Loan_Approval_Predictor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\joe\\Downloads\\Loan.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA CLEANING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0      False   False    False       False      False          False   \n",
       "1      False   False    False       False      False          False   \n",
       "2      False   False    False       False      False          False   \n",
       "3      False   False    False       False      False          False   \n",
       "4      False   False    False       False      False          False   \n",
       "..       ...     ...      ...         ...        ...            ...   \n",
       "609    False   False    False       False      False          False   \n",
       "610    False   False    False       False      False          False   \n",
       "611    False   False    False       False      False          False   \n",
       "612    False   False    False       False      False          False   \n",
       "613    False   False    False       False      False          False   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0              False              False        True             False   \n",
       "1              False              False       False             False   \n",
       "2              False              False       False             False   \n",
       "3              False              False       False             False   \n",
       "4              False              False       False             False   \n",
       "..               ...                ...         ...               ...   \n",
       "609            False              False       False             False   \n",
       "610            False              False       False             False   \n",
       "611            False              False       False             False   \n",
       "612            False              False       False             False   \n",
       "613            False              False       False             False   \n",
       "\n",
       "     Credit_History  Property_Area  Loan_Status  \n",
       "0             False          False        False  \n",
       "1             False          False        False  \n",
       "2             False          False        False  \n",
       "3             False          False        False  \n",
       "4             False          False        False  \n",
       "..              ...            ...          ...  \n",
       "609           False          False        False  \n",
       "610           False          False        False  \n",
       "611           False          False        False  \n",
       "612           False          False        False  \n",
       "613           False          False        False  \n",
       "\n",
       "[614 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = df.isnull().sum() #getting count for number of null values in each column\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      489\n",
       "Female    112\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].value_counts()  #finding the Male/Female split in the data in otrder to fill the mising values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Gender = df.Gender.fillna('Male') #filling missing values with value=Male as no of males is 4times more "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    398\n",
       "No     213\n",
       "Name: Married, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Married'].value_counts() #finding married data split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Married = df.Married.fillna('Yes') #filling missing values as \"Yes\" as majority data is Yes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     345\n",
       "1     102\n",
       "2     101\n",
       "3+     51\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Dependents'].value_counts() #finding the split for the dependent data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Dependents = df.Dependents.fillna('0') #filling missing values with '0' as '0' has majority "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     500\n",
       "Yes     82\n",
       "Name: Self_Employed, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Self_Employed'].value_counts() #finding the split of data here "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Self_Employed = df.Self_Employed.fillna('No') #filling missing data with No"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.LoanAmount = df.LoanAmount.fillna(df.LoanAmount.mean()) #filling the missing values with the mean of all the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0    512\n",
       "180.0     44\n",
       "480.0     15\n",
       "300.0     13\n",
       "84.0       4\n",
       "240.0      4\n",
       "120.0      3\n",
       "36.0       2\n",
       "60.0       2\n",
       "12.0       1\n",
       "Name: Loan_Amount_Term, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Loan_Amount_Term'].value_counts() #finding the split of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Loan_Amount_Term = df.Loan_Amount_Term.fillna(360.0) #filling missing data with 360"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    475\n",
       "0.0     89\n",
       "Name: Credit_History, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Credit_History'].value_counts() #finding the data split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Credit_History = df.Credit_History.fillna(1.0) #filling missing values with the majority "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = df.isnull().sum() #getting count for number of null values in each column with all the updated parts\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>146.412162</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>128.000000</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>66.000000</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>120.000000</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>141.000000</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>5</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.000000</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>6</th>\n",
       "      <td>LP001013</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2333</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95.000000</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>7</th>\n",
       "      <td>LP001014</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3036</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LP001018</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4006</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>168.000000</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>9</th>\n",
