exp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  customer_id            name  age gender owns_car owns_house  no_of_children  \\\n",
      "0  CST_115179        ita Bose   46      F        N          Y             0.0   \n",
      "1  CST_121920  Alper Jonathan   29      M        N          Y             0.0   \n",
      "2  CST_109330     Umesh Desai   37      M        N          Y             0.0   \n",
      "3  CST_128288             Rie   39      F        N          Y             0.0   \n",
      "4  CST_151355          McCool   46      M        Y          Y             0.0   \n",
      "5  CST_123268     Sarah Marsh   46      F        Y          N             0.0   \n",
      "6  CST_127502           Mason   38      M        N          Y             1.0   \n",
      "7  CST_151722            Saba   46      F        Y          Y             1.0   \n",
      "8  CST_133768        Ashutosh   40      F      NaN          Y             0.0   \n",
      "9  CST_111670  David Milliken   39      F        Y          Y             2.0   \n",
      "\n",
      "   net_yearly_income  no_of_days_employed        occupation_type  \\\n",
      "0          107934.04                612.0                Unknown   \n",
      "1          109862.62               2771.0               Laborers   \n",
      "2          230153.17                204.0               Laborers   \n",
      "3          122325.82              11941.0             Core staff   \n",
      "4          387286.00               1459.0             Core staff   \n",
      "5          252765.91               2898.0            Accountants   \n",
      "6          262389.20               5541.0  High skill tech staff   \n",
      "7          241211.39               1448.0             Core staff   \n",
      "8          210091.43              11551.0               Laborers   \n",
      "9          207109.13               2791.0  High skill tech staff   \n",
      "\n",
      "   total_family_members  migrant_worker  yearly_debt_payments  credit_limit  \\\n",
      "0                   1.0             1.0              33070.28      18690.93   \n",
      "1                   2.0             0.0              15329.53      37745.19   \n",
      "2                   2.0             0.0              48416.60      41598.36   \n",
      "3                   2.0             0.0              22574.36      32627.76   \n",
      "4                   1.0             0.0              38282.95      52950.64   \n",
      "5                   2.0             1.0              37046.86      40245.64   \n",
      "6                   3.0             0.0              50839.39      41311.08   \n",
      "7                   3.0             0.0              30008.46      32209.22   \n",
      "8                   2.0             0.0              21521.89      65037.74   \n",
      "9                   4.0             0.0               9509.10      28425.52   \n",
      "\n",
      "   credit_limit_used(%)  credit_score  prev_defaults  default_in_last_6months  \\\n",
      "0                    73         544.0              2                        1   \n",
      "1                    52         857.0              0                        0   \n",
      "2                    43         650.0              0                        0   \n",
      "3                    20         754.0              0                        0   \n",
      "4                    75         927.0              0                        0   \n",
      "5                    19         937.0              0                        0   \n",
      "6                    42         733.0              0                        0   \n",
      "7                    91         906.0              0                        0   \n",
      "8                    14         783.0              0                        0   \n",
      "9                    14         666.0              0                        0   \n",
      "\n",
      "   credit_card_default  \n",
      "0                    1  \n",
      "1                    0  \n",
      "2                    0  \n",
      "3                    0  \n",
      "4                    0  \n",
      "5                    0  \n",
      "6                    0  \n",
      "7                    0  \n",
      "8                    0  \n",
      "9                    0  \n"
     ]
    }
   ],
   "source": [
    "data=pd.read_csv(\"./dataset/train.csv\")\n",
    "test_data=pd.read_csv(\"./dataset/test.csv\")\n",
    "print(data.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(45528, 19)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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>customer_id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>owns_house</th>\n",
       "      <th>no_of_children</th>\n",
       "      <th>net_yearly_income</th>\n",
       "      <th>no_of_days_employed</th>\n",
       "      <th>occupation_type</th>\n",
       "      <th>total_family_members</th>\n",
       "      <th>migrant_worker</th>\n",
       "      <th>yearly_debt_payments</th>\n",
       "      <th>credit_limit</th>\n",
       "      <th>credit_limit_used(%)</th>\n",
       "      <th>credit_score</th>\n",
       "      <th>prev_defaults</th>\n",
       "      <th>default_in_last_6months</th>\n",
       "      <th>credit_card_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45528</td>\n",
       "      <td>45528</td>\n",
       "      <td>45528.000000</td>\n",
       "      <td>45528</td>\n",
       "      <td>44981</td>\n",
       "      <td>45528</td>\n",
       "      <td>44754.000000</td>\n",
       "      <td>4.552800e+04</td>\n",
       "      <td>45065.000000</td>\n",
       "      <td>45528</td>\n",
       "      <td>45445.000000</td>\n",
       "      <td>45441.000000</td>\n",
       "      <td>45433.000000</td>\n",
       "      <td>4.552800e+04</td>\n",
       "      <td>45528.00000</td>\n",
       "      <td>45520.000000</td>\n",
       "      <td>45528.000000</td>\n",
       "      <td>45528.000000</td>\n",
       "      <td>45528.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>45528</td>\n",
       "      <td>4010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>CST_115179</td>\n",
       "      <td>Jonathan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29957</td>\n",
       "      <td>29743</td>\n",
       "      <td>31642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14299</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.993411</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420655</td>\n",
       "      <td>2.006556e+05</td>\n",
       "      <td>67609.289293</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.158081</td>\n",
       "      <td>0.179111</td>\n",
       "      <td>31796.965311</td>\n",
       "      <td>4.354842e+04</td>\n",
       "      <td>52.23502</td>\n",
       "      <td>782.791257</td>\n",
       "      <td>0.060710</td>\n",
       "      <td>0.050540</td>\n",
       "      <td>0.081203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.543990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.724097</td>\n",
       "      <td>6.690740e+05</td>\n",
       "      <td>139323.524434</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.911572</td>\n",
       "      <td>0.383450</td>\n",
       "      <td>17269.727234</td>\n",
       "      <td>1.487847e+05</td>\n",
       "      <td>29.37691</td>\n",
       "      <td>100.619746</td>\n",
       "      <td>0.264629</td>\n",
       "      <td>0.219059</td>\n",
       "      <td>0.273149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.717061e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2237.470000</td>\n",
       "      <td>4.003140e+03</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>500.000000</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.263458e+05</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19231.140000</td>\n",
       "      <td>2.397381e+04</td>\n",
       "      <td>27.00000</td>\n",
       "      <td>704.000000</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.717149e+05</td>\n",
       "      <td>2224.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29081.650000</td>\n",
       "      <td>3.568804e+04</td>\n",
       "      <td>54.00000</td>\n",
       "      <td>786.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.406038e+05</td>\n",
       "      <td>5817.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40561.150000</td>\n",
       "      <td>5.343576e+04</td>\n",
       "      <td>78.00000</td>\n",
       "      <td>867.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.407590e+08</td>\n",
       "      <td>365252.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>328112.860000</td>\n",
       "      <td>3.112997e+07</td>\n",
       "      <td>99.00000</td>\n",
       "      <td>949.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id      name           age gender owns_car owns_house  \\\n",
       "count        45528     45528  45528.000000  45528    44981      45528   \n",
       "unique       45528      4010           NaN      3        2          2   \n",
       "top     CST_115179  Jonathan           NaN      F        N          Y   \n",
       "freq             1       207           NaN  29957    29743      31642   \n",
       "mean           NaN       NaN     38.993411    NaN      NaN        NaN   \n",
       "std            NaN       NaN      9.543990    NaN      NaN        NaN   \n",
       "min            NaN       NaN     23.000000    NaN      NaN        NaN   \n",
       "25%            NaN       NaN     31.000000    NaN      NaN        NaN   \n",
       "50%            NaN       NaN     39.000000    NaN      NaN        NaN   \n",
       "75%            NaN       NaN     47.000000    NaN      NaN        NaN   \n",
       "max            NaN       NaN     55.000000    NaN      NaN        NaN   \n",
       "\n",
       "        no_of_children  net_yearly_income  no_of_days_employed  \\\n",
