZct17eChb25a0vxzHn57H88+dCXD086TX4Hao1TH/DlYODA5dOn2TbmpXEREVSwMOTwEpV\nGfjxlzjnyfg4bXWaNicywpgPocE3KVoigOHfJM6H4BTmw/BvfmDqD9+y1jwf+r31LjUbJ86HkmWC\nGPrVt8yeOJ5FU37H08ub7i8PpLl5Pjg4OnD+1Ek2rVpBdFQkbh6eBFWpxluffYVz3swba27uhlPk\ndHLk9c7lye/sxLHzYQyfuJ1bdxIPhAt75uVGWOIteR4uuZkwtKHlva9nXtrW8ee/kzcZ+vM2y+cV\nS3pQpFA+vppl3fsqs63Yc5GcORzo07gkeXLl4PTVSL5ecojbsYk5FCqQ22pIDLe8Ofm8d+KT5r1c\nnWlSvjBHL4bxxUJjqIumFY0xHkd0tu4Rvmj7ORbvePCH5lVq2ISYyHA2zJlKREgw3v7F6f/JGNy8\njO1lWLJtsLu3D/0/HcOyX39k24oluHh40v7Vtyhfr5GlzMUTx5g4/A3L+7UzJrF2xiSqNm1F9yEf\n4ODgyLXzZ9i1dgUxERHkcXGhaOkyDPz6JwoXL/nAuSRVu0MP7t29w+rfv+d2dCS+JcvQ88Mx5Eqy\nvQ29dtnSgAhQxFSOjm+NZOOcSWycOxk3bx86vj0S3yQ9JY/v3srynxPHEl054RsA6nftQ4NufZ/Y\nmPeu/ZN/50+1lJn75QgA2r72HhUbt0w15jrP9jRi/m0ct6Ij8S1Vlt4jk8V89ZLV+LhFA8vRachI\n/pk1iX/mTsbNy4dOQz7GN8nQFOmpNz2effMD1k4Zz5wv3wddp2hgeXqP/BanXIkXXLLqd0+r3jwu\nBejxwddsnP07Mz95h7i4e3j6+tHl3c8oXKI0Se1ft5wSlapbDUVhS7U23bh39w4bpv7EnZhIvEsE\n0mnYl1bHG+HXr1geEAfgUzqI1oPeZ+uCKWxbOA1Xr8K0HvSBpUdrZMgNTu01tqmzPrJ+gFbzl4cS\n1MAYO79Ky07E3Ytl0+yJ3IqMwKOIP88O/TzVh8wlqNuxJ7F377Dyt7HcioqiSKkyPP/xN1bzIeTq\nZashMfwCy9HlnZFsmDWJv+f8gbuXD13eGUWRJMtSWvWe2r+LkCsXCblykbEvJx57ALw5cQ5uhRIb\nnfauXU5ApepWQ1GkpUrrrtyLvcPGGeO5Ex2FV0AgHd75Itm8uEy+JHcOFS5VlhYDRrB90VR2LJ5O\ngUKFaTFgBN4BxgVR5eBIyOXzHNm8httRkeTOlx8v/9J0Gj4Gz6IlLPUc+nsFu5bOtLxfPs64YNzk\nxSGUqZf4rIO0bDxxEydHB56t6IOzkyMXQmP4fesZ7t5L7C/injen1fAS50JimL37PM3LeNGsTCFC\nou8yc9d5LiS7y6WirytOjg7stvHwuPSUyYjWrWsQFhbNhF9WcONGOKVK+TBh4pv4+hpDzly5HHzf\nNJ06Wo8P/fff/+Hj48H6DYnb1gsXbrBj+zG+/e6VTInTlqYtGxMRHsHU32YQfCOEEiX9+Wb8l3j7\nGPvua1fvH4mwX3fruLZs3Ia3jxcLVxlDhS2au5R79+4xcph1rq3at+DDz97LokzSx71aOZr+M93y\nvsKnb1Dh0zc4PWUR2/uNsGNkMGfdSXI6OfJG1wrkz+PE0bOhvDd+K7eSDJXi45nXamg0jwK5+XV4\n4oUh34L5aFevOPtP3OSdHxKbJiqV8qRIoXx8MS3zHiAoxJNCPcRztkQ2pJSaAvgCyYcpiMPofbsV\nOK/repck00wAngEq6bpudS+TeXze13Vdn5LO7/cA5gN/AAcwhlqohjGUw0Fd15uZy9UC1gF/A18A\nF4EKGMNcKKCOruuh5rJ1gH8xetkuNP+9FzAMqKLr+sEkuXvqut42WUx7gB26rr9mft/BHONaYBxw\nAnAHOprra6OU8gfOYDyo7r9kaYbrum7zfmWl1GDz9AmN1g2AscDPuq4PtzXt3KPXnviVPjImc8YM\ntacqfpn71Gp7iIl98q+k58uZNePQPUrvfvG3vUN4aN7FMjZG7uOqS9vAtAs95sKjH+6WaJE5HBzs\n+3DSzBD/FPSuibn95K8PBfKl2l/giXEz7OGH8bG3i9ft3wv0YX3ZIav6tTxaIXdKpF3oMbfG+eHv\n9rG3Sa9/be8QHtr6Hzs8+TvrB1C368wnfwefAVvm936q57MMHyEeRFPgSrLXPmAEUAwYmKz8UIxe\nvJnx6OkoYDvwJrAROIzR6DsLsIxroOv6doxxf6OBpcApYDywmiQNwuayW4FWQHNgM8a4wXWBJgkN\nwmn4FnhRKVXMXN9SoDbGmMozMBpu5wNFMRqak1rN/b9lep5a4QiMxugVvRsYhNE7+VGMqyyEEEII\nIYQQQgghnmAyfITIEF3X+wJ9bRT5LPkHuq5HASlektV1PV9Kn9v4/jsYDZ9pNn7qun4E6JHOetdg\nDP1gq0zfVD6fhdEonfSzPUDXlMqb/36WjD+ULun04zB6IQshhBBCCCGEEEIIkSHSKCyEEEIIIYQQ\nQgghhLAtLj7tMuKJIcNHiMeKUspPKRVl4+Vn7xgfBaXUYRu/QXqGlxBCCCGEEEIIIYQQIkXSU1g8\nbi4DldL4e3bQGnBK5W/XUvlcCCGEEEIIIYQQQog0SaOweKzoun4POGnvOOxN1/Vz9o5BCCGEEEII\nIYQQQjydZPgIIYQQQgghhBBCCCGEyEakp7AQQgghhBBCCCGEEMImPV63dwgiE0lPYSGEEEIIIYQQ\nQgghhMhGpFFYCCGEEEIIIYQQQgghshFpFBZCCCGEEEIIIYQQQohsRMYUFkIIIYQQQgghhBBC2KTH\nyZjCTxPpKSyEEEIIIYQQQgghhBDZiDQKCyGEEEIIIYQQQgghRDYijcJCCCGEEEIIIYQQQgiRjUij\nsBBCCCGEEEIIIYQQQmQj8qA5IYQQQgghhBBCCCGETXq8PGjuaSI9hYUQQgghhBBCCCGEECIbkUZh\nIYQQQgghhBBCCCGEyEakUVgIIYQQQgghhBBCCCGyEWkUFkIIIYQQQgghhBBCiGxEHjQnhBBCCCGE\nEEIIIYSwSY+TB809TaSnsBBCCCGEEEIIIYQQQmQj0igshBBCCCGEEEIIIYQQ2Yg0CgshhBBCCCGE\nEEIIIUQ2ImMKCyGEEEIIIYQQQgghbIuPt3cEIhMpXZdBooXITuZr15/4ld7hKbjH4fKNaHuH8NAK\n5Mtl7xAeWnD4LXuH8NAal/GydwgPzUEpe4eQKQ5fi7R3CA/NVDCfvUN4aA5PweJ0IjjG3iEIno7j\njehb9+wdwkPL5eRo7xAemovzk98X66ffdtk7hEzh6PTkr9jRwU/+8euLPw2zdwgPrZeuPQVHHBlX\ns/mkJ749ISN2rHnxqZ7PT/4WUQghhBBCCCGEEEIIIUS6SaOwEEIIIYQQQgghhBBCZCPSKCyEEEII\nIYQQQgghhBDZyJM/uJEQQgghhBBCCCGEECJL6XHZakjhp570FBZCCCGEEEIIIYQQQohsRBqFhRBC\nCCGEEEIIIYQQIhuRRmEhhBBCCCGEEEIIIYTIRqRRWAghhBBCCCGEEEIIIbIRedCcEEIIIYQQQggh\nhBDCJj1eHjT3NJGewkIIIYQQQgghhBBCCJGNSKOwEEIIIYQQQgghhBBCZCPSKCyEEEIIIYQQQggh\nhBDZiIwpLIQQQgghhBBCCCGEsEmPkzGFnybSU1gIIYQQQgghhBBCCCGyEWkUFkIIIYQQQgghhBBC\niGxEGoWFEEIIIYQQQgghhBAiG5FGYSGEEEIIIYQQQgghhMhG5EFzQgghhBBCCCGEEEII2+Lj7R2B\nyETSU1gIIYQQQgghhBBCCCGyEWkUFkIIIYQQQgghhBBCiGxEGoWFEEIIIYQQQgghhBAiG5FGYSGE\nEEIIIYQQQgghhMhG5EFzQgghhBBCCCGEEEIIm/Q43d4hiEwkPYWFEEIIIYQQQgghhBAiG5FGYSGE\nEEIIIYQQQgghhMhGZPgIIbK5HSsX8++i2USFBlPIz5/WL72Bf1DFVMufObSPVZN+4vr5s+R396B+\np17UaPVshur8/f3BnD2032qa8vWfofu7nwAQHx/PrC/e58rpE0SHh5E7Xz4CKlSlRd+BuHgUTDOn\n7SuM748MMb6/zctvULxc6jmdPriPlb8n5tSgcy9qtrbOKT11XtCOsHb6b5w/dhgUeBcL4PmPviRv\nAdc0Y05O13V2Lp3B4Y2ruBMdhVcJEw2fH4SHr7/N6U7u3syOxdMIv36FAoUKU6tTHwKq1k3MY9FU\nTu76l6iQGzjmcKJgsZLU7PgChUuVBeB2VCQ7lkznwuG9RAZfxzmfC/6ValKzUx+c87lkOI+U8vp3\n/lT2rVvO7ahIfEqVoeVLb1KwaHGb0x3bvpGNcyYTeu0ybl4+NOz5IoE16yf+fccm9q1dxtXTJ4iJ\nDOe5UWMpFlTpoeNNLYfdf87k6KbV3ImJolBxE/V7v4a7bzGb053es5ldS6YTfuMKBQoWpkbHPhSv\nUifFshun/cjRTauo1fVFKrXo/FDx/rVoHstmTScs+CZFipegzxtDKVOpcqrlj+zbw7Qfv+PimdO4\neRakfa8XaNaxi1WZ0Js3mPXLj+zbvoXbMTEU8vHlpaEjKFu5KgBzf/2Z7X+vI/j6NXI4OVG8dCDd\nXh6IqXzq62GaeSycx9JZ0yx59HtzKGUqVUm1/OF9e5j6w7eWPDr07kPzFPKY+cuP7N222ZLHy+++\nT5A5j1sxMcya8CM7N/5NZHg4nt7eNH+2M217PPdAOei6zj9zp7BnzTJuR0fiW6osbV55i0J+tpf/\nI9s2smHWJEKvXsbN24cmvV+iTK0G6a439PoVNs2bxplD+4gKDSafmwfl6j5Dw259cMqVK0M5rFk0\nj+WzzcuTfwleeHMogRVtL08zfvyOi2dP4+ZRkLa9X6DZs4nzYcGkiSyc/KvVNAXcPZjw5xrL+7CQ\nYGb/8gMHdm4nJiqSwIpV6Pv2MAoX9ctQ7Ake9Tpx714sc3/9hf3bt3Dt0kWc8+YlqEo1eg0YjKd3\n4XTF/Kj31TGREWyYNYmT+3cTduMqeVxcCaxWm6bPvUwelwKWOi6f0vhrygQunTyGcnAgqHZDWr34\nOrmc89g9B4DFP43m9IG9RIbcJGduZ/wCy9O8z6sUKupvVc+JvTvYMHsyV8+cxNHJicIlSvPi59/f\nF5M9ji9+Gz6YMykcM/V87xPL+5uXzrNq8i+cO3KQe7F38fIrTpNe/SldteZ9MWXVfjg99QZfvsCG\nGRO5cOwgcbGxeBb1p0HXvgRUrmEpc+bgHjbOmcyN86dxypWbCo1a0Oz5l9k0fzp71izjVnQkRUqV\npe2rb6e57Ty89R82zJpEyNXLuHv70OS5lymbbNv595zJNuu9FRXJyt++59iuLQAEVq9L65ffxDlf\nfkuZE/t28vecyVw/d5ocTjnxK1OO5n1ew9O3qKXMng1rWD9/JjcunSd3nryUrlyNDi8PxsXdA4DN\nyxayYf4sIkKC8S5WnI4D3iSgfOrHMScP7GPJxB+4eu4MBTw8eaZrb+q27Wj5+9o50ziw5R+uXzxP\nDqec+AcG0bb/AAr7B1jKzPzmf+xau9Kq3mKBQbz9/W82f9eM6vVMSVpWL0I+Zye0C+H8suwI569H\npVrer1A+ejcpSUkfF7zd8zBz/UlmbTh5X529m5S0+iw08g7PffV3psaeoGejAJpX9SVfbieOXwpn\nwoqjXLgRnWr5ogXz0qtxSQIK58fbLQ+z/znF7H9O3Vdnz0YBVp+FRt2hzzcbsySHF1qZaFPXn/zO\nThw9F8oP8w5w7mpkquWLeeenb+tAShYtgI9nXqauPMa0VZpVmZmjmuHtcf/2fvvhq3wwYUem55Ae\nBetXo8zQF3GvGkQeXy+29R3OmamL7RKLEE8Cx1GjRtk7BiHEI3QkOHpUwv8P/rueJeO/pnmfV2n2\n/CtEhYawevLPVGzUnNx58903bcjVy/z23iDK1KjLs4Pfw7WgF8snjqWQn7/lBCvFMKf8AAAgAElE\nQVQ9de5dv4oS5avwwqgx1OvYg3ode1C2VgNy5MwJGAfp0WGh1O3QnQZdn8NUtRYHN//NgU3rqN6i\nHUqlnt+BTetZ/NPXtOzzKs37vEJkaAir//iZSjZymjhsEGVq1qXTm+/hVtCLPyeYc/LzT3edF7TD\nTB45lIqNmtGq/2vUavMs7j5F8PQpYskrqciYWJvzae/K+exdNZ/Gfd6kWpvu3Dh7gt3L5xDUsBWO\nTk4pTnPl5BGWj/2Iik07UL/XABwdHdk4/Sf8ylUln7snANGhwZSq2ZBqbXtgqtuEsCsX2LZwCmXr\nN8cptzPh169wZt82qrTqQvUOz1E0qDKHNiznwuG9BNZpYvV9uXNm/LritqVz2LZkNq1fHUKdjr25\nelpjy6KZVG7alhyp5HVRO8ycL4dTvXVnmvd7HQfHHKz+fRwlKlW3XCS4ckrDyTkPQXWfQdu5mQqN\nWuJayDvNeG7duZfhHPavXsD+VQto+MJgqrTuxo1zJ9i7Yi5lGrbEMUfKOVw9dZSV339MuSYdqNfj\nVZRjDv6dOZ6iQVXI5+ZpVfbU7s2c3LkR5eCAV0Ag3iXL2oyneMH7l+sEW9et4bfR/6PXwMH0ePV1\nwoKDmTF+HPWbtyJPkhPaBNc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I/G4eVnWnZ9tZslKybWflGpblI2HbmbRM8novaIfNw46U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41jgY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\nRERERERERERExBj5/5jGAnwK1I6kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1584x1584 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate and visualize correlations (relationships) between variables; heat map.\n",
    "\n",
    "# Display correlations\n",
    "plt.figure(figsize = (22, 22))\n",
    "\n",
    "sns.heatmap(df.corr(), cmap = plt.cm.RdYlBu_r, vmin = -0.25, annot = True, vmax = 0.6)\n",
    "plt.title('Correlation Heatmap');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "SQSqqjrAyT67",
    "outputId": "ff65591b-9830-4e50-a47e-8b09227ab69a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of columns to drop:  4\n",
      "columns to drop: ['AMT_ANNUITY', 'AMT_GOODS_PRICE', 'CNT_FAM_MEMBERS', 'REGION_RATING_CLIENT_W_CITY']\n"
     ]
    }
   ],
   "source": [
    "# remove collinear variables\n",
