ltfs_xgboost.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://datahack.analyticsvidhya.com/contest/ltfs-datascience-finhack-an-online-hackathon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 50)\n",
    "pd.set_option('display.width', 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_in = pd.read_csv('data/train.csv')\n",
    "df_test_in = pd.read_csv('data/test_bqCt9Pv.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_in['loan_default'] = -1\n",
    "df = pd.concat(( df_train_in, df_test_in ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UniqueID</th>\n",
       "      <th>disbursed_amount</th>\n",
       "      <th>asset_cost</th>\n",
       "      <th>ltv</th>\n",
       "      <th>branch_id</th>\n",
       "      <th>supplier_id</th>\n",
       "      <th>manufacturer_id</th>\n",
       "      <th>Current_pincode_ID</th>\n",
       "      <th>Date.of.Birth</th>\n",
       "      <th>Employment.Type</th>\n",
       "      <th>DisbursalDate</th>\n",
       "      <th>State_ID</th>\n",
       "      <th>Employee_code_ID</th>\n",
       "      <th>MobileNo_Avl_Flag</th>\n",
       "      <th>Aadhar_flag</th>\n",
       "      <th>PAN_flag</th>\n",
       "      <th>VoterID_flag</th>\n",
       "      <th>Driving_flag</th>\n",
       "      <th>Passport_flag</th>\n",
       "      <th>PERFORM_CNS.SCORE</th>\n",
       "      <th>PERFORM_CNS.SCORE.DESCRIPTION</th>\n",
       "      <th>PRI.NO.OF.ACCTS</th>\n",
       "      <th>PRI.ACTIVE.ACCTS</th>\n",
       "      <th>PRI.OVERDUE.ACCTS</th>\n",
       "      <th>PRI.CURRENT.BALANCE</th>\n",
       "      <th>PRI.SANCTIONED.AMOUNT</th>\n",
       "      <th>PRI.DISBURSED.AMOUNT</th>\n",
       "      <th>SEC.NO.OF.ACCTS</th>\n",
       "      <th>SEC.ACTIVE.ACCTS</th>\n",
       "      <th>SEC.OVERDUE.ACCTS</th>\n",
       "      <th>SEC.CURRENT.BALANCE</th>\n",
       "      <th>SEC.SANCTIONED.AMOUNT</th>\n",
       "      <th>SEC.DISBURSED.AMOUNT</th>\n",
       "      <th>PRIMARY.INSTAL.AMT</th>\n",
       "      <th>SEC.INSTAL.AMT</th>\n",
       "      <th>NEW.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>AVERAGE.ACCT.AGE</th>\n",
       "      <th>CREDIT.HISTORY.LENGTH</th>\n",
       "      <th>NO.OF_INQUIRIES</th>\n",
       "      <th>loan_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>420825</td>\n",
       "      <td>50578</td>\n",
       "      <td>58400</td>\n",
       "      <td>89.55</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1441</td>\n",
       "      <td>01-01-84</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>03-08-18</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No Bureau History Available</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>537409</td>\n",
       "      <td>47145</td>\n",
       "      <td>65550</td>\n",
       "      <td>73.23</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1502</td>\n",
       "      <td>31-07-85</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>26-09-18</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>598</td>\n",
       "      <td>I-Medium Risk</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27600</td>\n",
       "      <td>50200</td>\n",
       "      <td>50200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1yrs 11mon</td>\n",
       "      <td>1yrs 11mon</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>417566</td>\n",
       "      <td>53278</td>\n",
       "      <td>61360</td>\n",
       "      <td>89.63</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1497</td>\n",
       "      <td>24-08-85</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>01-08-18</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No Bureau History Available</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>624493</td>\n",
       "      <td>57513</td>\n",
       "      <td>66113</td>\n",
       "      <td>88.48</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1501</td>\n",
       "      <td>30-12-93</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>26-10-18</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>305</td>\n",
       "      <td>L-Very High Risk</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0yrs 8mon</td>\n",
       "      <td>1yrs 3mon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>539055</td>\n",
       "      <td>52378</td>\n",
       "      <td>60300</td>\n",
       "      <td>88.39</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1495</td>\n",
       "      <td>09-12-77</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>26-09-18</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No Bureau History Available</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  manufacturer_id  Current_pincode_ID Date.of.Birth Employment.Type DisbursalDate  State_ID  Employee_code_ID  MobileNo_Avl_Flag  Aadhar_flag  PAN_flag  VoterID_flag  Driving_flag  Passport_flag  PERFORM_CNS.SCORE PERFORM_CNS.SCORE.DESCRIPTION  PRI.NO.OF.ACCTS  PRI.ACTIVE.ACCTS  PRI.OVERDUE.ACCTS  PRI.CURRENT.BALANCE  PRI.SANCTIONED.AMOUNT  PRI.DISBURSED.AMOUNT  SEC.NO.OF.ACCTS  SEC.ACTIVE.ACCTS  SEC.OVERDUE.ACCTS  SEC.CURRENT.BALANCE  SEC.SANCTIONED.AMOUNT  SEC.DISBURSED.AMOUNT  PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS AVERAGE.ACCT.AGE CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default\n",
       "0    420825             50578       58400  89.55         67        22807               45                1441      01-01-84        Salaried      03-08-18         6              1998                  1            1         0             0             0              0                  0   No Bureau History Available                0                 0                  0                    0                      0                     0                0                 0                  0                    0                      0                     0                   0               0                             0                                    0        0yrs 0mon             0yrs 0mon                0             0\n",
       "1    537409             47145       65550  73.23         67        22807               45                1502      31-07-85   Self employed      26-09-18         6              1998                  1            1         0             0             0              0                598                 I-Medium Risk                1                 1                  1                27600                  50200                 50200                0                 0                  0                    0                      0                     0                1991               0                             0                                    1       1yrs 11mon            1yrs 11mon                0             1\n",
       "2    417566             53278       61360  89.63         67        22807               45                1497      24-08-85   Self employed      01-08-18         6              1998                  1            1         0             0             0              0                  0   No Bureau History Available                0                 0                  0                    0                      0                     0                0                 0                  0                    0                      0                     0                   0               0                             0                                    0        0yrs 0mon             0yrs 0mon                0             0\n",
       "3    624493             57513       66113  88.48         67        22807               45                1501      30-12-93   Self employed      26-10-18         6              1998                  1            1         0             0             0              0                305              L-Very High Risk                3                 0                  0                    0                      0                     0                0                 0                  0                    0                      0                     0                  31               0                             0                                    0        0yrs 8mon             1yrs 3mon                1             1\n",
       "4    539055             52378       60300  88.39         67        22807               45                1495      09-12-77   Self employed      26-09-18         6              1998                  1            1         0             0             0              0                  0   No Bureau History Available                0                 0                  0                    0                      0                     0                0                 0                  0                    0                      0                     0                   0               0                             0                                    0        0yrs 0mon             0yrs 0mon                1             1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((233154, 41), (112392, 41))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_in.shape, df_test_in.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2170711203753742"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_train_in['loan_default'])/len(df_train_in['loan_default'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.474463450223624e-11"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/sum(df_train_in['loan_default'])/len(df_train_in['loan_default'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UniqueID</th>\n",
       "      <th>disbursed_amount</th>\n",
       "      <th>asset_cost</th>\n",
       "      <th>ltv</th>\n",
       "      <th>branch_id</th>\n",
       "      <th>supplier_id</th>\n",
       "      <th>manufacturer_id</th>\n",
       "      <th>Current_pincode_ID</th>\n",
       "      <th>State_ID</th>\n",
       "      <th>Employee_code_ID</th>\n",
       "      <th>MobileNo_Avl_Flag</th>\n",
       "      <th>Aadhar_flag</th>\n",
       "      <th>PAN_flag</th>\n",
       "      <th>VoterID_flag</th>\n",
       "      <th>Driving_flag</th>\n",
       "      <th>Passport_flag</th>\n",
       "      <th>PERFORM_CNS.SCORE</th>\n",
       "      <th>PRI.NO.OF.ACCTS</th>\n",
       "      <th>PRI.ACTIVE.ACCTS</th>\n",
       "      <th>PRI.OVERDUE.ACCTS</th>\n",
       "      <th>PRI.CURRENT.BALANCE</th>\n",
       "      <th>PRI.SANCTIONED.AMOUNT</th>\n",
       "      <th>PRI.DISBURSED.AMOUNT</th>\n",
       "      <th>SEC.NO.OF.ACCTS</th>\n",
       "      <th>SEC.ACTIVE.ACCTS</th>\n",
       "      <th>SEC.OVERDUE.ACCTS</th>\n",
       "      <th>SEC.CURRENT.BALANCE</th>\n",
       "      <th>SEC.SANCTIONED.AMOUNT</th>\n",
       "      <th>SEC.DISBURSED.AMOUNT</th>\n",
       "      <th>PRIMARY.INSTAL.AMT</th>\n",
       "      <th>SEC.INSTAL.AMT</th>\n",
       "      <th>NEW.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>NO.OF_INQUIRIES</th>\n",
       "      <th>loan_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>3.455460e+05</td>\n",
       "      <td>345546.00000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.0</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>3.455460e+05</td>\n",
       "      <td>3.455460e+05</td>\n",
       "      <td>3.455460e+05</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>3.455460e+05</td>\n",
       "      <td>3.455460e+05</td>\n",
       "      <td>3.455460e+05</td>\n",
       "      <td>3.455460e+05</td>\n",
       "      <td>3.455460e+05</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "      <td>345546.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>593106.042648</td>\n",
       "      <td>54916.377391</td>\n",
       "      <td>7.629484e+04</td>\n",