       "count     44754.000000       4.552800e+04         45065.000000   \n",
       "unique             NaN                NaN                  NaN   \n",
       "top                NaN                NaN                  NaN   \n",
       "freq               NaN                NaN                  NaN   \n",
       "mean          0.420655       2.006556e+05         67609.289293   \n",
       "std           0.724097       6.690740e+05        139323.524434   \n",
       "min           0.000000       2.717061e+04             2.000000   \n",
       "25%           0.000000       1.263458e+05           936.000000   \n",
       "50%           0.000000       1.717149e+05          2224.000000   \n",
       "75%           1.000000       2.406038e+05          5817.000000   \n",
       "max           9.000000       1.407590e+08        365252.000000   \n",
       "\n",
       "       occupation_type  total_family_members  migrant_worker  \\\n",
       "count            45528          45445.000000    45441.000000   \n",
       "unique              19                   NaN             NaN   \n",
       "top            Unknown                   NaN             NaN   \n",
       "freq             14299                   NaN             NaN   \n",
       "mean               NaN              2.158081        0.179111   \n",
       "std                NaN              0.911572        0.383450   \n",
       "min                NaN              1.000000        0.000000   \n",
       "25%                NaN              2.000000        0.000000   \n",
       "50%                NaN              2.000000        0.000000   \n",
       "75%                NaN              3.000000        0.000000   \n",
       "max                NaN             10.000000        1.000000   \n",
       "\n",
       "        yearly_debt_payments  credit_limit  credit_limit_used(%)  \\\n",
       "count           45433.000000  4.552800e+04           45528.00000   \n",
       "unique                   NaN           NaN                   NaN   \n",
       "top                      NaN           NaN                   NaN   \n",
       "freq                     NaN           NaN                   NaN   \n",
       "mean            31796.965311  4.354842e+04              52.23502   \n",
       "std             17269.727234  1.487847e+05              29.37691   \n",
       "min              2237.470000  4.003140e+03               0.00000   \n",
       "25%             19231.140000  2.397381e+04              27.00000   \n",
       "50%             29081.650000  3.568804e+04              54.00000   \n",
       "75%             40561.150000  5.343576e+04              78.00000   \n",
       "max            328112.860000  3.112997e+07              99.00000   \n",
       "\n",
       "        credit_score  prev_defaults  default_in_last_6months  \\\n",
       "count   45520.000000   45528.000000             45528.000000   \n",
       "unique           NaN            NaN                      NaN   \n",
       "top              NaN            NaN                      NaN   \n",
       "freq             NaN            NaN                      NaN   \n",
       "mean      782.791257       0.060710                 0.050540   \n",
       "std       100.619746       0.264629                 0.219059   \n",
       "min       500.000000       0.000000                 0.000000   \n",
       "25%       704.000000       0.000000                 0.000000   \n",
       "50%       786.000000       0.000000                 0.000000   \n",
       "75%       867.000000       0.000000                 0.000000   \n",
       "max       949.000000       2.000000                 1.000000   \n",
       "\n",
       "        credit_card_default  \n",
       "count          45528.000000  \n",
       "unique                  NaN  \n",
       "top                     NaN  \n",
       "freq                    NaN  \n",
       "mean               0.081203  \n",
       "std                0.273149  \n",
       "min                0.000000  \n",
       "25%                0.000000  \n",
       "50%                0.000000  \n",
       "75%                0.000000  \n",
       "max                1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some initial observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### For every 25 percentile increase the age increases by 8.Hence the age seems to be uniformly distributed throughout the dataset.\n",
    "###### Some names seems to be repeated but the customer_id is not repeated which means that there might be entries of a person with multiple credit cards.\n",
    "###### 14299 entries contain occupation_type labelled as 'unknown'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for dtypes of columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_279d3_row0_col0, #T_279d3_row0_col1, #T_279d3_row1_col0, #T_279d3_row1_col1, #T_279d3_row2_col0, #T_279d3_row2_col1 {\n",
       "  background-color: yellow;\n",
       "  color: black;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_279d3_\" style='display:inline'>\n",
       "  <caption>Data types of the columns</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >dtype</th>\n",
       "      <th class=\"col_heading level0 col1\" >total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_279d3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_279d3_row0_col0\" class=\"data row0 col0\" >float64</td>\n",
       "      <td id=\"T_279d3_row0_col1\" class=\"data row0 col1\" >8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_279d3_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_279d3_row1_col0\" class=\"data row1 col0\" >object</td>\n",
       "      <td id=\"T_279d3_row1_col1\" class=\"data row1 col1\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_279d3_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_279d3_row2_col0\" class=\"data row2 col0\" >int64</td>\n",
       "      <td id=\"T_279d3_row2_col1\" class=\"data row2 col1\" >5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ed85013dc0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt=pd.DataFrame(columns=[\"dtype\",\"total\"])\n",
    "dtypes=data.dtypes.value_counts()\n",
    "dt.dtype=list(dtypes.index)\n",
    "dt.total=list(dtypes.values)\n",
    "dt=dt.style.set_properties(**{'background-color':'yellow','color':'black'})\n",
    "dt.set_table_attributes(\"style='display:inline'\").set_caption(\"Data types of the columns\")\n",
    "#print(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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>Column_names</th>\n",
       "      <th>data_types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>credit_card_default</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>prev_defaults</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>age</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>credit_limit_used(%)</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>default_in_last_6months</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>credit_score</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>credit_limit</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>no_of_children</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>net_yearly_income</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>no_of_days_employed</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>total_family_members</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>migrant_worker</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>yearly_debt_payments</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>customer_id</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>owns_car</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>gender</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>name</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>owns_house</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>occupation_type</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Column_names data_types\n",
       "0       credit_card_default      int64\n",
       "1             prev_defaults      int64\n",
       "2                       age      int64\n",
       "3      credit_limit_used(%)      int64\n",
       "4   default_in_last_6months      int64\n",
       "5              credit_score    float64\n",
       "6              credit_limit    float64\n",
       "7            no_of_children    float64\n",
       "8         net_yearly_income    float64\n",
       "9       no_of_days_employed    float64\n",
       "10     total_family_members    float64\n",
       "11           migrant_worker    float64\n",
       "12     yearly_debt_payments    float64\n",
       "13              customer_id     object\n",
       "14                 owns_car     object\n",
       "15                   gender     object\n",
       "16                     name     object\n",
       "17               owns_house     object\n",
       "18          occupation_type     object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_type=pd.DataFrame(columns=[\"Column_names\",\"data_types\"])\n",
    "col_data=data.dtypes\n",
    "col_type[\"Column_names\"]=list(col_data.index)\n",
    "col_type[\"data_types\"]=list(col_data.values)\n",
    "col_type.sort_values(by='data_types',ascending='True',inplace=True)\n",
    "col_type.reset_index(inplace=True,drop=True)\n",
    "col_type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "categorical_columns=list(col_type.Column_names[(col_type.data_types=='object')].values)\n",
    "numerical_columns=list(col_type.Column_names[(col_type.data_types=='float64') | (col_type.data_types=='int64')].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jonathan            207\n",
       "Jonathan Stempel    192\n",
       "David               170\n",
       "Stempel             165\n",
       "Jessica             162\n",
       "Sarah               147\n",
       "Lucia               142\n",
       "Nick                140\n",
       "Lucia Mutikani      137\n",