    "\n",
    "corr_matrix = df.corr().abs()\n",
    "\n",
    "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
    "upper.head(10)\n",
    "\n",
    "\n",
    "threshold = 0.75\n",
    "to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n",
    "print('number of columns to drop: ', len(to_drop))\n",
    "print('columns to drop:', to_drop)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uAmD7gVBwYpD"
   },
   "outputs": [],
   "source": [
    "df = df.drop(to_drop, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "-CAMj53OxmwW",
    "outputId": "87429f9b-dc8c-4ef6-c583-f10cec1ab919"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY',\n",
       "       'CNT_CHILDREN', 'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'NAME_INCOME_TYPE',\n",
       "       'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'DAYS_BIRTH',\n",
       "       'DAYS_EMPLOYED', 'OCCUPATION_TYPE', 'REGION_RATING_CLIENT',\n",
       "       'AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
       "       'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON',\n",
       "       'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR',\n",
       "       'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'TARGET'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uyySips4PjHw"
   },
   "outputs": [],
   "source": [
    "df['DAYS_BIRTH'] = abs(df['DAYS_BIRTH'])\n",
    "df['DAYS_EMPLOYED'] = abs(df['DAYS_EMPLOYED'])\n",
    "\n",
    "anomolous = df[df['DAYS_EMPLOYED'] == 365243].index\n",
    "df = df.drop(index=anomolous)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 292
    },
    "colab_type": "code",
    "id": "HmAozR2WSOd8",
    "outputId": "d2276a5e-5f7c-4da5-ceac-6df7b8987b0f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>NAME_INCOME_TYPE</th>\n",
       "      <th>NAME_EDUCATION_TYPE</th>\n",
       "      <th>NAME_FAMILY_STATUS</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>REGION_RATING_CLIENT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>9461</td>\n",
       "      <td>637</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.083037</td>\n",
       "      <td>0.262949</td>\n",
       "      <td>0.139376</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>171000.0</td>\n",
       "      <td>1560726.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>13778</td>\n",
       "      <td>3130</td>\n",
       "      <td>Accountants</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.774761</td>\n",
       "      <td>0.724000</td>\n",
       "      <td>0.492060</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>652500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>10197</td>\n",
       "      <td>679</td>\n",
       "      <td>Core staff</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.319760</td>\n",
       "      <td>0.651862</td>\n",
       "      <td>0.363945</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>80865.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>13439</td>\n",
       "      <td>2717</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.464831</td>\n",
       "      <td>0.715042</td>\n",
       "      <td>0.176653</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>299772.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>8728</td>\n",
       "      <td>1157</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.115634</td>\n",
       "      <td>0.346634</td>\n",
       "      <td>0.678568</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT      NAME_INCOME_TYPE            NAME_EDUCATION_TYPE    NAME_FAMILY_STATUS  DAYS_BIRTH  DAYS_EMPLOYED OCCUPATION_TYPE  REGION_RATING_CLIENT  AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  TARGET\n",
       "0   Cash loans         M           N            Y               0             202500.0          406597.5    Working               Secondary / secondary special  Single / not married  9461        637            Laborers        2                     0.0                         0.0                        0.0                         0.0                        0.0                        1.0                         0.083037      0.262949      0.139376      1     \n",
       "6   Cash loans         F           Y            Y               1             171000.0          1560726.0   Commercial associate  Higher education               Married               13778       3130           Accountants     2                     0.0                         0.0                        0.0                         1.0                        1.0                        2.0                         0.774761      0.724000      0.492060      0     \n",
       "10  Cash loans         F           N            Y               1             112500.0          652500.0    Working               Higher education               Married               10197       679            Core staff      2                     0.0                         0.0                        0.0                         1.0                        0.0                        0.0                         0.319760      0.651862      0.363945      0     \n",
       "12  Cash loans         F           N            Y               0             67500.0           80865.0     Working               Secondary / secondary special  Married               13439       2717           Laborers        2                     0.0                         0.0                        0.0                         1.0                        0.0                        0.0                         0.464831      0.715042      0.176653      0     \n",
       "15  Cash loans         M           Y            Y               0             157500.0          299772.0    Working               Secondary / secondary special  Single / not married  8728        1157           Laborers        3                     0.0                         0.0                        0.0                         0.0                        0.0                        1.0                         0.115634      0.346634      0.678568      0     "
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cC2AbM0CEsLH"
   },
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "kn77XHezEsLe",
    "outputId": "07d698ce-3c43-4962-e181-a98824041330"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(82454,)"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Split data to (1) Relevant features and (2) Target\n",
    "features = df.drop(['TARGET'], axis=1)\n",
    "target = df['TARGET']\n",
    "target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "86dotfm7QqSH",
    "outputId": "b0337c3a-4ee6-4517-d64c-06d2f5df896c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(82454, 57)"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "ZhHxXLzZHOP6",
    "outputId": "edf1bcf4-6f62-4718-cb32-76a5ec38a0be"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
      "  \"(https://pypi.org/project/six/).\", DeprecationWarning)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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VsS2OiEkRMWnUqFH9HZKZmfWirpCRdISkz1FMBvgBsAHYJWm+pCP7sb/NwNnAm4G/Bu6U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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# handle imbalance dataset\n",
    "from imblearn.under_sampling import RandomUnderSampler \n",
    "\n",
    "rus = RandomUnderSampler()\n",
    "X_sampled, y_sampled = rus.fit_resample(features, target)\n",
    "\n",
    "plt.hist(y_sampled)\n",
    "plt.ylabel('No of items')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 304
    },
    "colab_type": "code",
    "id": "B2UbkvQShJXf",
    "outputId": "375046bc-5947-4b6f-cb9f-7d99408d8f27"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2e6cf4978>"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAEOCAYAAABbxmo1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGUlSZwwZSVJnDBlJUmcMGUlSZ/4/qahD06h72WoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(y_sampled)\n",
