Loan-Status.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a5c69875",
   "metadata": {},
   "source": [
    "## Downloading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c80a0cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import opendatasets as od"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5c735e3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_url = 'https://www.kaggle.com/ninzaami/loan-predication'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2254878e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping, found downloaded files in \".\\loan-predication\" (use force=True to force download)\n"
     ]
    }
   ],
   "source": [
    "od.download(dataset_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa53c2a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "132f8c70",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = '.\\loan-predication'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "18b6e7d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.ipynb_checkpoints', 'loan.csv']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "06f73b55",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_csv = data_dir + '/loan.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1046911",
   "metadata": {},
   "source": [
    "## Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cedf2ed2",
   "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 math\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8e7073b5",
   "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",
       "    </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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read csv from pandas\n",
    "data = pd.read_csv(train_csv)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b2316631",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checks the shape of dataframe\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8b08879",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               object\n",
       "Gender                object\n",
       "Married               object\n",
       "Dependents            object\n",
       "Education             object\n",
       "Self_Employed         object\n",
       "ApplicantIncome        int64\n",
       "CoapplicantIncome    float64\n",
       "LoanAmount           float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History       float64\n",
       "Property_Area         object\n",
       "Loan_Status           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checks the datatypes of columns\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "06b74eb4",
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checks the null values in dataframe\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "754c6e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop all the null values \n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6a506325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Again check null values after dropping all the null values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8d3aff43",
   "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>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>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5364.231250</td>\n",
       "      <td>1581.093583</td>\n",
       "      <td>144.735417</td>\n",
       "      <td>342.050000</td>\n",
       "      <td>0.854167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5668.251251</td>\n",
       "      <td>2617.692267</td>\n",
       "      <td>80.508164</td>\n",
       "      <td>65.212401</td>\n",
       "      <td>0.353307</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>36.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2898.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3859.000000</td>\n",
       "      <td>1084.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5852.500000</td>\n",