       "Jones               133\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.name.value_counts().sort_values(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>customer_id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>owns_house</th>\n",
       "      <th>no_of_children</th>\n",
       "      <th>net_yearly_income</th>\n",
       "      <th>no_of_days_employed</th>\n",
       "      <th>occupation_type</th>\n",
       "      <th>total_family_members</th>\n",
       "      <th>migrant_worker</th>\n",
       "      <th>yearly_debt_payments</th>\n",
       "      <th>credit_limit</th>\n",
       "      <th>credit_limit_used(%)</th>\n",
       "      <th>credit_score</th>\n",
       "      <th>prev_defaults</th>\n",
       "      <th>default_in_last_6months</th>\n",
       "      <th>credit_card_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>CST_113854</td>\n",
       "      <td>Jonathan Stempel</td>\n",
       "      <td>41</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121548.28</td>\n",
       "      <td>365240.0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7385.18</td>\n",
       "      <td>35876.02</td>\n",
       "      <td>82</td>\n",
       "      <td>848.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>CST_109433</td>\n",
       "      <td>Jonathan Stempel</td>\n",
       "      <td>31</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>486061.51</td>\n",
       "      <td>5459.0</td>\n",
       "      <td>Managers</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48519.66</td>\n",
       "      <td>118311.52</td>\n",
       "      <td>15</td>\n",
       "      <td>896.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>CST_114457</td>\n",
       "      <td>Jonathan Stempel</td>\n",
       "      <td>44</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140730.45</td>\n",
       "      <td>365246.0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24214.94</td>\n",
       "      <td>16437.61</td>\n",
       "      <td>44</td>\n",
       "      <td>709.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048</th>\n",
       "      <td>CST_100560</td>\n",
       "      <td>Jonathan Stempel</td>\n",
       "      <td>30</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52400.34</td>\n",
       "      <td>365249.0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12266.65</td>\n",
       "      <td>7660.66</td>\n",
       "      <td>78</td>\n",
       "      <td>668.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>CST_105262</td>\n",
       "      <td>Jonathan Stempel</td>\n",
       "      <td>37</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>268306.08</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10086.52</td>\n",
       "      <td>94377.10</td>\n",
       "      <td>95</td>\n",
       "      <td>901.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1322</th>\n",
       "      <td>CST_115792</td>\n",
       "      <td>Jonathan Stempel</td>\n",
       "      <td>54</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>334404.45</td>\n",
       "      <td>7070.0</td>\n",
       "      <td>Managers</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41187.90</td>\n",
       "      <td>60366.88</td>\n",
       "      <td>92</td>\n",
       "      <td>806.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1492</th>\n",
       "      <td>CST_105671</td>\n",
       "      <td>Jonathan Stempel</td>\n",
       "      <td>39</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>286833.32</td>\n",
       "      <td>2455.0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50236.16</td>\n",
       "      <td>69358.03</td>\n",
       "      <td>37</td>\n",
       "      <td>881.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1776</th>\n",
       "      <td>CST_113496</td>\n",
       "      <td>Jonathan Stempel</td>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>292351.99</td>\n",
       "      <td>1793.0</td>\n",
       "      <td>Managers</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25432.46</td>\n",
       "      <td>38372.74</td>\n",
       "      <td>75</td>\n",
       "      <td>782.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1791</th>\n",
       "      <td>CST_104008</td>\n",
       "      <td>Jonathan Stempel</td>\n",
       "      <td>44</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>138463.53</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11511.52</td>\n",
       "      <td>48535.94</td>\n",
       "      <td>69</td>\n",
       "      <td>668.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>CST_111049</td>\n",
       "      <td>Jonathan Stempel</td>\n",
       "      <td>42</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>155590.54</td>\n",
       "      <td>704.0</td>\n",
       "      <td>Private service staff</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21134.33</td>\n",
       "      <td>31302.79</td>\n",
       "      <td>99</td>\n",
       "      <td>740.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     customer_id              name  age gender owns_car owns_house  \\\n",
       "254   CST_113854  Jonathan Stempel   41      F        N          Y   \n",
       "741   CST_109433  Jonathan Stempel   31      F        Y          Y   \n",
       "833   CST_114457  Jonathan Stempel   44      F        N          Y   \n",
       "1048  CST_100560  Jonathan Stempel   30      F        N          N   \n",
       "1223  CST_105262  Jonathan Stempel   37      F        Y          Y   \n",
       "1322  CST_115792  Jonathan Stempel   54      F        N          Y   \n",
       "1492  CST_105671  Jonathan Stempel   39      M        N          N   \n",
       "1776  CST_113496  Jonathan Stempel   38      M        Y          Y   \n",
       "1791  CST_104008  Jonathan Stempel   44      F        N          Y   \n",
       "1926  CST_111049  Jonathan Stempel   42      F        N          Y   \n",
       "\n",
       "      no_of_children  net_yearly_income  no_of_days_employed  \\\n",
       "254              0.0          121548.28             365240.0   \n",
       "741              0.0          486061.51               5459.0   \n",
       "833              0.0          140730.45             365246.0   \n",
       "1048             0.0           52400.34             365249.0   \n",
       "1223             0.0          268306.08               1114.0   \n",
       "1322             0.0          334404.45               7070.0   \n",
       "1492             1.0          286833.32               2455.0   \n",
       "1776             0.0          292351.99               1793.0   \n",
       "1791             0.0          138463.53                 99.0   \n",
       "1926             1.0          155590.54                704.0   \n",
       "\n",
       "            occupation_type  total_family_members  migrant_worker  \\\n",
       "254                 Unknown                   1.0             0.0   \n",
       "741                Managers                   2.0             0.0   \n",
       "833                 Unknown                   2.0             0.0   \n",
       "1048                Unknown                   2.0             0.0   \n",
       "1223                Unknown                   2.0             0.0   \n",
       "1322               Managers                   1.0             0.0   \n",
       "1492                Unknown                   3.0             1.0   \n",
       "1776               Managers                   1.0             0.0   \n",
       "1791               Laborers                   1.0             1.0   \n",
       "1926  Private service staff                   3.0             0.0   \n",
       "\n",
       "      yearly_debt_payments  credit_limit  credit_limit_used(%)  credit_score  \\\n",
       "254                7385.18      35876.02                    82         848.0   \n",
       "741               48519.66     118311.52                    15         896.0   \n",
       "833               24214.94      16437.61                    44         709.0   \n",
       "1048              12266.65       7660.66                    78         668.0   \n",
       "1223              10086.52      94377.10                    95         901.0   \n",
       "1322              41187.90      60366.88                    92         806.0   \n",
       "1492              50236.16      69358.03                    37         881.0   \n",
       "1776              25432.46      38372.74                    75         782.0   \n",
       "1791              11511.52      48535.94                    69         668.0   \n",
       "1926              21134.33      31302.79                    99         740.0   \n",
       "\n",
       "      prev_defaults  default_in_last_6months  credit_card_default  \n",
       "254               0                        0                    0  \n",
       "741               0                        0                    0  \n",
       "833               0                        0                    0  \n",
       "1048              0                        0                    0  \n",
       "1223              0                        0                    0  \n",
       "1322              0                        0                    0  \n",
       "1492              0                        0                    0  \n",
       "1776              0                        0                    0  \n",
       "1791              0                        0                    0  \n",
       "1926              0                        0                    0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.name==\"Jonathan Stempel\"][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Our assumption regarding multiple entries of a person with different uid holdilng different credit cards seems to be wrong since it seems that different people with same name exists and the uid is given to each person and not to each credit card of different companies owned by a person.Since,the name column doesn't seem to provide valuable information,it needs to be dropped so that the computational costs would be reduced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([\"name\"],axis=1,inplace=True)\n",
