new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "%matplotlib inline\n",
    "import plotly.offline as py\n",
    "from plotly import tools\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.offline as plo\n",
    "import plotly.graph_objs as go \n",
    "import cufflinks as cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "\n",
    "\n",
    "import cufflinks as cf\n",
    "\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_columns\",1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UniqueID</th>\n",
       "      <th>disbursed_amount</th>\n",
       "      <th>asset_cost</th>\n",
       "      <th>ltv</th>\n",
       "      <th>branch_id</th>\n",
       "      <th>supplier_id</th>\n",
       "      <th>manufacturer_id</th>\n",
       "      <th>Current_pincode_ID</th>\n",
       "      <th>Date.of.Birth</th>\n",
       "      <th>Employment.Type</th>\n",
       "      <th>DisbursalDate</th>\n",
       "      <th>State_ID</th>\n",
       "      <th>Employee_code_ID</th>\n",
       "      <th>MobileNo_Avl_Flag</th>\n",
       "      <th>Aadhar_flag</th>\n",
       "      <th>PAN_flag</th>\n",
       "      <th>VoterID_flag</th>\n",
       "      <th>Driving_flag</th>\n",
       "      <th>Passport_flag</th>\n",
       "      <th>PERFORM_CNS.SCORE</th>\n",
       "      <th>PERFORM_CNS.SCORE.DESCRIPTION</th>\n",
       "      <th>PRI.NO.OF.ACCTS</th>\n",
       "      <th>PRI.ACTIVE.ACCTS</th>\n",
       "      <th>PRI.OVERDUE.ACCTS</th>\n",
       "      <th>PRI.CURRENT.BALANCE</th>\n",
       "      <th>PRI.SANCTIONED.AMOUNT</th>\n",
       "      <th>PRI.DISBURSED.AMOUNT</th>\n",
       "      <th>SEC.NO.OF.ACCTS</th>\n",
       "      <th>SEC.ACTIVE.ACCTS</th>\n",
       "      <th>SEC.OVERDUE.ACCTS</th>\n",
       "      <th>SEC.CURRENT.BALANCE</th>\n",
       "      <th>SEC.SANCTIONED.AMOUNT</th>\n",
       "      <th>SEC.DISBURSED.AMOUNT</th>\n",
       "      <th>PRIMARY.INSTAL.AMT</th>\n",
       "      <th>SEC.INSTAL.AMT</th>\n",
       "      <th>NEW.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>AVERAGE.ACCT.AGE</th>\n",
       "      <th>CREDIT.HISTORY.LENGTH</th>\n",
       "      <th>NO.OF_INQUIRIES</th>\n",
       "      <th>loan_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>420825</td>\n",
       "      <td>50578</td>\n",
       "      <td>58400</td>\n",
       "      <td>89.55</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1441</td>\n",
       "      <td>01-01-1984</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>03-08-2018</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No Bureau History Available</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>537409</td>\n",
       "      <td>47145</td>\n",
       "      <td>65550</td>\n",
       "      <td>73.23</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1502</td>\n",
       "      <td>31-07-1985</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>26-09-2018</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>598</td>\n",
       "      <td>I-Medium Risk</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27600</td>\n",
       "      <td>50200</td>\n",
       "      <td>50200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1yrs 11mon</td>\n",
       "      <td>1yrs 11mon</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>417566</td>\n",
       "      <td>53278</td>\n",
       "      <td>61360</td>\n",
       "      <td>89.63</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1497</td>\n",
       "      <td>24-08-1985</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>01-08-2018</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No Bureau History Available</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>624493</td>\n",
       "      <td>57513</td>\n",
       "      <td>66113</td>\n",
       "      <td>88.48</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1501</td>\n",
       "      <td>30-12-1993</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>26-10-2018</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>305</td>\n",
       "      <td>L-Very High Risk</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0yrs 8mon</td>\n",
       "      <td>1yrs 3mon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>539055</td>\n",
       "      <td>52378</td>\n",
       "      <td>60300</td>\n",
       "      <td>88.39</td>\n",
       "      <td>67</td>\n",
       "      <td>22807</td>\n",
       "      <td>45</td>\n",
       "      <td>1495</td>\n",
       "      <td>09-12-1977</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>26-09-2018</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No Bureau History Available</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\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    01-01-1984        Salaried   \n",
       "1               45                1502    31-07-1985   Self employed   \n",
       "2               45                1497    24-08-1985   Self employed   \n",
       "3               45                1501    30-12-1993   Self employed   \n",
       "4               45                1495    09-12-1977   Self employed   \n",
       "\n",
       "  DisbursalDate  State_ID  Employee_code_ID  MobileNo_Avl_Flag  Aadhar_flag  \\\n",
       "0    03-08-2018         6              1998                  1            1   \n",
       "1    26-09-2018         6              1998                  1            1   \n",
       "2    01-08-2018         6              1998                  1            1   \n",
       "3    26-10-2018         6              1998                  1            1   \n",
       "4    26-09-2018         6              1998                  1            1   \n",
       "\n",
       "   PAN_flag  VoterID_flag  Driving_flag  Passport_flag  PERFORM_CNS.SCORE  \\\n",
       "0         0             0             0              0                  0   \n",
       "1         0             0             0              0                598   \n",
       "2         0             0             0              0                  0   \n",
       "3         0             0             0              0                305   \n",
       "4         0             0             0              0                  0   \n",
       "\n",
       "  PERFORM_CNS.SCORE.DESCRIPTION  PRI.NO.OF.ACCTS  PRI.ACTIVE.ACCTS  \\\n",
       "0   No Bureau History Available                0                 0   \n",
       "1                 I-Medium Risk                1                 1   \n",
       "2   No Bureau History Available                0                 0   \n",
       "3              L-Very High Risk                3                 0   \n",
       "4   No Bureau History Available                0                 0   \n",
       "\n",
       "   PRI.OVERDUE.ACCTS  PRI.CURRENT.BALANCE  PRI.SANCTIONED.AMOUNT  \\\n",
       "0                  0                    0                      0   \n",
       "1                  1                27600                  50200   \n",
       "2                  0                    0                      0   \n",
       "3                  0                    0                      0   \n",
       "4                  0                    0                      0   \n",
       "\n",
       "   PRI.DISBURSED.AMOUNT  SEC.NO.OF.ACCTS  SEC.ACTIVE.ACCTS  SEC.OVERDUE.ACCTS  \\\n",
       "0                     0                0                 0                  0   \n",
       "1                 50200                0                 0                  0   \n",
       "2                     0                0                 0                  0   \n",
       "3                     0                0                 0                  0   \n",
       "4                     0                0                 0                  0   \n",
       "\n",
       "   SEC.CURRENT.BALANCE  SEC.SANCTIONED.AMOUNT  SEC.DISBURSED.AMOUNT  \\\n",
       "0                    0                      0                     0   \n",
       "1                    0                      0                     0   \n",
       "2                    0                      0                     0   \n",
       "3                    0                      0                     0   \n",
       "4                    0                      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 CREDIT.HISTORY.LENGTH  \\\n",
       "0                                    0        0yrs 0mon             0yrs 0mon   \n",
       "1                                    1       1yrs 11mon            1yrs 11mon   \n",
       "2                                    0        0yrs 0mon             0yrs 0mon   \n",
       "3                                    0        0yrs 8mon             1yrs 3mon   \n",
       "4                                    0        0yrs 0mon             0yrs 0mon   \n",
       "\n",
       "   NO.OF_INQUIRIES  loan_default  \n",
       "0                0             0  \n",
       "1                0             1  \n",
       "2                0             0  \n",
       "3                1             1  \n",
       "4                1             1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(233154, 47)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disbursed Amount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Univariate Anlaysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    233154.000000\n",
       "mean      54356.993528\n",
       "std       12971.314171\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"disbursed_amount\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25c88d462e8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"disbursed_amount\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25c994582b0>"
      ]
     },
     "execution_count": 166,
     "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.distplot(np.log(df[\"disbursed_amount\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.4922396638902455\n"
     ]
    }
   ],
   "source": [
    "print(df[\"disbursed_amount\"].skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "249.9892735051841\n"
     ]
    }
   ],
   "source": [
    "print(df[\"disbursed_amount\"].kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25c87d59ac8>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df[\"disbursed_amount\"])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Most of the data are close towards the center as mean and median are almost identical."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loan to value ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"ltv\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_0=df[df[\"loan_default\"]==0]\n",
    "loan_1=df[df[\"loan_default\"]==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(loan_0[\"ltv\"])\n",
    "sns.distplot(loan_1[\"ltv\"])\n",
    "plt.legend([0,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### higher the ltv,There are chances of getting defaulted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0:Mean_of_0==Mean_of_1 \n",
    "### H1:Mean_of_0!=Mean_of_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-47.65077542766104, pvalue=0.0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(loan_0[\"ltv\"],loan_1[\"ltv\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_default\n",
       "0    74.154093\n",
       "1    76.883322\n",
       "Name: ltv, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"loan_default\").mean()[\"ltv\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### p-value less than .05 it failed to reject the null hypothesis which shows it is significant variable in spitting target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## asset_cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is the orginal price of the vehicle from which depreceated value can be calculated over the course of its useful life "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.331540e+05\n",
       "mean     7.586507e+04\n",
       "std      1.894478e+04\n",
       "min      3.700000e+04\n",
       "25%      6.571700e+04\n",
       "50%      7.094600e+04\n",
       "75%      7.920175e+04\n",
       "max      1.628992e+06\n",
       "Name: asset_cost, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"asset_cost\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25c87ff20f0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KpUazsNrUIBGRNtPVeKdRfaXdvkMnGS6UxssymcoYiBJERNJJPZCYSu5krKoHQuUQVrtaJCLSXgqQmMplH+91AJgG0UUk5RQgMY2VynRkJ3ZXRtN4RSTlFCAxjRWdfFWAqAciImmnAImpUCrTmavqgWQ0jVdE0k0BEtNYsUw+p0NYIiIVCpCYasdAKsPp6oGISFopQGIaK5bpyFWfSKhpvCKSbrECxMzWmdkuM+szsxvqvN5pZveG17eY2aqq124M5bvM7NJmdZrZuaGO3aHOjlD+FjP7npkVzezKF/OmWzF1Flb0XScSikhaNQ0QM8sCtwOXAWuAq8xsTc1i1wD97n4ecBuwIay7BlgPnA+sAz5tZtkmdW4AbnP31UB/qBtgP/AB4J7W3mrrSmWnVHY66g6iz3ZrREReGuL0QF4P9Ln7HncfAzYCl9csczlwd3h8P/AOi642eDmw0d1H3X0v0Bfqq1tnWOeSUAehzisA3H2fu+8Ayi2+15YVStEmO+pM41UPRETSKk6ALAMOVD0/GMrqLuPuReA4sHiadRuVLwYGQh2NtjXrxopRgNSbhaUeiIikVZwAsTpltR+bjZaZqfLYzOxaM9tuZtsPHz6cZNWGxqbpgWgWloikVZwAOQisqHq+HHiu0TJmlgMWAMemWbdR+RGgN9TRaFvTcvc73X2tu69dunRpklUbqvRAJo2BoBMJRSTd4gTINmB1mB3VQTQovqlmmU3A1eHxlcDDHg0ObALWh1la5wKrga2N6gzrbA51EOp8oPW3NzPqBsj4GAg8f2KErz/9gsZDRCRVmgZIGI+4HngI+AFwn7vvNLObzey9YbG7gMVm1gd8FLghrLsTuA94GvgacJ27lxrVGer6GPDRUNfiUDdmdrGZHQTeB9xhZpXlT7t6h7CqL2Wy48AAm3cd4uRose76IiIvR7FuKOXuDwIP1pTdVPV4hOiDvd66twC3xKkzlO8hmqVVW76N6JDWrKvXA7GqS5lUguPESJF5XfnZb6CISBvoTPQY6k3jrRzCKpV9IkCGC7PeNhGRdlGAxDBaZxrveA8EFCAikkoKkBgqPZDO6h5I+F72iR7IcQWIiKSIAiSGuicSZipjIM6pqjEQEZG0UIDEMFYqk8vY+NnnMHEm+nChTKEUTd/VISwRSRMFSAzRpdwn76pKlgxWhcaJEQWIiKSHAiSGQs2l3GGiBzJYddhKYyAikiYKkBhGp+mBVPc6TgxrDERE0kMBEkOhNDVAansgczqyOoQlIqmiAIlhrFgmP+UQVvS9EhqLezo0iC4iqaIAiaH2drYw0QMZGivRnc8ytzOnMRARSRUFSAxjRW84BgIwtzNHd0d20oC6iMjLnQIkhrFiqWEPBKCnM0dXPqtDWCKSKgqQGMamGUQHmNsVBcjgaJGS7nErIimhAImhEOcQVj4LwKBmYolISihAmiiVnZJ7nVlYVT2Qzux4gOhcEBFJCwVIE/VuJlVRmco7tzNPVyVA1AMRkZRQgDRR73a2FZV7gsztzNLVEb2ugXQRSYtYt7RNs2Y9kBLRGEjlUu/qgYhIWihAmpiuBxKNgzg9nTmy4XiWTiYUkbRQgDQxXQ+kMo4+tyuHh9m7GkQXkbRQgDRRGO+B2JTXMmbks0ZnLkvZnYzpEJaIpIcG0ZuodzvbCjNjbmeUwRkz5nXlNYguIqmhAGmiEiCdueyU17LGeIAALOjOawxERFJDAdJEZRA9X+cQlpnRUxUg87tznNAFFUUkJTQG0sR0g+ivWDyH5QvnjD+fr0NYIpIiCpAmJnogUwNk/cUrJz2f35Vnz5GTs9IuEZF20yGsJgrFMvmsTbr2VSPzuyduKvX88RH2HTl1upsnItI2CpAmRuvcjbCRBd358fNAfv/eJ/jw328/nU0TEWkrHcJqolCcei+QRuZ35RkulHj++Ahb9h6l7NF5IfO78qe5lSIis089kCbGSuW64x/1/OiFQQBueuApKveV+j/f2H26miYi0lYKkCbGEvRAKpd0f+zH/ePnhzw3MHza2iYi0k4KkCbGEoyBdHdEAXL01BgXreiltzvPswoQEXmZUoA0kWQMpHJXQoDzz5nPOb3dPNs/ESA7Dg7wk+MKFBF5eVCANDHawiGseV05li+aw7KF3Rw9NcaJkQKDIwXW3/kof3j/jtPZXBGRWaMAaaKQ4BBWJUDWnD2fjBnLersB2PnsCTZ9/zmGxko8svsI+48OATAwNMYVt3+bR3YfPj2NF5FU2vX8IF65x8RppABpYqxUrnsl3nrmd+X45TVn8pbVSwE4JwTIk88O8A9b97NiUTcZg3u37wfgb7/1DE8cGODm//s0pfLp/2GLyMvPqdHipM+PXc8P8p6/foTPPLL3tG871iejma0zs11m1mdmN9R5vdPM7g2vbzGzVVWv3RjKd5nZpc3qNLNzQx27Q50dzbZxOo0V4/dAzIy3v/oMFvZ0ANGVehd05/nH7Qd56tkT/OdfeiWXvOYM7tt+kIP9Q3z+O/t45ZIedh86yVcefxaAvkOD/NFXntRYiYhMsfuFQb63v3/8+Y+PnuKtn9zMb3zmUcaKZUpl54Yv72BeV55f+4Xlp709TT8ZzSwL3A5cBqwBrjKzNTWLXQP0u/t5wG3AhrDuGmA9cD6wDvi0mWWb1LkBuM3dVwP9oe6G2zidCqUyZa9/IcW4lvV2s/vQSbryGS6/cBnrL17J4cFRPvi5bZTKzuc+eDGvXbaA277+I548eJz33/Eo92zZz/vveJSD/UMcHyrw0Xuf4JdufZjNPzwEwGixxD1b9nP3d/YxPFYCwN15fH8/B44NTdr+kZOjlNW7EWmbEyMFRgql8efFUpmte49x5OToeNmPXhjk9s199B2KrqU3NFbklv/3NO/+q0f4991HAPjaU8/zH//m37nyb7/D57+9l+NDBT70+W0Mj5V4dM8xbvjyDr7w3X08vn+AP3nPz7Io/CN7Olmz42Rm9ibgT9390vD8RgB3/4uqZR4Ky3zXzHLA88BS4IbqZSvLhdWm1An8JXAYOMvdi9XbbrQNn+YNrF271rdvT345kecGhtm47QCf//ZeTowU+U8XLWPtqkWJ6wHYvOsQX3/6BS5a0cv71q6gVHY++dAPOTFS5Lfe+Ar+/IoLeGT3YX7rrq1kM8aSuR380bt+lj/+56eY35WnWC5z9OQYZ/d2ceDYMO9+7dk8cWBgfHrw0nmdXHHhOXzzB4fYc+QUZvDWn1nKBecs4Bs/eIEfPj/ImfM7ueyCs+npzPLI7iPsen6Qi1b28ubzljA4UmTbvmMcOzXGRSsXcsGyBTw3MMyTB48DcMGyBbxi8Rz2HjlF36GTzOvK8eqz5tHbnWfvkVMc6B/mzPmdvGrpXMyMHx89xdGTYyxb2M2KRXMYGi1ysH+Y4UKJZb3dnDm/i/6hMZ4bGCabMc7p7aa3O8/hwVEODY4ypyPLmfO76MpnOTQ4Qv+pMRZ05zljfhfuzuGTY5wcKbJ4bgeLezoYKZQ4emqMsVKZJT2d0eVkRgocOzVGNmMs6umgpyPHwPAYA0MFuvJZFvZ0kM8Y/UMFTo4WmNuZp3dOnrI7A0MFRgsl5nfnmd+VZ6RY4vhQgWLZ6Z2Tp6czx6nRIseHC2TN6J2TpzOf5cRwgcGRIl35LPO7c+QyxonhIqfGivR05JjfnaNYdk4MFxktlpjXlWdeV47RQokTI0XKZWd+d57ujixDo0VOjBTJZaOblHVmMwyOFjk5UqQrn2F+dx4DBkei+ud25pjXNVH/WLHMvK4cc7tyjBRKnBgu4M54/SdHipwYKZDPRnV1ZjMcHy4wOFqkO59lQXceMxgYKjA8VmRuV9STHis5A2FfLwj759RYkYGh6PpvvXPy9HTkODFSoH9ojI5slkU9efLZDMeGxjg+XGBuR45Fcztwh6Mnxzg5WqB3TvSzHC6UOHJylNFCmSXzOlk4J7q/zgsnRskYnDGvi3ldOQ4PjvLC4Ag9HTnOnN9FRy7DT44Pc3hwlEU9nZzT20Wh5OP/gJ21oItzers5Plzgx0dPMVIo84rFczhrQRfPDQyz5/ApctkMr1raw6KeDvoOnaTv0EkW9nTwmjPn0ZnP8OSzJ9hz+CSrlvTw2mULOBn+bp47PszPLe/lwuW9PHP4JI/sPsKpsSK/+KolnL9sPt/pO8p39xylM5fh7a85g5WL5vCV7z3L8ydGyGeNS88/CwcefPInuEPG4F2vPZsdB4+z/9gQZ8zr5NDgKG8+bwnffuYIr1vey5K5nXzjBy9w9oIujpwc5YvXvIFH9xzjtm/8iIzBm1cv5e4PXozFuH5fI2b2mLuvbbpcjAC5Eljn7h8Oz38LeIO7X1+1zFNhmYPh+TPAG4jC4lF3/2Iovwv4alhtSp1Vy58XylcAX3X3Cxptw92P1LT3WuDa8PTVwK4m+2AJcKTJMu2gdiWjdiWjdiX3Um3b6WjXK9x9abOF4lwLq16M1aZOo2Ualdc7JjTd8nHbgbvfCdxZZ9m6zGx7nKSdbWpXMmpXMmpXci/VtrWzXXEO7h8EVlQ9Xw4812iZcHhpAXBsmnUblR8BekMdtdtqtA0REWmDOAGyDVgdZkd1EA2Kb6pZZhNwdXh8JfBwGJvYBKwPM6jOBVYDWxvVGdbZHOog1PlAk22IiEgbND2EFQazrwceArLAZ919p5ndDGx3903AXcAXzKyPqFewPqy708zuA54GisB17l4CqFdn2OTHgI1m9gng8VA3jbYxA2If7pplalcyalcyaldyL9W2ta1dTQfRRURE6tGZ6CIi0hIFiIiItMbdU/lFdGb8LqAPuGEG611BNBHgB8BO4PdC+SLg68Du8H1hKDfgr0I7dgA/X1XX1WH53cDVVeW/ADwZ1vkrJg5F1t1GTfuyRGNL/xKenwtsCevcC3SE8s7wvC+8vqqqjhtD+S7g0mb7tNE2ql7vBe4Hfhj225teCvsL+P3wM3wK+Aegq0376/PAIeCpqmXatn9qtnEsfFW37ZPhZ7kD+ArQO9O/OzH29wmicdd9df4G/oDoFIAlbdhnlXb11bTpd8L73wnc2ob9NWUbsT7vZuqD86fpi+hD9BnglUAH8H1gzQzVfXblFxCYB/yI6HItt1Z+0ERn6G8Ij99FdHKlAW8EtlT9Iu4J3xeGx5Vfxq1EH7IW1r0slNfdRk37Pgrcw0SA3AesD4//Dvgv4fFvA38XHq8H7g2P14T91Rl+WZ8J+7PhPm20jao23Q18ODzuIAqUtu4vYBmwF+iueg8faNP++l/AzzP5Q7pt+6dmGx8h+iCtbtuvALnweEPVejP2uxNjf78DeA8wCmSr2raCaPLOj5kIkNncZ28hCqWhqja9HfgG0Bmen9GG/TVpG7E/79rxAd7ur/CDf6jq+Y3AjadpWw8Av0yU7meHsrOBXeHxHcBVVcvvCq9fBdxRVX5HKDsb+GFV+fhyjbZRtexy4JvAJcC/hF/+I0z8sY/vl/BH9qbwOBeWs9p9VVmu0T6dbhvh+XyiD2qraWtb9xdRgBwg+vDIhf11abv2F7CKyR/Sbds/dbbxTHUdNT/HXwW+VO/v7MX87sTZ32GfDVaWC2X3A68D9jERILO6z0K7RquWuw94Z519N6v7q3a5OF9pHQOpfDhUHAxlMypcMfgioi7jme7+E4Dw/YwmbZmu/GCDtjfaRsWngD8EyuH5YmDA3Yt16hrffnj9eFg+aXun2wZE/0UdBj5nZo+b2WfMrGea9zIr+8vdnwX+J7Af+El4/4+9BPZXRTv3T21dz9P4lIAPMXH5opncF3H3d6Gyjpm9F3jW3b9f08Z27LNC1To/A/xSuMr4v5nZxS22ayb/nmNJa4DEuizKi9qA2Vzgn4D/6u4nWmhL0vJm7XkPcMjdH4ux7ZlsV7P25ogOz/ytu18EnCJchLOB2dpfC4HLibr15wA9RFePblTXbO2vZmZje7Gu0mdmHyc63v+l09C2ROuY2Rzg48BN9Zo6g+1qZLp1ckSHyN4I/HfgPouuhNi2/VWnrK60Bkicy7O0zMzyROHxJXf/cih+wczODq+fTTQoOl1bpitfXqd8um0A/CLwXjPbB2wkOoz1KZJfOmYmL09T2c5Bd98Snt9PFCjt3l89uHlOAAAEu0lEQVTvBPa6+2F3LwBfBv7DNO9ltvZXRTv3T21dZxEFxTgzu5poDOI3PBwbaaFtrVzaqLaufFjnVUT/DHw//A0sB75nZme10K6Z2Gd5Jr+XL3tkK9ERgiVt2l/JPgvjHut6OX0RJf4eol+oyiDU+TNUtwF/D3yqpvyTTB5cuzU8fjeTB/C2hvJFRGMDC8PXXmBReG1bWLYygPeu6bZRp41vY2IQ/R+ZPOj22+HxdUwedLsvPD6fyYNue4gG9Rru00bbqGrPI8Crw+M/De+jrfuL6OrQO4E5Yb27iWbKtGV/MXUMpG37p842nqhp2zqiq08srfk5z9jvTsz9/WZgjDqDwkweA5ntffarTB5E/whwc3j8M0SHlKwN+2t8G7E/72bjA/ul+EU0K+JHRAOAH5/Bet9M1AXcEf6wngjbWkw0gL07fK/8IhrRzbWeIZrNsraqrg8RTa/rAz5YVb6WaGrpM8DfMDGFsO426rTxbUwEyCuJZpT0hV++ykyQrvC8L7z+yqr1Px62vYsw+2S6fdpoG1WvXwhsD/vsn4n+WNu+v4A/I5qO+hTwhfBH1o79dS/ROEyB6D/Ga9q5f2q2MUD0n2912/qIPgQrv/9/N9O/OzH29yBRr6hYaVfNz3Yfk6fxztY+Gwz7arxdRCHwxVDf94BL2rC/pmwjzpcuZSIiIi1J6xiIiIi8SAoQERFpiQJERERaogAREZGWKEBERKQlChCRWWJmF5rZu2ZhOx8ws3NO93ZEFCAis+dCovn7p9sHiC6/InJaKUBE6jCzfzazx8xsp5lda2ZZM/u8mT1lZk+a2e+H5X7XzJ42sx1mtjGU9ZjZZ81sW7hA5OVm1gHcDLzfzJ4ws/c32O5cM/tc2MYOM/u1UH5VKHvKzDaEsiltMrMriU5y+1LYTvds7C9Jp0ZX0BRJuw+5+7HwAbyN6Eq8y9z9AgAz6w3L3QCc6+6jVWUfBx529w+Fsq1E93u4iegs5+un2e6fAMfd/bVhOwvD4agNRDcx6gf+1cyuIDrbe1Kb3H3AzK4H/sDdt8/UzhCpRz0Qkfp+18y+DzxKdLG5DuCVZvbXZraO6M5yEF1+5Utm9ptMXFDwV4AbzOwJ4FtEl5FYGXO77yS65AUA7t4PXAx8y6MLO1aubvsWousW1WuTyKxQgIjUMLO3EX2Qv8ndX0d0C+BOohsRfYvownSfCYu/m+gD/xeAx8KVTg34NXe/MHytdPcfxN08Uy+nXffy6SFc6rVJZFYoQESmWgD0u/uQmb2G6KqrS4CMu/8T0WGmnzezDLDC3TcT3airF5hLdFe33wn3dMDMLgr1DhLd5ng6/wqMH+IK9yXZArzVzJaYWZboDnj/ZmZT2pRgOyIvmi6mKFLDzDqJrgq8jOgKpUuBrxDdy7ryT9eNROMam4kCx4AvuvtfhnGTTxHdP8SAfe7+HjNbRBQueeAv3P3eOtuey0SPpgT8mbt/2cx+nYnblj7o7n9oZq8DPlfdJnf/ahh4/x/AMFEvangGd4/IOAWIiIi0RIewRESkJZrGK9IGZvZB4Pdqir/t7te1oz0irdAhLBERaYkOYYmISEsUICIi0hIFiIiItEQBIiIiLVGAiIhIS/4/1e7aCtnhG9EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"asset_cost\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.133485335511084\n"
     ]
    }
   ],
   "source": [
    "print(df[\"asset_cost\"].skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "291.49393926915474\n"
     ]
    }
   ],
   "source": [
    "print(df[\"asset_cost\"].kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25c885b2080>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(df[\"disbursed_amount\"],df[\"asset_cost\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>disbursed_amount</th>\n",
       "      <th>asset_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>disbursed_amount</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.752668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asset_cost</th>\n",
       "      <td>0.752668</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  disbursed_amount  asset_cost\n",
       "disbursed_amount          1.000000    0.752668\n",
       "asset_cost                0.752668    1.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"disbursed_amount\",\"asset_cost\"]].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disbursed amount and asset cost seems to be positively correlated which is seen in both scatterplot as well as in correlation table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0:Mean_of_0==Mean_of_1 \n",
    "### H1:Mean_of_0!=Mean_of_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-6.886906209368185, pvalue=5.716223071536896e-12)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(loan_0[\"asset_cost\"],loan_1[\"asset_cost\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_default\n",
       "0    75722.806128\n",
       "1    76378.176661\n",
       "Name: asset_cost, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"loan_default\").mean()[\"asset_cost\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### p-value less than .05 it failed to reject the null hypothesis which shows it is significant variable in spitting target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Branch_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"branch_id\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25c88ad6400>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"branch_id\"].value_counts().plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#comparison with target variable:\n",
    "com_df=pd.crosstab(df[\"branch_id\"],df[\"loan_default\"],normalize=\"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manufacturer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.727469</td>\n",
       "      <td>0.272531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.771501</td>\n",
       "      <td>0.228499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.779250</td>\n",
       "      <td>0.220750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.781213</td>\n",
       "      <td>0.218787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.782536</td>\n",
       "      <td>0.217464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.792053</td>\n",
       "      <td>0.207947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.795406</td>\n",
       "      <td>0.204594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0.799486</td>\n",
       "      <td>0.200514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "loan_default            0         1\n",
       "manufacturer_id                    \n",
       "153              0.666667  0.333333\n",
       "48               0.727469  0.272531\n",
       "45               0.771501  0.228499\n",
       "120              0.779250  0.220750\n",
       "49               0.781213  0.218787\n",
       "67               0.782536  0.217464\n",
       "51               0.792053  0.207947\n",
       "86               0.795406  0.204594\n",
       "145              0.799486  0.200514\n",
       "152              1.000000  0.000000\n",
       "156              1.000000  0.000000"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "com_df.sort_values(1,ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(14,7))\n",
    "com_df.sort_values(1,ascending=False)[:15].plot.bar(figsize=(12,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### These branches have highest number of defaulters`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0-There is no siginificant relationship between the branches and no_of_defaulters\n",
    "### H1-There is  siginificant relationship between the branches and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import chi2_contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "conti_table=pd.crosstab(df[\"branch_id\"],df[\"loan_default\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_2,p_value,dof,exp=chi2_contingency(conti_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_2- 2930.842784292354 p_value- 0.0\n"
     ]
    }
   ],
   "source": [
    "print(\"chi_2-\",chi_2,\"p_value-\",p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### P_value less than .05 which rejects null hypotheis in favour of alternate hypothesis.There is significant relationship between the branches and no_of_defaulters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### manufacturer_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"manufacturer_id\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#comparison with target variable:\n",
    "com_df=pd.crosstab(df[\"manufacturer_id\"],df[\"loan_default\"],normalize=\"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manufacturer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.727469</td>\n",
       "      <td>0.272531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.771501</td>\n",
       "      <td>0.228499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.779250</td>\n",
       "      <td>0.220750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.781213</td>\n",
       "      <td>0.218787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.782536</td>\n",
       "      <td>0.217464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.792053</td>\n",
       "      <td>0.207947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.795406</td>\n",
       "      <td>0.204594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0.799486</td>\n",
       "      <td>0.200514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "loan_default            0         1\n",
       "manufacturer_id                    \n",
       "153              0.666667  0.333333\n",
       "48               0.727469  0.272531\n",
       "45               0.771501  0.228499\n",
       "120              0.779250  0.220750\n",
       "49               0.781213  0.218787\n",
       "67               0.782536  0.217464\n",
       "51               0.792053  0.207947\n",
       "86               0.795406  0.204594\n",
       "145              0.799486  0.200514\n",
       "152              1.000000  0.000000\n",
       "156              1.000000  0.000000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "com_df.sort_values(1,ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25c8a676cc0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7lZiYqPz8fFmWpaKiIpWXl8vn82nlypWSpIyMDB04cEDLly9XXFyc8vPzJUkul0sLFy5UQUGBJOm+++6Ty+WSJC1ZskQlJSXq6OhQenq6MjIyelIuAAAA0Occ1tfZLD1AXFp1/7q6Hp3fy5Vcn9jN79hy3p7ibavoQM/RIdp6jrZ+JXqOFvTcd/pkSwkAAACAr0bgBgAAAAwicAMAAAAGEbgBAAAAg3p0lxIMfD25ULQnHxQ0UC8UBQAA+LpY4QYAAAAMInADAAAABhG4AQAAAIMI3AAAAIBBBG4AAADAIAI3AAAAYBCBGwAAADCIwA0AAAAYROAGAAAADCJwAwAAAAYRuAEAAACDCNwAAACAQQRuAAAAwCACNwAAAGAQgRsAAAAwiMANAAAAGETgBgAAAAwicAMAAAAGEbgBAAAAgwjcAAAAgEEEbgAAAMAgAjcAAABgEIEbAAAAMIjADQAAABhE4AYAAAAMInADAAAABhG4AQAAAIMI3AAAAIBBBG4AAADAIAI3AAAAYBCBGwAAADCIwA0AAAAYROAGAAAADCJwAwAAAAYRuAEAAACDCNwAAACAQQRuAAAAwCACNwAAAGAQgRsAAAAwiMANAAAAGETgBgAAAAwicAMAAAAGOXv6BMFgUM8995w8Ho+ee+45NTU1acOGDQoEAho3bpyeeuopOZ1OXbhwQRs3blR9fb2GDh2qFStWKDExUZK0c+dOlZeXKyYmRosWLVJ6erokqbq6WqWlpQoGg5o7d67y8vJ6Wi4AAADQp3q8wv1v//ZvGjNmTOjxb37zG82bN0/FxcVKSEhQeXm5JKm8vFwJCQl6/fXXNW/ePL311luSpOPHj6uyslKvvvqqVq9era1btyoYDCoYDGrr1q16/vnnVVRUpPfff1/Hjx/vabkAAABAn+pR4G5padGBAwc0d+5cSZJlWfrwww+VlZUlSZo9e7aqqqokSfv379fs2bMlSVlZWTp06JAsy1JVVZWys7M1aNAgJSYmatSoUaqrq1NdXZ1GjRqlkSNHyul0Kjs7O/RcAAAAwEDRo8C9bds2Pfjgg3I4HJKks2fPKj4+XrGxsZIkj8cjv98vSfL7/fJ6vZKk2NhYxcfH6+zZs1eMX37M/x33er2h5wIAAAAGirD3cP/3f/+3hg8frtTUVH344YdfOd+yrKvGHA5Ht+NfNr87ZWVlKisrkyQVFhbK5/N9ZT3daQzrqJ4Lt97eEI0994TT6RywtYeLnqNDtPUcbf1K9Bwt6Ll/Cjtwf/zxx9q/f78OHjyojo4Otbe3a9u2bWpra1NXV5diY2Pl9/vl8XgkXVyhbmlpkdfrVVdXl9ra2uRyuULjl1x+zOXjLS0tcrvd3daSm5ur3Nzc0OPm5uZw27LFQKu3NwzUnn0+34CtPVz0HB2iredo61ei52hBz30nKSnpuueGvaXk+9//vt58801t2rRJK1as0K233qrly5crLS1Ne/fulSRVVFQoMzNTknT77beroqJCkrR3716lpaXJ4XAoMzNTlZWVunDhgpqamnTy5ElNmDBB48eP18mTJ9XU1KTOzk5VVlaGngsAAAAYKHp8W8D/64EHHtCGDRv0u9/9TuPGjVNOTo4kKScnRxs3btRTTz0ll8ulFStWSJJSUlJ01113aeXKlYqJidEjjzyimJiLfwcsXrxYa9asUTAY1Jw5c5SSktLb5QIAAABG9UrgTktLU1pamiRp5MiRWrdu3VVz4uLitHLlym6PX7BggRYsWHDV+PTp0zV9+vTeKBEAAACwBZ80CQAAABhE4AYAAAAMInADAAAABhG4AQAAAIMI3AAAAIBBBG4AAADAIAI3AAAAYBCBGwAAADCIwA0AAAAYROAGAAAADCJwAwAAAAYRuAEAAACDCNwAAACAQQRuAAAAwCACNwAAAGAQgRsAAAAwiMANAAAAGETgBgAAAAwicAMAAAAGEbgBAAAAgwjcAAAAgEEEbgAAAMAgAjcAAABgEIEbAAAAMIjADQAAABhE4AYAAAAMInADAAAABhG4AQAAAIMI3AAAAIBBBG4AAADAIAI3AAAAYBCBGwAAADDIaXcBQF/renR+2Mc29uC8sZvf6cHRAABgoGKFGwAAADCIwA0AAAAYROAGAAAADCJwAwAAAAYRuAEAAACDCNwAAACAQQRuAAAAwCACNwAAAGAQgRsAAAAwiMANAAAAGETgBgAAAAwicAMAAAAGEbgBAAAAgwjcAAAAgEEEbgAAAMAgZ7gHNjc3a9OmTTp16pQcDodyc3P1rW99S4FAQEVFRfr8889144036umnn5bL5ZJlWSotLdXBgwc1ePBg5efnKzU1VZJUUVGhHTt2SJIWLFig2bNnS5Lq6+u1adMmdXR0KCMjQ4sWLZLD4eh51wAAAEAfCXuFOzY2Vg899JCKioq0Zs0avffeezp+/Lh27dqlqVOnqri4WFOnTtWuXbskSQcPHlRDQ4OKi4u1dOlSbdmyRZIUCAS0fft2rV27VmvXrtX27dsVCAQkSZs3b9Zjjz2m4uJiNTQ0qLq6uhdaBgAAAPpO2IHb7XaHVqiHDBmiMWPGyO/3q6qqSrNmzZIkzZo1S1VVVZKk/fv3a+bMmXI4HJo0aZLOnTun1tZWVVdXa9q0aXK5XHK5XJo2bZqqq6vV2tqq9vZ2TZo0SQ6HQzNnzgw9FwAAADBQ9Moe7qamJn366aeaMGGCTp8+LbfbLeliKD9z5owkye/3y+fzhY7xer3y+/3y+/3yer2hcY/H0+34pfkAAADAQBL2Hu5LvvjiC61fv14PP/yw4uPjrznPsqyrxq61H9vhcHQ7/1rKyspUVlYmSSosLLwi2H8djWEd1XPh1tsb6Lnv2NlzTzidzgFbe7joOfJFW78SPUcLeu6fehS4Ozs7tX79en3jG9/QnXfeKUkaPny4Wltb5Xa71draqmHDhkm6uELd3NwcOralpUVut1sej0eHDx8Ojfv9fk2ZMkVer1ctLS1XzPd4PN3WkZubq9zc3NDjy88zEAy0ensDPQ8cPp9vwNYeLnqOfNHWr0TP0YKe+05SUtJ1zw17S4llWXrzzTc1ZswY3XPPPaHxzMxM7d69W5K0e/duzZgxIzS+Z88eWZalI0eOKD4+Xm63W+np6aqpqVEgEFAgEFBNTY3S09Pldrs1ZMgQHTlyRJZlac+ePcrMzAy3XAAAAMAWYa9wf/zxx9qzZ4/Gjh2rZ555RpL0ve99T3l5eSoqKlJ5ebl8Pp9WrlwpScrIyNCBAwe0fPlyxcXFKT8/X5Lkcrm0cOFCFRQUSJLuu+8+uVwuSdKSJUtUUlKijo4OpaenKyMjo0fNAgAAAH0t7MB9yy236Pe//32333vxxRevGnM4HFqyZEm383NycpSTk3PV+Pjx47V+/fpwSwQAAABsxydNAgAAAAYRuAEAAACDCNwAAACAQQRuAAAAwKAef/ANgP6v69H5YR/bkw8Kit38Tg+OBgAgMrDCDQAAABhE4AYAAAAMInADAAAABhG4AQAAAIMI3AAAAIBBBG4AAADAIAI3AAAAYBCBGwAAADCIwA0AAAAYROAGAAAADCJwAwAAAAYRuAEAAACDCNwAAACAQQRuAAAAwCACNwAAAGAQgRsAAAAwiMANAAAAGETgBgAAAAxy2l0AAJjQ9ej8sI9t7MF5Yze/04OjAQCRiMANABGgJ39gSPyRAQAmsaUEAAAAMIjADQAAABhE4AYAAAAMInADAAAABhG4AQAAAIMI3AAAAIBBBG4AAADAIAI3AAAAYBCBGwAAADCIwA0AAAAYROAGAAAADCJwAwAAAAYRuAEAAACDCNwAAACAQQRuAAAAwCACNwAAAGAQgRsAAAAwiMANAAAAGETgBgAAAAwicAMAAAAGOe0uAACAcHQ9Oj/sYxt7eO7Yze/08BkARBNWuAEAAACDCNwAAACAQf1+S0l1dbVKS0sVDAY1d+5c5eXl2V0SAAAAcN36deAOBoPaunWrfvzjH8vr9aqgoECZmZlKTk62uzQAAPoc+9aBgalfbympq6vTqFGjNHLkSDmdTmVnZ6uqqsrusgAAAIDr1q8Dt9/vl9frDT32er3y+/02VgQAAAB8PQ7Lsiy7i7iW//qv/1JNTY2WLVsmSdqzZ4/q6uq0ePHiK+aVlZWprKxMklRYWNjndQIAAADX0q9XuL1er1paWkKPW1pa5Ha7r5qXm5urwsJCW8P2c889Z9u57ULP0YGeo0O09Rxt/Ur0HC3ouX/q14F7/PjxOnnypJqamtTZ2anKykplZmbaXRYAAABw3fr1XUpiY2O1ePFirVmzRsFgUHPmzFFKSordZQEAAADXLfall156ye4ivszo0aP1zW9+U9/61rc0efJku8v5UqmpqXaX0OfoOTrQc3SItp6jrV+JnqMFPfc//fqiSQAAAGCg69d7uAEAAICBjsANAAAAGETgBgAAAAzq13cp6a/++Mc/Kjs7W8OGDVNDQ4PeeOMNffbZZ0pKStKyZcs0duxYu0vsdZ999pluuukmSVJnZ6f++Z//WXV1dUpJSdHChQs1ePBgmytETzU2Nurtt9+Wx+NRXl6etm3bpqNHj2rMmDF68MEHlZiYaHeJvS4YDKqiokIffPCB/H6/YmJiNHr0aP3N3/yN0tLS7C4PABAhWOEOw7//+79r2LBhkqTS0lLNmzdP27Zt0wMPPKDNmzfbXJ0ZJSUloa9/+9vfqqGhQffee686Ojoitucvs3btWrtL6HUlJSWaMGGCbrjhBq1evVpjxoxRQUGB0tPT9cYbb9hdnhFvvvmmmpub9e1vf1tpaWmaPn26cnJytGPHDv3hD3+wuzyjLMvS0aNH9cEHH2jfvn06evSoIvUa+lWrVuntt99WQ0OD3aXYKhAI2F2CccFgUMFgUNLFxaH6+vqo6Luzs/OqsTNnzthQiXmdnZ1X/Ft16NAh/cu//IsOHjxoY1VfjRXuMHR1dYW+PnPmjO644w5JUlpamtrb2+0qy6jLf7hra2u1bt06OZ1OTZ48Wc8884yNlZlTX19/ze/9z//8T98V0kfa29v1t3/7t5Kk9957T/fee68kKScnR3/84x/tLM2Y+vp65efnS5JuueUWrV69Wn//93+vyZMn69lnn9U3v/lNmys0o6amRlu2bNHo0aPl8XgkXfwk34aGBi1ZskS33XabzRX2rkAgoHPnzumnP/2pRowYobvvvlvZ2dmh3iPR22+/rYULF0qSjh8/rldeeSUUylasWKGJEyfaWZ4R+/bt0+bNm+VwOPToo49q586dGjx4sE6ePKklS5ZE5AfnHTp0SBs3btSFCxc0btw4LV26NPRu5Jo1a/Tyyy/bXGHvKygo0E9+8hO5XC6988472rdvnzIyMvTuu+/qo48+0ve//327S+wWgTsMWVlZ2rRpk+677z7NmDFD//qv/6o777xTtbW18vl8dpdnRFtbm/bt26dgMKjOzk45nRd/dBwOhxwOh83VmVFQUKApU6Z0+71z5871cTXmORwOnThxQm1tbero6NAnn3yi8ePHq6GhIbRiFGliY2PV0NCgUaNGqb6+PvRzPWjQIJsrM2vbtm164YUXrtom1NTUpHXr1qmoqMimysxwuVz6wQ9+oB/84Af66KOP9P7772vVqlVKTk7W3XffrdzcXLtL7HX79u0LBe5f//rXevjhh5WRkaG6ujpt27ZNP//5z22usPdt375dr7zyijo6OvTMM89o3bp1SkpK0ueff67169dHZOB+6623tHr1aqWkpGjv3r36+c9/rieffFKTJk2K2HesgsGgXC6XJKmyslI/+9nPFBcXp7y8PK1atYrAHUm+973vqaKiQq+99poaGxt14cIFlZWVacaMGVq+fLnd5RkxefJk7d+/X5I0ceJEnTp1SiNGjNCpU6c0dOhQm6szIzk5WUuXLtXo0aOv+t7jjz9uQ0VmPfjgg3r55ZcVExOjZ555Rjt37tSxY8fU1tampUuX2l2eEQ899JB++tOfatCgQerq6tIPf/hDSRffubr99tttrs6crq4ueb3eq8Y9Hk+3b00PdJcHj8mTJ2vy5MlavHix/vKXv6iysjIiA/flWltblZGRIUmaMGGCOjo6bK7InBEjRkiSfD7AyVEhAAAHQ0lEQVSfkpKSJEk33nhjxIbPzs7O0CdwZ2VlacyYMfrFL36hBx54IGIXw4YMGaJjx45p7NixGjp0qDo6OhQXF6eurq5+/ToTuMPQ2dkph8Oh7373u5o2bZr+/Oc/6+OPP5bH41FcXJzd5RnxxBNPXDW2ceNGPfnkk3rxxRdtqMi8+++//5r/8y5atKiPqzFv6tSpeu2110KPb7nlFhUWFurZZ59VTExkXu5x6623qqSkRGfPng1dl3Hp5/rBBx+0uTpz5syZo4KCAmVnZ4felWtublZlZaVycnJsrq73XQpel4uJiVF6errS09NtqMi8xsZGvfzyy7IsSy0tLTp//nzo4vbLt0VGmmAwqJiYmCsWRS69MxuJYmNjQwtgkpSSkqIXX3xRhYWFamxstLk6Mx599FG9/vrruummmzR8+HAVFBRo8uTJOnbsmL797W/bXd418UmTYSguLlZXV5fOnz+vhIQEnT9/XnfccYdqa2tlWZaefPJJu0vsdd3tAzt06JBuvfVWSRcvSop0f/3rX0N3Zom0Pa5SdL7G0djzJcePH9f+/fvl9/tlWZa8Xq8yMzOVnJxsd2m97tLdduLj49XR0aGdO3fq008/VXJyshYsWKD4+Hi7S+x1hw8fvuJxamqqbrjhBp06dUp79+7V3/3d39lUmTl1dXUaO3bsVQtfTU1N+utf/6qZM2faVJk5f/nLXzRs2DDdfPPNV4yfO3dO7733nhYsWGBPYYYFg0HV1NTo5MmToXfsbrvtNiUkJNhd2jWxwh2GY8eO6Re/+IW6urq0bNky/cM//INiYmL0jW98I2IvIGxpaVFycrLmzp0rh8Mhy7L0ySefhC6si0QFBQVat26dJKmsrEzvvfee7rjjDm3fvl2ffvqp8vLybK6wd0XjaxyNPV+SnJwckeG6O2+88YZeeeUVSRfvLDV48GDl5eWptrZWJSUl+tGPfmRzhb2vu+tPTp8+rREjRkRk2JYubpfpTmJiYkTe1lSSpk2b1u14QkJCxIZt6eI7VBkZGaGtUgNBZL5PbJhlWers7FR7e7vOnz+vtrY2SdKFCxci9q26wsJCpaamaseOHYqPj1daWpri4uI0ZcqUa15YONBd/lr+x3/8h1544QXdf//9Wr16tf785z/bWJkZ0fgaR2PP0sWLoH/729/q9ddf13/+539e8b0tW7bYVJU5lmUpNjZW0sU70zz88MO65ZZbdP/990fs2+6BQOCq/55//vnQ15Gouro69PW5c+f0xhtv6Ec/+pFee+01nTp1ysbKzLm857a2Nnruxz2zwh2GOXPmaMWKFQoGg/rud7+rV199VYmJiTp69Kiys7PtLs+ImJgY3XPPPbrrrrv0q1/9SsOHD4/YPy4usSxLgUBAlmXJsqzQHt8bbrgh9Ms7kkTjaxyNPUsX77k+evRo3XnnnfrTn/6kvXv36oc//KEGDRqko0eP2l1er0tJSdGf/vQnzZkzRzfddFPoDjwnTpwI3Zkm0jzyyCNX3TXL7/dr1apVcjgc2rhxo02VmfNP//RPoT35v/71r+V2u7Vq1Sp98MEH+uUvf6lnn33W5gp73+U9/+M//iM99+OeI/NfGsPuueeeULD2eDyaNWuWamtrlZube823tCKF1+vVypUrdeDAAQ0ZMsTucoxqa2vTc889J8uy5HA4QhemfPHFF/36SuieiqbX+JJo67mxsTG0jeKOO+7Qjh079LOf/azf/qLqqWXLlqm0tFQ7duzQ0KFD9eMf/1her1der1ePPfaY3eUZ8cADD6i2tlYPPfRQ6NOPn3jiCW3atMnmyvrGJ598EtpGdM8992j37t02V2QePffvngncYbr8AxMSEhKUlZVlYzV9b/r06Zo+fbrdZRh1rV9MDocjYvfqXy4aXuP/K1p67uzsDN3NQZIWLFggj8ejn/zkJ/riiy9srq73xcfH64knnlB7e7saGxsVDAbl8XhCd3aIRPPnz9fdd9+tX/3qV/J6vfrOd74TsbeJu+T06dN69913ZVmW2tvbQ4slkiJ2kYSeB07PsS+99NJLdhcBDCROp7NfXwkNfJXm5mY5HA6NHDkyNHbzzTdr5MiRqqmpidhP2Bw0aJBGjBght9utG264we5yjIuPj9ddd92lzs5O/fKXv9SZM2c0f/58u8sypr29XZ2dners7NTNN9+slJQUDR48WKdOndKxY8dCnwodSeh54PTMbQEBACGX9jojsnR0dKihoUFjx46NyteYnqNDf+6Zu5QAAEJ+//vf210CDIiLiwvt5Y7G15ieo0N/7pk93AAQZa5132nLsnT69Ok+rgYmRONrTM//Hz33PwRuAIgyp0+f1urVq6+6FsGyLL3wwgs2VYXeFI2vMT3/f/Tc/xC4ASDKTJ8+XV988cVVHwctdf8JhRh4ovE1pucr0XP/wkWTAAAAgEFcNAkAAAAYROAGAAAADCJwAwAAAAYRuAEAAACDCNwAAACAQf8P1HTiRK863A4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"manufacturer_id\"].value_counts().plot.bar(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Top 2 players in this market seems to have huge market share with them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0-There is no siginificant relationship between the manufacturer and no_of_defaulters\n",
    "### H1-There is  siginificant relationship between the manufacturer and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import chi2_contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "conti_table=pd.crosstab(df[\"manufacturer_id\"],df[\"loan_default\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_2,p_value,dof,exp=chi2_contingency(conti_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_2- 464.69764016358414 p_value- 1.5277380091688753e-93\n"
     ]
    }
   ],
   "source": [
    "print(\"chi_2-\",chi_2,\"p_value-\",p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### P_value less than .05 which rejects null hypotheis in favour of alternate hypothesis.There is significant relationship between the manufactuers and no_of_defaulters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Current_pincode_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6698"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Current_pincode_ID\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25c8a85bf60>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Current_pincode_ID\"].value_counts()[:15].plot.bar(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0-There is no siginificant relationship between the Pincodes and no_of_defaulters\n",
    "### H1-There is  siginificant relationship between the Pincodes and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import chi2_contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "conti_table=pd.crosstab(df[\"Current_pincode_ID\"],df[\"loan_default\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_2,p_value,dof,exp=chi2_contingency(conti_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_2- 12702.76993331368 p_value- 0.0\n"
     ]
    }
   ],
   "source": [
    "print(\"chi_2-\",chi_2,\"p_value-\",p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### P_value less than .05 which rejects null hypotheis in favour of alternate hypothesis.There is significant relationship between the Current pincode and no_of_defaulters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Date_of_Birth and Disbursal Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'26-09-2018'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"DisbursalDate\"][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Date.of.Birth\"]=pd.to_datetime(df[\"Date.of.Birth\"],format=\"%d-%m-%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"DisbursalDate\"]=pd.to_datetime(df[\"DisbursalDate\"],format=\"%d-%m-%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Age_at_time_of_disbursement\"]=((df[\"DisbursalDate\"]-df[\"Date.of.Birth\"])/365).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,2,figsize=(14,7))\n",
    "sns.distplot(df[df[\"loan_default\"]==0][\"Age_at_time_of_disbursement\"],ax=ax[0],label=\"0\")\n",
    "sns.distplot(df[df[\"loan_default\"]==1][\"Age_at_time_of_disbursement\"],ax=ax[1],label=\"1\")\n",
    "ax[0].legend()\n",
    "ax[1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    182543\n",
       "1     50611\n",
       "Name: loan_default, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"loan_default\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Both have same characteristic from the distplot.Let's do statistical test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_of_0=df[df[\"loan_default\"]==0][\"Age_at_time_of_disbursement\"]\n",
    "age_of_1=df[df[\"loan_default\"]==1][\"Age_at_time_of_disbursement\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0:mean_age_of_non_defaulters==mean_age_of_non_defaulters\n",
    "### H1:mean_age_of_non_defaulters!=mean_age_of_non_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=17.65984940031283, pvalue=9.491681743180959e-70)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(age_of_0,age_of_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pvalue less than .05 which rejects null hypothesis in favour of alternate hypothesis that mean age of both groups differ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df[\"year_of_disbursement\"]=df[\"DisbursalDate\"].dt.year\n",
    "df[\"month_of_disbursement\"]=df[\"DisbursalDate\"].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8,  9, 10], dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"month_of_disbursement\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "conti_table=pd.crosstab(df[\"month_of_disbursement\"],df[\"loan_default\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_of_disbursement</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>53336</td>\n",
       "      <td>14666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>53973</td>\n",
       "      <td>12815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>75234</td>\n",
       "      <td>23130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "loan_default               0      1\n",
       "month_of_disbursement              \n",
       "8                      53336  14666\n",
       "9                      53973  12815\n",
       "10                     75234  23130"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conti_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_2,p_value,dof,exp=chi2_contingency(conti_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_2- 439.3615166880304 p_value- 3.925173615141533e-96\n"
     ]
    }
   ],
   "source": [
    "print(\"chi_2-\",chi_2,\"p_value-\",p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### P_value less than .05 which rejects null hypotheis in favour of alternate hypothesis.There is significant relationship between the  month and no_of_defaulters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Employment.Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Self employed    127635\n",
       "Salaried          97858\n",
       "Name: Employment.Type, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Employment.Type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Employment.Type    7661\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()[df.isnull().sum()>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32639371402592277"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "761*100/df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There are 7661 null values in the column which is .32 of the total rows.So,replace them with mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Employment.Type\"]=df[\"Employment.Type\"].fillna(df[\"Employment.Type\"].mode()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### State_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x25c87f6cd30>"
      ]
     },
     "execution_count": 69,
     "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": [
    "print(df[\"State_ID\"].nunique())\n",
    "state=df[\"State_ID\"].value_counts()\n",
    "\n",
    "state[\"percent\"]=state*100/state.sum()\n",
    "\n",
    "state[\"cumsum\"]=state[\"percent\"].cumsum()\n",
    "\n",
    "plt.plot(state[\"cumsum\"].values,\"-\",c=\"black\")\n",
    "plt.scatter(np.arange(0,22),state[\"cumsum\"].values,c=\"r\")\n",
    "plt.axhline(80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### nearly seven states contribute to 80 percent of the customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0-There is no siginificant relationship between the Pincodes and no_of_defaulters\n",
    "### H1-There is  siginificant relationship between the Pincodes and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "conti_table=pd.crosstab(df[\"State_ID\"],df[\"loan_default\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_2,p_value,dof,exp=chi2_contingency(conti_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_2- 1626.8617814468685 p_value- 0.0\n"
     ]
    }
   ],
   "source": [
    "print(\"chi_2-\",chi_2,\"p_value-\",p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### P_value less than .05 which rejects null hypotheis in favour of alternate hypothesis.There is significant relationship between the State_id and no_of_defaulters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PERFORM_CNS.SCORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    233154.000000\n",
       "mean        289.462994\n",
       "std         338.374779\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%         678.000000\n",
       "max         890.000000\n",
       "Name: PERFORM_CNS.SCORE, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"PERFORM_CNS.SCORE\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_0=df[df[\"loan_default\"]==0]\n",
    "group_1=df[df[\"loan_default\"]==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A6Ud78Pl8pBwr5MzwiTii4vq7ikII0YocIfQS9cFGqK/FcM/30AxGAErHzcQTFMyYvbmgVD/XUAghWpNA6AWqrhaV+2e06+ajpY4JTPcGhVA6bhbRtacZXFHajzUUQoi2JBB6gdq9HXxetJu+ia4U9S4vuv+A4MzwCTRbwkg4fbB/KymEEBeRcwi9QO3eDsnDaYxJ4CebT1JS6wLCiTCEcn2YneqkNJJOHcDg9SAdR0KIK4UcIfQwVVcLpYdwTpnBT/LKOF7n4p6JsUwLcwPwV0ckR4dMwOjzEFt1rJ9rK4QQX5BA6GFqz6d4NQNPk8kxm4sfzhzCXRNiuTbCzS3hDfiA3wWNpT4kmvgzR/q7ukIIESCB0MPUnu18NPoGjjToPHxtItOSIwKfRRt93Bhm55xu5OWMBcRWfo7B4+7H2gohxBckEHqQqrfhKT3CHxJnkGYNZvbwyDZlks0exltcFISNpMYcgfX43n6oqRBCtCWB0INU0Q7yErI4q4K4Z2Ismqa1W25ScBOaBu8Nn0/ckV19XEshhGhfl64yKioqYv369ei6zvz587njjjtafe7xeFizZg3Hjh0jIiKC5cuXExfXcifu+++/T15eHgaDgUWLFpGZmQnA0qVLCQ4OxmAwYDQaWbVqVQ83re81H9zHH4bnMCY2mMmJYR2WCzfojAlysSV+CncWbQOlgybZLIToX50Ggq7rvPbaa6xYsQKr1coTTzxBVlYWycnJgTJ5eXmEhYWxevVqtm/fzoYNG3jkkUc4ffo0+fn5PP/889TV1fH000/z0ksvYTC0fPn9+Mc/JjKybbfKQKR0Hx/ZTNSOiOT/TRrc4dHBeZODmzjcbOGvsVMZe/Y0jrihfVRTIYRoX6e7paWlpSQkJBAfH4/JZCI7O5uCgoJWZXbt2sWcOXMAmD59OsXFxSilKCgoIDs7G7PZTFxcHAkJCZSWXqV36JYdJzc2kxFBHibGh3ZaPNKok25s5G9J0wk9cbgPKiiEEJfW6RGCzWbDarUG3lutVkpKSjosYzQaCQ0NxW63Y7PZSEtLC5SLiYnBZrMF3q9cuRKA66+/npycnHbXn5ubS25uLgCrVq0iNja2q23rU8X/+AefRyTz8MQ4Bg8e3OZzg2bAYrG0mjZxkIHDtiBO1rkYHB5+xbbtUkwm04Csd1dI2wYmaduXWH5nBVQ7g7Bd3B3SUZn2pp/39NNPExMTw7lz5/jZz35GUlIS48aNa1MuJyenVVjU1NR0VuV+8cExO6YQH9cMi2y3jrrSaW5ubjUtRsFgH+w0DGZOwzlqagbejeOxsbFX7Db5sqRtA5O0ra2kpKQuleu0y8hqtVJbWxt4X1tbS3R0dIdlfD4fTqeT8PDwNvPabDZiYmIAAj+joqK45pprBnRXksftZqspmSzNRmRw17/UNQ3Gh3o4EDUCVXa8F2sohBCd6zQQUlNTqaiooLq6Gq/XS35+PllZWa3KTJ06lS1btgCwY8cOMjIy0DSNrKws8vPz8Xg8VFdXU1FRwahRo3C5XDQ1NQHgcrnYt28fQ4cO3JOqewpLOBcUzryUkG7PO3xYIgDHyut6ulpCCNEtne7OGo1GFi9ezMqVK9F1nblz55KSksLGjRtJTU0lKyuLefPmsWbNGpYtW0Z4eDjLly8HICUlhWuvvZZHH30Ug8HA/fffj8Fg4Ny5c/zqV78CWo4oZsyYEbgcdSD6xwk7g9xGpk4e03nhi4QMimJMYymF5kiUUp1enSSEEL1FU5fq6L8ClZeX93cVWml0+/iXdw7zTw0HeGDJgg7LffSPgjbnEAC0tPHYPvmIty1j+cW8RMYkRvVmdXuc9NcOTNK2ganfzyGIS/vsZB1ezch11svfsx+aEINZ9/BJ8ekerJkQQnSPBMKXlH+kGqurnvTRwy97GU1D08i0HeXTsz70gXXAJoS4ikggfAlOj4/Cc3BtzX4M6WMvezk+SyiTm8upVUEcqWnqwRoKIUTXSSB8CTtPO/BiIFurQQsN/1LLGhEVhFn3sP14fQ/VTgghukcC4UvYfuIcMc3nGD207Z3J3dU0NJ1M21HyT5yTbiMhRL+QQLhMTo+PwopGrj27H+Po8V96efXJ6WSf3U+tR+NojasHaiiEEN0jgXCZdp1pxKM0ss/ug7S2Q250lzc4jKzgRkzKx/ZTDT1QQyGE6B4JhMu0/VQD0T4no8MVWnjPDOEdnjaazLqj5J+yS7eREKLPSSBchiaPzp7yRqZX78OY/uW7i87T0seTXbWXGqeXklrpNhJC9C0JhMuw64wDt0+RXVmINnpCzy04PYNrag9hQmf7Sek2EkL0LQmEy7D9lJ1ozcOYhpMwpucCQQuLIGzIEDKbzpB/yn7J4cOFEKKnSSB0k8urs7vcwXR7KcahI9HCInp0+dq4SVx76lPOOr0clW4jIUQfkkDopt3+7qJrj32CNq7nR2jVxmbytbPFmDRF/il7jy9fCCE6MvAe0dXPtp2yE2XSGWv7HG3sv/b8CkaNJcygM0nZyD9l5r7Jg2VIbHFV0rd+2OFnhlk39WFNxHlyhNANjW4fBacdzPBVYjSbYdTlj1/UEc0cBKMyyK7YQ3WjdBsJIfqOBEI3fFpmx6MrZp3cDmnjWr68e4E2LpNppVsJMsBHx871yjqEEOJiEgjdsOV4A0lhRkYdK+iV8wfnaeMyCfU1My3EybaTDXh8eq+tSwghzpNA6KKzjR6Kq5zMCqpHA7Sxk3pvZUOGQUQUc2zF2N06u8obe29dQgjhJ4HQRZ+caEABs07vhMhBkDyi19alGQxo46cwce9mooON0m0khOgTcpVRF318ooHRVgsJn36MNm0OmqF3s1Sbko3x04+YGeHiL+U+GlxeIoNlcwlxOTq6okmuZmpNjhC6oKS2iRP1zcwOqodmF9rUa3t/peMywRLMnKpCvDp8clLuSRBC9C4JhC74y9E6gk0GZp/YBqFhkN6D4xd1QAuyoE3IYnjh3xkZbeHDkjoZykII0aukD6IT51xePjlh5/qRkYT87lO0SV9DM/XRr23KtbBrG7cMcrL6uJF9VU4mJYT1zbqF6AXK1QRHilElxajCHdDoAJcTNANYgiEsHOKSUIMTIX08mtHY31X+SpFA6MTfPz+HR1f8k7kanA60KX3QXeSnTZiKMpmZcfJTfmuZzabDNgkEMeD4tvwFyk/BsaNw+gT4vOgGIw2D4mmKSaHZEoamFGZ3E2EOGxEH9kDxboiKQbtuPtqsG9Gscf3djK8ECYRL8OmKD4/WMTEhlOSDeaggC4yb3Gfr14JDIWMy5qJ8brrndt4ptlFhd5MY0Ts3xF3JNpfUd1rmxrRBfVAT0VXqXB1q29/h739sORKwBEPqaBiaykehqehGc7vzGT3NWE06k47tQP31XdTm99C+Ngvtpm+gJQ3t41Z8tUggXMLO0w7OOr3cnxmD+mA72sRr0IIsfVoH7ZqZqL07uUmd4V1DCB8cqePfsuL7tA5CdJXSdTi8D7V1M6poB/h8kJAMWddB8nA0Q0sXkO5oPwwAfGYLZ9PGY7zlepTtLOrvf2pZ3qcfQeZ0DDd/E21Eel816StFAqEDPl3x+/01JEaYyTq9Gxx2tNl9f4maNiUbFfHfRH3yATOm3k/u5+f41ngrg+QSVHEFUdXlqO15qB15YKuB0HC0ebeizboJdXR/95dXUoxe4Wh5k5gMX78HDu+Hg4XoRTtgzEQM//RNGDtJBn/sQfKt0oFPTjZwsr6Zx65LxPDbX7fcPdyTT0frIs1sRpt9E+r/3uFbty5i6wmdjftrePCahD6vS3/Rt36IcoS3ma6l9dzjS0X3KKWg8jRq366WI4HSQy0nhjMy0b65CC1zWmCsr/OB0Kxr2HUDdt1IpdeEAjQgSFNYNJ1QTdHRd7sWHAKZX0NlZKJ5fai//wn9hf+EpKFo0+eiTZuFFjO43Xk3l9S3+/8HQPN3RUp3YwsJhHZ4fIoNe2sYGW0hu/k0lB1Hu3dJv+2JaLNvQv31DyTt3MyNo27hw5J6bhkdTXJk33Zfia825WiAz4+gDuxB7d8FNVUtHwwZBpOn4xuRjs0yiMo6H1V/30mV10SV10ylL5FKrwmHfukrhowoIgw+oo0+Bhu9xJu8jAlqxmL44nJrzRyEYf5NqHm3oHZsQW3PRb33Juq9N1vCYdxkXFOnowYnoUVF9+av46okgdCOzaV1VDd6+P7XktH+vBYVGoY2bU6/1UcbZG3pOtqey1033sWW4w38tvAsT85O7rc6iaub8vngzEn0zw/jPP45DadOY29w0GAOwx4SiX3EPM5NH05NRDw1XiNnbXZqbUZ0vthpMqKIM7V8sV8X0shgk49Ig48Ig87+ZgtGwAd4lIZLN2DXDZzTjdT6TBz3WNjpCsOEYozFxUSLiwnBLtKCmjHQEgzazBtg5g0t3VWFn7UE1Za/cC73Ty0ViI1HSx1DcuQw6r0ajsjBqF4eYWCgk0C4SI3Tw9v7apgQH0qmsQFV+Clazu1oluB+rZc2/zZUwSdEffY3vpkxm7f2nqWoopHMRLkMVXTu4qu0wso9nK1voK7Jh8Ptw+VwEu11YDvnoK7JS4PXgN0UgsM8DN0yEtLaLtPgUIQ5m4gw6EQbfaSY3UQYfEQadKIMPsIMOoaLDqoV0KAbGGb2XLK+zbpGktnL/uZg9rmC+V1DNDRAiKYzfstpJiWEMikhjJSoILS4JLQb/xlu/GeUx0PUuRrq9+xAfX4EdXg/Y859DIDXZKYmfiRVQ8ZQk5CKbpSvv4vJb+QCPl3xYn4Fbp/ie9fEo954BoIsaDlf7++qwcjRMOlrqE1vc+uPZ/CPiCBe/LSCF28eLieYxSX5dEWN00OF3U2N00tdk5f6pkpcvtblQryKaLePSJ+bWKMiUfNgNLoxBgURbFAEG3QsmiJY0wnWFJZL9Pl/WRaDYmpIE1NDmgBo8Bko9ofDviofBWdaTjhHG7yMsTQzKqiZ9KxJjLIGM3jMBAyxiUDLuY6tuz8nance0TVlxJWXkHDmCO6gEM4Mn8Tp+ESaI62904gBSL5JLvDHQzb2VzlZNj2BpKMFqOI9aAv/DS26///DaJqGYeG/of94Keb//W9+dPej/GDzSV7YXs5/zk3BePGumPjKcvt0SmpdHKpu4uBZJ4fPNtHoaXmmRrjeTHJjNRPtZ0hurCLefY7gEAtEROGKSaAhPh7dFHLB0nSgf57a97d2TgSPDHIzMsiNXTdwxmPGh8ZRt4VPm8LgH2VowLCY0wyLNDMyxsKI6GDqw2JoShlHZco4Dk+6nuiaUyQfK2T40c8YVlpAxfgZqEHfRhv81blQoyMSCH6FFY1s2HuW64ZGMC/BhPrNf8PQVLS5N/d31QK02Hi0W+9Gvfcmw67bx79lpbP2s0re3l/Dtye1f4XFQOdw+zjdHMTRZgvNSqNZaSjApEFQlZOwIAMRQUaivsJHSX86ZKPS4abC3nIUUN3oQfefh01w1/O1+uNMrD3M2HMniPI1UR+bgj1xJGeT06mPjKXXdvN7UYRBZ4ylmRvCW44UGnwGjo3OpqTWxUm7j+JKOx+faAiUDzdEE2v0Emv0kRCdRtW04UQ4zzH87OcM2bcF/T+2tVytdMuCr3QwfHX/ii6w64yDZ7aeYeggC9+fakW9/itoqMPw0FOBG2muFNr1t6N2fIS+/iVy/v1pDo+M4p3iWnQF35kUO+Cvya5xethT3siBKieHapqocniApPYLn/ziD14D/lpSx8joYEZEWxgR0/LzautO8+mKU+eaKal1UVrr4nBNEyfrmwEwoBjqqeemus+ZWFXMmIYThOCjzppCXdxQPs+YhCNyMGgaFouF5ubmfm5Nz4k06kxJCmdKUjixsbHU1NRwzuXlWF0zm0vqOFtZS43PyAlPEBCKhmKwMYohYyYybPzNLDi9heBP/or6NM8fDN9Ci+vg/91V7Or6a+kmpRRbjjew5rMKhg0K5iezEgl983nYuxPt299DG97OmbR+ppnMGB5agf7LJ1Av/Jglj/0ck2EQfzhQS0Ozl+9mxWM2DpwrKXy64mhtE7vONLK73MHxupYvqWiTYgz13OAsY0jFERJtZQxqbiDE50JTCo/BRENoNOXW4ZQPSuGJukeAAAAWfElEQVREVDINKo5DVV62XhAUMSEmRka3dB0Mj7YQF2YmLsxMVLDxig7PZq9OpaNlj7/c7qbC7uZUvZtjdS7cvpbd/zC8pHlr+VrFASbUHiWtoQyD0Ui9dQh1iSkcnJiFPSqu5f6Ar6CoYBOTE01UOzwoxwkA3Eqj0muiwmum3GNmb2UjhQo2aTMYfftcJtWVMLHoQ0bteAjTtJlot9yFFv/VCQZNdWFM5aKiItavX4+u68yfP5877rij1ecej4c1a9Zw7NgxIiIiWL58OXFxLYNRvf/+++Tl5WEwGFi0aBGZmZldWmZHysvLu9vGdp1t9PCbnZXsLm9k3OAQnsoMJWTjy7CvAO2uBzDk3N4j6znvo38UtLtHduHNVd25OUZVnkZ/9smWZXx7CRsMqfzhQC1JEUF895p4JvfR1Ufn98a6w9bkpbjKye4zDnZXNGJv9mFAMdbgYKrtEFNLt5HsqGz5wk5IBrOZE5ZYPEEh+IwmUAqzt5mg4BBC6qsIs1UQcu6LOtgjB3NiWCbHB6dyPCSB4yqU000EulEAzAaN2DAT0cEmLCYDwSbN/9OAyX8+JiQkGFeTv/9ca/Xji5/thMoXn7U/3afAqys8PoVX13H7FI1uH3aXlwaXF7tbx3nRCd9IvZkh7jpS608wynaMUfbTJDbVoA1OoMISTb01mbrYZBojutYFdLUcIZzvMoIvHnZz8f/JzSX1qJLiduf3jsyg0uHGbNTYW9nIMVszCgjFy4TaI0yqPUrmsGgSZ81BSx3d7zsRl/P3BpCU1LVQ6/QIQdd1XnvtNVasWIHVauWJJ54gKyuL5OQvroHPy8sjLCyM1atXs337djZs2MAjjzzC6dOnyc/P5/nnn6euro6nn36al156CaDTZfYGn644dLaJv5XWk3/KjkGDxRmR3Fz+KYafvgM+L9rd38Uw79ZerUdP0BKSMTz6M/RXn0X95ufcM+lrjJv+DV49o/hJXhnj40KYNzKK7KGRhJj7bw/RpysqHR6O1DRxoNrJgWonFfaWSw4j8DKlqYyppwvIrDpAuK8Zho9CmzULbcwEGJGGFhyKvvVDSjq5U9nY3MT84DpU2XEiy44z4fQJJhz5GNxuAJrNFsrj0zgbmUBNqJWa4EHUqEjqHUHYMVKDAZcy0IwRj4LAbbRKoVpeBNalAE1dOIVWn7X8bJ0g6oLr8w1Kx6x7MSkfJt2HWXkJ8zQR4XWS6GkkwtNIlLuReJeNxKYa4tznMIaG4TJZaAobhD0xjtNjMjgcGYvPLDcnnnf+qWjO8HB0xxdB0dFdygBmo0ZKlCWwM9bg8rKvyklRRSNFIRP4zJoBQNwWGxkfvseI2FBGjEhi2Lg0oiJCe7E1/aPTQCgtLSUhIYH4+JYB1bKzsykoKGj15b1r1y6+9a1vATB9+nRef/11lFIUFBSQnZ2N2WwmLi6OhIQESktLATpdZk9690At+6ucHKlpwunRCTMbuH6Qi9sPfUBcXgEoHSZ9DcNdDwyoE0rakKEYVryAytuE+vPvydy7kxdj4vm/jNv4e+1wfl3dxNrPKhkRbiA9xkLioBAGRwYTHWIm2KQRYm7ZI75wr1jTWr7DLtwTUkqh+/dqfUrh8eotr306DmWnrNKOw+3D7vLgcDZz1tFMud1LuVOnqhl8/i/DcL2ZsY2nuaHqEOPOHWekoxzjkKFoaWPQbvt/MGYiWljHf7yX4rOEoI1KRBs17ot66z6oqkCdPk5w2XFG1lQRebaSMbWlmF0OzE2NaOqCXXFdoQBlMKIbjCijCV3T0M+/9//84rWhzWfKYGz58tf8IaBp/pC44LX/d6u0C443NPAag/AGW/BGWPCaY/GYkzkdGsHxoNABeeK3L7R3JZLFY6GrBz/njxzOj5sUDmT7/2n/fCMVdg9FZXUUHW2i0D6Sj3whUAqUniLG4yDJ4MIaBDHBRqxhQcREhRAWHkZIsJmQ4CCCgy1Ygi2YTEYMmobRAAZNw6C1/DxPVwqv24vP5cTX1BT412Sy0BiTRKNH75Oj/k4DwWazYbV+cdml1WqlpKSkwzJGo5HQ0FDsdjs2m420tC/64WNiYrDZbIHlXGqZPemz0w5cHp2ZwyIZHx/KtORwzNs+RNWXt5w8mpKNljKi19bfmzSTCe2Gf0bNvBG1dyfmXdu4o/Advu5o4HDkMHZbx3I0cih5dSm4TE3dW7bS0WjZQ1bd6IcO8rlJbKplaNNZpjtrSGyqYZTXRkqYAYM1Dm3qCLTU6wNHAJfr4m6ADzv8L6RB8EhIHgnd2Oe4WrpVxOXRNI2kyCCSMuK5OaNl57Wu3sHxg6UcL6vmRLOiym3koCeMOk8E3kYTVAM0+f91zqB8l/j7MgAe4CQA79zV+yO8dhoI7Z1iuLgfraMyHZ2e6Moyz8vNzSU3NxeAVatWdbkv7EL/s6idee55oOVfH/n2vX1wc1vqKLjznsDbocANvb/W3rdwMYv7uw5CAElJkDGuf4fevpzvwK7qdLfParVSW1sbeF9bW0t0dHSHZXw+H06nk/Dw8Dbz2mw2YmJiurTM83Jycli1ahWrVq3qXsuuMI8//nh/V6FXXK3tAmnbQCVtu3ydBkJqaioVFRVUV1fj9XrJz88nKyurVZmpU6eyZcsWAHbs2EFGRgaappGVlUV+fj4ej4fq6moqKioYNWpUl5YphBCib3XaZWQ0Glm8eDErV65E13Xmzp1LSkoKGzduJDU1laysLObNm8eaNWtYtmwZ4eHhLF++HICUlBSuvfZaHn30UQwGA/fffz8G/2iD7S1TCCFE/+nSfQjiy8vNzSUnJ6e/q9HjrtZ2gbRtoJK2XT4JBCGEEEAXziEIIYT4avhKj2XUFy53iI4rRU1NDWvXrqW+vh5N08jJyeHmm2/G4XDwwgsvcPbsWQYPHswjjzxCeHg4SinWr19PYWEhFouFJUuWMHLkyP5uRod0Xefxxx8nJiaGxx9/nOrqal588UUcDgcjRoxg2bJlmEymSw7PciVqbGzk5ZdfpqysDE3T+P73v09SUtJVsc0++OAD8vLy0DSNlJQUlixZQn19/YDcbuvWrWPPnj1ERUXx3HPPAVzW39aWLVt47733ALjzzjuZM2fO5VVIiV7j8/nUQw89pCorK5XH41GPPfaYKisr6+9qdYvNZlOff/65Ukopp9OpHn74YVVWVqbeeust9f777yullHr//ffVW2+9pZRSavfu3WrlypVK13V15MgR9cQTT/Rb3bti06ZN6sUXX1TPPPOMUkqp5557Tm3btk0ppdQrr7yiNm/erJRS6sMPP1SvvPKKUkqpbdu2qeeff75/KtxFq1evVrm5uUoppTwej3I4HFfFNqutrVVLlixRzc3NSqmW7fXRRx8N2O124MAB9fnnn6tHH300MK2728lut6ulS5cqu93e6vXlkC6jXnThsB8mkykwRMdAEh0dHdgLCQkJYciQIdhsNgoKCpg9ezYAs2fPDrRr165dzJo1C03TSE9Pp7Gxkbq6un6r/6XU1tayZ88e5s+fD7TcMHngwAGmT58OwJw5c1q16/xe1/Tp0ykuLu7wxsv+5nQ6OXToEPPmzQPAZDIRFhZ2VWwzaDmqc7vd+Hw+3G43gwYNGrDbbdy4cYSHtx5+o7vbqaioiIkTJxIeHk54eDgTJ06kqKjosuojXUa9qCvDfgwk1dXVHD9+nFGjRnHu3LnAzYTR0dE0NLQMOW2z2YiNjQ3MY7VasdlsHd542J/eeOMNvvOd79DU1DLMgN1uJzQ0FKOx5RkYFw610tHwLJGRkf1T+Uuorq4mMjKSdevWcfLkSUaOHMl99913VWyzmJgYbrvtNr7//e8TFBTEpEmTGDly5FWx3c7r7na6+HvmwvZ3lxwh9KL29kT6e/jcy+VyuXjuuee47777CA3tePyhgdLm3bt3ExUV1eW+8oHSLmgZLeD48ePccMMN/PKXv8RisfDHP/6xw/IDqW0Oh4OCggLWrl3LK6+8gsvluuTe8EBqW2e605bLbaMcIfSi7gzRcSXzer0899xzzJw5k2nTpgEQFRVFXV0d0dHR1NXVBfa4rFZrq/Har9Q2HzlyhF27dlFYWIjb7aapqYk33ngDp9OJz+fDaDQGhlqBL7al1WptNTzLlchqtWK1WgMDS06fPp0//vGPA36bAezfv5+4uLhA3adNm8aRI0euiu12Xne3U0xMDAcPHgxMt9lsjBs3rs1yu0KOEHrR1TBEh1KKl19+mSFDhnDrrV88JyIrK4uPP/4YgI8//phrrrkmMH3r1q0opTh69CihoaFX5JfLPffcw8svv8zatWtZvnw548eP5+GHHyYjI4MdO3YALVdunN9eHQ3PciUaNGgQVqs18DCp/fv3k5ycPOC3GbQ8IKakpITm5maUUoG2XQ3b7bzubqfMzEz27t2Lw+HA4XCwd+/ewIPIuktuTOtle/bs4c033wwM0XHnnXf2d5W65fDhw/znf/4nQ4cODfwh3X333aSlpfHCCy9QU1NDbGwsjz76aODSuNdee429e/cSFBTEkiVLSE1N7edWXNqBAwfYtGkTjz/+OFVVVW0uXzSbzbjdbtasWcPx48cDw7Ocf57HlejEiRO8/PLLeL1e4uLiWLJkCUqpq2KbvfPOO+Tn52M0Ghk+fDjf+973sNlsA3K7vfjiixw8eBC73U5UVBQLFizgmmuu6fZ2ysvL4/333wdaLjudO3fuZdVHAkEIIQQgXUZCCCH8JBCEEEIAEghCCCH8JBCEEEIAEghCCCH8JBCEEEIAEghCCCH8ZOgK8aUtXbqU+vp6DAYDwcHBTJ48mcWLF7Nq1SpKSkoCz9EGyMjI4PHHH+fAgQP89Kc/JSgoCE3TiI6O5o477mh1Q82CBQuwWCyB90ajkTfeeANoGe//d7/7HTt37qSpqYn4+HhuvfXWVvNfXK/MzEzuv/9+goODAVi7di0ff/wxP/zhD1vdQf7GG2/wl7/8hSVLlnQ6rnxdXR1vv/02hYWFuFwuYmJiyM7O5vbbbyc4OJgFCxaQkpLCs88+G/g9vP3229TW1rJ06VKg5aaiP//5z9hsNiwWCyNHjmT58uWEhIS0Wd/hw4f5n//5H8rKyjAYDCQnJ/Ov//qvjBo1qkv1UUqxadMmcnNzqa2tJTIykhkzZrBgwQLMZnPg97Jt2zZMJhMmk4mRI0eyePFihgwZArTcCfyb3/yGoKCgVnV76aWXAkNGiIFJAkH0iB/96EdMnDgRm83GypUreffddwFYvHhxYHjpi0VHR/Pyyy+jlKKwsJBf/vKXjB49mqSkpECZZ599loSEhFbzeb1enn76aaKioli5ciUxMTEUFxezdu1aGhsbWw2xcb5e9fX1rFy5kvfff5+777478HliYmKroQ58Ph+ffvppl+5mdTgcrFixgvT0dH72s58RFxdHTU0NmzZtoqqqimHDhgEtX9L5+fnMmDGjzTIOHjzI73//e5588klGjBiBw+Fg165d7a7P6XSyatUqHnjgAbKzs/F6vRw6dCjwRd6V+qxfv56ioiIeeughUlNTKS8vZ926dZw5c4Yf/vCHgXV9/etfZ+HChbjdbl599VVefvllnn766cDn6enprd6Lq4N0GYkeFRMTQ2ZmJmVlZV2eR9M0pkyZQnh4OCdPnuy0/NatW6mpqeGRRx4hLi4Ok8lEZmYmixYtYuPGjTidzjbzDBo0iEmTJnHixIlW06dOncqRI0dwOBxAyxPuhg0bxqBBgzqtxwcffEBwcDDLli0LPIUrNjaWRYsWBcIA4Pbbb+edd97B5/O1WUZpaSlpaWmMGDECgPDwcObMmdPu0UFFRQUAM2bMwGAwBIZ/Pr+uzupTUVHB5s2befjhh0lPT8doNJKSksK///u/U1RURHFxcZt1BgUFce2117b5vYmrkwSC6FE1NTUUFhYyfPjwLs+j6zq7du3Cbre3ORpoz759+8jMzAx0/Zw3bdo0PB4PR48ebTNPbW0thYWFbZYfFBREVlYW+fn5QMtgYrNmzepSvffv38+0adNadYm1Z9q0aYSEhAQGWbtQWloae/fu5Z133uHw4cN4PJ4Ol5OYmIjBYGDNmjUUFhYGQqyr9dm/fz9WqzXQvXRebGwsaWlp7Nu3r808LpeL7du3d2m7iIFPuoxEj3j22WcDDyCZMmUKd955Jz//+c9Zv349b731VqDcTTfdxMKFC4GWrpT77rsv8PSrf/mXfwnsKZ/3ox/9KDCo3qxZs1i8eDF2u73d5xgYjUYiIiKw2+2t6qVpGi6Xi/Hjx7NgwYI2882ePZu33nqLGTNmcOjQIR566CE2b97caZvtdnuXjiQ0TeOuu+7i1VdfbRM2Y8eO5bHHHmPz5s385S9/wefzkZOTw7333tvmiz00NJSf/vSn/OlPf+KVV16hvr6eyZMn8+CDDzJo0KBO62O32zscxfTCB7EAbNq0iQ8//JCmpiZiY2NbdScBlJSUcN999wXeR0REsHr16k5/F+LKJoEgesQPfvADJk6c2Gb6okWLOj2H4PF42LBhA8XFxdxyyy2tyvziF79os3caERHR7iMefT4fdrudiIiINvU6ePAgL730Ena7nbCwsFbzjRkzhoaGBt59912mTJnS5mRpRyIiIqivr+9S2SlTphAbG0tubm6bzyZPnszkyZPRdZ0DBw7w/PPPk5SUxPXXX9+mbHJycuBk9JkzZ1i9ejVvvPEGy5cv77Q+Hf3eoCWcR48eHXh/2223sXDhQmpqali5ciXl5eWtusHS0tLkHMJVSLqMRL8zm8185zvf4dSpU+zcubPT8hMmTKCoqAiXy9Vq+meffYbZbCY9Pb3NPOPGjWPOnDn89re/bXeZM2fO5IMPPgg8y7YrJkyYwM6dO9F1vUvlFy5cyHvvvUdzc3O7nxsMBiZMmMD48eO7dA5myJAhzJkzJ1C2s/qMHz+e2tpaSktLW02vqamhpKSE8ePHt5nn/DmIN954A7fb3WmdxMAmgSCuCCaTidtuuy1wddKlzJo1C6vVygsvvBB4+FBRURHr16/nW9/6VoeP+LzlllvYv39/uydIb775ZlasWMHYsWO7XOdbb72VpqYm1q5dy9mzZ4GWp1W9+eab7Z4cz8jIYOjQoYGHn0DLA9W3b9+Ow+FAKUVpaSkHDx4MPO3sQmfOnGHTpk2Bp/DV1NSwffv2QNnO6nP+qOPXv/41R48eRdd1ysrKeO6555gwYUK7R3gAEydOJDo6ut2jG3F1kS4j0atef/31wL0DAElJSfziF79ot+zcuXP53//9X3bt2nXJJ8uZzWb+4z/+g9/97nc89dRTOJ1O4uPjWbhwYYfdUwCRkZHMmjWLP/zhDzz22GOtPgsPD2fChAndalt4eDhPP/00b7/9Nk8++STNzc3ExMRw3XXXdXgSduHChTz11FOB92FhYfz1r3/l9ddfx+PxEB0dze23387MmTMBeO+99zh8+DBPPvkkISEhlJSU8MEHH+B0OgkNDWXq1Kl85zvf6XJ9Fi9ezJ///GdWr16NzWYjMjKS6667rt1zKxe6/fbbefPNNwPdWEePHuXee+9tVebHP/5xmxPWYmCRB+QIIYQApMtICCGEn3QZCXEJ//Vf/8Unn3zSZvrMmTP57ne/2w81EqL3SJeREEIIQLqMhBBC+EkgCCGEACQQhBBC+EkgCCGEACQQhBBC+P1/bRgGP5UDfIMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(group_0[\"PERFORM_CNS.SCORE\"])\n",
    "sns.distplot(group_1[\"PERFORM_CNS.SCORE\"])\n",
    "plt.legend([0,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    182543.000000\n",
       "mean        299.784270\n",
       "std         342.883794\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%          15.000000\n",
       "75%         690.000000\n",
       "max         890.000000\n",
       "Name: PERFORM_CNS.SCORE, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_0[\"PERFORM_CNS.SCORE\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    50611.000000\n",
       "mean       252.236372\n",
       "std        318.826242\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%        610.000000\n",
       "max        879.000000\n",
       "Name: PERFORM_CNS.SCORE, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_1[\"PERFORM_CNS.SCORE\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nearly 50% of the customers in the defaulter's list have cns score of zero"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0:mean_CNS_Score_of_non_defaulters==mean_CNS_Score_of_non_defaulters\n",
    "### H1:mean_CNS_Score_of_non_defaulters!=mean_CNS_Score_of_non_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=28.018568872971258, pvalue=1.869421119257312e-172)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(group_0[\"PERFORM_CNS.SCORE\"],group_1[\"PERFORM_CNS.SCORE\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pvalue less than .05 which rejects null hypothesis in favour of alternate hypothesis that CNS score of both groups differ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PERFORM_CNS.SCORE.DESCRIPTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No Bureau History Available', 'I-Medium Risk', 'L-Very High Risk',\n",
       "       'A-Very Low Risk',\n",
       "       'Not Scored: Not Enough Info available on the customer',\n",
       "       'D-Very Low Risk', 'M-Very High Risk', 'B-Very Low Risk',\n",
       "       'C-Very Low Risk', 'E-Low Risk', 'H-Medium Risk', 'F-Low Risk',\n",
       "       'K-High Risk',\n",
       "       'Not Scored: No Activity seen on the customer (Inactive)',\n",
       "       'Not Scored: Sufficient History Not Available',\n",
       "       'Not Scored: No Updates available in last 36 months', 'G-Low Risk',\n",
       "       'J-High Risk', 'Not Scored: Only a Guarantor',\n",
       "       'Not Scored: More than 50 active Accounts found'], dtype=object)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"PERFORM_CNS.SCORE.DESCRIPTION\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">PERFORM_CNS.SCORE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERFORM_CNS.SCORE.DESCRIPTION</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A-Very Low Risk</th>\n",
       "      <td>827.662631</td>\n",
       "      <td>806</td>\n",
       "      <td>890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B-Very Low Risk</th>\n",
       "      <td>774.183893</td>\n",
       "      <td>761</td>\n",
       "      <td>805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C-Very Low Risk</th>\n",
       "      <td>741.899221</td>\n",
       "      <td>736</td>\n",
       "      <td>760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-Very Low Risk</th>\n",
       "      <td>715.958091</td>\n",
       "      <td>706</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E-Low Risk</th>\n",
       "      <td>691.517437</td>\n",
       "      <td>681</td>\n",
       "      <td>705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F-Low Risk</th>\n",
       "      <td>666.064467</td>\n",
       "      <td>651</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G-Low Risk</th>\n",
       "      <td>640.853561</td>\n",
       "      <td>631</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H-Medium Risk</th>\n",
       "      <td>617.031947</td>\n",
       "      <td>601</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I-Medium Risk</th>\n",
       "      <td>586.832644</td>\n",
       "      <td>571</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J-High Risk</th>\n",
       "      <td>549.876734</td>\n",
       "      <td>521</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K-High Risk</th>\n",
       "      <td>440.594177</td>\n",
       "      <td>351</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Very High Risk</th>\n",
       "      <td>326.601411</td>\n",
       "      <td>301</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M-Very High Risk</th>\n",
       "      <td>300.000000</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_Score</th>\n",
       "      <td>1.590076</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              PERFORM_CNS.SCORE          \n",
       "                                           mean  min  max\n",
       "PERFORM_CNS.SCORE.DESCRIPTION                            \n",
       "A-Very Low Risk                      827.662631  806  890\n",
       "B-Very Low Risk                      774.183893  761  805\n",
       "C-Very Low Risk                      741.899221  736  760\n",
       "D-Very Low Risk                      715.958091  706  735\n",
       "E-Low Risk                           691.517437  681  705\n",
       "F-Low Risk                           666.064467  651  680\n",
       "G-Low Risk                           640.853561  631  650\n",
       "H-Medium Risk                        617.031947  601  630\n",
       "I-Medium Risk                        586.832644  571  600\n",
       "J-High Risk                          549.876734  521  570\n",
       "K-High Risk                          440.594177  351  520\n",
       "L-Very High Risk                     326.601411  301  350\n",
       "M-Very High Risk                     300.000000  300  300\n",
       "No_Score                               1.590076    0   18"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"PERFORM_CNS.SCORE.DESCRIPTION\").agg({\"PERFORM_CNS.SCORE\":[\"mean\",\"min\",\"max\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "val=['No Bureau History Available','Not Scored: Not Enough Info available on the customer',\n",
    "    'Not Scored: No Activity seen on the customer (Inactive)','Not Scored: Sufficient History Not Available',\n",
    "    'Not Scored: No Updates available in last 36 months','Not Scored: More than 50 active Accounts found','Not Scored: Only a Guarantor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in val:\n",
    "    df[\"PERFORM_CNS.SCORE.DESCRIPTION\"]=df[\"PERFORM_CNS.SCORE.DESCRIPTION\"].replace({i:\"No_Score\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERFORM_CNS.SCORE.DESCRIPTION</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>M-Very High Risk</th>\n",
       "      <td>0.695419</td>\n",
       "      <td>0.304581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Very High Risk</th>\n",
       "      <td>0.719577</td>\n",
       "      <td>0.280423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K-High Risk</th>\n",
       "      <td>0.721880</td>\n",
       "      <td>0.278120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I-Medium Risk</th>\n",
       "      <td>0.727371</td>\n",
       "      <td>0.272629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J-High Risk</th>\n",
       "      <td>0.747599</td>\n",
       "      <td>0.252401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H-Medium Risk</th>\n",
       "      <td>0.758133</td>\n",
       "      <td>0.241867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_Score</th>\n",
       "      <td>0.770274</td>\n",
       "      <td>0.229726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G-Low Risk</th>\n",
       "      <td>0.802909</td>\n",