       "      <td>2253.250000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>33837.000000</td>\n",
       "      <td>600.000000</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       480.000000         480.000000  480.000000        480.000000   \n",
       "mean       5364.231250        1581.093583  144.735417        342.050000   \n",
       "std        5668.251251        2617.692267   80.508164         65.212401   \n",
       "min         150.000000           0.000000    9.000000         36.000000   \n",
       "25%        2898.750000           0.000000  100.000000        360.000000   \n",
       "50%        3859.000000        1084.500000  128.000000        360.000000   \n",
       "75%        5852.500000        2253.250000  170.000000        360.000000   \n",
       "max       81000.000000       33837.000000  600.000000        480.000000   \n",
       "\n",
       "       Credit_History  \n",
       "count      480.000000  \n",
       "mean         0.854167  \n",
       "std          0.353307  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical measure\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6743768e",
   "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>ApplicantIncome</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.112588</td>\n",
       "      <td>0.495310</td>\n",
       "      <td>-0.010838</td>\n",
       "      <td>-0.056152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <td>-0.112588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.190740</td>\n",
       "      <td>-0.005775</td>\n",
       "      <td>-0.008692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoanAmount</th>\n",
       "      <td>0.495310</td>\n",
       "      <td>0.190740</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050867</td>\n",
       "      <td>-0.040773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <td>-0.010838</td>\n",
       "      <td>-0.005775</td>\n",
       "      <td>0.050867</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit_History</th>\n",
       "      <td>-0.056152</td>\n",
       "      <td>-0.008692</td>\n",
       "      <td>-0.040773</td>\n",
       "      <td>0.032937</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ApplicantIncome  CoapplicantIncome  LoanAmount  \\\n",
       "ApplicantIncome           1.000000          -0.112588    0.495310   \n",
       "CoapplicantIncome        -0.112588           1.000000    0.190740   \n",
       "LoanAmount                0.495310           0.190740    1.000000   \n",
       "Loan_Amount_Term         -0.010838          -0.005775    0.050867   \n",
       "Credit_History           -0.056152          -0.008692   -0.040773   \n",
       "\n",
       "                   Loan_Amount_Term  Credit_History  \n",
       "ApplicantIncome           -0.010838       -0.056152  \n",
       "CoapplicantIncome         -0.005775       -0.008692  \n",
       "LoanAmount                 0.050867       -0.040773  \n",
       "Loan_Amount_Term           1.000000        0.032937  \n",
       "Credit_History             0.032937        1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checks correlation \n",
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7de882f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.title('Correlation Matrix')\n",
    "sns.heatmap(data.corr(), annot=True, fmt='.2f', cbar=True, cmap='Greens');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b653ac8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# encoding categorical column\n",
    "data.replace({'Loan_Status':{'N':0, 'Y':1}}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "48bb3bac",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</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>1</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>1</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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\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",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\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",