    "sns.countplot(x = 0,data = df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "e3pTlOzAd4Bd",
    "outputId": "f7c67be4-d5e0-4947-aed9-4ec75c151bd8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12496, 57)\n",
      "(12496,)\n"
     ]
    }
   ],
   "source": [
    "print(X_sampled.shape)\n",
    "print(y_sampled.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9qlokA_JEsLm"
   },
   "outputs": [],
   "source": [
    "# Split features and target to x and y train and test (Indicate % split to test)\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_sampled, y_sampled, test_size=0.10, random_state=12)\n",
    "\n",
    "#feature scaling\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "sc_X = MinMaxScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "QHzDi6O0EsLt",
    "outputId": "4b58fa47-e7a0-42a1-d9f6-d2a80a20049b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11246, 57)\n",
      "(11246,)\n",
      "(1250, 57)\n",
      "(1250,)\n"
     ]
    }
   ],
   "source": [
    "# Check shape of x and y train and test\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R8HpaLvLEsL1"
   },
   "outputs": [],
   "source": [
    "# Use SMOTE to balance your data\n",
    "# smt = SMOTE()\n",
    "# X_train, y_train = smt.fit_sample(X_train, y_train)\n",
    "# print('after smote: \\n', np.bincount(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 337
    },
    "colab_type": "code",
    "id": "cnYtmzVrEsL6",
    "outputId": "6131db14-2dca-4de9-9768-5c84addb89a4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([5615.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "        5631.]),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kPg0jHJa0P/f17N/X1bYEmOp+9d/e3t9Tc6Q+uh/j+5KsSzKZZHJqamoOw5ck9TpmVyuV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\n9CgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Verify that target/label has been balanced\n",
    "plt.hist(y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L2l6m6uUEsMB"
   },
   "outputs": [],
   "source": [
    "# Use logistic regression to fit default or non-default\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression(C=10)\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "# Assign a variable to your prediction using train data\n",
    "train_pred = logreg.predict(X_train)\n",
    "\n",
    "# Assign a variable to your prediction using test data\n",
    "test_pred = logreg.predict(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "T56_LsmTEsMK",
    "outputId": "bd388b37-f746-4218-e4e1-62858f4739c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.688\n"
     ]
    }
   ],
   "source": [
    "# Obtain accuracy score of features to target with train data set\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "3dyMAkoaEsMS",
    "outputId": "eebc90cf-9070-47f1-bfb4-485c4c3ae174"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.69      0.69       633\n",
      "           1       0.68      0.69      0.69       617\n",
      "\n",
      "    accuracy                           0.69      1250\n",
      "   macro avg       0.69      0.69      0.69      1250\n",
      "weighted avg       0.69      0.69      0.69      1250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Use classification report to view precision and recall\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "colab_type": "code",
    "id": "-lzw4UfeVgV1",
    "outputId": "fcb7cac2-cdfa-496e-b83a-88710a42083c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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83VtEkoGvgbeBb4wx6Z4O7FJ16NDBrF+/3u4wSpVMh+Ge+b+xdv8pro+OYN69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\nnJOLmZlVzsnFzMwq9xOVy9F0WkO2YQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# roc curve\n",
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, test_pred)\n",
    "\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "\n",
    "plot_roc_curve(fpr, tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "3KTazh6kEsMg",
    "outputId": "5d41500a-c777-43ce-b2a7-aa8c018b6793"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[434, 199],\n",
       "       [191, 426]])"
      ]
     },
     "execution_count": 49,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, test_pred)\n",
    "\n",
    "# Interpret the results\n",
    "# What does each quarter/corner of the matrix mean?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QlhobWPpEsMn"
   },
   "source": [
    "### Model Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KPm_XBpLEsMs"
   },
   "outputs": [],
   "source": [
    "# Define 10 folds for stratified cross-validation\n",
    "from sklearn import model_selection as ms\n",
    "ten_fold_cv = ms.StratifiedKFold(n_splits=10, shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "yN3VJlshEsM7",
    "outputId": "5422d33c-a36a-45a7-8e3a-c5be0eca0fc9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.585828057519973"
      ]
     },
     "execution_count": 51,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average AUC across folds\n",
    "aucs = ms.cross_val_score(LogisticRegression(), features, target, scoring='roc_auc', cv=ten_fold_cv)\n",
    "np.mean(aucs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "568udbNgEsNB"
   },
   "outputs": [],
   "source": [
    "# Split features and target to x and y train and test (Indicate % split to test)\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_sampled, y_sampled, test_size=0.10, random_state=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LUBTiekjEsNF"
   },
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JHj3Dtc_EsNH"
   },
   "outputs": [],
   "source": [
    "# Decision tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "BFnpnMJ7EsNQ",
    "outputId": "8238d5d2-0bc2-4bf1-d71e-1fb7ad0fd602"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.688\n"
     ]
    }
   ],
   "source": [
    "# Run a decision tree\n",
    "tree = DecisionTreeClassifier()\n",
    "tree.fit(X_train, y_train)\n",
    "\n",
    "# Assess accuracy of predicted target with actual target\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, test_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "ui19aRiKEsNV",
    "outputId": "167cabef-09c0-4aa8-aef7-866ddaba1dea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.61      0.58      0.59       633\n",
      "           1       0.59      0.62      0.61       617\n",
      "\n",
      "    accuracy                           0.60      1250\n",
      "   macro avg       0.60      0.60      0.60      1250\n",
      "weighted avg       0.60      0.60      0.60      1250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Use classification report to view precision and recall\n",
    "y_pred = tree.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "49DaDtcX-shh",
    "outputId": "25e7cbd8-96c2-49e5-f71d-c538e8558f65"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5392710743533502"
      ]
     },
     "execution_count": 56,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ten_fold_cv = ms.StratifiedKFold(n_splits=10, shuffle=True)\n",
    "aucs = ms.cross_val_score(tree, features, target, scoring='roc_auc', cv=ten_fold_cv)\n",
    "np.mean(aucs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "colab_type": "code",
    "id": "gDyFzGqt8ZsZ",
    "outputId": "14e898a6-94f4-4f55-a883-3b2c8783495d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Yu2wba3efoEoFLz4a3JFO19d0d1gucanQZLdu3Rg9ejSvvvoq1apVc3dYqpyx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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# roc curve\n",
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "\n",
