LogisticRegression_loan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b38cef6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae9f34b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d8fa5a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <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>...</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>LP002978</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>LP002979</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>LP002983</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>LP002984</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>LP002990</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 13 columns</p>\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",
       "609  LP002978  Female      No          0      Graduate            No   \n",
       "610  LP002979    Male     Yes         3+      Graduate            No   \n",
       "611  LP002983    Male     Yes          1      Graduate            No   \n",
       "612  LP002984    Male     Yes          2      Graduate            No   \n",
       "613  LP002990  Female      No          0      Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "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",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "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",
       "..              ...           ...         ...  \n",
       "609             1.0         Rural           Y  \n",
       "610             1.0         Rural           Y  \n",
       "611             1.0         Urban           Y  \n",
       "612             1.0         Urban           Y  \n",
       "613             0.0     Semiurban           N  \n",
       "\n",
       "[614 rows x 13 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"loan.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f1a55c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4aa9a66a",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "60d02ad2",
   "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": "markdown",
   "id": "2fee439d",
   "metadata": {},
   "source": [
    "## Dropping Unwanted Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "03fe783e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([\"Loan_ID\",\"CoapplicantIncome\"], axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b4ea1c44",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([\"Self_Employed\",\"Loan_Amount_Term\",\"Property_Area\"], axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0f52b484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender             13\n",
       "Married             3\n",
       "Dependents         15\n",
       "Education           0\n",
       "ApplicantIncome     0\n",
       "LoanAmount         22\n",
       "Credit_History     50\n",
       "Loan_Status         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ffdc4161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fcae504a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      489\n",
       "Female    112\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Gender\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "469535e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married  Dependents  LoanAmount  Credit_History\n",
       "No       0           120.0       1.0               9\n",
       "Yes      0           110.0       1.0               6\n",
       "No       0           110.0       1.0               5\n",
       "Yes      0           120.0       1.0               5\n",
       "                     115.0       1.0               4\n",
       "                                                  ..\n",
       "No       3+          292.0       1.0               1\n",
       "                     255.0       1.0               1\n",
       "                     185.0       1.0               1\n",
       "                     148.0       1.0               1\n",
       "Yes      3+          700.0       1.0               1\n",
       "Length: 401, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[\"Married\",\"Dependents\",\"LoanAmount\",\"Credit_History\"]].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "73560a95",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Gender\"].fillna(method=\"ffill\",inplace=True)\n",
    "data[\"Married\"].fillna(method=\"bfill\",inplace=True)\n",
    "data[\"Dependents\"].fillna(method=\"ffill\",inplace=True)\n",
    "data['LoanAmount'].fillna(method='bfill',inplace=True)\n",
    "data[\"Credit_History\"].fillna(method=\"ffill\",inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "809ca873",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data[\"Gender\"].fillna(\"0\",inplace=True)\n",
    "#data[\"Married\"].fillna(\"0\",inplace=True)\n",
    "#data[\"Dependents\"].fillna(\"0\",inplace=True)\n",
    "#data[\"LoanAmount\"].fillna(\"0\",inplace=True)\n",
    "#data[\"Credit_History\"].fillna(\"0\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "25122c0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    513\n",
       "0.0    101\n",
       "Name: Credit_History, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Credit_History\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "21c9cf45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender             0\n",
       "Married            0\n",
       "Dependents         0\n",
       "Education          0\n",
       "ApplicantIncome    0\n",
       "LoanAmount         0\n",
       "Credit_History     0\n",
       "Loan_Status        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b330fe98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e4e82c66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>5849</td>\n",
       "      <td>128.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>4583</td>\n",
       "      <td>128.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>3000</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>2583</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>6000</td>\n",
       "      <td>141.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender Married Dependents     Education  ApplicantIncome  LoanAmount  \\\n",
       "0   Male      No          0      Graduate             5849       128.0   \n",
       "1   Male     Yes          1      Graduate             4583       128.0   \n",
       "2   Male     Yes          0      Graduate             3000        66.0   \n",
