Loan Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "59081c83",
   "metadata": {},
   "source": [
    "#### Steps to solve ML projects\n",
    "\n",
    "1. start\n",
    "2. data selection \n",
    "3. data description : A story of data is all about and the features present in the data\n",
    "4. Performing both statistical and graphical data analysis\n",
    "5. data transformation and derivation ofj new attributes, if necessary\n",
    "6. selection of machine learning algorithms based on the patterns observed in the 'EDA'\n",
    "7. data standardization and nomalization\n",
    "8. train test datasets\n",
    "9. model training\n",
    "10. model accuracy : both train and test dataset\n",
    "11. hyper parameter tuning to achieve a better accuracy\n",
    "12. saving the created model\n",
    "13. Deployment strategies for model\n",
    "14. production deployment and testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "832aad95",
   "metadata": {},
   "source": [
    "#### Importing required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "315418b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7834bf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the dataset into pandas dataframe\n",
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1f2c8734",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print first 5 rows of the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "336db2a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>609</th>\n",
       "      <td>LP002978</td>\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>610</th>\n",
       "      <td>LP002979</td>\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>611</th>\n",
       "      <td>LP002983</td>\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>612</th>\n",
       "      <td>LP002984</td>\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>613</th>\n",
       "      <td>LP002990</td>\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",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender Married Dependents Education Self_Employed  \\\n",
       "609  LP002978  Female      No          0  Graduate            No   \n",
       "610  LP002979    Male     Yes         3+  Graduate            No   \n",
       "611  LP002983    Male     Yes          1  Graduate            No   \n",
       "612  LP002984    Male     Yes          2  Graduate            No   \n",
       "613  LP002990  Female      No          0  Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "609             1.0         Rural           Y  \n",
       "610             1.0         Rural           Y  \n",
       "611             1.0         Urban           Y  \n",
       "612             1.0         Urban           Y  \n",
       "613             0.0     Semiurban           N  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# last 5 rows of the dataset\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5d0b1942",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9875be1a",
   "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": [
    "# Getting the information of the dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "92329bcb",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total missing values in the dataset\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e272724a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical measure of dataset (all the numerical column by default)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ec87c89",
   "metadata": {},
   "source": [
    "#### Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7c9da73",
   "metadata": {},
   "source": [
    "##### Handling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7c75701f",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total missing values in the dataset\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "018d14fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle Numerical data\n",
    "\n",
    "df['LoanAmount'] = df['LoanAmount'].fillna(df['LoanAmount'].mean())\n",
    "df['Loan_Amount_Term'] = df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mean())\n",
    "df['Credit_History'] = df['Credit_History'].fillna(df['Credit_History'].mean())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "83abddc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Male'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handle categorical data\n",
    "\n",
    "df['Gender'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2e0416eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gender'] = df['Gender'].fillna(df['Gender'].mode()[0])\n",
    "df['Married'] = df['Married'].fillna(df['Married'].mode()[0])\n",
    "df['Dependents'] = df['Dependents'].fillna(df['Dependents'].mode()[0])\n",
    "df['Self_Employed'] = df['Self_Employed'].fillna(df['Self_Employed'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dbee2a4f",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now again we will check for null values in the dataset\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "493498c2",
   "metadata": {},
   "source": [
    "### EDA (Exploratory Data Analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e7aac8e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for visualization\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2233a07f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vikash Kumawat\\AppData\\Local\\Programs\\Python\\Python39\\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='Gender', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting graphs for categorical data\n",
    "\n",
