Data Insights.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7605bd21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split,RandomizedSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de6c8e35",
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_df = pd.read_csv(r\"C:\\Users\\aryan\\Downloads\\data\\data\\loan.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4211672",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_application_id</th>\n",
       "      <th>applicant_id</th>\n",
       "      <th>Months_loan_taken_for</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Principal_loan_amount</th>\n",
       "      <th>EMI_rate_in_percentage_of_disposable_income</th>\n",
       "      <th>Property</th>\n",
       "      <th>Has_coapplicant</th>\n",
       "      <th>Has_guarantor</th>\n",
       "      <th>Other_EMI_plans</th>\n",
       "      <th>Number_of_existing_loans_at_this_bank</th>\n",
       "      <th>Loan_history</th>\n",
       "      <th>high_risk_applicant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d68d975e-edad-11ea-8761-1d6f9c1ff461</td>\n",
       "      <td>1469590</td>\n",
       "      <td>6</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>1169000</td>\n",
       "      <td>4</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d68d989e-edad-11ea-b1d5-2bcf65006448</td>\n",
       "      <td>1203873</td>\n",
       "      <td>48</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>5951000</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d68d995c-edad-11ea-814a-1b6716782575</td>\n",
       "      <td>1432761</td>\n",
       "      <td>12</td>\n",
       "      <td>education</td>\n",
       "      <td>2096000</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d68d99fc-edad-11ea-8841-17e8848060ae</td>\n",
       "      <td>1207582</td>\n",
       "      <td>42</td>\n",
       "      <td>FF&amp;E</td>\n",
       "      <td>7882000</td>\n",
       "      <td>2</td>\n",
       "      <td>building society savings agreement/life insurance</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d68d9a92-edad-11ea-9f3d-1f8682db006a</td>\n",
       "      <td>1674436</td>\n",
       "      <td>24</td>\n",
       "      <td>new vehicle</td>\n",
       "      <td>4870000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>delay in paying off loans in the past</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    loan_application_id  applicant_id  Months_loan_taken_for  \\\n",
       "0  d68d975e-edad-11ea-8761-1d6f9c1ff461       1469590                      6   \n",
       "1  d68d989e-edad-11ea-b1d5-2bcf65006448       1203873                     48   \n",
       "2  d68d995c-edad-11ea-814a-1b6716782575       1432761                     12   \n",
       "3  d68d99fc-edad-11ea-8841-17e8848060ae       1207582                     42   \n",
       "4  d68d9a92-edad-11ea-9f3d-1f8682db006a       1674436                     24   \n",
       "\n",
       "                Purpose  Principal_loan_amount  \\\n",
       "0  electronic equipment                1169000   \n",
       "1  electronic equipment                5951000   \n",
       "2             education                2096000   \n",
       "3                  FF&E                7882000   \n",
       "4           new vehicle                4870000   \n",
       "\n",
       "   EMI_rate_in_percentage_of_disposable_income  \\\n",
       "0                                            4   \n",
       "1                                            2   \n",
       "2                                            2   \n",
       "3                                            2   \n",
       "4                                            3   \n",
       "\n",
       "                                            Property  Has_coapplicant  \\\n",
       "0                                        real estate                0   \n",
       "1                                        real estate                0   \n",
       "2                                        real estate                0   \n",
       "3  building society savings agreement/life insurance                0   \n",
       "4                                                NaN                0   \n",
       "\n",
       "   Has_guarantor Other_EMI_plans  Number_of_existing_loans_at_this_bank  \\\n",
       "0              0             NaN                                      2   \n",
       "1              0             NaN                                      1   \n",
       "2              0             NaN                                      1   \n",
       "3              1             NaN                                      1   \n",
       "4              0             NaN                                      2   \n",
       "\n",
       "                             Loan_history  high_risk_applicant  \n",
       "0   critical/pending loans at other banks                    0  \n",
       "1  existing loans paid back duly till now                    1  \n",
       "2   critical/pending loans at other banks                    0  \n",
       "3  existing loans paid back duly till now                    0  \n",
       "4   delay in paying off loans in the past                    1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e512560c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Applicant_df = pd.read_csv(r\"C:\\Users\\aryan\\Downloads\\data\\data\\applicant.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ed3a5c07",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>applicant_id</th>\n",
       "      <th>Primary_applicant_age_in_years</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Number_of_dependents</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Years_at_current_residence</th>\n",
       "      <th>Employment_status</th>\n",
       "      <th>Has_been_employed_for_at_least</th>\n",
       "      <th>Has_been_employed_for_at_most</th>\n",
       "      <th>Telephone</th>\n",
       "      <th>Foreign_worker</th>\n",
       "      <th>Savings_account_balance</th>\n",
       "      <th>Balance_in_existing_bank_account_(lower_limit_of_bucket)</th>\n",
       "      <th>Balance_in_existing_bank_account_(upper_limit_of_bucket)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1469590</td>\n",
       "      <td>67</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>7 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Registered under the applicant's name</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1203873</td>\n",
       "      <td>22</td>\n",
       "      <td>female</td>\n",