       "      <td>74.92991</td>\n",
       "      <td>74.876752</td>\n",
       "      <td>19705.205342</td>\n",
       "      <td>68.812725</td>\n",
       "      <td>3272.345381</td>\n",
       "      <td>7.247122</td>\n",
       "      <td>1621.741403</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.849852</td>\n",
       "      <td>0.113311</td>\n",
       "      <td>0.137148</td>\n",
       "      <td>0.021117</td>\n",
       "      <td>0.002058</td>\n",
       "      <td>289.032062</td>\n",
       "      <td>2.371201</td>\n",
       "      <td>1.003586</td>\n",
       "      <td>0.155146</td>\n",
       "      <td>1.602702e+05</td>\n",
       "      <td>2.096509e+05</td>\n",
       "      <td>2.095608e+05</td>\n",
       "      <td>0.047528</td>\n",
       "      <td>0.022709</td>\n",
       "      <td>0.005921</td>\n",
       "      <td>4.565299e+03</td>\n",
       "      <td>6.133302e+03</td>\n",
       "      <td>6.038715e+03</td>\n",
       "      <td>1.249773e+04</td>\n",
       "      <td>2.727354e+02</td>\n",
       "      <td>0.360065</td>\n",
       "      <td>0.095660</td>\n",
       "      <td>0.212959</td>\n",
       "      <td>-0.178792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>101481.557990</td>\n",
       "      <td>13045.957158</td>\n",
       "      <td>1.873864e+04</td>\n",
       "      <td>11.31522</td>\n",
       "      <td>70.426229</td>\n",
       "      <td>3513.984528</td>\n",
       "      <td>22.142130</td>\n",
       "      <td>2240.489856</td>\n",
       "      <td>4.547494</td>\n",
       "      <td>1024.408918</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.357217</td>\n",
       "      <td>0.316972</td>\n",
       "      <td>0.344004</td>\n",
       "      <td>0.143776</td>\n",
       "      <td>0.045314</td>\n",
       "      <td>338.841228</td>\n",
       "      <td>5.013630</td>\n",
       "      <td>1.877087</td>\n",
       "      <td>0.543333</td>\n",
       "      <td>9.253457e+05</td>\n",
       "      <td>2.043866e+06</td>\n",
       "      <td>2.047483e+06</td>\n",
       "      <td>0.561836</td>\n",
       "      <td>0.283288</td>\n",
       "      <td>0.100497</td>\n",
       "      <td>1.612026e+05</td>\n",
       "      <td>1.893427e+05</td>\n",
       "      <td>1.889114e+05</td>\n",
       "      <td>1.997545e+05</td>\n",
       "      <td>1.626126e+04</td>\n",
       "      <td>0.920797</td>\n",
       "      <td>0.383594</td>\n",
       "      <td>0.717365</td>\n",
       "      <td>0.663144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>417428.000000</td>\n",
       "      <td>11613.000000</td>\n",
       "      <td>3.700000e+04</td>\n",
       "      <td>10.03000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10524.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.678296e+06</td>\n",
       "      <td>-4.815000e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.746470e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>505386.250000</td>\n",
       "      <td>47645.000000</td>\n",
       "      <td>6.628200e+04</td>\n",
       "      <td>69.28000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16565.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1449.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>737.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>592918.500000</td>\n",
       "      <td>54303.000000</td>\n",
       "      <td>7.154100e+04</td>\n",
       "      <td>77.14000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>20470.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>2790.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1511.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>680501.750000</td>\n",
       "      <td>60947.000000</td>\n",
       "      <td>7.960500e+04</td>\n",
       "      <td>83.69000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>23049.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>5458.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2470.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>679.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.136450e+04</td>\n",
       "      <td>5.941675e+04</td>\n",
       "      <td>5.764575e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.946000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>769909.000000</td>\n",
       "      <td>990572.000000</td>\n",
       "      <td>1.628992e+06</td>\n",
       "      <td>95.00000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>24901.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>7345.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>3923.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>890.000000</td>\n",
       "      <td>453.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>9.652492e+07</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.603285e+07</td>\n",
       "      <td>5.794500e+07</td>\n",
       "      <td>5.794500e+07</td>\n",
       "      <td>8.526233e+07</td>\n",
       "      <td>5.390000e+06</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            UniqueID  disbursed_amount    asset_cost           ltv      branch_id    supplier_id  manufacturer_id  Current_pincode_ID       State_ID  Employee_code_ID  MobileNo_Avl_Flag    Aadhar_flag       PAN_flag   VoterID_flag   Driving_flag  Passport_flag  PERFORM_CNS.SCORE  PRI.NO.OF.ACCTS  PRI.ACTIVE.ACCTS  PRI.OVERDUE.ACCTS  PRI.CURRENT.BALANCE  PRI.SANCTIONED.AMOUNT  PRI.DISBURSED.AMOUNT  SEC.NO.OF.ACCTS  SEC.ACTIVE.ACCTS  SEC.OVERDUE.ACCTS  SEC.CURRENT.BALANCE  SEC.SANCTIONED.AMOUNT  SEC.DISBURSED.AMOUNT  PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  NO.OF_INQUIRIES   loan_default\n",
       "count  345546.000000     345546.000000  3.455460e+05  345546.00000  345546.000000  345546.000000    345546.000000       345546.000000  345546.000000     345546.000000           345546.0  345546.000000  345546.000000  345546.000000  345546.000000  345546.000000      345546.000000    345546.000000     345546.000000      345546.000000         3.455460e+05           3.455460e+05          3.455460e+05    345546.000000     345546.000000      345546.000000         3.455460e+05           3.455460e+05          3.455460e+05        3.455460e+05    3.455460e+05                 345546.000000                        345546.000000    345546.000000  345546.000000\n",
       "mean   593106.042648      54916.377391  7.629484e+04      74.92991      74.876752   19705.205342        68.812725         3272.345381       7.247122       1621.741403                1.0       0.849852       0.113311       0.137148       0.021117       0.002058         289.032062         2.371201          1.003586           0.155146         1.602702e+05           2.096509e+05          2.095608e+05         0.047528          0.022709           0.005921         4.565299e+03           6.133302e+03          6.038715e+03        1.249773e+04    2.727354e+02                      0.360065                             0.095660         0.212959      -0.178792\n",
       "std    101481.557990      13045.957158  1.873864e+04      11.31522      70.426229    3513.984528        22.142130         2240.489856       4.547494       1024.408918                0.0       0.357217       0.316972       0.344004       0.143776       0.045314         338.841228         5.013630          1.877087           0.543333         9.253457e+05           2.043866e+06          2.047483e+06         0.561836          0.283288           0.100497         1.612026e+05           1.893427e+05          1.889114e+05        1.997545e+05    1.626126e+04                      0.920797                             0.383594         0.717365       0.663144\n",
       "min    417428.000000      11613.000000  3.700000e+04      10.03000       1.000000   10524.000000        45.000000            1.000000       1.000000          1.000000                1.0       0.000000       0.000000       0.000000       0.000000       0.000000           0.000000         0.000000          0.000000           0.000000        -6.678296e+06          -4.815000e+05          0.000000e+00         0.000000          0.000000           0.000000        -5.746470e+05           0.000000e+00          0.000000e+00        0.000000e+00    0.000000e+00                      0.000000                             0.000000         0.000000      -1.000000\n",
       "25%    505386.250000      47645.000000  6.628200e+04      69.28000      15.000000   16565.000000        45.000000         1449.000000       4.000000        737.000000                1.0       1.000000       0.000000       0.000000       0.000000       0.000000           0.000000         0.000000          0.000000           0.000000         0.000000e+00           0.000000e+00          0.000000e+00         0.000000          0.000000           0.000000         0.000000e+00           0.000000e+00          0.000000e+00        0.000000e+00    0.000000e+00                      0.000000                             0.000000         0.000000      -1.000000\n",
       "50%    592918.500000      54303.000000  7.154100e+04      77.14000      64.000000   20470.000000        86.000000         2790.000000       6.000000       1511.000000                1.0       1.000000       0.000000       0.000000       0.000000       0.000000           0.000000         0.000000          0.000000           0.000000         0.000000e+00           0.000000e+00          0.000000e+00         0.000000          0.000000           0.000000         0.000000e+00           0.000000e+00          0.000000e+00        0.000000e+00    0.000000e+00                      0.000000                             0.000000         0.000000       0.000000\n",
       "75%    680501.750000      60947.000000  7.960500e+04      83.69000     135.000000   23049.000000        86.000000         5458.000000      10.000000       2470.000000                1.0       1.000000       0.000000       0.000000       0.000000       0.000000         679.000000         3.000000          1.000000           0.000000         3.136450e+04           5.941675e+04          5.764575e+04         0.000000          0.000000           0.000000         0.000000e+00           0.000000e+00          0.000000e+00        1.946000e+03    0.000000e+00                      0.000000                             0.000000         0.000000       0.000000\n",
       "max    769909.000000     990572.000000  1.628992e+06      95.00000     261.000000   24901.000000       156.000000         7345.000000      22.000000       3923.000000                1.0       1.000000       1.000000       1.000000       1.000000       1.000000         890.000000       453.000000        144.000000          25.000000         9.652492e+07           1.000000e+09          1.000000e+09        57.000000         36.000000           8.000000         3.603285e+07           5.794500e+07          5.794500e+07        8.526233e+07    5.390000e+06                     35.000000                            20.000000        36.000000       1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UniqueID</th>\n",
       "      <th>disbursed_amount</th>\n",
       "      <th>asset_cost</th>\n",
       "      <th>ltv</th>\n",
       "      <th>branch_id</th>\n",
       "      <th>supplier_id</th>\n",
       "      <th>manufacturer_id</th>\n",
       "      <th>Current_pincode_ID</th>\n",
       "      <th>Date.of.Birth</th>\n",
       "      <th>Employment.Type</th>\n",
       "      <th>DisbursalDate</th>\n",
       "      <th>State_ID</th>\n",
       "      <th>Employee_code_ID</th>\n",
       "      <th>MobileNo_Avl_Flag</th>\n",
       "      <th>Aadhar_flag</th>\n",
       "      <th>PAN_flag</th>\n",
       "      <th>VoterID_flag</th>\n",
       "      <th>Driving_flag</th>\n",
       "      <th>Passport_flag</th>\n",
       "      <th>PERFORM_CNS.SCORE</th>\n",