    "sns.countplot(df['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d8bb1b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vikash Kumawat\\AppData\\Local\\Programs\\Python\\Python39\\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": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Dependents'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "097ccfea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vikash Kumawat\\AppData\\Local\\Programs\\Python\\Python39\\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='Married', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Married'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e09dd488",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vikash Kumawat\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CoapplicantIncome', ylabel='Density'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J+kV6bNPM/gxYAFzr7p0J7le/7TvaRlt7Z06XvABmTBzN4vkzeGjldl7fdzTh6ERE4pFYQgl9IrcAy4ENwFJ3X2dmd5jZh0K1e4FKM2sgOqu4Lay7DlgKrCfqC7nZ3Tt6ajO09U1gMvCcma0xs88ntW/9lcszKN3d+r65FKWNry7fmFRYIiKx6vUVwAPl7o8Cj3Yr+3zGdAvwsR7W/RLwpVzaDOWJ7stA5PIMSneTKsr49O/N4c4nNjFxdAlnT61IKjwRkVgMx075Qadr2JX+nKEAfPqK2Zw9tYKfvriLY216LkVEBjcllDzY09xCaVGKCaP79/B+SVGKf7n6PI62tfP4+saEohMRiYcSSh40NrcwuaIs63tQ+nJuzTjmz6pk5Wv72XOoJYHoRETioYSSB43NLUzpZciVvlx19iTKitM8uvaNGKMSEYmXEkoe7G1uZVKW1/7manRJEb93RjUNe4/oLEVEBi0llIS5O3vCJa+BqDttAkUp43m9IlhEBikllIQdaW3nWFsHkwdwhgIwurSI82eM58UdBzje1hFTdCIi8VFCSVhjcyvQ+7D1ubr09EpOdDgv7Tw44LZEROKmhJKwxuaozyOOhDJt/Ciqy0tZt/vQgNsSEYmbEkrC4kwoAOdMq+C1N49y4GhbLO2JiMRFCSVhb1/yGlgfSpd50yrodHhigx50FJHBRQklYY3NLYwtK2J0STxDjdWMH8W4UcUsX6eEIiKDixJKwhpjuGU4k5kxb2oFv97cRGu77vYSkcFj0I7QO1xECSW3y10PrtieU705k8p5bus+Xtx+kEtPrxxIeCIisdEZSsIam1uZPDa+MxSAWVVjSBk82/BmrO2KiAyEEkqCOjudvYdbmNyP96Dkoqw4zbumj+fZLXpqXkQGDyWUBB041saJDmfy2Hju8Mp02exK1uw4yNFWvSdFRAYHJZQExfmUfHeXz66ivdNZuW1/7G2LiJwKJZQENR6OHmqclEBCuei0CZSkUzyny14iMkgooSSoMQw1PyXmPhSAUSVpLjxtPM9uUce8iAwOSigJ6rrkVV0efx8KwGWzq1i3u5mDxzQMi4gUnhJKghoPt1A5poSSomQO8+VzKnFH70gRkUFBCSVBjYdaEuk/6XLe9PGMLknr9mERGRSUUBLUeLiFKTENCplNcTrF/FkT+a0ecBSRQUAJJUGNza2J3DKc6bLZlWxpOvrWMPkiIoWihJKQEx2dvHmkNdFLXhB1zIP6UUSk8JRQEvLmkVbc43sPSk/OnlpBRVkRzzYooYhIYSmhJKTrluEpCZ+hpFPGJadX8pzOUESkwBJNKGa20Mw2mlmDmd2WZXmpmT0clq8ws9qMZbeH8o1mtqCvNs3sllDmZlaV5H7lIu5X//bmstmVbN9/jJ0HjiW+LRGRniSWUMwsDdwFfACYB1xrZvO6VbsROODuc4A7ga+EdecBi4FzgIXA3WaW7qPN3wJXAa8ntU/90ZVQJiV8yQvgd2ZH70TR7cMiUkhJnqHMBxrcfau7twFLgEXd6iwC7g/TjwBXmpmF8iXu3ururwENob0e23T3F919W4L70y97DrWQThmVY5JPKGdMGktVeYluHxaRgkoyodQAOzLmd4ayrHXcvR04BFT2sm4ubfbKzG4ys3ozq29qaurPqv2y++BxplSUkU5ZYtvokkoZ751bza83v0lnpye+PRGRbEZcp7y73+Pude5eV11dndh2dh08Ts2EUYm1393vnlHF/qNtrN19KG/bFBHJlOQ75XcBMzLmp4eybHV2mlkRMA7Y18e6fbU5KOw6cDzR9713f//8kfCirWc2NXHe9PGJbVdEpCdJnqGsAuaa2SwzKyHqZF/Wrc4y4IYwfTXwpLt7KF8c7gKbBcwFVubYZsGd6OhkT3NLXs9QykuLmDa+jF9tSu4ynohIbxJLKKFP5BZgObABWOru68zsDjP7UKh2L1BpZg3A54DbwrrrgKXAeuAx4GZ37+ipTQAzu9XMdhKdtbxsZt9Jat/6sudQC50ONePzl1Ag6px/YftBDh07kdftiohAspe8cPdHgUe7lX0+Y7oF+FgP634J+FIubYbyrwNfH2DIsdh18DhAXs9QIHpq/ulNTfzy1Ub+5MLped22iMiI65TPh10HQkLJ8xlKzYRRTB1XxmNr9+R1uyIioISSiK4zlGl5TigpMxacM4VfbWriWFt7XrctIqKEkoBdB45TVV5KWXE679tecM4UWts7eUad8yKSZ0ooCcj3MyiZLq6dQOWYEpa9tLsg2xeRkUsJJQG7Dh5nep4vd3UpSqdYdH4NT6zfy4GjbQWJQURGJiWUmHV2ekHPUAA+Vjedto5OfrZmUD7zKSLDlBJKzPYebqWtvZMZBUwoZ0+t4NyaCn64emfBYhCRkUcJJWYNe48AMHtSeUHj+G91M1i3u5kXth8oaBwiMnIoocSsYe9hAOZUFzahfPTC6VSUFfHtZ7YWNA4RGTmUUGLW0HSEsWVFVI9N/j0ovRlTWsQnLj2Nx9btYdubRwsai4iMDEooMWvYe4Q5k8qJ3hNWWH96WS3FqRTfemZLoUMRkRFACSVmW5qOFvxyV5dJFWVcO38GS+t3sqXpSKHDEZFhLtHBIUeaQ8dP0HS4lTkF7JDv/p6UmgmjKStK8dXHXuVb19cVKCoRGQl0hhKjrju8CplQuisvLeLPf282y9c16p3zIpIonaHEaEvXLcOD5JJXl3GjiqkcU8JfPfQit75vLiVF0d8R110ys8CRichwojOUGDU0HaGkKMWMiaMLHco7FKdTfPiCGvYfbeOJDY2FDkdEhikllBit2X6Qs6dWkE4V/g6v7mZXlzN/1kR+0/AmmxsPFzocERmGlFBi0trewZqdB7n4tAmFDqVHH3zXVCZXlLJ09U6aj+s1wSISLyWUmKzd1Uxbeyd1tRMLHUqPitMpFl88kxMdnTzw/Oscb+sodEgiMowoocSkftt+AOpqB+8ZCsDkijKuqZvB7oPH+Z8/XENHpxc6JBEZJpRQYrJq2wFOrxpDVXlhh1zJxdlTK/jAu6by6Ct7+KefrsVdSUVEBk63DcfA3Vn9+n6uOntyoUPJ2XvmVFFbOZq7n95COgV3fOhcUoPwZgIRGTqUUGLwyq5DHDh2gvmzBm//STY140fxu3Or+P7z21m7q5k/uaCGT15WW+iwRGSIUkKJwQ/rd1JalGLBuVMKHUq/mBkLzplCWXGaX6xvZP/RNhacO4XJFWWFDk1EhiD1oQxQy4kOfrZmFwvPnUJFWXGhw+k3M+OKMydx7fyZvHHoOAv+9RmWvbRb/Soi0m9KKAP0+PpGmlvaufqi6YUOZUDeVTOOv/r9uZw2cTS3PvQiV3/zOZ56dS+dugtMRHKkS14D0Nbeyd1Pb6Fm/Cgum11V6HAGrGpsKT/+y8t5ZPUOvvb4Jj713VVMHVfGlWdP4sKZEzhrSgWzJ42htChd6FBFZBBSQhmAu55qYMMbzdxz/UWDcriVU5FOGddcPJOPXDCd5ev28F8v7ebHL+zi+89Hw+KnDCaOKaGqvJT3zq1iVlU5s6rGcHr1GCaNLR0ULxYTkcJINKGY2ULg34A08B13/3K35aXA94CLgH3ANe6+LSy7HbgR6ABudfflvbVpZrOAJUAlsBq43t3bktgvd2fJqh3c9VQDH7mghvefM7Q643NRUpTij989jT9+9zROdHTyjScbaGxuYc+hFpqOtLLvSBv/+dtttGdcEisrTnHutHGcOWUsZ00Zy5lTKjhz8ljGjR56fUsi0n+WVOermaWBTcAfADuBVcC17r4+o85fAue5+1+Y2WLgI+5+jZnNAx4C5gPTgCeAM8JqWds0s6XAj919iZl9E3jJ3f+jtxjr6uq8vr6+X/vl7vz9j15maf1O3ju3im9cdyHjRvX/F2b3F2ENRZ3uNB8/wZtH2mg60sre5hY63Xl1z2EOt7S/VW9KRRlzJ5czpaKMyvJSKseUUFKUIpUy0mZ0utPR6bR3Oh2dnazedoAOj461A8Up49LZlZQVpykrTjGqOE1pcZqyojBf0jUdzZcVpyktShXkbMndaevopKWtk5b2Do63dXCsrYNjbe38/OU3aG3vpK29k9b2DtrC9Lk14xhVkmZUcZrRJWlGlaQpLy2ivLSIMaVFjC2LvstLi0iHY2bGsD0b7P47qfuvqO6/sdydTif8H+rM+L8UvjtOLneH4iKjOJ2iJJ2iKB1Nd80Xp410yvJ6jDs6nRMdnZzo6KS9wznRGb47OjkR9qFrvusPuZKumItSlBZF313zRSkjZdF+pGL+/2Jmq939pDf2JXmGMh9ocPetIYAlwCJgfUadRcAXwvQjwDcs2utFwBJ3bwVeM7OG0B7Z2jSzDcD7gOtCnftDu70mlFNhZpw5pYLPXnUGt7xvzrC51HUqUmaMH13C+NEl73ipmLvT3NLOnkMt0VlNcwsHj51gc+MR9h1t5URH//+IWb6+f8PumxElFaJ/H8/4NZT5C+odkbyj/OT6mXUzf+m9s7xfYZIyeGZzE6dy70PKon+DlBmpFG/t69tx9e8X88kF/W/jpGSQJe6hdANhSTpF0jnFHU50diZ+XN76/xL+KPn5re+J/d1NSSaUGmBHxvxO4JKe6rh7u5kdIrpkVQM8323dmjCdrc1K4KC7t2ep/w5mdhNwU5g9YmYb+7FP7/CZU10xUgWM9Fco6hhEdBx0DLrk7TjM+ecBrX5atsIR1ynv7vcA9xQ6DjOrz3bKOJLoGER0HHQMugz145Dkcyi7gBkZ89NDWdY6ZlYEjCPqnO9p3Z7K9wHjQxs9bUtERBKUZEJZBcw1s1lmVgIsBpZ1q7MMuCFMXw086dFF2GXAYjMrDXdvzQVW9tRmWOep0AahzZ8luG8iItJNYpe8Qp/ILcByolt873P3dWZ2B1Dv7suAe4EHQqf7fqIEQai3lKgDvx242d07ALK1GTb598ASM/tn4MXQ9mBW8Mtug4COQUTHQcegy5A+DondNiwiIiOLxvISEZFYKKGIiEgslFDyzMwWmtlGM2sws9sKHU