       "      <td>divorced/separated/married</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>4 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>2 lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1432761</td>\n",
       "      <td>49</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>3</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>4 years</td>\n",
       "      <td>7 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1207582</td>\n",
       "      <td>45</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>2</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>4 years</td>\n",
       "      <td>7 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1674436</td>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>2</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>4 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   applicant_id  Primary_applicant_age_in_years  Gender  \\\n",
       "0       1469590                              67    male   \n",
       "1       1203873                              22  female   \n",
       "2       1432761                              49    male   \n",
       "3       1207582                              45    male   \n",
       "4       1674436                              53    male   \n",
       "\n",
       "               Marital_status  Number_of_dependents   Housing  \\\n",
       "0                      single                     1       own   \n",
       "1  divorced/separated/married                     1       own   \n",
       "2                      single                     2       own   \n",
       "3                      single                     2  for free   \n",
       "4                      single                     2  for free   \n",
       "\n",
       "   Years_at_current_residence            Employment_status  \\\n",
       "0                           4  skilled employee / official   \n",
       "1                           2  skilled employee / official   \n",
       "2                           3         unskilled - resident   \n",
       "3                           4  skilled employee / official   \n",
       "4                           4  skilled employee / official   \n",
       "\n",
       "  Has_been_employed_for_at_least Has_been_employed_for_at_most  \\\n",
       "0                        7 years                           NaN   \n",
       "1                         1 year                       4 years   \n",
       "2                        4 years                       7 years   \n",
       "3                        4 years                       7 years   \n",
       "4                         1 year                       4 years   \n",
       "\n",
       "                               Telephone  Foreign_worker  \\\n",
       "0  Registered under the applicant's name               1   \n",
       "1                                    NaN               1   \n",
       "2                                    NaN               1   \n",
       "3                                    NaN               1   \n",
       "4                                    NaN               1   \n",
       "\n",
       "  Savings_account_balance  \\\n",
       "0                     NaN   \n",
       "1                     Low   \n",
       "2                     Low   \n",
       "3                     Low   \n",
       "4                     Low   \n",
       "\n",
       "  Balance_in_existing_bank_account_(lower_limit_of_bucket)  \\\n",
       "0                                                NaN         \n",
       "1                                                  0         \n",
       "2                                                NaN         \n",
       "3                                                NaN         \n",
       "4                                                NaN         \n",
       "\n",
       "  Balance_in_existing_bank_account_(upper_limit_of_bucket)  \n",
       "0                                                  0        \n",
       "1                                              2 lac        \n",
       "2                                                NaN        \n",
       "3                                                  0        \n",
       "4                                                  0        "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Applicant_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "670e363b",
   "metadata": {},
   "source": [
    "### Task  Doing Exploratory Data Analysis & share the insights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b937ba5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>applicant_id</th>\n",
       "      <th>Months_loan_taken_for</th>\n",
       "      <th>Principal_loan_amount</th>\n",
       "      <th>EMI_rate_in_percentage_of_disposable_income</th>\n",
       "      <th>Has_coapplicant</th>\n",
       "      <th>Has_guarantor</th>\n",
       "      <th>Number_of_existing_loans_at_this_bank</th>\n",
       "      <th>high_risk_applicant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.514763e+06</td>\n",
       "      <td>20.903000</td>\n",
       "      <td>3.271258e+06</td>\n",
       "      <td>2.973000</td>\n",
       "      <td>0.041000</td>\n",
       "      <td>0.052000</td>\n",
       "      <td>1.407000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.286764e+05</td>\n",
       "      <td>12.058814</td>\n",
       "      <td>2.822737e+06</td>\n",
       "      <td>1.118715</td>\n",
       "      <td>0.198389</td>\n",
       "      <td>0.222138</td>\n",
       "      <td>0.577654</td>\n",
       "      <td>0.458487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.105364e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.321398e+06</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.365500e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.529114e+06</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.319500e+06</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.707752e+06</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>3.972250e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.903505e+06</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>1.842400e+07</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       applicant_id  Months_loan_taken_for  Principal_loan_amount  \\\n",
       "count  1.000000e+03            1000.000000           1.000000e+03   \n",
       "mean   1.514763e+06              20.903000           3.271258e+06   \n",
       "std    2.286764e+05              12.058814           2.822737e+06   \n",