       "      <th>PERFORM_CNS.SCORE.DESCRIPTION</th>\n",
       "      <th>PRI.NO.OF.ACCTS</th>\n",
       "      <th>PRI.ACTIVE.ACCTS</th>\n",
       "      <th>PRI.OVERDUE.ACCTS</th>\n",
       "      <th>PRI.CURRENT.BALANCE</th>\n",
       "      <th>PRI.SANCTIONED.AMOUNT</th>\n",
       "      <th>PRI.DISBURSED.AMOUNT</th>\n",
       "      <th>SEC.NO.OF.ACCTS</th>\n",
       "      <th>SEC.ACTIVE.ACCTS</th>\n",
       "      <th>SEC.OVERDUE.ACCTS</th>\n",
       "      <th>SEC.CURRENT.BALANCE</th>\n",
       "      <th>SEC.SANCTIONED.AMOUNT</th>\n",
       "      <th>SEC.DISBURSED.AMOUNT</th>\n",
       "      <th>PRIMARY.INSTAL.AMT</th>\n",
       "      <th>SEC.INSTAL.AMT</th>\n",
       "      <th>NEW.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>AVERAGE.ACCT.AGE</th>\n",
       "      <th>CREDIT.HISTORY.LENGTH</th>\n",
       "      <th>NO.OF_INQUIRIES</th>\n",
       "      <th>loan_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546.0</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>334442</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "      <td>345546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>345546</td>\n",
       "      <td>29271</td>\n",
       "      <td>53158</td>\n",
       "      <td>6819.0</td>\n",
       "      <td>82</td>\n",
       "      <td>3089</td>\n",
       "      <td>12</td>\n",
       "      <td>7096</td>\n",
       "      <td>15888</td>\n",
       "      <td>2</td>\n",
       "      <td>111</td>\n",
       "      <td>22</td>\n",
       "      <td>3398</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>574</td>\n",
       "      <td>20</td>\n",
       "      <td>114</td>\n",
       "      <td>42</td>\n",
       "      <td>23</td>\n",
       "      <td>97465</td>\n",
       "      <td>60681</td>\n",
       "      <td>65673</td>\n",
       "      <td>40</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>3947</td>\n",
       "      <td>2631</td>\n",
       "      <td>3031</td>\n",
       "      <td>34330</td>\n",
       "      <td>2295</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>200</td>\n",
       "      <td>307</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>524287</td>\n",
       "      <td>53303</td>\n",
       "      <td>68000</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2</td>\n",
       "      <td>15694</td>\n",
       "      <td>86</td>\n",
       "      <td>2578</td>\n",
       "      <td>01-01-88</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>31-10-18</td>\n",
       "      <td>4</td>\n",
       "      <td>2546</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No Bureau History Available</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>3131</td>\n",
       "      <td>852</td>\n",
       "      <td>5910.0</td>\n",
       "      <td>20527</td>\n",
       "      <td>1916</td>\n",
       "      <td>161203</td>\n",
       "      <td>2681</td>\n",
       "      <td>3230</td>\n",
       "      <td>187429</td>\n",
       "      <td>8826</td>\n",
       "      <td>70438</td>\n",
       "      <td>757</td>\n",
       "      <td>345546</td>\n",
       "      <td>293663</td>\n",
       "      <td>306392</td>\n",
       "      <td>298155</td>\n",
       "      <td>338249</td>\n",
       "      <td>344835</td>\n",
       "      <td>174843</td>\n",
       "      <td>174843</td>\n",
       "      <td>174843</td>\n",
       "      <td>205427</td>\n",
       "      <td>306917</td>\n",
       "      <td>212468</td>\n",
       "      <td>207051</td>\n",
       "      <td>207188</td>\n",
       "      <td>338438</td>\n",
       "      <td>340894</td>\n",
       "      <td>343928</td>\n",
       "      <td>341430</td>\n",
       "      <td>340989</td>\n",
       "      <td>341028</td>\n",
       "      <td>236874</td>\n",
       "      <td>342841</td>\n",
       "      <td>272341</td>\n",
       "      <td>319144</td>\n",
       "      <td>177481</td>\n",
       "      <td>177178</td>\n",
       "      <td>298020</td>\n",
       "      <td>182543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        UniqueID  disbursed_amount  asset_cost       ltv  branch_id  supplier_id  manufacturer_id  Current_pincode_ID Date.of.Birth Employment.Type DisbursalDate  State_ID  Employee_code_ID  MobileNo_Avl_Flag  Aadhar_flag  PAN_flag  VoterID_flag  Driving_flag  Passport_flag  PERFORM_CNS.SCORE PERFORM_CNS.SCORE.DESCRIPTION  PRI.NO.OF.ACCTS  PRI.ACTIVE.ACCTS  PRI.OVERDUE.ACCTS  PRI.CURRENT.BALANCE  PRI.SANCTIONED.AMOUNT  PRI.DISBURSED.AMOUNT  SEC.NO.OF.ACCTS  SEC.ACTIVE.ACCTS  SEC.OVERDUE.ACCTS  SEC.CURRENT.BALANCE  SEC.SANCTIONED.AMOUNT  SEC.DISBURSED.AMOUNT  PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS AVERAGE.ACCT.AGE CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default\n",
       "count     345546            345546      345546  345546.0     345546       345546           345546              345546        345546          334442        345546    345546            345546             345546       345546    345546        345546        345546         345546             345546                        345546           345546            345546             345546               345546                 345546                345546           345546            345546             345546               345546                 345546                345546              345546          345546                        345546                               345546           345546                345546           345546        345546\n",
       "unique    345546             29271       53158    6819.0         82         3089               12                7096         15888               2           111        22              3398                  1            2         2             2             2              2                574                            20              114                42                 23                97465                  60681                 65673               40                23                  9                 3947                   2631                  3031               34330            2295                            26                                   16              200                   307               26             3\n",
       "top       524287             53303       68000      85.0          2        15694               86                2578      01-01-88   Self employed      31-10-18         4              2546                  1            1         0             0             0              0                  0   No Bureau History Available                0                 0                  0                    0                      0                     0                0                 0                  0                    0                      0                     0                   0               0                             0                                    0        0yrs 0mon             0yrs 0mon                0             0\n",
       "freq           1              3131         852    5910.0      20527         1916           161203                2681          3230          187429          8826     70438               757             345546       293663    306392        298155        338249         344835             174843                        174843           174843            205427             306917               212468                 207051                207188           338438            340894             343928               341430                 340989                341028              236874          342841                        272341                               319144           177481                177178           298020        182543"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.astype('object').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "345546"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df.MobileNo_Avl_Flag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['is_self_employed'] = df['Employment.Type'].apply(lambda x: 1 if x == \"Self employed\" else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['dob'] = df['Date.of.Birth'].apply(lambda d: dt.strptime(d, '%d-%m-%y').date().toordinal())\n",
    "df['disbursal_date'] = df['DisbursalDate'].apply(lambda d: dt.strptime(d, '%d-%m-%y').date().toordinal())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.916666666666666\n"
     ]
    }
   ],
   "source": [
    "def parse_yrs_mon(cred):\n",
    "    try:\n",
    "        yr, mn = cred.split()\n",
    "        yr = int(yr[:-3])\n",
    "        mn = int(mn[:-3])/12.0\n",
    "        return yr + mn\n",
    "    except:\n",
    "        print(cred)\n",
    "        return 0.0\n",
    "    \n",
    "print(parse_yrs_mon(\"12yrs 11mon\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['credit_history_length'] = df['CREDIT.HISTORY.LENGTH'].apply(parse_yrs_mon)\n",
    "df['avg_acct_age'] = df['AVERAGE.ACCT.AGE'].apply(parse_yrs_mon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f321e30f320>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Current_pincode_ID'].hist(bins=50)\n",
    "# ??? Doesn't seem random we should maybe keep it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No Bureau History Available                                174843\n",
       "C-Very Low Risk                                             23870\n",
       "A-Very Low Risk                                             21683\n",
       "D-Very Low Risk                                             16472\n",
       "B-Very Low Risk                                             13696\n",
       "M-Very High Risk                                            12808\n",
       "F-Low Risk                                                  12176\n",
       "K-High Risk                                                 12141\n",
       "H-Medium Risk                                               10142\n",
       "E-Low Risk                                                   8393\n",
       "I-Medium Risk                                                8260\n",
       "G-Low Risk                                                   5795\n",
       "J-High Risk                                                  5526\n",
       "Not Scored: Sufficient History Not Available                 5025\n",
       "Not Scored: Not Enough Info available on the customer        4940\n",
       "Not Scored: No Activity seen on the customer (Inactive)      4246\n",
       "Not Scored: No Updates available in last 36 months           2299\n",
       "L-Very High Risk                                             1782\n",
       "Not Scored: Only a Guarantor                                 1446\n",
       "Not Scored: More than 50 active Accounts found                  3\n",
       "Name: PERFORM_CNS.SCORE.DESCRIPTION, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PERFORM_CNS.SCORE.DESCRIPTION'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import LabelEncoder\n",
    "# cns_encoder = LabelEncoder()\n",
    "# df_train['cns_encoded'] = cns_encoder.fit_transform(df_train['PERFORM_CNS.SCORE.DESCRIPTION'])\n",
    "# df_test['cns_encoded'] = cns_encoder.fit_transform(df_train['PERFORM_CNS.SCORE.DESCRIPTION'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cns_dummies = pd.get_dummies(df['PERFORM_CNS.SCORE.DESCRIPTION'], prefix = 'cns')\n",
    "df = pd.concat([df, cns_dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Salaried\n",
       "1    Self employed\n",
       "2    Self employed\n",
       "3    Self employed\n",
       "4    Self employed\n",
       "Name: Employment.Type, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Employment.Type'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_dump = ['UniqueId', 'MobileNo_Avl_Flag', 'PERFORM_CNS.SCORE.DESCRIPTION', 'Employment.Type']\n",