       "      <td>LP001020</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>12841</td>\n",
       "      <td>10968.0</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LP001024</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3200</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.000000</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>11</th>\n",
       "      <td>LP001027</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2500</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>109.000000</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>12</th>\n",
       "      <td>LP001028</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3073</td>\n",
       "      <td>8106.0</td>\n",
       "      <td>200.000000</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>13</th>\n",
       "      <td>LP001029</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1853</td>\n",
       "      <td>2840.0</td>\n",
       "      <td>114.000000</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>14</th>\n",
       "      <td>LP001030</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1299</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LP001032</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125.000000</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>16</th>\n",
       "      <td>LP001034</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LP001036</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LP001038</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4887</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.000000</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>19</th>\n",
       "      <td>LP001041</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2600</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>115.000000</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>20</th>\n",
       "      <td>LP001043</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LP001046</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5955</td>\n",
       "      <td>5625.0</td>\n",
       "      <td>315.000000</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>22</th>\n",
       "      <td>LP001047</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2600</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LP001050</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3365</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LP001052</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3717</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LP001066</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9560</td>\n",
       "      <td>0.0</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LP001068</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2799</td>\n",
       "      <td>2253.0</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>LP001073</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4226</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>110.000000</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>28</th>\n",
       "      <td>LP001086</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>LP001087</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3750</td>\n",
       "      <td>2083.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</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",
       "5   LP001011    Male     Yes          2      Graduate           Yes   \n",
       "6   LP001013    Male     Yes          0  Not Graduate            No   \n",
       "7   LP001014    Male     Yes         3+      Graduate            No   \n",
       "8   LP001018    Male     Yes          2      Graduate            No   \n",
       "9   LP001020    Male     Yes          1      Graduate            No   \n",
       "10  LP001024    Male     Yes          2      Graduate            No   \n",
       "11  LP001027    Male     Yes          2      Graduate            No   \n",
       "12  LP001028    Male     Yes          2      Graduate            No   \n",
       "13  LP001029    Male      No          0      Graduate            No   \n",
       "14  LP001030    Male     Yes          2      Graduate            No   \n",
       "15  LP001032    Male      No          0      Graduate            No   \n",
       "16  LP001034    Male      No          1  Not Graduate            No   \n",
       "17  LP001036  Female      No          0      Graduate            No   \n",
       "18  LP001038    Male     Yes          0  Not Graduate            No   \n",
       "19  LP001041    Male     Yes          0      Graduate            No   \n",
       "20  LP001043    Male     Yes          0  Not Graduate            No   \n",
       "21  LP001046    Male     Yes          1      Graduate            No   \n",
       "22  LP001047    Male     Yes          0  Not Graduate            No   \n",
       "23  LP001050    Male     Yes          2  Not Graduate            No   \n",
       "24  LP001052    Male     Yes          1      Graduate            No   \n",
       "25  LP001066    Male     Yes          0      Graduate           Yes   \n",
       "26  LP001068    Male     Yes          0      Graduate            No   \n",
       "27  LP001073    Male     Yes          2  Not Graduate            No   \n",
       "28  LP001086    Male      No          0  Not Graduate            No   \n",
       "29  LP001087  Female      No          2      Graduate            No   \n",
       "\n",
       "    ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0              5849                0.0  146.412162             360.0   \n",
       "1              4583             1508.0  128.000000             360.0   \n",
       "2              3000                0.0   66.000000             360.0   \n",
       "3              2583             2358.0  120.000000             360.0   \n",
       "4              6000                0.0  141.000000             360.0   \n",
       "5              5417             4196.0  267.000000             360.0   \n",
       "6              2333             1516.0   95.000000             360.0   \n",
       "7              3036             2504.0  158.000000             360.0   \n",
       "8              4006             1526.0  168.000000             360.0   \n",
       "9             12841            10968.0  349.000000             360.0   \n",
       "10             3200              700.0   70.000000             360.0   \n",
       "11             2500             1840.0  109.000000             360.0   \n",
       "12             3073             8106.0  200.000000             360.0   \n",
       "13             1853             2840.0  114.000000             360.0   \n",
       "14             1299             1086.0   17.000000             120.0   \n",
