Loan Amount Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e3766c18",
   "metadata": {},
   "source": [
    "# Loan Amount Prediction using Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86733951",
   "metadata": {},
   "source": [
    "#### Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0100b8d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5915c522",
   "metadata": {},
   "source": [
    "#### Importing the training Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6864b05d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "197c7c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.0</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>6</th>\n",
       "      <td>LP001013</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2333</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95.0</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>7</th>\n",
       "      <td>LP001014</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3036</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LP001018</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4006</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>168.0</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>9</th>\n",
       "      <td>LP001020</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>12841</td>\n",
       "      <td>10968.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</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",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "5  LP001011   Male     Yes          2      Graduate           Yes   \n",
       "6  LP001013   Male     Yes          0  Not Graduate            No   \n",
       "7  LP001014   Male     Yes         3+      Graduate            No   \n",
       "8  LP001018   Male     Yes          2      Graduate            No   \n",
       "9  LP001020   Male     Yes          1      Graduate            No   \n",
       "\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",
       "5             5417             4196.0       267.0             360.0   \n",
       "6             2333             1516.0        95.0             360.0   \n",
       "7             3036             2504.0       158.0             360.0   \n",
       "8             4006             1526.0       168.0             360.0   \n",
       "9            12841            10968.0       349.0             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  \n",
       "5             1.0         Urban           Y  \n",
       "6             1.0         Urban           Y  \n",
       "7             0.0     Semiurban           N  \n",
       "8             1.0         Urban           Y  \n",
       "9             1.0     Semiurban           N  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5f65fbf",
   "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>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "495da4d3",
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a89880a",
   "metadata": {},
   "source": [
    "#### Procressing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62595c83",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "513f6583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['loanAmount_log']= np.log(data['LoanAmount']) ## normalising the loan amount and visualizing\n",
    "data['loanAmount_log'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d763c00a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['totalincome']=data['ApplicantIncome']+data['CoapplicantIncome']  ## making the new attributes\n",
    "data['totalincome']=np.log(data['totalincome'])  #normalising the new attributes and normalisin g\n",
    "data['totalincome'].hist(bins=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "665dac7a",
   "metadata": {},
   "source": [
    "#### Handling the null values present in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d5ec40d1",
   "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",
       "loanAmount_log       0\n",
       "totalincome          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Gender'].fillna(data['Gender'].mode()[0], inplace= True)\n",
    "data['Married'].fillna(data['Married'].mode()[0], inplace= True)\n",
    "data['Dependents'].fillna(data['Dependents'].mode()[0], inplace= True)\n",
    "data['Self_Employed'].fillna(data['Self_Employed'].mode()[0], inplace= True)\n",
    "data['Loan_Amount_Term'].fillna(data['Loan_Amount_Term'].mode()[0], inplace= True)\n",
    "data['Credit_History'].fillna(data['Credit_History'].mode()[0], inplace= True)\n",
    "\n",
    "data.LoanAmount = data.LoanAmount.fillna(data.LoanAmount.mean())\n",
    "data.loanAmount_log = data.loanAmount_log.fillna(data.loanAmount_log.mean())\n",
    "\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ecebc774",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>loanAmount_log</th>\n",
       "      <th>totalincome</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>4.857444</td>\n",
       "      <td>8.674026</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>4.852030</td>\n",
       "      <td>8.714568</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>4.189655</td>\n",
       "      <td>8.006368</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>4.787492</td>\n",
       "      <td>8.505323</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>4.948760</td>\n",
       "      <td>8.699515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>5.587249</td>\n",
       "      <td>9.170872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LP001013</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2333</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>4.553877</td>\n",
       "      <td>8.255569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LP001014</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3036</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "      <td>5.062595</td>\n",
       "      <td>8.619750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LP001018</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4006</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>5.123964</td>\n",