       "      <td>0.197091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F-Low Risk</th>\n",
       "      <td>0.813789</td>\n",
       "      <td>0.186211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C-Very Low Risk</th>\n",
       "      <td>0.827361</td>\n",
       "      <td>0.172639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E-Low Risk</th>\n",
       "      <td>0.828208</td>\n",
       "      <td>0.171792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A-Very Low Risk</th>\n",
       "      <td>0.834254</td>\n",
       "      <td>0.165746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-Very Low Risk</th>\n",
       "      <td>0.850414</td>\n",
       "      <td>0.149586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B-Very Low Risk</th>\n",
       "      <td>0.868710</td>\n",
       "      <td>0.131290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "loan_default                          0         1\n",
       "PERFORM_CNS.SCORE.DESCRIPTION                    \n",
       "M-Very High Risk               0.695419  0.304581\n",
       "L-Very High Risk               0.719577  0.280423\n",
       "K-High Risk                    0.721880  0.278120\n",
       "I-Medium Risk                  0.727371  0.272629\n",
       "J-High Risk                    0.747599  0.252401\n",
       "H-Medium Risk                  0.758133  0.241867\n",
       "No_Score                       0.770274  0.229726\n",
       "G-Low Risk                     0.802909  0.197091\n",
       "F-Low Risk                     0.813789  0.186211\n",
       "C-Very Low Risk                0.827361  0.172639\n",
       "E-Low Risk                     0.828208  0.171792\n",
       "A-Very Low Risk                0.834254  0.165746\n",
       "D-Very Low Risk                0.850414  0.149586\n",
       "B-Very Low Risk                0.868710  0.131290"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"PERFORM_CNS.SCORE.DESCRIPTION\"],df[\"loan_default\"],normalize=\"index\").sort_values(1,ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This column looks like a ordinal data.However,the No_score inspite of being not scored has relatively low percent of defaulters than high risk people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a column whether they are scored are not\n",
    "df[\"Scored_Or_Not\"]=df[\"PERFORM_CNS.SCORE.DESCRIPTION\"].apply(lambda x:1 if x==\"No_Score\" else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0-There is no siginificant relationship between the CNS.desc and no_of_defaulters\n",
    "### H1-There is  siginificant relationship between the CNS.desc and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "conti_table=pd.crosstab(df[\"PERFORM_CNS.SCORE.DESCRIPTION\"],df[\"loan_default\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_2,p_value,dof,exp=chi2_contingency(conti_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_2- 2114.4654081820345 p_value- 0.0\n"
     ]
    }
   ],
   "source": [
    "print(\"chi_2-\",chi_2,\"p_value-\",p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### P_value less than .05 which rejects null hypotheis in favour of alternate hypothesis.There is significant relationship between the CNS.desc and no_of_defaulters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### with p_value exactly zero this seems important variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PRI.NO.OF.ACCTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    233154.000000\n",
       "mean          2.440636\n",
       "std           5.217233\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%           3.000000\n",
       "max         453.000000\n",
       "Name: PRI.NO.OF.ACCTS, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"PRI.NO.OF.ACCTS\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### with over 50% of people have No loan accounts before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    116950\n",
       "1     34978\n",
       "2     19784\n",
       "3     13015\n",
       "4      9323\n",
       "Name: PRI.NO.OF.ACCTS, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"PRI.NO.OF.ACCTS\"].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25c88b00cf8>"
      ]
     },
     "execution_count": 89,
     "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.distplot(df['PRI.NO.OF.ACCTS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"First_Time_Account_Holder\"]=df[\"PRI.NO.OF.ACCTS\"].apply(lambda x:1 if x==0 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "conti_table=pd.crosstab(df[\"First_Time_Account_Holder\"],df[\"loan_default\"],normalize=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0-There is no siginificant relationship between the First_Time_Account_Holder and no_of_defaulters\n",
    "### H1-There is  siginificant relationship between the First_Time_Account_Holder and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_2,p_value,dof,exp=chi2_contingency(conti_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_2- 2.7767129608850434 p_value- 0.0956442825239546\n"
     ]
    }
   ],
   "source": [
    "print(\"chi_2-\",chi_2,\"p_value-\",p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### With p_value less than .05 it fails to reject null hypothesis which stats that there is no significant relationship between first time account holders and no_of_defaulters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PRI.ACTIVE.ACCTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    233154.000000\n",
       "mean          1.039896\n",
       "std           1.941496\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%           1.000000\n",
       "max         144.000000\n",
       "Name: PRI.ACTIVE.ACCTS, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"PRI.ACTIVE.ACCTS\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### here also over 50% people have no active accounts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Active_accounts_Flag\"]=df[\"PRI.ACTIVE.ACCTS\"].apply(lambda x:0 if x==0 else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "conti_table=pd.crosstab(df[\"Active_accounts_Flag\"],df[\"loan_default\"],normalize=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0-There is no siginificant relationship between the Active_Accounts_Flag and no_of_defaulters\n",
    "### H1-There is  siginificant relationship between the Active_Accounts_Flag and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_2,p_value,dof,exp=chi2_contingency(conti_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_2- 2.826471242613229 p_value- 0.09272189051920196\n"
     ]
    }
   ],
   "source": [
    "print(\"chi_2-\",chi_2,\"p_value-\",p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This parameter also fails to reject null hypothesis which conveys there is no significant relationship between Active aaccounts flag and no_of_defaulters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PRI.OVERDUE.ACCTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    233154.000000\n",
       "mean          0.156549\n",
       "std           0.548787\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%           0.000000\n",
       "max          25.000000\n",
       "Name: PRI.OVERDUE.ACCTS, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"PRI.OVERDUE.ACCTS\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     206879\n",
       "1      19970\n",
       "2       4302\n",
       "3       1202\n",
       "4        404\n",
       "5        166\n",
       "6         96\n",
       "7         38\n",
       "8         27\n",
       "9         25\n",
       "11        12\n",
       "12         8\n",
       "10         6\n",
       "13         5\n",
       "14         5\n",
       "17         2\n",
       "18         2\n",
       "23         1\n",
       "15         1\n",
       "16         1\n",
       "19         1\n",
       "25         1\n",
       "Name: PRI.OVERDUE.ACCTS, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"PRI.OVERDUE.ACCTS\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### only around 25000 people have overdue accounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Overdue_accounts_Flag\"]=df[\"PRI.OVERDUE.ACCTS\"].apply(lambda x:0 if x==0 else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "conti_table=pd.crosstab(df[\"Overdue_accounts_Flag\"],df[\"loan_default\"],normalize=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0-There is no siginificant relationship between the Overdue_accounts_Flag and no_of_defaulters\n",
    "### H1-There is  siginificant relationship between the Overdue_accounts_Flag and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_2,p_value,dof,exp=chi2_contingency(conti_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_2- 2.386531795177552 p_value- 0.12238487774028094\n"
     ]
    }
   ],
   "source": [
    "print(\"chi_2-\",chi_2,\"p_value-\",p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This parameter also fails to reject null hypothesis which conveys there is no significant relationship between Overdue aaccounts flag and no_of_defaulters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PRI.CURRENT.BALANCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.331540e+05\n",
       "mean     1.659001e+05\n",
       "std      9.422736e+05\n",
       "min     -6.678296e+06\n",
       "25%      0.000000e+00\n",
       "50%      0.000000e+00\n",
       "75%      3.500650e+04\n",
       "max      9.652492e+07\n",
       "Name: PRI.CURRENT.BALANCE, dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"PRI.CURRENT.BALANCE\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25c8ad304a8>"
      ]
     },
     "execution_count": 106,
     "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.distplot(df[\"PRI.CURRENT.BALANCE\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There are some values less than zero which doesn't seem right"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PRI.SANCTIONED.AMOUNT AND PRI.DISBURSED.AMOUNT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.331540e+05\n",
       "mean     2.185039e+05\n",
       "std      2.374794e+06\n",
       "min      0.000000e+00\n",
       "25%      0.000000e+00\n",
       "50%      0.000000e+00\n",
       "75%      6.250000e+04\n",
       "max      1.000000e+09\n",
       "Name: PRI.SANCTIONED.AMOUNT, dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"PRI.SANCTIONED.AMOUNT\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.331540e+05\n",
       "mean     2.180659e+05\n",
       "std      2.377744e+06\n",
       "min      0.000000e+00\n",
       "25%      0.000000e+00\n",
       "50%      0.000000e+00\n",
       "75%      6.080000e+04\n",
       "max      1.000000e+09\n",
       "Name: PRI.DISBURSED.AMOUNT, dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"PRI.DISBURSED.AMOUNT\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### both feature seems to have same characteristic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import zscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25c8adfd2e8>"
      ]
     },
     "execution_count": 110,
     "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.distplot(df[\"PRI.SANCTIONED.AMOUNT\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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>PRI.SANCTIONED.AMOUNT</th>\n",
       "      <th>PRI.DISBURSED.AMOUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRI.SANCTIONED.AMOUNT</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRI.DISBURSED.AMOUNT</th>\n",
       "      <td>0.998747</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       PRI.SANCTIONED.AMOUNT  PRI.DISBURSED.AMOUNT\n",
       "PRI.SANCTIONED.AMOUNT               1.000000              0.998747\n",
       "PRI.DISBURSED.AMOUNT                0.998747              1.000000"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['PRI.SANCTIONED.AMOUNT','PRI.DISBURSED.AMOUNT']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>DisbursalDate</th>\n",
       "      <th>State_ID</th>\n",
       "      <th>Employee_code_ID</th>\n",
       "      <th>MobileNo_Avl_Flag</th>\n",
       "      <th>Aadhar_flag</th>\n",
       "      <th>PAN_flag</th>\n",
       "      <th>VoterID_flag</th>\n",
       "      <th>Driving_flag</th>\n",
       "      <th>Passport_flag</th>\n",
       "      <th>PERFORM_CNS.SCORE</th>\n",
       "      <th>PERFORM_CNS.SCORE.DESCRIPTION</th>\n",
       "      <th>PRI.NO.OF.ACCTS</th>\n",
       "      <th>PRI.ACTIVE.ACCTS</th>\n",
       "      <th>PRI.OVERDUE.ACCTS</th>\n",
       "      <th>PRI.CURRENT.BALANCE</th>\n",
       "      <th>PRI.SANCTIONED.AMOUNT</th>\n",
       "      <th>PRI.DISBURSED.AMOUNT</th>\n",
       "      <th>SEC.NO.OF.ACCTS</th>\n",
       "      <th>SEC.ACTIVE.ACCTS</th>\n",
       "      <th>SEC.OVERDUE.ACCTS</th>\n",
       "      <th>SEC.CURRENT.BALANCE</th>\n",
       "      <th>SEC.SANCTIONED.AMOUNT</th>\n",
       "      <th>SEC.DISBURSED.AMOUNT</th>\n",
       "      <th>PRIMARY.INSTAL.AMT</th>\n",
       "      <th>SEC.INSTAL.AMT</th>\n",
       "      <th>NEW.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>AVERAGE.ACCT.AGE</th>\n",
       "      <th>CREDIT.HISTORY.LENGTH</th>\n",
       "      <th>NO.OF_INQUIRIES</th>\n",
       "      <th>loan_default</th>\n",
       "      <th>Age_at_time_of_disbursement</th>\n",
       "      <th>month_of_disbursement</th>\n",
       "      <th>Scored_Or_Not</th>\n",
       "      <th>First_Time_Account_Holder</th>\n",
       "      <th>Active_accounts_Flag</th>\n",
       "      <th>Overdue_accounts_Flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71593</th>\n",
       "      <td>598208</td>\n",
       "      <td>47773</td>\n",
       "      <td>85964</td>\n",
       "      <td>59.33</td>\n",
       "      <td>20</td>\n",
       "      <td>16700</td>\n",
       "      <td>86</td>\n",
       "      <td>6434</td>\n",
       "      <td>1968-02-23</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>2018-10-22</td>\n",
       "      <td>5</td>\n",
       "      <td>152</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>748</td>\n",
       "      <td>C-Very Low Risk</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>36041</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2yrs 7mon</td>\n",
       "      <td>4yrs 2mon</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
       "71593    598208             47773       85964  59.33         20        16700   \n",
       "\n",
       "       manufacturer_id  Current_pincode_ID Date.of.Birth Employment.Type  \\\n",
       "71593               86                6434    1968-02-23   Self employed   \n",
       "\n",
       "      DisbursalDate  State_ID  Employee_code_ID  MobileNo_Avl_Flag  \\\n",
       "71593    2018-10-22         5               152                  1   \n",
       "\n",
       "       Aadhar_flag  PAN_flag  VoterID_flag  Driving_flag  Passport_flag  \\\n",
       "71593            1         0             0             0              0   \n",
       "\n",
       "       PERFORM_CNS.SCORE PERFORM_CNS.SCORE.DESCRIPTION  PRI.NO.OF.ACCTS  \\\n",
       "71593                748               C-Very Low Risk                2   \n",
       "\n",
       "       PRI.ACTIVE.ACCTS  PRI.OVERDUE.ACCTS  PRI.CURRENT.BALANCE  \\\n",
       "71593                 1                  0                36041   \n",
       "\n",
       "       PRI.SANCTIONED.AMOUNT  PRI.DISBURSED.AMOUNT  SEC.NO.OF.ACCTS  \\\n",
       "71593             1000000000            1000000000                0   \n",
       "\n",
       "       SEC.ACTIVE.ACCTS  SEC.OVERDUE.ACCTS  SEC.CURRENT.BALANCE  \\\n",
       "71593                 0                  0                    0   \n",
       "\n",
       "       SEC.SANCTIONED.AMOUNT  SEC.DISBURSED.AMOUNT  PRIMARY.INSTAL.AMT  \\\n",
       "71593                      0                     0                   0   \n",
       "\n",
       "       SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "71593               0                             0   \n",
       "\n",
       "       DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS AVERAGE.ACCT.AGE  \\\n",
       "71593                                    0        2yrs 7mon   \n",
       "\n",
       "      CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \\\n",
       "71593             4yrs 2mon                0             1   \n",
       "\n",
       "       Age_at_time_of_disbursement  month_of_disbursement  Scored_Or_Not  \\\n",
       "71593                           50                     10              0   \n",
       "\n",