8czOw+M9trZmszyiaa2eNmtjl8TwjlZmZfD/v/spldmLHODaH+ZjO7IaP8IjN7JazzdRuEj4ib2Qwze8rM1pvZOjP761A+Yo6DmZWZ2Uozeykcgy+G8llmtiLE/XC4oYZw083DoXyFmdVmtHV7KN9oZgsyyofEz4+Zpc3sRTP7eZgfGcfA3fXJ04foRoItwOlACfASMK/QccWwX78LXAiszSj7KnBbmL4N+EqY/kPg/wEGXAqsCOUTga3he0KYnhCWrQx1Laz7gULvc5ZjMBW4MEyPJRoiaN5IOg4hrvIwXQysCPEuBRaH8m8Cnw7Tfwl8M0wvBh4O0/PCz0YpMCv8zKSH0s8P8DngQeDnYX5EHAOdoeTXW8PReDRwZddwNEOauz9DdJdepkVEQ+AQvj+cUf49jzxP9PzQVGAB8Li773f3A8DjwMKwrMLdn/foJ+17GW0NGu7+hru/EKYPAxuIRmsYMcch7MuRMFscPk40LNIjobz7Meg6No8AV4azrreGXnL314CuoZeGxM+PmU0HPgh8J8wbI+QYKKHkV7bhaLIOETMMTHb3N8L0HmBymO7pGPRWvjNL+aAVLltcQPQX+og6DuFSzxpgL1Ey3ELPwyK9Y+glIHPopf4cm8HmX4G/AzrDfG9DQw2rY6CEIokLf1GPiPvTzawc+BHwGXdvzlw2Eo6Du3e4+/lEo1XMB84qbET5ZWZ/BOx199WFjqUQlFDyK5fhaIaLxnCZhvC9N5T3d1idXWG6e/mgY2bFRMnkB+7+41A84o4DgLsfJBq94nfoeVikuIZeGkwuBz5kZtuILke9j+j9TSPiGCih5Fcuw9EMF5nD6mQOhbMM+GS4y+lS4FC4JLQceL+ZTQh3Qr0fWB6WNZvZpeHa8icZhMPqhNjuBTa4+9cyFo2Y42Bm1WY2PkyPInpv0QZ6HhYplqGXEt+xfnD32919urvXEsX3pLt/nJFyDAp9V8BI+xDd3bOJ6NryPxY6npj26SHgDeAE0TXdG4muA/8S2Ez0grSJoa4Bd4X9fwWoy2jnvxN1PjYAn8oorwPWhnW+QRjhYTB9gPcQXc56GVgTPn84ko4DcB7RsEcvhzg/H8pPJ/pl2AD8ECgN5WVhviEsPz2jrX8M+7mRjLvZhtLPD3AFb9/lNSKOgYZeERGRWOiSl4iIxEIJRUREYqGEIiIisVBCERGRWCihiIhILJRQZFgzsylmtsTMtpjZajN71MzOyMN2v2BmfxOm7zCzq2Ju/zNmNjpjfpuZVcW5DZH+UkKRYSs8APgT4Gl3n+3uFwG38/Z4Wnnh7p939ydibvYzwOi+KonkkxKKDGe/D5xw9292Fbj7S8BvzOxfzGytRe8XuQaicbjM7Jdm9kIoXxTKa83sVTP7gZltMLNHus4OwpnBV0P9lWY2p3sQZvZdM7s6TF9sZs9a9M6QlWY2NrT/67DdF8zsslD3CjN7Omyva/tmZrcC04CnzOypbtuqDTF+26J3kvwiPLWOmc0xsyfCtl8ws9mhvWzH4goz+5WZ/czMtprZl83s4yHmV8xsdqhXbWY/MrNV4XN53P+IMoQU+slKffRJ6gPcCtyZpfyjRCPhponOVrYTvc+kiGiIeIAqoqeXDaglegr+8rDsPuBvwvQ2wtPKRMOhdD0Z/YWMOt8lGlajhOj9JheH8oqwzdFAWSibC9SH6SuIRp+dTvTH33PAezK2W5WxT9tCzLVAO3B+KF8KfCJMrwA+EqbLwnZ7OhZXAAfDdCnReFFfDOv+NfCvYfrBjJhmEg09U/B/e30K89EZioxE7wEe8mhk3EbgV8DFRMnjf5vZy0TDpNTw9uWxHe7+2zD9/dBGl4cyvn+nl+2eCbzh7qsA3L3ZoyHLi4Fvm9krRMNwzMtYZ6W773T3TqLhXGpz2L/X3H1NmF4N1JrZWKDG3X8Stt3i7sd6ORYAqzx6z0sr0TAfvwjlr2TEcRXwDYuGrF8GVFg04rKMQEV9VxEZstbx9oB8ufg4UA1c5O4nLBoxtiws6z5GkecwnavPAo3Au4nORFoylrVmTHeQ289s93VGnUJM3dvpzJjvzIgjBVzq7pkxywilMxQZzp4ESs3spq4CMzuP6FLONRa9DKqa6BXGK4mGDt8bksnvA6dltDXTzLrOPq4DfpOx7JqM7+d6iWcjMNXMLg6xjLW3hyx/I5yFXE90+akvh4leNZwTj94iudPMPhy2XRr6gX5N9mORq18Af9U1Y2bn92NdGWaUUGTYcncHPgJcFW4bXgf8H6Lr/i8TvY/7SeDv3H0P8AOgLlx6+iTwakZzG4GbzWwD0bve/yNj2YRwmeyvic42eoqnjSjp/LuZvUTUd1EG3A3cEMrOAo7msHv3AI9175Tvw/XArSHWZ4EpRHfBZTsWubqV6Ji9bGbrgb/ox7oyzGi0YZE+WPRK35+7+7lZlm0jGnr+zXzHJTLY6AxFRERioTMUERGJhc5QREQkFkooIiISCyUUERGJhRKKiIjEQglFRERi8f8B1w7JQapoH88AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting graph for numerical data\n",
    "\n",
    "sns.distplot(df['CoapplicantIncome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1ee3583f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vikash Kumawat\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LoanAmount', ylabel='Density'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['LoanAmount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "92382bca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vikash Kumawat\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Credit_History', ylabel='Density'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Credit_History'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5fb28168",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0  146.412162             360.0   \n",
       "1             4583             1508.0  128.000000             360.0   \n",
       "2             3000                0.0   66.000000             360.0   \n",
       "3             2583             2358.0  120.000000             360.0   \n",
       "4             6000                0.0  141.000000             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "625aed79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here column 'ApplicantIncome' and 'CoapplicantIncome' convert into one column\n",
    "\n",
    "df['Total_Income'] = df['ApplicantIncome'] + df['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "74ebcffb",
   "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",
       "      <th>Total_Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>5849.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "      <td>6091.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>4941.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0  146.412162             360.0   \n",
       "1             4583             1508.0  128.000000             360.0   \n",
       "2             3000                0.0   66.000000             360.0   \n",
       "3             2583             2358.0  120.000000             360.0   \n",
       "4             6000                0.0  141.000000             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  Total_Income  \n",
       "0             1.0         Urban           Y        5849.0  \n",
       "1             1.0         Rural           N        6091.0  \n",
       "2             1.0         Urban           Y        3000.0  \n",
       "3             1.0         Urban           Y        4941.0  \n",
       "4             1.0         Urban           Y        6000.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89da5e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Transformation\n",
    "\n",
    "\n",
    "df['ApplicantIncomeLog'] = np.log(df['ApplicantIncome'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dfde6bd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vikash Kumawat\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ApplicantIncomeLog', ylabel='Density'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['ApplicantIncomeLog'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "92334004",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vikash Kumawat\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "df[\"CoapplicantIncomeLog\"] = np.log(df['CoapplicantIncome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "26f337c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vikash Kumawat\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ApplicantIncomeLog', ylabel='Density'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['ApplicantIncomeLog'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dfc16620",
