Random forest classifier with standard scaler (Loan Prediction _AV).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading train dataset\n",
    "\n",
    "train_data=pd.read_csv(\"train_AV.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>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": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading Test dataset\n",
    "\n",
    "test_data=pd.read_csv(\"test_AV.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "    </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": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "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: 43.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "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": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#No. of null values\n",
    "\n",
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# filling missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Gender'].fillna(train_data['Gender'].mode().values[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Married'].fillna(train_data['Married'].mode().values[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Dependents'].fillna(train_data['Dependents'].mode().values[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Self_Employed'].fillna(train_data['Self_Employed'].mode().values[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Loan_Amount_Term'].fillna(train_data['Loan_Amount_Term'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['LoanAmount'].fillna(train_data['LoanAmount'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Credit_History'].fillna(train_data['Credit_History'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "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": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "# missing values have been filled for train_data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# now filling NaN of test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "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": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 367 entries, 0 to 366\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            367 non-null    object \n",
      " 1   Gender             356 non-null    object \n",
      " 2   Married            367 non-null    object \n",
      " 3   Dependents         357 non-null    object \n",
      " 4   Education          367 non-null    object \n",
      " 5   Self_Employed      344 non-null    object \n",
      " 6   ApplicantIncome    367 non-null    int64  \n",
      " 7   CoapplicantIncome  367 non-null    int64  \n",
      " 8   LoanAmount         362 non-null    float64\n",
      " 9   Loan_Amount_Term   361 non-null    float64\n",
      " 10  Credit_History     338 non-null    float64\n",
      " 11  Property_Area      367 non-null    object \n",
      "dtypes: float64(3), int64(2), object(7)\n",
      "memory usage: 24.4+ KB\n"
     ]
    }
   ],
   "source": [
    "test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Gender'].fillna(test_data['Gender'].mode().values[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Dependents'].fillna(test_data['Gender'].mode().values[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Self_Employed'].fillna(test_data['Self_Employed'].mode().values[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['LoanAmount'].fillna(test_data['LoanAmount'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Loan_Amount_Term'].fillna(test_data['Loan_Amount_Term'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Credit_History'].fillna(test_data['Credit_History'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Missing values for test_data is filled now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Loan_ID  Gender  Married Dependents  Education  Self_Employed  \\\n",
       "0        0       1        0          0          0              0   \n",
       "1        1       1        1          1          0              0   \n",
       "2        2       1        1          0          0              1   \n",
       "3        3       1        1          0          1              0   \n",
       "4        4       1        0          0          0              0   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0  146.412162             360.0   \n",
       "1             4583             1508.0  128.000000             360.0   \n",
       "2             3000                0.0   66.000000             360.0   \n",
       "3             2583             2358.0  120.000000             360.0   \n",
       "4             6000                0.0  141.000000             360.0   \n",
       "\n",
       "   Credit_History  Property_Area  Loan_Status  \n",
       "0             1.0              2            1  \n",
       "1             1.0              0            0  \n",
       "2             1.0              2            1  \n",
       "3             1.0              2            1  \n",
       "4             1.0              2            1  "
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now LabelEncoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we will not use dummies variable bcoz here we can use LabelEncoder as the data has only two cateogries\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Gender']=encoder.fit_transform(train_data['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Married']=encoder.fit_transform(train_data['Married'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Education']=encoder.fit_transform(train_data['Education'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Self_Employed']=encoder.fit_transform(train_data['Self_Employed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Property_Area']=encoder.fit_transform(train_data['Property_Area'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Loan_Status']=encoder.fit_transform(train_data['Loan_Status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Loan_ID']=encoder.fit_transform(train_data['Loan_ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Dependents']=encoder.fit_transform(train_data['Dependents'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2333</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>158.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>9</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>2500</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3073</td>\n",