       "3   Male     Yes          0  Not Graduate             2583       120.0   \n",
       "4   Male      No          0      Graduate             6000       141.0   \n",
       "\n",
       "   Credit_History Loan_Status  \n",
       "0             1.0           Y  \n",
       "1             1.0           N  \n",
       "2             1.0           Y  \n",
       "3             1.0           Y  \n",
       "4             1.0           Y  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0c6b54bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      614.000000\n",
       "mean      5403.459283\n",
       "std       6109.041673\n",
       "min        150.000000\n",
       "25%       2877.500000\n",
       "50%       3812.500000\n",
       "75%       5795.000000\n",
       "max      81000.000000\n",
       "Name: ApplicantIncome, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ApplicantIncome.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a6ad7699",
   "metadata": {},
   "outputs": [],
   "source": [
    "def salary(income):\n",
    "    if income<2877:\n",
    "        return 'low'\n",
    "    elif income<3812:\n",
    "        return 'medium'\n",
    "    else:\n",
    "        return 'high'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b72fb67f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"ApplicantIncome\"]=data[\"ApplicantIncome\"].apply(salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "60204266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>high</td>\n",
       "      <td>128.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>high</td>\n",
       "      <td>128.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>medium</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>low</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>high</td>\n",
       "      <td>141.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>high</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>high</td>\n",
       "      <td>253.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>high</td>\n",
       "      <td>187.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>high</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender Married Dependents     Education ApplicantIncome  LoanAmount  \\\n",
       "0      Male      No          0      Graduate            high       128.0   \n",
       "1      Male     Yes          1      Graduate            high       128.0   \n",
       "2      Male     Yes          0      Graduate          medium        66.0   \n",
       "3      Male     Yes          0  Not Graduate             low       120.0   \n",
       "4      Male      No          0      Graduate            high       141.0   \n",
       "..      ...     ...        ...           ...             ...         ...   \n",
       "609  Female      No          0      Graduate          medium        71.0   \n",
       "610    Male     Yes         3+      Graduate            high        40.0   \n",
       "611    Male     Yes          1      Graduate            high       253.0   \n",
       "612    Male     Yes          2      Graduate            high       187.0   \n",
       "613  Female      No          0      Graduate            high       133.0   \n",
       "\n",
       "     Credit_History Loan_Status  \n",
       "0               1.0           Y  \n",
       "1               1.0           N  \n",
       "2               1.0           Y  \n",
       "3               1.0           Y  \n",
       "4               1.0           Y  \n",
       "..              ...         ...  \n",
       "609             1.0           Y  \n",
       "610             1.0           Y  \n",
       "611             1.0           Y  \n",
       "612             1.0           Y  \n",
       "613             0.0           N  \n",
       "\n",
       "[614 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2f7ad971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tab=pd.crosstab(data[\"ApplicantIncome\"],data[\"Loan_Status\"])\n",
    "\n",
    "tab.plot(kind=\"bar\",stacked=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9684f1ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Status       N    Y\n",
      "ApplicantIncome         \n",
      "high             96  212\n",
      "low              48  106\n",
      "medium           48  104\n"
     ]
    }
   ],
   "source": [
    "print(tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2f515926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tab=pd.crosstab(data[\"Education\"],data[\"Loan_Status\"])\n",
    "\n",
    "tab.plot(kind=\"bar\",stacked=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5fef2677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Status    N    Y\n",
      "Gender               \n",
      "Female        37   77\n",
      "Male         155  345\n"
     ]
    }
   ],
   "source": [
    "tab_gender=pd.crosstab(data[\"Gender\"],data[\"Loan_Status\"])\n",
    "\n",
    "tab_gender.plot(kind=\"bar\",stacked=True)\n",
    "plt.show()\n",
    "print(tab_gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a1056da1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Status    N    Y\n",
      "Married              \n",
      "No            79  136\n",
      "Yes          113  286\n"
     ]
    }
   ],
   "source": [
    "tab=pd.crosstab(data[\"Married\"],data[\"Loan_Status\"])\n",
    "\n",
    "tab.plot(kind=\"bar\",stacked=True)\n",
    "plt.show()\n",
    "print(tab)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5b48741c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Status    N    Y\n",
      "Dependents           \n",
      "0            109  245\n",
      "1             37   67\n",
      "2             28   77\n",
      "3+            18   33\n"
     ]
    }
   ],
   "source": [
    "tab=pd.crosstab(data[\"Dependents\"],data[\"Loan_Status\"])\n",
    "\n",
    "tab.plot(kind=\"bar\",stacked=True)\n",
    "plt.show()\n",
    "print(tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27453dfc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ecd3eb49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    614.000000\n",
       "mean     146.416938\n",
       "std       84.917398\n",
       "min        9.000000\n",
       "25%      100.000000\n",
       "50%      128.000000\n",
       "75%      166.750000\n",
       "max      700.000000\n",