   "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",
       "       'Total_Income', 'ApplicantIncomeLog', 'CoapplicantIncomeLog'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7f301431",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vikash Kumawat\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LoanAmountLog', ylabel='Density'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['LoanAmountLog'] = np.log(df['LoanAmount'])\n",
    "sns.distplot(df['LoanAmountLog'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e0a3fa43",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vikash Kumawat\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Loan_Amount_Term_log', ylabel='Density'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Loan_Amount_Term_log'] = np.log(df['Loan_Amount_Term'])\n",
    "sns.distplot(df['Loan_Amount_Term_log'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "90ac27dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vikash Kumawat\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total_Income_Log', ylabel='Density'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Total_Income_Log'] = np.log(df['Total_Income'])\n",
    "sns.distplot(df['Total_Income_Log'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d07905e0",
   "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",
       "       'Total_Income', 'ApplicantIncomeLog', 'CoapplicantIncomeLog',\n",
       "       'LoanAmountLog', 'Loan_Amount_Term_log', 'Total_Income_Log'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c842958a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping all the unnecessary column they don't add much value for the dataset\n",
    "\n",
    "\n",
    "cols = ['ApplicantIncome', 'CoapplicantIncome', \"LoanAmount\", \"Loan_Amount_Term\", \"Total_Income\", 'Loan_ID', 'CoapplicantIncomeLog']\n",
    "df = df.drop(columns=cols, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b42ed76a",
   "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>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>ApplicantIncomeLog</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Loan_Amount_Term_log</th>\n",
       "      <th>Total_Income_Log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>4.986426</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.674026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "      <td>8.430109</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.714568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.006368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>7.856707</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.505323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.699515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender Married Dependents     Education Self_Employed  Credit_History  \\\n",
       "0   Male      No          0      Graduate            No             1.0   \n",
       "1   Male     Yes          1      Graduate            No             1.0   \n",
       "2   Male     Yes          0      Graduate           Yes             1.0   \n",
       "3   Male     Yes          0  Not Graduate            No             1.0   \n",
       "4   Male      No          0      Graduate            No             1.0   \n",
       "\n",
       "  Property_Area Loan_Status  ApplicantIncomeLog  LoanAmountLog  \\\n",
       "0         Urban           Y            8.674026       4.986426   \n",
       "1         Rural           N            8.430109       4.852030   \n",
       "2         Urban           Y            8.006368       4.189655   \n",
       "3         Urban           Y            7.856707       4.787492   \n",
       "4         Urban           Y            8.699515       4.948760   \n",
       "\n",
       "   Loan_Amount_Term_log  Total_Income_Log  \n",
       "0              5.886104          8.674026  \n",
       "1              5.886104          8.714568  \n",
       "2              5.886104          8.006368  \n",
       "3              5.886104          8.505323  \n",
       "4              5.886104          8.699515  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0c8ab282",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      502\n",
       "Female    112\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "809d0e31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    422\n",
       "N    192\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Loan_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d99cd5f6",
   "metadata": {},
   "source": [
    "#### Handling categorical data(One hot Encoding)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "100b7ba0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Gender                614 non-null    object \n",
      " 1   Married               614 non-null    object \n",
      " 2   Dependents            614 non-null    object \n",
      " 3   Education             614 non-null    object \n",
      " 4   Self_Employed         614 non-null    object \n",
      " 5   Credit_History        614 non-null    float64\n",
      " 6   Property_Area         614 non-null    object \n",
      " 7   Loan_Status           614 non-null    object \n",
      " 8   ApplicantIncomeLog    614 non-null    float64\n",
      " 9   LoanAmountLog         614 non-null    float64\n",
