LTFS - Loan Default Challenge - Version 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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>...</th>\n",
       "      <th>PRI.CURRENT.BALANCE</th>\n",
       "      <th>PRI.SANCTIONED.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>1984-01-01</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>0</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>1985-07-31</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>27600</td>\n",
       "      <td>50200</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>23</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>1985-08-24</td>\n",
       "      <td>1</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>0</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>1993-12-30</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>8</td>\n",
       "      <td>15</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>1977-09-12</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>518279</td>\n",
       "      <td>54513</td>\n",
       "      <td>61900</td>\n",
       "      <td>89.66</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1501</td>\n",
       "      <td>1990-08-09</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1347</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>529269</td>\n",
       "      <td>46349</td>\n",
       "      <td>61500</td>\n",
       "      <td>76.42</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1502</td>\n",
       "      <td>1988-01-06</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>510278</td>\n",
       "      <td>43894</td>\n",
       "      <td>61900</td>\n",
       "      <td>71.89</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1501</td>\n",
       "      <td>1989-04-10</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>72879</td>\n",
       "      <td>74500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>490213</td>\n",
       "      <td>53713</td>\n",
       "      <td>61973</td>\n",
       "      <td>89.56</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1497</td>\n",
       "      <td>1991-11-15</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-41</td>\n",
       "      <td>365384</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>510980</td>\n",
       "      <td>52603</td>\n",
       "      <td>61300</td>\n",
       "      <td>86.95</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1492</td>\n",
       "      <td>1968-01-06</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
       "0    420825             50578       58400  89.55         67        22807   \n",
       "1    537409             47145       65550  73.23         67        22807   \n",
       "2    417566             53278       61360  89.63         67        22807   \n",
       "3    624493             57513       66113  88.48         67        22807   \n",
       "4    539055             52378       60300  88.39         67        22807   \n",
       "5    518279             54513       61900  89.66         67        22807   \n",
       "6    529269             46349       61500  76.42         67        22807   \n",
       "7    510278             43894       61900  71.89         67        22807   \n",
       "8    490213             53713       61973  89.56         67        22807   \n",
       "9    510980             52603       61300  86.95         67        22807   \n",
       "\n",
       "   manufacturer_id  Current_pincode_ID Date.of.Birth  Employment.Type  \\\n",
       "0               45                1441    1984-01-01                0   \n",
       "1               45                1502    1985-07-31                1   \n",
       "2               45                1497    1985-08-24                1   \n",
       "3               45                1501    1993-12-30                1   \n",
       "4               45                1495    1977-09-12                1   \n",
       "5               45                1501    1990-08-09                1   \n",
       "6               45                1502    1988-01-06                0   \n",
       "7               45                1501    1989-04-10                0   \n",
       "8               45                1497    1991-11-15                1   \n",
       "9               45                1492    1968-01-06                0   \n",
       "\n",
       "       ...      PRI.CURRENT.BALANCE  PRI.SANCTIONED.AMOUNT  \\\n",
       "0      ...                        0                      0   \n",
       "1      ...                    27600                  50200   \n",
       "2      ...                        0                      0   \n",
       "3      ...                        0                      0   \n",
       "4      ...                        0                      0   \n",
       "5      ...                        0                      0   \n",
       "6      ...                        0                      0   \n",
       "7      ...                    72879                  74500   \n",
       "8      ...                      -41                 365384   \n",
       "9      ...                        0                      0   \n",
       "\n",
       "   PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0                   0               0                             0   \n",
       "1                1991               0                             0   \n",
       "2                   0               0                             0   \n",
       "3                  31               0                             0   \n",
       "4                   0               0                             0   \n",
       "5                1347               0                             0   \n",
       "6                   0               0                             0   \n",
       "7                   0               0                             0   \n",
       "8                   0               0                             0   \n",
       "9                2608               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0                 0   \n",
       "1                                    1                23   \n",
       "2                                    0                 0   \n",
       "3                                    0                 8   \n",
       "4                                    0                 0   \n",
       "5                                    0                21   \n",
       "6                                    0                 0   \n",
       "7                                    0                 2   \n",
       "8                                    0                56   \n",
       "9                                    0                19   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \n",
       "0                      0                0             0  \n",
       "1                     23                0             1  \n",
       "2                      0                0             0  \n",
       "3                     15                1             1  \n",
       "4                      0                1             1  \n",
       "5                     24                0             0  \n",
       "6                      0                0             0  \n",
       "7                      2                0             0  \n",
       "8                     56                1             0  \n",
       "9                     19                0             0  \n",
       "\n",
       "[10 rows x 32 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UniqueID                                 int64\n",
       "disbursed_amount                         int64\n",
       "asset_cost                               int64\n",
       "ltv                                    float64\n",
       "branch_id                                int64\n",
       "supplier_id                              int64\n",
       "manufacturer_id                          int64\n",
       "Current_pincode_ID                       int64\n",
       "Date.of.Birth                           object\n",
       "Employment.Type                         object\n",
       "DisbursalDate                           object\n",
       "State_ID                                 int64\n",
       "Employee_code_ID                         int64\n",
       "MobileNo_Avl_Flag                        int64\n",
       "Aadhar_flag                              int64\n",
       "PAN_flag                                 int64\n",
       "VoterID_flag                             int64\n",
       "Driving_flag                             int64\n",
       "Passport_flag                            int64\n",
       "PERFORM_CNS.SCORE                        int64\n",
       "PERFORM_CNS.SCORE.DESCRIPTION           object\n",
       "PRI.NO.OF.ACCTS                          int64\n",
       "PRI.ACTIVE.ACCTS                         int64\n",
       "PRI.OVERDUE.ACCTS                        int64\n",
       "PRI.CURRENT.BALANCE                      int64\n",
       "PRI.SANCTIONED.AMOUNT                    int64\n",
       "PRI.DISBURSED.AMOUNT                     int64\n",
       "SEC.NO.OF.ACCTS                          int64\n",
       "SEC.ACTIVE.ACCTS                         int64\n",
       "SEC.OVERDUE.ACCTS                        int64\n",
       "SEC.CURRENT.BALANCE                      int64\n",
       "SEC.SANCTIONED.AMOUNT                    int64\n",
       "SEC.DISBURSED.AMOUNT                     int64\n",
       "PRIMARY.INSTAL.AMT                       int64\n",
       "SEC.INSTAL.AMT                           int64\n",
       "NEW.ACCTS.IN.LAST.SIX.MONTHS             int64\n",
       "DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS      int64\n",
       "AVERAGE.ACCT.AGE                        object\n",
       "CREDIT.HISTORY.LENGTH                   object\n",
       "NO.OF_INQUIRIES                          int64\n",
       "loan_default                             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>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>...</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>233154.000000</td>\n",
       "      <td>233154.000000</td>\n",
       "      <td>2.331540e+05</td>\n",
       "      <td>233154.000000</td>\n",
       "      <td>233154.000000</td>\n",
       "      <td>233154.000000</td>\n",
       "      <td>233154.000000</td>\n",
       "      <td>233154.000000</td>\n",
       "      <td>233154.000000</td>\n",
       "      <td>233154.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>233154.000000</td>\n",
       "      <td>2.331540e+05</td>\n",
       "      <td>2.331540e+05</td>\n",
       "      <td>2.331540e+05</td>\n",
       "      <td>2.331540e+05</td>\n",
       "      <td>2.331540e+05</td>\n",
       "      <td>233154.000000</td>\n",
       "      <td>233154.000000</td>\n",
       "      <td>233154.000000</td>\n",
       "      <td>233154.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>535917.573376</td>\n",
       "      <td>54356.993528</td>\n",
       "      <td>7.586507e+04</td>\n",
       "      <td>74.746530</td>\n",
       "      <td>72.936094</td>\n",
       "      <td>19638.635035</td>\n",
       "      <td>69.028054</td>\n",
       "      <td>3396.880247</td>\n",
       "      <td>7.262243</td>\n",
       "      <td>1549.477148</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007244</td>\n",
       "      <td>5.427793e+03</td>\n",
       "      <td>7.295923e+03</td>\n",
       "      <td>7.179998e+03</td>\n",
       "      <td>1.310548e+04</td>\n",
       "      <td>3.232684e+02</td>\n",
       "      <td>0.381833</td>\n",
       "      <td>0.097481</td>\n",
       "      <td>0.206615</td>\n",
       "      <td>0.217071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>68315.693711</td>\n",
       "      <td>12971.314171</td>\n",
       "      <td>1.894478e+04</td>\n",
       "      <td>11.456636</td>\n",
       "      <td>69.834995</td>\n",
       "      <td>3491.949566</td>\n",
       "      <td>22.141304</td>\n",
       "      <td>2238.147502</td>\n",
       "      <td>4.482230</td>\n",
       "      <td>975.261278</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111079</td>\n",
       "      <td>1.702370e+05</td>\n",
       "      <td>1.831560e+05</td>\n",
       "      <td>1.825925e+05</td>\n",
       "      <td>1.513679e+05</td>\n",
       "      <td>1.555369e+04</td>\n",
       "      <td>0.955107</td>\n",
       "      <td>0.384439</td>\n",
       "      <td>0.706498</td>\n",
       "      <td>0.412252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>417428.000000</td>\n",
       "      <td>13320.000000</td>\n",
       "      <td>3.700000e+04</td>\n",
       "      <td>10.030000</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>...</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>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>476786.250000</td>\n",
       "      <td>47145.000000</td>\n",
       "      <td>6.571700e+04</td>\n",
       "      <td>68.880000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>16535.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1511.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>...</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>50%</th>\n",
       "      <td>535978.500000</td>\n",
       "      <td>53803.000000</td>\n",
       "      <td>7.094600e+04</td>\n",
       "      <td>76.800000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>20333.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>2970.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1451.000000</td>\n",
       "      <td>...</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>595039.750000</td>\n",
       "      <td>60413.000000</td>\n",
       "      <td>7.920175e+04</td>\n",
       "      <td>83.670000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>23000.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>5677.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2362.000000</td>\n",
       "      <td>...</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>1.999000e+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>671084.000000</td>\n",
       "      <td>990572.000000</td>\n",
       "      <td>1.628992e+06</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>24803.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>7345.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>3795.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.603285e+07</td>\n",
       "      <td>3.000000e+07</td>\n",
       "      <td>3.000000e+07</td>\n",
       "      <td>2.564281e+07</td>\n",
       "      <td>4.170901e+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",
       "<p>8 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            UniqueID  disbursed_amount    asset_cost            ltv  \\\n",
       "count  233154.000000     233154.000000  2.331540e+05  233154.000000   \n",
       "mean   535917.573376      54356.993528  7.586507e+04      74.746530   \n",
       "std     68315.693711      12971.314171  1.894478e+04      11.456636   \n",
       "min    417428.000000      13320.000000  3.700000e+04      10.030000   \n",
       "25%    476786.250000      47145.000000  6.571700e+04      68.880000   \n",
       "50%    535978.500000      53803.000000  7.094600e+04      76.800000   \n",
       "75%    595039.750000      60413.000000  7.920175e+04      83.670000   \n",
       "max    671084.000000     990572.000000  1.628992e+06      95.000000   \n",
       "\n",
       "           branch_id    supplier_id  manufacturer_id  Current_pincode_ID  \\\n",
       "count  233154.000000  233154.000000    233154.000000       233154.000000   \n",
       "mean       72.936094   19638.635035        69.028054         3396.880247   \n",
       "std        69.834995    3491.949566        22.141304         2238.147502   \n",
       "min         1.000000   10524.000000        45.000000            1.000000   \n",
       "25%        14.000000   16535.000000        48.000000         1511.000000   \n",
       "50%        61.000000   20333.000000        86.000000         2970.000000   \n",
       "75%       130.000000   23000.000000        86.000000         5677.000000   \n",
       "max       261.000000   24803.000000       156.000000         7345.000000   \n",
       "\n",
       "            State_ID  Employee_code_ID      ...        SEC.OVERDUE.ACCTS  \\\n",
       "count  233154.000000     233154.000000      ...            233154.000000   \n",
       "mean        7.262243       1549.477148      ...                 0.007244   \n",
       "std         4.482230        975.261278      ...                 0.111079   \n",
       "min         1.000000          1.000000      ...                 0.000000   \n",
       "25%         4.000000        713.000000      ...                 0.000000   \n",
       "50%         6.000000       1451.000000      ...                 0.000000   \n",
       "75%        10.000000       2362.000000      ...                 0.000000   \n",
       "max        22.000000       3795.000000      ...                 8.000000   \n",
       "\n",
       "       SEC.CURRENT.BALANCE  SEC.SANCTIONED.AMOUNT  SEC.DISBURSED.AMOUNT  \\\n",
       "count         2.331540e+05           2.331540e+05          2.331540e+05   \n",
       "mean          5.427793e+03           7.295923e+03          7.179998e+03   \n",
       "std           1.702370e+05           1.831560e+05          1.825925e+05   \n",
       "min          -5.746470e+05           0.000000e+00          0.000000e+00   \n",
       "25%           0.000000e+00           0.000000e+00          0.000000e+00   \n",
       "50%           0.000000e+00           0.000000e+00          0.000000e+00   \n",
       "75%           0.000000e+00           0.000000e+00          0.000000e+00   \n",
       "max           3.603285e+07           3.000000e+07          3.000000e+07   \n",
       "\n",
       "       PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "count        2.331540e+05    2.331540e+05                 233154.000000   \n",
       "mean         1.310548e+04    3.232684e+02                      0.381833   \n",
       "std          1.513679e+05    1.555369e+04                      0.955107   \n",
       "min          0.000000e+00    0.000000e+00                      0.000000   \n",
       "25%          0.000000e+00    0.000000e+00                      0.000000   \n",
       "50%          0.000000e+00    0.000000e+00                      0.000000   \n",
       "75%          1.999000e+03    0.000000e+00                      0.000000   \n",
       "max          2.564281e+07    4.170901e+06                     35.000000   \n",
       "\n",
       "       DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  NO.OF_INQUIRIES   loan_default  \n",
       "count                        233154.000000    233154.000000  233154.000000  \n",
       "mean                              0.097481         0.206615       0.217071  \n",
       "std                               0.384439         0.706498       0.412252  \n",
       "min                               0.000000         0.000000       0.000000  \n",
       "25%                               0.000000         0.000000       0.000000  \n",
       "50%                               0.000000         0.000000       0.000000  \n",
       "75%                               0.000000         0.000000       0.000000  \n",
       "max                              20.000000        36.000000       1.000000  \n",
       "\n",
       "[8 rows x 35 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    182543\n",
       "1     50611\n",
       "Name: loan_default, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['loan_default'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2      13138\n",
       "67     11328\n",
       "3       9230\n",
       "5       9218\n",
       "36      8832\n",
       "136     7833\n",
       "34      7794\n",
       "16      6466\n",
       "19      5860\n",
       "1       5709\n",
       "146     5376\n",
       "18      5032\n",
       "152     4933\n",
       "61      4906\n",
       "48      4725\n",
       "11      4506\n",
       "20      4431\n",
       "138     4352\n",
       "74      4297\n",
       "120     4210\n",
       "147     4160\n",
       "10      4125\n",
       "103     3878\n",
       "251     3844\n",
       "65      3509\n",
       "160     3505\n",
       "79      3413\n",
       "7       3222\n",
       "135     3203\n",
       "8       3146\n",
       "       ...  \n",
       "77      1445\n",
       "72      1294\n",
       "257     1256\n",
       "17      1160\n",
       "130     1069\n",
       "82      1035\n",
       "165     1021\n",
       "121      884\n",
       "249      858\n",
       "76       855\n",
       "69       810\n",
       "35       693\n",
       "153      692\n",
       "62       691\n",
       "207      689\n",
       "43       584\n",
       "117      558\n",
       "142      473\n",
       "97       389\n",
       "258      374\n",
       "260      372\n",
       "101      368\n",
       "259      346\n",
       "100      331\n",
       "66       314\n",
       "217      183\n",
       "261      176\n",
       "84       156\n",
       "111       89\n",
       "158       69\n",
       "Name: branch_id, Length: 82, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['branch_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(233154, 41)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8390450>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% matplotlib inline\n",
    "train['disbursed_amount'].hist(bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xab5b7f0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['asset_cost'].hist(bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(train.boxplot(column='disbursed_amount'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xac147b0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.boxplot(column='disbursed_amount', by = 'Employment.Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf57dff0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['ltv'].hist(bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    233154\n",
       "Name: MobileNo_Avl_Flag, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['MobileNo_Avl_Flag'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    195924\n",
       "0     37230\n",
       "Name: Aadhar_flag, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Aadhar_flag'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    215533\n",
       "1     17621\n",
       "Name: PAN_flag, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['PAN_flag'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    199360\n",
       "1     33794\n",
       "Name: VoterID_flag, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['VoterID_flag'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    227735\n",
       "1      5419\n",
       "Name: Driving_flag, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Driving_flag'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    232658\n",
       "1       496\n",
       "Name: Passport_flag, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Passport_flag'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UniqueID                                 int64\n",
       "disbursed_amount                         int64\n",
       "asset_cost                               int64\n",
       "ltv                                    float64\n",
       "branch_id                                int64\n",
       "supplier_id                              int64\n",
       "manufacturer_id                          int64\n",
       "Current_pincode_ID                       int64\n",
       "State_ID                                 int64\n",
       "Employee_code_ID                         int64\n",
       "MobileNo_Avl_Flag                        int64\n",
       "Aadhar_flag                              int64\n",
       "PAN_flag                                 int64\n",
       "VoterID_flag                             int64\n",
       "Driving_flag                             int64\n",
       "Passport_flag                            int64\n",
       "PERFORM_CNS.SCORE                        int64\n",