       "      <td>8.618305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LP001020</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>12841</td>\n",
       "      <td>10968.0</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "      <td>5.855072</td>\n",
       "      <td>10.077819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "5  LP001011   Male     Yes          2      Graduate           Yes   \n",
       "6  LP001013   Male     Yes          0  Not Graduate            No   \n",
       "7  LP001014   Male     Yes         3+      Graduate            No   \n",
       "8  LP001018   Male     Yes          2      Graduate            No   \n",
       "9  LP001020   Male     Yes          1      Graduate            No   \n",
       "\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",
       "\n",
       "   Credit_History Property_Area Loan_Status  loanAmount_log  totalincome  \n",
       "0             1.0         Urban           Y        4.857444     8.674026  \n",
       "1             1.0         Rural           N        4.852030     8.714568  \n",
       "2             1.0         Urban           Y        4.189655     8.006368  \n",
       "3             1.0         Urban           Y        4.787492     8.505323  \n",
       "4             1.0         Urban           Y        4.948760     8.699515  \n",
       "5             1.0         Urban           Y        5.587249     9.170872  \n",
       "6             1.0         Urban           Y        4.553877     8.255569  \n",
       "7             0.0     Semiurban           N        5.062595     8.619750  \n",
       "8             1.0         Urban           Y        5.123964     8.618305  \n",
       "9             1.0     Semiurban           N        5.855072    10.077819  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "017e23b9",
   "metadata": {},
   "source": [
    "### Dividing the dataset into Independent and Dependent dataset\n",
    "\n",
    "#### x have Independent dataset\n",
    "#### y have Dependent dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "911e3e3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:,np.r_[1:5,9:11,13:15]].values\n",
    "y = data.iloc[:,12].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "61c0bf83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Male', 'No', '0', ..., 1.0, 4.857444178729352,\n",
       "        8.674025985443025],\n",
       "       ['Male', 'Yes', '1', ..., 1.0, 4.852030263919617,\n",
       "        8.714567550836485],\n",
       "       ['Male', 'Yes', '0', ..., 1.0, 4.189654742026425,\n",
       "        8.006367567650246],\n",
       "       ...,\n",
       "       ['Male', 'Yes', '1', ..., 1.0, 5.53338948872752,\n",
       "        9.025455532779063],\n",
       "       ['Male', 'Yes', '2', ..., 1.0, 5.231108616854587,\n",
       "        8.933664178700935],\n",
       "       ['Female', 'No', '0', ..., 0.0, 4.890349128221754,\n",
       "        8.430109084509125]], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd105e7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'Y', 'N', 'N', 'N', 'Y',\n",
       "       'Y', 'Y', 'N', 'Y', 'N', 'N', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y',\n",
       "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'N', 'N', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'N',\n",
       "       'N', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'N', 'N',\n",
       "       'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'N', 'N', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'N', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'N',\n",
       "       'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'N', 'N', 'N',\n",
       "       'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y',\n",
       "       'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'N', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'N',\n",
       "       'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y',\n",
       "       'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'N', 'Y', 'N', 'N', 'N', 'N',\n",
       "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y',\n",
       "       'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N',\n",
       "       'N', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'N', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'Y', 'N', 'N', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'N', 'N', 'Y',\n",
       "       'Y', 'N', 'Y', 'Y', 'Y', 'N', 'N', 'N', 'Y', 'N', 'Y', 'N', 'Y',\n",
       "       'N', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y',\n",
       "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'N', 'N', 'Y', 'N', 'Y', 'Y',\n",
       "       'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y',\n",
       "       'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'N', 'N', 'N',\n",
       "       'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N',\n",
       "       'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'N', 'N', 'Y', 'N',\n",
       "       'Y', 'N', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'N',\n",
       "       'N', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'N',\n",
       "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'N'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0e31ef9",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cc100eee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people who take loan as group by Gender: \n",
      "Male      502\n",
      "Female    112\n",
      "Name: Gender, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Number of people who take loan as group by Gender: \")\n",
    "print(data['Gender'].value_counts())\n",
    "sns.countplot(data=data, x=\"Gender\", palette='Set2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7be49753",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people who take loan as group by Martial Status: \n",
      "Yes    401\n",
      "No     213\n",