    "plot_roc_curve(fpr, tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CiV9tiimEsNe"
   },
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "3brmu-KZBRVN",
    "outputId": "3a7a89e4-6e4d-4476-807f-be75f13ae694"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.63      0.70      0.66       633\n",
      "           1       0.65      0.58      0.61       617\n",
      "\n",
      "    accuracy                           0.64      1250\n",
      "   macro avg       0.64      0.64      0.64      1250\n",
      "weighted avg       0.64      0.64      0.64      1250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier(n_estimators=10)\n",
    "rfc.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rfc.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "colab_type": "code",
    "id": "Qr_rGTDpc9ss",
    "outputId": "29bb0b4f-b6cc-482f-8660-69712b915469"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PZvHixdSqVYvvvvuOli3Tq3ur7ORqchEgrfazEsAVz4WjlHJVoJdjyYrY2Fg+/fRTJkyY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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# roc curve\n",
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "\n",
    "plot_roc_curve(fpr, tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ghCwVARnc_qj"
   },
   "outputs": [],
   "source": [
    "feature_importances = rfc.feature_importances_\n",
    "\n",
    "feature_importances = pd.DataFrame({'feature': list(features.columns), 'importance': feature_importances}).sort_values('importance', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### External sources has the highest feature importance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 514
    },
    "colab_type": "code",
    "id": "T6o6QZMM_roU",
    "outputId": "40118c30-1025-4924-e192-78b6a43b8702"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>EXT_SOURCE_3</td>\n",
       "      <td>0.135662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>EXT_SOURCE_2</td>\n",
       "      <td>0.115416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>EXT_SOURCE_1</td>\n",
       "      <td>0.109158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAYS_EMPLOYED</td>\n",
       "      <td>0.086352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAYS_BIRTH</td>\n",
       "      <td>0.077349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMT_CREDIT</td>\n",
       "      <td>0.075920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMT_INCOME_TOTAL</td>\n",
       "      <td>0.058415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_YEAR</td>\n",
       "      <td>0.038086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CNT_CHILDREN</td>\n",
       "      <td>0.021703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>REGION_RATING_CLIENT</td>\n",
       "      <td>0.016202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_QRT</td>\n",
       "      <td>0.015844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_MON</td>\n",
       "      <td>0.015737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>NAME_FAMILY_STATUS_Married</td>\n",
       "      <td>0.011776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>OCCUPATION_TYPE_Laborers</td>\n",
       "      <td>0.011758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NAME_INCOME_TYPE_Working</td>\n",
       "      <td>0.011608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       feature  importance\n",
       "14  EXT_SOURCE_3                0.135662  \n",
       "13  EXT_SOURCE_2                0.115416  \n",
       "12  EXT_SOURCE_1                0.109158  \n",
       "4   DAYS_EMPLOYED               0.086352  \n",
       "3   DAYS_BIRTH                  0.077349  \n",
       "2   AMT_CREDIT                  0.075920  \n",
       "1   AMT_INCOME_TOTAL            0.058415  \n",
       "11  AMT_REQ_CREDIT_BUREAU_YEAR  0.038086  \n",
       "0   CNT_CHILDREN                0.021703  \n",
       "5   REGION_RATING_CLIENT        0.016202  \n",
       "10  AMT_REQ_CREDIT_BUREAU_QRT   0.015844  \n",
       "9   AMT_REQ_CREDIT_BUREAU_MON   0.015737  \n",
       "35  NAME_FAMILY_STATUS_Married  0.011776  \n",
       "47  OCCUPATION_TYPE_Laborers    0.011758  \n",
       "28  NAME_INCOME_TYPE_Working    0.011608  "
      ]
     },
     "execution_count": 61,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "wi92fDT_AEL0",
    "outputId": "5ae4b25c-7c54-4398-976c-d60747001430"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.653264886273125"
      ]
     },
     "execution_count": 62,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aucs = ms.cross_val_score(rfc,features, target, scoring='roc_auc', cv=ten_fold_cv)\n",
    "np.mean(aucs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "wTSDib7gAt7F",
    "outputId": "895d0aed-0ab3-44ee-c0a1-391c6378c904"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  2 trees: mean AUC 56.20%\n",
      "  5 trees: mean AUC 61.02%\n",
      " 10 trees: mean AUC 65.49%\n",
      " 20 trees: mean AUC 67.77%\n",
      " 50 trees: mean AUC 71.16%\n",
      "100 trees: mean AUC 72.65%\n"
     ]
    }
   ],
   "source": [
    "for n_trees in [2, 5, 10, 20, 50, 100]:\n",
    "    aucs = ms.cross_val_score(RandomForestClassifier(n_estimators=n_trees), features, target, scoring='roc_auc', cv=ten_fold_cv)\n",
    "    print('{:>3} trees: mean AUC {:.2%}'.format(n_trees, np.mean(aucs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5-Ao5kv2AyNe"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier,GradientBoostingClassifier\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "XoKWmrGVClQh",
    "outputId": "3d9e177f-c8d8-44b6-bc31-791cd4a07988"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.68      0.70      0.69       633\n",
      "           1       0.69      0.66      0.67       617\n",
      "\n",
      "    accuracy                           0.68      1250\n",
      "   macro avg       0.68      0.68      0.68      1250\n",
      "weighted avg       0.68      0.68      0.68      1250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Adaboost\n",
    "ada_clf = AdaBoostClassifier(n_estimators=10)\n",
    "ada_clf.fit(X_train, y_train)\n",
    "y_pred = ada_clf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "sJSh6aHgCw_k",
    "outputId": "d2e399c5-6ec0-43e7-d6cc-279cbbdbc25f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  2 trees: mean AUC 67.61%\n",
      "  5 trees: mean AUC 71.86%\n",
      " 10 trees: mean AUC 73.17%\n",
      " 20 trees: mean AUC 74.54%\n",
      " 50 trees: mean AUC 75.09%\n",
      "100 trees: mean AUC 75.17%\n"
     ]
    }
   ],
   "source": [
    "for n_trees in [2, 5, 10, 20, 50, 100]:\n",
    "    aucs = ms.cross_val_score(AdaBoostClassifier(n_estimators=n_trees), features, target, scoring='roc_auc', cv=ten_fold_cv)\n",
    "    print('{:>3} trees: mean AUC {:.2%}'.format(n_trees, np.mean(aucs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZXyrvWrbEFza"
   },
   "outputs": [],
   "source": [
    "feature_importances = ada_clf.feature_importances_\n",
    "\n",
    "feature_importances = pd.DataFrame({'feature': list(features.columns), 'importance': feature_importances}).sort_values('importance', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 514
    },
    "colab_type": "code",
    "id": "6zoPRnAaGs9U",
