Bank_loan_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a50d4a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cde1492f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv(\"C:/Users/91981/Documents/myprojects/Bank_Loan_prediction/train.csv\")\n",
    "train.Loan_Status=train.Loan_Status.map({'Y':1,'N':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9dac3015",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b9a76c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area  \n",
       "0             1.0         Urban  \n",
       "1             1.0         Rural  \n",
       "2             1.0         Urban  \n",
       "3             1.0         Urban  \n",
       "4             1.0         Urban  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Loan_status=train.Loan_Status\n",
    "train.drop('Loan_Status',axis=1,inplace=True)\n",
    "test=pd.read_csv('C:/Users/91981/Documents/myprojects/Bank_Loan_prediction/test.csv')\n",
    "Loan_ID=test.Loan_ID\n",
    "data=train.append(test)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3a7a0f9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               24\n",
       "Married               3\n",
       "Dependents           25\n",
       "Education             0\n",
       "Self_Employed        55\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           27\n",
       "Loan_Amount_Term     20\n",
       "Credit_History       79\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1e268504",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline \n",
    "corrmat=data.corr()\n",
    "f,ax=plt.subplots(figsize=(9,9))\n",
    "sns.heatmap(corrmat,vmax=.8,square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a627c4c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    775\n",
       "0.0    182\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Gender=data.Gender.map({'Male':1,'Female':0})\n",
    "data.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c5f48f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat=data.corr()\n",
    "f,ax=plt.subplots(figsize=(9,9))\n",
    "sns.heatmap(corrmat,vmax=.8,square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8590e864",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('Loan_ID',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1ec53e58",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Married=data.Married.map({'Yes':1,'No':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3abb840",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    631\n",
       "0.0    347\n",
       "Name: Married, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Married.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9889a9b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Dependents=data.Dependents.map({'0':0,'1':1,'2':2,'3+':3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea3073f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    545\n",
       "2.0    160\n",
       "1.0    160\n",
       "3.0     91\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Dependents.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "41b36796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat=data.corr()\n",
    "f,ax=plt.subplots(figsize=(9,9))\n",
    "sns.heatmap(corrmat,vmax=.8,square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f2025182",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Credit_History.fillna(np.random.randint(0,2),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dbc9f42e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender               24\n",
       "Married               3\n",
       "Dependents           25\n",
       "Education             0\n",
       "Self_Employed        55\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           27\n",
       "Loan_Amount_Term     20\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0dab78cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Married.fillna(np.random.randint(0,2),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ad90724e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender               24\n",
       "Married               0\n",
       "Dependents           25\n",
       "Education             0\n",
       "Self_Employed        55\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           27\n",
       "Loan_Amount_Term     20\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "943fbc03",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.LoanAmount.fillna(data.LoanAmount.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9d5abe10",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Loan_Amount_Term.fillna(data.Loan_Amount_Term.mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1bd1525b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender               24\n",
       "Married               0\n",
       "Dependents           25\n",
       "Education             0\n",
       "Self_Employed        55\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            0\n",
       "Loan_Amount_Term      0\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f88a367a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    775\n",
       "0.0    182\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "90dfa528",
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import randint \n",
    "data.Gender.fillna(np.random.randint(0,2),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b960c7c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    775\n",
       "0.0    206\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aaf8fd6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Dependents.fillna(data.Dependents.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3c7717f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                0\n",
       "Married               0\n",
       "Dependents            0\n",
       "Education             0\n",
       "Self_Employed        55\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            0\n",
       "Loan_Amount_Term      0\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "049a4e13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat=data.corr()\n",
    "f,ax=plt.subplots(figsize=(9,9))\n",
    "sns.heatmap(corrmat,vmax=.8,square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c0c30001",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Education=data.Education.map({'Graduate':1,'Not Graduate':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "01bb68aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    763\n",
       "0    218\n",
       "Name: Education, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Education.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "08e67037",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Self_Employed=data.Self_Employed.map({'Yes':1,'No':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "776ecaaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    807\n",
       "1.0    119\n",
       "Name: Self_Employed, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Self_Employed.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "600dc22e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Semiurban    349\n",
       "Urban        342\n",
       "Rural        290\n",
       "Name: Property_Area, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Property_Area.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f4f39ad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Property_Area=data.Property_Area.map({'Urban':2,'Rural':0,'Semiurban':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "28de2b01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat=data.corr()\n",
    "f,ax=plt.subplots(figsize=(9,9))\n",
    "sns.heatmap(corrmat,vmax=.8,square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "899c5294",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Self_Employed.fillna(np.random.randint(0,2),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "783ef7f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "58fed3a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    775\n",
       "0.0    206\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f6b238cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import randint \n",
