loan-eligibility-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loan Approval Prediction Machine Learning\n",
    "\n",
    "### Introduction\n",
    "In this notebook, we are going to predict whether a person is eligible for loan or not. The dataset is from Loan Approval Prediction Hackathon hosted by Analytics Vidhya.\n",
    "\n",
    "### Problem Statement\n",
    "Dream Housing Finance company deals in all kinds of home loans. They have a presence across all urban, semi-urban and rural areas. The customer first applies for a home loan and after that, the company validates the customer eligiblity for the loan.\n",
    "\n",
    "The company wants to automate the loan eligibility process (real-time) based on customer detail provided while filling out online application forms. These detials are Gender, Martial status, Education, number of dependents, Income, Loan Amount, Credit history, and others.\n",
    "\n",
    "This is a Binary Classification problem in which we need to predict our Target label which is `Loan Status`.\n",
    "\n",
    "Loan status can have two values: Yes or No.\n",
    "\n",
    "Yes: if the loan is approved\n",
    "\n",
    "No: if the loan is disapproved\n",
    "\n",
    "So using the training dataset we will train our model and try to predict our target column that is `Loan Status` on the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_train = pd.read_csv('Data/train.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_train.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shape of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Shape:  (614, 13)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Shape: \", loan_train.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             601 non-null    object \n",
      " 2   Married            611 non-null    object \n",
      " 3   Dependents         599 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      582 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         592 non-null    float64\n",
      " 9   Loan_Amount_Term   600 non-null    float64\n",
      " 10  Credit_History     564 non-null    float64\n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "loan_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dataset Information\n",
    "- Loan_ID: Unqiue Loan ID\n",
    "- Gender: Male/Female\n",
    "- Married: Applicant married (Y/N)\n",
    "- Dependents: Number of dependents\n",
    "- Education: Application Education (Graduate/Under Graduate)\n",
    "- Self_Employed: Self employed (Y/N)\n",
    "- ApplicantIncome: Applicant income\n",
    "- CoappliantIncome: Coappliant income\n",
    "- LoanAmount: Loan amount in thousands\n",
    "- Loan_Amount_Term: Term of loan in months\n",
    "- Credit_History: Credit history meets guidelines\n",
    "- Property_Area: Urban/Semi Urban/Rural\n",
    "- Loan_Status: (Target) Loan Approved (Y/N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Understanding the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be a lot of missing values in multiple columns. Let's impute the Mode values in `Gender, Married, Dependents, Self_Employed and Credit_History`\n",
    "\n",
    "Imputing Mode values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_train['Gender'].fillna(loan_train['Gender'].mode()[0],inplace=True)\n",
    "loan_train['Married'].fillna(loan_train['Married'].mode()[0],inplace=True)\n",
    "loan_train['Dependents'].fillna(loan_train['Dependents'].mode()[0],inplace=True)\n",