    "to_cat = ['branch_id', 'supplier_id', 'manufacturer_id', 'State_ID', 'Employee_code_ID']\n",
    "to_date = ['Date.of.Birth', 'DisbursalDate']\n",
    "to_bool = ['is_self_employed', 'Aadhar_flag', 'PAN_flag', 'VoterID_flag', 'Driving_flag', 'Passport_flag']\n",
    "to_custom = ['CREDIT.HISTORY.LENGTH', 'AVERAGE.ACCT.AGE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_to_delete = to_dump\n",
    "all_to_delete.extend(to_cat)\n",
    "all_to_delete.extend(to_date)\n",
    "all_to_delete.extend(to_custom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['UniqueId',\n",
       " 'MobileNo_Avl_Flag',\n",
       " 'PERFORM_CNS.SCORE.DESCRIPTION',\n",
       " 'Employment.Type',\n",
       " 'branch_id',\n",
       " 'supplier_id',\n",
       " 'manufacturer_id',\n",
       " 'State_ID',\n",
       " 'Employee_code_ID',\n",
       " 'Date.of.Birth',\n",
       " 'DisbursalDate',\n",
       " 'CREDIT.HISTORY.LENGTH',\n",
       " 'AVERAGE.ACCT.AGE']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_to_delete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_keep = set(df.columns) - set(all_to_delete)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>SEC.SANCTIONED.AMOUNT</th>\n",
       "      <th>cns_H-Medium Risk</th>\n",
       "      <th>PRI.OVERDUE.ACCTS</th>\n",
       "      <th>cns_E-Low Risk</th>\n",
       "      <th>avg_acct_age</th>\n",
       "      <th>Current_pincode_ID</th>\n",
       "      <th>cns_K-High Risk</th>\n",
       "      <th>SEC.INSTAL.AMT</th>\n",
       "      <th>DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>disbursed_amount</th>\n",
       "      <th>cns_No Bureau History Available</th>\n",
       "      <th>cns_Not Scored: No Updates available in last 36 months</th>\n",
       "      <th>cns_G-Low Risk</th>\n",
       "      <th>PERFORM_CNS.SCORE</th>\n",
       "      <th>PRI.CURRENT.BALANCE</th>\n",
       "      <th>cns_B-Very Low Risk</th>\n",
       "      <th>cns_Not Scored: Sufficient History Not Available</th>\n",
       "      <th>NO.OF_INQUIRIES</th>\n",
       "      <th>SEC.OVERDUE.ACCTS</th>\n",
       "      <th>UniqueID</th>\n",
       "      <th>cns_Not Scored: Not Enough Info available on the customer</th>\n",
       "      <th>cns_F-Low Risk</th>\n",
       "      <th>ltv</th>\n",
       "      <th>cns_I-Medium Risk</th>\n",
       "      <th>cns_A-Very Low Risk</th>\n",
       "      <th>...</th>\n",
       "      <th>cns_M-Very High Risk</th>\n",
       "      <th>SEC.ACTIVE.ACCTS</th>\n",
       "      <th>VoterID_flag</th>\n",
       "      <th>PRI.ACTIVE.ACCTS</th>\n",
       "      <th>PRI.DISBURSED.AMOUNT</th>\n",
       "      <th>cns_Not Scored: Only a Guarantor</th>\n",
       "      <th>PAN_flag</th>\n",
       "      <th>is_self_employed</th>\n",
       "      <th>loan_default</th>\n",
       "      <th>PRIMARY.INSTAL.AMT</th>\n",
       "      <th>SEC.DISBURSED.AMOUNT</th>\n",
       "      <th>cns_L-Very High Risk</th>\n",
       "      <th>cns_Not Scored: More than 50 active Accounts found</th>\n",
       "      <th>PRI.SANCTIONED.AMOUNT</th>\n",
       "      <th>dob</th>\n",
       "      <th>Driving_flag</th>\n",
       "      <th>cns_C-Very Low Risk</th>\n",
       "      <th>cns_D-Very Low Risk</th>\n",
       "      <th>NEW.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>disbursal_date</th>\n",
       "      <th>Aadhar_flag</th>\n",
       "      <th>Passport_flag</th>\n",
       "      <th>PRI.NO.OF.ACCTS</th>\n",
       "      <th>cns_Not Scored: No Activity seen on the customer (Inactive)</th>\n",
       "      <th>SEC.CURRENT.BALANCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>1441</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50578</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>420825</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>724276</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>736909</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.916667</td>\n",
       "      <td>1502</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>47145</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>598</td>\n",
       "      <td>27600</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>537409</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73.23</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1991</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>417566</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>724877</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>736907</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1501</td>\n",
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       "      <td>57513</td>\n",
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       "      <td>0</td>\n",
       "      <td>305</td>\n",
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       "      <td>624493</td>\n",
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       "      <td>0</td>\n",
       "      <td>88.48</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>727927</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>736993</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1495</td>\n",
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       "      <td>539055</td>\n",
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       "      <td>0</td>\n",
       "      <td>88.39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>722062</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>736963</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>0</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1501</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>54513</td>\n",
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       "      <td>825</td>\n",
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       "      <td>89.66</td>\n",
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       "    </tr>\n",
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       "      <td>0</td>\n",
       "      <td>725889</td>\n",
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       "      <td>0</td>\n",
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       "      <td>736960</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1501</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43894</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>72879</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>510278</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.89</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>74500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74500</td>\n",
       "      <td>726379</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>736953</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>1497</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53713</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>718</td>\n",
       "      <td>-41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>490213</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>365384</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>365384</td>\n",
       "      <td>727151</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>736942</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>1492</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52603</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>818</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>510980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>86.95</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2608</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>755109</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>736953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>1493</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53278</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>548567</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2270</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>722450</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>736966</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.083333</td>\n",
       "      <td>1446</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64769</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>786</td>\n",
       "      <td>676</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>486821</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>23374</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36154</td>\n",
       "      <td>724526</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>736940</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1497</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53278</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>478647</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>69900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69900</td>\n",
       "      <td>720775</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>736936</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1497</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49478</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
       "      <td>79750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>479533</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>187000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23309</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187000</td>\n",
       "      <td>724504</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>736936</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3.916667</td>\n",
       "      <td>1495</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49278</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>95597</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>483869</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>179252</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3514</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>179252</td>\n",
       "      <td>720307</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>736937</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>1440</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47549</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>600655</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>79.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>728114</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>736989</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1440</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>57713</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>29069</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>513916</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1067200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>47100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1067200</td>\n",
       "      <td>721506</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>736955</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>1498</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53503</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>688</td>\n",
       "      <td>1076657</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>522020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87.28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2277048</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4982</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2277048</td>\n",