       "      <td>8106.0</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>1853</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>120.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0.842199</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4887</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2600</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>342.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0         0       1        0           0          0              0   \n",
       "1         1       1        1           1          0              0   \n",
       "2         2       1        1           0          0              1   \n",
       "3         3       1        1           0          1              0   \n",
       "4         4       1        0           0          0              0   \n",
       "5         5       1        1           2          0              1   \n",
       "6         6       1        1           0          1              0   \n",
       "7         7       1        1           3          0              0   \n",
       "8         8       1        1           2          0              0   \n",
       "9         9       1        1           1          0              0   \n",
       "10       10       1        1           2          0              0   \n",
       "11       11       1        1           2          0              0   \n",
       "12       12       1        1           2          0              0   \n",
       "13       13       1        0           0          0              0   \n",
       "14       14       1        1           2          0              0   \n",
       "15       15       1        0           0          0              0   \n",
       "16       16       1        0           1          1              0   \n",
       "17       17       0        0           0          0              0   \n",
       "18       18       1        1           0          1              0   \n",
       "19       19       1        1           0          0              0   \n",
       "\n",
       "    ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0              5849                0.0  146.412162             360.0   \n",
       "1              4583             1508.0  128.000000             360.0   \n",
       "2              3000                0.0   66.000000             360.0   \n",
       "3              2583             2358.0  120.000000             360.0   \n",
       "4              6000                0.0  141.000000             360.0   \n",
       "5              5417             4196.0  267.000000             360.0   \n",
       "6              2333             1516.0   95.000000             360.0   \n",
       "7              3036             2504.0  158.000000             360.0   \n",
       "8              4006             1526.0  168.000000             360.0   \n",
       "9             12841            10968.0  349.000000             360.0   \n",
       "10             3200              700.0   70.000000             360.0   \n",
       "11             2500             1840.0  109.000000             360.0   \n",
       "12             3073             8106.0  200.000000             360.0   \n",
       "13             1853             2840.0  114.000000             360.0   \n",
       "14             1299             1086.0   17.000000             120.0   \n",
       "15             4950                0.0  125.000000             360.0   \n",
       "16             3596                0.0  100.000000             240.0   \n",
       "17             3510                0.0   76.000000             360.0   \n",
       "18             4887                0.0  133.000000             360.0   \n",
       "19             2600             3500.0  115.000000             342.0   \n",
       "\n",
       "    Credit_History  Property_Area  Loan_Status  \n",
       "0         1.000000              2            1  \n",
       "1         1.000000              0            0  \n",
       "2         1.000000              2            1  \n",
       "3         1.000000              2            1  \n",
       "4         1.000000              2            1  \n",
       "5         1.000000              2            1  \n",
       "6         1.000000              2            1  \n",
       "7         0.000000              1            0  \n",
       "8         1.000000              2            1  \n",
       "9         1.000000              1            0  \n",
       "10        1.000000              2            1  \n",
       "11        1.000000              2            1  \n",
       "12        1.000000              2            1  \n",
       "13        1.000000              0            0  \n",
       "14        1.000000              2            1  \n",
       "15        1.000000              2            1  \n",
       "16        0.842199              2            1  \n",
       "17        0.000000              2            0  \n",
       "18        1.000000              0            0  \n",
       "19        1.000000              2            1  "
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now LabelEncoding test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Loan_ID']=encoder.fit_transform(test_data['Loan_ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Gender']=encoder.fit_transform(test_data['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Married']=encoder.fit_transform(test_data['Married'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Education']=encoder.fit_transform(test_data['Education'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Self_Employed']=encoder.fit_transform(test_data['Self_Employed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Property_Area']=encoder.fit_transform(test_data['Property_Area'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Dependents']=encoder.fit_transform(test_data['Dependents'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.825444</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2165</td>\n",