    "loan_train['Self_Employed'].fillna(loan_train['Self_Employed'].mode()[0],inplace=True)\n",
    "loan_train['Credit_History'].fillna(loan_train['Credit_History'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will fill the missing values of `LoanAmount` and `Loan_Amount_Term` by mean value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_train['LoanAmount'].fillna(loan_train['LoanAmount'].mean(),inplace=True)\n",
    "loan_train['Loan_Amount_Term'].fillna(loan_train['Loan_Amount_Term'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's convert Categorical columns into numercial values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_train.replace({\n",
    "    'Married':{'No':0,'Yes':1},\n",
    "    'Gender':{'Male':1,'Female':0},\n",
    "    'Education':{'Not Graduate':0,'Graduate':1},\n",
    "    'Self_Employed':{'No':0,'Yes':1},\n",
    "    'Property_Area':{\"Rural\":0,\"Semiurban\":1,\"Urban\":2},\n",
    "    'Loan_Status': {'Y':1,'N':0},\n",
    "    'Dependents':{'0':0,'1':1,'2':2,'3+':3}\n",
    "},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             614 non-null    int64  \n",
      " 2   Married            614 non-null    int64  \n",
      " 3   Dependents         614 non-null    int64  \n",
      " 4   Education          614 non-null    int64  \n",
      " 5   Self_Employed      614 non-null    int64  \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         614 non-null    float64\n",
      " 9   Loan_Amount_Term   614 non-null    float64\n",
      " 10  Credit_History     614 non-null    float64\n",
      " 11  Property_Area      614 non-null    int64  \n",
      " 12  Loan_Status        614 non-null    int64  \n",
      "dtypes: float64(4), int64(8), object(1)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "loan_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 367 entries, 0 to 366\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            367 non-null    object \n",
      " 1   Gender             367 non-null    int64  \n",
      " 2   Married            367 non-null    int64  \n",
      " 3   Dependents         367 non-null    int64  \n",
      " 4   Education          367 non-null    int64  \n",
      " 5   Self_Employed      367 non-null    int64  \n",
      " 6   ApplicantIncome    367 non-null    int64  \n",
      " 7   CoapplicantIncome  367 non-null    int64  \n",
      " 8   LoanAmount         367 non-null    float64\n",
      " 9   Loan_Amount_Term   367 non-null    float64\n",
      " 10  Credit_History     367 non-null    float64\n",
      " 11  Property_Area      367 non-null    int64  \n",
      "dtypes: float64(3), int64(8), object(1)\n",
      "memory usage: 34.5+ KB\n"
     ]
    }
   ],
   "source": [
    "loan_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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>128.000000</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>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>66.000000</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>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>120.000000</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>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>141.000000</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>...