    "outputId": "5f7fd38c-5946-4b7b-ba13-7dc274c0fa6c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>EXT_SOURCE_3</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>EXT_SOURCE_2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>EXT_SOURCE_1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NAME_CONTRACT_TYPE_Revolving loans</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NAME_EDUCATION_TYPE_Secondary / secondary special</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>OCCUPATION_TYPE_Cleaning staff</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>OCCUPATION_TYPE_Drivers</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>OCCUPATION_TYPE_Core staff</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>OCCUPATION_TYPE_Cooking staff</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CNT_CHILDREN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>OCCUPATION_TYPE_HR staff</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>NAME_FAMILY_STATUS_Widow</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>NAME_FAMILY_STATUS_Single / not married</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>NAME_FAMILY_STATUS_Separated</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>NAME_FAMILY_STATUS_Married</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              feature  importance\n",
       "14  EXT_SOURCE_3                                       0.4       \n",
       "13  EXT_SOURCE_2                                       0.2       \n",
       "12  EXT_SOURCE_1                                       0.2       \n",
       "16  NAME_CONTRACT_TYPE_Revolving loans                 0.1       \n",
       "33  NAME_EDUCATION_TYPE_Secondary / secondary special  0.1       \n",
       "40  OCCUPATION_TYPE_Cleaning staff                     0.0       \n",
       "43  OCCUPATION_TYPE_Drivers                            0.0       \n",
       "42  OCCUPATION_TYPE_Core staff                         0.0       \n",
       "41  OCCUPATION_TYPE_Cooking staff                      0.0       \n",
       "0   CNT_CHILDREN                                       0.0       \n",
       "44  OCCUPATION_TYPE_HR staff                           0.0       \n",
       "38  NAME_FAMILY_STATUS_Widow                           0.0       \n",
       "37  NAME_FAMILY_STATUS_Single / not married            0.0       \n",
       "36  NAME_FAMILY_STATUS_Separated                       0.0       \n",
       "35  NAME_FAMILY_STATUS_Married                         0.0       "
      ]
     },
     "execution_count": 68,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "NNEh855eEF5r",
    "outputId": "a6ea7239-2188-4e90-8c16-1a52d85053a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.70      0.70       633\n",
      "           1       0.69      0.69      0.69       617\n",
      "\n",
      "    accuracy                           0.69      1250\n",
      "   macro avg       0.69      0.69      0.69      1250\n",
      "weighted avg       0.69      0.69      0.69      1250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = GradientBoostingClassifier(n_estimators=150)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "0VkBalNnEL3N",
    "outputId": "ac43517b-5161-44d1-91c7-e31f2f8eb78c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6927701434603045"
      ]
     },
     "execution_count": 70,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "59_PtHjsEF-7",
    "outputId": "da170e54-23d2-4788-d6d3-f8f81c7d87b7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  2 trees: mean AUC 71.54%\n",
      "  5 trees: mean AUC 72.45%\n",
      " 10 trees: mean AUC 73.31%\n",
      " 20 trees: mean AUC 74.04%\n",
      " 50 trees: mean AUC 75.23%\n",
      "100 trees: mean AUC 75.68%\n"
     ]
    }
   ],
   "source": [
    "for n_trees in [2, 5, 10, 20, 50, 100]:\n",
    "    aucs = ms.cross_val_score(GradientBoostingClassifier(n_estimators=n_trees), features, target, scoring='roc_auc', cv=ten_fold_cv)\n",
    "    print('{:>3} trees: mean AUC {:.2%}'.format(n_trees, np.mean(aucs)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "12n0pWRpLK2D"
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZYDlNkgiL9-o"
   },
   "outputs": [],
   "source": [
    "model=xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "colab_type": "code",
    "id": "5-YTgE3EMBtI",
    "outputId": "02c4b688-7b2a-432f-ebba-106cb5026756"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/xgboost/sklearn.py:235: DeprecationWarning: The seed parameter is deprecated as of version .6.Please use random_state instead.seed is deprecated.\n",
      "  'seed is deprecated.', DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.69      0.69       633\n",
      "           1       0.68      0.68      0.68       617\n",
      "\n",
      "    accuracy                           0.69      1250\n",
      "   macro avg       0.69      0.69      0.69      1250\n",
      "weighted avg       0.69      0.69      0.69      1250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xg_cl = xgb.XGBClassifier(objective='binary:logistic',n_estimators=100, seed=123)\n",
    "\n",
    "xg_cl.fit(X_train, y_train)\n",
    "preds = xg_cl.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "colab_type": "code",
    "id": "BYOwL553eN6d",
    "outputId": "d7dc2c76-0258-453a-ae08-240e76973fde"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vhnWkTLDLc3CVF5w6dYpRo0aRnp7Oe++9Z3c4yk+JyHpjTJsrfR9Xr7n0BjKx\nylTHROQtZ4lKFRPr9p1g1ordBAjM6NVCE4uP+fzzz2nSpAnvvvsu1apV00aTynauXnP50hhzH9bI\nrpFYF/JXiMgeEXnOkwEq+51Ny2Tkwo04DAy8oR5taleyOyTllJCQQK9evbjrrrsIDw9nzZo1vPTS\nS9poUtnukv4FGmPOGmPeNcbcAsQAp4FnPBKZ8hnPf72VAyeSaRJZjuFdGxa+gfKazMxMVq1axb/+\n9S/++9//0rp1a7tDUgq4hMaVACISDNwB/B24DYgHpnogLuUjftwax0f/PUhQYAAzercgKFD/Irbb\ngQMHeOutt3j++eeJjIxk9+7dhIaG2h2WUhdw6ZtCRLqIyH+wWrW8DZzASi61jDEuzXNR/ifxbBpP\nf7IJgDG3NKJR1bI2R1S8ORwO3nzzTZo2bcprr73G1q1bATSxKJ/k6p+h32I1rXwMiDDG9DXGLDeu\nDDVTfskYw9hP/+L4uXTa1Q2jT4c6dodUrO3YsYNOnToxZMgQ2rVrx+bNm2nWTG8jrXyXq2WxSGPM\nCY9GonzKonWH+HFrHGVDApnaK4YAbUppG4fDQY8ePTh69CjvvPMODz/8sHZFUD6vwOQiIqHGmGTn\n01QRKfDcO9d6qgg4cDyZyV9tAeC57s2oVkHvzWaHTZs20bBhQ0JCQnj//feJjIwkMjLS7rCUcsnF\nymJJIhLu/P0sVvfjgh6qiMhyGEYu3MC59Cz+dlUk3Vtoc0NvS01NZdy4cbRq1Sqn0WSrVq00sSi/\ncrGy2O1YF+6zf9frK8XA7J93s27/ScLLBvOvHs20/OJlq1evpm/fvsTGxvLwww/z+OOP2x2SUpel\nwORijPkh1++uttVXfmzz4dPM+HEHAFPujaFCaJDNERUvr7/+OsOGDaNGjRp8//333HLLLXaHpNRl\nc3UocrKIVMnn9UoiotdbioDUjCxGfLyBjCzDQ+1qcUPD//nPrTwkMzMTgM6dOzN06FA2b96siUX5\nPVeHIocA+dVHQi7hPZQPm/LDdnbGn6Vu5dKMvS3a7nCKhZMnT/Loo4/y8MMPA9C0aVNmzpxJ2bI6\nn0j5v4sORRaRwc5fDdZ9WM7mWlwCuAHY4aHYlJf8uiuReb/spUSAMKN3C0oF5XdHa+VOn376KUOG\nDCEhIYGnnnoKh8Oh/cBUkVLYPJdnnT8FGAXkbrWaDuwDBqP81umUDJ5ctBGAJzo3IKZGBZsjKtri\n4+MZPHgwn3zyCS1btuS7776jRYsWdoellNtdNLkYYyIBROQ3rJt6nfRKVMprJn25haOnU4mpUYEh\nN9azO5wiLysri19//ZUXX7d6QfkAACAASURBVHyRUaNGUbJkSbtDUsojXJqhb4xp5+lAlPd9vekI\nn/15mFIlSzCjVwyBJbQs4wn79+9n1qxZvPDCC0RGRrJnzx5KldKJqapou9gM/VeAycaYc87fC2SM\nGeP2yJRHHTudyrjPNgMw7m/R1K1SxuaIih6Hw8Ebb7zB2LFjEREefPBBmjVrpolFFQsXO3O5HiiZ\n6/eC6ORKP2OMYfTijZxOyaBToyr8/ZqadodU5Gzbto1+/fqxevVqbr31Vt566y1q1apld1hKec3F\nJlG2y+935f/e+30/q3YmUjG0JK/cfZXOwnczh8PB3XffTVxcHAsWLODBBx/UY6yKncu+EbqIVAeO\nGWMy3RiP8rBd8Wd54dttALxwV3PCy4XYHFHRsWHDBho1akSpUqX44IMPiIqKIiIiwu6wlLKFqzP0\nJ4nIg7mefw0cAI6JSBtPBafcKyPLwciFG0jNcNCzVTVua66NEN0hJSWFsWPH0qZNG6ZMmQJAy5Yt\nNbGoYs3V4UGPALsBROQWoB3QCVgEvOSJwJT7vf7TLjYdOk21CqWYdGdTu8MpElatWkWLFi146aWX\nePjhhxk6dKjdISnlE1xNLlWBQ87fbwcWGWN+BqYDrT0RmHKvPw+c5I3luxCBab1iKBei8yuu1MyZ\nM+nYsSPp6en8+OOPzJs3j4oVK9odllI+wdXkcgKo7vz9FmBZru21V4iPS07PZOTCjWQ5DI9dX5dr\n64bZHZJfy2402bVrV4YPH85ff/1F165dbY5KKd/i6gX9z4H3RWQbEA5kt+CPwVkuU77rhW+3sTfx\nHI2rlmXUzQ3tDsdvHT9+nJEjR5KZmckHH3xA06ZNmTFjht1hKeWTXD1zGQ78BzgM3GqMyb77ZC1g\njicCU+6xfHs87/9+gKASAczo3YLgQD3RvFTGGBYtWkSTJk348MMPqVu3Lg6Ho/ANlSrGXG3/kg78\nK5/Xp7g9IuU2J86lM2bxJgBG3dyQ6MhyNkfkf+Li4hg4cCCff/45rVu3ZsmSJcTExNgdllI+z+V5\nLiJSCRgINMGalb8FmGOMOXHRDZUtjDE88+lfJCSlcXWdSvS7vq7dIfklYwxr167llVdeYcSIEQQG\nXvbUMKWKFVfnuVyDdW1lIBCMdZOwwcAuEWnrufDU5fr0j8N8v+UYZYIDmXZvDCUCdIa4q/bs2cPo\n0aNxOBxUrVqV3bt3M3r0aE0sSl0CV6+5TMO6qF/XGHOvMeZeoC7wJaBXNH3MwRPJTPxyCwAT72hC\njUqhNkfkH7Kysnj11Vdp3rw5s2fPZuvWrQCEhGgXA6UulavJpTXwcu5WL87fXwFaeSIwdXmyHIZR\nizZyNi2TW5pGcE/r6oVvpNi6dSvXXXcdI0aMoFOnTmzZsoVmzZrZHZZSfsvV8/wkoAYQm+f16s5l\nykfM+2UPa/eeoHKZYF64q7k2THSBw+HgnnvuIT4+nvfff58HHnhAj5tSV8jVM5eFwDwRuVtEIp2P\ne4C5zmUuE5HBIrJXRFJFZL2IXKydf+7trhORTBHZfCn7K05ij51h6g87AHjlnuaElQm2OSLf9uef\nf5KSkkJAQAAffvghW7du5e9//7smFqXcwNXk8iTwHfARVhuYQ8D/Ad8Co13dmYj0BmYCLwAtgdXA\ndyJy0RuKiEhFYAHnOwOoPNIysxj+0QbSsxw8cE1NOjfWpokFSUlJ4amnnqJNmza88op1H7wWLVoQ\nHh5uc2RKFR0uJRdjTKoxZgBQBbjW+ahijBlkjEm9hP2NBOYbY+YaY7YZY4YCR4FBhWw3D3gX+O0S\n9lWsTP9xB7HHkqgdFsq426PtDsdnrVy5kquuuopXXnmFvn37MmzYMLtDUqpIKjS5iEiUiPxDRPoA\nlY0x/3U+Tl3KjkQkCGtgwJI8i5YA7S+y3WAgAnj+UvZXnKzZc5w5P+8hQGB67xaUDtYhs/l59dVX\n6dSpEw6Hg2XLljFnzhwqVKhgd1hKFUkXTS4i0h7YinXW8Daw2Xmt5XJUxmpyGZfn9Tisrsv57b85\nMBF40BiTVdgORKS/iKwTkXUJCQmXGaZ/SUrNYOTCjRgDj99Yn1Y1tStvXhkZGQDcfPPNjBo1ik2b\nNtG5c2ebo1KqaCvszOV54HegPtbIsA+BqZ4OCkBEgoGPgSeNMXtd2cYYM8cY08YY06ZKlSqeDdBH\nTP5qK4dPpdC8WnmGdmlgdzg+JTExkQcffJCHHnoIgCZNmjB16lRKly5tc2RKFX2FJZcYYIwxZo8x\n5ggwDKghIpdTS0gEsrBKXLlFAMfyWT8SiAbecY4SywQmAE2dz2++jBiKlO83H2Xx+kMEB1pNKUuW\ncHV8RtFmjOGjjz4iOjqahQsX0qhRI200qZSXFfZtVJFcX/zObsjJztcvibP55XrgpjyLbsIaNZbX\nYaA50CLX4y1gl/P3/LYpNuKTUhn76V8AjL2tMfXDy9gckW84duwYPXr04P7776dOnTqsX7+eSZMm\nERCgiVcpb3Llym9DEamc67kADUSkVPYLxpitLu5vOvCeiKwFfsXqVRaFlTQQkQXO93vIGJMBXDCn\nRUTigTRjTLGe62KM4anFmziZnMH1DSrzULvadofkU/744w+mTZvGsGHDKFFCbzGglB1cSS4r8zwX\nrJuFGefvBhfvRmmM+VhEwoDxWGWvzcDtxpj9zlUuOt9FWf5v7UGWb0+gfKmSTLknhoBi3pRy9+7d\nvPnmm0yZMoWqVauya9cugoN1AqlSdhJjTMELRRq58ibGmO1ui8hN2rRpY9atW2d3GG63L/Ect81c\nRUpGFq/f35I7YqLsDsk2WVlZzJw5k/Hjx1OyZElWr15N06ZN7Q5LKb8mIuuNMW2u9H0ueubii0mj\nOMvMcjBi4QZSMrLo3iKqWCeWzZs307dvX9auXUu3bt2YNWsW1atrk06lfIXOtvMjs1bs5s8Dp4gs\nH8I/7yy+HXsdDge9evUiISGBDz/8kPvuu0/7gSnlYzS5+IlNh04xc9lOAKbeG0P50JI2R+R969at\no0mTJoSGhvJ///d/REVFUVzmMynlb3R8ph9ISc9ixMcbyHQY+nSoQ4f6lQvfqAhJTk5m1KhRXHPN\nNUyZMgWAmJgYTSxK+TA9c/EDL38fy+6EczQIL8OYW10aY1FkLF++nH79+rFnzx4GDhzIiBEj7A5J\nKeWCSzpzEZEyIhIjIsWvJmOTn3ckMH/1PgIDhBm9WxBSsvjM25gxYwadO3cmICCAFStWMGvWLMqV\nK2d3WEopF7h05iIipYFZwIOAA2gI7BGRfwNHjTH/8lyIxdep5HRGL94IwIibGtKsWnmbI/KOjIwM\nSpYsya233sqxY8eYOHEioaGhdoellLoErp65vAg0wmqNn/v+LUuAe90dlLI8+8UW4s6k0bpWRQZ0\nrGt3OB6XkJDA/fffzz/+8Q8AoqOjefnllzWxKOWHXE0u3YEnjDG/Y83Iz7YVKPrfejb4YsNhvtp4\nhNCgEkzvFUNgEW5KaYzhww8/JDo6mk8++YSmTZtqo0ml/JyrF/SrAPH5vK69yz3gyKkUnv3cap82\noVsTaoUV3cN89OhRHnvsMb755huuueYa5s2bp7PslSoCXP1zeD1we67n2WcvfdBbD7uVw2EYvXgj\nZ1Iz6RodTu+2NewOyaMCAgLYtGkTM2bM4Ndff9XEolQR4eqZyzjgWxFp7NxmiIg0BToBN3gotmJp\n/up9/LrrOGGlg3ix51VFcub5zp07eeONN5g2bRoRERHs3LlTG00qVcS4dOZijPkZK4mEY91npSdw\nDuhgjFnrufCKl51xSbz0fSwAL/ZsTpWyResLNzMzkylTpnDVVVcxf/58YmOtz6qJRamix+VJlMaY\n9UBvD8ZSrKVnOhj+8QbSMx30blODm5tWtTskt9q0aRN9+/Zl3bp1dO/enTfffJOoqOLbeFOpos7V\neS4XHQtqjEl2TzjF18xlO9hy5Aw1KpXi2Tua2B2OWzkcDu677z6OHz/OwoULueeee4pkuU8pdZ6r\nZy5nuXAIcl7FZ9q4B6zbd4JZK3YjAtN7taBMcNHoyrN27VqaNWtGaGgoH3/8MVFRUYSFhdkdllLK\nC1wdLXYb1mix7Ed3YBJwCHjUI5EVE2fTMhm5cCMOAwNvqEfb2pXsDumKnTt3jhEjRnDttdfmNJps\n3ry5JhalihGX/kQ2xvyQz8tfi8gOrJYwC9waVTHyr2+2cuBEMtGR5RjRtaHd4VyxZcuW8dhjj7F3\n714GDx6sjSaVKqaudNr3OqCzOwIpjpZujeP/1h4kKDCAV3u3ICjQv2fhT5s2ja5duxIYGMjKlSt5\n4403tNGkUsXUZRf3RSQIGII1NFldosSzaTz96SYAxtzSiEZVy9oc0eVLT08nKCiI22+/ncTERCZM\nmECpUqXsDkspZSNXR4slcOEFfQEqAOnAQx6Iq0gzxjD2079IPJtOu7ph9OlQx+6QLktcXBxDhw4F\nYOHChURHR/Piiy/aHJVSyhe4euYyPs9zB5AArDbG5NdzTF3EonWH+HFrHGVDApnaK4aAAP8almuM\n4b333mP48OGcO3eOiRMn4nA4CAjw77KeUsp9Ck0uIhIIZADfGmOOeT6kou3A8WQmf7UFgOe6N6Na\nBf8qHx05coS+ffvy/fff0759e+bNm0fjxo3tDksp5WMK/VPTGJMJ/BvQHh1XKMthGLlwA+fSs/hb\n80i6t/C/GeqBgYFs27aN119/nVWrVmliUUrly9U6xlogxpOBFAezf97Nuv0nCS8bzPM9mvnNLPXt\n27fzxBNPkJWVRXh4ODt27ODxxx/XMphSqkCufjv8G5gmIgNFpK2INMn98GSARcWWI6eZ8eMOAKbc\nG0PF0kE2R1S4zMxMXnrpJWJiYnjvvffYvn07AEFBvh+7Usperl7QX+j8+abzZ/bIMXH+ru1fLiI1\nI4sRH28gI8vwULta3NCwit0hFWrDhg307duXP/74g549e/LGG29QtWrRaqaplPIcV5NLtEejKOKm\n/rCdHXFnqVu5NGNv8/1D6XA4eOCBBzhx4gSLFy/m7rvvtjskpZSfuWhyEZH/AMOMMdu9FE+Rs3pX\nIm//spcSAcKM3i0oFeS7J3m///47V111FaGhoSxcuJCoqCgqVfL/XmdKKe8r7JrLw4B/jZX1IadT\nMnhy0UYAnujcgJgaFWyOKH9nz57liSeeoH379kydOhWAZs2aaWJRSl22wspi/jGcyUdN+nILR06n\nElOjAkNurGd3OPlasmQJ/fv358CBAwwdOpSRI0faHZJSqghwZbTYxe7jogrw9aYjfPbnYUqVLMGM\nXjEElvC9YbtTpkzhlltuoVSpUqxatYqZM2dSpkwZu8NSShUBrnzjHRORrIs9LmWHIjJYRPaKSKqI\nrBeR6y+ybk8RWSIiCSKSJCJrROTOS9mfHY6dTmXcZ5sBGPe3aOpW8a0v7LS0NADuvPNOxo0bx59/\n/kmHDh1sjkopVZS4MlqsP3DKHTsTkd7ATGAw8Ivz53ci0sQYcyCfTW4AfsLqbXYC+DvwmYh0Msas\nckdM7maMYfTijZxOyaBToyr8/ZqadoeU49ixYwwZMoSAgAAWLVpEo0aNeP755+0OSylVBLmSXL5y\nY3PKkcB8Y8xc5/OhInIrMAgYm3dlY8ywPC9NFpG/AT0An0wu7/2+n1U7E6kQWpJX7r7KJ2bhG2N4\n9913GTlyJMnJyUyaNEkbTSqlPKqwbxe3XW9x3v+lNbAkz6IlQPtLeKuywEl3xeVOuxPO8sK32wB4\n8a7mhJcLsTkiOHz4MLfeeiuPPvooTZs2ZePGjTz99NOaWJRSHlXYN4w7/+yujDWTPy7P63GAS1O/\nRWQIUB14r4Dl/UVknYisS0hIuJJYL1lGloORH28gNcNBz1bVuK15pFf3X5CSJUuyY8cO3njjDVau\nXEmjRo3sDkkpVQxcNLkYYwJ85X4tInI3MAV4wBizP791jDFzjDFtjDFtqlTxbouVf/+0i42HTlOt\nQikm3dnUq/vOKzY2liFDhlzQaHLw4MF6tqKU8hpvftskAllARJ7XI4CL3idGRO7BOlt5yBjzlWfC\nu3x/HjjJv5fvQgSm9YqhXEhJW+LIyMjghRdeICYmho8++ogdO6xGmSVL2hOPUqr48lpyMcakA+uB\nm/IsuglYXdB2ItILK7E8YoxZ7LkIL09yeiYjF24ky2F47Pq6XFs3zJY4/vjjD9q2bcu4cePo0aMH\nW7duJTra9/uYKaWKJlcbV7rLdOA9EVkL/AoMBKKAtwBEZAGAMeYh5/P7sBLLk8DPIpJ9bSbdGHPC\ny7Hn64Vvt7E38RyNq5Zl1M0NbYnB4XDw4IMPcvLkST777DN69OhhSxxKKZXNq8nFGPOxiIRhzVuJ\nBDYDt+e6hpJ3UshArBhfdT6yrQQ6eTbawi3fHs/7vx8gqEQAM3q3IDjQu00pV69eTUxMDKVLl2bR\nokVERUVRsWJFr8aglFL58foVXmPMm8aY2saYYGNMa2PMz7mWdTLGdMrzXPJ5dMrvvb3pxLl0xize\nBMDImxsSHVnOa/tOSkpiyJAhdOjQIafRZNOmTTWxKKV8hrfLYkWCMYZxn/1FQlIaV9euxGPX1/Xa\nvr/77jsGDBjAoUOHGD58OE8++aTX9q2UUq7SsamX4bM/D/Pd5mOUCQ5kWq8YSgR4Zxb+yy+/zO23\n306ZMmX49ddfmTFjBqVLl/bKvpVS6lLomcslOnQymYlfbAFg4h1NqFEp1KP7M8aQlpZGSEgIPXr0\nIDk5mWeeeYbg4GCP7lcppa6EnrlcAofDMGrhRpLSMrmlaQT3tK7u0f0dOXKEnj178uCDDwLQqFEj\nJk+erIlFKeXzNLlcgnm/7GXN3hNULhPMC3c191hTSmMM8+bNo0mTJnz//fdcc801GKO31VFK+Q8t\ni7ko9tgZpvywHYBX7mlOWBnPnD0cOnSIRx55hGXLltGxY0fefvttGjRo4JF9KaWUp+iZiwvSMrMY\n/tEG0rMcPHBNTTo3ztvBxn2Cg4PZu3cvs2bNYvny5ZpYlFJ+SZOLC6b/uIPYY0nUDgtl3O3ub6my\ndetWBg8eTFZWFlWqVGH79u0MHDhQG00qpfyWfnsVYs2e48z5eQ8BAtN7t6B0sPsqienp6Tz33HO0\nbNmShQsXsnPnTgACA7VaqZTyb5pcLiIpNYORCzdiDAy5sT6tarpvBvy6deto27YtEyZMoGfPnmzb\nto3GjRu77f2VUspO+ifyRfzzq60cPpVC82rleaKL+659OBwOHn74YU6dOsUXX3zBnXfe6bb3Vkop\nX6DJpQDfbz7GovWHCA4MYEbvGEqWuPKTvFWrVtGqVasLGk1WqFDBDdEqpZRv0bJYPuKTUnnms78A\nGHtbY+qHl72i9zt9+jQDBw6kY8eOTJs2DYAmTZpoYlFKFVl65pKHMYanP/mLE+fSub5BZR5qV/uK\n3u+bb75hwIABHD16lFGjRmmjSaVUsaBnLnn839qD/BQbT/lSJZlyTwwBV9CU8qWXXqJbt25UrFiR\n3377jalTpxIa6tleZEop5Qv0zCWXfYnneO7rrQA836MZVcuHXPJ7GGNITU2lVKlS9OzZk/T0dJ5+\n+mmCgoLcHa5SSvksPXNxysxyMGLhBlIysujeIoo7YqIu+T0OHz5M9+7dcxpNNmzYkAkTJmhiUUoV\nO5pcnGat2M2fB04RWT6Ef97Z7JK2NcYwd+5cmjRpwtKlS+nQoYM2mlRKFWtaFgM2HTrFzGXW7Pip\n98ZQPrSky9sePHiQhx9+mOXLl3PjjTcyd+5c6tWr56lQlVLKLxT75JKakcWIjzeQ6TA82qE2HepX\nvqTtQ0JCOHDgAHPmzKFfv34ea8OvlFL+pNiXxV76LpbdCeeoH16Gp251rf3K5s2bGTBgQE6jydjY\nWB577DFNLEop5VSsk8uqnQnMX72PwADh1d4tCClZ4qLrp6enM2nSJFq1asVnn32mjSaVUqoAxTa5\nnEpO58lFGwEYcVNDmlUrf9H116xZQ6tWrZg8eTK9e/dm69at2mhSKaUKUGz/5H72iy3EnUmjda2K\nDOhY96LrOhwO+vTpw5kzZ/j666/529/+5qUolVLKPxXL5PLFhsN8tfEIoUElmN4rhsACmlKuXLmS\n1q1bU6ZMGT755BOioqIoV66cl6NVSin/U+zKYkdOpfDs55sBmNCtCbXCSv/POqdPn6Z///506tSJ\n6dOnA9C4cWNNLEop5aJidebicBhGL97ImdRMukaH07ttjf9Z58svv2TQoEEcO3aMMWPGMHr0aBsi\nVUop/1aszlzmr97Hr7uOE1Y6iBd7XvU/Q4dfeOEFunfvTlhYGGvWrOHll1+mVKlSNkWrlFL+q9ic\nueyMS+Kl72MBeKFnc6qUDQas1i0pKSmEhoZy77334nA4GDNmjPYDU0qpK1AszlzSM62mlOmZDnq1\nqc4tTasCVuuWbt268fe//x1jDA0aNGD8+PGaWJRS6goVi+Ty2rKdbD58hhqVSjHhjqY4HA5mzZpF\n06ZNWbFiBZ06dbI7RKWUKlKKfFls/f4TvLliFyIwvVcLTsQd4W//+Ac///wzXbt2Zc6cOdSpU8fu\nMJVSqkgp0snlXFomIz7eiMPAoE71aFu7EomJDo4dO8a8efN49NFHtR+YUkp5gNfLYiIyWET2ikiq\niKwXkesLWf8G53qpIrJHRAa6uq/nv9nKgRPJVHPEs2PRVLKysqhcuTJbt26lT58+mliUUspDvJpc\nRKQ3MBN4AWgJrAa+E5GaBaxfB/jWuV5L4EXgdRG5u7B9nUnN4MPVe0j65X3WzujP119/xa5duwAo\nUeLiDSqVUkpdGfHmHRNFZA2wyRjzWK7XdgKLjTFj81n/ZaCnMaZBrtfeBpoaY9pdbF8hVWoah4GM\n4wd56KGHmD59OmFhYe77MEopVQSJyHpjTJsrfR+vnbmISBDQGliSZ9ESoH0Bm7XLZ/0fgDYictHb\nRaafjKOkI52vv/6Gd999VxOLUkp5kTcv6FcGSgBxeV6PA7oWsE1VYGk+6wc63+9o7gUi0h/o73ya\nlnwybnO3btrBGOtYJdodhI/QY3GeHovz9Fic18gdb1KkRosZY+YAcwBEZJ07Tu2KAj0W5+mxOE+P\nxXl6LM4TkXXueB9vXtBPBLKAiDyvRwDHCtjmWAHrZ6J/ZSillM/yWnIxxqQD64Gb8iy6CWs0WH5+\nK2D9dcaYDPdGqJRSyl28Pc9lOvCIiPQTkWgRmQlEAW8BiMgCEVmQa/23gGoi8qpz/X7AI8BUF/Y1\nx82x+zM9FufpsThPj8V5eizOc8ux8OpQZLAmUQJjgEhgMzDCGPOzc9kKAGNMp1zr3wDMAJoCR4CX\njTFveTVopZRSl8TryUUppVTRVyy6IiullPIuv00u3uxR5usu5ViISE8RWSIiCSKSJCJrROROb8br\nSZf67yLXdteJSKaIbPZ0jN5yGf+PBInIP53bpInIARF5wlvxetJlHIsHRGSDiCSLyDEReV9Eqnor\nXk8RkY4i8qWIHBYRIyKPuLBNcxFZKSIpzu0miCuNGY0xfvcAegMZwGNANPA6cBaoWcD6dYBzzvWi\nndtlAHfb/VlsOBYzgaeBq4H6wESsIeLX2/1ZvH0scm1XEdiD1f1hs92fw65jAXwKrMUakVkbuAbo\nZPdn8faxADo4/58Y4fzuuBb4A1hm92dxw7G4Hau34z1AMvBIIeuXw5oSshBo5twuCRhV6L7s/rCX\neYDWAHPzvLYTeLGA9V8GduZ57W3gN7s/i7ePRQHvsRaYZvdnsetYOL9UJwKTilByudT/R24GTgOV\n7Y7dB47Fk8D+PK89Cpy1+7O4+bicdSG5DALOAKVyvTYeOIzzmn1BD78ri3m7R5kvu8xjkZ+ywEl3\nxWWHyz0WztGLEcDznovOuy7zWPQA/guMFJFDIrJTRF4TkTIeDNXjLvNY/ApEisgdYqkM3IfVob24\naQesMsak5HrtB6wpJLUvtqHfJRcu3qOsoJpo1QLWz+5R5q8u51hcQESGANWB99wbmtdd8rEQkeZY\nZywPGmOyPBueV13Ov4u6wHVADHA38DhwKzDfMyF6zSUfC2PMb1jJ5AMgHUgABHjYc2H6rIK+O7OX\nFcgfk4tyE+d9caYADxhj9tsdjzeJSDDwMfCkMWav3fH4gADAYP1bWGOM+QErwdwtInlbMBVpItIE\n67rMc1hnPbdifZHOtjMuf+OPjSu1R9l5l3MsABCRe4AFwEPGmK88E55XXeqxiMS6uPuOiLzjfC0A\nEBHJBG43xuQtpfiLy/l3cRQ4bIw5neu1bc6fNfnfv179xeUci7HAWmPMFOfzTSJyDlglIs8YYw55\nJlSfVNB3Z/ayAvndmYvRHmU5LvNYICK9sMpgjxhjFnsuQu+5jGNxGGgOtMj1eAvY5fy9wOPn6y7z\n38WvQFSeaywNnT/99qz2Mo9FKFZCyi37ud99Z16h34DrRSQk12s3YXVL2XfRLe0esXCZoxx6Y9VC\n+2H99TkTa+RDLefyBcCCXOtnD0V+1bl+P+f2RWUo8qUci/uwhmUOwzrVz35UsvuzePtY5LP9JIrO\naLFL/XdRBjgILMJqtdQBqz3TIrs/iw3H4hHn/yODsK5FdcAa7LDe7s/ihmNRhvN/TCUDE5y/13Qu\nf5FcQ66B8lhnKB9hDUXuiTV6rGgORXZ+6MFYmTMN6y+TjrmWrQBW5Fn/Bqyx6mnAXmCg3Z/BjmPh\nfG7yeazwdtx2H4t8ti0yyeVyjgXWTaKWOL90DgNvAGXt/hw2HYuhwBbnsTiKdXG/ut2fww3HoVMB\n///Pdy6fD+zLs01z4Gcg1XksJlLIMGRjjPYWU0op5X7FrX6olFLKCzS5KKWUcjtNLkoppdxOk4tS\nSim30+SilFLK7TS5KKWUcjtNLsqviEig8yZHPeyO5XKJSH3nZ2hRyHrvi8jn3opLKXfS5KK8SkTm\nO79Y8z4u+kXrTSLygp2VkwAABg9JREFUfK64spx3ZJwjImFu2sVerN5mm5376+rcV4U86w3Bmi3u\nMbn2nf04LiLLROTaS3wfv0/6yr00uSg7LMX6cs398LXbC2/BiqsmVnfgu3BT+3ljTJYx5pgxJrOQ\n9U4bY065Y58uaIT1eW/EurfPt877mCh1WTS5KDukOb9ccz8yAUTkdhH5RUROicgJEflORBoV9EbO\nmzlNEpH9zvu+H83V5RgRCRCRsSKyx3kP8L9E5H4XYsx0xnXYGPMl8G/gNmerfkQkRkR+cr7ncRH5\nj4iUy7Xf7OVnRCTJeT/2G5zLcspiIlIf+NG52Unn628718spi4l1D/gjInLB/7MislBEPs31vLuI\n/CHWveL3ishzzhtmFSbe+Xk3Af/CuvVz21zve42I/Cgiic7PtEpErs61/T7nz8+cn2GXG2JSfkyT\ni/I1pYFpWF9sN2L1dvpSCr5jaC9gODAQaADcidVkMNuLwENYTQibYN3yep6I3HKJcaVg/f9Swtk5\n+Aesv/Cvxrq5Vkdgbq71P8JqBHk10BL4J1Zvprz2Oj8DnD97GJnPeh9j3fiqc/YLzmR2B/C+8/nt\nWE0YX8NqPtkXq1HpP139kCJSmvOluNwdw8sC7wLXA9cAfwHfiUhF5/LsRPSo8zNc666YlJ+yu5Ga\nPorXA6u0lInVlTb78d1F1i8HOIBrnc8DsRrt9XA+HwNs5f/bO78Qq6ooDn8/DCQkGYoII4IoLcEo\nFIShegi1IAkjfKjUiGoGeo3AJpgwCIIGHLUeigbyz2AUaTWigvVg6gRJUT6Jg6EzRVGENTY6Jd1W\nD2sf3F7OvXdGDzQ31gcHZt+9zz7rzIH9u2utfc+Cq0rOvQZf0DvrPn8TGGpyzVeBb7P2QuA7YDi1\nnwPOAHOyMcuTXbek9jlgTYP5b0tj7647t6Nu3CDwcdYeAt7N2k/hAjc7tb8AeurmWA2MN7nX4trF\nsyheZPhl2f80O094hcbHyp5LNm7aNsXx/zjCcwn+Cw5xaR2VZ4sOSfMlvZfCWGfxuhHCcx9lvI+L\nyClJA5JWZyGXRcBs4FNJE8UBdAG3trDxzjR+Es+/nAbWpb6FwDEzO5eNH876ADYCWyV9JuklSQu4\ncgaBR7PaGmvwV+L/ldpLgJfr7nU7MFfS9S3mvg9YDDyOe1NPWpYTknRD2tQwImkc+AO4jsbPpeBK\nbAramHasRBm0P+fN7GSDvr344taFC8s/uGdSGqM3s9G0cC8HlgH9QK+kTi6GfVfir5DPudDCxhN4\niK0G/Jgt4K2wZFevpB3AQ8ADwAZJXWa2bYrzlDGEh94elnQED5Hdn/ULfx367pJzz7SY+5T55oGR\nFBr7SNJddrGY3iDQgYcgR/FX1x+kwXOpyKagjQlxCWYM8lrt84FnzOxw+mwpLXKDZjYJ7AH2SOoD\nfsBj/l/jInKzmX0+TXMuNBHA48BaSXMy7+WerK+wawQYATZJegfPN5SJSyF0s5oZZGZ/StqFeyw3\n4Tmdw9mQb4Dbm9g9VbYCvXj4b0v67F6g28z2AUiahxeZK6ilo/4eqrIpaDNCXIKZxK/4t9luST/h\nC2gf7r2UIunp9OdRPM/xBJ6IPmlm45L6gX5Js/CFeC7QiYvHwGXauQP/Nr5N0gY80f4W8IGZnU4J\n/9eAD/Fw2o24+BxqMF9RRnilpP3ApJlNNBg7COzHk/87zSwvyPQK8ImkoqJkDS/0tMTMXpzqzZlZ\nTdJmoEfSgJmdx0VynaSv8DBkH+69FOeYpDFgmaRhfEfgb1XZFLQfkXMJZgxmVsNL0i7Gf/fyBtDD\npbuW6vkd6AaO4DuYVuFJ5bHU34Mn6NfjXsUB4BE89Ha5dk4ADwLX4jvTduPC0ZWG/I0LznZ8Ud6F\nC9sLDeYbxRfh14Gf8XLcjTgI/ALcQdolls2zD989tiLZdRTf8DDG9BkArsZ/4wO+eaAD90R2Am/j\nnlPO8+na36frV21T0EZEJcogCIKgcsJzCYIgCConxCUIgiConBCXIAiCoHJCXIIgCILKCXEJgiAI\nKifEJQiCIKicEJcgCIKgckJcgiAIgsoJcQmCIAgq51/2wtUMJcwrswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, preds)\n",
    "\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "\n",
    "plot_roc_curve(fpr, tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ye9dXQhWOBkC"
   },
   "outputs": [],
   "source": [
    "feature_importances = xg_cl.feature_importances_\n",
    "\n",
    "feature_importances = pd.DataFrame({'feature': list(features.columns), 'importance': feature_importances}).sort_values('importance', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "UVSQIz0HVwwk",
    "outputId": "a98c5535-d2d5-4720-c30b-9ad3bf4cd4c8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>EXT_SOURCE_3</td>\n",
       "      <td>0.172910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>EXT_SOURCE_2</td>\n",
       "      <td>0.086298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>EXT_SOURCE_1</td>\n",
       "      <td>0.085492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NAME_EDUCATION_TYPE_Higher education</td>\n",
       "      <td>0.064525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NAME_CONTRACT_TYPE_Cash loans</td>\n",
       "      <td>0.043822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>FLAG_OWN_CAR_N</td>\n",
       "      <td>0.040862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NAME_EDUCATION_TYPE_Secondary / secondary special</td>\n",
       "      <td>0.038558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CODE_GENDER_F</td>\n",
       "      <td>0.028848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAYS_EMPLOYED</td>\n",
       "      <td>0.026703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMT_CREDIT</td>\n",
       "      <td>0.026031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>OCCUPATION_TYPE_Core staff</td>\n",
       "      <td>0.024039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAYS_BIRTH</td>\n",
       "      <td>0.021414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>OCCUPATION_TYPE_Laborers</td>\n",
       "      <td>0.020916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NAME_INCOME_TYPE_State servant</td>\n",
       "      <td>0.020571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>NAME_FAMILY_STATUS_Separated</td>\n",
       "      <td>0.019799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>REGION_RATING_CLIENT</td>\n",
       "      <td>0.019557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>OCCUPATION_TYPE_Medicine staff</td>\n",
       "      <td>0.019551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>OCCUPATION_TYPE_Sales staff</td>\n",
       "      <td>0.019251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CNT_CHILDREN</td>\n",
       "      <td>0.018505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_YEAR</td>\n",
       "      <td>0.017853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>NAME_FAMILY_STATUS_Single / not married</td>\n",
       "      <td>0.017180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_QRT</td>\n",
       "      <td>0.015878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NAME_FAMILY_STATUS_Civil marriage</td>\n",
       "      <td>0.015553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMT_INCOME_TOTAL</td>\n",
       "      <td>0.015428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_WEEK</td>\n",
       "      <td>0.015254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>OCCUPATION_TYPE_Cleaning staff</td>\n",
       "      <td>0.015052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>OCCUPATION_TYPE_High skill tech staff</td>\n",
       "      <td>0.014868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>NAME_FAMILY_STATUS_Married</td>\n",
       "      <td>0.014748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>FLAG_OWN_REALTY_N</td>\n",
       "      <td>0.014380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>OCCUPATION_TYPE_Security staff</td>\n",