      " 10  Loan_Amount_Term_log  614 non-null    float64\n",
      " 11  Total_Income_Log      614 non-null    float64\n",
      "dtypes: float64(5), object(7)\n",
      "memory usage: 57.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ead4ccf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = pd.get_dummies(df['Gender'], drop_first= True)\n",
    "d2 = pd.get_dummies(df['Married'], drop_first= True)\n",
    "d3 = pd.get_dummies(df['Dependents'], drop_first= True)\n",
    "d4 = pd.get_dummies(df['Education'], drop_first= True)\n",
    "d5 = pd.get_dummies(df['Self_Employed'], drop_first= True)\n",
    "d6 = pd.get_dummies(df['Property_Area'], drop_first= True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8b32f68e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.concat([df, d1, d2,  d3, d4, d5, d6], axis = 1)\n",
    "df = df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cef04bdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['Gender', 'Married', \"Dependents\", \"Education\", \"Self_Employed\", 'Property_Area']\n",
    "df = df.drop(columns=cols, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f1850684",
   "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>Credit_History</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>ApplicantIncomeLog</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Loan_Amount_Term_log</th>\n",
       "      <th>Total_Income_Log</th>\n",
       "      <th>Male</th>\n",
       "      <th>Yes</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3+</th>\n",
       "      <th>Not Graduate</th>\n",
       "      <th>Yes</th>\n",
       "      <th>Semiurban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>4.986426</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>8.430109</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.714568</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>7.856707</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.505323</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Credit_History Loan_Status  ApplicantIncomeLog  LoanAmountLog  \\\n",
       "0             1.0           Y            8.674026       4.986426   \n",
       "1             1.0           N            8.430109       4.852030   \n",
       "2             1.0           Y            8.006368       4.189655   \n",
       "3             1.0           Y            7.856707       4.787492   \n",
       "4             1.0           Y            8.699515       4.948760   \n",
       "\n",
       "   Loan_Amount_Term_log  Total_Income_Log  Male  Yes  1  2  3+  Not Graduate  \\\n",
       "0              5.886104          8.674026     1    0  0  0   0             0   \n",
       "1              5.886104          8.714568     1    1  1  0   0             0   \n",
       "2              5.886104          8.006368     1    1  0  0   0             0   \n",
       "3              5.886104          8.505323     1    1  0  0   0             1   \n",
       "4              5.886104          8.699515     1    0  0  0   0             0   \n",
       "\n",
       "   Yes  Semiurban  Urban  \n",
       "0    0          0      1  \n",
       "1    0          0      0  \n",
       "2    1          0      1  \n",
       "3    0          0      1  \n",
       "4    0          0      1  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1c777a13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 15 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Credit_History        614 non-null    float64\n",
      " 1   Loan_Status           614 non-null    object \n",
      " 2   ApplicantIncomeLog    614 non-null    float64\n",
      " 3   LoanAmountLog         614 non-null    float64\n",
      " 4   Loan_Amount_Term_log  614 non-null    float64\n",
      " 5   Total_Income_Log      614 non-null    float64\n",
      " 6   Male                  614 non-null    uint8  \n",
      " 7   Yes                   614 non-null    uint8  \n",
      " 8   1                     614 non-null    uint8  \n",
      " 9   2                     614 non-null    uint8  \n",
      " 10  3+                    614 non-null    uint8  \n",
      " 11  Not Graduate          614 non-null    uint8  \n",
      " 12  Yes                   614 non-null    uint8  \n",
      " 13  Semiurban             614 non-null    uint8  \n",
      " 14  Urban                 614 non-null    uint8  \n",
      "dtypes: float64(5), object(1), uint8(9)\n",
      "memory usage: 34.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9b0cf19f",
   "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>Credit_History</th>\n",
       "      <th>ApplicantIncomeLog</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Loan_Amount_Term_log</th>\n",
       "      <th>Total_Income_Log</th>\n",
       "      <th>Male</th>\n",
       "      <th>Yes</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3+</th>\n",
       "      <th>Not Graduate</th>\n",
       "      <th>Yes</th>\n",
       "      <th>Semiurban</th>\n",
       "      <th>Urban</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>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.842199</td>\n",
       "      <td>8.341213</td>\n",
       "      <td>4.862066</td>\n",
       "      <td>5.802065</td>\n",
       "      <td>8.669414</td>\n",
       "      <td>0.817590</td>\n",
       "      <td>0.653094</td>\n",
       "      <td>0.166124</td>\n",
       "      <td>0.164495</td>\n",
       "      <td>0.083062</td>\n",
       "      <td>0.218241</td>\n",
       "      <td>0.133550</td>\n",
       "      <td>0.379479</td>\n",
       "      <td>0.328990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.349681</td>\n",
       "      <td>0.645263</td>\n",
       "      <td>0.496575</td>\n",