       "       First_Time_Account_Holder  Active_accounts_Flag  Overdue_accounts_Flag  \n",
       "71593                          0                     1                      0  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"PRI.SANCTIONED.AMOUNT\"]==df[\"PRI.SANCTIONED.AMOUNT\"].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### His primary sanctioned amount is 100 crore while he had only two accounts before and his current disbursed amount seems to be less than 50000 which is two extremes.Eitherway we can't rule out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=5.386857453201851, pvalue=7.176942237800462e-08)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(group_0[\"PRI.DISBURSED.AMOUNT\"],group_1[\"PRI.DISBURSED.AMOUNT\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=5.458812718452121, pvalue=4.798158421546997e-08)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(group_0[\"PRI.SANCTIONED.AMOUNT\"],group_1[\"PRI.SANCTIONED.AMOUNT\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### both variables have p_values less than .05 which states that mean of both groups are not equal(both are significant features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SEC.NO.OF.ACCTS  SEC.ACTIVE.ACCTS SEC.OVERDUE.ACCTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    227289\n",
       "1      3466\n",
       "2      1036\n",
       "3       444\n",
       "4       292\n",
       "Name: SEC.NO.OF.ACCTS, dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"SEC.NO.OF.ACCTS\"].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### over 2 lakh people have no secondary accounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "conti_table=pd.crosstab(df['SEC.NO.OF.ACCTS'],df[\"loan_default\"],normalize=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0-There is no siginificant relationship between the SEC.NO.OF.ACCTS and no_of_defaulters\n",
    "### H1-There is  siginificant relationship between the SEC.NO.OF.ACCTS and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_2,p_value,dof,exp=chi2_contingency(conti_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_2- 6.0045936375142555 p_value- 0.9999999963867071\n"
     ]
    }
   ],
   "source": [
    "print(\"chi_2-\",chi_2,\"p_value-\",p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This parameter also fails to reject null hypothesis which conveys there is no significant relationship between SEC.NO.OF.ACCTS  and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    229337\n",
       "1      2684\n",
       "2       636\n",
       "3       195\n",
       "4       116\n",
       "Name: SEC.ACTIVE.ACCTS, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"SEC.ACTIVE.ACCTS\"].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "conti_table=pd.crosstab(df['SEC.ACTIVE.ACCTS'],df[\"loan_default\"],normalize=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0-There is no siginificant relationship between the SEC.ACTIVE.ACCTS and no_of_defaulters\n",
    "### H1-There is  siginificant relationship between the SEC.ACTIVE.ACCTS and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_2,p_value,dof,exp=chi2_contingency(conti_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_2- 4.483899677775443 p_value- 0.9999765449816578\n"
     ]
    }
   ],
   "source": [
    "print(\"chi_2-\",chi_2,\"p_value-\",p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This parameter also fails to reject null hypothesis which conveys there is no significant relationship between SEC.ACTIVE.ACCTS  and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    231817\n",
       "1      1129\n",
       "2       126\n",
       "3        47\n",
       "4        19\n",
       "Name: SEC.OVERDUE.ACCTS, dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"SEC.OVERDUE.ACCTS\"].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "conti_table=pd.crosstab(df['SEC.OVERDUE.ACCTS'],df[\"loan_default\"],normalize=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0-There is no siginificant relationship between the SEC.OVERDUE.ACCTS and no_of_defaulters\n",
    "### H1-There is  siginificant relationship between the SEC.OVERDUE.ACCTS and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_2,p_value,dof,exp=chi2_contingency(conti_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_2- 0.7006247696945116 p_value- 0.9995250753555058\n"
     ]
    }
   ],
   "source": [
    "print(\"chi_2-\",chi_2,\"p_value-\",p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This parameter also fails to reject null hypothesis which conveys there is no significant relationship between SEC.OVERDUE.ACCTS  and no_of_defaulters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SEC.CURRENT.BALANCE SEC.SANCTIONED.AMOUNT  SEC.DISBURSED.AMOUNT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.331540e+05\n",
       "mean     5.427793e+03\n",
       "std      1.702370e+05\n",
       "min     -5.746470e+05\n",
       "25%      0.000000e+00\n",
       "50%      0.000000e+00\n",
       "75%      0.000000e+00\n",
       "max      3.603285e+07\n",
       "Name: SEC.CURRENT.BALANCE, dtype: float64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"SEC.CURRENT.BALANCE\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>DisbursalDate</th>\n",
       "      <th>State_ID</th>\n",
       "      <th>Employee_code_ID</th>\n",
       "      <th>MobileNo_Avl_Flag</th>\n",
       "      <th>Aadhar_flag</th>\n",
       "      <th>PAN_flag</th>\n",
       "      <th>VoterID_flag</th>\n",
       "      <th>Driving_flag</th>\n",
       "      <th>Passport_flag</th>\n",
       "      <th>PERFORM_CNS.SCORE</th>\n",
       "      <th>PERFORM_CNS.SCORE.DESCRIPTION</th>\n",
       "      <th>PRI.NO.OF.ACCTS</th>\n",
       "      <th>PRI.ACTIVE.ACCTS</th>\n",
       "      <th>PRI.OVERDUE.ACCTS</th>\n",
       "      <th>PRI.CURRENT.BALANCE</th>\n",
       "      <th>PRI.SANCTIONED.AMOUNT</th>\n",
       "      <th>PRI.DISBURSED.AMOUNT</th>\n",
       "      <th>SEC.NO.OF.ACCTS</th>\n",
       "      <th>SEC.ACTIVE.ACCTS</th>\n",
       "      <th>SEC.OVERDUE.ACCTS</th>\n",
       "      <th>SEC.CURRENT.BALANCE</th>\n",
       "      <th>SEC.SANCTIONED.AMOUNT</th>\n",
       "      <th>SEC.DISBURSED.AMOUNT</th>\n",
       "      <th>PRIMARY.INSTAL.AMT</th>\n",
       "      <th>SEC.INSTAL.AMT</th>\n",
       "      <th>NEW.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>AVERAGE.ACCT.AGE</th>\n",
       "      <th>CREDIT.HISTORY.LENGTH</th>\n",
       "      <th>NO.OF_INQUIRIES</th>\n",
       "      <th>loan_default</th>\n",
       "      <th>Age_at_time_of_disbursement</th>\n",
       "      <th>month_of_disbursement</th>\n",
       "      <th>Scored_Or_Not</th>\n",
       "      <th>First_Time_Account_Holder</th>\n",
       "      <th>Active_accounts_Flag</th>\n",
       "      <th>Overdue_accounts_Flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2548</th>\n",
       "      <td>461175</td>\n",
       "      <td>49763</td>\n",
       "      <td>73616</td>\n",
       "      <td>70.64</td>\n",
       "      <td>142</td>\n",
       "      <td>17315</td>\n",
       "      <td>86</td>\n",
       "      <td>109</td>\n",
       "      <td>1994-10-08</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>2018-08-24</td>\n",
       "      <td>16</td>\n",
       "      <td>331</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>525</td>\n",
       "      <td>J-High Risk</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>96378</td>\n",
       "      <td>196500</td>\n",
       "      <td>196500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-30</td>\n",
       "      <td>100000</td>\n",
       "      <td>2239</td>\n",
       "      <td>2332</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0yrs 10mon</td>\n",
       "      <td>2yrs 6mon</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3657</th>\n",
       "      <td>577744</td>\n",
       "      <td>59259</td>\n",
       "      <td>77516</td>\n",
       "      <td>77.40</td>\n",
       "      <td>16</td>\n",
       "      <td>21498</td>\n",
       "      <td>86</td>\n",
       "      <td>2940</td>\n",
       "      <td>1984-07-21</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>2018-10-14</td>\n",
       "      <td>17</td>\n",
       "      <td>430</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>585</td>\n",
       "      <td>I-Medium Risk</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-76</td>\n",
       "      <td>156994</td>\n",
       "      <td>156994</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3yrs 2mon</td>\n",
       "      <td>10yrs 7mon</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13333</th>\n",
       "      <td>473381</td>\n",
       "      <td>26060</td>\n",
       "      <td>52674</td>\n",
       "      <td>52.51</td>\n",
       "      <td>3</td>\n",
       "      <td>16167</td>\n",
       "      <td>45</td>\n",
       "      <td>91</td>\n",
       "      <td>1963-02-26</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>2018-08-29</td>\n",
       "      <td>1</td>\n",
       "      <td>630</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>610</td>\n",
       "      <td>H-Medium Risk</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>225759</td>\n",
       "      <td>539100</td>\n",
       "      <td>539100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>28964</td>\n",
       "      <td>28964</td>\n",
       "      <td>12319</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2yrs 6mon</td>\n",
       "      <td>7yrs 0mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18205</th>\n",
       "      <td>529513</td>\n",
       "      <td>39619</td>\n",
       "      <td>63084</td>\n",
       "      <td>67.37</td>\n",
       "      <td>160</td>\n",
       "      <td>15700</td>\n",
       "      <td>86</td>\n",
       "      <td>92</td>\n",
       "      <td>1968-05-01</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>16</td>\n",
       "      <td>1191</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>839</td>\n",
       "      <td>A-Very Low Risk</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-24</td>\n",
       "      <td>5436</td>\n",
       "      <td>5436</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1yrs 7mon</td>\n",
       "      <td>3yrs 2mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19182</th>\n",
       "      <td>446097</td>\n",
       "      <td>23166</td>\n",
       "      <td>54340</td>\n",
       "      <td>45.09</td>\n",
       "      <td>160</td>\n",
       "      <td>14151</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "      <td>1983-01-12</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1839</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>690</td>\n",
       "      <td>E-Low Risk</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>206000</td>\n",
       "      <td>518740</td>\n",
       "      <td>521684</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-125</td>\n",
       "      <td>17000</td>\n",
       "      <td>17000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3yrs 2mon</td>\n",
       "      <td>12yrs 6mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
       "2548     461175             49763       73616  70.64        142        17315   \n",
       "3657     577744             59259       77516  77.40         16        21498   \n",
       "13333    473381             26060       52674  52.51          3        16167   \n",
       "18205    529513             39619       63084  67.37        160        15700   \n",
       "19182    446097             23166       54340  45.09        160        14151   \n",
       "\n",
       "       manufacturer_id  Current_pincode_ID Date.of.Birth Employment.Type  \\\n",
       "2548                86                 109    1994-10-08        Salaried   \n",
       "3657                86                2940    1984-07-21        Salaried   \n",
       "13333               45                  91    1963-02-26        Salaried   \n",
       "18205               86                  92    1968-05-01   Self employed   \n",
       "19182               45                  60    1983-01-12   Self employed   \n",
       "\n",
       "      DisbursalDate  State_ID  Employee_code_ID  MobileNo_Avl_Flag  \\\n",
       "2548     2018-08-24        16               331                  1   \n",
       "3657     2018-10-14        17               430                  1   \n",
       "13333    2018-08-29         1               630                  1   \n",
       "18205    2018-09-23        16              1191                  1   \n",
       "19182    2018-08-17         1              1839                  1   \n",
       "\n",
       "       Aadhar_flag  PAN_flag  VoterID_flag  Driving_flag  Passport_flag  \\\n",
       "2548             1         0             0             0              0   \n",
       "3657             1         0             0             0              0   \n",
       "13333            1         0             0             0              0   \n",
       "18205            1         1             0             0              0   \n",
       "19182            1         0             0             0              0   \n",
       "\n",
       "       PERFORM_CNS.SCORE PERFORM_CNS.SCORE.DESCRIPTION  PRI.NO.OF.ACCTS  \\\n",
       "2548                 525                   J-High Risk                9   \n",
       "3657                 585                 I-Medium Risk                3   \n",
       "13333                610                 H-Medium Risk                4   \n",
       "18205                839               A-Very Low Risk                1   \n",
       "19182                690                    E-Low Risk               14   \n",
       "\n",
       "       PRI.ACTIVE.ACCTS  PRI.OVERDUE.ACCTS  PRI.CURRENT.BALANCE  \\\n",
       "2548                  5                  1                96378   \n",
       "3657                  0                  0                    0   \n",
       "13333                 3                  1               225759   \n",
       "18205                 0                  0                    0   \n",
       "19182                 3                  0               206000   \n",
       "\n",
       "       PRI.SANCTIONED.AMOUNT  PRI.DISBURSED.AMOUNT  SEC.NO.OF.ACCTS  \\\n",
       "2548                  196500                196500                1   \n",
       "3657                       0                     0                1   \n",
       "13333                 539100                539100                1   \n",
       "18205                      0                     0                1   \n",
       "19182                 518740                521684                1   \n",
       "\n",
       "       SEC.ACTIVE.ACCTS  SEC.OVERDUE.ACCTS  SEC.CURRENT.BALANCE  \\\n",
       "2548                  1                  0                  -30   \n",
       "3657                  1                  0                  -76   \n",
       "13333                 1                  0                   -1   \n",
       "18205                 1                  0                  -24   \n",
       "19182                 1                  0                 -125   \n",
       "\n",
       "       SEC.SANCTIONED.AMOUNT  SEC.DISBURSED.AMOUNT  PRIMARY.INSTAL.AMT  \\\n",
       "2548                  100000                  2239                2332   \n",
       "3657                  156994                156994                   0   \n",
       "13333                  28964                 28964               12319   \n",
       "18205                   5436                  5436                   0   \n",
       "19182                  17000                 17000                   0   \n",
       "\n",
       "       SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "2548                0                             3   \n",
       "3657                0                             0   \n",
       "13333               0                             0   \n",
       "18205               0                             1   \n",
       "19182               0                             0   \n",
       "\n",
       "       DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS AVERAGE.ACCT.AGE  \\\n",
       "2548                                     1       0yrs 10mon   \n",
       "3657                                     0        3yrs 2mon   \n",
       "13333                                    0        2yrs 6mon   \n",
       "18205                                    0        1yrs 7mon   \n",