</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>609</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>71.000000</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>610</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.000000</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>611</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>253.000000</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>612</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>187.000000</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>613</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>133.000000</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>614 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "0         1        0           0          1              0             5849   \n",
       "1         1        1           1          1              0             4583   \n",
       "2         1        1           0          1              1             3000   \n",
       "3         1        1           0          0              0             2583   \n",
       "4         1        0           0          1              0             6000   \n",
       "..      ...      ...         ...        ...            ...              ...   \n",
       "609       0        0           0          1              0             2900   \n",
       "610       1        1           3          1              0             4106   \n",
       "611       1        1           1          1              0             8072   \n",
       "612       1        1           2          1              0             7583   \n",
       "613       0        0           0          1              1             4583   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                  0.0  146.412162             360.0             1.0   \n",
       "1               1508.0  128.000000             360.0             1.0   \n",
       "2                  0.0   66.000000             360.0             1.0   \n",
       "3               2358.0  120.000000             360.0             1.0   \n",
       "4                  0.0  141.000000             360.0             1.0   \n",
       "..                 ...         ...               ...             ...   \n",
       "609                0.0   71.000000             360.0             1.0   \n",
       "610                0.0   40.000000             180.0             1.0   \n",
       "611              240.0  253.000000             360.0             1.0   \n",
       "612                0.0  187.000000             360.0             1.0   \n",
       "613                0.0  133.000000             360.0             0.0   \n",
       "\n",
       "     Property_Area  Loan_Status  \n",
       "0                2            1  \n",
       "1                0            0  \n",
       "2                2            1  \n",
       "3                2            1  \n",
       "4                2            1  \n",
       "..             ...          ...  \n",
       "609              0            1  \n",
       "610              0            1  \n",
       "611              2            1  \n",
       "612              2            1  \n",
       "613              1            0  \n",
       "\n",
       "[614 rows x 12 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_train.drop(\"Loan_ID\",axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Saurab\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Saurab\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Saurab\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Saurab\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Saurab\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Saurab\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Saurab\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Saurab\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Dependents', ylabel='count'>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1152x720 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,4,figsize=(16,10))\n",
