Loan_prediction_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e203dd71",
   "metadata": {},
   "source": [
    "### Importing neccesary libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "a418fd10",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "2051b2cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f377393d",
   "metadata": {},
   "source": [
    "Read the dataset from the csv and convert it to dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "6af37398",
   "metadata": {},
   "outputs": [],
   "source": [
    "read_data = pd.read_csv('train_u6lujuX_CVtuZ9i.csv')\n",
    "data = pd.DataFrame(read_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "4edf1a4d",
   "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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99061889",
   "metadata": {},
   "source": [
    "## Analyzing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6fe4180",
   "metadata": {},
   "source": [
    "Check for null values in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "d8d9952c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "7bfca24c",
   "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>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "27929900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc78812b",
   "metadata": {},
   "source": [
    "Checking each column for unique values which has NaN in the columns and fill the null values with the lowest value counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "dbb1bcf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Male', 'Female', nan], dtype=object)"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Gender.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "e3d87ff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      489\n",
       "Female    112\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "485a7902",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Gender']=data['Gender'].fillna('Female')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "4b24bb84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes', nan], dtype=object)"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Married.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "a5ccad63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    398\n",
       "No     213\n",
       "Name: Married, dtype: int64"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Married.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "d8527fcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Married']=data['Married'].fillna('No')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "6ec99c68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '1', '2', '3+', nan], dtype=object)"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Dependents.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "cda9c17e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     345\n",
       "1     102\n",
       "2     101\n",
       "3+     51\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Dependents.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "424e6215",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Dependents'] = data['Dependents'].fillna('3+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "04186ee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes', nan], dtype=object)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Self_Employed.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "69625504",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     500\n",
       "Yes     82\n",
       "Name: Self_Employed, dtype: int64"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Self_Employed.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "68773163",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Self_Employed']=data['Self_Employed'].fillna('Yes')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05ec1d9e",
   "metadata": {},
   "source": [
    "The NaNs in the numeric dtypes columns are filled with thier mean values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "cea6442c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['LoanAmount']=data['LoanAmount'].fillna(data['LoanAmount'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "60b0d03c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Loan_Amount_Term.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "f87fdb4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Loan_Amount_Term']=data['Loan_Amount_Term'].fillna(data['Loan_Amount_Term'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "9676f930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  0., nan])"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Credit_History.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "8c568fe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    475\n",
       "0.0     89\n",
       "Name: Credit_History, dtype: int64"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Credit_History.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "dae80764",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Credit_History']= data['Credit_History'].fillna(0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "779319bd",
   "metadata": {},
   "source": [
    "Checking again for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "5ab05b29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "628dde63",
   "metadata": {},
   "source": [
    "Got rid of the null values and now we can continue with the analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4cac34e",
   "metadata": {},
   "source": [
    "Grouping by different columns to further explore the data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f78a0d97",
   "metadata": {},
   "source": [