       "19182                                    1        3yrs 2mon   \n",
       "\n",
       "      CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \\\n",
       "2548              2yrs 6mon                0             1   \n",
       "3657             10yrs 7mon                0             1   \n",
       "13333             7yrs 0mon                0             0   \n",
       "18205             3yrs 2mon                0             0   \n",
       "19182            12yrs 6mon                0             0   \n",
       "\n",
       "       Age_at_time_of_disbursement  month_of_disbursement  Scored_Or_Not  \\\n",
       "2548                            23                      8              0   \n",
       "3657                            34                     10              0   \n",
       "13333                           55                      8              0   \n",
       "18205                           50                      9              0   \n",
       "19182                           35                      8              0   \n",
       "\n",
       "       First_Time_Account_Holder  Active_accounts_Flag  Overdue_accounts_Flag  \n",
       "2548                           0                     1                      1  \n",
       "3657                           0                     0                      0  \n",
       "13333                          0                     1                      1  \n",
       "18205                          0                     0                      0  \n",
       "19182                          0                     1                      0  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"SEC.CURRENT.BALANCE\"]<0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### here also current balance have values less than zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25c8adfdef0>"
      ]
     },
     "execution_count": 128,
     "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.distplot(df[\"SEC.CURRENT.BALANCE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=2.6709443833814404, pvalue=0.0075643427363124875)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(group_0[\"SEC.CURRENT.BALANCE\"],group_1[\"SEC.CURRENT.BALANCE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=3.017101025367004, pvalue=0.0025523226185338705)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(group_0[\"SEC.DISBURSED.AMOUNT\"],group_1[\"SEC.DISBURSED.AMOUNT\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=3.068298386446011, pvalue=0.002153062273491789)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(group_0[\"SEC.SANCTIONED.AMOUNT\"],group_1[\"SEC.SANCTIONED.AMOUNT\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PRIMARY.INSTAL.AMT SEC.INSTAL.AMT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.331540e+05\n",
       "mean     1.310548e+04\n",
       "std      1.513679e+05\n",
       "min      0.000000e+00\n",
       "25%      0.000000e+00\n",
       "50%      0.000000e+00\n",
       "75%      1.999000e+03\n",
       "max      2.564281e+07\n",
       "Name: PRIMARY.INSTAL.AMT, dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"PRIMARY.INSTAL.AMT\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.331540e+05\n",
       "mean     3.232684e+02\n",
       "std      1.555369e+04\n",
       "min      0.000000e+00\n",
       "25%      0.000000e+00\n",
       "50%      0.000000e+00\n",
       "75%      0.000000e+00\n",
       "max      4.170901e+06\n",
       "Name: SEC.INSTAL.AMT, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"SEC.INSTAL.AMT\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25c940708d0>"
      ]
     },
     "execution_count": 134,
     "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.distplot(df[\"PRIMARY.INSTAL.AMT\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25c940874e0>"
      ]
     },
     "execution_count": 135,
     "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.distplot(df[\"SEC.INSTAL.AMT\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### in both cases,we see abnormal max values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>PRI.DISBURSED.AMOUNT</th>\n",
       "      <th>PRIMARY.INSTAL.AMT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125308</th>\n",
       "      <td>1200000</td>\n",
       "      <td>25642806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PRI.DISBURSED.AMOUNT  PRIMARY.INSTAL.AMT\n",
       "125308               1200000            25642806"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"PRIMARY.INSTAL.AMT\"]==df[\"PRIMARY.INSTAL.AMT\"].max()][[\"PRI.DISBURSED.AMOUNT\",\"PRIMARY.INSTAL.AMT\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### how come primary install amt is greater than the primary disbursed amount.may be typo error which we should analyze"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0:mean_emi_of_non_defaulters==mean_emi_of_non_defaulters\n",
    "### H1:mean_emi_of_non_defaulters!=mean_emi_of_non_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=5.126237219178528, pvalue=2.958254960232989e-07)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(group_0[\"PRIMARY.INSTAL.AMT\"],group_1[\"PRIMARY.INSTAL.AMT\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pvalue less than .05 which rejects null hypothesis in favour of alternate hypothesis that emi of both groups differ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.7476974360618777, pvalue=0.4546434321302706)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(group_0[\"SEC.INSTAL.AMT\"],group_1[\"SEC.INSTAL.AMT\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pvalue greater than .05 it fails to reject which stats that mean of two groups are same (insignificant features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 'NEW.ACCTS.IN.LAST.SIX.MONTHS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    181494\n",
       "1     32099\n",
       "2     11015\n",
       "3      4458\n",
       "4      1957\n",
       "Name: NEW.ACCTS.IN.LAST.SIX.MONTHS, dtype: int64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"NEW.ACCTS.IN.LAST.SIX.MONTHS\"].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "conti_table=pd.crosstab(df['NEW.ACCTS.IN.LAST.SIX.MONTHS'],df[\"loan_default\"],normalize=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0-There is no siginificant relationship between the NEW.ACCTS.IN.LAST.SIX.MONTHS and no_of_defaulters\n",
    "### H1-There is  siginificant relationship between the NEW.ACCTS.IN.LAST.SIX.MONTHS and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_2,p_value,dof,exp=chi2_contingency(conti_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_2- 6.8198968740511186 p_value- 0.9998828825427777\n"
     ]
    }
   ],
   "source": [
    "print(\"chi_2-\",chi_2,\"p_value-\",p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This parameter also fails to reject null hypothesis which conveys there is no significant relationship between   NEW.ACCTS.IN.LAST.SIX.MONTHS and no_of_defaulters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    214959\n",
       "1     14941\n",
       "2      2470\n",
       "3       537\n",
       "4       138\n",
       "Name: DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS, dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS\"].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "conti_table=pd.crosstab(df['DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS'],df[\"loan_default\"],normalize=\"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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>DisbursalDate</th>\n",
       "      <th>State_ID</th>\n",
       "      <th>Employee_code_ID</th>\n",
       "      <th>MobileNo_Avl_Flag</th>\n",
       "      <th>Aadhar_flag</th>\n",
       "      <th>PAN_flag</th>\n",
       "      <th>VoterID_flag</th>\n",
       "      <th>Driving_flag</th>\n",
       "      <th>Passport_flag</th>\n",
       "      <th>PERFORM_CNS.SCORE</th>\n",
       "      <th>PERFORM_CNS.SCORE.DESCRIPTION</th>\n",
       "      <th>PRI.NO.OF.ACCTS</th>\n",
       "      <th>PRI.ACTIVE.ACCTS</th>\n",
       "      <th>PRI.OVERDUE.ACCTS</th>\n",
       "      <th>PRI.CURRENT.BALANCE</th>\n",
       "      <th>PRI.SANCTIONED.AMOUNT</th>\n",
       "      <th>PRI.DISBURSED.AMOUNT</th>\n",
       "      <th>SEC.NO.OF.ACCTS</th>\n",
       "      <th>SEC.ACTIVE.ACCTS</th>\n",
       "      <th>SEC.OVERDUE.ACCTS</th>\n",
       "      <th>SEC.CURRENT.BALANCE</th>\n",
       "      <th>SEC.SANCTIONED.AMOUNT</th>\n",
       "      <th>SEC.DISBURSED.AMOUNT</th>\n",
       "      <th>PRIMARY.INSTAL.AMT</th>\n",
       "      <th>SEC.INSTAL.AMT</th>\n",
       "      <th>NEW.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
       "      <th>AVERAGE.ACCT.AGE</th>\n",
       "      <th>CREDIT.HISTORY.LENGTH</th>\n",
       "      <th>NO.OF_INQUIRIES</th>\n",
       "      <th>loan_default</th>\n",
       "      <th>Age_at_time_of_disbursement</th>\n",
       "      <th>month_of_disbursement</th>\n",
       "      <th>Scored_Or_Not</th>\n",
       "      <th>First_Time_Account_Holder</th>\n",
       "      <th>Active_accounts_Flag</th>\n",
       "      <th>Overdue_accounts_Flag</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>2018-08-03</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No_Score</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>2018-08-01</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No_Score</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1977-12-09</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No_Score</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1988-06-01</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No_Score</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1994-07-14</td>\n",
       "      <td>Self employed</td>\n",
       "      <td>2018-10-08</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No_Score</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0yrs 0mon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
       "0     420825             50578       58400  89.55         67        22807   \n",
       "2     417566             53278       61360  89.63         67        22807   \n",
       "4     539055             52378       60300  88.39         67        22807   \n",
       "6     529269             46349       61500  76.42         67        22807   \n",
       "22    563215             43594       78256  57.50         67        22744   \n",
       "\n",
       "    manufacturer_id  Current_pincode_ID Date.of.Birth Employment.Type  \\\n",
       "0                45                1441    1984-01-01        Salaried   \n",
       "2                45                1497    1985-08-24   Self employed   \n",
       "4                45                1495    1977-12-09   Self employed   \n",
       "6                45                1502    1988-06-01        Salaried   \n",
       "22               86                1499    1994-07-14   Self employed   \n",
       "\n",
       "   DisbursalDate  State_ID  Employee_code_ID  MobileNo_Avl_Flag  Aadhar_flag  \\\n",
       "0     2018-08-03         6              1998                  1            1   \n",
       "2     2018-08-01         6              1998                  1            1   \n",
       "4     2018-09-26         6              1998                  1            1   \n",
       "6     2018-09-23         6              1998                  1            1   \n",
       "22    2018-10-08         6              1998                  1            1   \n",
       "\n",
       "    PAN_flag  VoterID_flag  Driving_flag  Passport_flag  PERFORM_CNS.SCORE  \\\n",
       "0          0             0             0              0                  0   \n",
       "2          0             0             0              0                  0   \n",
       "4          0             0             0              0                  0   \n",
       "6          0             0             0              0                  0   \n",
       "22         0             0             0              0                  0   \n",
       "\n",
       "   PERFORM_CNS.SCORE.DESCRIPTION  PRI.NO.OF.ACCTS  PRI.ACTIVE.ACCTS  \\\n",
       "0                       No_Score                0                 0   \n",
       "2                       No_Score                0                 0   \n",
       "4                       No_Score                0                 0   \n",
       "6                       No_Score                0                 0   \n",
       "22                      No_Score                0                 0   \n",
       "\n",
       "    PRI.OVERDUE.ACCTS  PRI.CURRENT.BALANCE  PRI.SANCTIONED.AMOUNT  \\\n",
       "0                   0                    0                      0   \n",
       "2                   0                    0                      0   \n",
       "4                   0                    0                      0   \n",
       "6                   0                    0                      0   \n",
       "22                  0                    0                      0   \n",
       "\n",
       "    PRI.DISBURSED.AMOUNT  SEC.NO.OF.ACCTS  SEC.ACTIVE.ACCTS  \\\n",
       "0                      0                0                 0   \n",
       "2                      0                0                 0   \n",
       "4                      0                0                 0   \n",
       "6                      0                0                 0   \n",
       "22                     0                0                 0   \n",
       "\n",
       "    SEC.OVERDUE.ACCTS  SEC.CURRENT.BALANCE  SEC.SANCTIONED.AMOUNT  \\\n",
       "0                   0                    0                      0   \n",
       "2                   0                    0                      0   \n",
       "4                   0                    0                      0   \n",
       "6                   0                    0                      0   \n",
       "22                  0                    0                      0   \n",
       "\n",
       "    SEC.DISBURSED.AMOUNT  PRIMARY.INSTAL.AMT  SEC.INSTAL.AMT  \\\n",
       "0                      0                   0               0   \n",
       "2                      0                   0               0   \n",
       "4                      0                   0               0   \n",
       "6                      0                   0               0   \n",
       "22                     0                   0               0   \n",
       "\n",
       "    NEW.ACCTS.IN.LAST.SIX.MONTHS  DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
       "0                              0                                    0   \n",
       "2                              0                                    0   \n",
       "4                              0                                    0   \n",
       "6                              0                                    0   \n",
       "22                             0                                    0   \n",
       "\n",
       "    AVERAGE.ACCT.AGE CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \\\n",
       "0                  0             0yrs 0mon                0             0   \n",
       "2                  0             0yrs 0mon                0             0   \n",
       "4                  0             0yrs 0mon                1             1   \n",
       "6                  0             0yrs 0mon                0             0   \n",
       "22                 0             0yrs 0mon                0             0   \n",
       "\n",
       "    Age_at_time_of_disbursement  month_of_disbursement  Scored_Or_Not  \\\n",
       "0                            34                      8              1   \n",
       "2                            32                      8              1   \n",
       "4                            40                      9              1   \n",
       "6                            30                      9              1   \n",
       "22                           24                     10              1   \n",
       "\n",
       "    First_Time_Account_Holder  Active_accounts_Flag  Overdue_accounts_Flag  \n",
       "0                           1                     0                      0  \n",
       "2                           1                     0                      0  \n",
       "4                           1                     0                      0  \n",