      "Name: Married, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Married', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Number of people who take loan as group by Martial Status: \")\n",
    "print(data['Married'].value_counts())\n",
    "sns.countplot(data=data, x=\"Married\", palette='Set2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8db641f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people who take loan as group by Dependents: \n",
      "0     360\n",
      "1     102\n",
      "2     101\n",
      "3+     51\n",
      "Name: Dependents, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Dependents', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Number of people who take loan as group by Dependents: \")\n",
    "print(data['Dependents'].value_counts())\n",
    "sns.countplot(data=data, x=\"Dependents\", palette='Set2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3a181420",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people who take loan as group by Self Employed: \n",
      "No     532\n",
      "Yes     82\n",
      "Name: Self_Employed, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Self_Employed', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Number of people who take loan as group by Self Employed: \")\n",
    "print(data['Self_Employed'].value_counts())\n",
    "sns.countplot(data=data, x=\"Self_Employed\", palette='Set2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c7f4aae9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people who take loan as group by Loan Amount: \n",
      "360.0    526\n",
      "180.0     44\n",
      "480.0     15\n",
      "300.0     13\n",
      "240.0      4\n",
      "84.0       4\n",
      "120.0      3\n",
      "60.0       2\n",
      "36.0       2\n",
      "12.0       1\n",
      "Name: Loan_Amount_Term, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Loan_Amount_Term', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Number of people who take loan as group by Loan Amount: \")\n",
    "print(data['Loan_Amount_Term'].value_counts())\n",
    "sns.countplot(data=data, x=\"Loan_Amount_Term\", palette='Set2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "16bad520",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people who take loan as group by Credit History: \n",
      "1.0    525\n",
      "0.0     89\n",
      "Name: Credit_History, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Credit_History', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Number of people who take loan as group by Credit History: \")\n",
    "print(data['Credit_History'].value_counts())\n",
    "sns.countplot(data=data, x=\"Credit_History\", palette='Set2')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bdc0368",
   "metadata": {},
   "source": [
    "## Label Encoding\n",
    "\n",
    "### Test and Train Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d4721328",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=0)\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelEncoder_x= LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8826ef4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, ..., 1.0, 3.784189633918261, 235],\n",
       "       [1, 0, 0, ..., 1.0, 4.836281906951478, 155],\n",
       "       [1, 0, 2, ..., 1.0, 5.087596335232384, 372],\n",
       "       ...,\n",
       "       [1, 1, 3, ..., 1.0, 5.298317366548036, 343],\n",
       "       [1, 1, 0, ..., 1.0, 5.075173815233827, 258],\n",
       "       [0, 1, 0, ..., 1.0, 5.204006687076795, 285]], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0, 5):\n",
    "    X_train[:,i]= labelEncoder_x.fit_transform(X_train[:,i])\n",
    "    X_train[:,7]= labelEncoder_x.fit_transform(X_train[:,7])\n",
    "    \n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5213c9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0,\n",
       "       1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1,\n",
       "       0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "       1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0,\n",
       "       1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1,\n",
       "       1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1,\n",
       "       1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1,\n",
       "       0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
       "       0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labelEncoder_y= LabelEncoder()\n",
    "y_train = labelEncoder_y.fit_transform(y_train)\n",
    "\n",
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d7f7fd3",
   "metadata": {},
   "source": [
    "### Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ae95faad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, ..., 1.0, 4.430816798843313, 105],\n",
       "       [0, 0, 0, ..., 1.0, 4.718498871295094, 38],\n",
       "       [1, 1, 0, ..., 1.0, 5.780743515792329, 127],\n",
       "       ...,\n",
       "       [1, 1, 0, ..., 1.0, 4.912654885736052, 132],\n",
       "       [1, 0, 0, ..., 1.0, 4.882801922586371, 83],\n",
       "       [1, 1, 1, ..., 0.0, 4.795790545596741, 44]], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0, 5):\n",
    "    X_test[:,i]= labelEncoder_x.fit_transform(X_test[:,i])\n",
    "    X_test[:,7]= labelEncoder_x.fit_transform(X_test[:,7])\n",
    "    \n",
    "X_test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8c93ed8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "       1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labelEncoder_y= LabelEncoder()\n",
    "y_test = labelEncoder_y.fit_transform(y_test)\n",
    "\n",
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a0a96cd",
   "metadata": {},
   "source": [
    "### Standardize features by removing the mean and scaling to unit variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fa2ab6cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "SS = StandardScaler()\n",
    "X_train = SS.fit_transform(X_train)\n",
    "X_test = SS.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af4a1a76",
   "metadata": {},
   "source": [
    "### To find which classifier is best for the prediction by accuracy\n",
    "\n",