       "min    1.105364e+06               4.000000           2.500000e+05   \n",
       "25%    1.321398e+06              12.000000           1.365500e+06   \n",
       "50%    1.529114e+06              18.000000           2.319500e+06   \n",
       "75%    1.707752e+06              24.000000           3.972250e+06   \n",
       "max    1.903505e+06              72.000000           1.842400e+07   \n",
       "\n",
       "       EMI_rate_in_percentage_of_disposable_income  Has_coapplicant  \\\n",
       "count                                  1000.000000      1000.000000   \n",
       "mean                                      2.973000         0.041000   \n",
       "std                                       1.118715         0.198389   \n",
       "min                                       1.000000         0.000000   \n",
       "25%                                       2.000000         0.000000   \n",
       "50%                                       3.000000         0.000000   \n",
       "75%                                       4.000000         0.000000   \n",
       "max                                       4.000000         1.000000   \n",
       "\n",
       "       Has_guarantor  Number_of_existing_loans_at_this_bank  \\\n",
       "count    1000.000000                            1000.000000   \n",
       "mean        0.052000                               1.407000   \n",
       "std         0.222138                               0.577654   \n",
       "min         0.000000                               1.000000   \n",
       "25%         0.000000                               1.000000   \n",
       "50%         0.000000                               1.000000   \n",
       "75%         0.000000                               2.000000   \n",
       "max         1.000000                               4.000000   \n",
       "\n",
       "       high_risk_applicant  \n",
       "count          1000.000000  \n",
       "mean              0.300000  \n",
       "std               0.458487  \n",
       "min               0.000000  \n",
       "25%               0.000000  \n",
       "50%               0.000000  \n",
       "75%               1.000000  \n",
       "max               1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5abdf86",
   "metadata": {},
   "source": [
    "### Crosstab relationship between the two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "06a3e543",
   "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>high_risk_applicant</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number_of_existing_loans_at_this_bank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>433</td>\n",
       "      <td>200</td>\n",
       "      <td>633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>241</td>\n",
       "      <td>92</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>700</td>\n",
       "      <td>300</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "high_risk_applicant                      0    1   All\n",
       "Number_of_existing_loans_at_this_bank                \n",
       "1                                      433  200   633\n",
       "2                                      241   92   333\n",
       "3                                       22    6    28\n",
       "4                                        4    2     6\n",
       "All                                    700  300  1000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(loan_df['Number_of_existing_loans_at_this_bank'], loan_df['high_risk_applicant'], margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47ad8877",
   "metadata": {},
   "source": [
    "### Merging the two dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6e5acd06",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.merge(loan_df,Applicant_df,on=\"applicant_id\", how=\"outer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2383b27a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Registered under the applicant's name\n",
       "1                                        NaN\n",
       "2                                        NaN\n",
       "3                                        NaN\n",
       "4                                        NaN\n",
       "                       ...                  \n",
       "995                                      NaN\n",
       "996    Registered under the applicant's name\n",
       "997                                      NaN\n",
       "998    Registered under the applicant's name\n",
       "999                                      NaN\n",
       "Name: Telephone, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,\"Telephone\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f71ff032",
   "metadata": {},
   "source": [
    "### Removing non essential columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "53b19209",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df.drop(['Other_EMI_plans', 'Telephone', 'Has_been_employed_for_at_most', 'Foreign_worker','loan_application_id'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1248a011",
   "metadata": {},
   "source": [
    "### Checking null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "249bffe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "applicant_id                                                  0\n",
       "Months_loan_taken_for                                         0\n",
       "Purpose                                                      12\n",
       "Principal_loan_amount                                         0\n",
       "EMI_rate_in_percentage_of_disposable_income                   0\n",
       "Property                                                    154\n",
       "Has_coapplicant                                               0\n",
       "Has_guarantor                                                 0\n",
       "Number_of_existing_loans_at_this_bank                         0\n",
       "Loan_history                                                  0\n",
       "high_risk_applicant                                           0\n",
       "Primary_applicant_age_in_years                                0\n",
       "Gender                                                        0\n",
       "Marital_status                                                0\n",
       "Number_of_dependents                                          0\n",
       "Housing                                                       0\n",
       "Years_at_current_residence                                    0\n",
       "Employment_status                                             0\n",