       "Name: LoanAmount, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.LoanAmount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e234daa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loan(amount):\n",
    "    if amount<100:\n",
    "        return 'low'\n",
    "    elif amount<128:\n",
    "        return 'medium'\n",
    "    else:\n",
    "        return 'high'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "540f7098",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"LoanAmount\"]=data[\"LoanAmount\"].apply(loan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5440d277",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Status    N    Y\n",
      "LoanAmount           \n",
      "high         102  211\n",
      "low           45   97\n",
      "medium        45  114\n"
     ]
    }
   ],
   "source": [
    "tab=pd.crosstab(data[\"LoanAmount\"],data[\"Loan_Status\"])\n",
    "print(tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f98833ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tab.plot(kind=\"bar\",stacked=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8dedc4bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Status       N    Y\n",
      "Credit_History          \n",
      "0.0              87   14\n",
      "1.0             105  408\n"
     ]
    }
   ],
   "source": [
    "tab=pd.crosstab(data[\"Credit_History\"],data[\"Loan_Status\"])\n",
    "\n",
    "tab.plot(kind=\"bar\",stacked=True)\n",
    "plt.show()\n",
    "print(tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cdfc2a1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data = data.replace([\"Male\", 1], [\"Female\", 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "43c33731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>high</td>\n",
       "      <td>low</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender Married Dependents     Education ApplicantIncome LoanAmount  \\\n",
       "0      Male      No          0      Graduate            high       high   \n",
       "1      Male     Yes          1      Graduate            high       high   \n",
       "2      Male     Yes          0      Graduate          medium        low   \n",
       "3      Male     Yes          0  Not Graduate             low     medium   \n",
       "4      Male      No          0      Graduate            high       high   \n",
       "..      ...     ...        ...           ...             ...        ...   \n",
       "609  Female      No          0      Graduate          medium        low   \n",
       "610    Male     Yes         3+      Graduate            high        low   \n",
       "611    Male     Yes          1      Graduate            high       high   \n",
       "612    Male     Yes          2      Graduate            high       high   \n",
       "613  Female      No          0      Graduate            high       high   \n",
       "\n",
       "     Credit_History Loan_Status  \n",
       "0               1.0           Y  \n",
       "1               1.0           N  \n",
       "2               1.0           Y  \n",
       "3               1.0           Y  \n",
       "4               1.0           Y  \n",
       "..              ...         ...  \n",
       "609             1.0           Y  \n",
       "610             1.0           Y  \n",
       "611             1.0           Y  \n",
       "612             1.0           Y  \n",
       "613             0.0           N  \n",
       "\n",
       "[614 rows x 8 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9cffa4ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Gender\"].replace({\"Male\":1,\"Female\":0},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3d963948",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Married\"].replace({\"Yes\":1,\"No\":0},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c39e4191",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Education\"].replace({\"Graduate\":1,\"Not Graduate\":0},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5f70b2a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"ApplicantIncome\"].replace({\"low\":1,\"medium\":2,\"high\":3},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e6f3b8cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"LoanAmount\"].replace({\"low\":1,\"medium\":2,\"high\":3},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8f822a0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Dependents\"].replace({\"0\":0,\"1\":1,\"2\":2,\"3+\":3},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "718f5b86",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Married\"].replace({\"Yes\":1,\"No\":0},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bc1340e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Credit_History\"]=data[\"Credit_History\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c1e0512c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  ApplicantIncome  LoanAmount  \\\n",
       "0         1        0           0          1                3           3   \n",
       "1         1        1           1          1                3           3   \n",
       "2         1        1           0          1                2           1   \n",
       "3         1        1           0          0                1           2   \n",
       "4         1        0           0          1                3           3   \n",
       "..      ...      ...         ...        ...              ...         ...   \n",
       "609       0        0           0          1                2           1   \n",
       "610       1        1           3          1                3           1   \n",
       "611       1        1           1          1                3           3   \n",
       "612       1        1           2          1                3           3   \n",
       "613       0        0           0          1                3           3   \n",
       "\n",
       "     Credit_History Loan_Status  \n",
       "0                 1           Y  \n",
       "1                 1           N  \n",
       "2                 1           Y  \n",
       "3                 1           Y  \n",
       "4                 1           Y  \n",
       "..              ...         ...  \n",
       "609               1           Y  \n",
       "610               1           Y  \n",
       "611               1           Y  \n",
       "612               1           Y  \n",
       "613               0           N  \n",
       "\n",