       "      <td>3422</td>\n",
       "      <td>152.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2226</td>\n",
       "      <td>0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3881</td>\n",
       "      <td>0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13633</td>\n",
       "      <td>0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>123.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3091</td>\n",
       "      <td>0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2185</td>\n",
       "      <td>1516</td>\n",
       "      <td>162.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4166</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>0.825444</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12173</td>\n",
       "      <td>0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4666</td>\n",
       "      <td>0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5667</td>\n",
       "      <td>0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4583</td>\n",
       "      <td>2916</td>\n",
       "      <td>200.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3786</td>\n",
       "      <td>333</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9226</td>\n",
       "      <td>7916</td>\n",
       "      <td>300.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1300</td>\n",
       "      <td>3470</td>\n",
       "      <td>100.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0         0       1        1           0          0              0   \n",
       "1         1       1        1           1          0              0   \n",
       "2         2       1        1           2          0              0   \n",
       "3         3       1        1           2          0              0   \n",
       "4         4       1        0           0          1              0   \n",
       "5         5       1        1           0          1              1   \n",
       "6         6       0        0           1          1              0   \n",
       "7         7       1        1           2          1              0   \n",
       "8         8       1        1           2          0              0   \n",
       "9         9       1        0           0          1              0   \n",
       "10       10       1        0           0          1              0   \n",
       "11       11       1        1           1          0              0   \n",
       "12       12       1        0           3          0              0   \n",
       "13       13       1        1           2          0              0   \n",
       "14       14       0        0           0          0              0   \n",
       "15       15       1        0           1          0              0   \n",
       "16       16       1        1           2          0              0   \n",
       "17       17       1        1           3          0              0   \n",
       "18       18       1        1           0          0              0   \n",
       "19       19       1        0           0          0              0   \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",
       "5              2165               3422       152.0             360.0   \n",
       "6              2226                  0        59.0             360.0   \n",
       "7              3881                  0       147.0             360.0   \n",
       "8             13633                  0       280.0             240.0   \n",
       "9              2400               2400       123.0             360.0   \n",
       "10             3091                  0        90.0             360.0   \n",
       "11             2185               1516       162.0             360.0   \n",
       "12             4166                  0        40.0             180.0   \n",
       "13            12173                  0       166.0             360.0   \n",
       "14             4666                  0       124.0             360.0   \n",
       "15             5667                  0       131.0             360.0   \n",
       "16             4583               2916       200.0             360.0   \n",
       "17             3786                333       126.0             360.0   \n",
       "18             9226               7916       300.0             360.0   \n",
       "19             1300               3470       100.0             180.0   \n",
       "\n",
       "    Credit_History  Property_Area  \n",
       "0         1.000000              2  \n",
       "1         1.000000              2  \n",
       "2         1.000000              2  \n",
       "3         0.825444              2  \n",
       "4         1.000000              2  \n",
       "5         1.000000              2  \n",
       "6         1.000000              1  \n",
       "7         0.000000              0  \n",
       "8         1.000000              2  \n",
       "9         1.000000              1  \n",
       "10        1.000000              2  \n",
       "11        1.000000              1  \n",
       "12        0.825444              2  \n",
       "13        0.000000              1  \n",
       "14        1.000000              1  \n",
       "15        1.000000              2  \n",
       "16        1.000000              2  \n",
       "17        1.000000              1  \n",
       "18        1.000000              2  \n",
       "19        1.000000              1  "
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(367, 12)"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "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>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>Loan_ID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.028029</td>\n",
       "      <td>-0.016013</td>\n",
       "      <td>0.039442</td>\n",
       "      <td>0.032874</td>\n",
       "      <td>0.016925</td>\n",
       "      <td>0.039211</td>\n",
       "      <td>0.035684</td>\n",
       "      <td>-0.028945</td>\n",