       "15             4950                0.0  125.000000             360.0   \n",
       "16             3596                0.0  100.000000             240.0   \n",
       "17             3510                0.0   76.000000             360.0   \n",
       "18             4887                0.0  133.000000             360.0   \n",
       "19             2600             3500.0  115.000000             360.0   \n",
       "20             7660                0.0  104.000000             360.0   \n",
       "21             5955             5625.0  315.000000             360.0   \n",
       "22             2600             1911.0  116.000000             360.0   \n",
       "23             3365             1917.0  112.000000             360.0   \n",
       "24             3717             2925.0  151.000000             360.0   \n",
       "25             9560                0.0  191.000000             360.0   \n",
       "26             2799             2253.0  122.000000             360.0   \n",
       "27             4226             1040.0  110.000000             360.0   \n",
       "28             1442                0.0   35.000000             360.0   \n",
       "29             3750             2083.0  120.000000             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  \n",
       "5              1.0         Urban           Y  \n",
       "6              1.0         Urban           Y  \n",
       "7              0.0     Semiurban           N  \n",
       "8              1.0         Urban           Y  \n",
       "9              1.0     Semiurban           N  \n",
       "10             1.0         Urban           Y  \n",
       "11             1.0         Urban           Y  \n",
       "12             1.0         Urban           Y  \n",
       "13             1.0         Rural           N  \n",
       "14             1.0         Urban           Y  \n",
       "15             1.0         Urban           Y  \n",
       "16             1.0         Urban           Y  \n",
       "17             0.0         Urban           N  \n",
       "18             1.0         Rural           N  \n",
       "19             1.0         Urban           Y  \n",
       "20             0.0         Urban           N  \n",
       "21             1.0         Urban           Y  \n",
       "22             0.0     Semiurban           N  \n",
       "23             0.0         Rural           N  \n",
       "24             1.0     Semiurban           N  \n",
       "25             1.0     Semiurban           Y  \n",
       "26             1.0     Semiurban           Y  \n",
       "27             1.0         Urban           Y  \n",
       "28             1.0         Urban           N  \n",
       "29             1.0     Semiurban           Y  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converting Categorical Varibale into Numeric\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder \n",
    "var_mod = ['Gender','Married','Dependents','Education','Self_Employed','Property_Area','Loan_Status']\n",
    "le = LabelEncoder()\n",
    "for i in var_mod:\n",
    "    df[i]=le.fit_transform(df[i])  #converting all the non numerical data into machine readable numeric data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LP001013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2333</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LP001014</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3036</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LP001018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4006</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LP001020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12841</td>\n",
       "      <td>10968.0</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LP001024</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3200</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LP001027</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2500</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LP001028</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3073</td>\n",
       "      <td>8106.0</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LP001029</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1853</td>\n",
       "      <td>2840.0</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LP001030</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1299</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LP001032</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LP001034</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LP001036</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LP001038</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4887</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LP001041</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2600</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LP001043</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LP001046</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5955</td>\n",
       "      <td>5625.0</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LP001047</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2600</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LP001050</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3365</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LP001052</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3717</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LP001066</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9560</td>\n",
       "      <td>0.0</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LP001068</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2799</td>\n",
       "      <td>2253.0</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>LP001073</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4226</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>LP001086</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>LP001087</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3750</td>\n",
       "      <td>2083.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0   LP001002       1        0           0          0              0   \n",
       "1   LP001003       1        1           1          0              0   \n",
       "2   LP001005       1        1           0          0              1   \n",
       "3   LP001006       1        1           0          1              0   \n",
       "4   LP001008       1        0           0          0              0   \n",
       "5   LP001011       1        1           2          0              1   \n",
       "6   LP001013       1        1           0          1              0   \n",
       "7   LP001014       1        1           3          0              0   \n",