    "test_data.drop([\"name\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for correlation between variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(45528, 13)\n"
     ]
    }
   ],
   "source": [
    "#numeric_data=mod_data.select_dtypes(exclude=['object'])\n",
    "numeric_data=data[numerical_columns]\n",
    "print(numeric_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def correlation_plot(numeric_data):\n",
    "    plt.imshow(numeric_data.corr(),cmap='coolwarm',interpolation='none')\n",
    "    plt.colorbar()\n",
    "    plt.xticks(range(len(numeric_data.columns)),numeric_data.columns,rotation=90)\n",
    "    plt.yticks(range(len(numeric_data.columns)),numeric_data.columns)\n",
    "    plt.gcf().set_size_inches(10,10)\n",
    "\n",
    "    labels=numeric_data.corr().values\n",
    "    #print(labels.shape)\n",
    "    for y in range(labels.shape[0]):\n",
    "        for x in range(labels.shape[1]):\n",
    "            plt.text(x,y,'{:.2f}'.format(labels[y,x]),ha='center',va='center',color='white')\n",
    "\n",
    "\n",
    "correlation_plot(numeric_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above figure we can see that \"no_of_children\" and \"total_family_members\" are highly correlated.\n",
    "Hence,dropping one of them\n",
    "also,\"prev_default\" and \"default_in_last_6months\" seems to be highly correlated.\n",
    "\"credit_limit\" and \"net_yearly_income\" also seems to be highly correlated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The important features with respect to the target variable are default_in_last_6_months,prev_Defaults,credit_score and credit_limit_used(%)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature exploration and visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26662\n"
     ]
    }
   ],
   "source": [
    "mod_data=data.copy()\n",
    "mod_data.dropna(inplace=True)\n",
    "max_val=mod_data.net_yearly_income.max()\n",
    "indx=mod_data[mod_data.net_yearly_income==max_val].index\n",
    "#indx=mod_data.net_yearly_income[max_val].index\n",
    "print(indx.values[0])\n",
    "mod_data.drop(labels=[indx.values[0]],axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "idxs=mod_data.index\n",
    "data1=data.yearly_debt_payments[idxs]\n",
    "data2=data.total_family_members[idxs]\n",
    "data3=data.no_of_children[idxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, ' Occupation type vs Net yearly income')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3960x2880 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ROWS=3\n",
    "COLS=2\n",
    "fig,ax=plt.subplots(nrows=ROWS,ncols=COLS,figsize=(55,40),tight_layout=True)\n",
    "ax[0][0].scatter(data3,data1,marker='x')\n",
    "ax[0][0].set_xlabel(\"No of children\",fontsize=20)\n",
    "ax[0][0].set_ylabel(\"Yearly debt payments\",fontsize=20)\n",
    "ax[0][0].set_title(\"No. of children vs Yearly debt payment\",fontsize=30)\n",
    "ax[0][1].scatter(mod_data.occupation_type[idxs],data1,marker='x')\n",
    "ax[0][1].tick_params(axis='x', labelrotation = 90)\n",
    "ax[0][1].set_xlabel(\"Occupation type\",fontsize=20)\n",
    "ax[0][1].set_ylabel(\"Yearly debt payments\",fontsize=20)\n",
    "ax[0][1].set_title(\"Occupation type vs Yearly debt payments\",fontsize=30)\n",
    "#ax[0][1].x(rotation=90)\n",
    "#ax[0][1].set_xticklabels(ax[0][1].get_xticks(),rotation=90)\n",
    "ax[1][0].scatter(data2,data1,marker='x')\n",
    "ax[1][0].set_xlabel(\"Total number of family members\",fontsize=20)\n",
    "ax[1][0].set_ylabel(\"Yearly debt payments\",fontsize=20)\n",
    "ax[1][0].set_title(\"Total number of family members vs Yeary debt payments\",fontsize=30)\n",
    "ax[1][1].scatter(mod_data.net_yearly_income[idxs],data1,marker='x')\n",
    "ax[1][1].set_xlabel(\"Net yearly income\",fontsize=20)\n",
    "ax[1][1].set_ylabel(\"Yearly debt payments\",fontsize=20)\n",
    "ax[1][1].set_title(\" Net yearly income vs Yearly debt payments\",fontsize=30)\n",
    "ax[2][0].scatter(mod_data.occupation_type[idxs],mod_data.net_yearly_income[idxs],marker='x')\n",
    "ax[2][0].set_xlabel(\"Occupation_type\",fontsize=20)\n",
    "ax[2][0].set_ylabel(\"Yearly debt payments\",fontsize=20)\n",
    "ax[2][0].set_title(\" Occupation type vs Net yearly income\",fontsize=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no_of_children             774\n",
       "owns_car                   547\n",
       "no_of_days_employed        463\n",
       "yearly_debt_payments        95\n",
       "migrant_worker              87\n",
       "total_family_members        83\n",
       "credit_score                 8\n",
       "default_in_last_6months      0\n",
       "prev_defaults                0\n",
       "credit_limit_used(%)         0\n",
       "credit_limit                 0\n",
       "customer_id                  0\n",
       "age                          0\n",
       "occupation_type              0\n",
       "net_yearly_income            0\n",
       "owns_house                   0\n",
       "gender                       0\n",
       "credit_card_default          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(sorted(data.isnull().sum(),reverse=True))\n",
    "data.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA of features + dealing with missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### a.)age column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def age_eda():\n",
    "    fig,(ax1,ax2)=plt.subplots(nrows=1,ncols=2,figsize=(10,5),tight_layout=True,squeeze=True,sharex='col')\n",
    "    red_circle=dict(markerfacecolor='red',marker='o')\n",
    "    mean=dict(markerfacecolor='green',marker='D')\n",
    "    ax1.boxplot(x=data[\"age\"],vert=False, flierprops=red_circle,showmeans=True,meanprops=mean)\n",
    "    ax1.set_title(\"Age Distribution of Customers.\")\n",
    "    ax1.set_xlabel(\"Age\")\n",
    "    ax2.hist(data.age)\n",
    "    ax2.set_xlabel(\"Age\")\n",
    "\n",
    "age_eda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The Age attribute almost follows a uniform distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### b.) no_of_days_employed column\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0 365252.0 0    365246.0\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "365246.0    684\n",
       "365244.0    669\n",
       "365240.0    641\n",
       "365245.0    631\n",
       "365241.0    628\n",
       "365247.0    625\n",
       "365250.0    609\n",
       "365251.0    607\n",
       "365243.0    607\n",
       "365252.0    602\n",
       "365249.0    601\n",
       "365242.0    600\n",
       "365248.0    594\n",
       "183.0        29\n",
       "436.0        26\n",
       "Name: no_of_days_employed, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=data[\"no_of_days_employed\"]\n",
    "print(x.min(),x.max(),x.mode())\n",
    "x.value_counts().sort_values(ascending=False).head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8098, 18)\n",
      "[365247. 365251. 365241. 365240. 365249. 365244. 365242. 365246. 365245.\n",
      " 365243. 365248. 365250. 365252.]\n"
     ]
    }
   ],
   "source": [
    "ddata=data[data.no_of_days_employed > 300000]\n",
    "#ddata.no_of_days_employed.value_counts()\n",
    "print(ddata.shape)\n",
    "y=ddata.no_of_days_employed.unique()\n",
    "print(y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Converting some values in no. of days employed to days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hrs_to_dys(column):\n",
    "    if column>300000:\n",
    "        column=column/(365*24)\n",
    "        column=column*365\n",
    "        column=round(column,2)\n",
    "    return column\n",
    "\n",
    "data['no_of_days_employed']=data.no_of_days_employed.apply(lambda x:(hrs_to_dys(x)),1)\n",
    "test_data['no_of_days_employed']=test_data.no_of_days_employed.apply(lambda x:(hrs_to_dys(x)),1)\n",
    "\n",
    "#data['no_of_days_employed']=data.no_of_days_employed.apply()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def days_employed_processing(data,value):\n",
    "    data.no_of_days_employed.replace(np.nan,value,inplace=True)\n",
    "    #return data\n",
    "value1=data['no_of_days_employed'].median()\n",
    "days_employed_processing(data,value1)\n",
    "days_employed_processing(test_data,value1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discretizing the no._of_days_employed feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import KBinsDiscretizer\n",
    "\n",
    "kbins=KBinsDiscretizer(n_bins=3,encode='ordinal',strategy='kmeans',dtype=np.float64)\n",
    "datatrans=kbins.fit_transform(pd.DataFrame(data.no_of_days_employed))\n",
    "def no_of_days_employed():\n",
    "    red_circle=dict(markerfacecolor='red',marker='o')\n",
    "    mean=dict(markerfacecolor='green',marker='D')\n",
    "    #fig=plt.figure(figsize=(13,7))\n",
    "    #plt.boxplot(x=mod_data.no_of_days_employed,vert=False, flierprops=red_circle,showmeans=True,meanprops=mean)\n",
    "    #plt.title(\"no_of_days_employed Distribution of Customers.\")\n",
    "    #plt.xlabel(\"days_employed\")\n",
    "    plt.hist(datatrans,bins=3)\n",
    "no_of_days_employed()\n",
    "#plt.scatter(mod_data.index,mod_data.no_of_days_employed,marker='x')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here,we can see that most of the customers do not have greater than 10 or 15 years of work experience which justifies the higher number of customers having 25 and 35 years of age."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### c. yearly_debt_payments column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29081.65 31796.96531111747 2237.47 328112.86\n"
     ]
    }
   ],
   "source": [
    "debt=data.yearly_debt_payments\n",