       "      <td>0.014321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NAME_INCOME_TYPE_Working</td>\n",
       "      <td>0.011983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>OCCUPATION_TYPE_Cooking staff</td>\n",
       "      <td>0.011623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_MON</td>\n",
       "      <td>0.008227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NAME_EDUCATION_TYPE_Incomplete higher</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CODE_GENDER_M</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_HOUR</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>OCCUPATION_TYPE_Secretaries</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AMT_REQ_CREDIT_BUREAU_DAY</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>OCCUPATION_TYPE_Realty agents</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>OCCUPATION_TYPE_Private service staff</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>OCCUPATION_TYPE_Managers</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>OCCUPATION_TYPE_Low-skill Laborers</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NAME_CONTRACT_TYPE_Revolving loans</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>OCCUPATION_TYPE_IT staff</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>OCCUPATION_TYPE_HR staff</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NAME_EDUCATION_TYPE_Lower secondary</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>OCCUPATION_TYPE_Drivers</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CODE_GENDER_XNA</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>FLAG_OWN_CAR_Y</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>OCCUPATION_TYPE_Accountants</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>NAME_FAMILY_STATUS_Widow</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>FLAG_OWN_REALTY_Y</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NAME_INCOME_TYPE_Businessman</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NAME_INCOME_TYPE_Commercial associate</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NAME_INCOME_TYPE_Student</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NAME_EDUCATION_TYPE_Academic degree</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>OCCUPATION_TYPE_Waiters/barmen staff</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              feature  importance\n",
       "14  EXT_SOURCE_3                                       0.172910  \n",
       "13  EXT_SOURCE_2                                       0.086298  \n",
       "12  EXT_SOURCE_1                                       0.085492  \n",
       "30  NAME_EDUCATION_TYPE_Higher education               0.064525  \n",
       "15  NAME_CONTRACT_TYPE_Cash loans                      0.043822  \n",
       "20  FLAG_OWN_CAR_N                                     0.040862  \n",
       "33  NAME_EDUCATION_TYPE_Secondary / secondary special  0.038558  \n",
       "17  CODE_GENDER_F                                      0.028848  \n",
       "4   DAYS_EMPLOYED                                      0.026703  \n",
       "2   AMT_CREDIT                                         0.026031  \n",
       "42  OCCUPATION_TYPE_Core staff                         0.024039  \n",
       "3   DAYS_BIRTH                                         0.021414  \n",
       "47  OCCUPATION_TYPE_Laborers                           0.020916  \n",
       "26  NAME_INCOME_TYPE_State servant                     0.020571  \n",
       "36  NAME_FAMILY_STATUS_Separated                       0.019799  \n",
       "5   REGION_RATING_CLIENT                               0.019557  \n",
       "50  OCCUPATION_TYPE_Medicine staff                     0.019551  \n",
       "53  OCCUPATION_TYPE_Sales staff                        0.019251  \n",
       "0   CNT_CHILDREN                                       0.018505  \n",
       "11  AMT_REQ_CREDIT_BUREAU_YEAR                         0.017853  \n",
       "37  NAME_FAMILY_STATUS_Single / not married            0.017180  \n",
       "10  AMT_REQ_CREDIT_BUREAU_QRT                          0.015878  \n",
       "34  NAME_FAMILY_STATUS_Civil marriage                  0.015553  \n",
       "1   AMT_INCOME_TOTAL                                   0.015428  \n",
       "8   AMT_REQ_CREDIT_BUREAU_WEEK                         0.015254  \n",
       "40  OCCUPATION_TYPE_Cleaning staff                     0.015052  \n",
       "45  OCCUPATION_TYPE_High skill tech staff              0.014868  \n",
       "35  NAME_FAMILY_STATUS_Married                         0.014748  \n",
       "22  FLAG_OWN_REALTY_N                                  0.014380  \n",
       "55  OCCUPATION_TYPE_Security staff                     0.014321  \n",
       "28  NAME_INCOME_TYPE_Working                           0.011983  \n",
       "41  OCCUPATION_TYPE_Cooking staff                      0.011623  \n",
       "9   AMT_REQ_CREDIT_BUREAU_MON                          0.008227  \n",
       "31  NAME_EDUCATION_TYPE_Incomplete higher              0.000000  \n",
       "18  CODE_GENDER_M                                      0.000000  \n",
       "6   AMT_REQ_CREDIT_BUREAU_HOUR                         0.000000  \n",
       "54  OCCUPATION_TYPE_Secretaries                        0.000000  \n",
       "7   AMT_REQ_CREDIT_BUREAU_DAY                          0.000000  \n",
       "52  OCCUPATION_TYPE_Realty agents                      0.000000  \n",
       "51  OCCUPATION_TYPE_Private service staff              0.000000  \n",
       "49  OCCUPATION_TYPE_Managers                           0.000000  \n",
       "48  OCCUPATION_TYPE_Low-skill Laborers                 0.000000  \n",
       "16  NAME_CONTRACT_TYPE_Revolving loans                 0.000000  \n",
       "46  OCCUPATION_TYPE_IT staff                           0.000000  \n",
       "44  OCCUPATION_TYPE_HR staff                           0.000000  \n",
       "32  NAME_EDUCATION_TYPE_Lower secondary                0.000000  \n",
       "43  OCCUPATION_TYPE_Drivers                            0.000000  \n",
       "19  CODE_GENDER_XNA                                    0.000000  \n",
       "21  FLAG_OWN_CAR_Y                                     0.000000  \n",
       "39  OCCUPATION_TYPE_Accountants                        0.000000  \n",
       "38  NAME_FAMILY_STATUS_Widow                           0.000000  \n",
       "23  FLAG_OWN_REALTY_Y                                  0.000000  \n",
       "24  NAME_INCOME_TYPE_Businessman                       0.000000  \n",
       "25  NAME_INCOME_TYPE_Commercial associate              0.000000  \n",
       "27  NAME_INCOME_TYPE_Student                           0.000000  \n",
       "29  NAME_EDUCATION_TYPE_Academic degree                0.000000  \n",
       "56  OCCUPATION_TYPE_Waiters/barmen staff               0.000000  "
      ]
     },
     "execution_count": 77,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zw9cFTa6Vyvk"
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   "source": [
    "### Conclusion\n",
    "\n",
    "\n",
    "In this project, I used Logistic Regression, Tree Based models, and Ensemble models to predict whether a borrower will default based only on economic/financial capability features.\n",
    "\n",
    "- Gradient Boosting has the highest recall (70%) among all ML models. However, we note that the performance of Ensemble methods are highly dependent on the hyperparameters used (risking overfitting - high variance) \n",
    "\n",
    "- External sources has the highest feature importance, followed by Education Type\n",
    "\n",
    "- Use of external sources significantly improve ML models. Extracting external sources may be a worthy investment (e.g. time spent by employees, purchase of software)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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