       "Has_been_employed_for_at_least                               62\n",
       "Savings_account_balance                                     183\n",
       "Balance_in_existing_bank_account_(lower_limit_of_bucket)    668\n",
       "Balance_in_existing_bank_account_(upper_limit_of_bucket)    457\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "879cf9fd",
   "metadata": {},
   "source": [
    "### Handling the missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "780018b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5d9b159b",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['Purpose'].fillna(df['Purpose'].mode()[0],inplace=True)\n",
    "new_df['Property'].fillna(df['Property'].mode()[0],inplace=True)\n",
    "new_df['Has_been_employed_for_at_least'].fillna(df['Has_been_employed_for_at_least'].mode()[0],inplace=True)\n",
    "new_df['Savings_account_balance'].fillna(df['Savings_account_balance'].mode()[0],inplace=True)\n",
    "new_df['Balance_in_existing_bank_account_(lower_limit_of_bucket)'].fillna(df['Balance_in_existing_bank_account_(lower_limit_of_bucket)'].mode()[0],inplace=True)\n",
    "new_df['Balance_in_existing_bank_account_(upper_limit_of_bucket)'].fillna(df['Balance_in_existing_bank_account_(upper_limit_of_bucket)'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dc979049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "applicant_id                                                0\n",
       "Months_loan_taken_for                                       0\n",
       "Purpose                                                     0\n",
       "Principal_loan_amount                                       0\n",
       "EMI_rate_in_percentage_of_disposable_income                 0\n",
       "Property                                                    0\n",
       "Has_coapplicant                                             0\n",
       "Has_guarantor                                               0\n",
       "Number_of_existing_loans_at_this_bank                       0\n",
       "Loan_history                                                0\n",
       "high_risk_applicant                                         0\n",
       "Primary_applicant_age_in_years                              0\n",
       "Gender                                                      0\n",
       "Marital_status                                              0\n",
       "Number_of_dependents                                        0\n",
       "Housing                                                     0\n",
       "Years_at_current_residence                                  0\n",
       "Employment_status                                           0\n",
       "Has_been_employed_for_at_least                              0\n",
       "Savings_account_balance                                     0\n",
       "Balance_in_existing_bank_account_(lower_limit_of_bucket)    0\n",
       "Balance_in_existing_bank_account_(upper_limit_of_bucket)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79f7bff7",
   "metadata": {},
   "source": [
    "### Histogram of Target Risk_Applicants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2344345e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "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": [
    "new_df['high_risk_applicant'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0f4a762a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Principal_loan_amount', ylabel='Balance_in_existing_bank_account_(lower_limit_of_bucket)'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Principal_loan_amount',y='Balance_in_existing_bank_account_(lower_limit_of_bucket)',data=new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7622ea99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "sns.heatmap(new_df.corr(),annot=True, cmap=\"coolwarm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "152bb892",
   "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>applicant_id</th>\n",
       "      <th>Months_loan_taken_for</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Principal_loan_amount</th>\n",
       "      <th>EMI_rate_in_percentage_of_disposable_income</th>\n",
       "      <th>Property</th>\n",
       "      <th>Has_coapplicant</th>\n",
       "      <th>Has_guarantor</th>\n",
       "      <th>Number_of_existing_loans_at_this_bank</th>\n",
       "      <th>Loan_history</th>\n",
       "      <th>...</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Number_of_dependents</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Years_at_current_residence</th>\n",
       "      <th>Employment_status</th>\n",
       "      <th>Has_been_employed_for_at_least</th>\n",
       "      <th>Savings_account_balance</th>\n",
       "      <th>Balance_in_existing_bank_account_(lower_limit_of_bucket)</th>\n",
       "      <th>Balance_in_existing_bank_account_(upper_limit_of_bucket)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1469590</td>\n",
       "      <td>6</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>1169000</td>\n",
       "      <td>4</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>7 years</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1203873</td>\n",
       "      <td>48</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>5951000</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>...</td>\n",
       "      <td>female</td>\n",
       "      <td>divorced/separated/married</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>2 lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1432761</td>\n",
       "      <td>12</td>\n",
       "      <td>education</td>\n",
       "      <td>2096000</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>3</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>4 years</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1207582</td>\n",
       "      <td>42</td>\n",
       "      <td>FF&amp;E</td>\n",
       "      <td>7882000</td>\n",
       "      <td>2</td>\n",
       "      <td>building society savings agreement/life insurance</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>2</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>4 years</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1674436</td>\n",
       "      <td>24</td>\n",
       "      <td>new vehicle</td>\n",
       "      <td>4870000</td>\n",
       "      <td>3</td>\n",
       "      <td>car or other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>delay in paying off loans in the past</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>2</td>\n",