    "print(debt.median(),debt.mean(),debt.min(),debt.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40561.15"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "debt.describe()['75%']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### c.) yearly_debt_payments column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def debt_payments_processing(data,value):\n",
    "    data.yearly_debt_payments.replace(np.nan,value,inplace=True)\n",
    "    #return data\n",
    "value1=data['yearly_debt_payments'].mean()\n",
    "debt_payments_processing(data,value1)\n",
    "debt_payments_processing(test_data,value1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def box_plot_debt_payments():\n",
    "    fig,(ax1,ax2)=plt.subplots(nrows=1,ncols=2,figsize=(10,5),tight_layout=True,squeeze=True,sharex='col')\n",
    "    red_circle=dict(markerfacecolor='red',marker='o')\n",
    "    mean=dict(markerfacecolor='green',marker='D')\n",
    "    ax1.boxplot(x=data[\"yearly_debt_payments\"],vert=False, flierprops=red_circle,showmeans=True,meanprops=mean)\n",
    "    ax1.set_title(\"Yearly debt payments.\")\n",
    "    ax1.set_xlabel(\"debt_payments\")\n",
    "    ax2.hist(data.yearly_debt_payments,bins=30)\n",
    "    ax2.set_xlabel(\"debt_payments\")\n",
    "box_plot_debt_payments()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kbin=KBinsDiscretizer(n_bins=30,encode='ordinal',strategy='kmeans',dtype=np.float64)\n",
    "datat=kbin.fit_transform(pd.DataFrame(data['yearly_debt_payments']))\n",
    "plt.hist(datat,bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### d.) credit_limit_used column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1edaabd3760>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['credit_limit_used(%)'],debt,marker='x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### e.) total_family_members column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "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>customer_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>owns_house</th>\n",
       "      <th>no_of_children</th>\n",
       "      <th>net_yearly_income</th>\n",
       "      <th>no_of_days_employed</th>\n",
       "      <th>occupation_type</th>\n",
       "      <th>migrant_worker</th>\n",
       "      <th>yearly_debt_payments</th>\n",
       "      <th>credit_limit</th>\n",
       "      <th>credit_limit_used(%)</th>\n",
       "      <th>credit_score</th>\n",
       "      <th>prev_defaults</th>\n",
       "      <th>default_in_last_6months</th>\n",
       "      <th>credit_card_default</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_family_members</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>9913</td>\n",
       "      <td>9913</td>\n",
       "      <td>9913</td>\n",
       "      <td>9802</td>\n",
       "      <td>9913</td>\n",
       "      <td>9721</td>\n",
       "      <td>9913</td>\n",
       "      <td>9913</td>\n",
       "      <td>9913</td>\n",
       "      <td>9891</td>\n",
       "      <td>9913</td>\n",
       "      <td>9913</td>\n",
       "      <td>9913</td>\n",
       "      <td>9910</td>\n",
       "      <td>9913</td>\n",
       "      <td>9913</td>\n",
       "      <td>9913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>23455</td>\n",
       "      <td>23455</td>\n",
       "      <td>23455</td>\n",
       "      <td>23160</td>\n",
       "      <td>23455</td>\n",
       "      <td>23058</td>\n",
       "      <td>23455</td>\n",
       "      <td>23455</td>\n",
       "      <td>23455</td>\n",
       "      <td>23406</td>\n",
       "      <td>23455</td>\n",
       "      <td>23455</td>\n",
       "      <td>23455</td>\n",
       "      <td>23451</td>\n",
       "      <td>23455</td>\n",
       "      <td>23455</td>\n",
       "      <td>23455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>7812</td>\n",
       "      <td>7812</td>\n",
       "      <td>7812</td>\n",
       "      <td>7709</td>\n",
       "      <td>7812</td>\n",
       "      <td>7698</td>\n",
       "      <td>7812</td>\n",
       "      <td>7812</td>\n",
       "      <td>7812</td>\n",
       "      <td>7799</td>\n",
       "      <td>7812</td>\n",
       "      <td>7812</td>\n",
       "      <td>7812</td>\n",
       "      <td>7812</td>\n",
       "      <td>7812</td>\n",
       "      <td>7812</td>\n",
       "      <td>7812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>3623</td>\n",
       "      <td>3623</td>\n",
       "      <td>3623</td>\n",
       "      <td>3591</td>\n",
       "      <td>3623</td>\n",
       "      <td>3564</td>\n",
       "      <td>3623</td>\n",
       "      <td>3623</td>\n",
       "      <td>3623</td>\n",
       "      <td>3620</td>\n",
       "      <td>3623</td>\n",
       "      <td>3623</td>\n",
       "      <td>3623</td>\n",
       "      <td>3622</td>\n",
       "      <td>3623</td>\n",
       "      <td>3623</td>\n",
       "      <td>3623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>564</td>\n",
       "      <td>564</td>\n",
       "      <td>564</td>\n",
       "      <td>560</td>\n",
       "      <td>564</td>\n",
       "      <td>555</td>\n",
       "      <td>564</td>\n",
       "      <td>564</td>\n",
       "      <td>564</td>\n",
       "      <td>564</td>\n",
       "      <td>564</td>\n",
       "      <td>564</td>\n",
       "      <td>564</td>\n",
       "      <td>564</td>\n",
       "      <td>564</td>\n",
       "      <td>564</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>55</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>81</td>\n",
       "      <td>83</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      customer_id    age  gender  owns_car  owns_house  \\\n",
       "total_family_members                                                     \n",
       "1.0                          9913   9913    9913      9802        9913   \n",
       "2.0                         23455  23455   23455     23160       23455   \n",
       "3.0                          7812   7812    7812      7709        7812   \n",
       "4.0                          3623   3623    3623      3591        3623   \n",
       "5.0                           564    564     564       560         564   \n",
       "6.0                            57     57      57        57          57   \n",
       "7.0                            12     12      12        12          12   \n",
       "8.0                             6      6       6         6           6   \n",
       "9.0                             1      1       1         1           1   \n",
       "10.0                            2      2       2         2           2   \n",
       "NaN                            83     83      83        81          83   \n",
       "\n",
       "                      no_of_children  net_yearly_income  no_of_days_employed  \\\n",
       "total_family_members                                                           \n",
       "1.0                             9721               9913                 9913   \n",
       "2.0                            23058              23455                23455   \n",
       "3.0                             7698               7812                 7812   \n",
       "4.0                             3564               3623                 3623   \n",
       "5.0                              555                564                  564   \n",
       "6.0                               55                 57                   57   \n",
       "7.0                               12                 12                   12   \n",
       "8.0                                6                  6                    6   \n",
       "9.0                                1                  1                    1   \n",
       "10.0                               2                  2                    2   \n",
       "NaN                               82                 83                   83   \n",
       "\n",
       "                      occupation_type  migrant_worker  yearly_debt_payments  \\\n",
       "total_family_members                                                          \n",
       "1.0                              9913            9891                  9913   \n",
       "2.0                             23455           23406                 23455   \n",
       "3.0                              7812            7799                  7812   \n",
       "4.0                              3623            3620                  3623   \n",
       "5.0                               564             564                   564   \n",
       "6.0                                57              57                    57   \n",
       "7.0                                12              12                    12   \n",
       "8.0                                 6               6                     6   \n",
       "9.0                                 1               1                     1   \n",
       "10.0                                2               2                     2   \n",
       "NaN                                83              83                    83   \n",
       "\n",
       "                      credit_limit  credit_limit_used(%)  credit_score  \\\n",
       "total_family_members                                                     \n",
       "1.0                           9913                  9913          9910   \n",
       "2.0                          23455                 23455         23451   \n",
       "3.0                           7812                  7812          7812   \n",
       "4.0                           3623                  3623          3622   \n",
       "5.0                            564                   564           564   \n",
       "6.0                             57                    57            57   \n",
       "7.0                             12                    12            12   \n",
       "8.0                              6                     6             6   \n",
