loan_approval.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b6b1d706",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "511718a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "LA=pd.read_excel(\"C:\\\\Users\\\\kavya\\\\Downloads\\\\LoanApprovalmg.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e4367eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146</td>\n",
       "      <td>360</td>\n",
       "      <td>1</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</td>\n",
       "      <td>360</td>\n",
       "      <td>1</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</td>\n",
       "      <td>360</td>\n",
       "      <td>1</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</td>\n",
       "      <td>360</td>\n",
       "      <td>1</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</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married  Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No           0      Graduate            No   \n",
       "1  LP001003   Male     Yes           1      Graduate            No   \n",
       "2  LP001005   Male     Yes           0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes           0  Not Graduate            No   \n",
       "4  LP001008   Male      No           0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         146               360   \n",
       "1             4583             1508.0         128               360   \n",
       "2             3000                0.0          66               360   \n",
       "3             2583             2358.0         120               360   \n",
       "4             6000                0.0         141               360   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0               1         Urban           Y  \n",
       "1               1         Rural           N  \n",
       "2               1         Urban           Y  \n",
       "3               1         Urban           Y  \n",
       "4               1         Urban           Y  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "277d171d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LA.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2912be6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             614 non-null    object \n",
      " 2   Married            614 non-null    object \n",
      " 3   Dependents         614 non-null    int64  \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      614 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         614 non-null    int64  \n",
      " 9   Loan_Amount_Term   614 non-null    int64  \n",
      " 10  Credit_History     614 non-null    int64  \n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    object \n",
      "dtypes: float64(1), int64(5), object(7)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "LA.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "558a8161",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LA.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4fde782e",
   "metadata": {},
   "outputs": [],
   "source": [
    "LA.drop([\"Loan_ID\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c762e4de",
   "metadata": {},
   "outputs": [],
   "source": [
    "LA[\"Loan_Status\"]=LA.Loan_Status.map({\"Y\":1,\"N\":0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "baedd102",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender Married  Dependents     Education Self_Employed  ApplicantIncome  \\\n",
       "0   Male      No           0      Graduate            No             5849   \n",
       "1   Male     Yes           1      Graduate            No             4583   \n",
       "2   Male     Yes           0      Graduate           Yes             3000   \n",
       "3   Male     Yes           0  Not Graduate            No             2583   \n",
       "4   Male      No           0      Graduate            No             6000   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                0.0         146               360               1   \n",
       "1             1508.0         128               360               1   \n",
       "2                0.0          66               360               1   \n",
       "3             2358.0         120               360               1   \n",
       "4                0.0         141               360               1   \n",
       "\n",
       "  Property_Area  Loan_Status  \n",
       "0         Urban            1  \n",
       "1         Rural            0  \n",
       "2         Urban            1  \n",
       "3         Urban            1  \n",
       "4         Urban            1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e12b9a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Education', ylabel='Loan_Status'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.barplot(data=LA,x='Education',y='Loan_Status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5fc61415",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Loan_Status'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.barplot(data=LA,x='Gender',y='Loan_Status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "81b8ec61",
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_approval=pd.get_dummies(LA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5c241d39",
   "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>Dependents</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>Loan_Status</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Married_No</th>\n",
       "      <th>Married_Yes</th>\n",
       "      <th>Education_Graduate</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_No</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Rural</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dependents  ApplicantIncome  CoapplicantIncome  LoanAmount  \\\n",
       "0           0             5849                0.0         146   \n",
       "1           1             4583             1508.0         128   \n",
       "2           0             3000                0.0          66   \n",
       "3           0             2583             2358.0         120   \n",
       "4           0             6000                0.0         141   \n",
       "\n",
       "   Loan_Amount_Term  Credit_History  Loan_Status  Gender_Female  Gender_Male  \\\n",
       "0               360               1            1              0            1   \n",
       "1               360               1            0              0            1   \n",
       "2               360               1            1              0            1   \n",
       "3               360               1            1              0            1   \n",
       "4               360               1            1              0            1   \n",
       "\n",
       "   Married_No  Married_Yes  Education_Graduate  Education_Not Graduate  \\\n",
       "0           1            0                   1                       0   \n",
       "1           0            1                   1                       0   \n",
       "2           0            1                   1                       0   \n",
       "3           0            1                   0                       1   \n",
       "4           1            0                   1                       0   \n",
       "\n",
       "   Self_Employed_No  Self_Employed_Yes  Property_Area_Rural  \\\n",
       "0                 1                  0                    0   \n",
       "1                 1                  0                    1   \n",
       "2                 0                  1                    0   \n",
       "3                 1                  0                    0   \n",
       "4                 1                  0                    0   \n",
       "\n",
       "   Property_Area_Semiurban  Property_Area_Urban  \n",
       "0                        0                    1  \n",
       "1                        0                    0  \n",
       "2                        0                    1  \n",
       "3                        0                    1  \n",
       "4                        0                    1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_approval.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "012d6309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1,\n",
       "       0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "       0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1,\n",
       "       1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
       "       1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0,\n",
       "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1,\n",
       "       0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0,\n",
       "       1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=loan_approval.Loan_Status.values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2eb6c314",
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_approval.drop(\"Loan_Status\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "90b8ee9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.000e+00, 5.849e+03, 0.000e+00, ..., 0.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       [1.000e+00, 4.583e+03, 1.508e+03, ..., 1.000e+00, 0.000e+00,\n",
       "        0.000e+00],\n",
       "       [0.000e+00, 3.000e+03, 0.000e+00, ..., 0.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       ...,\n",
       "       [1.000e+00, 8.072e+03, 2.400e+02, ..., 0.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       [2.000e+00, 7.583e+03, 0.000e+00, ..., 0.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       [0.000e+00, 4.583e+03, 0.000e+00, ..., 0.000e+00, 1.000e+00,\n",
       "        0.000e+00]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=loan_approval.values\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "50e055d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.20,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "bcad1e2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=0)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "RFC = RandomForestClassifier(random_state=0)\n",
    "RFC.fit(x_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f9206ed2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 100.00%\n",
      "Testing Accuracy: 81.30%\n",
      "Overall Accuracy: 96.25%\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.48      0.58        33\n",
      "           1       0.83      0.93      0.88        90\n",
      "\n",
      "    accuracy                           0.81       123\n",
      "   macro avg       0.78      0.71      0.73       123\n",
      "weighted avg       0.80      0.81      0.80       123\n",
      "\n"
     ]
    }
   ],
   "source": [
    "RFC.fit(x_train, y_train)\n",
    "\n",
    "train_score = RFC.score(x_train, y_train) \n",
    "test_score = RFC.score(x_test, y_test)\n",
    "\n",
    "overall_score = RFC.score(x, y)\n",
    "\n",
    "y_pred = RFC.predict(x_test)\n",
    "\n",
    "print (\"Training Accuracy: {:.2f}%\".format(train_score*100)) \n",
    "print(\"Testing Accuracy: {:.2f}%\".format(test_score*100)) \n",
    "print(\"Overall Accuracy: {:.2f}%\".format(overall_score*100))\n",
    "\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, y_pred))"
   ]
  }
 ],
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