       "      <td>0.312482</td>\n",
       "      <td>0.545102</td>\n",
       "      <td>0.386497</td>\n",
       "      <td>0.476373</td>\n",
       "      <td>0.372495</td>\n",
       "      <td>0.371027</td>\n",
       "      <td>0.276201</td>\n",
       "      <td>0.413389</td>\n",
       "      <td>0.340446</td>\n",
       "      <td>0.485653</td>\n",
       "      <td>0.470229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.010635</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>2.484907</td>\n",
       "      <td>7.273786</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.964677</td>\n",
       "      <td>4.607658</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.334712</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.246040</td>\n",
       "      <td>4.859812</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.597205</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.664750</td>\n",
       "      <td>5.104426</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.925549</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.302204</td>\n",
       "      <td>6.551080</td>\n",
       "      <td>6.173786</td>\n",
       "      <td>11.302204</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Credit_History  ApplicantIncomeLog  LoanAmountLog  \\\n",
       "count      614.000000          614.000000     614.000000   \n",
       "mean         0.842199            8.341213       4.862066   \n",
       "std          0.349681            0.645263       0.496575   \n",
       "min          0.000000            5.010635       2.197225   \n",
       "25%          1.000000            7.964677       4.607658   \n",
       "50%          1.000000            8.246040       4.859812   \n",
       "75%          1.000000            8.664750       5.104426   \n",
       "max          1.000000           11.302204       6.551080   \n",
       "\n",
       "       Loan_Amount_Term_log  Total_Income_Log        Male         Yes  \\\n",
       "count            614.000000        614.000000  614.000000  614.000000   \n",
       "mean               5.802065          8.669414    0.817590    0.653094   \n",
       "std                0.312482          0.545102    0.386497    0.476373   \n",
       "min                2.484907          7.273786    0.000000    0.000000   \n",
       "25%                5.886104          8.334712    1.000000    0.000000   \n",
       "50%                5.886104          8.597205    1.000000    1.000000   \n",
       "75%                5.886104          8.925549    1.000000    1.000000   \n",
       "max                6.173786         11.302204    1.000000    1.000000   \n",
       "\n",
       "                1           2          3+  Not Graduate         Yes  \\\n",
       "count  614.000000  614.000000  614.000000    614.000000  614.000000   \n",
       "mean     0.166124    0.164495    0.083062      0.218241    0.133550   \n",
       "std      0.372495    0.371027    0.276201      0.413389    0.340446   \n",
       "min      0.000000    0.000000    0.000000      0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000      0.000000    0.000000   \n",
       "50%      0.000000    0.000000    0.000000      0.000000    0.000000   \n",
       "75%      0.000000    0.000000    0.000000      0.000000    0.000000   \n",
       "max      1.000000    1.000000    1.000000      1.000000    1.000000   \n",
       "\n",
       "        Semiurban       Urban  \n",
       "count  614.000000  614.000000  \n",
       "mean     0.379479    0.328990  \n",
       "std      0.485653    0.470229  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    0.000000  \n",
       "50%      0.000000    0.000000  \n",
       "75%      1.000000    1.000000  \n",
       "max      1.000000    1.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f49bb0e",
   "metadata": {},
   "source": [
    "#### Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0300cada",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "06cc5e99",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001015   Male     Yes          0      Graduate            No   \n",
       "1  LP001022   Male     Yes          1      Graduate            No   \n",
       "2  LP001031   Male     Yes          2      Graduate            No   \n",
       "3  LP001035   Male     Yes          2      Graduate            No   \n",
       "4  LP001051   Male      No          0  Not Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5720                  0       110.0             360.0   \n",
       "1             3076               1500       126.0             360.0   \n",
       "2             5000               1800       208.0             360.0   \n",
       "3             2340               2546       100.0             360.0   \n",
       "4             3276                  0        78.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area  \n",
       "0             1.0         Urban  \n",
       "1             1.0         Urban  \n",
       "2             1.0         Urban  \n",
       "3             NaN         Urban  \n",
       "4             1.0         Urban  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first five rows of the dataset\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "dac30cb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(367, 12)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of the dataset\n",
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b25fc52a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               11\n",
       "Married               0\n",
       "Dependents           10\n",
       "Education             0\n",
       "Self_Employed        23\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            5\n",
       "Loan_Amount_Term      6\n",
