Loan_status.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4b6d8e5a",
   "metadata": {},
   "source": [
    "# LOAN_DATASET\n",
    "\n",
    "# Prediction of Loan Status Using Logistic Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f222d4b",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "The model is expected to predict whether a loan would be approved or not. We have 8 features in total out of which 7 are independent variables and 1 dependent variable. \n",
    "\n",
    "The model should predict whether a loan should be given or not, we decided to use the Logistic Regression Model since is a classification algorithm to predict binary outcome with Loan Status as the response and Gender, Married, Dependents, Education, CoapplicantIncome, Credit_History , Property_Area as the features. \n",
    "\n",
    "Our work is structured as follows :\n",
    "\n",
    "1. Data cleaning\n",
    "2. Data set Exploration\n",
    "3. Data visualization\n",
    "4. Model Building (Logistic Regression)\n",
    "5. Confusion Matrix and Accuracy\n",
    "6. Prediction\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46ce031a",
   "metadata": {},
   "source": [
    "The model was implemented with the important libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7ac9e8f",
   "metadata": {},
   "source": [
    "# Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b8ef19ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b49103bb",
   "metadata": {},
   "source": [
    "# Dataset Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c6d2fe",
   "metadata": {},
   "source": [
    "# 1. Reading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0d3ad2c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>LP001002</th>\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>LP001003</th>\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>LP001005</th>\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>LP001006</th>\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>LP001008</th>\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",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002978</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002979</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002983</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</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>LP002984</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</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>LP002990</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Gender Married Dependents     Education Self_Employed  \\\n",
       "Loan_ID                                                           \n",
       "LP001002    Male      No          0      Graduate            No   \n",
       "LP001003    Male     Yes          1      Graduate            No   \n",
       "LP001005    Male     Yes          0      Graduate           Yes   \n",
       "LP001006    Male     Yes          0  Not Graduate            No   \n",
       "LP001008    Male      No          0      Graduate            No   \n",
       "...          ...     ...        ...           ...           ...   \n",
       "LP002978  Female      No          0      Graduate            No   \n",
       "LP002979    Male     Yes         3+      Graduate            No   \n",
       "LP002983    Male     Yes          1      Graduate            No   \n",
       "LP002984    Male     Yes          2      Graduate            No   \n",
       "LP002990  Female      No          0      Graduate           Yes   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001002             5849                0.0         NaN             360.0   \n",
       "LP001003             4583             1508.0       128.0             360.0   \n",
       "LP001005             3000                0.0        66.0             360.0   \n",
       "LP001006             2583             2358.0       120.0             360.0   \n",
       "LP001008             6000                0.0       141.0             360.0   \n",
       "...                   ...                ...         ...               ...   \n",
       "LP002978             2900                0.0        71.0             360.0   \n",
       "LP002979             4106                0.0        40.0             180.0   \n",
       "LP002983             8072              240.0       253.0             360.0   \n",
       "LP002984             7583                0.0       187.0             360.0   \n",
       "LP002990             4583                0.0       133.0             360.0   \n",
       "\n",
       "          Credit_History Property_Area Loan_Status  \n",
       "Loan_ID                                             \n",
       "LP001002             1.0         Urban           Y  \n",
       "LP001003             1.0         Rural           N  \n",
       "LP001005             1.0         Urban           Y  \n",
       "LP001006             1.0         Urban           Y  \n",
       "LP001008             1.0         Urban           Y  \n",
       "...                  ...           ...         ...  \n",
       "LP002978             1.0         Rural           Y  \n",
       "LP002979             1.0         Rural           Y  \n",
       "LP002983             1.0         Urban           Y  \n",
       "LP002984             1.0         Urban           Y  \n",
       "LP002990             0.0     Semiurban           N  \n",
       "\n",
       "[614 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan = pd.read_csv(\"loan_dataset.csv\", index_col = 0)\n",
    "loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c6d1f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "411a91ba",
   "metadata": {},
   "source": [
    "# 2. Statistical Summary of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3ed40b3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76378a68",
   "metadata": {},
   "source": [
    "# 3. Finding the sum of missing values in the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60cc0e8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f62d0521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a18f783d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP002453</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>7085</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</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>LP001164</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4230</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112.0</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>LP002734</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6133</td>\n",