    "\n",
    "sns.countplot('Loan_Status',data=loan_train,ax=ax[0][0])\n",
    "sns.countplot('Gender',data=loan_train,ax=ax[0][1])\n",
    "sns.countplot('Married',data=loan_train,ax=ax[0][2])\n",
    "sns.countplot('Education',data=loan_train,ax=ax[0][3])\n",
    "sns.countplot('Self_Employed',data=loan_train,ax=ax[1][0])\n",
    "sns.countplot('Property_Area',data=loan_train,ax=ax[1][1])\n",
    "sns.countplot('Credit_History',data=loan_train,ax=ax[1][2])\n",
    "sns.countplot('Dependents',data=loan_train,ax=ax[1][3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Loan_Status', ylabel='ApplicantIncome'>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Loan_Status',y='ApplicantIncome',data=loan_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Loan_Status', ylabel='CoapplicantIncome'>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Loan_Status',y='CoapplicantIncome',data=loan_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plt.figure(figsize=(18, 6))\n",
    "plt.title(\"Relation Between Applicant Income vs Loan Amount (in thousands)\")\n",
    "\n",
    "plt.grid()\n",
    "plt.scatter(loan_train['ApplicantIncome'] , loan_train['LoanAmount'], c='k', marker='x')\n",
    "plt.xlabel(\"Applicant Income\")\n",
    "plt.ylabel(\"Loan Amount\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x26a4427b9a0>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 784.75x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Gender',y='LoanAmount',data=loan_train,kind='box',hue='Loan_Status',col='Married')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loan amount is slightly higher for married than non-married."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x26a4427b490>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1144.75x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Gender',y='CoapplicantIncome',data=loan_train,kind='box',hue='Loan_Status',col='Property_Area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Male have higher Co-applicant income than females in all three property areas.\n",
    "\n",
    "#### Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from filecmp import cmp\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "corr_matrix = loan_train.corr()\n",
    "\n",
    "sns.heatmap(corr_matrix,annot=True,cmap='coolwarm',fmt='.1f',linewidths=.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building Machine Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = loan_train.drop(['Loan_ID','Loan_Status'],axis=1)\n",
    "y = loan_train['Loan_Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (491, 11)\n",
      "X_test shape: (123, 11)\n",
      "y_train shape: (491,)\n",
      "y_test shape: (123,)\n"
     ]
    }
   ],
   "source": [
    "from cgi import test\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "print(\"X_train shape:\",X_train.shape)\n",
    "print(\"X_test shape:\",X_test.shape)\n",
    "print(\"y_train shape:\",y_train.shape)\n",
    "print(\"y_test shape:\",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>77</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>3022.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3762</td>\n",