       "Credit_History       29\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## missiing values\n",
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c1fd024f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## filling missing values\n",
    "\n",
    "test['LoanAmount']=test['LoanAmount'].fillna(test['LoanAmount'].mean())\n",
    "test['Loan_Amount_Term']=test['Loan_Amount_Term'].fillna(test['Loan_Amount_Term'].mean())\n",
    "test['Credit_History']=test['Credit_History'].fillna(test['Credit_History'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e1e33766",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling categorical missing data\n",
    "\n",
    "test['Gender']=test['Gender'].fillna(test['Gender'].mode()[0])\n",
    "test['Married']=test['Married'].fillna(test['Married'].mode()[0])\n",
    "test['Dependents']=test['Dependents'].fillna(test['Dependents'].mode()[0])\n",
    "test['Self_Employed']=test['Self_Employed'].fillna(test['Self_Employed'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "38d6a13c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Total_income'] = test['ApplicantIncome']+test['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9501c874",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vikash Kumawat\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# apply log transformation to the attribute\n",
    "test['ApplicantIncomeLog'] = np.log(test['ApplicantIncome'])\n",
    "\n",
    "test['CoapplicantIncomeLog'] = np.log(test['CoapplicantIncome'])\n",
    "\n",
    "test['LoanAmountLog'] = np.log(test['LoanAmount'])\n",
    "\n",
    "test['Loan_Amount_Term_Log'] = np.log(test['Loan_Amount_Term'])\n",
    "\n",
    "test['Total_Income_Log'] = np.log(test['Total_income'])\n",
    "\n",
    "cols = ['ApplicantIncome', 'CoapplicantIncome', \"LoanAmount\", \"Loan_Amount_Term\", \"Total_income\", 'Loan_ID', 'CoapplicantIncomeLog']\n",
    "test = test.drop(columns=cols, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "t1 = pd.get_dummies(test['Gender'], drop_first= True)\n",
    "t2 = pd.get_dummies(test['Married'], drop_first= True)\n",
    "t3 = pd.get_dummies(test['Dependents'], drop_first= True)\n",
    "t4 = pd.get_dummies(test['Education'], drop_first= True)\n",
    "t5 = pd.get_dummies(test['Self_Employed'], drop_first= True)\n",
    "t6 = pd.get_dummies(test['Property_Area'], drop_first= True)\n",
    "\n",
    "\n",
    "\n",
    "df1 = pd.concat([test, t1, t2, t3, t4, t5, t6], axis = 1)\n",
    "test=df1\n",
    "\n",
    "cols = ['Gender', 'Married', \"Dependents\", \"Education\", \"Self_Employed\", 'Property_Area']\n",
    "test = test.drop(columns=cols, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ac60c64a",
   "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>Credit_History</th>\n",
       "      <th>ApplicantIncomeLog</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Loan_Amount_Term_Log</th>\n",
       "      <th>Total_Income_Log</th>\n",
       "      <th>Male</th>\n",
       "      <th>Yes</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3+</th>\n",
       "      <th>Not Graduate</th>\n",
       "      <th>Yes</th>\n",
       "      <th>Semiurban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.651724</td>\n",
       "      <td>4.700480</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.651724</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.031385</td>\n",
       "      <td>4.836282</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.428581</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.517193</td>\n",
       "      <td>5.337538</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.824678</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.825444</td>\n",
       "      <td>7.757906</td>\n",
       "      <td>4.605170</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.494129</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.094378</td>\n",
       "      <td>4.356709</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.094378</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Credit_History  ApplicantIncomeLog  LoanAmountLog  Loan_Amount_Term_Log  \\\n",
       "0        1.000000            8.651724       4.700480              5.886104   \n",
       "1        1.000000            8.031385       4.836282              5.886104   \n",
       "2        1.000000            8.517193       5.337538              5.886104   \n",
       "3        0.825444            7.757906       4.605170              5.886104   \n",
       "4        1.000000            8.094378       4.356709              5.886104   \n",
       "\n",
       "   Total_Income_Log  Male  Yes  1  2  3+  Not Graduate  Yes  Semiurban  Urban  \n",
       "0          8.651724     1    1  0  0   0             0    0          0      1  \n",
       "1          8.428581     1    1  1  0   0             0    0          0      1  \n",
       "2          8.824678     1    1  0  1   0             0    0          0      1  \n",
       "3          8.494129     1    1  0  1   0             0    0          0      1  \n",
       "4          8.094378     1    0  0  0   0             1    0          0      1  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "286b3afb",
   "metadata": {},
   "source": [
    "## Split datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "babd7f37",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Loan_Status'], axis=1)\n",
    "Y = df['Loan_Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5869d948",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Credit_History  ApplicantIncomeLog  LoanAmountLog  Loan_Amount_Term_log  \\\n",
      "0               1.0            8.674026       4.986426              5.886104   \n",