       "      <td>723968</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>736957</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1479</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70017</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>585</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>492995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82.99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>725959</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>736943</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1468</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>568857</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>86.13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>722921</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>736978</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    SEC.SANCTIONED.AMOUNT  cns_H-Medium Risk  PRI.OVERDUE.ACCTS  cns_E-Low Risk  avg_acct_age  Current_pincode_ID  cns_K-High Risk  SEC.INSTAL.AMT  DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  disbursed_amount  cns_No Bureau History Available  cns_Not Scored: No Updates available in last 36 months  cns_G-Low Risk  PERFORM_CNS.SCORE  PRI.CURRENT.BALANCE  cns_B-Very Low Risk  cns_Not Scored: Sufficient History Not Available  NO.OF_INQUIRIES  SEC.OVERDUE.ACCTS  UniqueID  cns_Not Scored: Not Enough Info available on the customer  cns_F-Low Risk    ltv  cns_I-Medium Risk  cns_A-Very Low Risk         ...           cns_M-Very High Risk  SEC.ACTIVE.ACCTS  VoterID_flag  PRI.ACTIVE.ACCTS  PRI.DISBURSED.AMOUNT  cns_Not Scored: Only a Guarantor  PAN_flag  is_self_employed  loan_default  PRIMARY.INSTAL.AMT  SEC.DISBURSED.AMOUNT  cns_L-Very High Risk  cns_Not Scored: More than 50 active Accounts found  PRI.SANCTIONED.AMOUNT     dob  Driving_flag  cns_C-Very Low Risk  cns_D-Very Low Risk  \\\n",
       "0                       0                  0                  0               0      0.000000                1441                0               0                                    0             50578                                1                                                  0                    0                  0                    0                    0                                                 0                0                  0    420825                                                  0                       0  89.55                  0                    0         ...                              0                 0             0                 0                     0                                 0         0                 0             0                   0                     0                     0                                                  0                       0  724276             0                    0                    0   \n",
       "1                       0                  0                  1               0      1.916667                1502                0               0                                    1             47145                                0                                                  0                    0                598                27600                    0                                                 0                0                  0    537409                                                  0                       0  73.23                  1                    0         ...                              0                 0             0                 1                 50200                                 0         0                 1             1                1991                     0                     0                                                  0                   50200  724853             0                    0                    0   \n",
       "2                       0                  0                  0               0      0.000000                1497                0               0                                    0             53278                                1                                                  0                    0                  0                    0                    0                                                 0                0                  0    417566                                                  0                       0  89.63                  0                    0         ...                              0                 0             0                 0                     0                                 0         0                 1             0                   0                     0                     0                                                  0                       0  724877             0                    0                    0   \n",
       "3                       0                  0                  0               0      0.666667                1501                0               0                                    0             57513                                0                                                  0                    0                305                    0                    0                                                 0                1                  0    624493                                                  0                       0  88.48                  0                    0         ...                              0                 0             0                 0                     0                                 0         0                 1             1                  31                     0                     1                                                  0                       0  727927             0                    0                    0   \n",
       "4                       0                  0                  0               0      0.000000                1495                0               0                                    0             52378                                1                                                  0                    0                  0                    0                    0                                                 0                1                  0    539055                                                  0                       0  88.39                  0                    0         ...                              0                 0             0                 0                     0                                 0         0                 1             1                   0                     0                     0                                                  0                       0  722062             0                    0                    0   \n",
       "5                       0                  0                  0               0      1.750000                1501                0               0                                    0             54513                                0                                                  0                    0                825                    0                    0                                                 0                0                  0    518279                                                  0                       0  89.66                  0                    1         ...                              0                 0             0                 0                     0                                 0         0                 1             0                1347                     0                     0                                                  0                       0  726718             0                    0                    0   \n",
       "6                       0                  0                  0               0      0.000000                1502                0               0                                    0             46349                                1                                                  0                    0                  0                    0                    0                                                 0                0                  0    529269                                                  0                       0  76.42                  0                    0         ...                              0                 0             0                 0                     0                                 0         0                 0             0                   0                     0                     0                                                  0                       0  725889             0                    0                    0   \n",
       "7                       0                  0                  0               0      0.166667                1501                0               0                                    0             43894                                0                                                  0                    0                 17                72879                    0                                                 0                0                  0    510278                                                  1                       0  71.89                  0                    0         ...                              0                 0             0                 1                 74500                                 0         0                 0             0                   0                     0                     0                                                  0                   74500  726379             0                    0                    0   \n",
       "8                       0                  0                  0               0      4.666667                1497                0               0                                    0             53713                                0                                                  0                    0                718                  -41                    0                                                 0                1                  0    490213                                                  0                       0  89.56                  0                    0         ...                              0                 0             0                 1                365384                                 0         0                 1             0                   0                     0                     0                                                  0                  365384  727151             0                    0                    1   \n",
       "9                       0                  0                  0               0      1.583333                1492                0               0                                    0             52603                                0                                                  0                    0                818                    0                    0                                                 0                0                  0    510980                                                  0                       0  86.95                  0                    1         ...                              0                 0             1                 0                     0                                 0         0                 0             0                2608                     0                     0                                                  0                       0  755109             0                    0                    0   \n",