       "\n",
       "   Credit_History Property_Area  Loan_Status  \n",
       "1             1.0         Rural            0  \n",
       "2             1.0         Urban            1  \n",
       "3             1.0         Urban            1  \n",
       "4             1.0         Urban            1  \n",
       "5             1.0         Urban            1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4824c93f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Male' 'Female']\n",
      "['1' '0' '2' '3+']\n",
      "['Graduate' 'Not Graduate']\n",
      "[1. 0.]\n",
      "['Rural' 'Urban' 'Semiurban']\n"
     ]
    }
   ],
   "source": [
    "# prints the unique value\n",
    "print(data['Gender'].unique())\n",
    "print(data['Dependents'].unique())\n",
    "print(data['Education'].unique())\n",
    "print(data['Credit_History'].unique())\n",
    "print(data['Property_Area'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9a8daa62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     274\n",
       "2      85\n",
       "1      80\n",
       "3+     41\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checks the value counts\n",
    "data['Dependents'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "de16fc30",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.replace(to_replace='3+', value=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4d618ef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    274\n",
       "2     85\n",
       "1     80\n",
       "4     41\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checks the value counts\n",
    "data['Dependents'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "63332d00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    410\n",
       "0.0     70\n",
       "Name: Credit_History, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checks the value counts\n",
    "data['Credit_History'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8d017caf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    332\n",
       "0    148\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checks the value counts\n",
    "data['Loan_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "017c49f8",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a320a854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.countplot(x='Gender', data=data);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35bd82e9",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Nearly 80% of the applicants in dataset are Male."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "af7b9c05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AbEaLil4n+e9VdUNVbZvWju7uu6ftryQ5ejGjAbBZLeQ9vSR/v7t3VdX3J7m2qv5w5cHu7qrqfT1wiuS2JDnxxBPnnxSATWMhr/S6e9f0894kH0pyepJ7quqYJJl+3vswj72ku7d299alpaX1GhmATWDdo1dVT66qp+zZTvKTSW5JcnWS86e7nZ/kqvWeDYDNbRGnN49O8qGq2vP7f6e7f6+qPpvkyqq6IMmXk5yzgNkA2MTWPXrd/cUkz9zH+teSPHe95wFgHBvpTxYAYFaiB8AwRA+AYYgeAMMQPQCGIXoADEP0ABiG6AEwDNEDYBiiB8AwRA+AYYgeAMMQPQCGIXoADEP0ABiG6AEwDNEDYBiiB8AwRA+AYYgeAMMQPQCGIXoADEP0ABiG6AEwDNEDYBiiB8AwRA+AYYgeAMMQPQCGIXoADEP0ABiG6AEwDNEDYBiiB8AwRA+AYYgeAMMQPQCGIXoADEP0ABiG6AEwDNEDYBiiB8AwRA+AYYgeAMMQPQCGIXoADEP0ABiG6AEwDNEDYBiiB8AwNlz0qur5VXVHVe2sqtcveh4ANo8NFb2qOijJf0rygiSnJDmvqk5Z7FQAbBYbKnpJTk+ys7u/2N1/keSKJGcteCYANomDFz3AXo5LcueK/buS/N2Vd6iqbUm2Tbv3VdUd6zQbD++oJF9d9BCPN/WW8xc9AmvPv4UD8YZa62f8Gw93YKNF71F19yVJLln0HDykqnZ099ZFzwGL5t/CxrfRTm/uSnLCiv3jpzUAeMw2WvQ+m+Tkqjqpqp6Y5NwkVy94JgA2iQ11erO7H6iqVyf5aJKDklzW3bcueCwendPNsMy/hQ2uunvRMwDAuthopzcBYDaiB8AwRI9HVcs+WVUvWLH20qr6vUXOBYtSVV1VF6/Y/9dVddECR2KVRI9H1ctv/P6LJG+tqkOr6rAkv5LkwsVOBgtzf5IXV9VRix6E/SN6rEp335Lkw0n+bZJfTPKeJL9QVddX1eeq6qwkqaqnT2s3VdXNVXXyAseGuTyQ5Ss1/9XeB6pqS1V9bPrv/3VVdeL6j8fDcfUmq1ZVT05yY5K/SPKRJLd293uq6vAk1yc5Lcmbk3y6u987/a3lQd39Z4uaGeZQVfclOTbJzUmemeRnkhzW3RdV1YeTvL+7t1fVTyd5YXefvbhpWUn02C9V9cYk9yU5J8mhWf5/vElyZJLnZTl8v5Dk8iQf7O4vLGJOmFNV3dfdh03/Hv4yyZ/loeh9Nckx3f2XVXVIkru722nQDcLpTfbXd6dbJfmn3X3qdDuxu2/v7t9J8sIs/4/ANVX1nEUOCzP7j0kuSPLkBc/BKokeB+qjSV5TVZUkVXXa9PMHknyxu9+e5Kokz1jciDCv7v56kiuzHL49/iDLH6GYJP88yf9a77l4eKLHgfrlJIckubmqbp32k+XTnrdU1U1JfjjLpzlhM7s4y18ptMdrkryyqm5O8rIkr13IVOyT9/QAGIZXegAMQ/QAGIboATAM0QNgGKIHwDBEDxZo+rT+96zYP7iqdlfVRx7j8x5bVe/fz8e8q6pe8lh+L2x0ogeL9Z0kP1xV3zPt/0SSXfvzBFV18N773f3H3S1gsBfRg8W7Jsk/nrbPS/K+PQeq6vSq+tT0TRZ/UFU/NK2/oqqurqqPJbluH/tbquqW6b4HVdWvVdVnp0/+f9W0XlX1jqq6o6p+P8n3r+d/aFgE0YPFuyLJuVV1aJY/tu0zK479YZJ/0N2nZfkrnX5lxbFnJXlJd/+jh9nf44Ik3+zuH0nyI0l+pqpOSvKiJD+U5JQkL0/yo2v7Hws2noMf/S7AnLr75qrakuVXedfsdfipSbZP30vYWf7otz2unT778eH29/jJJM9Y8X7dU5OcnOQfJnlfdz+Y5I+nV4mwqYkebAxXJ3lLkmcn+b4V67+c5OPd/aIpjP9jxbHv7PUce+/vUUle090f/f8Wq858DPPC45LTm7AxXJbkl7r783utPzUPXdjyigN87o8m+ZfTd7ulqn5w+kLgTyT5Z9N7fsck+fEDfH543BA92AC6+67p65j29qtJ/n1VfS4HfmbmvyS5LcmN08Utvzk914eSfGE6dnmSTx3g88Pjhm9ZAGAYXukBMAzRA2AYogfAMEQPgGGIHgDDED0AhiF6AAzj/wEOjU8jU0PEfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.countplot(x='Married', data=data);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c8d767e",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Nearly 65% of the applicants in dataset are Married."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e4bcf2bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.countplot(x='Dependents', data=data);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37a0974d",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Most of the applicants doesn't have any dependents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "75b5367d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.countplot(x='Education', data=data);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c263914",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Around 80% of applicant are Graduate's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9daa90ce",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.countplot(x='Self_Employed', data=data);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdaf7140",
   "metadata": {},
   "source": [
    "## insight\n",
    "Nearly 15% of the applicants in dataset are Self Employed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9382d3fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.countplot(x='Credit_History', data=data);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c7e699b",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Nearly 85% of the applicants have repaid their debts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c7396adf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.countplot(x='Property_Area', data=data);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ac90bfa",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Most of the applicant are from semiurban area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "03b57ff2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.countplot(x='Loan_Status', data=data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a391648f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.histplot(data['ApplicantIncome'], bins=30, kde=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8f6b05f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AOud2OueeCocHJG2UNFfSNZJWhrOtlHRtVDVEIWGm5YtatXdwRDv2HYq7HADACSrJMUAzWyTpLZJWS+pwzu0MX+qW1DHJe643s7Vmtra3t7zOvDx1VoPSyYQ2vN4fdykAgBMUeQCaWYOk70v6tHPuiMRwwT7ECfcjOuduc84td84tb29vj7rM45JKJtTV2agtPQc0nKN3GACoRJEGoJmlFITfN51zPwgn7zKz2eHrsyX1RFlDVM6Y06Rc3mlT90DcpQAATkCUZ4GapK9K2uicu7XgpXskrQiHV0j6YVQ1RGlWY0YzGtJ6gQAEgIoU5RbgRZI+LOkyM3smfFwt6WZJ7zKzzZIuD8crjplpycwGde8f0hCdZANAxamJ6oOdc49JsklefmdUyy2lhTOyWrN1r7bt5Y7xAFBp6AnmJHQ21SpTk9CrewhAAKg0BOBJSCRMC9qy9A0KABWIADxJi2bU6+DImFLti+IuBQBwHAjAk7RwRlaSVHfKeTFXAgA4HgTgSarP1Kg1m1Jm3rK4SwEAHAcCcBp0NtcqM/d0OscGgApCAE6D2c11SmabORsUACoIATgNOptqJUlPbeuLuRIAQLEIwGkwoyGt/PAgAQgAFYQAnAYJMw3vfFFPb9sXdykAgCIRgNNk+PUX9EL3gAZHcnGXAgAoAgE4TUZ2vKCxvNOzr+2PuxQAQBEIwGky3L1ZkrThdQIQACoBAThN8oP71dGU0YbX+489MwAgdgTgNFo2u0nPE4AAUBEIwGl0xpxmbek9wA1yAaACEIDTaNmcJo3lnV7cNRB3KQCAYyAAp9EZc5okid2gAFABCMBpNL81q4ZMDSfCAEAFIACnUSJhwYkwOwlAACh3BOA0WzanSRt39mssz62RAKCcEYDTbNnsJg2OjGnbXm6NBADljACcZl2djZKkTd3sBgWAckYATrOlHQ0ykzZ1H4i7FADAFAjAaZZN12hBW1abdrEFCADljACMQFdHozZ1czE8AJQzAjACXZ2N2rpnkC7RAKCMEYAR6Ops1Fje6aVejgMCQLkiACPQ1TF+Jii7QQGgXBGAEVg0s17pZIIABIAyRgBGIJVMaMmsBm3irhAAULYIwIh0dTSwBQgAZYwAjEhXZ5N27h/S/kOjcZcCAJgAARiRrs4GSeLmuABQpgjAiHR1BjfHZTcoAJQnAjAic5pr1ZipIQABoEwRgBExM53WSZdoAFCuCMAIdXU2atOuATnHzXEBoNwQgBHq6mjU/kOj2tU/HHcpAICjEIAROnxzXM4EBYCyQwBG6I0+Qbk3IACUGwIwQq31ac1qzHB3eAAoQwRgxIITYdgCBIByQwBGrKujUZt3HdBYnjNBAaCcEIAR6+ps1HAur1f3HIy7FABAAQIwYofPBOWCeAAoKwRgxJbOapQZl0IAQLkhACNWl05qYVuWLUAAKDMEYAmMd4kGACgfBGAJdHU0auvugxoaHYu7FABAiAAsga7OJuWdtKWHC+IBoFwQgCUwfiboCxwHBICyQQBOF0vIzCZ8LO1sVn50WNd/9guHp81fsDDuigHAazVxF1A1XF63rto06cvffmKbUlf8rn77ps9Ikm68oqtUlQEAJsAWYIm0N2TUOzDMzXEBoEwQgCXS3pjRcC6vgaFc3KUAAEQAlsysxlpJUs8Ad4cHgHJAAJbIjIa0TFIvAQgAZYEALJFUMqHW+rR6DxCAAFAOCMASam/MsAUIAGWCACyh9oaMDgznNDjCiTAAEDcCsIRmNWYkcSIMAJQDArCEZjUFAbhr/1DMlQAAIgtAM7vDzHrM7LmCaW1mdr+ZbQ6fW6NafjnK1CTVVp9Wdz8BCABxi3IL8OuSrjxq2k2SHnDOLZX0QDjulY6mjHb1swsUAOIWWQA6534mae9Rk6+RtDIcXinp2qiWX646m2p1aHRMNc0dcZcCAF4r9THADufcznC4W9KkKWBm15vZWjNb29vbW5rqSqCzKegRJj2HzrABIE6xnQTjgl6hJ+0Z2jl3m3NuuXNueXt7ewkri9aMhoySCVNm9mlxlwIAXit1AO4ys9mSFD73lHj5sUsmTLMaM0oTgAAQq1IH4D2SVoTDKyT9sMTLLwudTbVKdyzRSC4fdykA4K0oL4O4U9IvJHWZ2XYz+6ikmyW9y8w2S7o8HPfOnJY6JVIZrd+xP+5SAMBbkd0R3jn3u5O89M6ollkp5rQEJ8KsfmWPzlvo1aWQAFA26AkmBtl0jUZ2b9OaV46+SgQAUCoEYEyGX9ugtVv7NJaf9ERYAECECMCYDL32nA4M57RxZ3/cpQCAlwjAmAxvD7pIXc1uUACIBQEYk7GBPVrQltXql/fEXQoAeIkAjNEFi9v0i5f3KDfG9YAAUGoEYIzecdosDQzl9Oz2fXGXAgDeIQBjdPGpM5Uw6ZFN1dPZNwBUCgIwRs3ZlM6Z36JHXiQAAaDUCMCYveO0WVq3Y7/2HhyJuxQA8AoBGLN3dLXLOenRzWwFAkApEYAxe/PcZrVmU3rwBe/uDAUAsSIAY5ZMmC5/U4ce3NjD7ZEAoIQIwDJw1Zs7NTCc089f2h13KQDgDQKwDFx06kw1ZGp033PdcZcCAN4gAMtApiapy06fpVXP7+LuEABQIgRgmbjyzE7tPTii1a/QNygAlAIBWCYu6WpXNp3UPc+8HncpAOAFArBMZNM1uurM2frPdTs1NDoWdzkAUPUIwLhYQmZ2xONfP/MRDQznNOOsS3/ltfkLFsZdMQBUlZq4C/CWy+vWVZuOnOSc7vj5Vi36/S/omnPmHvHajVd0lbI6AKh6bAGWETPT6Z2NenXvoA4O5+IuBwCqGgFYZpbNaZJz0obX++MuBQCqGgFYZlqzaS1oy2r9jv3Kc00gAESGACxDb57brAPDOb2y52DcpQBA1SIAy9DimfVqyNRo3fb9cZcCAFWLACxDiYTpzLlN2rZ3UHsODMddDgBUJQKwTJ01t0XJhOmpbfviLgUAqhIBWKbq0kktm92kTd0DXBIBABEgAMvYuQtaNOacnnltX9ylAEDVIQDLWEs2rVNnNWjd9v2yTH3c5QBAVSEAy9xbF7VpZCyvpuW/GXcpAFBVCMAy196Y0eKZ9Wpcfo36h0bjLgcAqgYBWAF+7ZQ2JWsb9LXHtsZdCgBUDQKwAsxqqtXgi7/QVx59WXsPjsRdDgBUBQKwQvT9bKUGR3L6l4e2xF0KAFQFArBC5PZs13XnzdM3fvGqtvcNxl0OAFQ8ArCCfPry05RISLf8ZNOxZwYATIkArCBzWup0/duX6EfPvq61W/fGXQ4AVDQCsMJ87B2L1dlUq7++93nuFwgAJ4EArDDZdI1uuup0rdu+X99asy3ucgCgYhGAFeiac+bobUtm6JafvKDeAW6XBAAnggCsQGamv7n2TA2P5vU39z4fdzkAUJEIwAq1pL1BH790ie559nXdt6E77nIAoOIQgBXs45ecqmWzm/Tnd6+nhxgAOE4EYKWwhMzsiEcmldRP//Yj6t1/UF0f+vwRr81fsDDuigGgrNXEXQCK5PK6ddXEF8A/+WqfHktepHffsVpnz2uRJN14RVcJiwOAysMWYBU4d0GLFs7I6tHNu9UzMBR3OQBQEQjAKmBmumJZh+pSSf3o2Z06OJyLuyQAKHsEYJXIpmv03rNma2h0TP+5fqeUTMVdEgCUNQKwisxqqtUVyzq0c/+Q2n/zs8qN5eMuCQDKFgFYZZZ2NOqS09qVPe1CfeaudYQgAEyCAKxCZ89vUd8jK3X30zv0iW89paHRsbhLAoCyQwBWqf5ffk9/9d5lum/DLn3w9tXq3s/ZoQBQiACsYr930Sn65w+8RRt39uvqf3xUqzZ0yzluoQQAEgFY9d5z1hzd88mLNasxo+u/8aQ+unKtNry+P+6yACB2BKAHTp3VoB/90cX6i3e/SWte2at3/+Nj+t3bfqlvr9mm3Qe4nRIAP9EVmidSyYT+4NcX63eWz9eda7bpW6u36aYfrJd+sF6LZmT18lM/U/+2FzS6e5tG9mxTrm+nlJ/45Jl58xfotW2vlrgFADC9CEDPNNel9LF3LNEfvn2xNrzer8e27NZTr/Zpc/M8tfz68sPzJUxqzabVVv/GY3ZzrRprU/QzCqAqEICeMjOdObdZZ85tDsZXnK9b/muj+g6OaM/BEe0NHz0Dw9rcc+Dw+9rq02q59Pf1+JbdOv+UNqWS7EUHUJkIwGoV3j7peKSSCc1qqtWsptojpufG8tp7cETb+w5p656DajrvvfrA7avVkk3p8jd16MozOnXx0pmqTSWnswUAECkCsFpNcfukiUy1W7OmIBjPXdiqP3n32frxU6/ovg3dum9Dt+56crvq00ldevosXXlmpy7tmqX6DF8tAOWNv1I4bm50SFee2akrz+zUSC6vx1/arfs2dGvVhl26d91OpWsSWr6wVRcunqELl8zQWfNalK5hVymA8hJLAJrZlZL+QVJS0u3OuZvjqAMnaLLdq5ZQZt4yZZdeoId3nKXHX1os3S+5sVGdMW+G3jS7SW+a3ah5rXXBFmVjRjPqM0omTMmEKWHScC6v/qFR9R/KqX9oVH0FxyP3Do6o7+CIhnN55fJO+byTmdSYSaklm1JTXUrtDRl1NtdqdnOtZrfUqYEt0eMyf8FCvd7bp2TjTNU0zlCycaaSTTNV0zBDibomyeXl8mNSPqf80EHV1+T1d1/4n5rVVKu5LXWa21LH1j8qRsm/qWaWlPQvkt4labukJ8zsHufc86WuBSeoyN2rh0bGtH3foO68/V804/Q/1M829+r7T20/8cXmRjV2qF9udFhyY3IuL8mUrG1QorZBVpP+lffkhw8qN7BbYwN7lOvv1djAHjWl8lr55X9QU22NsukaZdNJ1aWTqk/XKF2TUMKCMD7eY6ilNt6rT2HnPu6o14dzeR0aHdOhkbHDz/1Do9p9YFi7B0a0+8CwdvUPaef+IXX3D8l+50uan8ocsRyTVJ+pUV0qKSenvJPyeaeh3JiGRvP63PfXHzF/azalea1ZzW2p07zWOs1pqVNbfVpNdTVqqg3+UWmqTakulVQyaaoJ/wGqSVjZ/8wrTWHPT1N9T454zxGvFU53Ggm/T0MjwfPgSE6HRsf0vg+u0N7+A0qk65TI1MsyWSXSWSUy2WA8nVUiUxf8jlpCNZt+qm2Pfn96G3sC4vhX7a2StjjnXpYkM/u2pGskEYBVpi6d1NJZjdr38Nf0jYfukCTtPTii1/cdUu/AsHoGhtQ3OKqxvNNY3imXd6pNJfS5P75BH7jxr5WpSaouFYRTXSqpVHLiP5A3XtGlW1dtUm4sr4MjYzowlNOB4ZwGhkd1YKhZB4ZnBeNDOQ2OBNc2/t7XniiqDQlTGIgmC4fHOR37j4uO+gNy9PyT/SGa7I/QdEsnE+pozmh2U53OnteidT/+ht77oT9UQ22NGjMp1WeCfwwSiYmD6carztBrvfvVvX9IO/Yd0va+Q9qx75B29B3S5p4BPfxij4ZGi78jScKkmkRCZtKJZKHp+N90Mpl7dEBM/pomGSnue1RsSMUlecnH1V4wbpLSNYk3HsngefyfnJ8/eHtcpR7BSt03pJldJ+lK59wfhOMflvRrzrlPHjXf9ZKuD0e7JBV/RsfkZkraPQ2fUy6qrT1S9bWJ9pS/amtTtbVHmrhNC51z7RPNXKyy3VnvnLtN0m3T+ZlmttY5t/zYc1aGamuPVH1toj3lr9raVG3tkaJrUxyn5u2QNL9gfF44DQCAkokjAJ+QtNTMTjGztKT3S7onhjoAAB4r+S5Q51zOzD4p6T4Fl0Hc4ZzbUKLFT+su1TJQbe2Rqq9NtKf8VVubqq09UkRtKvlJMAAAlAO65wAAeIkABAB4yYsANLMrzWyTmW0xs5virmcqZrbVzNab2TNmtjac1mZm95vZ5vC5NZxuZvaPYbvWmdm5BZ+zIpx/s5mtKHEb7jCzHjN7rmDatLXBzM4Lf0ZbwvdG2n3IJO35vJntCNfTM2Z2dcFrfxbWtsnMfqNg+oTfw/CEsNXh9O+EJ4dFyszmm9lDZva8mW0ws0+F0ytyPU3RnopdT2ZWa2ZrzOzZsE1fmKoOM8uE41vC1xedaFtL3J6vm9krBevonHB69N8551xVPxScaPOSpMWS0pKelbQs7rqmqHerpJlHTfuipJvC4Zsk3RIOXy3pvxR0vHCBpNXh9DZJL4fPreFwawnb8HZJ50p6Loo2SFoTzmvhe6+KoT2fl/SnE8y7LPyOZSSdEn73klN9DyV9V9L7w+EvS/ofJVhHsyWdGw43SnoxrL0i19MU7anY9RT+3BrC4ZSk1eHPc8I6JH1c0pfD4fdL+s6JtrXE7fm6pOsmmD/y75wPW4CHu15zzo1IGu96rZJcI2llOLxS0rUF0//dBX4pqcXMZkv6DUn3O+f2Ouf6JN0v6cpSFeuc+5mkvUdNnpY2hK81Oed+6YJv/L8XfFYp2zOZayR92zk37Jx7RdIWBd/BCb+H4X+ol0m6K3x/4c8mMs65nc65p8LhAUkbJc1Vha6nKdozmbJfT+HPevxu1Knw4aaoo3Dd3SXpnWHdx9XWGNozmci/cz4E4FxJrxWMb9fUvxhxc5JWmdmTFnQHJ0kdzrmd4XC3pI5weLK2lWObp6sNc8Pho6fH4ZPhrpk7xncV6vjbM0PSPudc7qjpJRPuKnuLgv/IK349HdUeqYLXk5klzewZST0K/tC/NEUdh2sPX98f1l02fyeObo9zbnwd/W24jr5kZuO9sUf+nfMhACvNxc65cyVdJekTZvb2whfD/2wq+tqVamiDpH+TtETSOZJ2Svp/sVZzgsysQdL3JX3aOddf+FolrqcJ2lPR68k5N+acO0dBj1lvlXR6vBWdnKPbY2ZnSvozBe06X8Fuzc+Vqh4fArCiul5zzu0In3sk3a3gS78r3LxX+NwTzj5Z28qxzdPVhh3h8NHTS8o5tyv8Zc5L+oqC9SQdf3v2KNi1U3PU9MiZWUpBWHzTOfeDcHLFrqeJ2lMN60mSnHP7JD0k6cIp6jhce/h6s4K6y+7vREF7rgx3Xzvn3LCkr+nE19Hxf+emOkBYDQ8Fvd28rODg7/iB3jPirmuSWuslNRYMP67g2N3f6cgTE74YDr9bRx4kXuPeOEj8ioIDxK3hcFuJ27JIR540Mm1t0K8e6L46hvbMLhj+YwXHWCTpDB15wsHLCk42mPR7KOl7OvKkho+XoD2m4BjJ3x81vSLX0xTtqdj1JKldUks4XCfpUUnvmawOSZ/QkSfBfPdE21ri9swuWId/L+nmUn3nIv0lK5eHgrOJXlSw//zP465nijoXh1/CZyVtGK9VwX78ByRtlvTTgpVtCm4u/JKk9ZKWF3zW7ys42L1F0u+VuB13KtjdNKpgP/xHp7MNkpZLei58zz8r7NGoxO35RljvOgV92Rb+of3zsLZNKjgLbbLvYbje14Tt/J6kTAnW0cUKdm+uk/RM+Li6UtfTFO2p2PUk6SxJT4e1PyfpL6eqQ1JtOL4lfH3xiba1xO15MFxHz0n6D71xpmjk3zm6QgMAeMmHY4AAAPwKAhAA4CUCEADgJQIQAOAlAhAA4CUCEF4ys04z+7aZvRR2O/djMzutBMv9vJn9aTj812Z2+TR//qfNLFswvtXMZk7nMoBqQQDCO2EHwXdLetg5t8Q5d56C7pg6pn7n9HLO/aVz7qfT/LGflpQ91kwACED46VJJo865L49PcM49K+kxM/s7M3suvKfY+6Sgf0kze8DMngqnXxNOX2RmL5jZN81so5ndNb71FW55fTGcf42ZnXp0EeF90K4Lh883s8fDe6WtMbPG8PMfDZf7lJm9LZz3EjN7OFze+PLNzG6QNEfSQ2b20FHLWhTW+BUL7sW2yszqwtdONbOfhst+ysyWhJ830c/iEjN7xMx+aGYvm9nNZvbBsOb1ZrYknK/dzL5vZk+Ej4umeyUCJy3KK/958CjHh6QbJH1pgum/raDH/aSCrcFtCu4zV6PgNiuSNFNB7xOmoHs0J+mi8LU7FN57TsF9Hcd78vmIpHvD4c8XzPN1Sdcp6IbqZUnnh9ObwmVmJdWG05ZKWhsOX6Kgp/95Cv6J/YWCTtTHlzuzoE1bw5oXScpJOiec/l1JHwqHV0v6rXC4NlzuZD+LSyTtC4czCvpa/EL43k8p7IpM0rcKalogaWPc650Hj6MfbAECb7hY0p0u6Dx5l6RHFPRQb5L+t5mtU9A92Fy9sbv0Nefcz8Ph/wg/Y9ydBc8XTrHcLkk7nXNPSJJzrt8Ft7NJSfqKma1X0MXVsoL3rHHObXdBJ8/PKAi4Y3nFOfdMOPykpEVm1ihprnPu7nDZQ865wSl+FpL0hAs6MB5W0OXUqnD6+oI6Lpf0z+Gtb+6R1BTeqQEoGzXHngWoOhsUbHkV64MKOvI9zzk3amZbFWwpSb96uyBXxHCx/ljSLklnK9jSGyp4bbhgeEzF/S4f/Z66E6jp6M/JF4znC+pISLrAOVdYM1BW2AKEjx6UlLE3bjgsMztLwa6994U37WyX9HYFnQo3S+oJw+9SSQsLPmuBmY1v3X1A0mMFr72v4PkXU9SzSdJsMzs/rKWx4HY2O8OtvA8r2B15LAOSGouYT9Lhu6dvN7Nrw2VnwuOYj2rin0WxVkn6o/ERMzvnON4LlAQBCO8455yk35J0eXgZxAZJ/0fBcat1Cu7G8aCkzzrnuiV9U9LycFfkRyS9UPBxmxTcuHijgluz/FvBa63hbtNPKdiam6yeEQUh+U9m9qyCY2+1kv5V0opw2umSDhbRvNsk/eTok2CO4cOSbghrfVxSp4KzZCf6WRTrBgU/s3Vm9rykjx3He4GS4G4QwAkys0UKTm45c4LXtiq4fcvuUtcFoDhsAQIAvMQWIADAS2wBAgC8RAACALxEAAIAvEQAAgC8RAACALz0/wFnwfdwxJECPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.histplot(data['CoapplicantIncome'], bins=30, kde=True, stat=\"percent\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b1252fd7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.histplot(data['LoanAmount'], bins=30, kde=True, stat=\"percent\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0bb59f84",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.histplot(data['Loan_Amount_Term'], kde=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86b5819a",
   "metadata": {},
   "source": [
    "## Encoding Categorical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bc50c113",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.replace({'Gender': {'Male':0, 'Female':1}, 'Married': {'No':0, 'Yes':1}, 'Education':{'Not Graduate':0, 'Graduate':1}, \n",
    "              'Self_Employed': {'No':0, 'Yes':1}, 'Property_Area': {'Rural':0, 'Urban':1, 'Semiurban':2}}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ab4ff30",
   "metadata": {},
   "source": [
    "## Separate Dependent & Independent Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2f6ce097",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(columns=['Loan_ID', 'Loan_Status'], axis=1)\n",
    "Y = data['Loan_Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "82c853a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
      "1         0        1          1          1              0             4583   \n",
      "2         0        1          0          1              1             3000   \n",
      "3         0        1          0          0              0             2583   \n",
      "4         0        0          0          1              0             6000   \n",
      "5         0        1          2          1              1             5417   \n",
      "..      ...      ...        ...        ...            ...              ...   \n",
      "609       1        0          0          1              0             2900   \n",
      "610       0        1          4          1              0             4106   \n",
      "611       0        1          1          1              0             8072   \n",
      "612       0        1          2          1              0             7583   \n",
      "613       1        0          0          1              1             4583   \n",
      "\n",
      "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
      "1               1508.0       128.0             360.0             1.0   \n",
      "2                  0.0        66.0             360.0             1.0   \n",
      "3               2358.0       120.0             360.0             1.0   \n",
      "4                  0.0       141.0             360.0             1.0   \n",
      "5               4196.0       267.0             360.0             1.0   \n",
      "..                 ...         ...               ...             ...   \n",
      "609                0.0        71.0             360.0             1.0   \n",
      "610                0.0        40.0             180.0             1.0   \n",
      "611              240.0       253.0             360.0             1.0   \n",
      "612                0.0       187.0             360.0             1.0   \n",
      "613                0.0       133.0             360.0             0.0   \n",