    "#### Using Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "969cf020",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf_clasifier= RandomForestClassifier()\n",
    "rf_clasifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b389d155",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "y_pred= rf_clasifier.predict(X_test)\n",
    "\n",
    "print(\"According to Random Forest Prediction Percentage is \", metrics.accuracy_score(y_pred, y_test))\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f66d5bfa",
   "metadata": {},
   "source": [
    "#### Using Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cb2c277",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "NB_CLF= GaussianNB()\n",
    "NB_CLF.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "eaedc023",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "According to GaussianNB Prediction Percentage is  0.8311688311688312\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred= NB_CLF.predict(X_test)\n",
    "\n",
    "print(\"According to GaussianNB Prediction Percentage is \", metrics.accuracy_score(y_pred, y_test))\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce843c43",
   "metadata": {},
   "source": [
    "#### Using Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c265bda4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "DT_CLSIFIER= DecisionTreeClassifier()\n",
    "DT_CLSIFIER.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "92891dcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "According to DecisionTreeClassifier, Prediction Percentage is  0.6558441558441559\n"
     ]
    }
   ],
   "source": [
    "y_pred= DT_CLSIFIER.predict(X_test)\n",
    "\n",
    "print(\"According to DecisionTreeClassifier, Prediction Percentage is \", metrics.accuracy_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a845812a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "       1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f41e167",
   "metadata": {},
   "source": [
    "#### Using KNeighbors Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0729c9f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "KN_CLSIFIER= KNeighborsClassifier()\n",
    "KN_CLSIFIER.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0fea569a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "According to KNeighborsClassifier, Prediction Percentage is  0.8116883116883117\n"
     ]
    }
   ],
   "source": [
    "y_pred= KN_CLSIFIER.predict(X_test)\n",
    "\n",
    "print(\"According to KNeighborsClassifier, Prediction Percentage is \", metrics.accuracy_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5d479399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be852404",
   "metadata": {},
   "source": [
    "#### Using SVM Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1dcc2a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "SCV_clf = SVC()\n",
    "SCV_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4c39f210",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "According to SVC classifier, Prediction Percentage is  0.8311688311688312\n"
     ]
    }
   ],
   "source": [
    "y_pred = SCV_clf.predict(X_test)\n",
    "\n",
    "print(\"According to SVC classifier, Prediction Percentage is \", metrics.accuracy_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "40db92fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8df6e4d4",
   "metadata": {},
   "source": [
    "# After using the various classifier, the SVM classifier has the best accuracy of the prediction with 83.1%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ca4c79f",
   "metadata": {},
   "source": [
    "#### Importing the Testing Data and Preprocessing it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "238599e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfd = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6d5a4835",
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>LP002971</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4009</td>\n",
       "      <td>1777</td>\n",
       "      <td>113.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>LP002975</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4158</td>\n",
       "      <td>709</td>\n",
       "      <td>115.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>LP002980</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3250</td>\n",
       "      <td>1993</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>LP002986</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>2393</td>\n",
       "      <td>158.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>LP002989</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9200</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367 rows × 12 columns</p>\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",
       "362  LP002971   Male     Yes         3+  Not Graduate           Yes   \n",
       "363  LP002975   Male     Yes          0      Graduate            No   \n",
       "364  LP002980   Male      No          0      Graduate            No   \n",
       "365  LP002986   Male     Yes          0      Graduate            No   \n",
       "366  LP002989   Male      No          0      Graduate           Yes   \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",
       "362             4009               1777       113.0             360.0   \n",
       "363             4158                709       115.0             360.0   \n",
       "364             3250               1993       126.0             360.0   \n",
       "365             5000               2393       158.0             360.0   \n",
       "366             9200                  0        98.0             180.0   \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  \n",
       "..              ...           ...  \n",
       "362             1.0         Urban  \n",
       "363             1.0         Urban  \n",
       "364             NaN     Semiurban  \n",
       "365             1.0         Rural  \n",
       "366             1.0         Rural  \n",
       "\n",
       "[367 rows x 12 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5d20a3bc",