       "9.0                              1                     1             1   \n",
       "10.0                             2                     2             2   \n",
       "NaN                             83                    83            83   \n",
       "\n",
       "                      prev_defaults  default_in_last_6months  \\\n",
       "total_family_members                                           \n",
       "1.0                            9913                     9913   \n",
       "2.0                           23455                    23455   \n",
       "3.0                            7812                     7812   \n",
       "4.0                            3623                     3623   \n",
       "5.0                             564                      564   \n",
       "6.0                              57                       57   \n",
       "7.0                              12                       12   \n",
       "8.0                               6                        6   \n",
       "9.0                               1                        1   \n",
       "10.0                              2                        2   \n",
       "NaN                              83                       83   \n",
       "\n",
       "                      credit_card_default  \n",
       "total_family_members                       \n",
       "1.0                                  9913  \n",
       "2.0                                 23455  \n",
       "3.0                                  7812  \n",
       "4.0                                  3623  \n",
       "5.0                                   564  \n",
       "6.0                                    57  \n",
       "7.0                                    12  \n",
       "8.0                                     6  \n",
       "9.0                                     1  \n",
       "10.0                                    2  \n",
       "NaN                                    83  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt=data.groupby('total_family_members',dropna=False)\n",
    "tt.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func(row,val):\n",
    "    if pd.isnull(row.total_family_members):\n",
    "        row.total_family_members=val\n",
    "    return row\n",
    "u_values=sorted(list(data.total_family_members.unique()))[:5]\n",
    "data=data.apply(lambda x:func(x,random.choice(u_values)),axis=1)\n",
    "test_data=test_data.apply(lambda x:func(x,random.choice(u_values)),axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### f.) migrant_worker column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def migrant_w_processing(data,value):\n",
    "    data.migrant_worker.replace(np.nan,value,inplace=True)\n",
    "    #return data\n",
    "value=data.migrant_worker.unique()[0]\n",
    "migrant_w_processing(data,value)\n",
    "migrant_w_processing(test_data,value)\n",
    "#data.migrant_worker=migrant_w_processing(data['migrant_worker'],value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### g.) credit score column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "neg_scr=data.credit_score[data.credit_card_default==1].mean()\n",
    "pos_scr=data.credit_score[data.credit_card_default==0].mean()\n",
    "pos_scr=round(pos_scr,2)\n",
    "neg_scr=round(neg_scr,2)\n",
    "\n",
    "#print(pos_scr,neg_scr)\n",
    "b1=data.credit_card_default==1\n",
    "b2=data.credit_card_default==0\n",
    "\n",
    "#data[data.credit_card_default==1].isnull()\n",
    "#fillna(pos_scr,inplace=True)\n",
    "#data[data.credit_card_default==0].fillna(neg_scr,inplace=True)\n",
    "#data[data.credit_score.isnull().values & b1.values].assign(credit_score=pos_scr)\n",
    "#data[data.credit_score.isnull().values & b2.values].assign(credit_score=neg_scr)\n",
    "\n",
    "def fill_na(row,test=False):\n",
    "    if (test!=True):\n",
    "        if pd.isnull(row.credit_score) and row.credit_card_default==1:\n",
    "            row.credit_score=neg_scr\n",
    "        if pd.isnull(row.credit_score) and row.credit_card_default==0:\n",
    "            row.credit_score=pos_scr\n",
    "    elif pd.isnull(row.credit_score) and test==True:\n",
    "        row.credit_score=(pos_scr+neg_scr)/2\n",
    "    return row\n",
    "\n",
    "data=data.apply(lambda x:fill_na(x),axis=1)\n",
    "test_data=test_data.apply(lambda x:fill_na(x,test=True),axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    43060\n",
       "1     2172\n",
       "2      296\n",
       "Name: prev_defaults, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.prev_defaults.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    43227\n",
       "1     2301\n",
       "Name: default_in_last_6months, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.default_in_last_6months.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--prev_default contains more information than default_in_last_6months since it describes the no. of times a customer became a defaulter rather than just describing weather the customer was a defaulter or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Dropping columns which are not important with respect to the variable to be predicted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('owns_car',axis=1,inplace=True)\n",
    "data.drop(['no_of_children'],axis=1,inplace=True)\n",
    "data.drop(['default_in_last_6months'],axis=1,inplace=True)\n",
    "test_data.drop('owns_car',axis=1,inplace=True)\n",
    "test_data.drop(['no_of_children'],axis=1,inplace=True)\n",
    "test_data.drop(['default_in_last_6months'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### creating customer_id as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mod_data=data.copy()\n",
    "data['customer_id']=data['customer_id'].apply(lambda x:int(re.findall(\"\\d.*[0-9]\",str(x))[0]))\n",
    "data.set_index('customer_id',inplace=True)\n",
    "#commend below line for competition purpose.\n",
    "test_data['customer_id']=test_data['customer_id'].apply(lambda x:int(re.findall(\"\\d.*[0-9]\",str(x))[0]))\n",
    "test_data.set_index('customer_id',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Scaling for numeric values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler,MaxAbsScaler,QuantileTransformer\n",
    "\n",
    "data3=data[['net_yearly_income','no_of_days_employed']]\n",
    "scaler=RobustScaler()\n",
    "data3=pd.DataFrame(scaler.fit_transform(data3),columns=data3.columns)\n",
    "data3.set_index(data.index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Fscaling():\n",
    "    def __init__(self):\n",
    "        pass \n",
    "\n",
    "    #Scales data based on percentiles.\n",
    "    def Robust_Scaler(self,data):\n",
    "        typ=str(type(data)).split(\".\")[-1][:-2]\n",
    "        if typ==\"Series\":\n",
    "            data=pd.DataFrame(data)\n",
    "        scaler=RobustScaler()\n",
    "        mod_data=pd.DataFrame(scaler.fit_transform(data),columns=data.columns)\n",
    "        mod_data.index=data.index\n",
    "        return mod_data\n",
    "\n",
    "    def Max_Abs_Scaler(self,data):\n",
    "        typ=str(type(data)).split(\".\")[-1][:-2]\n",
    "        if typ==\"Series\":\n",
    "            data=pd.DataFrame(data)\n",
    "        scaler=MaxAbsScaler()\n",
    "        mod_data=pd.DataFrame(scaler.fit_transform(data),columns=data.columns)\n",
    "        mod_data.index=data.index\n",
    "        return mod_data\n",
    "\n",
    "    #transforms the features to follow a uniform or normal distribution.\n",
    "    #this transformation tends to spread out the most frequent values.\n",
    "    #the cumulative distribution function(c.d.f) is used to project original values.\n",
    "    #this transformation is non-linear\n",
    "    def quantile_transformer(self,data):\n",
    "        typ=str(type(data)).split(\".\")[-1][:-2]\n",
    "        if typ==\"Series\":\n",
    "            data=pd.DataFrame(data)\n",
    "        scaler=QuantileTransformer()\n",
    "        mod_data=pd.DataFrame(scaler.fit_transform(data),columns=data.columns)\n",
    "        mod_data.index=data.index\n",
    "        return mod_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(45528, 14)\n",
      "(11383, 13)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "print(test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_obj=Fscaling()\n",
    "ret_data=f_obj.Robust_Scaler(data['net_yearly_income'])\n",
    "data.drop(['net_yearly_income'],axis=1,inplace=True)\n",
    "data=pd.concat([data,ret_data],axis=1)\n",
    "ret_data=f_obj.Max_Abs_Scaler(data['no_of_days_employed'])\n",
    "data.drop(['no_of_days_employed'],axis=1,inplace=True)\n",
    "data=pd.concat([data,ret_data],axis=1)\n",
    "ret_data=f_obj.quantile_transformer(data['yearly_debt_payments'])\n",
    "data.drop(['yearly_debt_payments'],axis=1,inplace=True)\n",
    "data=pd.concat([data,ret_data],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "ret_data=f_obj.Robust_Scaler(test_data['net_yearly_income'])\n",
    "test_data.drop(['net_yearly_income'],axis=1,inplace=True)\n",
    "test_data=pd.concat([test_data,ret_data],axis=1)\n",
    "ret_data=f_obj.Max_Abs_Scaler(test_data['no_of_days_employed'])\n",
    "test_data.drop(['no_of_days_employed'],axis=1,inplace=True)\n",
    "test_data=pd.concat([test_data,ret_data],axis=1)\n",
    "ret_data=f_obj.quantile_transformer(test_data['yearly_debt_payments'])\n",
    "test_data.drop(['yearly_debt_payments'],axis=1,inplace=True)\n",
    "test_data=pd.concat([test_data,ret_data],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping unknown(XNA) category in Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "id=data[data.gender==\"XNA\"].index\n",
    "data.drop(id,axis=0,inplace=True)\n",
    "idt=test_data[test_data.gender==\"XNA\"].index\n",
    "test_data.drop(idt,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    33463\n",
      "1     2958\n",
      "Name: credit_card_default, dtype: int64\n",
      "0    8367\n",