       "      <td>3906.0</td>\n",
       "      <td>324.0</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>LP002505</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4333</td>\n",
       "      <td>2451.0</td>\n",
       "      <td>110.0</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>LP001194</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2708</td>\n",
       "      <td>1167.0</td>\n",
       "      <td>97.0</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>LP001207</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2609</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002740</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002386</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>405.0</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>LP002188</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5124</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002537</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2083</td>\n",
       "      <td>3150.0</td>\n",
       "      <td>128.0</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": [
       "          Gender Married Dependents     Education Self_Employed  \\\n",
       "Loan_ID                                                           \n",
       "LP002453    Male      No          0      Graduate           Yes   \n",
       "LP001164  Female      No          0      Graduate            No   \n",
       "LP002734    Male     Yes          0      Graduate            No   \n",
       "LP002505    Male     Yes          0      Graduate            No   \n",
       "LP001194    Male     Yes          2      Graduate            No   \n",
       "LP001207    Male     Yes          0  Not Graduate           Yes   \n",
       "LP002740    Male     Yes         3+      Graduate            No   \n",
       "LP002386    Male      No          0      Graduate           NaN   \n",
       "LP002188    Male      No          0      Graduate            No   \n",
       "LP002537    Male     Yes          0      Graduate            No   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP002453             7085                0.0        84.0             360.0   \n",
       "LP001164             4230                0.0       112.0             360.0   \n",
       "LP002734             6133             3906.0       324.0             360.0   \n",
       "LP002505             4333             2451.0       110.0             360.0   \n",
       "LP001194             2708             1167.0        97.0             360.0   \n",
       "LP001207             2609             3449.0       165.0             180.0   \n",
       "LP002740             6417                0.0       157.0             180.0   \n",
       "LP002386            12876                0.0       405.0             360.0   \n",
       "LP002188             5124                0.0       124.0               NaN   \n",
       "LP002537             2083             3150.0       128.0             360.0   \n",
       "\n",
       "          Credit_History Property_Area Loan_Status  \n",
       "Loan_ID                                             \n",
       "LP002453             1.0     Semiurban           Y  \n",
       "LP001164             1.0     Semiurban           N  \n",
       "LP002734             1.0         Urban           Y  \n",
       "LP002505             1.0         Urban           N  \n",
       "LP001194             1.0     Semiurban           Y  \n",
       "LP001207             0.0         Rural           N  \n",
       "LP002740             1.0         Rural           Y  \n",
       "LP002386             1.0     Semiurban           Y  \n",
       "LP002188             0.0         Rural           N  \n",
       "LP002537             1.0     Semiurban           Y  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan1=loan.sample(10, random_state=0)\n",
    "loan1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f029fc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "loan=loan.dropna()\n",
    "# loan2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "372fe0e7",
   "metadata": {
    "scrolled": true
   },
   "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",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>LP001003</th>\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>LP001005</th>\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>LP001006</th>\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>LP001008</th>\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",
       "    <tr>\n",
       "      <th>LP001011</th>\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>267.0</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002978</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002979</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002983</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</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>LP002984</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</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>LP002990</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Gender Married Dependents     Education Self_Employed  \\\n",
       "Loan_ID                                                           \n",
       "LP001003    Male     Yes          1      Graduate            No   \n",
       "LP001005    Male     Yes          0      Graduate           Yes   \n",
       "LP001006    Male     Yes          0  Not Graduate            No   \n",
       "LP001008    Male      No          0      Graduate            No   \n",
       "LP001011    Male     Yes          2      Graduate           Yes   \n",
       "...          ...     ...        ...           ...           ...   \n",
       "LP002978  Female      No          0      Graduate            No   \n",
       "LP002979    Male     Yes         3+      Graduate            No   \n",
       "LP002983    Male     Yes          1      Graduate            No   \n",
       "LP002984    Male     Yes          2      Graduate            No   \n",
       "LP002990  Female      No          0      Graduate           Yes   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001003             4583             1508.0       128.0             360.0   \n",
       "LP001005             3000                0.0        66.0             360.0   \n",
       "LP001006             2583             2358.0       120.0             360.0   \n",
       "LP001008             6000                0.0       141.0             360.0   \n",