       "      <td>1666.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4652</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2214</td>\n",
       "      <td>1398.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "77        1        1           1          1              1             1000   \n",
       "4         1        0           0          1              0             6000   \n",
       "306       0        0           0          1              0             3762   \n",
       "446       1        1           2          0              0             4652   \n",
       "117       1        1           1          1              0             2214   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "77              3022.0       110.0             360.0             1.0   \n",
       "4                  0.0       141.0             360.0             1.0   \n",
       "306             1666.0       135.0             360.0             1.0   \n",
       "446                0.0       110.0             360.0             1.0   \n",
       "117             1398.0        85.0             360.0             1.0   \n",
       "\n",
       "     Property_Area  \n",
       "77               2  \n",
       "4                2  \n",
       "306              0  \n",
       "446              0  \n",
       "117              2  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Saurab\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: 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": [
       "0.8048780487804879"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from random import random\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "log_clf = LogisticRegression(random_state=0)\n",
    "log_clf.fit(X_train,y_train)\n",
    "\n",
    "y_pred = log_clf.predict(X_test)\n",
    "accuracy_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With *Logistic Rergression*, we get an accuracy score of 80%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVgAAAD3CAYAAABYUUzPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAU9ElEQVR4nO3dfXRU9Z3H8c9NIgKTBLRY24qwRCM9qVUOphN8IGJLjHVrtSx6hW7UIiqUPRgLNeEpYQUJDyVbTVFYut22xMjtUaTudj1biQ/BRKeUFlpTq1tFTuWpa6UlGTHMw90/3I6gOHcG5pdJLu+X557jnYfffP/x4y/f+/vda7mu6woAkHE52S4AAPyKgAUAQwhYADCEgAUAQwhYADAkz+Tg14641uTw6Kc+m1OY7RLQBzW+ufGkx4i8/UbKnz1tWNFJ/54XowELAL0qHst2BccgYAH4hxvPdgXHIGAB+EecgAUAI1xmsABgSCya7QqOQcAC8I8MXeSKRCKqra3Vnj17lJOToyVLligvL0+1tbWyLEvFxcWqr69XTk7yla4ELAD/yFCL4Pnnn1c0GtXGjRvV3t6u7373u4pEIqqurlZZWZnq6urU2tqqioqKpOMQsAD8I42LXI7jyHGcxLlt27JtW5I0atQoxWIxxeNxdXd3Ky8vTzt27FAwGJQklZeXq729nYAFcOpI5yLX0YH6YYMHD9aePXv05S9/WQcPHtTatWu1bds2WZYlSQoEAurq6vL8DQIWgH9kaJnWD3/4Q11xxRWaM2eO9u3bp1tvvVWRSCTxfjgcVmGh945E7kUAwD9ikdSPJAoLC1VQUCBJGjJkiKLRqEpKShQKhSRJbW1tKi0t9SyHGSwA/8jQRa7bbrtN8+fP19SpUxWJRHTPPffowgsv1KJFi9TY2KiioiJVVlZ6jkPAAvCPDLUIAoGAHnjggY+83tzcnNY4BCwA/2AnFwAYwr0IAMAMN5784lVvI2AB+AczWAAwhB4sABjCEw0AwBBmsABgCD1YADCEG24DgCHMYAHADNflIhcAmMEMFgAMYRUBABjCDBYADGEVAQAYQosAAAyhRQAAhhCwAGBIhloEmzZt0hNPPCFJ6unp0SuvvKKWlhYtW7ZMlmWpuLhY9fX1yslJ/txYnioLwD9i0dSPJCZNmqQNGzZow4YN+tznPqeFCxdqzZo1qq6uVktLi1zXVWtrq2c5BCwA/4jHUz4cx9GkSZMSh+M4Hxnut7/9rf7whz/Itm11dnYqGAxKksrLy9XR0eFZDi0CAP6RRovAtm3Ztp30M+vWrdOsWbPeH9p1ZVmWpPefOtvV1eX5GwQsAP/I4EWuQ4cO6Y033tC4ceMk6Zh+azgcVmFhoecYtAgA+EcaLQIv27Zt02WXXZY4LykpUSgUkiS1tbWptLTUcwwCFoB/uG7qh4ddu3Zp+PDhifOamho1NTXJtm1FIhFVVlZ6jkGLAIB/RDO3VXb69OnHnI8aNUrNzc1pjUHAAvAPtsoCgCHs5AIAQ1LorfYmAhaAfzCDBQBDCFgAMMON8dBDADCDGSwAGMIyLQAwJM4qAgAwgxYBABjCRa5Tx+gxo/WNed9QrV2rIZ8YotkrZqtgSIFycnP0nXu+o/2792e7RPSinLxc3bxyhs4cfpbyBuTp6e89oc4t2yVJY796ua64rVIPTqrLcpX9HDPYU8PkGZP1xUlf1HvvvidJmjZ/mp7b/Jy2/udWXXTpRTr3vHMJ2FPMJV+7Qu/+pUst31qjwUPzNedny9W5Zbs+UzJSQfuqxM2ccRL6WA825dsVxvvY/xn6un2792npnUsT5yWlJRr2qWG6v+V+XXXDVfrNi7/JYnXIhp0/e0lPrf5J4jwei2nw0Hx9pWaKfnrfj7JYmY+48dSPXpA0YP/4xz/qm9/8psrLyzVx4kRNmDBBd955p3bt2tUrxfVn7U+1K3rUrdPOHn62uv/arQVTF+hPe/+kG2femMXqkA1H3u1RT/g9nR4YqNsevkdPrf6J7JV3afN9P9Z74feyXZ4/xN3Uj16QtEWwYMECzZkzRxdffHHitR07dmjevHnauHGj8eL85NDBQ3rp6ZckSaEtId367VuzXBGyYeinP6FvrPuW2jc8rbff3K+z/u5Tmnz/dJ12+mk6+/xzdEPdLdp834+zXWa/5faxv7STBuyRI0eOCVdJGjNmjMl6fOt3v/ydvvDFL+iZTc/o82Wf1+7Xdme7JPSy/GFDdNeG+dpU9+/6n46XJUkrr/62JOmM4WfplqbZhOvJ6k+rCEaPHq158+Zp/PjxKigoUDgc1vPPP6/Ro0f3Vn2+8f0l39fdK+/Wtf94rd7telcrZ6/MdknoZRNn3aBBQwKqmD1JFbMnSZLW39qgSE8ky5X5SB+7yGW57sffQNF1XW3ZskXbt29Xd3e38vPzNXbsWFVUVKR0xfPaEddmtFj4w2dzvJ/GiVNP45sn33YML56S8mcDix9N+v66dev0zDPPKBKJaMqUKQoGg6qtrZVlWSouLlZ9ff0xT5o9nqQzWMuyVFFRoYqKipSLBoCsydAMNhQK6de//rUeffRRHT58WD/4wQ/U0NCg6upqlZWVqa6uTq2trZ7ZyFNlAfhHhpZpvfDCC7rgggs0a9YszZgxQxMmTFBnZ6eCwaAkqby8XB0dHZ7lsNEAgH+kMYN1HEeO4yTObduWbduSpIMHD2rv3r1au3at3nrrLc2cOVOu6yZao4FAQF1dXZ6/QcAC8A03mvoqgqMD9cOGDh2qoqIiDRgwQEVFRTr99NO1f/8HOy/D4bAKC72vJdAiAOAfGdpocMkll2jr1q1yXVcHDhzQ4cOHdemllyoUCkmS2traVFpa6lkOM1gA/pGhLbBXXXWVtm3bpsmTJ8t1XdXV1Wn48OFatGiRGhsbVVRUpMrKSs9xCFgA/pHBdbD33nvvR15rbm5OawwCFoBvuH1sowEBC8A/0rjI1RsIWAD+wQwWAAwhYAHAjCS3VskKAhaAfzCDBQBDCFgAMMON9qMnGgBAv9K38pWABeAfbDQAAFMIWAAwhBYBAJhBiwAADHGjBCwAmEGLAADMyND9tjOGgAXgHwQsAJjBDBYADHGjmRvrhhtuUEFBgSRp+PDhmjFjhmpra2VZloqLi1VfX6+cnOTPjSVgAfhGpmawPT09kqQNGzYkXpsxY4aqq6tVVlamuro6tba2qqKiIuk4PLYbgG+48dSPZH7/+9/r8OHDmjZtmm655Rbt2LFDnZ2dCgaDkqTy8nJ1dHR41sMMFoB/uFbKH3UcR47jJM5t25Zt25KkgQMH6vbbb9eNN96oN998U3fccYdc15VlvT9+IBBQV1eX528QsAB8I50WwdGB+mGjRo3SyJEjZVmWRo0apaFDh6qzszPxfjgcVmFhoedv0CIA4Btu3Er5SOaxxx7T8uXLJUkHDhxQd3e3Lr/8coVCIUlSW1ubSktLPethBgvAN+Kx1FsEyUyePFnz5s3TlClTZFmWli1bpjPOOEOLFi1SY2OjioqKVFlZ6TkOAQvANzK1imDAgAFavXr1R15vbm5OaxwCFoBveP3p39sIWAC+0cee2k3AAvAPZrAAYEimLnJlCgELwDeYwQKAIW4aO7l6AwELwDe4XSEAGBJnBgsAZtAiAABDWEUAAIawigAADKEHCwCG0IMFAEO4FwEAGEKLAAAMiXORCwDMOKVmsD/fv9Pk8Oinfrp3a7ZLgE/1tYtcPPQQgG/EXSvlIxV//vOfdeWVV+r111/X7t27NWXKFE2dOlX19fWKx71vfEDAAvANN43DSyQSUV1dnQYOHChJamhoUHV1tVpaWuS6rlpbWz3HIGAB+EYsnpPy4WXFihW6+eab9clPflKS1NnZqWAwKEkqLy9XR0eH5xgELADfiKdxOI6jSZMmJQ7HcRLjbNq0SWeeeabGjx+feM11XVnW+62FQCCgrq4uz3pYRQDAN1ylfpHLtm3Ztn3c9x5//HFZlqUXX3xRr7zyimpqavTOO+8k3g+HwyosLPT8DQIWgG/EM7ST65FHHkn8e1VVlRYvXqxVq1YpFAqprKxMbW1tGjdunOc4tAgA+EZcVspHumpqatTU1CTbthWJRFRZWen5Hct1ze3ezRtwjqmh0Y8dZh0sjuO0YUUnPUbr2cf/k/94vnTA8f7QSaJFAMA3YicwMzWJgAXgG33smYcELAD/IGABwJB0lmn1BgIWgG/0sbsVErAA/ONEll+ZRMAC8I1Ytgv4EAIWgG/ELWawAGBEH3vmIQELwD9YpgUAhrCKAAAMYassABjCDBYADKEHCwCGsIoAAAyhRQAAhtAiAABDYsxgAcAMZrAAYEimAjYWi2nhwoXatWuXcnNz1dDQINd1VVtbK8uyVFxcrPr6euXkJH9uLAELwDcytYrg2WeflSRt3LhRoVAoEbDV1dUqKytTXV2dWltbVVFRkXQcAhaAb6SzisBxHDnOB0+WtW1btv3+U2knTpyoCRMmSJL27t2rYcOG6bnnnlMwGJQklZeXq729nYAFcOpIp0VwdKAeT15enmpqavT000/rwQcf1LPPPivr/2+HGAgE1NXV5fkbBCwA38j0DbdXrFihuXPn6qabblJPT0/i9XA4rMLCQs/vJ+/QAkA/ErdSP5LZvHmz1q1bJ0kaNGiQLMvShRdeqFAoJElqa2tTaWmpZz3MYAH4RqZWEVx99dWaN2+evv71rysajWr+/Pk677zztGjRIjU2NqqoqEiVlZWe4xCwAHwjU6sIBg8erAceeOAjrzc3N6c1DgELwDfifex2LwQsAN/gqbIAYAhbZQHAEG5XCACG0IMFAEP6VrwSsAB8hB4sABgS62NzWAIWgG8wgwUAQ7jIBQCG9K14JWAB+AgtAgAwhItcAGAIPVgAMKRvxSsBa5xlWfpeU4MuvqhEPT09unPGt/X6629muyxkQSQa1YKlq7Vn3wHl5uRoce3dcuNxLV75oFxXGn3+KM2/Z6Zyc3OzXWq/xQz2FHP99ddo4MDTdUX5V1UWHKtVK+s06R+mZbssZMHWF7cpFovpkXWN6vjFr/Tguh8pHo/r7rtuU+mYz2vB0tV69oWXNPHKy7Ndar/FRa5TzBWXBfXfP3//GeuhX/xKl4y9KMsVIVtGnnuOotGY4vG4wuF3lZeXqxX185Sbm6tIJKK33zmoT5x5RrbL7NdcZrCnloLCfB366weP943F4