      "1               1.0            8.430109       4.852030              5.886104   \n",
      "2               1.0            8.006368       4.189655              5.886104   \n",
      "3               1.0            7.856707       4.787492              5.886104   \n",
      "4               1.0            8.699515       4.948760              5.886104   \n",
      "..              ...                 ...            ...                   ...   \n",
      "609             1.0            7.972466       4.262680              5.886104   \n",
      "610             1.0            8.320205       3.688879              5.192957   \n",
      "611             1.0            8.996157       5.533389              5.886104   \n",
      "612             1.0            8.933664       5.231109              5.886104   \n",
      "613             0.0            8.430109       4.890349              5.886104   \n",
      "\n",
      "     Total_Income_Log  Male  Yes  1  2  3+  Not Graduate  Yes  Semiurban  \\\n",
      "0            8.674026     1    0  0  0   0             0    0          0   \n",
      "1            8.714568     1    1  1  0   0             0    0          0   \n",
      "2            8.006368     1    1  0  0   0             0    1          0   \n",
      "3            8.505323     1    1  0  0   0             1    0          0   \n",
      "4            8.699515     1    0  0  0   0             0    0          0   \n",
      "..                ...   ...  ... .. ..  ..           ...  ...        ...   \n",
      "609          7.972466     0    0  0  0   0             0    0          0   \n",
      "610          8.320205     1    1  0  0   1             0    0          0   \n",
      "611          9.025456     1    1  1  0   0             0    0          0   \n",
      "612          8.933664     1    1  0  1   0             0    0          0   \n",
      "613          8.430109     0    0  0  0   0             0    1          1   \n",
      "\n",
      "     Urban  \n",
      "0        1  \n",
      "1        0  \n",
      "2        1  \n",
      "3        1  \n",
      "4        1  \n",
      "..     ...  \n",
      "609      0  \n",
      "610      0  \n",
      "611      1  \n",
      "612      1  \n",
      "613      0  \n",
      "\n",
      "[614 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ecd289ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      Y\n",
      "1      N\n",
      "2      Y\n",
      "3      Y\n",
      "4      Y\n",
      "      ..\n",
      "609    Y\n",
      "610    Y\n",
      "611    Y\n",
      "612    Y\n",
      "613    N\n",
      "Name: Loan_Status, Length: 614, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "863c32cb",
   "metadata": {},
   "source": [
    "### Splitting data into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "da48278f",
   "metadata": {},
   "outputs": [],
   "source": [
    "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 = 0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f09c7679",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(460, 14) (154, 14) (614, 14)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, X_test.shape, X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "196c8ee4",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c18d07df",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b248ddfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "28a36049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "307179a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_data_accuracy: 100.0\n"
     ]
    }
   ],
   "source": [
    "# Accuracy on train data\n",
    "\n",
    "train_data_accuracy = model.score(X_train, Y_train)*100\n",
    "print(f\"train_data_accuracy: {train_data_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7a118911",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_data_accuracy: 77.27272727272727\n"
     ]
    }
   ],
   "source": [
    "# Accuracy on test data\n",
    "\n",
    "test_data_accuracy = model.score(X_test, Y_test)*100\n",
    "print(f\"test_data_accuracy: {test_data_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "acad0a3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy is 100.0\n",
      "Test Accuracy is 67.53246753246754\n"
     ]
    }
   ],
   "source": [
    "# decision tree classifier\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "\n",
    "model2 = DecisionTreeClassifier()\n",
    "model2.fit(X_train, Y_train)\n",
    "print(\"Train Accuracy is\", model2.score(X_train, Y_train)*100)\n",
    "print(\"Test Accuracy is\", model2.score(X_test, Y_test)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cc4f036b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy is 81.95652173913044\n",
      "Test Accuracy is 77.27272727272727\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vikash Kumawat\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: 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"
     ]
    }
   ],
   "source": [
    "# logistic regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model3 = LogisticRegression()\n",
    "\n",
    "model3.fit(X_train, Y_train)\n",
    "\n",
    "print(\"Training Accuracy is\", model3.score(X_train, Y_train)*100)\n",
    "\n",
    "\n",
    "print(\"Test Accuracy is\", model3.score(X_test, Y_test)*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b885a616",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the model\n",
    "\n",
    "\n",
    "import pickle\n",
    "file = open(\"model.pkl\", 'wb')\n",
    "pickle.dump(model, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fdff4d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40148935",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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