      "1     739\n",
      "Name: credit_card_default, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "X=data.drop([\"credit_card_default\"],axis=1,inplace=False)\n",
    "y=data[\"credit_card_default\"]\n",
    "from sklearn.model_selection import train_test_split\n",
    "xtrain,xval,ytrain,yval=train_test_split(X,y,train_size=0.80,test_size=0.20,random_state=42,shuffle=True,stratify=y)\n",
    "print(ytrain.value_counts())\n",
    "print(yval.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'gender', 'owns_house', 'occupation_type',\n",
      "       'total_family_members', 'migrant_worker', 'credit_limit',\n",
      "       'credit_limit_used(%)', 'credit_score', 'prev_defaults',\n",
      "       'credit_card_default', 'net_yearly_income', 'no_of_days_employed',\n",
      "       'yearly_debt_payments'],\n",
      "      dtype='object') 14\n"
     ]
    }
   ],
   "source": [
    "print(data.columns,len(data.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 13 13\n"
     ]
    }
   ],
   "source": [
    "print(len(xtrain.columns),len(xval.columns),len(test_data.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding Categorical Variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['customer_id', 'owns_car', 'gender', 'name', 'owns_house', 'occupation_type']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encoding categorical variables.\n",
    "from sklearn.preprocessing import OneHotEncoder,OrdinalEncoder\n",
    "\n",
    "class Encoding:\n",
    "    def __init__(self):\n",
    "        self.ohenc=OneHotEncoder(handle_unknown='ignore',sparse=False)\n",
    "        self.olenc=OrdinalEncoder()\n",
    "        #self.data=data\n",
    "        #print(type(self.data))\n",
    "        #print(self.data.head(10))\n",
    "\n",
    "    def ordinalencode(self,var_list,train=None,val=None,data=None):\n",
    "        #self.intrdata=self.data.copy()\n",
    "        if train is None and val is None:\n",
    "            data[var_list]=self.olenc.transform(data[var_list])\n",
    "            return data\n",
    "        else:\n",
    "            train[var_list]=self.olenc.fit_transform(train[var_list])\n",
    "            val[var_list]=self.olenc.transform(val[var_list])\n",
    "            return train,val\n",
    "\n",
    "    def onehotencode(self,categorical_var_list,train=None,val=None,data=None):\n",
    "        print(train,val)\n",
    "        if train is None and val is None:\n",
    "            self.test_data=data\n",
    "            self.test_data=pd.DataFrame(self.ohenc.transform(self.test_data[categorical_var_list]))\n",
    "            self.test_data.index=data.index\n",
    "            self.num_test_data=data.drop(columns=categorical_var_list,axis=1)\n",
    "            self.final_test_data=pd.concat([self.num_test_data,self.test_data],axis=1)\n",
    "            return self.final_test_data\n",
    "        else:\n",
    "            self.oh_cols_train=pd.DataFrame(self.ohenc.fit_transform(train[categorical_var_list]))\n",
    "            self.oh_cols_val=pd.DataFrame(self.ohenc.transform(val[categorical_var_list]))\n",
    "\n",
    "            self.oh_cols_train.index=train.index\n",
    "            self.oh_cols_val.index=val.index\n",
    "\n",
    "            self.num_ohcols_train=train.drop(columns=categorical_var_list,axis=1)\n",
    "            self.num_ohcols_val=val.drop(columns=categorical_var_list,axis=1)\n",
    "\n",
    "            self.final_data_train=pd.concat([self.num_ohcols_train,self.oh_cols_train],axis=1)\n",
    "            self.final_data_val=pd.concat([self.num_ohcols_val,self.oh_cols_val],axis=1)\n",
    "\n",
    "            return self.final_data_train,self.final_data_val\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "en=Encoding()\n",
    "xtrain,xval=en.ordinalencode([\"owns_house\"],train=xtrain,val=xval)\n",
    "test_data=en.ordinalencode([\"owns_house\"],data=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             age gender  owns_house        occupation_type  \\\n",
      "customer_id                                                  \n",
      "164920        38      F         1.0             Core staff   \n",
      "153597        38      F         1.0               Laborers   \n",
      "106429        46      M         1.0                Drivers   \n",
      "110261        42      F         1.0                Unknown   \n",
      "150536        39      F         1.0               Laborers   \n",
      "...          ...    ...         ...                    ...   \n",
      "107930        29      F         0.0                Unknown   \n",
      "125336        48      F         1.0  High skill tech staff   \n",
      "109884        33      M         1.0                Unknown   \n",
      "118738        38      F         1.0          Cooking staff   \n",
      "112155        55      F         1.0             Core staff   \n",
      "\n",
      "             total_family_members  migrant_worker  credit_limit  \\\n",
      "customer_id                                                       \n",
      "164920                        2.0             0.0      22335.12   \n",
      "153597                        2.0             1.0      26166.07   \n",
      "106429                        2.0             0.0      40183.42   \n",
      "110261                        1.0             0.0      43625.75   \n",
      "150536                        1.0             0.0      24872.90   \n",
      "...                           ...             ...           ...   \n",
      "107930                        4.0             0.0      52130.28   \n",
      "125336                        2.0             0.0      40784.48   \n",
      "109884                        3.0             0.0      36047.43   \n",
      "118738                        2.0             0.0      65247.47   \n",
      "112155                        2.0             0.0      22772.58   \n",
      "\n",
      "             credit_limit_used(%)  credit_score  prev_defaults  \\\n",
      "customer_id                                                      \n",
      "164920                         12         681.0              0   \n",
      "153597                         87         820.0              0   \n",
      "106429                         41         949.0              0   \n",
      "110261                          2         904.0              0   \n",
      "150536                         25         722.0              0   \n",
      "...                           ...           ...            ...   \n",
      "107930                          3         752.0              0   \n",
      "125336                         88         897.0              0   \n",
      "109884                         85         925.0              0   \n",
      "118738                         58         750.0              0   \n",
      "112155                         10         808.0              0   \n",
      "\n",
      "             net_yearly_income  no_of_days_employed  yearly_debt_payments  \n",
      "customer_id                                                                \n",
      "164920               -0.670040             0.082450              0.016676  \n",
      "153597               -0.409035             0.099441              0.247230  \n",
      "106429               -0.261465             0.048637              0.921859  \n",
      "110261               -0.412854             0.867749              0.284100  \n",
      "150536               -0.857526             0.005873              0.056214  \n",
      "...                        ...                  ...                   ...  \n",
      "107930               -0.126077             0.311666              0.813765  \n",
      "125336               -0.127892             0.173851              0.512485  \n",
      "109884               -0.571352             0.062550              0.051554  \n",
      "118738                2.196774             0.085015              0.789988  \n",
      "112155               -0.434038             0.534154              0.087186  \n",
      "\n",
      "[36421 rows x 13 columns]              age gender  owns_house       occupation_type  \\\n",
      "customer_id                                                 \n",
      "104993        42      F         1.0           Accountants   \n",
      "135019        35      F         1.0        Cleaning staff   \n",
      "151112        25      F         1.0  Waiters/barmen staff   \n",
      "105261        26      F         0.0               Unknown   \n",
      "115645        27      F         1.0           Sales staff   \n",
      "...          ...    ...         ...                   ...   \n",
      "145794        43      M         0.0               Unknown   \n",
      "108530        39      M         1.0              Laborers   \n",
      "123411        40      F         0.0           Sales staff   \n",
      "165583        51      F         1.0        Medicine staff   \n",
      "157981        33      M         1.0        Security staff   \n",
      "\n",
      "             total_family_members  migrant_worker  credit_limit  \\\n",
      "customer_id                                                       \n",
      "104993                        3.0             0.0      37112.57   \n",
      "135019                        4.0             1.0      35379.04   \n",
      "151112                        3.0             0.0      40387.32   \n",
      "105261                        1.0             0.0      27800.96   \n",
      "115645                        3.0             0.0      42407.67   \n",
      "...                           ...             ...           ...   \n",
      "145794                        1.0             0.0      64207.70   \n",
      "108530                        2.0             1.0     104528.23   \n",
      "123411                        1.0             0.0      42519.57   \n",
      "165583                        1.0             1.0      16430.91   \n",