    "By Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "763cf280",
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>654405</td>\n",
       "      <td>143151.000000</td>\n",
       "      <td>16960.236486</td>\n",
       "      <td>43866.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2663319</td>\n",
       "      <td>852293.919989</td>\n",
       "      <td>72936.831081</td>\n",
       "      <td>166122.0</td>\n",
       "      <td>382.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ApplicantIncome  CoapplicantIncome    LoanAmount  Loan_Amount_Term  \\\n",
       "Gender                                                                       \n",
       "Female           654405      143151.000000  16960.236486           43866.0   \n",
       "Male            2663319      852293.919989  72936.831081          166122.0   \n",
       "\n",
       "        Credit_History  \n",
       "Gender                  \n",
       "Female            93.0  \n",
       "Male             382.0  "
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(by=\"Gender\").sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90ee44f4",
   "metadata": {},
   "source": [
    "By Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "27621e3f",
   "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",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_Status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>1045647</td>\n",
       "      <td>360539.000000</td>\n",
       "      <td>28981.533784</td>\n",
       "      <td>66048.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>2272077</td>\n",
       "      <td>634905.919989</td>\n",
       "      <td>60915.533784</td>\n",
       "      <td>143940.0</td>\n",
       "      <td>378.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ApplicantIncome  CoapplicantIncome    LoanAmount  \\\n",
       "Loan_Status                                                     \n",
       "N                    1045647      360539.000000  28981.533784   \n",
       "Y                    2272077      634905.919989  60915.533784   \n",
       "\n",
       "             Loan_Amount_Term  Credit_History  \n",
       "Loan_Status                                    \n",
       "N                     66048.0            97.0  \n",
       "Y                    143940.0           378.0  "
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(by=\"Loan_Status\").sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd383a8c",
   "metadata": {},
   "source": [
    "By Credit_History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "d59f9978",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit_History</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>740124</td>\n",
       "      <td>269526.000000</td>\n",
       "      <td>21096.060811</td>\n",
       "      <td>47448.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>2577600</td>\n",
       "      <td>725918.919989</td>\n",
       "      <td>68801.006757</td>\n",
       "      <td>162540.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ApplicantIncome  CoapplicantIncome    LoanAmount  \\\n",
       "Credit_History                                                     \n",
       "0.0                      740124      269526.000000  21096.060811   \n",
       "1.0                     2577600      725918.919989  68801.006757   \n",
       "\n",
       "                Loan_Amount_Term  \n",
       "Credit_History                    \n",
       "0.0                      47448.0  \n",
       "1.0                     162540.0  "
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(by=\"Credit_History\").sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e6f9ecb",
   "metadata": {},
   "source": [
    "By Married"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "c314222b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\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>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>1077313</td>\n",
       "      <td>281181.000000</td>\n",
       "      <td>28039.297297</td>\n",
       "      <td>75822.0</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>2240411</td>\n",
       "      <td>714263.919989</td>\n",
       "      <td>61857.770270</td>\n",
       "      <td>134166.0</td>\n",
       "      <td>305.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ApplicantIncome  CoapplicantIncome    LoanAmount  Loan_Amount_Term  \\\n",
       "Married                                                                       \n",
       "No               1077313      281181.000000  28039.297297           75822.0   \n",
       "Yes              2240411      714263.919989  61857.770270          134166.0   \n",
       "\n",
       "         Credit_History  \n",
       "Married                  \n",
       "No                170.0  \n",
       "Yes               305.0  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(by=\"Married\").sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea16e27",
   "metadata": {},
   "source": [
    "By Education "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "924e9ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Graduate</th>\n",
       "      <td>2811568</td>\n",
       "      <td>824387.919989</td>\n",
       "      <td>73834.182432</td>\n",
       "      <td>165420.0</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not Graduate</th>\n",
       "      <td>506156</td>\n",
       "      <td>171057.000000</td>\n",
       "      <td>16062.885135</td>\n",
       "      <td>44568.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ApplicantIncome  CoapplicantIncome    LoanAmount  \\\n",
       "Education                                                        \n",