       "      <td>-0.032429</td>\n",
       "      <td>-0.155416</td>\n",
       "      <td>0.011773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>-0.028029</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.364569</td>\n",
       "      <td>0.045364</td>\n",
       "      <td>-0.000525</td>\n",
       "      <td>0.058809</td>\n",
       "      <td>0.082912</td>\n",
       "      <td>0.107930</td>\n",
       "      <td>-0.073567</td>\n",
       "      <td>0.012819</td>\n",
       "      <td>-0.025752</td>\n",
       "      <td>0.017987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>-0.016013</td>\n",
       "      <td>0.364569</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012304</td>\n",
       "      <td>0.004489</td>\n",
       "      <td>0.051708</td>\n",
       "      <td>0.075948</td>\n",
       "      <td>0.147141</td>\n",
       "      <td>-0.100863</td>\n",
       "      <td>0.005852</td>\n",
       "      <td>0.004257</td>\n",
       "      <td>0.091478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>0.039442</td>\n",
       "      <td>0.045364</td>\n",
       "      <td>0.012304</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010383</td>\n",
       "      <td>-0.140760</td>\n",
       "      <td>-0.062290</td>\n",
       "      <td>-0.166998</td>\n",
       "      <td>-0.077242</td>\n",
       "      <td>-0.077936</td>\n",
       "      <td>-0.065243</td>\n",
       "      <td>-0.085884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self_Employed</th>\n",
       "      <td>0.032874</td>\n",
       "      <td>-0.000525</td>\n",
       "      <td>0.004489</td>\n",
       "      <td>-0.010383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.127180</td>\n",
       "      <td>-0.016100</td>\n",
       "      <td>0.115260</td>\n",
       "      <td>-0.033943</td>\n",
       "      <td>-0.002260</td>\n",
       "      <td>-0.030860</td>\n",
       "      <td>-0.003700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <td>0.016925</td>\n",
       "      <td>0.058809</td>\n",
       "      <td>0.051708</td>\n",
       "      <td>-0.140760</td>\n",
       "      <td>0.127180</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.116605</td>\n",
       "      <td>0.565620</td>\n",
       "      <td>-0.045242</td>\n",
       "      <td>-0.014477</td>\n",
       "      <td>-0.009500</td>\n",
       "      <td>-0.004710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <td>0.039211</td>\n",
       "      <td>0.082912</td>\n",
       "      <td>0.075948</td>\n",
       "      <td>-0.062290</td>\n",
       "      <td>-0.016100</td>\n",
       "      <td>-0.116605</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.187828</td>\n",
       "      <td>-0.059675</td>\n",
       "      <td>-0.001665</td>\n",
       "      <td>0.010522</td>\n",
       "      <td>-0.059187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoanAmount</th>\n",
       "      <td>0.035684</td>\n",
       "      <td>0.107930</td>\n",
       "      <td>0.147141</td>\n",
       "      <td>-0.166998</td>\n",
       "      <td>0.115260</td>\n",
       "      <td>0.565620</td>\n",
       "      <td>0.187828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.038801</td>\n",
       "      <td>-0.007738</td>\n",
       "      <td>-0.044776</td>\n",
       "      <td>-0.036416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <td>-0.028945</td>\n",
       "      <td>-0.073567</td>\n",
       "      <td>-0.100863</td>\n",
       "      <td>-0.077242</td>\n",
       "      <td>-0.033943</td>\n",
       "      <td>-0.045242</td>\n",
       "      <td>-0.059675</td>\n",
       "      <td>0.038801</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001395</td>\n",
       "      <td>-0.077620</td>\n",
       "      <td>-0.020974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit_History</th>\n",
       "      <td>-0.032429</td>\n",
       "      <td>0.012819</td>\n",
       "      <td>0.005852</td>\n",
       "      <td>-0.077936</td>\n",
       "      <td>-0.002260</td>\n",
       "      <td>-0.014477</td>\n",
       "      <td>-0.001665</td>\n",
       "      <td>-0.007738</td>\n",
       "      <td>0.001395</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001880</td>\n",
       "      <td>0.540483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Property_Area</th>\n",
       "      <td>-0.155416</td>\n",
       "      <td>-0.025752</td>\n",
       "      <td>0.004257</td>\n",
       "      <td>-0.065243</td>\n",
       "      <td>-0.030860</td>\n",
       "      <td>-0.009500</td>\n",
       "      <td>0.010522</td>\n",
       "      <td>-0.044776</td>\n",
       "      <td>-0.077620</td>\n",
       "      <td>-0.001880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_Status</th>\n",
       "      <td>0.011773</td>\n",
       "      <td>0.017987</td>\n",
       "      <td>0.091478</td>\n",
       "      <td>-0.085884</td>\n",
       "      <td>-0.003700</td>\n",
       "      <td>-0.004710</td>\n",
       "      <td>-0.059187</td>\n",
       "      <td>-0.036416</td>\n",
       "      <td>-0.020974</td>\n",
       "      <td>0.540483</td>\n",
       "      <td>0.032112</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Loan_ID    Gender   Married  Education  Self_Employed  \\\n",
       "Loan_ID            1.000000 -0.028029 -0.016013   0.039442       0.032874   \n",
       "Gender            -0.028029  1.000000  0.364569   0.045364      -0.000525   \n",
       "Married           -0.016013  0.364569  1.000000   0.012304       0.004489   \n",
       "Education          0.039442  0.045364  0.012304   1.000000      -0.010383   \n",
       "Self_Employed      0.032874 -0.000525  0.004489  -0.010383       1.000000   \n",
       "ApplicantIncome    0.016925  0.058809  0.051708  -0.140760       0.127180   \n",
       "CoapplicantIncome  0.039211  0.082912  0.075948  -0.062290      -0.016100   \n",
       "LoanAmount         0.035684  0.107930  0.147141  -0.166998       0.115260   \n",
       "Loan_Amount_Term  -0.028945 -0.073567 -0.100863  -0.077242      -0.033943   \n",
       "Credit_History    -0.032429  0.012819  0.005852  -0.077936      -0.002260   \n",