       "PRI.NO.OF.ACCTS                          int64\n",
       "PRI.ACTIVE.ACCTS                         int64\n",
       "PRI.OVERDUE.ACCTS                        int64\n",
       "PRI.CURRENT.BALANCE                      int64\n",
       "PRI.SANCTIONED.AMOUNT                    int64\n",
       "PRI.DISBURSED.AMOUNT                     int64\n",
       "SEC.NO.OF.ACCTS                          int64\n",
       "SEC.ACTIVE.ACCTS                         int64\n",
       "SEC.OVERDUE.ACCTS                        int64\n",
       "SEC.CURRENT.BALANCE                      int64\n",
       "SEC.SANCTIONED.AMOUNT                    int64\n",
       "SEC.DISBURSED.AMOUNT                     int64\n",
       "PRIMARY.INSTAL.AMT                       int64\n",
       "SEC.INSTAL.AMT                           int64\n",
       "NEW.ACCTS.IN.LAST.SIX.MONTHS             int64\n",
       "DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS      int64\n",
       "AVERAGE.ACCT.AGE                         int64\n",
       "CREDIT.HISTORY.LENGTH                    int64\n",
       "NO.OF_INQUIRIES                          int64\n",
       "loan_default                             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_features = train.select_dtypes(include=[np.number])\n",
    "numeric_features.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12626f10>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train.loan_default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12697c70>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train.State_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b2fd070>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train.manufacturer_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UniqueID                                  0\n",
       "disbursed_amount                          0\n",
       "asset_cost                                0\n",
       "ltv                                       0\n",
       "branch_id                                 0\n",
       "supplier_id                               0\n",
       "manufacturer_id                           0\n",
       "Current_pincode_ID                        0\n",
       "Date.of.Birth                             0\n",
       "Employment.Type                        7661\n",
       "DisbursalDate                             0\n",
       "State_ID                                  0\n",
       "Employee_code_ID                          0\n",
       "MobileNo_Avl_Flag                         0\n",
       "Aadhar_flag                               0\n",
       "PAN_flag                                  0\n",
       "VoterID_flag                              0\n",
       "Driving_flag                              0\n",
       "Passport_flag                             0\n",
       "PERFORM_CNS.SCORE                         0\n",
       "PERFORM_CNS.SCORE.DESCRIPTION             0\n",
       "PRI.NO.OF.ACCTS                           0\n",
       "PRI.ACTIVE.ACCTS                          0\n",
       "PRI.OVERDUE.ACCTS                         0\n",
       "PRI.CURRENT.BALANCE                       0\n",
       "PRI.SANCTIONED.AMOUNT                     0\n",
       "PRI.DISBURSED.AMOUNT                      0\n",
       "SEC.NO.OF.ACCTS                           0\n",
       "SEC.ACTIVE.ACCTS                          0\n",
       "SEC.OVERDUE.ACCTS                         0\n",
       "SEC.CURRENT.BALANCE                       0\n",
       "SEC.SANCTIONED.AMOUNT                     0\n",
       "SEC.DISBURSED.AMOUNT                      0\n",
       "PRIMARY.INSTAL.AMT                        0\n",
       "SEC.INSTAL.AMT                            0\n",
       "NEW.ACCTS.IN.LAST.SIX.MONTHS              0\n",
       "DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS       0\n",
       "AVERAGE.ACCT.AGE                          0\n",
       "CREDIT.HISTORY.LENGTH                     0\n",
       "NO.OF_INQUIRIES                           0\n",
       "loan_default                              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.apply(lambda x: sum(x.isnull()),axis=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Self employed    127635\n",
       "Salaried          97858\n",
       "Name: Employment.Type, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Employment.Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>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>...</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>1/1/1984</td>\n",
       "      <td>Salaried</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</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>...</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>1 yrs 11 mon</td>\n",
       "      <td>1 yrs 11 mon</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>...</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</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>...</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>0 yrs 8 mon</td>\n",
       "      <td>1 yrs 3 mon</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>9/12/1977</td>\n",
       "      <td>Self employed</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>518279</td>\n",
       "      <td>54513</td>\n",
       "      <td>61900</td>\n",
       "      <td>89.66</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1501</td>\n",
       "      <td>8/9/1990</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1347</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1 yrs 9 mon</td>\n",
       "      <td>2 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>529269</td>\n",
       "      <td>46349</td>\n",
       "      <td>61500</td>\n",
       "      <td>76.42</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1502</td>\n",
       "      <td>1/6/1988</td>\n",
       "      <td>Salaried</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>510278</td>\n",
       "      <td>43894</td>\n",
       "      <td>61900</td>\n",
       "      <td>71.89</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1501</td>\n",
       "      <td>4/10/1989</td>\n",
       "      <td>Salaried</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 yrs 2 mon</td>\n",
       "      <td>0 yrs 2 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>490213</td>\n",
       "      <td>53713</td>\n",
       "      <td>61973</td>\n",
       "      <td>89.56</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1497</td>\n",
       "      <td>15-11-91</td>\n",
       "      <td>Self employed</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>4 yrs 8 mon</td>\n",
       "      <td>4 yrs 8 mon</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>510980</td>\n",
       "      <td>52603</td>\n",
       "      <td>61300</td>\n",
       "      <td>86.95</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1492</td>\n",
       "      <td>1/6/1968</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>...</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>1 yrs 7 mon</td>\n",
       "      <td>1 yrs 7 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>548567</td>\n",
       "      <td>53278</td>\n",
       "      <td>61230</td>\n",
       "      <td>89.83</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1493</td>\n",
       "      <td>1/1/1979</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2270</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 yrs 7 mon</td>\n",
       "      <td>0 yrs 7 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>486821</td>\n",
       "      <td>64769</td>\n",
       "      <td>74190</td>\n",
       "      <td>89.23</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1446</td>\n",
       "      <td>7/9/1984</td>\n",
       "      <td>Salaried</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>2 yrs 1 mon</td>\n",
       "      <td>2 yrs 3 mon</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>478647</td>\n",
       "      <td>53278</td>\n",
       "      <td>61330</td>\n",
       "      <td>89.68</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1497</td>\n",
       "      <td>1/6/1974</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1 yrs 3 mon</td>\n",
       "      <td>2 yrs 9 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>479533</td>\n",
       "      <td>49478</td>\n",
       "      <td>57010</td>\n",
       "      <td>89.46</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1497</td>\n",
       "      <td>16-08-84</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23309</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1 yrs 0 mon</td>\n",
       "      <td>2 yrs 1 mon</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>483869</td>\n",
       "      <td>49278</td>\n",
       "      <td>57080</td>\n",
       "      <td>89.35</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1495</td>\n",
       "      <td>18-02-73</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3514</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3 yrs 11 mon</td>\n",
       "      <td>7 yrs 2 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>600655</td>\n",
       "      <td>47549</td>\n",
       "      <td>61400</td>\n",
       "      <td>79.80</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1440</td>\n",
       "      <td>5/7/1994</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7900</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0 yrs 1 mon</td>\n",
       "      <td>0 yrs 1 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>513916</td>\n",
       "      <td>57713</td>\n",
       "      <td>65750</td>\n",
       "      <td>89.28</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1440</td>\n",
       "      <td>1/6/1976</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2 yrs 6 mon</td>\n",
       "      <td>5 yrs 6 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>522020</td>\n",
       "      <td>53503</td>\n",
       "      <td>62100</td>\n",
       "      <td>87.28</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1498</td>\n",
       "      <td>27-02-83</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4982</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1 yrs 10 mon</td>\n",
       "      <td>4 yrs 7 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>492995</td>\n",
       "      <td>70017</td>\n",
       "      <td>86760</td>\n",
       "      <td>82.99</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1479</td>\n",
       "      <td>10/8/1988</td>\n",
       "      <td>Self employed</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>1 yrs 9 mon</td>\n",
       "      <td>1 yrs 9 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>568857</td>\n",
       "      <td>58259</td>\n",
       "      <td>68500</td>\n",
       "      <td>86.13</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1468</td>\n",
       "      <td>16-04-80</td>\n",
       "      <td>Self employed</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 yrs 11 mon</td>\n",
       "      <td>0 yrs 11 mon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>590630</td>\n",
       "      <td>58013</td>\n",
       "      <td>69650</td>\n",
       "      <td>84.71</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1497</td>\n",
       "      <td>1/11/1978</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>557</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0 yrs 6 mon</td>\n",
       "      <td>0 yrs 10 mon</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>467015</td>\n",
       "      <td>31184</td>\n",
       "      <td>57110</td>\n",
       "      <td>56.91</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1498</td>\n",
       "      <td>29-02-84</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11083</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2 yrs 9 mon</td>\n",
       "      <td>5 yrs 10 mon</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>563215</td>\n",
       "      <td>43594</td>\n",
       "      <td>78256</td>\n",
       "      <td>57.50</td>\n",
       "      <td>67</td>\n",
       "      <td>22744</td>\n",
       "      <td>86</td>\n",
       "      <td>1499</td>\n",
       "      <td>14-07-94</td>\n",
       "      <td>Self employed</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>513139</td>\n",
       "      <td>54513</td>\n",
       "      <td>61900</td>\n",
       "      <td>89.66</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1468</td>\n",
       "      <td>31-05-79</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1 yrs 3 mon</td>\n",
       "      <td>1 yrs 3 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>498082</td>\n",
       "      <td>73123</td>\n",
       "      <td>92900</td>\n",
       "      <td>79.66</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1480</td>\n",
       "      <td>2/1/1989</td>\n",
       "      <td>Self employed</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>586411</td>\n",
       "      <td>55213</td>\n",
       "      <td>68600</td>\n",
       "      <td>83.09</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1494</td>\n",
       "      <td>1/1/1986</td>\n",
       "      <td>Salaried</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>440293</td>\n",
       "      <td>53713</td>\n",
       "      <td>61780</td>\n",
       "      <td>89.83</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1468</td>\n",
       "      <td>2/8/1968</td>\n",
       "      <td>Self employed</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>566763</td>\n",
       "      <td>57713</td>\n",
       "      <td>68040</td>\n",
       "      <td>86.27</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1497</td>\n",
       "      <td>1/1/1976</td>\n",
       "      <td>Self employed</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>605314</td>\n",
       "      <td>57513</td>\n",
       "      <td>65750</td>\n",
       "      <td>88.97</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1497</td>\n",
       "      <td>12/9/1972</td>\n",
       "      <td>Self employed</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>3 yrs 1 mon</td>\n",
       "      <td>3 yrs 1 mon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>519075</td>\n",
       "      <td>54513</td>\n",
       "      <td>61900</td>\n",
       "      <td>89.66</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1473</td>\n",
       "      <td>27-06-69</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8972</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2 yrs 0 mon</td>\n",
       "      <td>5 yrs 4 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233124</th>\n",
       "      <td>585873</td>\n",
       "      <td>52803</td>\n",
       "      <td>68027</td>\n",
       "      <td>79.38</td>\n",
       "      <td>101</td>\n",
       "      <td>24031</td>\n",
       "      <td>120</td>\n",
       "      <td>701</td>\n",
       "      <td>30-09-84</td>\n",
       "      <td>Salaried</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233125</th>\n",
       "      <td>573573</td>\n",
       "      <td>40894</td>\n",
       "      <td>72123</td>\n",
       "      <td>58.23</td>\n",
       "      <td>101</td>\n",
       "      <td>24031</td>\n",
       "      <td>120</td>\n",
       "      <td>696</td>\n",
       "      <td>13-05-88</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16547</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2 yrs 3 mon</td>\n",
       "      <td>5 yrs 5 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233126</th>\n",
       "      <td>559119</td>\n",
       "      <td>42894</td>\n",
       "      <td>73505</td>\n",
       "      <td>59.86</td>\n",
       "      <td>101</td>\n",
       "      <td>24031</td>\n",
       "      <td>120</td>\n",
       "      <td>692</td>\n",
       "      <td>2/12/1974</td>\n",
       "      <td>Self employed</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233127</th>\n",
       "      <td>575725</td>\n",
       "      <td>63447</td>\n",
       "      <td>82220</td>\n",
       "      <td>79.06</td>\n",
       "      <td>101</td>\n",
       "      <td>24031</td>\n",
       "      <td>120</td>\n",
       "      <td>701</td>\n",
       "      <td>5/3/1976</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1 yrs 1 mon</td>\n",
       "      <td>3 yrs 2 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233128</th>\n",
       "      <td>607646</td>\n",
       "      <td>57259</td>\n",
       "      <td>73914</td>\n",
       "      <td>79.15</td>\n",
       "      <td>101</td>\n",
       "      <td>24031</td>\n",
       "      <td>120</td>\n",
       "      <td>705</td>\n",
       "      <td>6/4/1969</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11830</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0 yrs 2 mon</td>\n",
       "      <td>0 yrs 2 mon</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233129</th>\n",
       "      <td>497980</td>\n",
       "      <td>58513</td>\n",
       "      <td>81739</td>\n",
       "      <td>73.40</td>\n",
       "      <td>101</td>\n",
       "      <td>24031</td>\n",
       "      <td>120</td>\n",
       "      <td>696</td>\n",
       "      <td>18-07-80</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79611</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2 yrs 1 mon</td>\n",
       "      <td>6 yrs 2 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233130</th>\n",
       "      <td>450782</td>\n",
       "      <td>60447</td>\n",
       "      <td>78437</td>\n",
       "      <td>79.04</td>\n",
       "      <td>101</td>\n",
       "      <td>24031</td>\n",
       "      <td>120</td>\n",
       "      <td>687</td>\n",
       "      <td>16-01-64</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23356</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4 yrs 1 mon</td>\n",
       "      <td>9 yrs 5 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233131</th>\n",
       "      <td>542403</td>\n",
       "      <td>29029</td>\n",
       "      <td>68935</td>\n",
       "      <td>43.52</td>\n",
       "      <td>101</td>\n",
       "      <td>24031</td>\n",
       "      <td>120</td>\n",
       "      <td>690</td>\n",
       "      <td>31-12-94</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8244</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0 yrs 5 mon</td>\n",
       "      <td>0 yrs 9 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233132</th>\n",
       "      <td>479107</td>\n",
       "      <td>60447</td>\n",
       "      <td>78437</td>\n",
       "      <td>79.04</td>\n",
       "      <td>101</td>\n",
       "      <td>24031</td>\n",
       "      <td>120</td>\n",
       "      <td>687</td>\n",
       "      <td>3/9/1992</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10480</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0 yrs 11 mon</td>\n",
       "      <td>1 yrs 11 mon</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233133</th>\n",
       "      <td>623933</td>\n",
       "      <td>53303</td>\n",
       "      <td>69351</td>\n",
       "      <td>77.86</td>\n",
       "      <td>34</td>\n",
       "      <td>23024</td>\n",
       "      <td>86</td>\n",
       "      <td>977</td>\n",
       "      <td>31-12-80</td>\n",
       "      <td>Salaried</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>6 yrs 10 mon</td>\n",
       "      <td>12 yrs 5 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233134</th>\n",
       "      <td>609494</td>\n",
       "      <td>48349</td>\n",
       "      <td>96022</td>\n",
       "      <td>51.55</td>\n",
       "      <td>34</td>\n",
       "      <td>20700</td>\n",
       "      <td>48</td>\n",
       "      <td>1050</td>\n",
       "      <td>1/6/1981</td>\n",
       "      <td>Self employed</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233135</th>\n",
       "      <td>624780</td>\n",
       "      <td>39439</td>\n",
       "      <td>60334</td>\n",
       "      <td>66.30</td>\n",
       "      <td>34</td>\n",
       "      <td>20700</td>\n",
       "      <td>48</td>\n",
       "      <td>1051</td>\n",
       "      <td>1/1/1996</td>\n",
       "      <td>Salaried</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233136</th>\n",
       "      <td>606171</td>\n",
       "      <td>50155</td>\n",
       "      <td>60679</td>\n",
       "      <td>85.00</td>\n",
       "      <td>34</td>\n",
       "      <td>21081</td>\n",
       "      <td>45</td>\n",
       "      <td>1051</td>\n",
       "      <td>2/11/1972</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8288</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4 yrs 6 mon</td>\n",
       "      <td>4 yrs 6 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233137</th>\n",
       "      <td>631394</td>\n",
       "      <td>43210</td>\n",
       "      <td>60334</td>\n",
       "      <td>72.93</td>\n",
       "      <td>34</td>\n",
       "      <td>20700</td>\n",
       "      <td>48</td>\n",
       "      <td>1050</td>\n",
       "      <td>14-09-73</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14583</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1 yrs 3 mon</td>\n",
       "      <td>2 yrs 4 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233138</th>\n",
       "      <td>607521</td>\n",
       "      <td>28029</td>\n",
       "      <td>45953</td>\n",
       "      <td>63.11</td>\n",
       "      <td>34</td>\n",
       "      <td>20700</td>\n",
       "      <td>48</td>\n",
       "      <td>992</td>\n",
       "      <td>20-03-83</td>\n",
       "      <td>Salaried</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233139</th>\n",
       "      <td>610384</td>\n",
       "      <td>42894</td>\n",
       "      <td>69351</td>\n",
       "      <td>63.45</td>\n",
       "      <td>34</td>\n",
       "      <td>23024</td>\n",
       "      <td>86</td>\n",
       "      <td>1050</td>\n",
       "      <td>14-08-81</td>\n",
       "      <td>Self employed</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 yrs 10 mon</td>\n",
       "      <td>1 yrs 2 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233140</th>\n",
       "      <td>623179</td>\n",
       "      <td>42894</td>\n",
       "      <td>60334</td>\n",
       "      <td>72.93</td>\n",
       "      <td>34</td>\n",
       "      <td>20700</td>\n",
       "      <td>48</td>\n",
       "      <td>1051</td>\n",
       "      <td>23-01-89</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12880</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3 yrs 0 mon</td>\n",
       "      <td>8 yrs 2 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233141</th>\n",
       "      <td>606375</td>\n",
       "      <td>29029</td>\n",
       "      <td>45233</td>\n",
       "      <td>66.32</td>\n",
       "      <td>34</td>\n",
       "      <td>20700</td>\n",
       "      <td>48</td>\n",
       "      <td>1067</td>\n",
       "      <td>9/1/1996</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2034</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 yrs 6 mon</td>\n",
       "      <td>0 yrs 6 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233142</th>\n",
       "      <td>606173</td>\n",
       "      <td>57013</td>\n",
       "      <td>69001</td>\n",
       "      <td>84.78</td>\n",
       "      <td>34</td>\n",
       "      <td>23024</td>\n",
       "      <td>86</td>\n",
       "      <td>987</td>\n",
       "      <td>3/3/1994</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4665</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0 yrs 5 mon</td>\n",
       "      <td>0 yrs 8 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233143</th>\n",
       "      <td>626823</td>\n",
       "      <td>61213</td>\n",
       "      <td>89708</td>\n",
       "      <td>69.11</td>\n",
       "      <td>34</td>\n",
       "      <td>20700</td>\n",
       "      <td>48</td>\n",
       "      <td>1051</td>\n",
       "      <td>25-01-67</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12602</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1 yrs 7 mon</td>\n",
       "      <td>3 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233144</th>\n",
       "      <td>613161</td>\n",
       "      <td>56059</td>\n",
       "      <td>69001</td>\n",