       "10                      0                  0                  0               0      0.583333                1493                0               0                                    0             53278                                0                                                  0                    0                300                    0                    0                                                 0                0                  0    548567                                                  0                       0  89.83                  0                    0         ...                              1                 0             0                 0                     0                                 0         0                 1             1                2270                     0                     0                                                  0                       0  722450             0                    0                    0   \n",
       "11                      0                  0                  0               0      2.083333                1446                0               0                                    0             64769                                0                                                  0                    0                786                  676                    1                                                 0                1                  0    486821                                                  0                       0  89.23                  0                    0         ...                              0                 0             0                 2                 23374                                 0         0                 0             0                   0                     0                     0                                                  0                   36154  724526             0                    0                    0   \n",
       "12                      0                  0                  1               0      1.250000                1497                0               0                                    0             53278                                0                                                  0                    0                300                    0                    0                                                 0                0                  0    478647                                                  0                       0  89.68                  0                    0         ...                              1                 0             1                 2                 69900                                 0         0                 0             1                3300                     0                     0                                                  0                   69900  720775             0                    0                    0   \n",
       "13                      0                  0                  0               0      1.000000                1497                0               0                                    0             49478                                0                                                  0                    0                738                79750                    0                                                 0                4                  0    479533                                                  0                       0  89.46                  0                    0         ...                              0                 0             0                 5                187000                                 0         0                 0             1               23309                     0                     0                                                  0                  187000  724504             0                    1                    0   \n",
       "14                      0                  0                  3               0      3.916667                1495                0               0                                    0             49278                                0                                                  0                    0                300                95597                    0                                                 0                0                  0    483869                                                  0                       0  89.35                  0                    0         ...                              1                 0             0                 5                179252                                 0         0                 1             1                3514                     0                     0                                                  0                  179252  720307             0                    0                    0   \n",
       "15                      0                  0                  0               0      0.083333                1440                0               0                                    0             47549                                0                                                  0                    0                 17                    0                    0                                                 0                0                  0    600655                                                  1                       0  79.80                  0                    0         ...                              0                 0             1                 0                     0                                 0         0                 0             1                7900                     0                     0                                                  0                       0  728114             0                    0                    0   \n",
       "16                      0                  0                  2               0      2.500000                1440                0               0                                    1             57713                                0                                                  0                    0                300                29069                    0                                                 0                0                  0    513916                                                  0                       0  89.28                  0                    0         ...                              1                 0             0                 4               1067200                                 0         0                 1             0               47100                     0                     0                                                  0                 1067200  721506             0                    0                    0   \n",
       "17                      0                  0                  0               1      1.833333                1498                0               0                                    0             53503                                0                                                  0                    0                688              1076657                    0                                                 0                0                  0    522020                                                  0                       0  87.28                  0                    0         ...                              0                 0             0                 8               2277048                                 0         0                 1             0                4982                     0                     0                                                  0                 2277048  723968             0                    0                    0   \n",
       "18                      0                  0                  1               0      1.750000                1479                0               0                                    0             70017                                0                                                  0                    0                585                    0                    0                                                 0                0                  0    492995                                                  0                       0  82.99                  1                    0         ...                              0                 0             1                 0                     0                                 0         0                 1             1                   0                     0                     0                                                  0                       0  725959             0                    0                    0   \n",
       "19                      0                  1                  0               0      0.916667                1468                0               0                                    0             58259                                0                                                  0                    0                615                    0                    0                                                 0                1                  0    568857                                                  0                       0  86.13                  0                    0         ...                              0                 0             0                 0                     0                                 0         0                 1             1                   0                     0                     0                                                  0                       0  722921             0                    0                    0   \n",
       "\n",
       "    NEW.ACCTS.IN.LAST.SIX.MONTHS  disbursal_date  Aadhar_flag  Passport_flag  PRI.NO.OF.ACCTS  cns_Not Scored: No Activity seen on the customer (Inactive)  SEC.CURRENT.BALANCE  \n",
       "0                              0          736909            1              0                0                                                  0                              0  \n",
       "1                              0          736963            1              0                1                                                  0                              0  \n",
       "2                              0          736907            1              0                0                                                  0                              0  \n",
       "3                              0          736993            1              0                3                                                  0                              0  \n",
       "4                              0          736963            1              0                0                                                  0                              0  \n",
       "5                              0          736956            1              0                2                                                  0                              0  \n",
       "6                              0          736960            1              0                0                                                  0                              0  \n",
       "7                              0          736953            1              0                1                                                  0                              0  \n",
       "8                              0          736942            1              0                1                                                  0                              0  \n",
       "9                              0          736953            0              0                1                                                  0                              0  \n",