       "LP001011             5417             4196.0       267.0             360.0   \n",
       "...                   ...                ...         ...               ...   \n",
       "LP002978             2900                0.0        71.0             360.0   \n",
       "LP002979             4106                0.0        40.0             180.0   \n",
       "LP002983             8072              240.0       253.0             360.0   \n",
       "LP002984             7583                0.0       187.0             360.0   \n",
       "LP002990             4583                0.0       133.0             360.0   \n",
       "\n",
       "          Credit_History Property_Area Loan_Status  \n",
       "Loan_ID                                             \n",
       "LP001003             1.0         Rural           N  \n",
       "LP001005             1.0         Urban           Y  \n",
       "LP001006             1.0         Urban           Y  \n",
       "LP001008             1.0         Urban           Y  \n",
       "LP001011             1.0         Urban           Y  \n",
       "...                  ...           ...         ...  \n",
       "LP002978             1.0         Rural           Y  \n",
       "LP002979             1.0         Rural           Y  \n",
       "LP002983             1.0         Urban           Y  \n",
       "LP002984             1.0         Urban           Y  \n",
       "LP002990             0.0     Semiurban           N  \n",
       "\n",
       "[480 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan2=loan.dropna()\n",
    "loan2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1432e19d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan2.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd0d03b4",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be9e0576",
   "metadata": {},
   "source": [
    "We used the bivariate analysis in finding some kind of empirical relationship between two variables. Specifically the dependent variable (Loan_Status) and the independent variables (Gender, Married, Dependents, Education, CoapplicantIncome, Credit_History , Property_Area as the features)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b015ed7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "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(data=loan, x='Gender', hue='Loan_Status')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34be928e",
   "metadata": {},
   "source": [
    "There is no substancial difference between male and female approval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9592073a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Married', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=loan, x='Married', hue='Loan_Status')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff781ac1",
   "metadata": {},
   "source": [
    "Married applicants have a slightly higher chances of loan approval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "95155fea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Dependents', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=loan, x='Dependents', hue='Loan_Status')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d84f50a7",
   "metadata": {},
   "source": [
    "Applicant with not dependents has high chance of approval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f36b80f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Education', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=loan, x='Education', hue='Loan_Status')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00bb33f0",
   "metadata": {},
   "source": [
    "Graduate has high chance of loan approval compared to non-graduates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b9e7adc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Self_Employed', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=loan, x='Self_Employed', hue='Loan_Status')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcba001f",
   "metadata": {},
   "source": [
    "Self Employees have slight chance of loan approval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7208c056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ApplicantIncome', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=loan.sample(10, random_state=0), x='ApplicantIncome', hue='Loan_Status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "efa1174d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CoapplicantIncome', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "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(data=loan.sample(10), x='CoapplicantIncome', hue='Loan_Status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7dd26795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LoanAmount', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "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(data=loan.sample(10, random_state=0), x='LoanAmount', hue='Loan_Status')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8c654e0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Loan_Amount_Term', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "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(data=loan, x='Loan_Amount_Term', hue='Loan_Status')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ab779f56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Credit_History', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "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(data=loan, x='Credit_History', hue='Loan_Status')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e6ce1e15",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Property_Area', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "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(data=loan, x='Property_Area', hue='Loan_Status')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4822e1f8",
   "metadata": {},
   "source": [
    "# Conventing Categorical Variables to Numerical\n",
    "\n",
    "We convented the categorical data to numerical because logistic regression works only numerical values as input.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1f3d742f",
   "metadata": {
    "scrolled": true
   },