       "      <td>83.04</td>\n",
       "      <td>34</td>\n",
       "      <td>23024</td>\n",
       "      <td>86</td>\n",
       "      <td>1044</td>\n",
       "      <td>15-06-63</td>\n",
       "      <td>Salaried</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233145</th>\n",
       "      <td>606146</td>\n",
       "      <td>49803</td>\n",
       "      <td>66973</td>\n",
       "      <td>76.15</td>\n",
       "      <td>34</td>\n",
       "      <td>21081</td>\n",
       "      <td>45</td>\n",
       "      <td>1051</td>\n",
       "      <td>23-12-85</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1672</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0 yrs 9 mon</td>\n",
       "      <td>2 yrs 6 mon</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233146</th>\n",
       "      <td>622612</td>\n",
       "      <td>38439</td>\n",
       "      <td>52965</td>\n",
       "      <td>74.58</td>\n",
       "      <td>34</td>\n",
       "      <td>20700</td>\n",
       "      <td>48</td>\n",
       "      <td>1051</td>\n",
       "      <td>23-07-82</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0 yrs 3 mon</td>\n",
       "      <td>0 yrs 3 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233147</th>\n",
       "      <td>645697</td>\n",
       "      <td>72623</td>\n",
       "      <td>105405</td>\n",
       "      <td>69.73</td>\n",
       "      <td>34</td>\n",
       "      <td>20700</td>\n",
       "      <td>48</td>\n",
       "      <td>1051</td>\n",
       "      <td>19-06-89</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0 yrs 9 mon</td>\n",
       "      <td>1 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233148</th>\n",
       "      <td>613494</td>\n",
       "      <td>42894</td>\n",
       "      <td>60334</td>\n",
       "      <td>72.93</td>\n",
       "      <td>34</td>\n",
       "      <td>20700</td>\n",
       "      <td>48</td>\n",
       "      <td>1051</td>\n",
       "      <td>8/7/1993</td>\n",
       "      <td>Salaried</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233149</th>\n",
       "      <td>626432</td>\n",
       "      <td>63213</td>\n",
       "      <td>105405</td>\n",
       "      <td>60.72</td>\n",
       "      <td>34</td>\n",
       "      <td>20700</td>\n",
       "      <td>48</td>\n",
       "      <td>1050</td>\n",
       "      <td>1/8/1988</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4084</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1 yrs 9 mon</td>\n",
       "      <td>3 yrs 3 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233150</th>\n",
       "      <td>606141</td>\n",
       "      <td>73651</td>\n",
       "      <td>100600</td>\n",
       "      <td>74.95</td>\n",
       "      <td>34</td>\n",
       "      <td>23775</td>\n",
       "      <td>51</td>\n",
       "      <td>990</td>\n",
       "      <td>5/12/1988</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1565</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 yrs 6 mon</td>\n",
       "      <td>0 yrs 6 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233151</th>\n",
       "      <td>613658</td>\n",
       "      <td>33484</td>\n",
       "      <td>71212</td>\n",
       "      <td>48.45</td>\n",
       "      <td>77</td>\n",
       "      <td>22186</td>\n",
       "      <td>86</td>\n",
       "      <td>2299</td>\n",
       "      <td>1/6/1976</td>\n",
       "      <td>Salaried</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233152</th>\n",
       "      <td>548084</td>\n",
       "      <td>34259</td>\n",
       "      <td>73286</td>\n",
       "      <td>49.10</td>\n",
       "      <td>77</td>\n",
       "      <td>22186</td>\n",
       "      <td>86</td>\n",
       "      <td>2299</td>\n",
       "      <td>26-03-94</td>\n",
       "      <td>Salaried</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233153</th>\n",
       "      <td>630213</td>\n",
       "      <td>75751</td>\n",
       "      <td>116009</td>\n",
       "      <td>66.81</td>\n",
       "      <td>77</td>\n",
       "      <td>22186</td>\n",
       "      <td>86</td>\n",
       "      <td>2299</td>\n",
       "      <td>18-02-84</td>\n",
       "      <td>Salaried</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>233154 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
       "0         420825             50578       58400  89.55         67        22807   \n",
       "1         537409             47145       65550  73.23         67        22807   \n",
       "2         417566             53278       61360  89.63         67        22807   \n",
       "3         624493             57513       66113  88.48         67        22807   \n",
       "4         539055             52378       60300  88.39         67        22807   \n",
       "5         518279             54513       61900  89.66         67        22807   \n",
       "6         529269             46349       61500  76.42         67        22807   \n",
       "7         510278             43894       61900  71.89         67        22807   \n",
       "8         490213             53713       61973  89.56         67        22807   \n",
       "9         510980             52603       61300  86.95         67        22807   \n",
       "10        548567             53278       61230  89.83         67        22807   \n",
       "11        486821             64769       74190  89.23         67        22807   \n",
       "12        478647             53278       61330  89.68         67        22807   \n",
       "13        479533             49478       57010  89.46         67        22807   \n",
       "14        483869             49278       57080  89.35         67        22807   \n",
       "15        600655             47549       61400  79.80         67        22807   \n",
       "16        513916             57713       65750  89.28         67        22807   \n",
       "17        522020             53503       62100  87.28         67        22807   \n",
       "18        492995             70017       86760  82.99         67        22807   \n",
       "19        568857             58259       68500  86.13         67        22807   \n",
       "20        590630             58013       69650  84.71         67        22807   \n",
       "21        467015             31184       57110  56.91         67        22807   \n",
       "22        563215             43594       78256  57.50         67        22744   \n",
       "23        513139             54513       61900  89.66         67        22807   \n",
       "24        498082             73123       92900  79.66         67        22807   \n",
       "25        586411             55213       68600  83.09         67        22807   \n",
       "26        440293             53713       61780  89.83         67        22807   \n",
       "27        566763             57713       68040  86.27         67        22807   \n",
       "28        605314             57513       65750  88.97         67        22807   \n",
       "29        519075             54513       61900  89.66         67        22807   \n",
       "...          ...               ...         ...    ...        ...          ...   \n",
       "233124    585873             52803       68027  79.38        101        24031   \n",
       "233125    573573             40894       72123  58.23        101        24031   \n",
       "233126    559119             42894       73505  59.86        101        24031   \n",
       "233127    575725             63447       82220  79.06        101        24031   \n",
       "233128    607646             57259       73914  79.15        101        24031   \n",
       "233129    497980             58513       81739  73.40        101        24031   \n",
       "233130    450782             60447       78437  79.04        101        24031   \n",
       "233131    542403             29029       68935  43.52        101        24031   \n",
       "233132    479107             60447       78437  79.04        101        24031   \n",
       "233133    623933             53303       69351  77.86         34        23024   \n",
       "233134    609494             48349       96022  51.55         34        20700   \n",
       "233135    624780             39439       60334  66.30         34        20700   \n",
       "233136    606171             50155       60679  85.00         34        21081   \n",
       "233137    631394             43210       60334  72.93         34        20700   \n",
       "233138    607521             28029       45953  63.11         34        20700   \n",
       "233139    610384             42894       69351  63.45         34        23024   \n",
       "233140    623179             42894       60334  72.93         34        20700   \n",
       "233141    606375             29029       45233  66.32         34        20700   \n",
       "233142    606173             57013       69001  84.78         34        23024   \n",
       "233143    626823             61213       89708  69.11         34        20700   \n",
       "233144    613161             56059       69001  83.04         34        23024   \n",
       "233145    606146             49803       66973  76.15         34        21081   \n",
       "233146    622612             38439       52965  74.58         34        20700   \n",
       "233147    645697             72623      105405  69.73         34        20700   \n",
       "233148    613494             42894       60334  72.93         34        20700   \n",
       "233149    626432             63213      105405  60.72         34        20700   \n",
       "233150    606141             73651      100600  74.95         34        23775   \n",
       "233151    613658             33484       71212  48.45         77        22186   \n",
       "233152    548084             34259       73286  49.10         77        22186   \n",
       "233153    630213             75751      116009  66.81         77        22186   \n",
       "\n",
       "        manufacturer_id  Current_pincode_ID Date.of.Birth Employment.Type  \\\n",
       "0                    45                1441      1/1/1984        Salaried   \n",
       "1                    45                1502      31-07-85   Self employed   \n",
       "2                    45                1497      24-08-85   Self employed   \n",
       "3                    45                1501      30-12-93   Self employed   \n",
       "4                    45                1495     9/12/1977   Self employed   \n",
       "5                    45                1501      8/9/1990   Self employed   \n",
       "6                    45                1502      1/6/1988        Salaried   \n",
       "7                    45                1501     4/10/1989        Salaried   \n",
       "8                    45                1497      15-11-91   Self employed   \n",
       "9                    45                1492      1/6/1968        Salaried   \n",
       "10                   45                1493      1/1/1979   Self employed   \n",
       "11                   45                1446      7/9/1984        Salaried   \n",
       "12                   45                1497      1/6/1974        Salaried   \n",
       "13                   45                1497      16-08-84        Salaried   \n",
       "14                   45                1495      18-02-73   Self employed   \n",
       "15                   45                1440      5/7/1994        Salaried   \n",
       "16                   45                1440      1/6/1976   Self employed   \n",
       "17                   45                1498      27-02-83   Self employed   \n",
       "18                   45                1479     10/8/1988   Self employed   \n",
       "19                   45                1468      16-04-80   Self employed   \n",
       "20                   45                1497     1/11/1978   Self employed   \n",
       "21                   45                1498      29-02-84        Salaried   \n",
       "22                   86                1499      14-07-94   Self employed   \n",
       "23                   45                1468      31-05-79   Self employed   \n",
       "24                   45                1480      2/1/1989   Self employed   \n",
       "25                   45                1494      1/1/1986        Salaried   \n",
       "26                   45                1468      2/8/1968   Self employed   \n",
       "27                   45                1497      1/1/1976   Self employed   \n",
       "28                   45                1497     12/9/1972   Self employed   \n",
       "29                   45                1473      27-06-69   Self employed   \n",
       "...                 ...                 ...           ...             ...   \n",
       "233124              120                 701      30-09-84        Salaried   \n",
       "233125              120                 696      13-05-88        Salaried   \n",
       "233126              120                 692     2/12/1974   Self employed   \n",
       "233127              120                 701      5/3/1976   Self employed   \n",
       "233128              120                 705      6/4/1969        Salaried   \n",
       "233129              120                 696      18-07-80        Salaried   \n",
       "233130              120                 687      16-01-64        Salaried   \n",
       "233131              120                 690      31-12-94   Self employed   \n",
       "233132              120                 687      3/9/1992   Self employed   \n",
       "233133               86                 977      31-12-80        Salaried   \n",
       "233134               48                1050      1/6/1981   Self employed   \n",
       "233135               48                1051      1/1/1996        Salaried   \n",
       "233136               45                1051     2/11/1972        Salaried   \n",
       "233137               48                1050      14-09-73   Self employed   \n",
       "233138               48                 992      20-03-83        Salaried   \n",
       "233139               86                1050      14-08-81   Self employed   \n",
       "233140               48                1051      23-01-89   Self employed   \n",
       "233141               48                1067      9/1/1996        Salaried   \n",
       "233142               86                 987      3/3/1994   Self employed   \n",
       "233143               48                1051      25-01-67   Self employed   \n",
       "233144               86                1044      15-06-63        Salaried   \n",
       "233145               45                1051      23-12-85   Self employed   \n",
       "233146               48                1051      23-07-82   Self employed   \n",
       "233147               48                1051      19-06-89        Salaried   \n",
       "233148               48                1051      8/7/1993        Salaried   \n",
       "233149               48                1050      1/8/1988        Salaried   \n",
       "233150               51                 990     5/12/1988   Self employed   \n",
       "233151               86                2299      1/6/1976        Salaried   \n",
       "233152               86                2299      26-03-94        Salaried   \n",
       "233153               86                2299      18-02-84        Salaried   \n",
       "\n",
       "           ...      SEC.SANCTIONED.AMOUNT  SEC.DISBURSED.AMOUNT  \\\n",
       "0          ...                          0                     0   \n",
       "1          ...                          0                     0   \n",
       "2          ...                          0                     0   \n",
       "3          ...                          0                     0   \n",
       "4          ...                          0                     0   \n",
       "5          ...                          0                     0   \n",
       "6          ...                          0                     0   \n",
       "7          ...                          0                     0   \n",
       "8          ...                          0                     0   \n",
       "9          ...                          0                     0   \n",
       "10         ...                          0                     0   \n",
       "11         ...                          0                     0   \n",
       "12         ...                          0                     0   \n",
       "13         ...                          0                     0   \n",
       "14         ...                          0                     0   \n",
       "15         ...                          0                     0   \n",
       "16         ...                          0                     0   \n",
       "17         ...                          0                     0   \n",
       "18         ...                          0                     0   \n",
       "19         ...                          0                     0   \n",
       "20         ...                          0                     0   \n",
       "21         ...                          0                     0   \n",
       "22         ...                          0                     0   \n",
       "23         ...                          0                     0   \n",
       "24         ...                          0                     0   \n",
       "25         ...                          0                     0   \n",
       "26         ...                          0                     0   \n",
       "27         ...                          0                     0   \n",
       "28         ...                          0                     0   \n",
       "29         ...                          0                     0   \n",
       "...        ...                        ...                   ...   \n",
       "233124     ...                          0                     0   \n",
       "233125     ...                          0                     0   \n",
       "233126     ...                          0                     0   \n",
       "233127     ...                          0                     0   \n",
       "233128     ...                          0                     0   \n",
       "233129     ...                          0                     0   \n",
       "233130     ...                          0                     0   \n",
       "233131     ...                          0                     0   \n",
       "233132     ...                          0                     0   \n",
       "233133     ...                          0                     0   \n",
       "233134     ...                          0                     0   \n",
       "233135     ...                          0                     0   \n",
       "233136     ...                          0                     0   \n",
       "233137     ...                          0                     0   \n",
       "233138     ...                          0                     0   \n",
       "233139     ...                          0                     0   \n",
       "233140     ...                          0                     0   \n",
       "233141     ...                          0                     0   \n",
       "233142     ...                          0                     0   \n",
       "233143     ...                          0                     0   \n",
       "233144     ...                          0                     0   \n",
       "233145     ...                          0                     0   \n",
       "233146     ...                          0                     0   \n",
       "233147     ...                          0                     0   \n",
       "233148     ...                          0                     0   \n",
       "233149     ...                          0                     0   \n",
       "233150     ...                          0                     0   \n",
       "233151     ...                          0                     0   \n",
       "233152     ...                          0                     0   \n",
       "233153     ...                          0                     0   \n",
       "\n",
       "        PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0                        0               0                             0   \n",
       "1                     1991               0                             0   \n",
       "2                        0               0                             0   \n",
       "3                       31               0                             0   \n",
       "4                        0               0                             0   \n",
       "5                     1347               0                             0   \n",
       "6                        0               0                             0   \n",
       "7                        0               0                             0   \n",
       "8                        0               0                             0   \n",
       "9                     2608               0                             0   \n",
       "10                    2270               0                             0   \n",
       "11                       0               0                             0   \n",
       "12                    3300               0                             0   \n",
       "13                   23309               0                             1   \n",
       "14                    3514               0                             0   \n",
       "15                    7900               0                             1   \n",
       "16                   47100               0                             1   \n",
       "17                    4982               0                             1   \n",
       "18                       0               0                             0   \n",
       "19                       0               0                             0   \n",
       "20                     557               0                             1   \n",
       "21                   11083               0                             0   \n",
       "22                       0               0                             0   \n",
       "23                    1700               0                             0   \n",
       "24                       0               0                             0   \n",
       "25                       0               0                             0   \n",
       "26                       0               0                             0   \n",
       "27                       0               0                             0   \n",
       "28                       0               0                             0   \n",
       "29                    8972               0                             1   \n",
       "...                    ...             ...                           ...   \n",
       "233124                   0               0                             0   \n",
       "233125               16547               0                             1   \n",
       "233126                   0               0                             0   \n",
       "233127                   0               0                             3   \n",
       "233128               11830               0                             1   \n",
       "233129               79611               0                             1   \n",
       "233130               23356               0                             1   \n",
       "233131                8244               0                             1   \n",
       "233132               10480               0                             1   \n",
       "233133                   0               0                             0   \n",
       "233134                   0               0                             0   \n",
       "233135                   0               0                             0   \n",
       "233136                8288               0                             0   \n",