       "      <td>for free</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   applicant_id  Months_loan_taken_for               Purpose  \\\n",
       "0       1469590                      6  electronic equipment   \n",
       "1       1203873                     48  electronic equipment   \n",
       "2       1432761                     12             education   \n",
       "3       1207582                     42                  FF&E   \n",
       "4       1674436                     24           new vehicle   \n",
       "\n",
       "   Principal_loan_amount  EMI_rate_in_percentage_of_disposable_income  \\\n",
       "0                1169000                                            4   \n",
       "1                5951000                                            2   \n",
       "2                2096000                                            2   \n",
       "3                7882000                                            2   \n",
       "4                4870000                                            3   \n",
       "\n",
       "                                            Property  Has_coapplicant  \\\n",
       "0                                        real estate                0   \n",
       "1                                        real estate                0   \n",
       "2                                        real estate                0   \n",
       "3  building society savings agreement/life insurance                0   \n",
       "4                                       car or other                0   \n",
       "\n",
       "   Has_guarantor  Number_of_existing_loans_at_this_bank  \\\n",
       "0              0                                      2   \n",
       "1              0                                      1   \n",
       "2              0                                      1   \n",
       "3              1                                      1   \n",
       "4              0                                      2   \n",
       "\n",
       "                             Loan_history  ...  Gender  \\\n",
       "0   critical/pending loans at other banks  ...    male   \n",
       "1  existing loans paid back duly till now  ...  female   \n",
       "2   critical/pending loans at other banks  ...    male   \n",
       "3  existing loans paid back duly till now  ...    male   \n",
       "4   delay in paying off loans in the past  ...    male   \n",
       "\n",
       "               Marital_status Number_of_dependents   Housing  \\\n",
       "0                      single                    1       own   \n",
       "1  divorced/separated/married                    1       own   \n",
       "2                      single                    2       own   \n",
       "3                      single                    2  for free   \n",
       "4                      single                    2  for free   \n",
       "\n",
       "   Years_at_current_residence            Employment_status  \\\n",
       "0                           4  skilled employee / official   \n",
       "1                           2  skilled employee / official   \n",
       "2                           3         unskilled - resident   \n",
       "3                           4  skilled employee / official   \n",
       "4                           4  skilled employee / official   \n",
       "\n",
       "   Has_been_employed_for_at_least Savings_account_balance  \\\n",
       "0                         7 years                     Low   \n",
       "1                          1 year                     Low   \n",
       "2                         4 years                     Low   \n",
       "3                         4 years                     Low   \n",
       "4                          1 year                     Low   \n",
       "\n",
       "  Balance_in_existing_bank_account_(lower_limit_of_bucket)  \\\n",
       "0                                                  0         \n",
       "1                                                  0         \n",
       "2                                                  0         \n",
       "3                                                  0         \n",
       "4                                                  0         \n",
       "\n",
       "  Balance_in_existing_bank_account_(upper_limit_of_bucket)  \n",
       "0                                                  0        \n",
       "1                                              2 lac        \n",
       "2                                                  0        \n",
       "3                                                  0        \n",
       "4                                                  0        \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "000a23b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = new_df.drop(['Has_coapplicant', 'Has_guarantor',], axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "756288fd",
   "metadata": {},
   "source": [
    "### TASK - Segmenting Customers ordered by Their Risk (of default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "51a0a74f",
   "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>applicant_id</th>\n",
       "      <th>Months_loan_taken_for</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Principal_loan_amount</th>\n",
       "      <th>EMI_rate_in_percentage_of_disposable_income</th>\n",
       "      <th>Property</th>\n",
       "      <th>Number_of_existing_loans_at_this_bank</th>\n",
       "      <th>Loan_history</th>\n",
       "      <th>high_risk_applicant</th>\n",
       "      <th>Primary_applicant_age_in_years</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Number_of_dependents</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Years_at_current_residence</th>\n",
       "      <th>Employment_status</th>\n",
       "      <th>Has_been_employed_for_at_least</th>\n",
       "      <th>Savings_account_balance</th>\n",
       "      <th>Balance_in_existing_bank_account_(lower_limit_of_bucket)</th>\n",
       "      <th>Balance_in_existing_bank_account_(upper_limit_of_bucket)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1469590</td>\n",
       "      <td>6</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>1169000</td>\n",
       "      <td>4</td>\n",
       "      <td>real estate</td>\n",
       "      <td>2</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>7 years</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>1462065</td>\n",
       "      <td>10</td>\n",
       "      <td>FF&amp;E</td>\n",
       "      <td>1275000</td>\n",
       "      <td>4</td>\n",