       "8   LP001018       1        1           2          0              0   \n",
       "9   LP001020       1        1           1          0              0   \n",
       "10  LP001024       1        1           2          0              0   \n",
       "11  LP001027       1        1           2          0              0   \n",
       "12  LP001028       1        1           2          0              0   \n",
       "13  LP001029       1        0           0          0              0   \n",
       "14  LP001030       1        1           2          0              0   \n",
       "15  LP001032       1        0           0          0              0   \n",
       "16  LP001034       1        0           1          1              0   \n",
       "17  LP001036       0        0           0          0              0   \n",
       "18  LP001038       1        1           0          1              0   \n",
       "19  LP001041       1        1           0          0              0   \n",
       "20  LP001043       1        1           0          1              0   \n",
       "21  LP001046       1        1           1          0              0   \n",
       "22  LP001047       1        1           0          1              0   \n",
       "23  LP001050       1        1           2          1              0   \n",
       "24  LP001052       1        1           1          0              0   \n",
       "25  LP001066       1        1           0          0              1   \n",
       "26  LP001068       1        1           0          0              0   \n",
       "27  LP001073       1        1           2          1              0   \n",
       "28  LP001086       1        0           0          1              0   \n",
       "29  LP001087       0        0           2          0              0   \n",
       "\n",
       "    ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0              5849                0.0  146.412162             360.0   \n",
       "1              4583             1508.0  128.000000             360.0   \n",
       "2              3000                0.0   66.000000             360.0   \n",
       "3              2583             2358.0  120.000000             360.0   \n",
       "4              6000                0.0  141.000000             360.0   \n",
       "5              5417             4196.0  267.000000             360.0   \n",
       "6              2333             1516.0   95.000000             360.0   \n",
       "7              3036             2504.0  158.000000             360.0   \n",
       "8              4006             1526.0  168.000000             360.0   \n",
       "9             12841            10968.0  349.000000             360.0   \n",
       "10             3200              700.0   70.000000             360.0   \n",
       "11             2500             1840.0  109.000000             360.0   \n",
       "12             3073             8106.0  200.000000             360.0   \n",
       "13             1853             2840.0  114.000000             360.0   \n",
       "14             1299             1086.0   17.000000             120.0   \n",
       "15             4950                0.0  125.000000             360.0   \n",
       "16             3596                0.0  100.000000             240.0   \n",
       "17             3510                0.0   76.000000             360.0   \n",
       "18             4887                0.0  133.000000             360.0   \n",
       "19             2600             3500.0  115.000000             360.0   \n",
       "20             7660                0.0  104.000000             360.0   \n",
       "21             5955             5625.0  315.000000             360.0   \n",
       "22             2600             1911.0  116.000000             360.0   \n",
       "23             3365             1917.0  112.000000             360.0   \n",
       "24             3717             2925.0  151.000000             360.0   \n",
       "25             9560                0.0  191.000000             360.0   \n",
       "26             2799             2253.0  122.000000             360.0   \n",
       "27             4226             1040.0  110.000000             360.0   \n",
       "28             1442                0.0   35.000000             360.0   \n",
       "29             3750             2083.0  120.000000             360.0   \n",
       "\n",
       "    Credit_History  Property_Area  Loan_Status  \n",
       "0              1.0              2            1  \n",
       "1              1.0              0            0  \n",
       "2              1.0              2            1  \n",
       "3              1.0              2            1  \n",
       "4              1.0              2            1  \n",
       "5              1.0              2            1  \n",
       "6              1.0              2            1  \n",
       "7              0.0              1            0  \n",
       "8              1.0              2            1  \n",
       "9              1.0              1            0  \n",
       "10             1.0              2            1  \n",
       "11             1.0              2            1  \n",
       "12             1.0              2            1  \n",
       "13             1.0              0            0  \n",
       "14             1.0              2            1  \n",
       "15             1.0              2            1  \n",
       "16             1.0              2            1  \n",
       "17             0.0              2            0  \n",
       "18             1.0              0            0  \n",
       "19             1.0              2            1  \n",
       "20             0.0              2            0  \n",
       "21             1.0              2            1  \n",
       "22             0.0              1            0  \n",
       "23             0.0              0            0  \n",
       "24             1.0              1            0  \n",
       "25             1.0              1            1  \n",
       "26             1.0              1            1  \n",
       "27             1.0              2            1  \n",
       "28             1.0              2            0  \n",
       "29             1.0              1            1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,1:12] #independent variable\n",
    "Y=df.iloc[:,12]   #dependent variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.20,random_state=0)\n",
    "#splitting the data into training and test set in the ratio of 8:2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2958</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3948</td>\n",