       "233137               14583               0                             1   \n",
       "233138                   0               0                             0   \n",
       "233139                   0               0                             0   \n",
       "233140               12880               0                             0   \n",
       "233141                2034               0                             0   \n",
       "233142                4665               0                             1   \n",
       "233143               12602               0                             1   \n",
       "233144                   0               0                             0   \n",
       "233145                1672               0                             2   \n",
       "233146                   0               0                             2   \n",
       "233147                   0               0                             1   \n",
       "233148                   0               0                             0   \n",
       "233149                4084               0                             0   \n",
       "233150                1565               0                             0   \n",
       "233151                   0               0                             0   \n",
       "233152                   0               0                             0   \n",
       "233153                   0               0                             0   \n",
       "\n",
       "        DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                         0       0 yrs 0 mon   \n",
       "1                                         1      1 yrs 11 mon   \n",
       "2                                         0       0 yrs 0 mon   \n",
       "3                                         0       0 yrs 8 mon   \n",
       "4                                         0       0 yrs 0 mon   \n",
       "5                                         0       1 yrs 9 mon   \n",
       "6                                         0       0 yrs 0 mon   \n",
       "7                                         0       0 yrs 2 mon   \n",
       "8                                         0       4 yrs 8 mon   \n",
       "9                                         0       1 yrs 7 mon   \n",
       "10                                        0       0 yrs 7 mon   \n",
       "11                                        0       2 yrs 1 mon   \n",
       "12                                        0       1 yrs 3 mon   \n",
       "13                                        0       1 yrs 0 mon   \n",
       "14                                        0      3 yrs 11 mon   \n",
       "15                                        0       0 yrs 1 mon   \n",
       "16                                        1       2 yrs 6 mon   \n",
       "17                                        0      1 yrs 10 mon   \n",
       "18                                        0       1 yrs 9 mon   \n",
       "19                                        0      0 yrs 11 mon   \n",
       "20                                        0       0 yrs 6 mon   \n",
       "21                                        0       2 yrs 9 mon   \n",
       "22                                        0       0 yrs 0 mon   \n",
       "23                                        0       1 yrs 3 mon   \n",
       "24                                        0       0 yrs 0 mon   \n",
       "25                                        0       0 yrs 0 mon   \n",
       "26                                        0       0 yrs 0 mon   \n",
       "27                                        0       0 yrs 0 mon   \n",
       "28                                        0       3 yrs 1 mon   \n",
       "29                                        0       2 yrs 0 mon   \n",
       "...                                     ...               ...   \n",
       "233124                                    0       0 yrs 0 mon   \n",
       "233125                                    0       2 yrs 3 mon   \n",
       "233126                                    0       0 yrs 0 mon   \n",
       "233127                                    0       1 yrs 1 mon   \n",
       "233128                                    0       0 yrs 2 mon   \n",
       "233129                                    1       2 yrs 1 mon   \n",
       "233130                                    0       4 yrs 1 mon   \n",
       "233131                                    0       0 yrs 5 mon   \n",
       "233132                                    0      0 yrs 11 mon   \n",
       "233133                                    0      6 yrs 10 mon   \n",
       "233134                                    0       0 yrs 0 mon   \n",
       "233135                                    0       0 yrs 0 mon   \n",
       "233136                                    0       4 yrs 6 mon   \n",
       "233137                                    1       1 yrs 3 mon   \n",
       "233138                                    0       0 yrs 0 mon   \n",
       "233139                                    0      0 yrs 10 mon   \n",
       "233140                                    0       3 yrs 0 mon   \n",
       "233141                                    0       0 yrs 6 mon   \n",
       "233142                                    0       0 yrs 5 mon   \n",
       "233143                                    0       1 yrs 7 mon   \n",
       "233144                                    0       0 yrs 0 mon   \n",
       "233145                                    0       0 yrs 9 mon   \n",
       "233146                                    0       0 yrs 3 mon   \n",
       "233147                                    0       0 yrs 9 mon   \n",
       "233148                                    0       0 yrs 0 mon   \n",
       "233149                                    0       1 yrs 9 mon   \n",
       "233150                                    0       0 yrs 6 mon   \n",
       "233151                                    0       0 yrs 0 mon   \n",
       "233152                                    0       0 yrs 0 mon   \n",
       "233153                                    0       0 yrs 0 mon   \n",
       "\n",
       "        CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES loan_default  \n",
       "0                 0 yrs 0 mon                0            0  \n",
       "1                1 yrs 11 mon                0            1  \n",
       "2                 0 yrs 0 mon                0            0  \n",
       "3                 1 yrs 3 mon                1            1  \n",
       "4                 0 yrs 0 mon                1            1  \n",
       "5                 2 yrs 0 mon                0            0  \n",
       "6                 0 yrs 0 mon                0            0  \n",
       "7                 0 yrs 2 mon                0            0  \n",
       "8                 4 yrs 8 mon                1            0  \n",
       "9                 1 yrs 7 mon                0            0  \n",
       "10                0 yrs 7 mon                0            1  \n",
       "11                2 yrs 3 mon                1            0  \n",
       "12                2 yrs 9 mon                0            1  \n",
       "13                2 yrs 1 mon                4            1  \n",
       "14                7 yrs 2 mon                0            1  \n",
       "15                0 yrs 1 mon                0            1  \n",
       "16                5 yrs 6 mon                0            0  \n",
       "17                4 yrs 7 mon                0            0  \n",
       "18                1 yrs 9 mon                0            1  \n",
       "19               0 yrs 11 mon                1            1  \n",
       "20               0 yrs 10 mon                1            0  \n",
       "21               5 yrs 10 mon                2            0  \n",
       "22                0 yrs 0 mon                0            0  \n",
       "23                1 yrs 3 mon                0            0  \n",
       "24                0 yrs 0 mon                0            0  \n",
       "25                0 yrs 0 mon                0            0  \n",
       "26                0 yrs 0 mon                0            1  \n",
       "27                0 yrs 0 mon                0            1  \n",
       "28                3 yrs 1 mon                1            1  \n",
       "29                5 yrs 4 mon                0            0  \n",
       "...                       ...              ...          ...  \n",
       "233124            0 yrs 0 mon                0            0  \n",
       "233125            5 yrs 5 mon                0            0  \n",
       "233126            0 yrs 0 mon                0            0  \n",
       "233127            3 yrs 2 mon                0            0  \n",
       "233128            0 yrs 2 mon                2            1  \n",
       "233129            6 yrs 2 mon                0            0  \n",
       "233130            9 yrs 5 mon                0            0  \n",
       "233131            0 yrs 9 mon                0            0  \n",
       "233132           1 yrs 11 mon                1            0  \n",
       "233133           12 yrs 5 mon                0            0  \n",
       "233134            0 yrs 0 mon                0            0  \n",
       "233135            0 yrs 0 mon                0            0  \n",
       "233136            4 yrs 6 mon                0            0  \n",
       "233137            2 yrs 4 mon                0            0  \n",
       "233138            0 yrs 0 mon                0            0  \n",
       "233139            1 yrs 2 mon                0            0  \n",
       "233140            8 yrs 2 mon                0            0  \n",
       "233141            0 yrs 6 mon                0            0  \n",
       "233142            0 yrs 8 mon                0            0  \n",
       "233143            3 yrs 0 mon                0            0  \n",
       "233144            0 yrs 0 mon                0            0  \n",
       "233145            2 yrs 6 mon                1            0  \n",
       "233146            0 yrs 3 mon                0            0  \n",
       "233147            1 yrs 0 mon                0            0  \n",
       "233148            0 yrs 0 mon                0            0  \n",
       "233149            3 yrs 3 mon                0            0  \n",
       "233150            0 yrs 6 mon                0            0  \n",
       "233151            0 yrs 0 mon                0            0  \n",
       "233152            0 yrs 0 mon                0            0  \n",
       "233153            0 yrs 0 mon                0            0  \n",
       "\n",
       "[233154 rows x 40 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop('MobileNo_Avl_Flag', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loan_default                           1.000000\n",
      "ltv                                    0.098208\n",
      "disbursed_amount                       0.077675\n",
      "State_ID                               0.048075\n",
      "VoterID_flag                           0.043747\n",
      "NO.OF_INQUIRIES                        0.043678\n",
      "PRI.OVERDUE.ACCTS                      0.040872\n",
      "DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS    0.034462\n",
      "UniqueID                               0.033848\n",
      "branch_id                              0.030193\n",
      "Current_pincode_ID                     0.028419\n",
      "supplier_id                            0.027357\n",
      "Employee_code_ID                       0.020657\n",
      "asset_cost                             0.014261\n",
      "PAN_flag                               0.002046\n",
      "SEC.OVERDUE.ACCTS                     -0.001371\n",
      "SEC.INSTAL.AMT                        -0.001548\n",
      "SEC.CURRENT.BALANCE                   -0.005531\n",
      "Driving_flag                          -0.005821\n",
      "SEC.ACTIVE.ACCTS                      -0.005993\n",
      "SEC.DISBURSED.AMOUNT                  -0.006248\n",
      "SEC.SANCTIONED.AMOUNT                 -0.006354\n",
      "Passport_flag                         -0.007602\n",
      "SEC.NO.OF.ACCTS                       -0.008385\n",
      "PRIMARY.INSTAL.AMT                    -0.010616\n",
      "PRI.DISBURSED.AMOUNT                  -0.011155\n",
      "PRI.SANCTIONED.AMOUNT                 -0.011304\n",
      "manufacturer_id                       -0.025039\n",
      "PRI.CURRENT.BALANCE                   -0.027386\n",
      "NEW.ACCTS.IN.LAST.SIX.MONTHS          -0.029400\n",
      "PRI.NO.OF.ACCTS                       -0.035456\n",
      "PRI.ACTIVE.ACCTS                      -0.041451\n",
      "Aadhar_flag                           -0.041593\n",
      "PERFORM_CNS.SCORE                     -0.057929\n",
      "MobileNo_Avl_Flag                           NaN\n",
      "Name: loan_default, dtype: float64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (corr['loan_default'].sort_values(ascending=False), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = train.corr()\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(corr,cmap='coolwarm', vmin=-1, vmax=1)\n",
    "fig.colorbar(cax)\n",
    "ticks = np.arange(0,len(train.columns),1)\n",
    "ax.set_xticks(ticks)\n",
    "plt.xticks(rotation=90)\n",
    "ax.set_yticks(ticks)\n",
    "ax.set_xticklabels(train.columns)\n",
    "ax.set_yticklabels(train.columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "branch_pivot = train.pivot_table(index='branch_id', values=\"loan_default\", aggfunc=np.mean)\n",
    "branch_pivot.plot(kind='bar', color='blue',figsize=(12,7))\n",
    "plt.xlabel(\"Branch_ID\")\n",
    "plt.ylabel(\"Loan\")\n",
    "plt.title(\"Branch_ID and Loan\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "state_pivot = train.pivot_table(index='State_ID', values=\"loan_default\", aggfunc=np.mean)\n",
    "state_pivot.plot(kind='bar', color='blue',figsize=(12,7))\n",
    "plt.xlabel(\"State_ID\")\n",
    "plt.ylabel(\"Loan\")\n",
    "plt.title(\"State_ID and Loan\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "voter_pivot = train.pivot_table(index='VoterID_flag', values=\"loan_default\", aggfunc=np.mean)\n",
    "voter_pivot.plot(kind='bar', color='blue',figsize=(12,7))\n",
    "plt.xlabel(\"Voter_ID\")\n",
    "plt.ylabel(\"Loan\")\n",
    "plt.title(\"Voter_ID and Loan \")\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6,  4,  3,  9,  5, 10, 18, 15,  1, 16, 13, 14, 17,  2, 11,  7, 12,\n",
       "        8, 20, 19, 22, 21], dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['State_ID'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['loan_default'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['loan_default'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 67,  78,  34, 130,  74,  11,   5,  20,  63,  48,  79,   3,  42,\n",
       "       142,  36,  16, 146, 147,  65,   9,   1, 152,  29,  10,  70,  19,\n",
       "         7,  85,  61,  17,   8, 153,  18, 162,  68,  72,  64,   2, 160,\n",
       "       251, 103, 104, 120, 136,  77,  13, 138, 135,  73, 248,  15, 165,\n",
       "        62,  76, 105, 249, 250, 255, 254,  82, 158, 159, 117, 202, 259,\n",
       "       207,  35,  69,  97,  43, 257, 258, 260, 111,  66, 261, 101,  14,\n",
       "       121, 217,  84, 100], dtype=int64)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['branch_id'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['branch_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22807, 22744, 17014, ..., 23618, 22289, 24031], dtype=int64)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['supplier_id'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2953"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['supplier_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-197-bb84c9bdce55>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Employee_code_ID'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "train['Employee_code_ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['manufacturer_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['State_ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['SEC.NO.OF.ACCTS'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['SEC.ACTIVE.ACCTS'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['SEC.OVERDUE.ACCTS'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=[0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>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>...</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>1/1/1984</td>\n",
       "      <td>Salaried</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</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>...</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>1 yrs 11 mon</td>\n",
       "      <td>1 yrs 11 mon</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>...</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</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>...</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>0 yrs 8 mon</td>\n",
       "      <td>1 yrs 3 mon</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>9/12/1977</td>\n",
       "      <td>Self employed</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
       "0    420825             50578       58400  89.55         67        22807   \n",
       "1    537409             47145       65550  73.23         67        22807   \n",
       "2    417566             53278       61360  89.63         67        22807   \n",
       "3    624493             57513       66113  88.48         67        22807   \n",
       "4    539055             52378       60300  88.39         67        22807   \n",
       "\n",
       "   manufacturer_id  Current_pincode_ID Date.of.Birth Employment.Type  \\\n",
       "0               45                1441      1/1/1984        Salaried   \n",
       "1               45                1502      31-07-85   Self employed   \n",
       "2               45                1497      24-08-85   Self employed   \n",
       "3               45                1501      30-12-93   Self employed   \n",
       "4               45                1495     9/12/1977   Self employed   \n",
       "\n",
       "       ...      SEC.SANCTIONED.AMOUNT  SEC.DISBURSED.AMOUNT  \\\n",
       "0      ...                          0                     0   \n",
       "1      ...                          0                     0   \n",
       "2      ...                          0                     0   \n",
       "3      ...                          0                     0   \n",
       "4      ...                          0                     0   \n",
       "\n",
       "   PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0                   0               0                             0   \n",
       "1                1991               0                             0   \n",
       "2                   0               0                             0   \n",
       "3                  31               0                             0   \n",
       "4                   0               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0       0 yrs 0 mon   \n",
       "1                                    1      1 yrs 11 mon   \n",
       "2                                    0       0 yrs 0 mon   \n",
       "3                                    0       0 yrs 8 mon   \n",
       "4                                    0       0 yrs 0 mon   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \n",
       "0            0 yrs 0 mon                0             0  \n",
       "1           1 yrs 11 mon                0             1  \n",
       "2            0 yrs 0 mon                0             0  \n",
       "3            1 yrs 3 mon                1             1  \n",
       "4            0 yrs 0 mon                1             1  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No Bureau History Available                                116950\n",
       "C-Very Low Risk                                             16045\n",
       "A-Very Low Risk                                             14124\n",
       "D-Very Low Risk                                             11358\n",
       "B-Very Low Risk                                              9201\n",
       "M-Very High Risk                                             8776\n",
       "F-Low Risk                                                   8485\n",
       "K-High Risk                                                  8277\n",
       "H-Medium Risk                                                6855\n",
       "E-Low Risk                                                   5821\n",
       "I-Medium Risk                                                5557\n",
       "G-Low Risk                                                   3988\n",
       "Not Scored: Sufficient History Not Available                 3765\n",
       "J-High Risk                                                  3748\n",
       "Not Scored: Not Enough Info available on the customer        3672\n",
       "Not Scored: No Activity seen on the customer (Inactive)      2885\n",
       "Not Scored: No Updates available in last 36 months           1534\n",
       "L-Very High Risk                                             1134\n",
       "Not Scored: Only a Guarantor                                  976\n",
       "Not Scored: More than 50 active Accounts found                  3\n",
       "Name: PERFORM_CNS.SCORE.DESCRIPTION, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['PERFORM_CNS.SCORE.DESCRIPTION'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UniqueID                                 int64\n",
       "disbursed_amount                         int64\n",
       "asset_cost                               int64\n",
       "ltv                                    float64\n",
       "branch_id                                int64\n",
       "supplier_id                              int64\n",
       "manufacturer_id                          int64\n",
       "Current_pincode_ID                       int64\n",
       "Date.of.Birth                           object\n",
       "Employment.Type                         object\n",
       "DisbursalDate                           object\n",
       "State_ID                                 int64\n",
       "Employee_code_ID                         int64\n",
       "MobileNo_Avl_Flag                        int64\n",
       "Aadhar_flag                              int64\n",
       "PAN_flag                                 int64\n",
       "VoterID_flag                             int64\n",
       "Driving_flag                             int64\n",
       "Passport_flag                            int64\n",
       "PERFORM_CNS.SCORE                        int64\n",
       "PERFORM_CNS.SCORE.DESCRIPTION           object\n",
       "PRI.NO.OF.ACCTS                          int64\n",
       "PRI.ACTIVE.ACCTS                         int64\n",
       "PRI.OVERDUE.ACCTS                        int64\n",
       "PRI.CURRENT.BALANCE                      int64\n",
       "PRI.SANCTIONED.AMOUNT                    int64\n",
       "PRI.DISBURSED.AMOUNT                     int64\n",
       "SEC.NO.OF.ACCTS                          int64\n",
       "SEC.ACTIVE.ACCTS                         int64\n",
       "SEC.OVERDUE.ACCTS                        int64\n",
       "SEC.CURRENT.BALANCE                      int64\n",
       "SEC.SANCTIONED.AMOUNT                    int64\n",
       "SEC.DISBURSED.AMOUNT                     int64\n",
       "PRIMARY.INSTAL.AMT                       int64\n",
       "SEC.INSTAL.AMT                           int64\n",
       "NEW.ACCTS.IN.LAST.SIX.MONTHS             int64\n",
       "DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS      int64\n",
       "AVERAGE.ACCT.AGE                        object\n",
       "CREDIT.HISTORY.LENGTH                   object\n",
       "NO.OF_INQUIRIES                          int64\n",