srNzVUs1tduDQzTBg8apL37D+i6qXfq4F/+qjWr/lm5ubnau/+Apt89X/mBgEaNGJ7tMvu1TK0iiEQimj9/vvbs2aMjR45o5syZOv/881VbWyvLslRcXKz6+nrl5CR/bixPlTWs61C38gvyE+c5OTmE6ylqg/OELgteop9t/L4e/9FDWrB0tXp6jugznzpb/+X8m2664VqtfPBfs11mvxZP40jmySef1NChQ9XS0qL169dryZIlamhoUHV1tVpaWuS6rlpbWz3rSTqDraqqUiQSOeY113VlWZY2btzoOTik9he36St/X6HHHvsPlQXH6uWXX8l2SciSwoJ85eW9/5/ckMICRaNRzbp3sRbNnaWR556jwOBBnjMiJBd3U5/BOo4jx3ES57Zty7ZtSdI111xzzFNjc3Nz1dnZqWAwKEkqLy9Xe3u7Kioqkv5G0oCdO3euFi5cqDVr1nBl8wRt3vyUJn6pXFuf/6ksy9Ltd9yT7ZKQJbfYX9Oihn/RLTPnKhKN6u67btNnPn22FtzfqNPy8jRw4Om6r7Y622X2a+k0CI4O1A8LBAKSpO7ubs2ePVvV1dVasWKFLMtKvN/V1XXc7x4tacBefPHFuv766/Xqq696JjWOz3Vdzfqn2myXgT5g8OBBWr1k/kdeb167OgvV+FMml2nt27dPs2bN0tSpU3Xddddp1apViffC4bAKCws9x/C8yDV9+vSTqxIAekmmVhG8/fbbmjZtmurq6nTppZdKkkpKShQKhVRWVqa2tjaNGzfOcxxWEQDwjWiGAnbt2rU6dOiQHnroIT300EOSpAULFmjp0qVqbGxUUVHRMT3aj2O5bhpd4TTlDTjH1NDoxw7v3ZrtEtAHnTas6KTHmDzyqyl/9rHdT57073lhBgvAN9jJBQCGGPyD/IQQsAB8g5u9AIAh3HAbAAxhBgsAhtCDBQBDWEUAAIZwP1gAMIQeLAAYEnP7VpOAgAXgG7QIAMCQdG643RsIWAC+0bfilYAF4CNc5AIAQwhYADCEVQQAYAirCADAkL52LwIewg7AN+JyUz5SsXPnTlVVVUmSdu/erSlTpmjq1Kmqr69XPO7djiBgAfiG67opH17Wr1+vhQsXqqenR5LU0NCg6upqtbS0yHVdtba2eo5BwALwjZjiKR9eRowYoaampsR5Z2engsGgJKm8vFwdHR2eY9CDBeAb6ezkchxHjuMkzm3blm3bifPKykq99dZbiXPXdWVZliQpEAioq6vL8zcIWAC+kc4qgg8HqpecnA/+4A+HwyosLPT+TsqjA0AfF3fdlI90lZSUKBQKSZLa2tpUWlrq+R0CFoBvuGn8k66amho1NTXJtm1FIhFVVlZ6fsdyDS4cyxtwjqmh0Y8d3rs12yWgDzptWNFJj3HBWd6zyr957X9/edK/54UeLADfYKssABjCVlkAMMRlBgsAZnC7QgAwpK/d7IWABeAbzGABwJBYCne46k0ELADfYBUBABhCDxYADKEHCwCGMIMFAEO4yAUAhtAiAABDaBEAgCEnciNtkwhYAL7BOlgAMIQZLAAYEud2hQBgRqYucsXjcS1evFivvvqqBgwYoKVLl2rkyJFpj8NDDwH4huu6KR/JbNmyRUeOHJHjOJozZ46WL19+QvUwgwXgG5nqwG7fvl3jx4+XJI0ZM0Yvv/zyCY1jNGCjR/aYHB4AjpFO5jiOI8dxEue2bcu2bUlSd3e38vPzE+/l5uYqGo0qLy+9yGQGC+CUdHSgflh+fr7C4XDiPB6Ppx2uEj1YAPiIsWPHqq2tTZK0Y8cOXXDBBSc0juX2tb1lAJBlf1tF8Nprr8l1XS1btkznnXde2uMQsABgCC0CADCEgAUAQwhYADCEgDUsHo+rrq5Otm2rqqpKu3fvznZJ6CN27typqqqqbJcBg1gHa9jRW+527Nih5cuX6+GHH852Wciy9evX68knn9SgQYOyXQoMYgZrWKa23MFfRowYoaampmyXAcMIWMM+bssdTm2VlZUntDMI/QsBa1imttwB6H8IWMMyteUOQP/DVMqwiooKtbe36+abb05suQNwamCrLAAYQosAAAwhYAHAEAIWAAwhYAHAEAIWAAwhYAHAEAIWAAz5PwgLmt0/U6msAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "\n",
    "sns.heatmap(cm,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classfier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=0)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier(n_estimators=10,criterion='entropy',random_state=0)\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7723577235772358"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_rf_pred = classifier.predict(X_test)\n",
    "accuracy_score(y_test,y_rf_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,y_rf_pred)\n",
    "\n",
    "sns.heatmap(cm,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "svm_clss = svm.SVC(kernel='linear')\n",
    "svm_clss.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7886178861788617"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = svm_clss.predict(X_test)\n",
    "accuracy_score(y_test,pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,y_rf_pred)\n",
    "\n",
    "sns.heatmap(cm,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "Among the different algorithms we have used, LogisticRegression seems to perfom better among LogisticRegression, SVM and RandomForest with an accuracy score of 80%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.5 ('base')",
   "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": "3.8.5"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}