       "10                             0          736966            1              0                1                                                  0                              0  \n",
       "11                             0          736940            1              0                3                                                  0                              0  \n",
       "12                             0          736936            0              0                7                                                  0                              0  \n",
       "13                             1          736936            1              0               10                                                  0                              0  \n",
       "14                             0          736937            1              0                5                                                  0                              0  \n",
       "15                             1          736989            0              0                1                                                  0                              0  \n",
       "16                             1          736955            1              0                6                                                  0                              0  \n",
       "17                             1          736957            1              0               13                                                  0                              0  \n",
       "18                             0          736943            0              0                1                                                  0                              0  \n",
       "19                             0          736978            1              0                1                                                  0                              0  \n",
       "\n",
       "[20 rows x 54 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[list(cols_to_keep)].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cols_to_keep:\n",
    "    if col not in ['loan_default', 'UniqueID']:\n",
    "         # Normalise the other fields so all fields are between -/+ 1 standard deviation\n",
    "        mean = df[col].mean(axis=0)\n",
    "        df[col] -= mean\n",
    "        std = df[col].std(axis=0)\n",
    "        df[col] /= std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_cols = list(cols_to_keep)\n",
    "# x_cols.remove('loan_default')\n",
    "# y_cols = 'loan_default'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SEC.SANCTIONED.AMOUNT                                          False\n",
       "cns_H-Medium Risk                                              False\n",
       "PRI.OVERDUE.ACCTS                                              False\n",
       "cns_E-Low Risk                                                 False\n",
       "avg_acct_age                                                   False\n",
       "Current_pincode_ID                                             False\n",
       "cns_K-High Risk                                                False\n",
       "SEC.INSTAL.AMT                                                 False\n",
       "DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS                            False\n",
       "disbursed_amount                                               False\n",
       "cns_No Bureau History Available                                False\n",
       "cns_Not Scored: No Updates available in last 36 months         False\n",
       "cns_G-Low Risk                                                 False\n",
       "PERFORM_CNS.SCORE                                              False\n",
       "PRI.CURRENT.BALANCE                                            False\n",
       "cns_B-Very Low Risk                                            False\n",
       "cns_Not Scored: Sufficient History Not Available               False\n",
       "NO.OF_INQUIRIES                                                False\n",
       "SEC.OVERDUE.ACCTS                                              False\n",
       "UniqueID                                                       False\n",
       "cns_Not Scored: Not Enough Info available on the customer      False\n",
       "cns_F-Low Risk                                                 False\n",
       "ltv                                                            False\n",
       "cns_I-Medium Risk                                              False\n",
       "cns_A-Very Low Risk                                            False\n",
       "asset_cost                                                     False\n",
       "cns_J-High Risk                                                False\n",
       "credit_history_length                                          False\n",
       "SEC.NO.OF.ACCTS                                                False\n",
       "cns_M-Very High Risk                                           False\n",
       "SEC.ACTIVE.ACCTS                                               False\n",
       "VoterID_flag                                                   False\n",
       "PRI.ACTIVE.ACCTS                                               False\n",
       "PRI.DISBURSED.AMOUNT                                           False\n",
       "cns_Not Scored: Only a Guarantor                               False\n",
       "PAN_flag                                                       False\n",
       "is_self_employed                                               False\n",
       "loan_default                                                   False\n",
       "PRIMARY.INSTAL.AMT                                             False\n",
       "SEC.DISBURSED.AMOUNT                                           False\n",
       "cns_L-Very High Risk                                           False\n",
       "cns_Not Scored: More than 50 active Accounts found             False\n",
       "PRI.SANCTIONED.AMOUNT                                          False\n",
       "dob                                                            False\n",
       "Driving_flag                                                   False\n",
       "cns_C-Very Low Risk                                            False\n",
       "cns_D-Very Low Risk                                            False\n",
       "NEW.ACCTS.IN.LAST.SIX.MONTHS                                   False\n",
       "disbursal_date                                                 False\n",
       "Aadhar_flag                                                    False\n",
       "Passport_flag                                                  False\n",
       "PRI.NO.OF.ACCTS                                                False\n",
       "cns_Not Scored: No Activity seen on the customer (Inactive)    False\n",
       "SEC.CURRENT.BALANCE                                            False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[x_cols].isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df[df['loan_default'] != -1]\n",
    "df_test = df[df['loan_default'] == -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((233154, 66), (112392, 66))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape, df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train, X_test, y_train, y_test = train_test_split(df_train[x_cols], df_train[y_cols], \n",
    "#                                                     test_size=0.2, \n",
    "#                                                     random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_train[list(cols_to_keep)]['UniqueID']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGboost Gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.model_selection import cross_validate, GridSearchCV\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'loan_default'\n",
    "IDcol = 'UniqueID'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def modelfit(alg, dtrain, predictors,useTrainCV=True, cv_folds=5, early_stopping_rounds=50):\n",
    "    \n",
    "    if useTrainCV:\n",
    "        xgb_param = alg.get_xgb_params()\n",
    "        xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values)\n",
    "        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\n",
    "            metrics='auc', early_stopping_rounds=early_stopping_rounds, verbose_eval=10)\n",
    "        alg.set_params(n_estimators=cvresult.shape[0])\n",
    "    \n",
    "    #Fit the algorithm on the data\n",
    "    alg.fit(dtrain[predictors], dtrain['loan_default'],eval_metric='auc')\n",
    "        \n",
    "    #Predict training set:\n",
    "    dtrain_predictions = alg.predict(dtrain[predictors])\n",
    "    dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1]\n",
    "        \n",
    "    #Print model report:\n",
    "    print(\"\\nModel Report\")\n",
    "    print(\"Accuracy : %.4g\" % metrics.accuracy_score(dtrain['loan_default'].values, dtrain_predictions))\n",
    "    print(\"AUC Score (Train): %f\" % metrics.roc_auc_score(dtrain['loan_default'], dtrain_predprob))\n",
    "                    \n",
    "#     feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)\n",
    "#     feat_imp.plot(kind='bar', title='Feature Importances')\n",
    "#     plt.ylabel('Feature Importance Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.607554+0.00936586\ttest-auc:0.603956+0.0068954\n",
      "[10]\ttrain-auc:0.645353+0.00122152\ttest-auc:0.637536+0.00388921\n",
      "[20]\ttrain-auc:0.65541+0.00111044\ttest-auc:0.645383+0.00341112\n",
      "[30]\ttrain-auc:0.663965+0.00128709\ttest-auc:0.6513+0.00338024\n",
      "[40]\ttrain-auc:0.670226+0.00160046\ttest-auc:0.655031+0.00268031\n",
      "[50]\ttrain-auc:0.674904+0.00120921\ttest-auc:0.657322+0.00304007\n",
      "[60]\ttrain-auc:0.678906+0.00111077\ttest-auc:0.659113+0.00334144\n",
      "[70]\ttrain-auc:0.682074+0.00113362\ttest-auc:0.660175+0.0033029\n",
      "[80]\ttrain-auc:0.685016+0.00112959\ttest-auc:0.661133+0.00343731\n",
      "[90]\ttrain-auc:0.687625+0.00100171\ttest-auc:0.661661+0.00353555\n",
      "[100]\ttrain-auc:0.689834+0.000888567\ttest-auc:0.662315+0.00366341\n",
      "[110]\ttrain-auc:0.692048+0.00097081\ttest-auc:0.662773+0.00364164\n",
      "[120]\ttrain-auc:0.694265+0.0011238\ttest-auc:0.663166+0.00345736\n",
      "[130]\ttrain-auc:0.696138+0.00115227\ttest-auc:0.663349+0.00355468\n",
      "[140]\ttrain-auc:0.697911+0.000986406\ttest-auc:0.66369+0.00371605\n",
      "[150]\ttrain-auc:0.699611+0.000988261\ttest-auc:0.663892+0.00364175\n",
      "[160]\ttrain-auc:0.701321+0.00110495\ttest-auc:0.664076+0.00364635\n",
      "[170]\ttrain-auc:0.702917+0.0010118\ttest-auc:0.664151+0.00374958\n",
      "[180]\ttrain-auc:0.704671+0.00105776\ttest-auc:0.664333+0.0037691\n",
      "[190]\ttrain-auc:0.706155+0.000931663\ttest-auc:0.664453+0.00362224\n",
      "[200]\ttrain-auc:0.707611+0.000845425\ttest-auc:0.664533+0.00364078\n",
      "[210]\ttrain-auc:0.709136+0.000902067\ttest-auc:0.664761+0.00358837\n",
      "[220]\ttrain-auc:0.71065+0.000916912\ttest-auc:0.664812+0.0034799\n",
      "[230]\ttrain-auc:0.712199+0.000844889\ttest-auc:0.664927+0.003351\n",
      "[240]\ttrain-auc:0.713709+0.000833456\ttest-auc:0.664927+0.00342771\n",
      "[250]\ttrain-auc:0.715193+0.000824434\ttest-auc:0.664861+0.00347836\n",
      "[260]\ttrain-auc:0.716698+0.000837151\ttest-auc:0.664921+0.00339775\n",
      "[270]\ttrain-auc:0.717961+0.000817814\ttest-auc:0.664833+0.00344727\n",
      "[280]\ttrain-auc:0.719275+0.000730944\ttest-auc:0.664697+0.00332389\n",
      "\n",
      "Model Report\n",
      "Accuracy : 0.3581\n",
      "AUC Score (Train): 0.706493\n"
     ]
    }
   ],
   "source": [