       "loan_default                             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['PERFORM_CNS.SCORE.DESCRIPTION'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Date.of.Birth']= pd.to_datetime(train['Date.of.Birth']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['DisbursalDate']= pd.to_datetime(train['DisbursalDate']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>disbursed_amount</th>\n",
       "      <th>asset_cost</th>\n",
       "      <th>ltv</th>\n",
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       "      <td>1441</td>\n",
       "      <td>1984-01-01</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>537409</td>\n",
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       "      <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>1985-07-31</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</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>1 yrs 11 mon</td>\n",
       "      <td>1 yrs 11 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>61360</td>\n",
       "      <td>89.63</td>\n",
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       "      <td>45</td>\n",
       "      <td>1497</td>\n",
       "      <td>1985-08-24</td>\n",
       "      <td>Self employed</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 yrs 0 mon</td>\n",
       "      <td>0 yrs 0 mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "      <td>45</td>\n",
       "      <td>1501</td>\n",
       "      <td>1993-12-30</td>\n",
       "      <td>Self employed</td>\n",
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       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 yrs 8 mon</td>\n",
       "      <td>1 yrs 3 mon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>539055</td>\n",
       "      <td>52378</td>\n",
       "      <td>60300</td>\n",
       "      <td>88.39</td>\n",
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       "      <td>0 yrs 0 mon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "<p>5 rows × 41 columns</p>\n",
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      ],
      "text/plain": [
       "   UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
       "0    420825             50578       58400  89.55         67        22807   \n",
       "1    537409             47145       65550  73.23         67        22807   \n",
       "2    417566             53278       61360  89.63         67        22807   \n",
       "3    624493             57513       66113  88.48         67        22807   \n",
       "4    539055             52378       60300  88.39         67        22807   \n",
       "\n",
       "   manufacturer_id  Current_pincode_ID Date.of.Birth Employment.Type  \\\n",
       "0               45                1441    1984-01-01        Salaried   \n",
       "1               45                1502    1985-07-31   Self employed   \n",
       "2               45                1497    1985-08-24   Self employed   \n",
       "3               45                1501    1993-12-30   Self employed   \n",
       "4               45                1495    1977-09-12   Self employed   \n",
       "\n",
       "       ...      SEC.SANCTIONED.AMOUNT  SEC.DISBURSED.AMOUNT  \\\n",
       "0      ...                          0                     0   \n",
       "1      ...                          0                     0   \n",
       "2      ...                          0                     0   \n",
       "3      ...                          0                     0   \n",
       "4      ...                          0                     0   \n",
       "\n",
       "   PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0                   0               0                             0   \n",
       "1                1991               0                             0   \n",
       "2                   0               0                             0   \n",
       "3                  31               0                             0   \n",
       "4                   0               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0       0 yrs 0 mon   \n",
       "1                                    1      1 yrs 11 mon   \n",
       "2                                    0       0 yrs 0 mon   \n",
       "3                                    0       0 yrs 8 mon   \n",
       "4                                    0       0 yrs 0 mon   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \n",
       "0            0 yrs 0 mon                0             0  \n",
       "1           1 yrs 11 mon                0             1  \n",
       "2            0 yrs 0 mon                0             0  \n",
       "3            1 yrs 3 mon                1             1  \n",
       "4            0 yrs 0 mon                1             1  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop('MobileNo_Avl_Flag', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
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       "4    539055             52378       60300  88.39         67        22807   \n",
       "\n",
       "   manufacturer_id  Current_pincode_ID Date.of.Birth Employment.Type  \\\n",
       "0               45                1441    1984-01-01        Salaried   \n",
       "1               45                1502    1985-07-31   Self employed   \n",
       "2               45                1497    1985-08-24   Self employed   \n",
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       "4               45                1495    1977-09-12   Self employed   \n",
       "\n",
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       "2                   0               0                             0   \n",
       "3                  31               0                             0   \n",
       "4                   0               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0                 0   \n",
       "1                                    1                23   \n",
       "2                                    0                 0   \n",
       "3                                    0                 8   \n",
       "4                                    0                 0   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES loan_default  \n",
       "0                      0                0            0  \n",
       "1                     23                0            1  \n",
       "2                      0                0            0  \n",
       "3                     15                1            1  \n",
       "4                      0                1            1  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
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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",
       "    </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>1993-12-30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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>8</td>\n",
       "      <td>15</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>1977-09-12</td>\n",
       "      <td>0.0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
       "0    420825             50578       58400  89.55         67        22807   \n",
       "1    537409             47145       65550  73.23         67        22807   \n",
       "2    417566             53278       61360  89.63         67        22807   \n",
       "3    624493             57513       66113  88.48         67        22807   \n",
       "4    539055             52378       60300  88.39         67        22807   \n",
       "\n",
       "   manufacturer_id  Current_pincode_ID Date.of.Birth  Employment.Type  \\\n",
       "0               45                1441    1984-01-01              1.0   \n",
       "1               45                1502    1985-07-31              0.0   \n",
       "2               45                1497    1985-08-24              0.0   \n",
       "3               45                1501    1993-12-30              0.0   \n",
       "4               45                1495    1977-09-12              0.0   \n",
       "\n",
       "      ...      SEC.SANCTIONED.AMOUNT  SEC.DISBURSED.AMOUNT  \\\n",
       "0     ...                          0                     0   \n",
       "1     ...                          0                     0   \n",
       "2     ...                          0                     0   \n",
       "3     ...                          0                     0   \n",
       "4     ...                          0                     0   \n",
       "\n",
       "   PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0                   0               0                             0   \n",
       "1                1991               0                             0   \n",
       "2                   0               0                             0   \n",
       "3                  31               0                             0   \n",
       "4                   0               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0                 0   \n",
       "1                                    1                23   \n",
       "2                                    0                 0   \n",
       "3                                    0                 8   \n",
       "4                                    0                 0   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES loan_default  \n",
       "0                      0                0            0  \n",
       "1                     23                0            1  \n",
       "2                      0                0            0  \n",
       "3                     15                1            1  \n",
       "4                      0                1            1  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict = {'Self employed':0, 'Salaried':1}\n",
    "train.replace(dict, inplace=True)\n",
    "train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     227289\n",
       "1       3466\n",
       "2       1036\n",
       "3        444\n",
       "4        292\n",
       "5        148\n",
       "6        119\n",
       "7         75\n",
       "8         68\n",
       "9         38\n",
       "10        35\n",
       "11        29\n",
       "13        17\n",
       "12        13\n",
       "16        11\n",
       "14        11\n",
       "15        10\n",
       "18         6\n",
       "19         6\n",
       "17         5\n",
       "20         4\n",
       "22         4\n",
       "23         4\n",
       "31         4\n",
       "21         3\n",
       "46         2\n",
       "24         2\n",
       "38         2\n",
       "30         2\n",
       "34         2\n",
       "37         1\n",
       "42         1\n",
       "28         1\n",
       "35         1\n",
       "29         1\n",
       "25         1\n",
       "52         1\n",
       "Name: SEC.NO.OF.ACCTS, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['SEC.NO.OF.ACCTS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     229337\n",
       "1       2684\n",
       "2        636\n",
       "3        195\n",
       "4        116\n",
       "5         65\n",
       "6         32\n",
       "7         22\n",
       "8         17\n",
       "9         11\n",
       "10         9\n",
       "11         7\n",
       "12         6\n",
       "15         4\n",
       "13         3\n",
       "16         2\n",
       "22         2\n",
       "26         1\n",
       "14         1\n",
       "17         1\n",
       "20         1\n",
       "21         1\n",
       "36         1\n",
       "Name: SEC.ACTIVE.ACCTS, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['SEC.ACTIVE.ACCTS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    231817\n",
       "1      1129\n",
       "2       126\n",
       "3        47\n",
       "4        19\n",
       "5         8\n",
       "6         6\n",
       "8         1\n",
       "7         1\n",
       "Name: SEC.OVERDUE.ACCTS, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['SEC.OVERDUE.ACCTS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0          229790\n",
       " 800            10\n",
       " 100             8\n",
       " 400             8\n",
       " 1200            6\n",
       " 589             6\n",
       "-1               5\n",
       " 1070            4\n",
       " 1               4\n",
       " 1600            4\n",
       " 5000            3\n",
       " 20000           3\n",
       " 1755            3\n",
       " 72000           3\n",
       " 26127           3\n",
       " 30000           3\n",
       " 4000            3\n",
       " 12950           3\n",
       " 1398            2\n",
       " 5625            2\n",
       " 13500           2\n",
       " 44000           2\n",
       " 5700            2\n",
       " 46000           2\n",
       " 8               2\n",
       " 11120           2\n",
       " 9014            2\n",
       " 3183            2\n",
       " 5100            2\n",
       " 5900            2\n",
       "             ...  \n",
       " 1007672         1\n",
       " 1441812         1\n",
       " 1701741         1\n",
       " 647016          1\n",
       " 274515          1\n",
       " 1603802         1\n",
       " 9937            1\n",
       " 325962          1\n",
       " 224952          1\n",
       " 358011          1\n",
       " 421400          1\n",
       " 2989542         1\n",
       " 1580502         1\n",
       " 40390           1\n",
       " 11600           1\n",
       " 50509           1\n",
       " 1109            1\n",
       " 15442           1\n",
       " 314444          1\n",
       " 238625          1\n",
       " 23518           1\n",
       " 19420           1\n",
       " 455563          1\n",
       " 201527          1\n",
       " 2772            1\n",
       " 62155           1\n",
       " 1722908         1\n",
       " 225723          1\n",
       " 22878           1\n",
       " 195358          1\n",
       "Name: SEC.CURRENT.BALANCE, Length: 3246, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['SEC.CURRENT.BALANCE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(['SEC.NO.OF.ACCTS','SEC.ACTIVE.ACCTS','SEC.OVERDUE.ACCTS','SEC.CURRENT.BALANCE','SEC.SANCTIONED.AMOUNT','SEC.DISBURSED.AMOUNT'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>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>...</th>\n",
       "      <th>PRI.SANCTIONED.AMOUNT</th>\n",
       "      <th>PRI.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>1984-01-01</td>\n",
       "      <td>Salaried</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>0</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>1985-07-31</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</td>\n",
       "      <td>50200</td>\n",
       "      <td>50200</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>23</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>1985-08-24</td>\n",
       "      <td>Self employed</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>0</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>1993-12-30</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>...</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>8</td>\n",
       "      <td>15</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>1977-09-12</td>\n",
       "      <td>Self employed</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
       "0    420825             50578       58400  89.55         67        22807   \n",
       "1    537409             47145       65550  73.23         67        22807   \n",
       "2    417566             53278       61360  89.63         67        22807   \n",
       "3    624493             57513       66113  88.48         67        22807   \n",
       "4    539055             52378       60300  88.39         67        22807   \n",
       "\n",
       "   manufacturer_id  Current_pincode_ID Date.of.Birth Employment.Type  \\\n",
       "0               45                1441    1984-01-01        Salaried   \n",
       "1               45                1502    1985-07-31   Self employed   \n",
       "2               45                1497    1985-08-24   Self employed   \n",
       "3               45                1501    1993-12-30   Self employed   \n",
       "4               45                1495    1977-09-12   Self employed   \n",
       "\n",
       "      ...      PRI.SANCTIONED.AMOUNT  PRI.DISBURSED.AMOUNT  \\\n",
       "0     ...                          0                     0   \n",
       "1     ...                      50200                 50200   \n",
       "2     ...                          0                     0   \n",
       "3     ...                          0                     0   \n",
       "4     ...                          0                     0   \n",
       "\n",
       "   PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0                   0               0                             0   \n",
       "1                1991               0                             0   \n",
       "2                   0               0                             0   \n",
       "3                  31               0                             0   \n",
       "4                   0               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0                 0   \n",
       "1                                    1                23   \n",
       "2                                    0                 0   \n",
       "3                                    0                 8   \n",
       "4                                    0                 0   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES loan_default  \n",
       "0                      0                0            0  \n",
       "1                     23                0            1  \n",
       "2                      0                0            0  \n",
       "3                     15                1            1  \n",
       "4                      0                1            1  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['PERFORM_CNS.SCORE.DESCRIPTION'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No Bureau History Available                                116950\n",
       "C-Very Low Risk                                             16045\n",
       "A-Very Low Risk                                             14124\n",
       "D-Very Low Risk                                             11358\n",
       "B-Very Low Risk                                              9201\n",
       "M-Very High Risk                                             8776\n",
       "F-Low Risk                                                   8485\n",
       "K-High Risk                                                  8277\n",
       "H-Medium Risk                                                6855\n",
       "E-Low Risk                                                   5821\n",
       "I-Medium Risk                                                5557\n",
       "G-Low Risk                                                   3988\n",
       "Not Scored: Sufficient History Not Available                 3765\n",
       "J-High Risk                                                  3748\n",
       "Not Scored: Not Enough Info available on the customer        3672\n",
       "Not Scored: No Activity seen on the customer (Inactive)      2885\n",
       "Not Scored: No Updates available in last 36 months           1534\n",
       "L-Very High Risk                                             1134\n",
       "Not Scored: Only a Guarantor                                  976\n",
       "Not Scored: More than 50 active Accounts found                  3\n",
       "Name: PERFORM_CNS.SCORE.DESCRIPTION, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['PERFORM_CNS.SCORE.DESCRIPTION'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['PERFORM_CNS.SCORE.DESCRIPTION'] = train['PERFORM_CNS.SCORE.DESCRIPTION'].map({'No Bureau History Available':0, \n",
    "'C-Very Low Risk':1, 'A-Very Low Risk':2, 'D-Very Low Risk':3, 'B-Very Low Risk': 4, 'M-Very High Risk':5, 'F-Low Risk':6, \n",
    "'K-High Risk':7, 'H-Medium Risk':8, 'E-Low Risk':9, 'I-Medium Risk':10, 'G-Low Risk':11, \n",
    "'Not Scored: Sufficient History Not Available': 12, 'J-High Risk':13, 'Not Scored: Not Enough Info available on the customer':14,\n",
    "'Not Scored: No Activity seen on the customer (Inactive)':15, 'Not Scored: No Updates available in last 36 months':16, \n",
    "'L-Very High Risk':17, 'Not Scored: Only a Guarantor':18, 'Not Scored: More than 50 active Accounts found':19})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "          ..\n",
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       "233149     3\n",
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       "233151     0\n",
       "233152     0\n",
       "233153     0\n",
       "Name: PERFORM_CNS.SCORE.DESCRIPTION, Length: 233154, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['PERFORM_CNS.SCORE.DESCRIPTION']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Self employed    127635\n",
       "Salaried          97858\n",
       "Name: Employment.Type, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Employment.Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Employment.Type'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Employment.Type_random'] = train['Employment.Type']\n",
    "random_sample_train = train['Employment.Type'].dropna().sample(train['Employment.Type'].isnull().sum(),random_state = 0)\n",
    "random_sample_train.index = train[train['Employment.Type'].isnull()].index\n",
    "train.loc[train['Employment.Type'].isnull(), 'Employment.Type_random'] = random_sample_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Self employed    131918\n",
       "Salaried         101236\n",
       "Name: Employment.Type_random, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Employment.Type_random'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Self employed    131918\n",
       "Salaried         101236\n",
       "Name: Employment.Type_random, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Employment.Type_random'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Self employed    127635\n",
       "Salaried          97858\n",
       "Name: Employment.Type, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Employment.Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Employment.Type'] = train['Employment.Type_random']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <td>60300</td>\n",
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       "   UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
       "0    420825             50578       58400  89.55         67        22807   \n",
       "1    537409             47145       65550  73.23         67        22807   \n",
       "2    417566             53278       61360  89.63         67        22807   \n",
       "3    624493             57513       66113  88.48         67        22807   \n",
       "4    539055             52378       60300  88.39         67        22807   \n",
       "\n",
       "   manufacturer_id  Current_pincode_ID Date.of.Birth Employment.Type  \\\n",
       "0               45                1441    1984-01-01        Salaried   \n",
       "1               45                1502    1985-07-31   Self employed   \n",
       "2               45                1497    1985-08-24   Self employed   \n",
       "3               45                1501    1993-12-30   Self employed   \n",
       "4               45                1495    1977-09-12   Self employed   \n",
       "\n",
       "            ...           PRI.DISBURSED.AMOUNT  PRIMARY.INSTAL.AMT  \\\n",
       "0           ...                              0                   0   \n",
       "1           ...                          50200                1991   \n",
       "2           ...                              0                   0   \n",
       "3           ...                              0                  31   \n",
       "4           ...                              0                   0   \n",
       "\n",
       "   SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0               0                             0   \n",
       "1               0                             0   \n",
       "2               0                             0   \n",