       "      <td>building society savings agreement/life insurance</td>\n",
       "      <td>1</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>female</td>\n",
       "      <td>divorced/separated/married</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>0 year</td>\n",
       "      <td>Low</td>\n",
       "      <td>2 lac</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>1474361</td>\n",
       "      <td>24</td>\n",
       "      <td>FF&amp;E</td>\n",
       "      <td>2828000</td>\n",
       "      <td>4</td>\n",
       "      <td>real estate</td>\n",
       "      <td>1</td>\n",
       "      <td>all loans at this bank paid back duly</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1 year</td>\n",
       "      <td>High</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>1764990</td>\n",
       "      <td>24</td>\n",
       "      <td>business</td>\n",
       "      <td>4526000</td>\n",
       "      <td>3</td>\n",
       "      <td>real estate</td>\n",
       "      <td>1</td>\n",
       "      <td>critical/pending loans at other banks</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>management / self-employed / highly qualified ...</td>\n",
       "      <td>1 year</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>1365958</td>\n",
       "      <td>18</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>2051000</td>\n",
       "      <td>4</td>\n",
       "      <td>real estate</td>\n",
       "      <td>1</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>0 year</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>1234944</td>\n",
       "      <td>12</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>1534000</td>\n",
       "      <td>1</td>\n",
       "      <td>real estate</td>\n",
       "      <td>1</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>married/widowed</td>\n",
       "      <td>1</td>\n",
       "      <td>rent</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>0 year</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>2 lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>1351247</td>\n",
       "      <td>36</td>\n",
       "      <td>business</td>\n",
       "      <td>9572000</td>\n",
       "      <td>1</td>\n",
       "      <td>car or other</td>\n",
       "      <td>2</td>\n",
       "      <td>delay in paying off loans in the past</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>divorced/separated</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>0 year</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>1324848</td>\n",
       "      <td>36</td>\n",
       "      <td>business</td>\n",
       "      <td>4455000</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>2</td>\n",
       "      <td>delay in paying off loans in the past</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>male</td>\n",
       "      <td>divorced/separated</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>management / self-employed / highly qualified ...</td>\n",
       "      <td>1 year</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>2 lac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>1612689</td>\n",
       "      <td>18</td>\n",
       "      <td>new vehicle</td>\n",
       "      <td>976000</td>\n",
       "      <td>1</td>\n",
       "      <td>car or other</td>\n",
       "      <td>1</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>female</td>\n",
       "      <td>divorced/separated/married</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>0 year</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>1766191</td>\n",
       "      <td>24</td>\n",
       "      <td>electronic equipment</td>\n",
       "      <td>2039000</td>\n",
       "      <td>1</td>\n",
       "      <td>building society savings agreement/life insurance</td>\n",
       "      <td>1</td>\n",
       "      <td>existing loans paid back duly till now</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>male</td>\n",
       "      <td>married/widowed</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>0 year</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>2 lac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     applicant_id  Months_loan_taken_for               Purpose  \\\n",
       "0         1469590                      6  electronic equipment   \n",
       "604       1462065                     10                  FF&E   \n",
       "605       1474361                     24                  FF&E   \n",
       "606       1764990                     24              business   \n",
       "608       1365958                     18  electronic equipment   \n",
       "..            ...                    ...                   ...   \n",
       "540       1234944                     12  electronic equipment   \n",
       "180       1351247                     36              business   \n",
       "181       1324848                     36              business   \n",
       "751       1612689                     18           new vehicle   \n",
       "405       1766191                     24  electronic equipment   \n",
       "\n",
       "     Principal_loan_amount  EMI_rate_in_percentage_of_disposable_income  \\\n",
       "0                  1169000                                            4   \n",
       "604                1275000                                            4   \n",
       "605                2828000                                            4   \n",
       "606                4526000                                            3   \n",
       "608                2051000                                            4   \n",
       "..                     ...                                          ...   \n",
       "540                1534000                                            1   \n",
       "180                9572000                                            1   \n",
       "181                4455000                                            2   \n",
       "751                 976000                                            1   \n",
       "405                2039000                                            1   \n",
       "\n",
       "                                              Property  \\\n",
       "0                                          real estate   \n",
       "604  building society savings agreement/life insurance   \n",
       "605                                        real estate   \n",
       "606                                        real estate   \n",
       "608                                        real estate   \n",
