Loan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "47397530",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn as svm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from collections import Counter\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ec6c7d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              False\n",
       "Gender               False\n",
       "Married              False\n",
       "Dependents           False\n",
       "Education            False\n",
       "Self_Employed        False\n",
       "ApplicantIncome      False\n",
       "CoapplicantIncome    False\n",
       "LoanAmount           False\n",
       "Loan_Amount_Term     False\n",
       "Credit_History       False\n",
       "Property_Area        False\n",
       "Loan_Status          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('bankloan.csv')\n",
    "df = df.dropna()\n",
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "71e439c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('Loan_ID',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "213e1dbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</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>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</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>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</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",
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       "      <td>4196.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>Graduate</td>\n",
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       "      <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",
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       "      <td>3.0</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>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</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",
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       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</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",
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       "      <td>Female</td>\n",
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       "      <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>480 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender Married  Dependents     Education Self_Employed  ApplicantIncome  \\\n",
       "1      Male     Yes         1.0      Graduate            No             4583   \n",
       "2      Male     Yes         0.0      Graduate           Yes             3000   \n",
       "3      Male     Yes         0.0  Not Graduate            No             2583   \n",
       "4      Male      No         0.0      Graduate            No             6000   \n",
       "5      Male     Yes         2.0      Graduate           Yes             5417   \n",
       "..      ...     ...         ...           ...           ...              ...   \n",
       "609  Female      No         0.0      Graduate            No             2900   \n",
       "610    Male     Yes         3.0      Graduate            No             4106   \n",
       "611    Male     Yes         1.0      Graduate            No             8072   \n",
       "612    Male     Yes         2.0      Graduate            No             7583   \n",
       "613  Female      No         0.0      Graduate           Yes             4583   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "1               1508.0       128.0             360.0             1.0   \n",
       "2                  0.0        66.0             360.0             1.0   \n",
       "3               2358.0       120.0             360.0             1.0   \n",
       "4                  0.0       141.0             360.0             1.0   \n",
       "5               4196.0       267.0             360.0             1.0   \n",
       "..                 ...         ...               ...             ...   \n",
       "609                0.0        71.0             360.0             1.0   \n",
       "610                0.0        40.0             180.0             1.0   \n",
       "611              240.0       253.0             360.0             1.0   \n",
       "612                0.0       187.0             360.0             1.0   \n",
       "613                0.0       133.0             360.0             0.0   \n",
       "\n",
       "    Property_Area Loan_Status  \n",
       "1           Rural           N  \n",
       "2           Urban           Y  \n",
       "3           Urban           Y  \n",
       "4           Urban           Y  \n",
       "5           Urban           Y  \n",
       "..            ...         ...  \n",
       "609         Rural           Y  \n",
       "610         Rural           Y  \n",
       "611         Urban           Y  \n",
       "612         Urban           Y  \n",
       "613     Semiurban           N  \n",
       "\n",
       "[480 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3704ebfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['LoanAmount'] = (df['LoanAmount']*1000).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16b17e55",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Graduate</td>\n",
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       "      <td>Urban</td>\n",
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       "      <td>267000</td>\n",
       "      <td>360.0</td>\n",
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       "      <td>Female</td>\n",
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       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>133000</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>480 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "     Gender Married  Dependents     Education Self_Employed  ApplicantIncome  \\\n",
       "1      Male     Yes         1.0      Graduate            No             4583   \n",
       "2      Male     Yes         0.0      Graduate           Yes             3000   \n",
       "3      Male     Yes         0.0  Not Graduate            No             2583   \n",
       "4      Male      No         0.0      Graduate            No             6000   \n",
       "5      Male     Yes         2.0      Graduate           Yes             5417   \n",
       "..      ...     ...         ...           ...           ...              ...   \n",
       "609  Female      No         0.0      Graduate            No             2900   \n",
       "610    Male     Yes         3.0      Graduate            No             4106   \n",
       "611    Male     Yes         1.0      Graduate            No             8072   \n",
       "612    Male     Yes         2.0      Graduate            No             7583   \n",
       "613  Female      No         0.0      Graduate           Yes             4583   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "1               1508.0      128000             360.0             1.0   \n",
       "2                  0.0       66000             360.0             1.0   \n",
       "3               2358.0      120000             360.0             1.0   \n",
       "4                  0.0      141000             360.0             1.0   \n",
       "5               4196.0      267000             360.0             1.0   \n",
       "..                 ...         ...               ...             ...   \n",
       "609                0.0       71000             360.0             1.0   \n",
       "610                0.0       40000             180.0             1.0   \n",
       "611              240.0      253000             360.0             1.0   \n",
       "612                0.0      187000             360.0             1.0   \n",
       "613                0.0      133000             360.0             0.0   \n",
       "\n",
       "    Property_Area Loan_Status  \n",
       "1           Rural           N  \n",
       "2           Urban           Y  \n",
       "3           Urban           Y  \n",
       "4           Urban           Y  \n",
       "5           Urban           Y  \n",
       "..            ...         ...  \n",
       "609         Rural           Y  \n",
       "610         Rural           Y  \n",
       "611         Urban           Y  \n",
       "612         Urban           Y  \n",
       "613     Semiurban           N  \n",