   "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",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>LP001003</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001005</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001006</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001008</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001011</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.0</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002978</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002979</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002983</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</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>LP002984</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</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>LP002990</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "Loan_ID                                                           \n",
       "LP001003       1        1           1          1              0   \n",
       "LP001005       1        1           0          1              1   \n",
       "LP001006       1        1           0          0              0   \n",
       "LP001008       1        0           0          1              0   \n",
       "LP001011       1        1           2          1              1   \n",
       "...          ...      ...         ...        ...            ...   \n",
       "LP002978       0        0           0          1              0   \n",
       "LP002979       1        1           4          1              0   \n",
       "LP002983       1        1           1          1              0   \n",
       "LP002984       1        1           2          1              0   \n",
       "LP002990       0        0           0          1              1   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001003             4583             1508.0       128.0             360.0   \n",
       "LP001005             3000                0.0        66.0             360.0   \n",
       "LP001006             2583             2358.0       120.0             360.0   \n",
       "LP001008             6000                0.0       141.0             360.0   \n",
       "LP001011             5417             4196.0       267.0             360.0   \n",
       "...                   ...                ...         ...               ...   \n",
       "LP002978             2900                0.0        71.0             360.0   \n",
       "LP002979             4106                0.0        40.0             180.0   \n",
       "LP002983             8072              240.0       253.0             360.0   \n",
       "LP002984             7583                0.0       187.0             360.0   \n",
       "LP002990             4583                0.0       133.0             360.0   \n",
       "\n",
       "          Credit_History  Property_Area  Loan_Status  \n",
       "Loan_ID                                               \n",
       "LP001003             1.0              0            0  \n",
       "LP001005             1.0              2            1  \n",
       "LP001006             1.0              2            1  \n",
       "LP001008             1.0              2            1  \n",
       "LP001011             1.0              2            1  \n",
       "...                  ...            ...          ...  \n",
       "LP002978             1.0              0            1  \n",
       "LP002979             1.0              0            1  \n",
       "LP002983             1.0              2            1  \n",
       "LP002984             1.0              2            1  \n",
       "LP002990             0.0              1            0  \n",
       "\n",
       "[480 rows x 12 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.replace({'Gender': {'Male': 1,'Female':0},'Married':{'No':0,'Yes':1}, 'Education':{'Graduate':1,'Not Graduate':0},'Self_Employed':{'No':0,'Yes':1}, 'Property_Area':{'Rural':0,'Semiurban':1,'Urban':2},'Dependents':{'1':1, '0':0, '2':2, '3':3, '3+':4}, 'Loan_Status' : {'Y':1, 'N':0} })"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "923c2ac0",
   "metadata": {},
   "source": [
    "# Visualizing Features Correlation via Heatmap\n",
    "\n",
    "The variables with darker color means their correlation is more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6f1ecade",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(8,6))\n",
    "sns.heatmap(loan1.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ec81d6e",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "47181caf",
   "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",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>LP001003</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001005</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>2</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001006</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001008</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001011</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002978</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002979</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002983</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002984</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP002990</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "Loan_ID                                                           \n",
       "LP001003       1        1           1          1              0   \n",
       "LP001005       1        1           0          1              1   \n",
       "LP001006       1        1           0          0              0   \n",
       "LP001008       1        0           0          1              0   \n",
       "LP001011       1        1           2          1              1   \n",
       "...          ...      ...         ...        ...            ...   \n",
       "LP002978       0        0           0          1              0   \n",
       "LP002979       1        1           4          1              0   \n",
       "LP002983       1        1           1          1              0   \n",
       "LP002984       1        1           2          1              0   \n",
       "LP002990       0        0           0          1              1   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001003             4583             1508.0       128.0             360.0   \n",
       "LP001005             3000                0.0        66.0             360.0   \n",
       "LP001006             2583             2358.0       120.0             360.0   \n",
       "LP001008             6000                0.0       141.0             360.0   \n",
       "LP001011             5417             4196.0       267.0             360.0   \n",
       "...                   ...                ...         ...               ...   \n",
       "LP002978             2900                0.0        71.0             360.0   \n",