       "..                                                 ...   \n",
       "540                                        real estate   \n",
       "180                                       car or other   \n",
       "181                                        real estate   \n",
       "751                                       car or other   \n",
       "405  building society savings agreement/life insurance   \n",
       "\n",
       "     Number_of_existing_loans_at_this_bank  \\\n",
       "0                                        2   \n",
       "604                                      1   \n",
       "605                                      1   \n",
       "606                                      1   \n",
       "608                                      1   \n",
       "..                                     ...   \n",
       "540                                      1   \n",
       "180                                      2   \n",
       "181                                      2   \n",
       "751                                      1   \n",
       "405                                      1   \n",
       "\n",
       "                               Loan_history  high_risk_applicant  \\\n",
       "0     critical/pending loans at other banks                    0   \n",
       "604  existing loans paid back duly till now                    0   \n",
       "605   all loans at this bank paid back duly                    0   \n",
       "606   critical/pending loans at other banks                    0   \n",
       "608  existing loans paid back duly till now                    0   \n",
       "..                                      ...                  ...   \n",
       "540  existing loans paid back duly till now                    1   \n",
       "180   delay in paying off loans in the past                    1   \n",
       "181   delay in paying off loans in the past                    1   \n",
       "751  existing loans paid back duly till now                    1   \n",
       "405  existing loans paid back duly till now                    1   \n",
       "\n",
       "     Primary_applicant_age_in_years  Gender              Marital_status  \\\n",
       "0                                67    male                      single   \n",
       "604                              23  female  divorced/separated/married   \n",
       "605                              22    male                      single   \n",
       "606                              74    male                      single   \n",
       "608                              33    male                      single   \n",
       "..                              ...     ...                         ...   \n",
       "540                              23    male             married/widowed   \n",
       "180                              28    male          divorced/separated   \n",
       "181                              30    male          divorced/separated   \n",
       "751                              23  female  divorced/separated/married   \n",
       "405                              22    male             married/widowed   \n",
       "\n",
       "     Number_of_dependents Housing  Years_at_current_residence  \\\n",
       "0                       1     own                           4   \n",
       "604                     1     own                           2   \n",
       "605                     1     own                           4   \n",
       "606                     1     own                           2   \n",
       "608                     1     own                           1   \n",
       "..                    ...     ...                         ...   \n",
       "540                     1    rent                           1   \n",
       "180                     1     own                           1   \n",
       "181                     1     own                           2   \n",
       "751                     1     own                           2   \n",
       "405                     1     own                           1   \n",
       "\n",
       "                                     Employment_status  \\\n",
       "0                          skilled employee / official   \n",
       "604                        skilled employee / official   \n",
       "605                        skilled employee / official   \n",
       "606  management / self-employed / highly qualified ...   \n",
       "608                        skilled employee / official   \n",
       "..                                                 ...   \n",
       "540                        skilled employee / official   \n",
       "180                        skilled employee / official   \n",
       "181  management / self-employed / highly qualified ...   \n",
       "751                               unskilled - resident   \n",
       "405                        skilled employee / official   \n",
       "\n",
       "    Has_been_employed_for_at_least Savings_account_balance  \\\n",
       "0                          7 years                     Low   \n",
       "604                         0 year                     Low   \n",
       "605                         1 year                    High   \n",
       "606                         1 year                     Low   \n",
       "608                         0 year                     Low   \n",
       "..                             ...                     ...   \n",
       "540                         0 year                     Low   \n",
       "180                         0 year                     Low   \n",
       "181                         1 year                     Low   \n",
       "751                         0 year                     Low   \n",
       "405                         0 year                     Low   \n",
       "\n",
       "    Balance_in_existing_bank_account_(lower_limit_of_bucket)  \\\n",
       "0                                                    0         \n",
       "604                                              2 lac         \n",
       "605                                                  0         \n",
       "606                                                  0         \n",
       "608                                                  0         \n",
       "..                                                 ...         \n",
       "540                                                  0         \n",
       "180                                                  0         \n",
       "181                                                  0         \n",