       "3               0                             0   \n",
       "4               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0                 0   \n",
       "1                                    1                23   \n",
       "2                                    0                 0   \n",
       "3                                    0                 8   \n",
       "4                                    0                 0   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \\\n",
       "0                      0                0             0   \n",
       "1                     23                0             1   \n",
       "2                      0                0             0   \n",
       "3                     15                1             1   \n",
       "4                      0                1             1   \n",
       "\n",
       "   Employment.Type_random  \n",
       "0                Salaried  \n",
       "1           Self employed  \n",
       "2           Self employed  \n",
       "3           Self employed  \n",
       "4           Self employed  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Self employed    131918\n",
       "Salaried         101236\n",
       "Name: Employment.Type, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Employment.Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Employment.Type'] = train['Employment.Type'].map({'Salaried':0, 'Self employed':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      ],
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       "   UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
       "0    420825             50578       58400  89.55         67        22807   \n",
       "1    537409             47145       65550  73.23         67        22807   \n",
       "2    417566             53278       61360  89.63         67        22807   \n",
       "3    624493             57513       66113  88.48         67        22807   \n",
       "4    539055             52378       60300  88.39         67        22807   \n",
       "\n",
       "   manufacturer_id  Current_pincode_ID Date.of.Birth  Employment.Type  \\\n",
       "0               45                1441    1984-01-01                0   \n",
       "1               45                1502    1985-07-31                1   \n",
       "2               45                1497    1985-08-24                1   \n",
       "3               45                1501    1993-12-30                1   \n",
       "4               45                1495    1977-09-12                1   \n",
       "\n",
       "            ...           PRI.DISBURSED.AMOUNT  PRIMARY.INSTAL.AMT  \\\n",
       "0           ...                              0                   0   \n",
       "1           ...                          50200                1991   \n",
       "2           ...                              0                   0   \n",
       "3           ...                              0                  31   \n",
       "4           ...                              0                   0   \n",
       "\n",
       "   SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0               0                             0   \n",
       "1               0                             0   \n",
       "2               0                             0   \n",
       "3               0                             0   \n",
       "4               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0                 0   \n",
       "1                                    1                23   \n",
       "2                                    0                 0   \n",
       "3                                    0                 8   \n",
       "4                                    0                 0   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \\\n",
       "0                      0                0             0   \n",
       "1                     23                0             1   \n",
       "2                      0                0             0   \n",
       "3                     15                1             1   \n",
       "4                      0                1             1   \n",
       "\n",
       "   Employment.Type_random  \n",
       "0                Salaried  \n",
       "1           Self employed  \n",
       "2           Self employed  \n",
       "3           Self employed  \n",
       "4           Self employed  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('train_updated.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop('Employment.Type_random', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
       "0    420825             50578       58400  89.55         67        22807   \n",
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       "1               45                1502    1985-07-31                1   \n",
       "2               45                1497    1985-08-24                1   \n",
       "3               45                1501    1993-12-30                1   \n",
       "4               45                1495    1977-09-12                1   \n",
       "\n",
       "       ...      PRI.SANCTIONED.AMOUNT  PRI.DISBURSED.AMOUNT  \\\n",
       "0      ...                          0                     0   \n",
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       "2      ...                          0                     0   \n",
       "3      ...                          0                     0   \n",
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       "   PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
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       "1                1991               0                             0   \n",
       "2                   0               0                             0   \n",
       "3                  31               0                             0   \n",
       "4                   0               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0                 0   \n",
       "1                                    1                23   \n",
       "2                                    0                 0   \n",
       "3                                    0                 8   \n",
       "4                                    0                 0   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \n",
       "0                      0                0             0  \n",
       "1                     23                0             1  \n",
       "2                      0                0             0  \n",
       "3                     15                1             1  \n",
       "4                      0                1             1  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('train_updated.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(['VoterID_flag', 'PRI.DISBURSED.AMOUNT'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "text/plain": [
       "   UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
       "0    420825             50578       58400  89.55         67        22807   \n",
       "1    537409             47145       65550  73.23         67        22807   \n",
       "2    417566             53278       61360  89.63         67        22807   \n",
       "3    624493             57513       66113  88.48         67        22807   \n",
       "4    539055             52378       60300  88.39         67        22807   \n",
       "\n",
       "   manufacturer_id  Current_pincode_ID Date.of.Birth  Employment.Type  \\\n",
       "0               45                1441    1984-01-01                0   \n",
       "1               45                1502    1985-07-31                1   \n",
       "2               45                1497    1985-08-24                1   \n",
       "3               45                1501    1993-12-30                1   \n",
       "4               45                1495    1977-09-12                1   \n",
       "\n",
       "       ...      PRI.CURRENT.BALANCE  PRI.SANCTIONED.AMOUNT  \\\n",
       "0      ...                        0                      0   \n",
       "1      ...                    27600                  50200   \n",
       "2      ...                        0                      0   \n",
       "3      ...                        0                      0   \n",
       "4      ...                        0                      0   \n",
       "\n",
       "   PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0                   0               0                             0   \n",
       "1                1991               0                             0   \n",
       "2                   0               0                             0   \n",
       "3                  31               0                             0   \n",
       "4                   0               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0                 0   \n",
       "1                                    1                23   \n",
       "2                                    0                 0   \n",
       "3                                    0                 8   \n",
       "4                                    0                 0   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \n",
       "0                      0                0             0  \n",
       "1                     23                0             1  \n",
       "2                      0                0             0  \n",
       "3                     15                1             1  \n",
       "4                      0                1             1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25123db0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=train['disbursed_amount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf5a2bd0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=train['asset_cost'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf380f10>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=train['ltv'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18317    1432\n",
       "15694    1300\n",
       "15663    1275\n",
       "17980    1268\n",
       "14234    1258\n",
       "18166    1210\n",
       "21980    1125\n",
       "14375    1119\n",
       "22727    1062\n",
       "14145    1060\n",
       "14115    1042\n",
       "21124    1032\n",
       "18532    1009\n",
       "18473     976\n",
       "14347     963\n",
       "14178     887\n",
       "15142     816\n",
       "14770     795\n",
       "21043     764\n",
       "14791     762\n",
       "16633     737\n",
       "20470     714\n",
       "21003     710\n",
       "21556     710\n",
       "20292     707\n",
       "14078     686\n",
       "16565     678\n",
       "23137     661\n",
       "14293     659\n",
       "16694     641\n",
       "         ... \n",
       "15490       1\n",
       "23438       1\n",
       "23575       1\n",
       "24679       1\n",
       "21963       1\n",
       "24592       1\n",
       "16677       1\n",
       "23463       1\n",
       "22562       1\n",
       "24715       1\n",
       "24498       1\n",
       "20943       1\n",
       "20613       1\n",
       "20869       1\n",
       "24659       1\n",
       "23551       1\n",
       "22751       1\n",
       "24742       1\n",
       "22421       1\n",
       "24084       1\n",
       "21204       1\n",
       "21683       1\n",
       "23188       1\n",
       "24629       1\n",
       "21847       1\n",
       "22811       1\n",
       "23189       1\n",
       "23712       1\n",
       "15045       1\n",
       "18019       1\n",
       "Name: supplier_id, Length: 2953, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['supplier_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2953"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['supplier_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['branch_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['manufacturer_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86     109534\n",
       "45      56626\n",
       "51      27204\n",
       "48      16710\n",
       "49      10220\n",
       "120      9658\n",
       "67       2405\n",
       "145       778\n",
       "153        12\n",
       "152         6\n",
       "156         1\n",
       "Name: manufacturer_id, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['manufacturer_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = train[(train['manufacturer_id'] == 156)].index\n",
    "train.drop(index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(233153, 32)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2523add0>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=train['disbursed_amount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    233153.000000\n",
       "mean      54352.991877\n",
       "std       12826.618894\n",
       "min       13320.000000\n",
       "25%       47145.000000\n",
       "50%       53803.000000\n",
       "75%       60413.000000\n",
       "max      990572.000000\n",
       "Name: disbursed_amount, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.disbursed_amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100217.0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR = train.disbursed_amount.quantile(0.75) - train.disbursed_amount.quantile(0.25)\n",
    "upper_limit = train.disbursed_amount.quantile(0.75) + (IQR * 3)\n",
    "upper_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "233153"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "833"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.disbursed_amount>upper_limit].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0035727612340394504"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.disbursed_amount>upper_limit].shape[0]/np.float(train.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100217.0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[train.disbursed_amount>upper_limit, 'disbursed_amount'] = upper_limit \n",
    "train.disbursed_amount.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x33934cf0>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=train['asset_cost'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.331530e+05\n",
       "mean     7.585969e+04\n",
       "std      1.876623e+04\n",
       "min      3.700000e+04\n",
       "25%      6.571700e+04\n",
       "50%      7.094600e+04\n",
       "75%      7.920100e+04\n",
       "max      1.628992e+06\n",
       "Name: asset_cost, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.asset_cost.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(['asset_cost','disbursed_amount'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>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>...</th>\n",
       "      <th>PRI.CURRENT.BALANCE</th>\n",
       "      <th>PRI.SANCTIONED.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>89.55</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1441</td>\n",
       "      <td>1984-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-03-08</td>\n",
       "      <td>6</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>537409</td>\n",
       "      <td>73.23</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1502</td>\n",
       "      <td>1985-07-31</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>27600</td>\n",
       "      <td>50200</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>417566</td>\n",
       "      <td>89.63</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1497</td>\n",
       "      <td>1985-08-24</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>6</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>624493</td>\n",
       "      <td>88.48</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1501</td>\n",
       "      <td>1993-12-30</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-10-26</td>\n",
       "      <td>6</td>\n",
       "      <td>...</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>8</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>539055</td>\n",
       "      <td>88.39</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1495</td>\n",
       "      <td>1977-09-12</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>6</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UniqueID    ltv  branch_id  supplier_id  manufacturer_id  \\\n",
       "0    420825  89.55         67        22807               45   \n",
       "1    537409  73.23         67        22807               45   \n",
       "2    417566  89.63         67        22807               45   \n",
       "3    624493  88.48         67        22807               45   \n",
       "4    539055  88.39         67        22807               45   \n",
       "\n",
       "   Current_pincode_ID Date.of.Birth  Employment.Type DisbursalDate  State_ID  \\\n",
       "0                1441    1984-01-01                0    2018-03-08         6   \n",
       "1                1502    1985-07-31                1    2018-09-26         6   \n",
       "2                1497    1985-08-24                1    2018-01-08         6   \n",
       "3                1501    1993-12-30                1    2018-10-26         6   \n",
       "4                1495    1977-09-12                1    2018-09-26         6   \n",
       "\n",
       "       ...       PRI.CURRENT.BALANCE  PRI.SANCTIONED.AMOUNT  \\\n",
       "0      ...                         0                      0   \n",
       "1      ...                     27600                  50200   \n",
       "2      ...                         0                      0   \n",
       "3      ...                         0                      0   \n",
       "4      ...                         0                      0   \n",
       "\n",
       "   PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0                   0               0                             0   \n",
       "1                1991               0                             0   \n",
       "2                   0               0                             0   \n",
       "3                  31               0                             0   \n",
       "4                   0               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0                 0   \n",
       "1                                    1                23   \n",
       "2                                    0                 0   \n",
       "3                                    0                 8   \n",
       "4                                    0                 0   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \n",
       "0                      0                0             0  \n",
       "1                     23                0             1  \n",
       "2                      0                0             0  \n",
       "3                     15                1             1  \n",
       "4                      0                1             1  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3270"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Employee_code_ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x37a6cf70>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train.manufacturer_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x37afb890>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train.State_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "State_ID\n",
      "1      3.832676\n",
      "2      1.784236\n",
      "3     14.616153\n",
      "4     19.244445\n",
      "5      4.364945\n",
      "6     14.370392\n",
      "7      2.910535\n",
      "8      6.089135\n",
      "9      6.871882\n",
      "10     1.546195\n",
      "11     2.882656\n",
      "12     1.805681\n",
      "13     7.670500\n",
      "14     4.037692\n",
      "15     2.165531\n",
      "16     1.151604\n",
      "17     1.711752\n",
      "18     2.321223\n",
      "19     0.443915\n",
      "20     0.079347\n",
      "21     0.066909\n",
      "22     0.032597\n",
      "Name: State_ID, dtype: float64\n",
      "\n",
      "manufacturer_id\n",
      "45     24.287056\n",
      "48      7.166968\n",
      "49      4.383388\n",
      "51     11.667875\n",
      "67      1.031511\n",
      "86     46.979451\n",
      "120     4.142344\n",
      "145     0.333686\n",
      "152     0.002573\n",
      "153     0.005147\n",
      "Name: manufacturer_id, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cols = ['State_ID','manufacturer_id']\n",
    "for cols in cols: \n",
    "    print(train.groupby(cols)[cols].count()/np.float(len(train))*100)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>loan_default</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manufacturer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>43687</td>\n",
       "      <td>12939</td>\n",
       "      <td>56626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>12156</td>\n",
       "      <td>4554</td>\n",
       "      <td>16710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>7984</td>\n",
       "      <td>2236</td>\n",
       "      <td>10220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>21547</td>\n",
       "      <td>5657</td>\n",
       "      <td>27204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1882</td>\n",
       "      <td>523</td>\n",
       "      <td>2405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>87124</td>\n",
       "      <td>22410</td>\n",
       "      <td>109534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>7526</td>\n",
       "      <td>2132</td>\n",
       "      <td>9658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>622</td>\n",
       "      <td>156</td>\n",
       "      <td>778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>182543</td>\n",
       "      <td>50611</td>\n",
       "      <td>233154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "loan_default          0      1     All\n",
       "manufacturer_id                       \n",
       "45                43687  12939   56626\n",
       "48                12156   4554   16710\n",
       "49                 7984   2236   10220\n",
       "51                21547   5657   27204\n",
       "67                 1882    523    2405\n",
       "86                87124  22410  109534\n",
       "120                7526   2132    9658\n",
       "145                 622    156     778\n",
       "152                   6      0       6\n",
       "153                   8      4      12\n",
       "156                   1      0       1\n",
       "All              182543  50611  233154"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_table = pd.crosstab(\n",
    "    train['manufacturer_id'],\n",
    "    train['loan_default'],\n",
    "    margins = True\n",
    ")\n",
    "contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>loan_default</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7353</td>\n",
       "      <td>1583</td>\n",
       "      <td>8936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3031</td>\n",
       "      <td>1129</td>\n",
       "      <td>4160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27733</td>\n",
       "      <td>6345</td>\n",
       "      <td>34078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35544</td>\n",
       "      <td>9326</td>\n",
       "      <td>44870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8154</td>\n",
       "      <td>2023</td>\n",
       "      <td>10177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26615</td>\n",
       "      <td>6890</td>\n",
       "      <td>33505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5417</td>\n",
       "      <td>1369</td>\n",
       "      <td>6786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10939</td>\n",
       "      <td>3258</td>\n",
       "      <td>14197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12530</td>\n",
       "      <td>3492</td>\n",
       "      <td>16022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2972</td>\n",
       "      <td>633</td>\n",
       "      <td>3605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5348</td>\n",
       "      <td>1373</td>\n",
       "      <td>6721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3092</td>\n",