       "[614 rows x 8 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d2e82987",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender              int64\n",
       "Married             int64\n",
       "Dependents          int64\n",
       "Education           int64\n",
       "ApplicantIncome     int64\n",
       "LoanAmount          int64\n",
       "Credit_History      int32\n",
       "Loan_Status        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "65adc07d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=data.Loan_Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "00babebb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Y\n",
       "1      N\n",
       "2      Y\n",
       "3      Y\n",
       "4      Y\n",
       "      ..\n",
       "609    Y\n",
       "610    Y\n",
       "611    Y\n",
       "612    Y\n",
       "613    N\n",
       "Name: Loan_Status, Length: 614, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b1634ee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.drop(\"Loan_Status\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "565f33ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  ApplicantIncome  LoanAmount  \\\n",
       "0         1        0           0          1                3           3   \n",
       "1         1        1           1          1                3           3   \n",
       "2         1        1           0          1                2           1   \n",
       "3         1        1           0          0                1           2   \n",
       "4         1        0           0          1                3           3   \n",
       "..      ...      ...         ...        ...              ...         ...   \n",
       "609       0        0           0          1                2           1   \n",
       "610       1        1           3          1                3           1   \n",
       "611       1        1           1          1                3           3   \n",
       "612       1        1           2          1                3           3   \n",
       "613       0        0           0          1                3           3   \n",
       "\n",
       "     Credit_History  \n",
       "0                 1  \n",
       "1                 1  \n",
       "2                 1  \n",
       "3                 1  \n",
       "4                 1  \n",
       "..              ...  \n",
       "609               1  \n",
       "610               1  \n",
       "611               1  \n",
       "612               1  \n",
       "613               0  \n",
       "\n",
       "[614 rows x 7 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f0fd8bb",
   "metadata": {},
   "source": [
    "## split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "291d07f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "aa550735",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c6d3d40",
   "metadata": {},
   "source": [
    "## LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d02c4f02",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "316dab0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5db13c7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(429, 7)\n",
      "(185, 7)\n",
      "(429,)\n",
      "(185,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b489cd6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a139c1ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bd5755a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['N', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y',\n",
       "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y',\n",
       "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'N', 'Y', 'Y', 'Y', 'N',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y',\n",
       "       'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'Y',\n",
       "       'Y', 'N', 'Y', 'N', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'N', 'N', 'Y'], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4b074f43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8158508158508159"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train,Y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9cab4083",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7837837837837838"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2b16c9f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df=pd.DataFrame({'Actual':Y_test,'Predicted':y_pred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "26cbfda0",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual Predicted\n",
       "79       Y         N\n",
       "410      N         Y\n",
       "302      Y         Y\n",
       "450      N         N\n",
       "582      Y         Y\n",
       "..     ...       ...\n",
       "260      Y         Y\n",
       "197      Y         Y\n",
       "162      N         N\n",
       "554      N         N\n",
       "82       N         Y\n",
       "\n",
       "[185 rows x 2 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "947a01c3",
   "metadata": {},
   "source": [
    "## accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b49a03c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7fec3fc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7837837837837838"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ffcfdf45",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "eaa8b5c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "p=confusion_matrix(Y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "25bfd611",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 32,  36],\n",
       "       [  4, 113]], dtype=int64)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b09e5557",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0f034e82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model,X_test,Y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6bdacfe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e02d0bfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           N       0.89      0.47      0.62        68\n",
      "           Y       0.76      0.97      0.85       117\n",
      "\n",
      "    accuracy                           0.78       185\n",
      "   macro avg       0.82      0.72      0.73       185\n",
      "weighted avg       0.81      0.78      0.76       185\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9cd66e4",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}