       "6                           1                     0                      0  \n",
       "22                          1                     0                      0  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"First_Time_Account_Holder\"]==1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0-There is no siginificant relationship between the DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS and no_of_defaulters\n",
    "### H1-There is  siginificant relationship between the DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_2,p_value,dof,exp=chi2_contingency(conti_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_2- 2.28208875754001 p_value- 0.9995269170827656\n"
     ]
    }
   ],
   "source": [
    "print(\"chi_2-\",chi_2,\"p_value-\",p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This parameter also fails to reject null hypothesis which conveys there is no significant relationship between   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS and no_of_defaulters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NO.OF_INQUIRIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    201961\n",
       "1     22285\n",
       "2      5409\n",
       "3      1767\n",
       "4       760\n",
       "Name: NO.OF_INQUIRIES, dtype: int64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"NO.OF_INQUIRIES\"].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "conti_table=pd.crosstab(df['NO.OF_INQUIRIES'],df[\"loan_default\"],normalize=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0-There is no siginificant relationship between the NO.OF_INQUIRIES and no_of_defaulters\n",
    "### H1-There is  siginificant relationship between the NO.OF_INQUIRIES and no_of_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_2,p_value,dof,exp=chi2_contingency(conti_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_2- 4.777288778135122 p_value- 0.9999919312983875\n"
     ]
    }
   ],
   "source": [
    "print(\"chi_2-\",chi_2,\"p_value-\",p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This parameter also fails to reject null hypothesis which conveys there is no significant relationship between   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS and no_of_defaulters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AVERAGE.ACCT.AGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        233154\n",
       "unique          192\n",
       "top       0yrs 0mon\n",
       "freq         119373\n",
       "Name: AVERAGE.ACCT.AGE, dtype: object"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"AVERAGE.ACCT.AGE\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"AVERAGE.ACCT.AGE\"]=df[\"AVERAGE.ACCT.AGE\"].apply(lambda x:(int((x.split(\"y\"))[0])*12)+(int((x.split())[1].split(\"m\")[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    233154.000000\n",
       "mean          8.915764\n",
       "std          15.106416\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%          13.000000\n",
       "max         369.000000\n",
       "Name: AVERAGE.ACCT.AGE, dtype: float64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"AVERAGE.ACCT.AGE\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_0=df[df[\"loan_default\"]==0]\n",
    "group_1=df[df[\"loan_default\"]==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ACV4Xcnv004iIri4BjdNPT09Henq67rVZs2Zp3w8bNgyvvvqqz20nT56MyZMnd6HEjhECkKSWlr7K1Cci0hjwjlx3K1/yfoGIiAAYMPRVISBBgmeQkMrUJyLSGC70W3fvMPOJiFoYL/Sh795Rr7QyEVEfY7jQ93TvaHfksqVPRKQxXOgLoW/p8+HoREQtDBf6Kjx9+u5ltvSJiFoYLvSFEJAkqeWO3CDXQ0QUSgwY+u6D8tyRy5uziIhaBHRH7tVEbR6yKb4/CyASrrMnoVbt096XJ/5D0GojIgo247X04e7e8cyyqaLz00ATERmN4UK/9YRrHKdPRNTCcKHvuSNXaV5mnz4RUQvjhT6E7kKui907REQaw4W++0KuBEXr0yciIg/Dhb7njtyWIZts6RMReRgv9KHv03cFsxgiohBjvNDXJlxr7t5hS5+ISGO40Fc93Tue5aBWQ0QUWgwX+lr3DsfpExG1YbzQFwIypJYhm+zeISLSGC70Ve3mLA7ZJCJqzXCh7+neaZmGgS19IiIP44W+EJC9h2xyGgYiIo3hQt894ZoEzyNU2NInImphuND3TLgmNQ/b5IRrREQtDBf6Klr68xW29ImIdAwX+p47cgF3+HMaBiKiFgYM/ZaWPrt3iIj0DBf6Ktz9+QAgs3uHiEjHcKEvhPsZuYB7KgZ27xARtTBg6LcclAzBWTaJiLwYLvQ90zAA7r59TsNARNTCcKEv4NW9wz59IiIdw4W+quve4TQMRETeDBf6gt07RER+GS/0IbzG6fNCLhGRN1MgKxUXF2Pjxo1QVRVTpkzBtGnTdO87HA6sW7cOp0+fRkxMDBYsWIB+/fqhvLwcjz/+OAYMGAAAGD58OB555JHuPwov7gu5nj59oKlHP42I6OrSbuirqooNGzZg6dKlsFqtWLx4MTIyMjBo0CBtne3btyM6OhovvfQS9uzZg82bN+Pxxx8HAKSkpGDlypU9dwStCAHt0q0sCajCcH/MEBF1WruJWFJSgpSUFCQnJ8NkMmH8+PEoKirSrbNv3z5MmjQJAJCZmYlvvvkGQgTnCqquTx+choGIyFu7LX2bzQar1aotW61WnDx50u86iqIgKioKNTU1AIDy8nI8+eSTiIyMxC9+8QvccMMN3Vl/GypaJlxTJA7ZJCLy1m7o+2qxe/rM21snISEB69evR0xMDE6fPo2VK1di9erViIqK0q2bn5+P/Px8AEBubi6SkpI6dBDeFOUcIiPCITtlmGQFKiRYLBbt/agu7Lu7mUymLh1rTwv1+gDW2B1CvT4g9GsM9fq8tRv6VqsVlZWV2nJlZSUSEhJ8rmO1WuFyuVBXVweLxQJJkmA2mwEA119/PZKTk1FaWorU1FTd9tnZ2cjOztaWKyoqOn1AjQ4nnE1NUIUKqC64hAy73a69X9eFfXe3pKSkLh1rTwv1+gDW2B1CvT4g9GsMhfo8A2ba026ffmpqKkpLS1FeXg6n04nCwkJkZGTo1hk3bhx27NgBAPjiiy8watQoSJKE6upqqKp7pHxZWRlKS0uRnJzcwUPpmCaXgFnxzKfP7h0iIm/ttvQVRcGcOXOQk5MDVVWRlZWFwYMHIy8vD6mpqcjIyMDkyZOxbt06PPbYY7BYLFiwYAEA4OjRo9iyZQsURYEsy/j1r3+t62rpCU6v0FfAO3KJiLwFNE4/PT0d6enputdmzZqlfR8WFoaFCxe22S4zMxOZmZldLLFjHKpXS59z7xAR6RhuEHuTS8AstzwukdMwEBG1MFToCyHgVAXCvFr6AhK7eIiImhkq9B3Nd2KZZfdhKc2vu9jFQ0QEwGih39ykbxm9436dLX0iIjdjhz7cyw7OtElEBMBooa9177Q8GB0AnOzeISICYLTQ99PSZ/cOEZGboUK/yeUeoNkS+m5Odu8QEQEwWOh7unfCmkfvyJJ7md07RERuxgr9Vt07niGbTnbvEBEBMFrot7qQq/Xps6VPRATAaKHvZ5w++/SJiNwMHfot3TsMfSIiwGihr7Zu6Xsu5BIREWCw0NeGbMocsklE5IuhQt/TvROmNA/Z1G7OYugTEQEGC32n32kYiIgIMFjoN/mZhsEpJIhzpyD+8h5EzeWg1UdEFGyGCn3PhVxTqz59h5CAixeAKhvEu68HqToiouAzVui7BBQJUJpDP0J2/xK4pCqorW1ARXgcRNEuiP2FEEJAHD0AcepYMEsmIupVAT0Y/WrhcKkwKy2/x8IkgRjZhfNNYfhTQiYOXDcMr5//b0jvvALlo81QSy9AiomF8seNkEyGOhVERD4ZKukcqtD68z2sihPnHGY0WIagyhSN//n7R7H1WDV+aduLgyNmovxyHXK/PQjcOC5IVRMR9R5DhX6TSyBM1od+ouLC1w2RQEQCAODNUw7AHIVXkie7Z9+MA8r+57+RwtAnoj7AUH36Tpfvlj6aJ1zrJ+oASEgLa4CqvQfsb4iGaKjv3WKJiILAUKHfpApt5I5HouICAJhUJ/4+zIYRYQ2YGGXHvTGXMdVSjXjRiP0JaTj0t8+DUTIRUa8yVOg7XAJhrVr68bILinDhOvsPiI0Ow+RoO8wSkGJywiKrGBzuwqGEYag/dTxIVRMR9R5jhb6PC7myBGRdOoKsykOA1PZwR0U0Ilp14A/Jd+Kbb8/1VqlEREFhrNBvNWTT48Fz/4txDRd8bpOguHB/5EVYnPX4y2cHIM6V9HSZRERBY7DQF9q8OxohEFlbhfroeL/bCUss0pKiUBQ/HLYXVkCc+KaHKyUiCg5jhb7atk/f3FQPk7MJdVcIfQAYJqrgkhRsvH4qLr+6Bq5PP4S68289WS4RUa8zVui72o7eiaqtAoArtvQBdzfPvTGXsSdxFJb+3b/BtX9vj9VJRBQshgr9Jh/j9CNrLwFoP/QB4KH4S1ho/RHfRSdjd00ExIWzEHV2iOpLPVIvEVFvM1To++reaWnpx7W7/f/aLbC7ZFhlBzYPm4qPTtrx4xP/DvX//hvU//duj9RMRNSbDDUNg9OltrmQG2mvQkOEBapiDmgfkgT8LKoOn6tReGvYPdg07G6kuOwYdfwIHtlXiLCM8T1ROhFRrzBU6Lu7d/R/vLQ3cseXgWYHHki4jGqXjKPRg1FVF4ZPlUx8/9UZLLIcQ+KIEZAkPoKRiK4+hgp9h6ofsmlqqkfM5XKUDhnVqf3FKioyG84BMnDGrGK7ZRD+zxe1+EXeStwWWYPYu6ZB4kRtRHQVMUzou1QBVUDXpz/49AGYXA5cuO4nXd7/dRYZv8BF7Ky34I1hP8cbAGL32XHdvh24zSowDDUYGBsGc+btkCIiu/x5REQ9IaDQLy4uxsaNG6GqKqZMmYJp06bp3nc4HFi3bh1Onz6NmJgYLFiwAP369QMAbNu2Ddu3b4csy/jlL3+JsWPHdv9RwOtRiZ7n4zodGHJqP35Mvh72uGu65TOiLZH4h2gXSp1VKHcqiG6qxBGXBetqkgD0R3hVE9KO7sDfJYVj5GArRgxORFRKCiRTYNcTiIh6Wruhr6oqNmzYgKVLl8JqtWLx4sXIyMjAoEGDtHW2b9+O6OhovPTSS9izZw82b96Mxx9/HBcuXEBhYSHWrFmDS5cuYdmyZXjxxRchy90/aMjheSh6c/dOyulihDXV42zaT7v1cyQJGGB2YoDZCURakOZ0wuYsQwUicKnOgR/DLfhzkxXvn5Yhn6pBUuN3SGsow8gYgQaY0F+1w2JNQMoNI2C1WiFcLsDpBMLCeJ2AiHpcu6FfUlKClJQUJCcnAwDGjx+PoqIiXejv27cP//zP/wwAyMzMxJ/+9CcIIVBUVITx48fDbDajX79+SElJQUlJCdLS0rr9QCQJuO3aGAyMViAuXsCgY3tRlTgQVdZB7W/cBcJkQoIJSIADiAAACaj/DpdqHShzmlClyii2DMVupbnLRwFQDeDLJsTu+gQAEOewwymbkCQa0E+tw2U5HP2dNXCFhSMy3IxoE+CSTYgzCQjFhCiThEizDKGYEB+uQDaHQTWZoEoyJFlGmMn9X7giQ1YkuIQMlyTBBQmSJEOWJciyBEWWocgSZEVu/l6GLMuQTRJkSUJ9WDiqa2phkmUonnVMsvsYJck97YUiab+sXKpAo0t1f67k+f8iQQihfRXurd0PvFEkCM//PwBOFTDJ0L3muU7T/IccZAlwCUCRAFW4P1MIAZcATLIEpyogNa/n+Ywml4AsSZAloMGpIsoso9ahwiRLUCSgutGF+AgTKuuciDDLkAD8WOtA/5gwnLnUgNhwBU5V4GxVI4ZbI7HrXDVSLGaU1zpQdMGOf78lBamJET34r4yCTQgBCAFIkq5xJlwuz+M63MuqCjiaAMUEyWRyb+d0AE2NQFgEJLPZvU2dHWhqAiwxQFg40FAP2CoAlxPSkOt79FjaDX2bzQar1aotW61WnDx50u86iqIgKioKNTU1sNlsGD58uLZeYmIibDZbd9WuYwlTsPDE+xBvfw5VCEQA+PambPdvg94WGY2ESCChefFmUYt6UQ8zBC6rCtBQj9pqO6qFCSok2JVwmNU6lMvROBuWiBhnAw5aBsKsOtEozHC5FMAFwBFoAQLuDVxdPJDv27wiCRWKUCFBwCGboaguyFDhkhSozbOYSkLV1jerLjQpZphVB1ySAgFAESqcsgmyULVtTKoTTtkEk+qEq/k1z3qe91qv537tuI/XAEV1wSUrutdk4YIqKdpXT61CkrWvHRXhasSTH9vx9PF3kV510uc65c2/8Nrw9VrblTpck2/+fw7KJEkLtA7xWb+44mJAx+xdR/O3ZZCadyb5Xk/bt2j5zNbLEPrPl6TmfTR/leCeidcT8EIFVLVl2UMxAYrs/gtddf9bLzOZ3Ns6mvTrAYDLqX/Ne7n1a9elQVmyyv+56Qbthr6vf6ytuyH8rePzH7oP+fn5yM/PBwDk5uZiwIABAW3XxtI/6hYHd24vRJ1wR7ALIApIu00bq9WKyspKbbmyshIJCQl+13G5XKirq4PFYmmzrc1mQ2JiYpvPyM7ORm5uLnJzczt9IK09/fTT3bavnhLqNYZ6fQBr7A6hXh8Q+jWGen3e2g391NRUlJaWory8HE6nE4WFhcjIyNCtM27cOOzYsQMA8MUXX2DUqFGQJAkZGRkoLCyEw+FAeXk5SktLMWzYsB45ECIial+73TuKomDOnDnIycmBqqrIysrC4MGDkZeXh9TUVGRkZGDy5MlYt24dHnvsMVgsFixYsAAAMHjwYNx6661YuHAhZFnGww8/3CMjd4iIKDABjdNPT09Henq67rVZs2Zp34eFhWHhwoU+t50+fTqmT5/ehRI7Jzs7u9c/s6NCvcZQrw9gjd0h1OsDQr/GUK/PmyQCvdpKRERXPfa1EBH1IYaZe8dbe9NGBMO8efMQEREBWZahKApyc3Nht9uxdu1a/Pjjj7jmmmvw+OOPw2Kx9FpN69evx9dff424uDisXr0aAPzWJITAxo0bceDAAYSHh2Pu3Lm4/vqevYnEX41btmzBZ599htjYWADA7Nmzte7H3pr2w6OiogIvv/wyqqqqIEkSsrOzMXXq1JA5j/7qC6Vz2NTUhGeffRZOpxMulwuZmZmYOXMmysvL8cILL8But+O6667DY489BpPJdMVpX3q7xpdffhlHjx5FVFQUAPfP+dChQ4P28xIQYTAul0vMnz9fXLx4UTgcDrFo0SLx3XffBbssMXfuXHH58mXda5s2bRLbtm0TQgixbds2sWnTpl6t6ciRI+LUqVNi4cKF7da0f/9+kZOTI1RVFcePHxeLFy8OWo15eXnio48+arPud999JxYtWiSamppEWVmZmD9/vnC5XD1an81mE6dOnRJCCFFXVyf+4z/+Q3z33Xchcx791RdK51BVVVFfXy+EEMLhcIjFixeL48ePi9WrV4vdu3cLIYR47bXXxCeffCKEEOJvf/ubeO2114QQQuzevVusWbOmR+u7Uo3r1q0Te/fubbN+sH5eAmG47h3vaSNMJpM2bUQoKioqwu233w4AuP3223u9zpEjR7b5y8JfTfv27cPEiRMhSRLS0tJQW1uLS5d6/jGSvmr0x9+0Hz0pISFBa8FFRkZi4MCBsNlsIXMe/dXnTzDOoSRJiIhwT2PXUHegAAALxUlEQVThcrngcrkgSRKOHDmCzMxMAMCkSZN053DSpEkA3NO+fPPNNwHfCNrdNfoTrJ+XQBgu9H1NG9FTUz90VE5ODp566int7uPLly9rN7olJCSguro6mOUB8F+TzWZDUlKStl6wz+snn3yCRYsWYf369bDb7QDa/r/vyWk/fCkvL8eZM2cwbNiwkDyP3vUBoXUOVVXFE088gV/96le48cYbkZycjKioKCiK0qYOf9O+9HaNnilm3n33XSxatAhvvvkmHA6HVmMo/bx4M1yfvq/f+KEwe+WyZcuQmJiIy5cvY/ny5Z2faiJIQum83nHHHZgxYwYAIC8vD2+//Tbmzp3b4629K2loaMDq1avx0EMPaf27vgTrPLauL9TOoSzLWLlyJWpra7Fq1Sp8/33beZ88gnUOW9d4/vx53H///YiPj4fT6cRrr72Gjz76CDNmzAipn5fWDNfSD2TaiGDwTD8RFxeHm2++GSUlJYiLi9P+5Lt06ZJ2US2Y/NVktVpRUVGhrRfM8xofH++eDVSWMWXKFJw6dUqrMZBpP7qb0+nE6tWrcdttt+GnP3VP5R1K59FXfaF2Dj2io6MxcuRInDx5EnV1dXC5XG3q8DftS2/XWFxcjISEBEiSBLPZjKysLK0rLJR+XlozXOgHMm1Eb2toaEB9fb32/aFDhzBkyBBkZGSgoKAAAFBQUICbb745mGUCgN+aMjIysHPnTgghcOLECURFRQXtH7F33+hXX32FwYMHazX29rQfQgi8+uqrGDhwIO655x7t9VA5j/7qC6VzWF1djdraWgDuUTKHDx/GwIEDMWrUKHzxxRcAgB07dmg/x/6mfQlGjZ7zKJqnkvc+j6Hy89KaIW/O+vrrr/HWW29p00YE445gb2VlZVi1yj1dqsvlwoQJEzB9+nTU1NRg7dq1qKioQFJSEhYuXNirLZYXXngBR48eRU1NDeLi4jBz5kzcfPPNPmsSQmDDhg04ePAgwsLCMHfuXKSmpgalxiNHjuDs2bOQJAnXXHMNHnnkEe0H6oMPPsDnn38OWZbx0EMP4Sc/6fqjMq/k2LFjeOaZZzBkyBAteGbPno3hw4eHxHn0V9+ePXtC5hyeO3cOL7/8MlRVhRACt956K2bMmIGysrI2QzbNZjOampqwbt06nDlzRpv2xfO8j96u8fnnn9eu11x77bV45JFHEBEREbSfl0AYMvSJiMg3w3XvEBGRfwx9IqI+hKFPRNSHMPSJiPoQhj4RUR/C0Cci6kMMNw0DBcdzzz2Hc+fO4fXXX4fZbMa2bdtQXFyM559/XrdedXU1fvOb3+APf/gDTp8+jVdeeQVhYWG6dV588UUkJiZi3rx5qKqqgizLiIiIwNixY/Hwww9rE195bNmyBVu3bsWKFSva3Eh06dIl5OXl4cCBA6irq0NcXBxuuOEGTJs2DQMHDkR5eTnmz5+P8PBw3XaPPvooxo8fH/DxeispKcH777+P48ePQ5IkpKSk4I477kBWVhYAoK6uDlu2bMGXX34Ju92O+Ph4jBs3DtOnT8e8efO0/TQ1NcFkMmmPGH3kkUdw2223+aznP//zP7F371688soriI+P1733ww8/4L333sORI0fgdDoRHx+PsWPH4t5770ViYiIOHTqEnJycNv8fnn32WT7T2oAY+tRl5eXl+PbbbxEVFYV9+/bh1ltvxcSJE5GXl4fy8nLdXOeFhYUYMmQIhgwZgtOnTyMtLQ3Lli3zu++nnnoKY8aMQVVVFXJycrBt2zbMnj1be18IgV27dsFisWDHjh26kKqpqcHSpUsxYsQIPP/880hOTkZdXR2++uorHDp0CAMHDtTWffPNN7XJvTpzvB4nTpzAsmXLcN9992HevHmIiYnBmTNn8OGHHyIrKwtOpxPLli1DVFQUfvvb32LAgAGoqanBp59+ipKSEmzatEnb17x58/Cb3/wGY8aMuWI99fX1KCoqQmRkJHbv3q278/aHH37AkiVLMGXKFKxcuRKJiYmoqqrCrl27cPz4ca32pKQkvPzyywEdP13d2L1DXbZz506kpaVh0qRJ2tQDVqsVo0ePxs6dO3XrFhQUaFMOd0R8fDxuuukmnD17Vvf6t99+C5vNhoceegiFhYVwOp3aex9//DGioqIwf/58pKSkQJIkREdHIysrC3fddVfHD7SZr+P12LRpE26//XZMmzYNsbGxkCQJ119/vfYM6YKCAlRUVOCJJ57AoEGDIMsy4uLiMGPGjDbPoQ7U3r17ERsbi3/6p39qU8+WLVswatQoPPjgg9rcNfHx8fjHf/xH3S8r6jsY+tRlBQUFmDBhAm677TYcPHgQVVVVANzzyHuH/g8//ICzZ89iwoQJHf6MyspKHDhwACkpKW0+e9y4cVpXzP79+7X3Dh8+jJtvvlnrHuku/o63sbERJ06c0OaA9+Xw4cO46aab2nRRdbWen/3sZ/jZz36G8+fP634xHj58WJtkjQhg9w510bFjx1BRUYFbb70VsbGxSE5O1roYbrnlFvzXf/0Xjh8/jhEjRqCgoAA/+clPdLOJnjx5Eg899JC2HBMTg5deeklbXrlyJSRJQkNDA0aPHo2ZM2dq7zU2NuKLL77AvHnzYDKZkJmZiYKCAi3kqqurdf3b+/btw7p166CqKtLS0rB06VLtvYcfflh3XMuXL8egQYM6dLy1tbUQQlxxYq2amppufWxeeXk5jh07hocffhiJiYkYNWoUdu7ciaFDhwKAds3A4+OPP8b7778Pl8uFiRMn4te//jUA92MVvf8/AMAbb7zR5noFXf0Y+tQlO3bswJgxY7QgnzBhAgoKCnDPPfcgPDxcC+K0tDTs3r0b//qv/6rbfvjw4Vfs03/iiScwZswYHD16FC+++CJqamoQHR0NwD07pCzLWrfIhAkTsGzZMlRXVyM2NhYxMTFaKxxwz3z45ptv4rPPPsOuXbt0n7Nhw4aA+vSvdLzR0dGQJAmXLl3SXS/wFhMT061PUCooKNCukXjqeffdd/HAAw9AlmVER0frzsHdd9+Nu+++G5s3b9Y9eIR9+n0HQ586rampCXv37oWqqlqL0el0ora2FmfPnsXQoUNx++23Y+XKlfjpT3+K+vp6jBs3rlOfNXLkSEyaNAlvv/02nnzySQDuwGtoaMCjjz6qredyubB7925MnToVo0ePRlFREWbMmNEtXTyBHG9aWhq+/PJLjB492uc+brzxRrz33ntoaGjochePEAI7d+6EzWbT6lFVFTU1NSguLkZ6ejpGjx6NL7/8EhMnTuzSZ5FxMPSp0zwt7dWrV8NkavmntHbtWq2L4YYbbkB0dDRef/11jB8/XrdeR919992YN28ezp49i9jYWBw+fBhLlizBtddeq63z8ccfo6CgAFOnTsU999yDXbt2Yd26dZg5cyaSk5PR0NDQ5mJwdx7vAw88gOXLl+Oaa65BVlYWYmJicPbsWXz44YdYsGABJk6ciPz8fO0pVv3790dtbS0+/fRTDB06tEMXcz1dTX/84x8RExOjvb5x40YUFBQgPT0ds2bNwpIlS7Bp0ybcfffdSExMRHV1Nb7//vuQeGgP9T6GPnVaQUEBsrKydM8CBYA777wTGzduxL/8y79AURRMnDgRW7du9Tlq58SJE3jwwQd1r/kbHx4bG6vta9iwYRg6dChuuukm3Tp33XUX/vrXv+L8+fMYMmQIcnJykJeXh2eeeQb19fWIj4/HiBEj8Ktf/Uq3Xev+7FmzZuGee+7BBx98gGPHjmHJkiUBHe+IESPw7LPPYsuWLfjzn/8MWZbRv39/3HnnnQAAs9mM3/3ud9iyZQuWL1+u9blnZGRoz1y9kh07duDjjz/GypUrUVBQgFtuuUV7cIfH1KlT8fvf/x61tbUYOHCgdg4WLVoEp9OJhIQEjB07Fj//+c+1bSoqKtr8f3jsscdwyy23tFsTXV04nz4RUR/CIZtERH0IQ5+IqA9h6BMR9SEMfSKiPoShT0TUhzD0iYj6EIY+EVEfwtAnIupDGPpERH3I/wcyDv1pp1H1ZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(group_0[\"AVERAGE.ACCT.AGE\"])\n",
    "sns.distplot(group_1[\"AVERAGE.ACCT.AGE\"])\n",
    "plt.legend([0,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### H0:mean_avg_age_of_non_defaulters==mean_avg_age_of_non_defaulters\n",
    "### H1:mean_avg_age_of_non_defaulters!=mean_avg_age_of_non_defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=11.969316978054646, pvalue=5.261091482095756e-33)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(group_0[\"AVERAGE.ACCT.AGE\"],group_1[\"AVERAGE.ACCT.AGE\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pvalue less than .05 which rejects null hypothesis in favour of alternate hypothesis that emi of both groups differ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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",
       "       'MobileNo_Avl_Flag', 'Aadhar_flag', 'PAN_flag', 'VoterID_flag',\n",
       "       '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",
       "       'PRI.DISBURSED.AMOUNT', 'SEC.NO.OF.ACCTS', 'SEC.ACTIVE.ACCTS',\n",
       "       'SEC.OVERDUE.ACCTS', 'SEC.CURRENT.BALANCE', 'SEC.SANCTIONED.AMOUNT',\n",
       "       'SEC.DISBURSED.AMOUNT', 'PRIMARY.INSTAL.AMT', 'SEC.INSTAL.AMT',\n",
       "       'NEW.ACCTS.IN.LAST.SIX.MONTHS', 'DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS',\n",
       "       'AVERAGE.ACCT.AGE', 'CREDIT.HISTORY.LENGTH', 'NO.OF_INQUIRIES',\n",
       "       'loan_default', 'Age_at_time_of_disbursement', 'month_of_disbursement',\n",
       "       'Scored_Or_Not', 'First_Time_Account_Holder', 'Active_accounts_Flag',\n",
       "       'Overdue_accounts_Flag'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=chi2(df[['branch_id','supplier_id', 'manufacturer_id', 'Current_pincode_ID','State_ID']],df[\"loan_default\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "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>chi_2</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>branch_id</th>\n",
       "      <td>14212.508752</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>supplier_id</th>\n",
       "      <td>108342.654906</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manufacturer_id</th>\n",
       "      <td>1038.158660</td>\n",
       "      <td>9.122256e-228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Current_pincode_ID</th>\n",
       "      <td>277680.338195</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State_ID</th>\n",
       "      <td>1490.699660</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            chi_2        p_value\n",
       "branch_id            14212.508752   0.000000e+00\n",
       "supplier_id         108342.654906   0.000000e+00\n",
       "manufacturer_id       1038.158660  9.122256e-228\n",
       "Current_pincode_ID  277680.338195   0.000000e+00\n",
       "State_ID              1490.699660   0.000000e+00"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(a,columns=['branch_id','supplier_id', 'manufacturer_id', 'Current_pincode_ID','State_ID'],index=[\"chi_2\",\"p_value\"]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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