      "\n",
      "     Property_Area  \n",
      "1                0  \n",
      "2                1  \n",
      "3                1  \n",
      "4                1  \n",
      "5                1  \n",
      "..             ...  \n",
      "609              0  \n",
      "610              0  \n",
      "611              1  \n",
      "612              1  \n",
      "613              2  \n",
      "\n",
      "[480 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3367f68b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1      0\n",
      "2      1\n",
      "3      1\n",
      "4      1\n",
      "5      1\n",
      "      ..\n",
      "609    1\n",
      "610    1\n",
      "611    1\n",
      "612    1\n",
      "613    0\n",
      "Name: Loan_Status, Length: 480, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0a8718b",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "46969fdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, stratify=Y, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9ef859e7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X : (480, 11)\n",
      "X_train : (432, 11)\n",
      "X_test : (48, 11)\n"
     ]
    }
   ],
   "source": [
    "print('X :', X.shape)\n",
    "print('X_train :', X_train.shape)\n",
    "print('X_test :', X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e52bb73",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2186e70a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = svm.SVC(kernel='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2861f4e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8d93becb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 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, 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, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_prediction = model.predict(X_train)\n",
    "X_train_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "72cc0a05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_score : 0.7731481481481481\n"
     ]
    }
   ],
   "source": [
    "train_score = accuracy_score(Y_train, X_train_prediction)\n",
    "print('train_score :', accuracy_score(Y_train, X_train_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "dbcaa740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_prediction = model.predict(X_test)\n",
    "X_test_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "389a8553",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_score : 0.75\n"
     ]
    }
   ],
   "source": [
    "test_score = accuracy_score(Y_test, X_test_prediction)\n",
    "print('test_score :', accuracy_score(Y_test, X_test_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d1e5039",
   "metadata": {},
   "source": [
    "##  Losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "450a1f00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse : 0.22685185185185186\n",
      "rmse: 0.4762896722078402\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(Y_train, X_train_prediction)\n",
    "print('mse :', mean_squared_error(Y_train, X_train_prediction))\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(Y_train, X_train_prediction))\n",
    "print('rmse:', np.sqrt(mean_squared_error(Y_train, X_train_prediction)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d5e85e42",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse : 0.25\n",
      "rmse: 0.5\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(Y_test, X_test_prediction)\n",
    "print('mse :', mean_squared_error(Y_test, X_test_prediction))\n",
    "\n",
    "rmse =  np.sqrt(mean_squared_error(Y_test, X_test_prediction))\n",
    "print('rmse:', np.sqrt(mean_squared_error(Y_test, X_test_prediction)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7af459fe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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