       "751                                                  0         \n",
       "405                                                  0         \n",
       "\n",
       "    Balance_in_existing_bank_account_(upper_limit_of_bucket)  \n",
       "0                                                    0        \n",
       "604                                                  0        \n",
       "605                                                  0        \n",
       "606                                                  0        \n",
       "608                                                  0        \n",
       "..                                                 ...        \n",
       "540                                              2 lac        \n",
       "180                                                  0        \n",
       "181                                              2 lac        \n",
       "751                                                  0        \n",
       "405                                              2 lac        \n",
       "\n",
       "[1000 rows x 20 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.sort_values(by=['high_risk_applicant'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "41db79ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['applicant_id',\n",
       " 'Months_loan_taken_for',\n",
       " 'Purpose',\n",
       " 'Principal_loan_amount',\n",
       " 'EMI_rate_in_percentage_of_disposable_income',\n",
       " 'Property',\n",
       " 'Number_of_existing_loans_at_this_bank',\n",
       " 'Loan_history',\n",
       " 'high_risk_applicant',\n",
       " 'Primary_applicant_age_in_years',\n",
       " 'Gender',\n",
       " 'Marital_status',\n",
       " 'Number_of_dependents',\n",
       " 'Housing',\n",
       " 'Years_at_current_residence',\n",
       " 'Employment_status',\n",
       " 'Has_been_employed_for_at_least',\n",
       " 'Savings_account_balance',\n",
       " 'Balance_in_existing_bank_account_(lower_limit_of_bucket)',\n",
       " 'Balance_in_existing_bank_account_(upper_limit_of_bucket)']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_headers = list(df_2.columns.values)\n",
    "column_headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b55393cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Target = df_2.drop(['high_risk_applicant'], axis=1)\n",
    "df_2 = df_2.drop(['applicant_id',], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "766ce228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Months_loan_taken_for',\n",
       " 'Purpose',\n",
       " 'Principal_loan_amount',\n",
       " 'EMI_rate_in_percentage_of_disposable_income',\n",
       " 'Property',\n",
       " 'Number_of_existing_loans_at_this_bank',\n",
       " 'Loan_history',\n",
       " 'high_risk_applicant',\n",
       " 'Primary_applicant_age_in_years',\n",
       " 'Gender',\n",
       " 'Marital_status',\n",
       " 'Number_of_dependents',\n",
       " 'Housing',\n",
       " 'Years_at_current_residence',\n",
       " 'Employment_status',\n",
       " 'Has_been_employed_for_at_least',\n",
       " 'Savings_account_balance',\n",
       " 'Balance_in_existing_bank_account_(lower_limit_of_bucket)',\n",
       " 'Balance_in_existing_bank_account_(upper_limit_of_bucket)']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_headers = list(df_2.columns.values)\n",
    "column_headers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab7eb57f",
   "metadata": {},
   "source": [
    "### Handling Categorical variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dedf4458",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'used vehicle', 'domestic appliances', 'FF&E', 'education', 'electronic equipment', 'business', 'career development', 'repair costs', 'new vehicle'}\n",
      "{'female', 'male'}\n",
      "{'real estate', 'car or other', 'building society savings agreement/life insurance'}\n",
      "{'no loans taken/all loans paid back duly', 'existing loans paid back duly till now', 'critical/pending loans at other banks', 'all loans at this bank paid back duly', 'delay in paying off loans in the past'}\n",
      "{'single', 'divorced/separated/married', 'divorced/separated', 'married/widowed'}\n",
      "{'for free', 'rent', 'own'}\n",
      "{'unemployed / unskilled - non-resident', 'management / self-employed / highly qualified employee / officer', 'unskilled - resident', 'skilled employee / official'}\n",
      "{'4 years', '7 years', '1 year', '0 year'}\n",
      "{'Medium', 'Low', 'Very high', 'High'}\n"
     ]
    }
   ],
   "source": [
    "print(set(df_2['Purpose'].values.tolist()))\n",
    "print(set(df_2['Gender'].values.tolist()))\n",
    "print(set(df_2['Property'].values.tolist()))\n",
    "print(set(df_2['Loan_history'].values.tolist()))\n",
    "print(set(df_2['Marital_status'].values.tolist()))\n",
    "print(set(df_2['Housing'].values.tolist()))\n",
    "print(set(df_2['Employment_status'].values.tolist()))\n",
    "print(set(df_2['Has_been_employed_for_at_least'].values.tolist()))\n",
    "print(set(df_2['Savings_account_balance'].values.tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a8e34acf",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_2['Balance_in_existing_bank_account_(upper_limit_of_bucket)']= df_2['Balance_in_existing_bank_account_(upper_limit_of_bucket)'].map({'0':0,'2 lac':1}).astype(int)\n",
    "\n",
    "df_2['Balance_in_existing_bank_account_(lower_limit_of_bucket)']= df_2['Balance_in_existing_bank_account_(lower_limit_of_bucket)'].map({'0':0,'2 lac':1}).astype(int)\n",
    "\n",
    "df_2 = pd.get_dummies(df_2,columns = ['Gender', 'Purpose','Marital_status','Property','Loan_history'\n",
    "                                          ,'Housing','Employment_status','Savings_account_balance','Loan_history','Has_been_employed_for_at_least'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "778188f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1. ... 1. 1. 1.]\n",
      " [1. 1. 1. ... 1. 1. 1.]\n",
      " [1. 1. 1. ... 1. 1. 1.]\n",
      " ...\n",
      " [1. 1. 1. ... 1. 1. 1.]\n",
      " [1. 1. 1. ... 1. 1. 1.]\n",
      " [1. 1. 1. ... 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "matrix = np.corrcoef(df_2)\n",
    "print(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb60496b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bb68b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "888a853f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2.to_csv(r'C:\\Users\\aryan\\Downloads\\data\\df.csv')\n",
    "\n"
   ]
  }
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