    "#Choose all predictors except target & IDcols\n",
    "predictors = [x for x in cols_to_keep if x not in [target, IDcol]]\n",
    "xgb1 = XGBClassifier(\n",
    "    learning_rate =0.1,\n",
    "    n_estimators=1000,\n",
    "    max_depth=5,\n",
    "    min_child_weight=1,\n",
    "    gamma=0,\n",
    "    subsample=0.8,\n",
    "    colsample_bytree=0.8,\n",
    "    objective= 'binary:logistic',\n",
    "    n_jobs=6,\n",
    "    scale_pos_weight=8.4,\n",
    "    seed=42)\n",
    "modelfit(xgb1, df_train, predictors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune max_depth and min_child_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'GridSearchCV' object has no attribute 'grid_scores_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-2790ff048d9e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m     param_grid = param_test1, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n\u001b[1;32m      9\u001b[0m \u001b[0mgsearch1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mgsearch1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid_scores_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgsearch1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgsearch1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m#         cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'grid_scores_'"
     ]
    }
   ],
   "source": [
    "param_test1 = {\n",
    "    'max_depth':range(3,10,2),\n",
    "    'min_child_weight':range(1,6,2)\n",
    "}\n",
    "gsearch1 = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=140, max_depth=5,\n",
    "    min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    "    objective= 'binary:logistic', nthread=6, scale_pos_weight=8.4, seed=27), \n",
    "    param_grid = param_test1, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch1.fit(df_train[predictors],df_train[target])\n",
    "gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_\n",
    "\n",
    "#         cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\n",
    "#             metrics='auc', early_stopping_rounds=early_stopping_rounds, verbose_eval=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 5, 'min_child_weight': 5}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch1.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 4, 'min_child_weight': 6}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_test2 = {\n",
    "    'max_depth':[4,5,6],\n",
    "    'min_child_weight':[4,5,6]\n",
    "}\n",
    "gsearch2 = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=140, max_depth=5,\n",
    "    min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    "    objective= 'binary:logistic', nthread=6, scale_pos_weight=8.4, seed=27), \n",
    "    param_grid = param_test2, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch2.fit(df_train[predictors],df_train[target])\n",
    "gsearch2.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gamma': 0.3}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_test3 = {\n",
    "    'gamma':[i/10.0 for i in range(0,5)]\n",
    "}\n",
    "gsearch3 = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=140,\n",
    "    gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    "    objective= 'binary:logistic', nthread=6, scale_pos_weight=8.4, seed=27,\n",
    "    max_depth=4, min_child_weight=6), \n",
    "    param_grid = param_test3, scoring='roc_auc',n_jobs=6,iid=False, cv=5)\n",
    "gsearch3.fit(df_train[predictors],df_train[target])\n",
    "gsearch3.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 0.7, 'subsample': 0.7}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_test4 = {\n",
    "    'subsample':[i/10.0 for i in range(6,10)],\n",
    "    'colsample_bytree':[i/10.0 for i in range(6,10)]\n",
    "}\n",
    "gsearch4 = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=140,\n",
    "    subsample=0.8, colsample_bytree=0.8,\n",
    "    objective= 'binary:logistic', nthread=6, scale_pos_weight=8.4, seed=27,\n",
    "    max_depth=4, min_child_weight=6, gamma=0), \n",
    "    param_grid = param_test4, scoring='roc_auc',n_jobs=6,iid=False, cv=5)\n",
    "gsearch4.fit(df_train[predictors],df_train[target])\n",
    "gsearch4.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 0.7, 'subsample': 0.7}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_test5 = {\n",
    "    'subsample':[0.65, 0.7, 0.75],\n",
    "    'colsample_bytree':[0.65, 0.7, 0.75]\n",
    "}\n",
    "gsearch5 = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=140,\n",
    "    subsample=0.8, colsample_bytree=0.8,\n",
    "    objective= 'binary:logistic', nthread=6, scale_pos_weight=8.4, seed=27,\n",
    "    max_depth=4, min_child_weight=6, gamma=0), \n",
    "    param_grid=param_test5, scoring='roc_auc',n_jobs=6,iid=False, cv=5)\n",
    "gsearch5.fit(df_train[predictors],df_train[target])\n",
    "gsearch5.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'reg_alpha': 100}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_test6 = {\n",
    "    'reg_alpha':[1e-5, 1e-2, 0.1, 1, 100]\n",
    "}\n",
    "gsearch6 = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=140,\n",
    "    subsample=0.7, colsample_bytree=0.7,\n",
    "    objective= 'binary:logistic', nthread=6, scale_pos_weight=8.4, seed=27,\n",
    "    max_depth=4, min_child_weight=6, gamma=0), \n",
    "    param_grid=param_test6, scoring='roc_auc',n_jobs=6,iid=False, cv=5)\n",
    "gsearch6.fit(df_train[predictors],df_train[target])\n",
    "gsearch6.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'reg_alpha': 100}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_test7 = {\n",
    "    'reg_alpha':[50, 100, 150]\n",
    "}\n",
    "gsearch7 = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=140,\n",
    "    subsample=0.7, colsample_bytree=0.7,\n",
    "    objective= 'binary:logistic', nthread=6, scale_pos_weight=8.4, seed=27,\n",
    "    max_depth=4, min_child_weight=6, gamma=0), \n",
    "    param_grid=param_test7, scoring='roc_auc',n_jobs=6,iid=False, cv=5)\n",
    "gsearch7.fit(df_train[predictors],df_train[target])\n",
    "gsearch7.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_test7 = {\n",
    "    'reg_alpha':[50, 100, 150]\n",
    "}\n",
    "gsearch7 = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=140,\n",
    "    subsample=0.7, colsample_bytree=0.7,\n",
    "    objective= 'binary:logistic', nthread=6, scale_pos_weight=8.4, seed=27,\n",
    "    max_depth=4, min_child_weight=6, gamma=0), \n",
    "    param_grid=param_test7, scoring='roc_auc',n_jobs=6,iid=False, cv=5)\n",
    "gsearch7.fit(df_train[predictors],df_train[target])\n",
    "gsearch7.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.609394+0.000726629\ttest-auc:0.606299+0.00431848\n",
      "[10]\ttrain-auc:0.636754+0.00289071\ttest-auc:0.63271+0.00339507\n",
      "[20]\ttrain-auc:0.645734+0.00117105\ttest-auc:0.640363+0.00325149\n",
      "[30]\ttrain-auc:0.653241+0.000698516\ttest-auc:0.646711+0.00384403\n",
      "[40]\ttrain-auc:0.659076+0.00100018\ttest-auc:0.651254+0.00335593\n",
      "[50]\ttrain-auc:0.662968+0.00093517\ttest-auc:0.653957+0.00343213\n",
      "[60]\ttrain-auc:0.665819+0.00100645\ttest-auc:0.655741+0.00360906\n",
      "[70]\ttrain-auc:0.668381+0.00117364\ttest-auc:0.657372+0.00327072\n",
      "[80]\ttrain-auc:0.670868+0.00136968\ttest-auc:0.658772+0.0031551\n",
      "[90]\ttrain-auc:0.672922+0.00134195\ttest-auc:0.659685+0.00322803\n",
      "[100]\ttrain-auc:0.674658+0.00133266\ttest-auc:0.66048+0.00336015\n",
      "[110]\ttrain-auc:0.676293+0.00123633\ttest-auc:0.661257+0.0035775\n",
      "[120]\ttrain-auc:0.677757+0.00116379\ttest-auc:0.66177+0.00359736\n",
      "[130]\ttrain-auc:0.679149+0.00117568\ttest-auc:0.662303+0.00365395\n",
      "[139]\ttrain-auc:0.68032+0.000996655\ttest-auc:0.662626+0.00376389\n",
      "\n",
      "Model Report\n",
      "Accuracy : 0.3219\n",
      "AUC Score (Train): 0.678602\n"
     ]
    }
   ],
   "source": [
    "#Choose all predictors except target & IDcols\n",
    "xgb8 = XGBClassifier(\n",
    "    learning_rate=0.1,\n",
    "    n_estimators=140,\n",
    "    subsample=0.7,\n",
    "    colsample_bytree=0.7,\n",
    "    objective= 'binary:logistic',\n",
    "    nthread=6,\n",
    "    scale_pos_weight=8.4,\n",
    "    seed=27,\n",
    "    max_depth=4,\n",
    "    min_child_weight=6,\n",
    "    gamma=0\n",
    ")\n",
    "modelfit(xgb8, df_train, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.611668+0.00153359\ttest-auc:0.608803+0.00300232\n",
      "[10]\ttrain-auc:0.631225+0.00272459\ttest-auc:0.627736+0.0038214\n",
      "[20]\ttrain-auc:0.633709+0.00162682\ttest-auc:0.629922+0.00297688\n",
      "[30]\ttrain-auc:0.634769+0.00131575\ttest-auc:0.630838+0.0036459\n",
      "[40]\ttrain-auc:0.635932+0.00129506\ttest-auc:0.631689+0.0035775\n",
      "[50]\ttrain-auc:0.636943+0.00135644\ttest-auc:0.632629+0.00388746\n",
      "[60]\ttrain-auc:0.637475+0.00123975\ttest-auc:0.633108+0.00422211\n",
      "[70]\ttrain-auc:0.638289+0.00130039\ttest-auc:0.633919+0.00420436\n",
      "[80]\ttrain-auc:0.639047+0.00104911\ttest-auc:0.634603+0.00414037\n",
      "[90]\ttrain-auc:0.63945+0.0010408\ttest-auc:0.635032+0.00420011\n",
      "[100]\ttrain-auc:0.640034+0.000948543\ttest-auc:0.635602+0.00411942\n",
      "[110]\ttrain-auc:0.640728+0.000784888\ttest-auc:0.636163+0.00430921\n",
      "[120]\ttrain-auc:0.641245+0.00102943\ttest-auc:0.636666+0.00407203\n",
      "[130]\ttrain-auc:0.641916+0.000975583\ttest-auc:0.637263+0.00399518\n",
      "[139]\ttrain-auc:0.642435+0.000939737\ttest-auc:0.637663+0.00402355\n",
      "\n",
      "Model Report\n",
      "Accuracy : 0.7829\n",
      "AUC Score (Train): 0.640954\n"
     ]
    }
   ],
   "source": [
    "#Choose all predictors except target & IDcols\n",
    "xgb8 = XGBClassifier(\n",
    "    learning_rate=0.01,\n",
    "    n_estimators=140,\n",
    "    subsample=0.7,\n",
    "    colsample_bytree=0.7,\n",
    "    objective= 'binary:logistic',\n",
    "    nthread=6,\n",
    "    scale_pos_weight=1,\n",
    "    seed=27,\n",
    "    max_depth=4,\n",
    "    min_child_weight=6,\n",
    "    gamma=0\n",
    ")\n",
    "modelfit(xgb8, df_train, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = xgb8.predict_proba(df_test[predictors])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame()\n",
    "submission['UniqueID'] = df_test['UniqueID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>UniqueID</th>\n",
       "      <th>loan_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>655269</td>\n",
       "      <td>0.339714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>723482</td>\n",
       "      <td>0.322102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>758529</td>\n",
       "      <td>0.355418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>763449</td>\n",
       "      <td>0.320745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>708663</td>\n",
       "      <td>0.347641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UniqueID  loan_default\n",
       "0    655269      0.339714\n",
       "1    723482      0.322102\n",
       "2    758529      0.355418\n",
       "3    763449      0.320745\n",
       "4    708663      0.347641"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission['loan_default'] = predictions[:,1]\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'submission1.csv'\n",
    "submission.to_csv(filename, index=False)\n",
    "import gzip\n",
    "import shutil\n",
    "with open(filename, 'rb') as f_in:\n",
    "    with gzip.open(filename + '.gz', 'wb') as f_out:\n",
    "        shutil.copyfileobj(f_in, f_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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