       "      <td>1118</td>\n",
       "      <td>4210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12401</td>\n",
       "      <td>5483</td>\n",
       "      <td>17884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6817</td>\n",
       "      <td>2597</td>\n",
       "      <td>9414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3981</td>\n",
       "      <td>1068</td>\n",
       "      <td>5049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2192</td>\n",
       "      <td>493</td>\n",
       "      <td>2685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3010</td>\n",
       "      <td>981</td>\n",
       "      <td>3991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4221</td>\n",
       "      <td>1191</td>\n",
       "      <td>5412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>848</td>\n",
       "      <td>187</td>\n",
       "      <td>1035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>153</td>\n",
       "      <td>32</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>125</td>\n",
       "      <td>31</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>67</td>\n",
       "      <td>9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>182543</td>\n",
       "      <td>50611</td>\n",
       "      <td>233154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "loan_default       0      1     All\n",
       "State_ID                           \n",
       "1               7353   1583    8936\n",
       "2               3031   1129    4160\n",
       "3              27733   6345   34078\n",
       "4              35544   9326   44870\n",
       "5               8154   2023   10177\n",
       "6              26615   6890   33505\n",
       "7               5417   1369    6786\n",
       "8              10939   3258   14197\n",
       "9              12530   3492   16022\n",
       "10              2972    633    3605\n",
       "11              5348   1373    6721\n",
       "12              3092   1118    4210\n",
       "13             12401   5483   17884\n",
       "14              6817   2597    9414\n",
       "15              3981   1068    5049\n",
       "16              2192    493    2685\n",
       "17              3010    981    3991\n",
       "18              4221   1191    5412\n",
       "19               848    187    1035\n",
       "20               153     32     185\n",
       "21               125     31     156\n",
       "22                67      9      76\n",
       "All           182543  50611  233154"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_table_2 = pd.crosstab(\n",
    "    train['State_ID'],\n",
    "    train['loan_default'],\n",
    "    margins = True\n",
    ")\n",
    "contingency_table_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import chi2_contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi2, p, dof, expected = chi2_contingency(contingency_table.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "464.6976401635842\n"
     ]
    }
   ],
   "source": [
    "print(chi2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6324874941967802e-84\n"
     ]
    }
   ],
   "source": [
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n"
     ]
    }
   ],
   "source": [
    "print(dof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "contingency_table_2 = pd.crosstab(\n",
    "    train['State_ID'],\n",
    "    train['loan_default'],\n",
    "    margins = True\n",
    ")\n",
    "chi2, p, dof, expected = chi2_contingency(contingency_table_2.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.593347523885013e-37\n"
     ]
    }
   ],
   "source": [
    "contingency_table_3 = pd.crosstab(\n",
    "    train['Employment.Type'],\n",
    "    train['loan_default'],\n",
    "    margins = True\n",
    ")\n",
    "chi2, p, dof, expected = chi2_contingency(contingency_table_3.values)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>loan_default</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Employment.Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80568</td>\n",
       "      <td>20668</td>\n",
       "      <td>101236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101975</td>\n",
       "      <td>29943</td>\n",
       "      <td>131918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>182543</td>\n",
       "      <td>50611</td>\n",
       "      <td>233154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "loan_default          0      1     All\n",
       "Employment.Type                       \n",
       "0                 80568  20668  101236\n",
       "1                101975  29943  131918\n",
       "All              182543  50611  233154"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_table_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9133590492134818\n"
     ]
    }
   ],
   "source": [
    "contingency_table_4 = pd.crosstab(\n",
    "    train['PAN_flag'],\n",
    "    train['loan_default'],\n",
    "    margins = True\n",
    ")\n",
    "chi2, p, dof, expected = chi2_contingency(contingency_table_4.values)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>loan_default</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAN_flag</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>168799</td>\n",
       "      <td>46734</td>\n",
       "      <td>215533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13744</td>\n",
       "      <td>3877</td>\n",
       "      <td>17621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>182543</td>\n",
       "      <td>50611</td>\n",
       "      <td>233154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "loan_default       0      1     All\n",
       "PAN_flag                           \n",
       "0             168799  46734  215533\n",
       "1              13744   3877   17621\n",
       "All           182543  50611  233154"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_table_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09525254363019199\n"
     ]
    }
   ],
   "source": [
    "contingency_table_5 = pd.crosstab(\n",
    "    train['Driving_flag'],\n",
    "    train['loan_default'],\n",
    "    margins = True\n",
    ")\n",
    "chi2, p, dof, expected = chi2_contingency(contingency_table_5.values)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.009172891827200989\n"
     ]
    }
   ],
   "source": [
    "contingency_table_6 = pd.crosstab(\n",
    "    train['Passport_flag'],\n",
    "    train['loan_default'],\n",
    "    margins = True\n",
    ")\n",
    "chi2, p, dof, expected = chi2_contingency(contingency_table_6.values)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0686739534176258e-97\n"
     ]
    }
   ],
   "source": [
    "contingency_table_7 = pd.crosstab(\n",
    "    train['PRI.OVERDUE.ACCTS'],\n",
    "    train['loan_default'],\n",
    "    margins = True\n",
    ")\n",
    "chi2, p, dof, expected = chi2_contingency(contingency_table_7.values)\n",
    "print(p)"
   ]
  },
  {
   "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>loan_default</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRI.OVERDUE.ACCTS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>163471</td>\n",
       "      <td>43408</td>\n",
       "      <td>206879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14588</td>\n",
       "      <td>5382</td>\n",
       "      <td>19970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3061</td>\n",
       "      <td>1241</td>\n",
       "      <td>4302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>841</td>\n",
       "      <td>361</td>\n",
       "      <td>1202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>284</td>\n",
       "      <td>120</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>118</td>\n",
       "      <td>48</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>74</td>\n",
       "      <td>22</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>182543</td>\n",
       "      <td>50611</td>\n",
       "      <td>233154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "loan_default            0      1     All\n",
       "PRI.OVERDUE.ACCTS                       \n",
       "0                  163471  43408  206879\n",
       "1                   14588   5382   19970\n",
       "2                    3061   1241    4302\n",
       "3                     841    361    1202\n",
       "4                     284    120     404\n",
       "5                     118     48     166\n",
       "6                      74     22      96\n",
       "7                      31      7      38\n",
       "8                      20      7      27\n",
       "9                      21      4      25\n",
       "10                      5      1       6\n",
       "11                      8      4      12\n",
       "12                      6      2       8\n",
       "13                      4      1       5\n",
       "14                      4      1       5\n",
       "15                      1      0       1\n",
       "16                      0      1       1\n",
       "17                      2      0       2\n",
       "18                      1      1       2\n",
       "19                      1      0       1\n",
       "23                      1      0       1\n",
       "25                      1      0       1\n",
       "All                182543  50611  233154"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_table_7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['UniqueID', 'ltv', 'branch_id', 'supplier_id', 'manufacturer_id',\n",
       "       'Current_pincode_ID', 'Date.of.Birth', 'Employment.Type',\n",
       "       'DisbursalDate', 'State_ID', 'Employee_code_ID', 'Aadhar_flag',\n",
       "       'PAN_flag', 'Driving_flag', 'Passport_flag', 'PERFORM_CNS.SCORE',\n",
       "       'PERFORM_CNS.SCORE.DESCRIPTION', 'PRI.NO.OF.ACCTS', 'PRI.ACTIVE.ACCTS',\n",
       "       'PRI.OVERDUE.ACCTS', 'PRI.CURRENT.BALANCE', 'PRI.SANCTIONED.AMOUNT',\n",
       "       'PRIMARY.INSTAL.AMT', 'SEC.INSTAL.AMT', 'NEW.ACCTS.IN.LAST.SIX.MONTHS',\n",
       "       'DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS', 'AVERAGE.ACCT.AGE',\n",
       "       'CREDIT.HISTORY.LENGTH', 'NO.OF_INQUIRIES', 'loan_default'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(['PAN_flag','Driving_flag'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>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>...</th>\n",
       "      <th>PRI.CURRENT.BALANCE</th>\n",
       "      <th>PRI.SANCTIONED.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>89.55</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1441</td>\n",
       "      <td>1984-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-03-08</td>\n",
       "      <td>6</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>537409</td>\n",
       "      <td>73.23</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1502</td>\n",
       "      <td>1985-07-31</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>27600</td>\n",
       "      <td>50200</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>417566</td>\n",
       "      <td>89.63</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1497</td>\n",
       "      <td>1985-08-24</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>6</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>624493</td>\n",
       "      <td>88.48</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1501</td>\n",
       "      <td>1993-12-30</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-10-26</td>\n",
       "      <td>6</td>\n",
       "      <td>...</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>8</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>539055</td>\n",
       "      <td>88.39</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1495</td>\n",
       "      <td>1977-09-12</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>6</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UniqueID    ltv  branch_id  supplier_id  manufacturer_id  \\\n",
       "0    420825  89.55         67        22807               45   \n",
       "1    537409  73.23         67        22807               45   \n",
       "2    417566  89.63         67        22807               45   \n",
       "3    624493  88.48         67        22807               45   \n",
       "4    539055  88.39         67        22807               45   \n",
       "\n",
       "   Current_pincode_ID Date.of.Birth  Employment.Type DisbursalDate  State_ID  \\\n",
       "0                1441    1984-01-01                0    2018-03-08         6   \n",
       "1                1502    1985-07-31                1    2018-09-26         6   \n",
       "2                1497    1985-08-24                1    2018-01-08         6   \n",
       "3                1501    1993-12-30                1    2018-10-26         6   \n",
       "4                1495    1977-09-12                1    2018-09-26         6   \n",
       "\n",
       "       ...       PRI.CURRENT.BALANCE  PRI.SANCTIONED.AMOUNT  \\\n",
       "0      ...                         0                      0   \n",
       "1      ...                     27600                  50200   \n",
       "2      ...                         0                      0   \n",
       "3      ...                         0                      0   \n",
       "4      ...                         0                      0   \n",
       "\n",
       "   PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0                   0               0                             0   \n",
       "1                1991               0                             0   \n",
       "2                   0               0                             0   \n",
       "3                  31               0                             0   \n",
       "4                   0               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0                 0   \n",
       "1                                    1                23   \n",
       "2                                    0                 0   \n",
       "3                                    0                 8   \n",
       "4                                    0                 0   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \n",
       "0                      0                0             0  \n",
       "1                     23                0             1  \n",
       "2                      0                0             0  \n",
       "3                     15                1             1  \n",
       "4                      0                1             1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('train_updated.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import KFold   \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['UniqueID', 'ltv', 'branch_id', 'supplier_id', 'manufacturer_id',\n",
       "       'Current_pincode_ID', 'Date.of.Birth', 'Employment.Type',\n",
       "       'DisbursalDate', 'State_ID', 'Employee_code_ID', 'Aadhar_flag',\n",
       "       'Passport_flag', 'PERFORM_CNS.SCORE', 'PERFORM_CNS.SCORE.DESCRIPTION',\n",
       "       'PRI.NO.OF.ACCTS', 'PRI.ACTIVE.ACCTS', 'PRI.OVERDUE.ACCTS',\n",
       "       'PRI.CURRENT.BALANCE', 'PRI.SANCTIONED.AMOUNT', 'PRIMARY.INSTAL.AMT',\n",
       "       'SEC.INSTAL.AMT', 'NEW.ACCTS.IN.LAST.SIX.MONTHS',\n",
       "       'DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS', 'AVERAGE.ACCT.AGE',\n",
       "       'CREDIT.HISTORY.LENGTH', 'NO.OF_INQUIRIES', 'loan_default'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classification_model(model, data, predictors, outcome):\n",
    "  #Fit the model:\n",
    "  model.fit(data[predictors],data[outcome])\n",
    "  \n",
    "  #Make predictions on training set:\n",
    "  predictions = model.predict(data[predictors])\n",
    "  \n",
    "  #Print accuracy\n",
    "  accuracy = metrics.accuracy_score(predictions,data[outcome])\n",
    "  print (\"Accuracy : %s\" % \"{0:.3%}\".format(accuracy))\n",
    "\n",
    "  #Perform k-fold cross-validation with 10 folds\n",
    "  kf = KFold(data.shape[0], n_folds=10)\n",
    "  error = []\n",
    "  for train, test in kf:\n",
    "    # Filter training data\n",
    "    train_predictors = (data[predictors].iloc[train,:])\n",
    "    \n",
    "    # The target we're using to train the algorithm.\n",
    "    train_target = data[outcome].iloc[train]\n",
    "    \n",
    "    # Training the algorithm using the predictors and target.\n",
    "    model.fit(train_predictors, train_target)\n",
    "    \n",
    "    #Record error from each cross-validation run\n",
    "    error.append(model.score(data[predictors].iloc[test,:], data[outcome].iloc[test]))\n",
    " \n",
    "  print (\"Cross-Validation Score : %s\" % \"{0:.3%}\".format(np.mean(error)))\n",
    "\n",
    "  #Fit the model again so that it can be refered outside the function:\n",
    "  model.fit(data[predictors],data[outcome]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 78.293%\n",
      "Cross-Validation Score : 78.292%\n"
     ]
    }
   ],
   "source": [
    "outcome_var = 'loan_default'\n",
    "model = LogisticRegression()\n",
    "predictor_var = ['ltv', 'branch_id', 'supplier_id', 'manufacturer_id',\n",
    "       'Current_pincode_ID', 'Employment.Type', 'State_ID', 'Employee_code_ID', 'Aadhar_flag',\n",
    "       'Passport_flag', 'PERFORM_CNS.SCORE', 'PERFORM_CNS.SCORE.DESCRIPTION',\n",
    "       'PRI.NO.OF.ACCTS', 'PRI.ACTIVE.ACCTS', 'PRI.OVERDUE.ACCTS',\n",
    "       'PRI.CURRENT.BALANCE', 'PRI.SANCTIONED.AMOUNT', 'PRIMARY.INSTAL.AMT',\n",
    "       'SEC.INSTAL.AMT', 'NEW.ACCTS.IN.LAST.SIX.MONTHS',\n",
    "       'DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS', 'AVERAGE.ACCT.AGE',\n",
    "       'CREDIT.HISTORY.LENGTH', 'NO.OF_INQUIRIES']\n",
    "classification_model(model, train ,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 78.377%\n",
      "Cross-Validation Score : 78.299%\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators=100, max_depth=10)\n",
    "outcome_var = 'loan_default'\n",
    "predictor_var = ['ltv', 'branch_id', 'supplier_id', 'manufacturer_id',\n",
    "       'Current_pincode_ID', 'Employment.Type', 'State_ID', 'Employee_code_ID', 'Aadhar_flag',\n",
    "       'Passport_flag', 'PERFORM_CNS.SCORE', 'PERFORM_CNS.SCORE.DESCRIPTION',\n",
    "       'PRI.NO.OF.ACCTS', 'PRI.ACTIVE.ACCTS', 'PRI.OVERDUE.ACCTS',\n",
    "       'PRI.CURRENT.BALANCE', 'PRI.SANCTIONED.AMOUNT', 'PRIMARY.INSTAL.AMT',\n",
    "       'SEC.INSTAL.AMT', 'NEW.ACCTS.IN.LAST.SIX.MONTHS',\n",
    "       'DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS', 'AVERAGE.ACCT.AGE',\n",
    "       'CREDIT.HISTORY.LENGTH', 'NO.OF_INQUIRIES']\n",
    "classification_model(model, train ,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ltv                                    0.236837\n",
      "Current_pincode_ID                     0.158920\n",
      "Employee_code_ID                       0.112126\n",
      "supplier_id                            0.092133\n",
      "branch_id                              0.042276\n",
      "PRI.SANCTIONED.AMOUNT                  0.033660\n",
      "PRI.CURRENT.BALANCE                    0.033436\n",
      "CREDIT.HISTORY.LENGTH                  0.033178\n",
      "PERFORM_CNS.SCORE                      0.031776\n",
      "AVERAGE.ACCT.AGE                       0.030681\n",
      "PRIMARY.INSTAL.AMT                     0.030277\n",
      "manufacturer_id                        0.028897\n",
      "State_ID                               0.025165\n",
      "PRI.NO.OF.ACCTS                        0.019033\n",
      "Employment.Type                        0.016197\n",
      "PERFORM_CNS.SCORE.DESCRIPTION          0.015435\n",
      "NO.OF_INQUIRIES                        0.015415\n",
      "PRI.ACTIVE.ACCTS                       0.011927\n",
      "Aadhar_flag                            0.010028\n",
      "NEW.ACCTS.IN.LAST.SIX.MONTHS           0.008915\n",
      "PRI.OVERDUE.ACCTS                      0.005936\n",
      "DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS    0.005243\n",
      "SEC.INSTAL.AMT                         0.001941\n",
      "Passport_flag                          0.000569\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "featimp = pd.Series(model.feature_importances_, index=predictor_var).sort_values(ascending=False)\n",
    "print (featimp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = train[(train['manufacturer_id'] > 150)].index\n",
    "train.drop(index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(233135, 28)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = train[(train['State_ID'] == 22)].index\n",
    "train.drop(index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(233059, 28)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['State_ID','manufacturer_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State_ID           0\n",
       "manufacturer_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[cols].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "State_ID\n",
      "1      3.834222\n",
      "2      1.784956\n",
      "3     14.620761\n",
      "4     19.251777\n",
      "5      4.366705\n",
      "6     14.376188\n",
      "7      2.911709\n",
      "8      6.091591\n",
      "9      6.872080\n",
      "10     1.546819\n",
      "11     2.883819\n",
      "12     1.806410\n",
      "13     7.670161\n",
      "14     4.039321\n",
      "15     2.166404\n",
      "16     1.152069\n",
      "17     1.712442\n",
      "18     2.322159\n",
      "19     0.444094\n",
      "20     0.079379\n",
      "21     0.066936\n",
      "Name: State_ID, dtype: float64\n",
      "\n",
      "manufacturer_id\n",
      "45     24.296423\n",
      "48      7.169858\n",
      "49      4.385156\n",
      "51     11.663570\n",
      "67      1.031928\n",
      "86     46.975229\n",
      "120     4.144015\n",
      "145     0.333821\n",
      "Name: manufacturer_id, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for cols in cols: \n",
    "    print(train.groupby(cols)[cols].count()/np.float(len(train))*100)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PRI.OVERDUE.ACCTS'"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'PRI.OVERDUE.ACCTS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = train.groupby('PRI.OVERDUE.ACCTS')['PRI.OVERDUE.ACCTS'].count()/np.float(len(train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "rare_cat = [x for x in temp.loc[temp<0.0005].index.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 23, 25]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rare_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['rare'] = np.where(train['PRI.OVERDUE.ACCTS'].isin(rare_cat), 6 , train['PRI.OVERDUE.ACCTS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"train_updated.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>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>...</th>\n",
       "      <th>PRI.CURRENT.BALANCE</th>\n",
       "      <th>PRI.SANCTIONED.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>89.55</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1441</td>\n",
       "      <td>1/1/1984</td>\n",
       "      <td>0</td>\n",
       "      <td>3/8/2018</td>\n",
       "      <td>6</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>537409</td>\n",
       "      <td>73.23</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1502</td>\n",
       "      <td>7/31/1985</td>\n",
       "      <td>1</td>\n",
       "      <td>9/26/2018</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>27600</td>\n",
       "      <td>50200</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>417566</td>\n",
       "      <td>89.63</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1497</td>\n",
       "      <td>8/24/1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1/8/2018</td>\n",
       "      <td>6</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>624493</td>\n",
       "      <td>88.48</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1501</td>\n",
       "      <td>12/30/1993</td>\n",
       "      <td>1</td>\n",
       "      <td>10/26/2018</td>\n",
       "      <td>6</td>\n",
       "      <td>...</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>8</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>539055</td>\n",