       "Graduate              2811568      824387.919989  73834.182432   \n",
       "Not Graduate           506156      171057.000000  16062.885135   \n",
       "\n",
       "              Loan_Amount_Term  Credit_History  \n",
       "Education                                       \n",
       "Graduate              165420.0           380.0  \n",
       "Not Graduate           44568.0            95.0  "
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(by=\"Education\").sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c9d5a05",
   "metadata": {},
   "source": [
    "By Self_Employed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "f881b979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self_Employed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>2524874</td>\n",
       "      <td>790494.919989</td>\n",
       "      <td>70958.418919</td>\n",
       "      <td>171618.0</td>\n",
       "      <td>387.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>792850</td>\n",
       "      <td>204950.000000</td>\n",
       "      <td>18938.648649</td>\n",
       "      <td>38370.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ApplicantIncome  CoapplicantIncome    LoanAmount  \\\n",
       "Self_Employed                                                     \n",
       "No                     2524874      790494.919989  70958.418919   \n",
       "Yes                     792850      204950.000000  18938.648649   \n",
       "\n",
       "               Loan_Amount_Term  Credit_History  \n",
       "Self_Employed                                    \n",
       "No                     171618.0           387.0  \n",
       "Yes                     38370.0            88.0  "
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(by=\"Self_Employed\").sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31c06313",
   "metadata": {},
   "source": [
    "By Property Area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "2c4faffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Property_Area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>994181</td>\n",
       "      <td>294551.120001</td>\n",
       "      <td>27219.472973</td>\n",
       "      <td>61836.0</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semiurban</th>\n",
       "      <td>1233097</td>\n",
       "      <td>354191.000000</td>\n",
       "      <td>33907.060811</td>\n",
       "      <td>80862.0</td>\n",
       "      <td>187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>1090446</td>\n",
       "      <td>346702.799988</td>\n",
       "      <td>28770.533784</td>\n",
       "      <td>67290.0</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ApplicantIncome  CoapplicantIncome    LoanAmount  \\\n",
       "Property_Area                                                     \n",
       "Rural                   994181      294551.120001  27219.472973   \n",
       "Semiurban              1233097      354191.000000  33907.060811   \n",
       "Urban                  1090446      346702.799988  28770.533784   \n",
       "\n",
       "               Loan_Amount_Term  Credit_History  \n",
       "Property_Area                                    \n",
       "Rural                   61836.0           137.0  \n",
       "Semiurban               80862.0           187.0  \n",
       "Urban                   67290.0           151.0  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(by=\"Property_Area\").sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb69875e",
   "metadata": {},
   "source": [
    "##### Plotting several plots and graphs for further analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a53359b",
   "metadata": {},
   "source": [
    "Countplots for Loan_status and Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "d2d576fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Loan_Status', ylabel='count'>"
      ]
     },
     "execution_count": 254,
     "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.countplot(x=data['Loan_Status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "bf299cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender', ylabel='count'>"
      ]
     },
     "execution_count": 255,
     "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.countplot(x=data['Gender'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72206a0f",
   "metadata": {},
   "source": [
    "Countplots for LoanAmount and Proerty Area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "c9a465a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LoanAmount', ylabel='count'>"
      ]
     },
     "execution_count": 256,
     "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.countplot(x=data['LoanAmount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "e35ef6f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Property_Area', ylabel='count'>"
      ]
     },
     "execution_count": 257,
     "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.countplot(x=data['Property_Area'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cb9f5b9",
   "metadata": {},
   "source": [
    "Scatterplots for Loanamount and ApplicatnIncome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "e6a01669",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LoanAmount', ylabel='ApplicantIncome'>"
      ]
     },
     "execution_count": 258,
     "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(x='LoanAmount',y='ApplicantIncome',data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007fd9d5",
   "metadata": {},
   "source": [
    "Scatterplot for Loan amount and Applicant Income based on Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "6ba40242",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LoanAmount', ylabel='ApplicantIncome'>"