      "157981                        2.0             0.0      14419.68   \n",
      "\n",
      "             credit_limit_used(%)  credit_score  prev_defaults  \\\n",
      "customer_id                                                      \n",
      "104993                          5         749.0              0   \n",
      "135019                         53         782.0              0   \n",
      "151112                         38         816.0              0   \n",
      "105261                         22         827.0              0   \n",
      "115645                         70         631.0              1   \n",
      "...                           ...           ...            ...   \n",
      "145794                         85         812.0              0   \n",
      "108530                         34         669.0              0   \n",
      "123411                         90         537.0              0   \n",
      "165583                         72         684.0              0   \n",
      "157981                         97         702.0              0   \n",
      "\n",
      "             net_yearly_income  no_of_days_employed  yearly_debt_payments  \n",
      "customer_id                                                                \n",
      "104993                0.411630             0.031360              0.969508  \n",
      "135019                0.462982             0.134793              0.645813  \n",
      "151112               -0.152620             0.108279              0.794868  \n",
      "105261               -0.066512             0.867749              0.548627  \n",
      "115645               -0.000987             0.084502              0.271759  \n",
      "...                        ...                  ...                   ...  \n",
      "145794                1.501139             0.022408              0.564203  \n",
      "108530                1.308638             0.334303              0.622943  \n",
      "123411                0.220906             0.037462              0.248947  \n",
      "165583               -0.261902             0.077831              0.050420  \n",
      "157981               -0.817484             0.037918              0.028049  \n",
      "\n",
      "[9106 rows x 13 columns]\n",
      "None None\n"
     ]
    }
   ],
   "source": [
    "xtrain,xval=en.onehotencode([\"gender\",\"occupation_type\"],train=xtrain,val=xval)\n",
    "test_data=en.onehotencode([\"gender\",\"occupation_type\"],data=test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
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       "             age  owns_house  total_family_members  migrant_worker  \\\n",
       "customer_id                                                          \n",
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       "\n",
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       "customer_id                                                                    \n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123812</th>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29179.39</td>\n",
       "      <td>9</td>\n",
       "      <td>815.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.110226</td>\n",
       "      <td>0.030821</td>\n",
       "      <td>...</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144450</th>\n",
       "      <td>52</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>82331.82</td>\n",
       "      <td>82</td>\n",
       "      <td>613.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.537397</td>\n",
       "      <td>0.009620</td>\n",
       "      <td>...</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107341</th>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20346.31</td>\n",
       "      <td>71</td>\n",
       "      <td>720.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.244480</td>\n",
       "      <td>0.293750</td>\n",
       "      <td>...</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147879</th>\n",
       "      <td>37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31383.40</td>\n",
       "      <td>29</td>\n",
       "      <td>944.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.255719</td>\n",
       "      <td>0.065502</td>\n",
       "      <td>...</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156027</th>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25486.65</td>\n",
       "      <td>5</td>\n",
       "      <td>939.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.409900</td>\n",
       "      <td>0.019547</td>\n",
       "      <td>...</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109067</th>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13458.33</td>\n",
       "      <td>12</td>\n",
       "      <td>763.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.814242</td>\n",
       "      <td>0.184559</td>\n",
       "      <td>...</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>0.0</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>114556</th>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24354.25</td>\n",
       "      <td>90</td>\n",
       "      <td>897.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.624845</td>\n",
       "      <td>0.027574</td>\n",
       "      <td>...</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             age  owns_house  total_family_members  migrant_worker  \\\n",
       "customer_id                                                          \n",
       "142525        52         0.0                   2.0             0.0   \n",
       "129215        48         0.0                   3.0             0.0   \n",
       "138443        50         0.0                   3.0             0.0   \n",
       "123812        30         0.0                   2.0             1.0   \n",
       "144450        52         1.0                   2.0             1.0   \n",
       "107341        29         1.0                   2.0             0.0   \n",
       "147879        37         1.0                   1.0             0.0   \n",
       "156027        29         1.0                   1.0             0.0   \n",
       "109067        41         1.0                   2.0             0.0   \n",
       "114556        41         1.0                   3.0             1.0   \n",
       "\n",
       "             credit_limit  credit_limit_used(%)  credit_score  prev_defaults  \\\n",
       "customer_id                                                                    \n",
       "142525           26524.40                     4         779.0              0   \n",
       "129215           68998.72                    70         806.0              0   \n",
       "138443           25187.80                    71         528.0              2   \n",
       "123812           29179.39                     9         815.0              0   \n",
       "144450           82331.82                    82         613.0              1   \n",
       "107341           20346.31                    71         720.0              0   \n",
       "147879           31383.40                    29         944.0              0   \n",
       "156027           25486.65                     5         939.0              0   \n",
       "109067           13458.33                    12         763.0              0   \n",
       "114556           24354.25                    90         897.0              0   \n",
       "\n",
       "             net_yearly_income  no_of_days_employed  ...   11   12   13   14  \\\n",
       "customer_id                                          ...                       \n",
       "142525                0.529951             0.061152  ...  0.0  0.0  0.0  0.0   \n",
       "129215                0.988840             0.081985  ...  0.0  0.0  0.0  0.0   \n",
       "138443               -0.207918             0.074142  ...  0.0  0.0  0.0  0.0   \n",
       "123812               -0.110226             0.030821  ...  0.0  0.0  0.0  0.0   \n",
       "144450                0.537397             0.009620  ...  0.0  0.0  0.0  0.0   \n",
       "107341               -0.244480             0.293750  ...  0.0  0.0  0.0  0.0   \n",
       "147879               -0.255719             0.065502  ...  0.0  0.0  0.0  0.0   \n",
       "156027               -0.409900             0.019547  ...  0.0  0.0  0.0  0.0   \n",
       "109067               -0.814242             0.184559  ...  0.0  0.0  1.0  0.0   \n",
       "114556               -0.624845             0.027574  ...  0.0  0.0  0.0  0.0   \n",
       "\n",
       "              15   16   17   18   19   20  \n",
       "customer_id                                \n",
       "142525       0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "129215       0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "138443       0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "123812       0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "144450       0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "107341       0.0  1.0  0.0  0.0  0.0  0.0  \n",
       "147879       0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "156027       0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "109067       0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "114556       0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "\n",
       "[10 rows x 32 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(xtrain.columns),len(xval.columns),len(test_data.columns))\n",
    "test_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain.to_csv(\"/Amexpert/dataset/train_processed.csv\")\n",
    "xval.to_csv(\"/Amexpert/dataset/validation_processed.csv\")\n",
    "test_data.to_csv(\"/Amexpert/dataset/test_processed.csv\")\n",
    "test_data.to_csv(\"/Amexpert/dataset/test_processed.csv\")\n",
    "ytrain.to_csv(\"/Amexpert/dataset/ytrain_processed.csv\")\n",
    "yval.to_csv(\"/Amexpert/dataset/yval_processed.csv\")"
   ]
  }
 ],
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