       "LP002979             4106                0.0        40.0             180.0   \n",
       "LP002983             8072              240.0       253.0             360.0   \n",
       "LP002984             7583                0.0       187.0             360.0   \n",
       "LP002990             4583                0.0       133.0             360.0   \n",
       "\n",
       "          Credit_History  Property_Area Loan_Status  \n",
       "Loan_ID                                              \n",
       "LP001003             1.0              0           N  \n",
       "LP001005             1.0              2           Y  \n",
       "LP001006             1.0              2           Y  \n",
       "LP001008             1.0              2           Y  \n",
       "LP001011             1.0              2           Y  \n",
       "...                  ...            ...         ...  \n",
       "LP002978             1.0              0           Y  \n",
       "LP002979             1.0              0           Y  \n",
       "LP002983             1.0              2           Y  \n",
       "LP002984             1.0              2           Y  \n",
       "LP002990             0.0              1           N  \n",
       "\n",
       "[480 rows x 12 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan1=loan.replace({'Gender': {'Male': 1,'Female':0},'Married':{'No':0,'Yes':1}, 'Education':{'Graduate':1,'Not Graduate':0},'Self_Employed':{'No':0,'Yes':1}, 'Property_Area':{'Rural':0,'Semiurban':1,'Urban':2},'Dependents':{'1':1, '0':0, '2':2, '3':3, '3+':4} })\n",
    "loan1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26cf317e",
   "metadata": {},
   "source": [
    "# Prediction of Loan Status Using Logistics Regression\n",
    "\n",
    "We decided to use logistic regression because our response variable is a categorical variable. linear regression is not appropriate for our data set since our response is qualitative.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3f41d53",
   "metadata": {},
   "source": [
    "Defining features and the response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "06c0e47b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=loan1[[\"Gender\",\"Married\",\"Dependents\",\"Education\",\"CoapplicantIncome\",\"Credit_History\",\"Property_Area\"]].values\n",
    "y=loan1[\"Loan_Status\"].values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db5667ac",
   "metadata": {},
   "source": [
    "Splitting the feature and response set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9fa1816b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaa598f9",
   "metadata": {},
   "source": [
    "# Logistic Regression\n",
    "\n",
    "Training and Testing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "56f81f88",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = LogisticRegression(random_state = 0, solver='lbfgs', multi_class='auto')\n",
    "log_reg.fit(X_train, y_train)\n",
    "y_pred = log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "994e9f76",
   "metadata": {},
   "source": [
    "Predicting probabilities "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "56a90b57",
   "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>Test</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>N</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Test Predicted     N     Y\n",
       "72     Y         Y  0.12  0.88\n",
       "6      Y         Y  0.16  0.84\n",
       "83     N         N  0.66  0.34\n",
       "67     Y         Y  0.15  0.85\n",
       "30     Y         Y  0.18  0.82\n",
       "51     N         Y  0.15  0.85\n",
       "103    N         Y  0.15  0.85\n",
       "17     Y         Y  0.18  0.82\n",
       "85     N         Y  0.14  0.86\n",
       "5      N         Y  0.17  0.83\n",
       "66     N         Y  0.21  0.79\n",
       "116    Y         Y  0.21  0.79\n",
       "104    Y         Y  0.14  0.86\n",
       "45     Y         Y  0.21  0.79\n",
       "3      N         Y  0.08  0.92\n",
       "19     Y         Y  0.14  0.86\n",
       "79     N         Y  0.26  0.74\n",
       "57     N         N  0.68  0.32\n",
       "33     N         Y  0.24  0.76\n",
       "87     Y         Y  0.23  0.77"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs_y=log_reg.predict_proba(X_test) \n",
    "probs_y = np.round(probs_y, 2)\n",
    "result = pd.DataFrame(probs_y, columns= np.unique(loan1.Loan_Status))\n",
    "predictedt_index = [np.argmax(result.iloc[i,:]) for i in range(result.shape[0])]\n",
    "predicted_Status =[np.unique(loan1.Loan_Status)[i] for i in predictedt_index]\n",
    "result.insert(loc=0, column=\"Test\", value=y_test)\n",
    "result.insert(loc=1, column ='Predicted', value=predicted_Status)\n",
    "result.sample(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aa849ca",
   "metadata": {},
   "source": [
    "Computing the confusion matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "521c2fb3",
   "metadata": {
    "scrolled": true
   },
   "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": [
    "con_mat = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(con_mat, annot=True, annot_kws={\"size\": 20},cmap=\"summer_r\", xticklabels=np.unique(loan1.Loan_Status),\n",
    "           yticklabels=np.unique(loan1.Loan_Status))\n",
    "plt.title(\"Confusion Matrix of the classifier\")\n",
    "plt.ylabel('Actual labels')\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88e40581",
   "metadata": {},
   "source": [
    "The accuracy of the model is 78%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "53f3ffea",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7833333333333333\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "295c7cae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4.82693262e-01,  3.19078092e-01,  2.15233986e-01,\n",
       "        -2.25854107e-01, -6.79665108e-05,  2.74063044e+00,\n",
       "        -3.06936804e-02]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3634d147",
   "metadata": {},
   "source": [
    "We predicted the model with the following assumptions:\n",
    "\n",
    "Gender : Female,Married : Yes , Dependents : 1, Education : graduate, CoapplicantIncome : 1508, Credit_History : Yes, Property_Area : Semiurban."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a67c1368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Y'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_testn = [[0,1,1,1,1508.0,1,1]]\n",
    "y_pred = log_reg.predict(X_testn)\n",
    "y_pred"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}