      ]
     },
     "execution_count": 259,
     "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(x='LoanAmount',y='ApplicantIncome',data=data,hue='Gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1ed16a7",
   "metadata": {},
   "source": [
    "Barplots for Gender and amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "9cdbfebc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender', ylabel='LoanAmount'>"
      ]
     },
     "execution_count": 260,
     "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.barplot(x='Gender',y='LoanAmount',data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c558f4",
   "metadata": {},
   "source": [
    "Scatterplot for Loan amount and Gendere based on Loan Status\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "06bbeee6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender', ylabel='LoanAmount'>"
      ]
     },
     "execution_count": 261,
     "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(y='LoanAmount',x='Gender',data=data,hue='Loan_Status')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c25bedb",
   "metadata": {},
   "source": [
    "##### Preprocessing Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a945a553",
   "metadata": {},
   "source": [
    "Sepreating Categorical and numerical columns for easy processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "2558c7a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = []\n",
    "numeric = []\n",
    "types = data.dtypes\n",
    "for n,c in enumerate(types):\n",
    "    if c== object:\n",
    "        categorical.append(data.iloc[:,n])\n",
    "    else:\n",
    "        numeric.append(data.iloc[:,n])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49be0b41",
   "metadata": {},
   "source": [
    "Converting to dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "647b66b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_df = pd.DataFrame(categorical)\n",
    "num_df = pd.DataFrame(numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "66095e5f",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>604</th>\n",
       "      <th>605</th>\n",
       "      <th>606</th>\n",
       "      <th>607</th>\n",
       "      <th>608</th>\n",
       "      <th>609</th>\n",
       "      <th>610</th>\n",
       "      <th>611</th>\n",
       "      <th>612</th>\n",
       "      <th>613</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>LP001003</td>\n",
       "      <td>LP001005</td>\n",
       "      <td>LP001006</td>\n",
       "      <td>LP001008</td>\n",
       "      <td>LP001011</td>\n",
       "      <td>LP001013</td>\n",
       "      <td>LP001014</td>\n",
       "      <td>LP001018</td>\n",
       "      <td>LP001020</td>\n",
       "      <td>...</td>\n",
       "      <td>LP002959</td>\n",
       "      <td>LP002960</td>\n",
       "      <td>LP002961</td>\n",
       "      <td>LP002964</td>\n",
       "      <td>LP002974</td>\n",
       "      <td>LP002978</td>\n",
       "      <td>LP002979</td>\n",
       "      <td>LP002983</td>\n",
       "      <td>LP002984</td>\n",
       "      <td>LP002990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3+</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3+</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>Graduate</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>...</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Graduate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 614 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0         1         2             3         4         5    \\\n",
       "Loan_ID     LP001002  LP001003  LP001005      LP001006  LP001008  LP001011   \n",
       "Gender          Male      Male      Male          Male      Male      Male   \n",
       "Married           No       Yes       Yes           Yes        No       Yes   \n",
       "Dependents         0         1         0             0         0         2   \n",
       "Education   Graduate  Graduate  Graduate  Not Graduate  Graduate  Graduate   \n",
       "\n",
       "                     6         7         8         9    ...       604  \\\n",
       "Loan_ID         LP001013  LP001014  LP001018  LP001020  ...  LP002959   \n",
       "Gender              Male      Male      Male      Male  ...    Female   \n",
       "Married              Yes       Yes       Yes       Yes  ...       Yes   \n",
       "Dependents             0        3+         2         1  ...         1   \n",
       "Education   Not Graduate  Graduate  Graduate  Graduate  ...  Graduate   \n",
       "\n",
       "                     605       606           607       608       609  \\\n",
       "Loan_ID         LP002960  LP002961      LP002964  LP002974  LP002978   \n",
       "Gender              Male      Male          Male      Male    Female   \n",
       "Married              Yes       Yes           Yes       Yes        No   \n",
       "Dependents             0         1             2         0         0   \n",
       "Education   Not Graduate  Graduate  Not Graduate  Graduate  Graduate   \n",
       "\n",
       "                 610       611       612       613  \n",
       "Loan_ID     LP002979  LP002983  LP002984  LP002990  \n",
       "Gender          Male      Male      Male    Female  \n",
       "Married          Yes       Yes       Yes        No  \n",
       "Dependents        3+         1         2         0  \n",
       "Education   Graduate  Graduate  Graduate  Graduate  \n",
       "\n",
       "[5 rows x 614 columns]"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "8bf21ce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>604</th>\n",
       "      <th>605</th>\n",