       "\n",
       "[480 rows x 12 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3d601201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'N': 148, 'Y': 332})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(df['Loan_Status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82badce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_y = df['Loan_Status']\n",
    "pre_X = df.drop('Loan_Status',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "02c3bec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dm_X = pd.get_dummies(pre_X)\n",
    "dm_y = pre_y.map(dict(Y=1,N=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "88ff9657",
   "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>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>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <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>0.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</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>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>0.0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</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>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <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>0.0</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</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>611</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Dependents  ApplicantIncome  CoapplicantIncome  LoanAmount  \\\n",
       "1           1.0             4583             1508.0      128000   \n",
       "2           0.0             3000                0.0       66000   \n",
       "3           0.0             2583             2358.0      120000   \n",
       "4           0.0             6000                0.0      141000   \n",
       "5           2.0             5417             4196.0      267000   \n",
       "..          ...              ...                ...         ...   \n",
       "609         0.0             2900                0.0       71000   \n",
       "610         3.0             4106                0.0       40000   \n",
       "611         1.0             8072              240.0      253000   \n",
       "612         2.0             7583                0.0      187000   \n",
       "613         0.0             4583                0.0      133000   \n",
       "\n",
       "     Loan_Amount_Term  Credit_History  Gender_Female  Gender_Male  Married_No  \\\n",
       "1               360.0             1.0              0            1           0   \n",
       "2               360.0             1.0              0            1           0   \n",
       "3               360.0             1.0              0            1           0   \n",
       "4               360.0             1.0              0            1           1   \n",
       "5               360.0             1.0              0            1           0   \n",
       "..                ...             ...            ...          ...         ...   \n",
       "609             360.0             1.0              1            0           1   \n",
       "610             180.0             1.0              0            1           0   \n",
       "611             360.0             1.0              0            1           0   \n",
       "612             360.0             1.0              0            1           0   \n",
       "613             360.0             0.0              1            0           1   \n",
       "\n",
       "     Married_Yes  Education_Graduate  Education_Not Graduate  \\\n",
       "1              1                   1                       0   \n",
       "2              1                   1                       0   \n",
       "3              1                   0                       1   \n",
       "4              0                   1                       0   \n",
       "5              1                   1                       0   \n",
       "..           ...                 ...                     ...   \n",
       "609            0                   1                       0   \n",
       "610            1                   1                       0   \n",
       "611            1                   1                       0   \n",
       "612            1                   1                       0   \n",
       "613            0                   1                       0   \n",
       "\n",
       "     Self_Employed_No  Self_Employed_Yes  Property_Area_Rural  \\\n",
       "1                   1                  0                    1   \n",
       "2                   0                  1                    0   \n",
       "3                   1                  0                    0   \n",
       "4                   1                  0                    0   \n",
       "5                   0                  1                    0   \n",
       "..                ...                ...                  ...   \n",
       "609                 1                  0                    1   \n",
       "610                 1                  0                    1   \n",
       "611                 1                  0                    0   \n",
       "612                 1                  0                    0   \n",
       "613                 0                  1                    0   \n",
       "\n",
       "     Property_Area_Semiurban  Property_Area_Urban  \n",
       "1                          0                    0  \n",
       "2                          0                    1  \n",
       "3                          0                    1  \n",
       "4                          0                    1  \n",
       "5                          0                    1  \n",
       "..                       ...                  ...  \n",
       "609                        0                    0  \n",
       "610                        0                    0  \n",
       "611                        0                    1  \n",
       "612                        0                    1  \n",
       "613                        1                    0  \n",
       "\n",
       "[480 rows x 17 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f4b84b19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      0\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "5      1\n",
       "      ..\n",
       "609    1\n",
       "610    1\n",
       "611    1\n",
       "612    1\n",
       "613    0\n",
       "Name: Loan_Status, Length: 480, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c2729a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE(sampling_strategy='minority')\n",
    "X, y = smote.fit_resample(dm_X,dm_y)\n",
    "# sc = StandardScaler()\n",
    "# X = sc.fit_transform(X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e387ec79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 332, 1: 332})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bcaf4b79",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9135553b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "27986932",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "y_pred = (y_pred>0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c9ea454b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f2255847",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'No'), Text(0, 1.5, 'Yes')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "sns.heatmap(cm,annot=True, ax=ax)\n",
    "ax.set_xlabel('Predicted')\n",
    "ax.set_ylabel('Actual')\n",
    "ax.set_title('Confusion Matrix')\n",
    "ax.xaxis.set_ticklabels(['No','Yes'])\n",
    "ax.yaxis.set_ticklabels(['No','Yes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dd1b65d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.pkl']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "import joblib\n",
    "filename = 'model.pkl'\n",
    "joblib.dump(classifier,filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e21a28db",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=dm_X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "607fb70f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['columns.pkl']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = 'columns.pkl'\n",
    "joblib.dump(cols,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d0e331a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}