       "      <td>88.39</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1495</td>\n",
       "      <td>9/12/1977</td>\n",
       "      <td>1</td>\n",
       "      <td>9/26/2018</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UniqueID    ltv  branch_id  supplier_id  manufacturer_id  \\\n",
       "0    420825  89.55         67        22807               45   \n",
       "1    537409  73.23         67        22807               45   \n",
       "2    417566  89.63         67        22807               45   \n",
       "3    624493  88.48         67        22807               45   \n",
       "4    539055  88.39         67        22807               45   \n",
       "\n",
       "   Current_pincode_ID Date.of.Birth  Employment.Type DisbursalDate  State_ID  \\\n",
       "0                1441      1/1/1984                0      3/8/2018         6   \n",
       "1                1502     7/31/1985                1     9/26/2018         6   \n",
       "2                1497     8/24/1985                1      1/8/2018         6   \n",
       "3                1501    12/30/1993                1    10/26/2018         6   \n",
       "4                1495     9/12/1977                1     9/26/2018         6   \n",
       "\n",
       "       ...       PRI.CURRENT.BALANCE  PRI.SANCTIONED.AMOUNT  \\\n",
       "0      ...                         0                      0   \n",
       "1      ...                     27600                  50200   \n",
       "2      ...                         0                      0   \n",
       "3      ...                         0                      0   \n",
       "4      ...                         0                      0   \n",
       "\n",
       "   PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0                   0               0                             0   \n",
       "1                1991               0                             0   \n",
       "2                   0               0                             0   \n",
       "3                  31               0                             0   \n",
       "4                   0               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0                 0   \n",
       "1                                    1                23   \n",
       "2                                    0                 0   \n",
       "3                                    0                 8   \n",
       "4                                    0                 0   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \n",
       "0                      0                0             0  \n",
       "1                     23                0             1  \n",
       "2                      0                0             0  \n",
       "3                     15                1             1  \n",
       "4                      0                1             1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['UniqueID', 'ltv', 'branch_id', 'supplier_id', 'manufacturer_id',\n",
       "       'Current_pincode_ID', 'Date.of.Birth', 'Employment.Type',\n",
       "       'DisbursalDate', 'State_ID', 'Employee_code_ID', 'Aadhar_flag',\n",
       "       'Passport_flag', 'PERFORM_CNS.SCORE', 'PERFORM_CNS.SCORE.DESCRIPTION',\n",
       "       'PRI.NO.OF.ACCTS', 'PRI.ACTIVE.ACCTS', 'PRI.OVERDUE.ACCTS',\n",
       "       'PRI.CURRENT.BALANCE', 'PRI.SANCTIONED.AMOUNT', 'PRIMARY.INSTAL.AMT',\n",
       "       'SEC.INSTAL.AMT', 'NEW.ACCTS.IN.LAST.SIX.MONTHS',\n",
       "       'DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS', 'AVERAGE.ACCT.AGE',\n",
       "       'CREDIT.HISTORY.LENGTH', 'NO.OF_INQUIRIES', 'loan_default'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>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>...</th>\n",
       "      <th>PRI.CURRENT.BALANCE</th>\n",
       "      <th>PRI.SANCTIONED.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",
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       "      <th>NO.OF_INQUIRIES</th>\n",
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       "      <th>1</th>\n",
       "      <td>537409</td>\n",
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       "      <td>22807</td>\n",
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       "      <td>8/24/1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1/8/2018</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>624493</td>\n",
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       "      <td>6</td>\n",
       "      <td>...</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>8</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>539055</td>\n",
       "      <td>88.39</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1495</td>\n",
       "      <td>9/12/1977</td>\n",
       "      <td>1</td>\n",
       "      <td>9/26/2018</td>\n",
       "      <td>6</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UniqueID    ltv  branch_id  supplier_id  manufacturer_id  \\\n",
       "0    420825  89.55         67        22807               45   \n",
       "1    537409  73.23         67        22807               45   \n",
       "2    417566  89.63         67        22807               45   \n",
       "3    624493  88.48         67        22807               45   \n",
       "4    539055  88.39         67        22807               45   \n",
       "\n",
       "   Current_pincode_ID Date.of.Birth  Employment.Type DisbursalDate  State_ID  \\\n",
       "0                1441      1/1/1984                0      3/8/2018         6   \n",
       "1                1502     7/31/1985                1     9/26/2018         6   \n",
       "2                1497     8/24/1985                1      1/8/2018         6   \n",
       "3                1501    12/30/1993                1    10/26/2018         6   \n",
       "4                1495     9/12/1977                1     9/26/2018         6   \n",
       "\n",
       "       ...       PRI.CURRENT.BALANCE  PRI.SANCTIONED.AMOUNT  \\\n",
       "0      ...                         0                      0   \n",
       "1      ...                     27600                  50200   \n",
       "2      ...                         0                      0   \n",
       "3      ...                         0                      0   \n",
       "4      ...                         0                      0   \n",
       "\n",
       "   PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0                   0               0                             0   \n",
       "1                1991               0                             0   \n",
       "2                   0               0                             0   \n",
       "3                  31               0                             0   \n",
       "4                   0               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0                 0   \n",
       "1                                    1                23   \n",
       "2                                    0                 0   \n",
       "3                                    0                 8   \n",
       "4                                    0                 0   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \n",
       "0                      0                0             0  \n",
       "1                     23                0             1  \n",
       "2                      0                0             0  \n",
       "3                     15                1             1  \n",
       "4                      0                1             1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = data[(data['manufacturer_id'] > 150)].index\n",
    "data.drop(index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = data[(data['State_ID'] == 22)].index\n",
    "data.drop(index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(233059, 28)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = data.groupby('PRI.OVERDUE.ACCTS')['PRI.OVERDUE.ACCTS'].count()/np.float(len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "rare_cat = [x for x in temp.loc[temp<0.0005].index.values]\n",
    "data['PRI.OVERDUE.ACCTS'] = np.where(data['PRI.OVERDUE.ACCTS'].isin(rare_cat), 6 , data['PRI.OVERDUE.ACCTS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "    .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>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>...</th>\n",
       "      <th>PRI.CURRENT.BALANCE</th>\n",
       "      <th>PRI.SANCTIONED.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>89.55</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1441</td>\n",
       "      <td>1/1/1984</td>\n",
       "      <td>0</td>\n",
       "      <td>3/8/2018</td>\n",
       "      <td>6</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>537409</td>\n",
       "      <td>73.23</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1502</td>\n",
       "      <td>7/31/1985</td>\n",
       "      <td>1</td>\n",
       "      <td>9/26/2018</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>27600</td>\n",
       "      <td>50200</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>417566</td>\n",
       "      <td>89.63</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1497</td>\n",
       "      <td>8/24/1985</td>\n",
       "      <td>1</td>\n",
       "      <td>1/8/2018</td>\n",
       "      <td>6</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>624493</td>\n",
       "      <td>88.48</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1501</td>\n",
       "      <td>12/30/1993</td>\n",
       "      <td>1</td>\n",
       "      <td>10/26/2018</td>\n",
       "      <td>6</td>\n",
       "      <td>...</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>8</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>539055</td>\n",
       "      <td>88.39</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1495</td>\n",
       "      <td>9/12/1977</td>\n",
       "      <td>1</td>\n",
       "      <td>9/26/2018</td>\n",
       "      <td>6</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UniqueID    ltv  branch_id  supplier_id  manufacturer_id  \\\n",
       "0    420825  89.55         67        22807               45   \n",
       "1    537409  73.23         67        22807               45   \n",
       "2    417566  89.63         67        22807               45   \n",
       "3    624493  88.48         67        22807               45   \n",
       "4    539055  88.39         67        22807               45   \n",
       "\n",
       "   Current_pincode_ID Date.of.Birth  Employment.Type DisbursalDate  State_ID  \\\n",
       "0                1441      1/1/1984                0      3/8/2018         6   \n",
       "1                1502     7/31/1985                1     9/26/2018         6   \n",
       "2                1497     8/24/1985                1      1/8/2018         6   \n",
       "3                1501    12/30/1993                1    10/26/2018         6   \n",
       "4                1495     9/12/1977                1     9/26/2018         6   \n",
       "\n",
       "       ...       PRI.CURRENT.BALANCE  PRI.SANCTIONED.AMOUNT  \\\n",
       "0      ...                         0                      0   \n",
       "1      ...                     27600                  50200   \n",
       "2      ...                         0                      0   \n",
       "3      ...                         0                      0   \n",
       "4      ...                         0                      0   \n",
       "\n",
       "   PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0                   0               0                             0   \n",
       "1                1991               0                             0   \n",
       "2                   0               0                             0   \n",
       "3                  31               0                             0   \n",
       "4                   0               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0                 0   \n",
       "1                                    1                23   \n",
       "2                                    0                 0   \n",
       "3                                    0                 8   \n",
       "4                                    0                 0   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \n",
       "0                      0                0             0  \n",
       "1                     23                0             1  \n",
       "2                      0                0             0  \n",
       "3                     15                1             1  \n",
       "4                      0                1             1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    206793\n",
       "1     19961\n",
       "2      4302\n",
       "3      1202\n",
       "4       404\n",
       "6       231\n",
       "5       166\n",
       "Name: PRI.OVERDUE.ACCTS, dtype: int64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['PRI.OVERDUE.ACCTS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf6fc7b0>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['ltv'].hist(bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 78.362%\n",
      "Cross-Validation Score : 78.293%\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators=100, max_depth=10)\n",
    "outcome_var = 'loan_default'\n",
    "predictor_var = ['ltv', 'branch_id', 'supplier_id',\n",
    "       'Current_pincode_ID', 'Employee_code_ID', \n",
    "        'PERFORM_CNS.SCORE',\n",
    "       'PRI.NO.OF.ACCTS', 'PRI.ACTIVE.ACCTS', 'PRI.OVERDUE.ACCTS',\n",
    "       'PRI.CURRENT.BALANCE', 'PRI.SANCTIONED.AMOUNT', 'PRIMARY.INSTAL.AMT',\n",
    "       'SEC.INSTAL.AMT', 'NEW.ACCTS.IN.LAST.SIX.MONTHS', 'AVERAGE.ACCT.AGE',\n",
    "       'CREDIT.HISTORY.LENGTH']\n",
    "classification_model(model, data ,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pre-processing\n",
    "\n",
    "dataset['Date.of.Birth']= pd.to_datetime(dataset['Date.of.Birth']) \n",
    "\n",
    "dataset['DisbursalDate']= pd.to_datetime(dataset['DisbursalDate']) \n",
    "\n",
    "dataset = dataset.drop('MobileNo_Avl_Flag', axis = 1)\n",
    "\n",
    "dataset = dataset.drop(['SEC.NO.OF.ACCTS','SEC.ACTIVE.ACCTS','SEC.OVERDUE.ACCTS','SEC.CURRENT.BALANCE','SEC.SANCTIONED.AMOUNT','SEC.DISBURSED.AMOUNT'], axis = 1)\n",
    "\n",
    "dataset['PERFORM_CNS.SCORE.DESCRIPTION'] = dataset['PERFORM_CNS.SCORE.DESCRIPTION'].map({'No Bureau History Available':0, \n",
    "'C-Very Low Risk':1, 'A-Very Low Risk':2, 'D-Very Low Risk':3, 'B-Very Low Risk': 4, 'M-Very High Risk':5, 'F-Low Risk':6, \n",
    "'K-High Risk':7, 'H-Medium Risk':8, 'E-Low Risk':9, 'I-Medium Risk':10, 'G-Low Risk':11, \n",
    "'Not Scored: Sufficient History Not Available': 12, 'J-High Risk':13, 'Not Scored: Not Enough Info available on the customer':14,\n",
    "'Not Scored: No Activity seen on the customer (Inactive)':15, 'Not Scored: No Updates available in last 36 months':16, \n",
    "'L-Very High Risk':17, 'Not Scored: Only a Guarantor':18, 'Not Scored: More than 50 active Accounts found':19})\n",
    "\n",
    "dataset['Employment.Type_random'] = dataset['Employment.Type']\n",
    "random_sample_dataset = dataset['Employment.Type'].dropna().sample(dataset['Employment.Type'].isnull().sum(),random_state = 0)\n",
    "random_sample_dataset.index = dataset[dataset['Employment.Type'].isnull()].index\n",
    "dataset.loc[dataset['Employment.Type'].isnull(), 'Employment.Type_random'] = random_sample_dataset\n",
    "dataset['Employment.Type'] = dataset['Employment.Type_random']\n",
    "dataset = dataset.drop('Employment.Type_random', axis = 1)\n",
    "\n",
    "dataset['Employment.Type'] = dataset['Employment.Type'].map({'Salaried':0, 'Self employed':1})\n",
    "\n",
    "dataset = dataset.drop(['VoterID_flag', 'PRI.DISBURSED.AMOUNT'], axis = 1)\n",
    "\n",
    "index = dataset[(dataset['manufacturer_id'] > 150)].index\n",
    "dataset.drop(index, inplace=True)\n",
    "\n",
    "index = dataset[(dataset['State_ID'] == 22)].index\n",
    "dataset.drop(index, inplace=True)\n",
    "\n",
    "temp = dataset.groupby('PRI.OVERDUE.ACCTS')['PRI.OVERDUE.ACCTS'].count()/np.float(len(dataset))\n",
    "rare_cat = [x for x in temp.loc[temp<0.0005].index.values]\n",
    "dataset['PRI.OVERDUE.ACCTS'] = np.where(dataset['PRI.OVERDUE.ACCTS'].isin(rare_cat), 6 , dataset['PRI.OVERDUE.ACCTS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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>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>...</th>\n",
       "      <th>PRI.CURRENT.BALANCE</th>\n",
       "      <th>PRI.SANCTIONED.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>1984-01-01</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>0</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>1985-07-31</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>27600</td>\n",
       "      <td>50200</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>23</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>1985-08-24</td>\n",
       "      <td>1</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>0</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>1993-12-30</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>8</td>\n",
       "      <td>15</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>1977-09-12</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
       "0    420825             50578       58400  89.55         67        22807   \n",
       "1    537409             47145       65550  73.23         67        22807   \n",
       "2    417566             53278       61360  89.63         67        22807   \n",
       "3    624493             57513       66113  88.48         67        22807   \n",
       "4    539055             52378       60300  88.39         67        22807   \n",
       "\n",
       "   manufacturer_id  Current_pincode_ID Date.of.Birth  Employment.Type  \\\n",
       "0               45                1441    1984-01-01                0   \n",
       "1               45                1502    1985-07-31                1   \n",
       "2               45                1497    1985-08-24                1   \n",
       "3               45                1501    1993-12-30                1   \n",
       "4               45                1495    1977-09-12                1   \n",
       "\n",
       "       ...      PRI.CURRENT.BALANCE  PRI.SANCTIONED.AMOUNT  \\\n",
       "0      ...                        0                      0   \n",
       "1      ...                    27600                  50200   \n",
       "2      ...                        0                      0   \n",
       "3      ...                        0                      0   \n",
       "4      ...                        0                      0   \n",
       "\n",
       "   PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0                   0               0                             0   \n",
       "1                1991               0                             0   \n",
       "2                   0               0                             0   \n",
       "3                  31               0                             0   \n",
       "4                   0               0                             0   \n",
       "\n",
       "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
       "0                                    0                 0   \n",
       "1                                    1                23   \n",
       "2                                    0                 0   \n",
       "3                                    0                 8   \n",
       "4                                    0                 0   \n",
       "\n",
       "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \n",
       "0                      0                0             0  \n",
       "1                     23                0             1  \n",
       "2                      0                0             0  \n",
       "3                     15                1             1  \n",
       "4                      0                1             1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(233154, 32)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.drop(['PAN_flag','Driving_flag'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(233154, 30)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = dataset[(dataset['manufacturer_id'] > 150)].index\n",
    "dataset.drop(index, inplace=True)\n",
    "index = dataset[(dataset['State_ID'] == 22)].index\n",
    "dataset.drop(index, inplace=True)\n",
    "\n",
    "temp = dataset.groupby('PRI.OVERDUE.ACCTS')['PRI.OVERDUE.ACCTS'].count()/np.float(len(dataset))\n",
    "rare_cat = [x for x in temp.loc[temp<0.0005].index.values]\n",
    "dataset['PRI.OVERDUE.ACCTS'] = np.where(dataset['PRI.OVERDUE.ACCTS'].isin(rare_cat), 6 , dataset['PRI.OVERDUE.ACCTS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['UniqueID', 'disbursed_amount', 'asset_cost', 'ltv', 'branch_id',\n",
       "       'supplier_id', 'manufacturer_id', 'Current_pincode_ID', 'Date.of.Birth',\n",
       "       'Employment.Type', 'DisbursalDate', 'State_ID', 'Employee_code_ID',\n",
       "       'Aadhar_flag', 'Passport_flag', 'PERFORM_CNS.SCORE',\n",
       "       'PERFORM_CNS.SCORE.DESCRIPTION', 'PRI.NO.OF.ACCTS', 'PRI.ACTIVE.ACCTS',\n",
       "       'PRI.OVERDUE.ACCTS', 'PRI.CURRENT.BALANCE', 'PRI.SANCTIONED.AMOUNT',\n",
       "       'PRIMARY.INSTAL.AMT', 'SEC.INSTAL.AMT', 'NEW.ACCTS.IN.LAST.SIX.MONTHS',\n",
       "       'DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS', 'AVERAGE.ACCT.AGE',\n",
       "       'CREDIT.HISTORY.LENGTH', 'NO.OF_INQUIRIES', 'loan_default'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 78.293%\n",
      "Cross-Validation Score : 78.288%\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators=100, max_depth=10)\n",
    "outcome_var = 'loan_default'\n",
    "predictor_var = ['UniqueID', 'disbursed_amount', 'asset_cost']\n",
    "classification_model(model, dataset ,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.to_csv('train_updated_version2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['DisbursalDate'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9566424505431713e-80\n"
     ]
    }
   ],
   "source": [
    "contingency_table = pd.crosstab(\n",
    "    data['NO.OF_INQUIRIES'],\n",
    "    data['loan_default'],\n",
    "    margins = True\n",
    ")\n",
    "chi2, p, dof, expected = chi2_contingency(contingency_table.values)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['UniqueID', 'disbursed_amount', 'asset_cost', 'ltv', 'branch_id',\n",
       "       'supplier_id', 'manufacturer_id', 'Current_pincode_ID', 'Date.of.Birth',\n",
       "       'Employment.Type', 'DisbursalDate', 'State_ID', 'Employee_code_ID',\n",
       "       'Aadhar_flag', 'Passport_flag', 'PERFORM_CNS.SCORE',\n",
       "       'PERFORM_CNS.SCORE.DESCRIPTION', 'PRI.NO.OF.ACCTS', 'PRI.ACTIVE.ACCTS',\n",
       "       'PRI.OVERDUE.ACCTS', 'PRI.CURRENT.BALANCE', 'PRI.SANCTIONED.AMOUNT',\n",
       "       'PRIMARY.INSTAL.AMT', 'SEC.INSTAL.AMT', 'NEW.ACCTS.IN.LAST.SIX.MONTHS',\n",
       "       'DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS', 'AVERAGE.ACCT.AGE',\n",
       "       'CREDIT.HISTORY.LENGTH', 'NO.OF_INQUIRIES', 'loan_default'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier(n_estimators=100, max_depth=10)\n",
    "outcome_var = 'loan_default'\n",
    "predictor_var = ['branch_id']\n",
    "classification_model(model, dataset ,predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'branch_id', 'supplier_id',\n",
    "       'Current_pincode_ID', 'Employee_code_ID', \n",
    "        'PERFORM_CNS.SCORE',\n",
    "       'PRI.NO.OF.ACCTS', 'PRI.ACTIVE.ACCTS', 'PRI.OVERDUE.ACCTS',\n",
    "       'PRI.CURRENT.BALANCE', 'PRI.SANCTIONED.AMOUNT', 'PRIMARY.INSTAL.AMT',\n",
    "       'SEC.INSTAL.AMT', 'NEW.ACCTS.IN.LAST.SIX.MONTHS', 'AVERAGE.ACCT.AGE',\n",
    "       'CREDIT.HISTORY.LENGTH','disbursed_amount', 'asset_cost','ltv' "
   ]
  }
 ],
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