       "      <th>606</th>\n",
       "      <th>607</th>\n",
       "      <th>608</th>\n",
       "      <th>609</th>\n",
       "      <th>610</th>\n",
       "      <th>611</th>\n",
       "      <th>612</th>\n",
       "      <th>613</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <td>5849.000000</td>\n",
       "      <td>4583.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>2583.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>5417.0</td>\n",
       "      <td>2333.0</td>\n",
       "      <td>3036.0</td>\n",
       "      <td>4006.0</td>\n",
       "      <td>12841.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>3987.0</td>\n",
       "      <td>3232.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>4106.0</td>\n",
       "      <td>8072.0</td>\n",
       "      <td>7583.0</td>\n",
       "      <td>4583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>10968.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3800.000000</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>1411.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoanAmount</th>\n",
       "      <td>146.412162</td>\n",
       "      <td>128.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>...</td>\n",
       "      <td>496.0</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>173.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <td>360.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>360.0</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit_History</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 614 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0       1       2       3       4       5    \\\n",
       "ApplicantIncome    5849.000000  4583.0  3000.0  2583.0  6000.0  5417.0   \n",
       "CoapplicantIncome     0.000000  1508.0     0.0  2358.0     0.0  4196.0   \n",
       "LoanAmount          146.412162   128.0    66.0   120.0   141.0   267.0   \n",
       "Loan_Amount_Term    360.000000   360.0   360.0   360.0   360.0   360.0   \n",
       "Credit_History        1.000000     1.0     1.0     1.0     1.0     1.0   \n",
       "\n",
       "                      6       7       8        9    ...      604          605  \\\n",
       "ApplicantIncome    2333.0  3036.0  4006.0  12841.0  ...  12000.0  2400.000000   \n",
       "CoapplicantIncome  1516.0  2504.0  1526.0  10968.0  ...      0.0  3800.000000   \n",
       "LoanAmount           95.0   158.0   168.0    349.0  ...    496.0   146.412162   \n",
       "Loan_Amount_Term    360.0   360.0   360.0    360.0  ...    360.0   180.000000   \n",
       "Credit_History        1.0     0.0     1.0      1.0  ...      1.0     1.000000   \n",
       "\n",
       "                      606     607     608     609     610     611     612  \\\n",
       "ApplicantIncome    3400.0  3987.0  3232.0  2900.0  4106.0  8072.0  7583.0   \n",
       "CoapplicantIncome  2500.0  1411.0  1950.0     0.0     0.0   240.0     0.0   \n",
       "LoanAmount          173.0   157.0   108.0    71.0    40.0   253.0   187.0   \n",
       "Loan_Amount_Term    360.0   360.0   360.0   360.0   180.0   360.0   360.0   \n",
       "Credit_History        1.0     1.0     1.0     1.0     1.0     1.0     1.0   \n",
       "\n",
       "                      613  \n",
       "ApplicantIncome    4583.0  \n",
       "CoapplicantIncome     0.0  \n",
       "LoanAmount          133.0  \n",
       "Loan_Amount_Term    360.0  \n",
       "Credit_History        0.0  \n",
       "\n",
       "[5 rows x 614 columns]"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "8753242d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_df = cat_df.transpose()\n",
    "num_df = num_df.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "7669a568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "  Property_Area Loan_Status  \n",
       "0         Urban           Y  \n",
       "1         Rural           N  \n",
       "2         Urban           Y  \n",
       "3         Urban           Y  \n",
       "4         Urban           Y  "
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#catergorical data\n",
    "cat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "1ea6fd88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5849.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4583.0</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2583.0</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0           5849.0                0.0  146.412162             360.0   \n",
       "1           4583.0             1508.0  128.000000             360.0   \n",
       "2           3000.0                0.0   66.000000             360.0   \n",
       "3           2583.0             2358.0  120.000000             360.0   \n",
       "4           6000.0                0.0  141.000000             360.0   \n",
       "\n",
       "   Credit_History  \n",
       "0             1.0  \n",
       "1             1.0  \n",
       "2             1.0  \n",
       "3             1.0  \n",
       "4             1.0  "
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#numeric data\n",
    "num_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "068670aa",
   "metadata": {},
   "source": [
    "Checking for any null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "a96750f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID          False\n",
       "Gender           False\n",
       "Married          False\n",
       "Dependents       False\n",
       "Education        False\n",
       "Self_Employed    False\n",
       "Property_Area    False\n",
       "Loan_Status      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "fe6225ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ApplicantIncome      False\n",