       "Property_Area     -0.155416 -0.025752  0.004257  -0.065243      -0.030860   \n",
       "Loan_Status        0.011773  0.017987  0.091478  -0.085884      -0.003700   \n",
       "\n",
       "                   ApplicantIncome  CoapplicantIncome  LoanAmount  \\\n",
       "Loan_ID                   0.016925           0.039211    0.035684   \n",
       "Gender                    0.058809           0.082912    0.107930   \n",
       "Married                   0.051708           0.075948    0.147141   \n",
       "Education                -0.140760          -0.062290   -0.166998   \n",
       "Self_Employed             0.127180          -0.016100    0.115260   \n",
       "ApplicantIncome           1.000000          -0.116605    0.565620   \n",
       "CoapplicantIncome        -0.116605           1.000000    0.187828   \n",
       "LoanAmount                0.565620           0.187828    1.000000   \n",
       "Loan_Amount_Term         -0.045242          -0.059675    0.038801   \n",
       "Credit_History           -0.014477          -0.001665   -0.007738   \n",
       "Property_Area            -0.009500           0.010522   -0.044776   \n",
       "Loan_Status              -0.004710          -0.059187   -0.036416   \n",
       "\n",
       "                   Loan_Amount_Term  Credit_History  Property_Area  \\\n",
       "Loan_ID                   -0.028945       -0.032429      -0.155416   \n",
       "Gender                    -0.073567        0.012819      -0.025752   \n",
       "Married                   -0.100863        0.005852       0.004257   \n",
       "Education                 -0.077242       -0.077936      -0.065243   \n",
       "Self_Employed             -0.033943       -0.002260      -0.030860   \n",
       "ApplicantIncome           -0.045242       -0.014477      -0.009500   \n",
       "CoapplicantIncome         -0.059675       -0.001665       0.010522   \n",
       "LoanAmount                 0.038801       -0.007738      -0.044776   \n",
       "Loan_Amount_Term           1.000000        0.001395      -0.077620   \n",
       "Credit_History             0.001395        1.000000      -0.001880   \n",
       "Property_Area             -0.077620       -0.001880       1.000000   \n",
       "Loan_Status               -0.020974        0.540483       0.032112   \n",
       "\n",
       "                   Loan_Status  \n",
       "Loan_ID               0.011773  \n",
       "Gender                0.017987  \n",
       "Married               0.091478  \n",
       "Education            -0.085884  \n",
       "Self_Employed        -0.003700  \n",
       "ApplicantIncome      -0.004710  \n",
       "CoapplicantIncome    -0.059187  \n",
       "LoanAmount           -0.036416  \n",
       "Loan_Amount_Term     -0.020974  \n",
       "Credit_History        0.540483  \n",
       "Property_Area         0.032112  \n",
       "Loan_Status           1.000000  "
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# correlation of dataset\n",
    "\n",
    "train_data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature Extraction\n",
    "# for train dataset\n",
    "x=train_data.drop(columns=['Loan_ID','Loan_Status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=train_data['Loan_Status'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "       0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1,\n",
       "       1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
       "       1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0,\n",
       "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1,\n",
       "       0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0,\n",
       "       1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=x.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.,   0.,   0., ..., 360.,   1.,   2.],\n",
       "       [  1.,   1.,   1., ..., 360.,   1.,   0.],\n",
       "       [  1.,   1.,   0., ..., 360.,   1.,   2.],\n",
       "       ...,\n",
       "       [  1.,   1.,   1., ..., 360.,   1.,   2.],\n",
       "       [  1.,   1.,   2., ..., 360.,   1.,   2.],\n",
       "       [  0.,   0.,   0., ..., 360.,   0.,   1.]])"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature extraction from test dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test=test_data.drop(columns=['Loan_ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test=x_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.        ,   1.        ,   0.        , ..., 360.        ,\n",
       "          1.        ,   2.        ],\n",
       "       [  1.        ,   1.        ,   1.        , ..., 360.        ,\n",
       "          1.        ,   2.        ],\n",
       "       [  1.        ,   1.        ,   2.        , ..., 360.        ,\n",
       "          1.        ,   2.        ],\n",
       "       ...,\n",
       "       [  1.        ,   0.        ,   0.        , ..., 360.        ,\n",
       "          0.82544379,   1.        ],\n",
       "       [  1.        ,   1.        ,   0.        , ..., 360.        ,\n",
       "          1.        ,   0.        ],\n",
       "       [  1.        ,   0.        ,   0.        , ..., 180.        ,\n",
       "          1.        ,   0.        ]])"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()\n",
    "x_train=scaler.fit_transform(x_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test=scaler.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.47234264, -1.37208932, -0.73780632, ...,  0.27985054,\n",
       "         0.45164045,  1.22329839],\n",
       "       [ 0.47234264,  0.72881553,  0.25346957, ...,  0.27985054,\n",
       "         0.45164045, -1.31851281],\n",
       "       [ 0.47234264,  0.72881553, -0.73780632, ...,  0.27985054,\n",
       "         0.45164045,  1.22329839],\n",
       "       ...,\n",
       "       [ 0.47234264,  0.72881553,  0.25346957, ...,  0.27985054,\n",
       "         0.45164045,  1.22329839],\n",
       "       [ 0.47234264,  0.72881553,  1.24474546, ...,  0.27985054,\n",
       "         0.45164045,  1.22329839],\n",
       "       [-2.11710719, -1.37208932, -0.73780632, ...,  0.27985054,\n",