   "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: 34.5+ KB\n"
     ]
    }
   ],
   "source": [
    "dfd.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bb2f74c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>361.000000</td>\n",
       "      <td>338.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4805.599455</td>\n",
       "      <td>1569.577657</td>\n",
       "      <td>136.132597</td>\n",
       "      <td>342.537396</td>\n",
       "      <td>0.825444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4910.685399</td>\n",
       "      <td>2334.232099</td>\n",
       "      <td>61.366652</td>\n",
       "      <td>65.156643</td>\n",
       "      <td>0.380150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2864.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.250000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3786.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5060.000000</td>\n",
       "      <td>2430.500000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72529.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       367.000000         367.000000  362.000000        361.000000   \n",
       "mean       4805.599455        1569.577657  136.132597        342.537396   \n",
       "std        4910.685399        2334.232099   61.366652         65.156643   \n",
       "min           0.000000           0.000000   28.000000          6.000000   \n",
       "25%        2864.000000           0.000000  100.250000        360.000000   \n",
       "50%        3786.000000        1025.000000  125.000000        360.000000   \n",
       "75%        5060.000000        2430.500000  158.000000        360.000000   \n",
       "max       72529.000000       24000.000000  550.000000        480.000000   \n",
       "\n",
       "       Credit_History  \n",
       "count      338.000000  \n",
       "mean         0.825444  \n",
       "std          0.380150  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfd.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9b3e5637",
   "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfd.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dd8a878e",
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001054</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2165</td>\n",
       "      <td>3422</td>\n",
       "      <td>152.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LP001055</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2226</td>\n",
       "      <td>0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LP001056</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3881</td>\n",
       "      <td>0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LP001059</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13633</td>\n",
       "      <td>0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LP001067</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>123.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</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",
       "5  LP001054    Male     Yes          0  Not Graduate           Yes   \n",
       "6  LP001055  Female      No          1  Not Graduate            No   \n",
       "7  LP001056    Male     Yes          2  Not Graduate            No   \n",
       "8  LP001059    Male     Yes          2      Graduate           NaN   \n",
       "9  LP001067    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",
       "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",
       "\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  \n",
       "5             1.0         Urban  \n",
       "6             1.0     Semiurban  \n",
       "7             0.0         Rural  \n",
       "8             1.0         Urban  \n",
       "9             1.0     Semiurban  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfd.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "201bb3bc",
   "metadata": {},
   "source": [
    "### Dividing the dataset into Independent and Dependent dataset\n",
    "\n",
    "#### x have Independent dataset\n",
    "#### y have Dependent dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7f51ec9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_x = data.iloc[:,np.r_[1:5,9:11,13:15]].values\n",
    "test_y = data.iloc[:,12].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9505d341",
   "metadata": {},
   "source": [
    "#### Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2bb3b9a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0, 5):\n",
    "    test_x[:,i]= labelEncoder_x.fit_transform(test_x[:,i])\n",
    "    test_x[:,7]= labelEncoder_x.fit_transform(test_x[:,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "20013493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, ..., 1.0, 4.857444178729352, 320],\n",
       "       [1, 1, 1, ..., 1.0, 4.852030263919617, 333],\n",
       "       [1, 1, 0, ..., 1.0, 4.189654742026425, 42],\n",
       "       ...,\n",
       "       [1, 1, 1, ..., 1.0, 5.53338948872752, 436],\n",
       "       [1, 1, 2, ..., 1.0, 5.231108616854587, 416],\n",
       "       [0, 0, 0, ..., 0.0, 4.890349128221754, 185]], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e307bbc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_y = labelEncoder_y.fit_transform(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9bbfc310",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_x=SS.fit_transform(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "345cf3ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,\n",
       "       0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 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, 1, 0, 1, 0,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = SCV_clf.predict(test_x)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a7f6c60",
   "metadata": {},
   "source": [
    "# 1 predict that loan can approved\n",
    "# 0 predict loan can not approved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ba6a05a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
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