       "CoapplicantIncome    False\n",
       "LoanAmount           False\n",
       "Loan_Amount_Term     False\n",
       "Credit_History       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ad91f5",
   "metadata": {},
   "source": [
    "Dropping Loan ID column from dataframe as it is not that neccesary for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "ee4a0b92",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_df.drop('Loan_ID',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "2dbd4efa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>No</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender Married Dependents     Education Self_Employed Property_Area  \\\n",
       "0   Male      No          0      Graduate            No         Urban   \n",
       "1   Male     Yes          1      Graduate            No         Rural   \n",
       "2   Male     Yes          0      Graduate           Yes         Urban   \n",
       "3   Male     Yes          0  Not Graduate            No         Urban   \n",
       "4   Male      No          0      Graduate            No         Urban   \n",
       "\n",
       "  Loan_Status  \n",
       "0           Y  \n",
       "1           N  \n",
       "2           Y  \n",
       "3           Y  \n",
       "4           Y  "
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b97693",
   "metadata": {},
   "source": [
    "Using Label Encoder for the Categorical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "5cebd3ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "encode = LabelEncoder()\n",
    "\n",
    "# Transforming the Categorical Columns\n",
    "for i in cat_df:\n",
    "    cat_df[i] = encode.fit_transform(cat_df[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "63d08a53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Married  Dependents  Education  Self_Employed  Property_Area  \\\n",
       "0       1        0           0          0              0              2   \n",
       "1       1        1           1          0              0              0   \n",
       "2       1        1           0          0              1              2   \n",
       "3       1        1           0          1              0              2   \n",
       "4       1        0           0          0              0              2   \n",
       "\n",
       "   Loan_Status  \n",
       "0            1  \n",
       "1            0  \n",
       "2            1  \n",
       "3            1  \n",
       "4            1  "
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "35fba71a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mapping for credit history\n",
    "mapping = {  1.0 : 1, 0.0 : 0}\n",
    "num_df.loc[:, \"Credit_History\"] = num_df.Credit_History.map(mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "7c9f451c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1508.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2583.0</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0           5849.0                0.0  146.412162             360.0   \n",
       "1           4583.0             1508.0  128.000000             360.0   \n",
       "2           3000.0                0.0   66.000000             360.0   \n",
       "3           2583.0             2358.0  120.000000             360.0   \n",
       "4           6000.0                0.0  141.000000             360.0   \n",
       "\n",
       "   Credit_History  \n",
       "0               1  \n",
       "1               1  \n",
       "2               1  \n",
       "3               1  \n",
       "4               1  "
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b4bd3f",
   "metadata": {},
   "source": [
    "Concatenate both dataframes for further operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "e29f3aa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([cat_df,num_df],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "f90d5c31",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2583.0</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Gender  Married  Dependents  Education  Self_Employed  Property_Area  \\\n",
       "0       1        0           0          0              0              2   \n",
       "1       1        1           1          0              0              0   \n",
       "2       1        1           0          0              1              2   \n",
       "3       1        1           0          1              0              2   \n",
       "4       1        0           0          0              0              2   \n",
       "\n",
       "   Loan_Status  ApplicantIncome  CoapplicantIncome  LoanAmount  \\\n",
       "0            1           5849.0                0.0  146.412162   \n",
       "1            0           4583.0             1508.0  128.000000   \n",
       "2            1           3000.0                0.0   66.000000   \n",
       "3            1           2583.0             2358.0  120.000000   \n",
       "4            1           6000.0                0.0  141.000000   \n",
       "\n",
       "   Loan_Amount_Term  Credit_History  \n",
       "0             360.0               1  \n",
       "1             360.0               1  \n",
       "2             360.0               1  \n",
       "3             360.0               1  \n",
       "4             360.0               1  "
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bc95f25",
   "metadata": {},
   "source": [
    "Saving the processed data to a csv file for future use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "2287b8cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('Processed_Data.csv',index=False)"
   ]
  }
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