       "        -2.41044061, -0.04760721]])"
      ]
     },
     "execution_count": 545,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datta Cleaning is Done, Now we are reaady to deploy on ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now my dataset is ready to deploy on Machine Learning model\n",
    "# here we will perform on various model (Classification model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random forest classifier\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf=RandomForestClassifier(n_estimators=50,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=50, random_state=1)"
      ]
     },
     "execution_count": 549,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=rf.predict(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction is done Successfully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Accuracy from Decision Tree Classifier is  0.75\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample=pd.read_csv(\"Sample_AV.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Loan_Status\n",
       "0  LP001015           N\n",
       "1  LP001022           N\n",
       "2  LP001031           N\n",
       "3  LP001035           N\n",
       "4  LP001051           N"
      ]
     },
     "execution_count": 554,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.DataFrame(y_pred,columns=[\"Loan_Status\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df1.replace(1,'Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df1.replace(0,'N')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Y</td>\n",
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       "      <th>1</th>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Loan_Status\n",
       "0           Y\n",
       "1           Y\n",
       "2           Y\n",
       "3           Y\n",
       "4           Y"
      ]
     },
     "execution_count": 558,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample=sample.drop(columns=['Loan_Status'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample['Loan_Status']=df1['Loan_Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .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>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Loan_Status\n",
       "0  LP001015           Y\n",
       "1  LP001022           Y\n",
       "2  LP001031           Y\n",
       "3  LP001035           Y\n",
       "4  LP001051           Y"
      ]
     },
     "execution_count": 561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample.to_csv(\"randomforest_output_3.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "XGBoostError",
     "evalue": "XGBoost Library (xgboost.dll) could not be loaded.\nLikely causes:\n  * OpenMP runtime is not installed (vcomp140.dll or libgomp-1.dll for Windows, libomp.dylib for Mac OSX, libgomp.so for Linux and other UNIX-like OSes). Mac OSX users: Run `brew install libomp` to install OpenMP runtime.\n  * You are running 32-bit Python on a 64-bit OS\nError message(s): [\"Could not find module 'c:\\\\users\\\\rishi\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\xgboost\\\\lib\\\\xgboost.dll' (or one of its dependencies). Try using the full path with constructor syntax.\"]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mXGBoostError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-5943d1bfe3f1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mxgboost\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mxgb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\rishi\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\xgboost\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDMatrix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDeviceQuantileDMatrix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBooster\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mtraining\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrabit\u001b[0m  \u001b[1;31m# noqa\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\rishi\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\xgboost\\core.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    173\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    174\u001b[0m \u001b[1;31m# load the XGBoost library globally\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 175\u001b[1;33m \u001b[0m_LIB\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_load_lib\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    176\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    177\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\rishi\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\xgboost\\core.py\u001b[0m in \u001b[0;36m_load_lib\u001b[1;34m()\u001b[0m\n\u001b[0;32m    156\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mlib_success\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m         \u001b[0mlibname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbasename\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlib_paths\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 158\u001b[1;33m         raise XGBoostError(\n\u001b[0m\u001b[0;32m    159\u001b[0m             \u001b[1;34m'XGBoost Library ({}) could not be loaded.\\n'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlibname\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    160\u001b[0m             \u001b[1;34m'Likely causes:\\n'\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mXGBoostError\u001b[0m: XGBoost Library (xgboost.dll) could not be loaded.\nLikely causes:\n  * OpenMP runtime is not installed (vcomp140.dll or libgomp-1.dll for Windows, libomp.dylib for Mac OSX, libgomp.so for Linux and other UNIX-like OSes). Mac OSX users: Run `brew install libomp` to install OpenMP runtime.\n  * You are running 32-bit Python on a 64-bit OS\nError message(s): [\"Could not find module 'c:\\\\users\\\\rishi\\\\appdata\\\\local\\\\programs\\\\python\\\\python38\\\\lib\\\\site-packages\\\\xgboost\\\\lib\\\\xgboost.dll' (or one of its dependencies). Try using the full path with constructor syntax.\"]\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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