       "      <td>1733.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3813</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4708</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3103</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12841</td>\n",
       "      <td>10968.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5167</td>\n",
       "      <td>3167.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4180</td>\n",
       "      <td>2306.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>491 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "90        1        1           0          0              0             2958   \n",
       "533       1        0           1          0              0            11250   \n",
       "452       1        1           0          0              0             3948   \n",
       "355       0        0           0          0              0             3813   \n",
       "266       1        1           2          0              0             4708   \n",
       "..      ...      ...         ...        ...            ...              ...   \n",
       "277       1        1           0          0              0             3103   \n",
       "9         1        1           1          0              0            12841   \n",
       "359       1        1           3          0              0             5167   \n",
       "192       1        1           0          1              0             6033   \n",
       "559       0        1           0          0              0             4180   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "90              2900.0       131.0             360.0             1.0   \n",
       "533                0.0       196.0             360.0             1.0   \n",
       "452             1733.0       149.0             360.0             0.0   \n",
       "355                0.0       116.0             180.0             1.0   \n",
       "266             1387.0       150.0             360.0             1.0   \n",
       "..                 ...         ...               ...             ...   \n",
       "277             1300.0        80.0             360.0             1.0   \n",
       "9              10968.0       349.0             360.0             1.0   \n",
       "359             3167.0       200.0             360.0             1.0   \n",
       "192                0.0       160.0             360.0             1.0   \n",
       "559             2306.0       182.0             360.0             1.0   \n",
       "\n",
       "     Property_Area  \n",
       "90               1  \n",
       "533              1  \n",
       "452              0  \n",
       "355              2  \n",
       "266              1  \n",
       "..             ...  \n",
       "277              2  \n",
       "9                1  \n",
       "359              1  \n",
       "192              2  \n",
       "559              1  \n",
       "\n",
       "[491 rows x 11 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90     1\n",
       "533    0\n",
       "452    0\n",
       "355    1\n",
       "266    1\n",
       "      ..\n",
       "277    1\n",
       "9      0\n",
       "359    1\n",
       "192    0\n",
       "559    1\n",
       "Name: Loan_Status, Length: 491, dtype: int32"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Decision Tree Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model=DecisionTreeClassifier()\n",
    "model.fit(x_train,y_train)\n",
    "y_prediction = model.predict(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0,\n",
       "       1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "454    1\n",
       "52     0\n",
       "536    1\n",
       "469    0\n",
       "55     1\n",
       "      ..\n",
       "337    1\n",
       "376    1\n",
       "278    1\n",
       "466    0\n",
       "303    1\n",
       "Name: Loan_Status, Length: 123, dtype: int32"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation of Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6910569105691057\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,y_prediction))\n",
    "#accuracy is lower than ideal and changing according to the tain test split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20, 13],\n",
       "       [25, 65]], dtype=int64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnf_matrix=metrics.confusion_matrix(y_test,y_prediction)\n",
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of Confusion Matrix for Decesion Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.heatmap(cnf_matrix ,annot=True)\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.ylabel(\"Actual Label\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Buliding Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joe\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logistic_regression=LogisticRegression()\n",
    "logistic_regression.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=logistic_regression.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "454    1\n",
       "52     0\n",
       "536    1\n",
       "469    0\n",
       "55     1\n",
       "      ..\n",
       "337    1\n",
       "376    1\n",
       "278    1\n",
       "466    0\n",
       "303    1\n",
       "Name: Loan_Status, Length: 123, dtype: int32"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation For Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[15 18]\n",
      " [ 2 88]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "matrix=metrics.confusion_matrix(y_test,y_pred)\n",
    "print (matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8373983739837398\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,y_pred))\n",
    "#here the accuracy is higher than decision tree algorithm "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.heatmap(matrix ,annot=True)\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.ylabel(\"Actual Label\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the Loan aprroval for a Applicant "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using Logistic regression as accuracy is higher \n",
    "#taking input data as \n",
    "#Gender :Male\n",
    "#Married:Yes             \n",
    "#Dependents:1           \n",
    "#Education:Graduate             \n",
    "#Self_Employed :No       \n",
    "#ApplicantIncome :8000      \n",
    "#CoapplicantIncome  :2000   \n",
    "#LoanAmount :130          \n",
    "#Loan_Amount_Term :24 months    \n",
    "#Credit_History  :0.0     \n",
    "#Property_Area  :Urban \n",
    "\n",
    "#putting data in numeric form w.r.t the conversion done before\n",
    "loanstatus=logistic_regression.predict([[1,1,1,0,0,8000,2000,130,24,0.0,2]])\n",
    "\n",
    "#putting data in numeric form w.r.t the convrsion before\n",
    "loanstatus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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