    "data.Gender.fillna(np.random.randint(0,2),inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b3d4e901",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2d6b559b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Self_Employed.fillna(np.random.randint(0,2),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7f9a94dd",
   "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>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</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>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "0     1.0      0.0         0.0          1            0.0             5849   \n",
       "1     1.0      1.0         1.0          1            0.0             4583   \n",
       "2     1.0      1.0         0.0          1            1.0             3000   \n",
       "3     1.0      1.0         0.0          0            0.0             2583   \n",
       "4     1.0      0.0         0.0          1            0.0             6000   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                0.0       126.0             360.0             1.0   \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",
       "\n",
       "   Property_Area  \n",
       "0              2  \n",
       "1              0  \n",
       "2              2  \n",
       "3              2  \n",
       "4              2  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "59e66e4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "981"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Credit_History.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "305165b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X=data.iloc[:614,]\n",
    "train_y=Loan_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "40e0279f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_X,test_X,train_y,test_y=train_test_split(train_X,train_y,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f93ae74c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>46</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5649</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>2833</td>\n",
       "      <td>1857.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5532</td>\n",
       "      <td>4648.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1811</td>\n",
       "      <td>1666.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "46      1.0      1.0         1.0          1            0.0             5649   \n",
       "272     1.0      0.0         0.0          1            0.0             2833   \n",
       "474     1.0      0.0         2.0          1            0.0             5532   \n",
       "382     0.0      0.0         0.0          1            0.0             6000   \n",
       "283     0.0      0.0         0.0          1            0.0             1811   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "46                 0.0        44.0             360.0             1.0   \n",
       "272             1857.0       126.0             360.0             1.0   \n",
       "474             4648.0       162.0             360.0             1.0   \n",
       "382                0.0       156.0             360.0             1.0   \n",
       "283             1666.0        54.0             360.0             1.0   \n",
       "\n",
       "     Property_Area  \n",
       "46               2  \n",
       "272              0  \n",
       "474              0  \n",
       "382              2  \n",
       "283              2  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "train_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "df020e5c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
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       "      <th>LoanAmount</th>\n",
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       "      <th>Credit_History</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>324.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "      <th>469</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4333</td>\n",
       "      <td>2451.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2708</td>\n",
       "      <td>1167.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "454     1.0      0.0         0.0          1            1.0             7085   \n",
       "52      0.0      0.0         0.0          1            0.0             4230   \n",
       "536     1.0      1.0         0.0          1            0.0             6133   \n",
       "469     1.0      1.0         0.0          1            0.0             4333   \n",
       "55      1.0      1.0         2.0          1            0.0             2708   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "454                0.0        84.0             360.0             1.0   \n",
       "52                 0.0       112.0             360.0             1.0   \n",
       "536             3906.0       324.0             360.0             1.0   \n",
       "469             2451.0       110.0             360.0             1.0   \n",
       "55              1167.0        97.0             360.0             1.0   \n",
       "\n",
       "     Property_Area  \n",
       "454              1  \n",
       "52               1  \n",
       "536              2  \n",
       "469              2  \n",
       "55               1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "42c5de26",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "3966480c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "acc81209",
   "metadata": {},
   "outputs": [],
   "source": [
    "models=[]\n",
    "models.append((\"Decision Tree\",DecisionTreeClassifier()))\n",
    "models.append((\"Support Vector Classifier\",SVC()))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "10989b3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring='accuracy'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "095978ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold \n",
    "from sklearn.model_selection import cross_val_score\n",
    "result=[]\n",
    "names=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "d00678e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier()\n",
      "Decision Tree 0.654288\n",
      "SVC()\n",
      "Support Vector Classifier 0.667402\n"
     ]
    }
   ],
   "source": [
    "for name,model in models:\n",
    "    kfold=KFold(n_splits=3,random_state=None)\n",
    "    cv_result=cross_val_score(model,train_X,train_y,cv=kfold,scoring=scoring)\n",
    "    result.append(cv_result)\n",
    "    names.append(name)\n",
    "    print(model)\n",
    "    print(\"%s %f\" % (name,cv_result.mean()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "63f3e2a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy:-  0.8116883116883117\n",
      "[[ 23  20]\n",
      " [  9 102]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.53      0.61        43\n",
      "           1       0.84      0.92      0.88       111\n",
      "\n",
      "    accuracy                           0.81       154\n",
      "   macro avg       0.78      0.73      0.74       154\n",
      "weighted avg       0.80      0.81      0.80       154\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\91981\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "LR=LogisticRegression()\n",
    "LR.fit(train_X,train_y)\n",
    "pred=LR.predict(test_X)\n",
    "print(\"Model Accuracy:- \",accuracy_score(test_y,pred))\n",
    "print(confusion_matrix(test_y,pred))\n",
    "print(classification_report(test_y,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "7638e50a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 0 1 1 1 0 0 1 1 1 1 1 0 1\n",
      " 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 0 1 1 0 0 1 1 1\n",
      " 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1\n",
      " 0 0 1 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "36d2ecb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test=data.iloc[614:,] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "fcfc94a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>5000</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3276</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "0     1.0      1.0         0.0          1            0.0             5720   \n",
       "1     1.0      1.0         1.0          1            0.0             3076   \n",
       "2     1.0      1.0         2.0          1            0.0             5000   \n",
       "3     1.0      1.0         2.0          1            0.0             2340   \n",
       "4     1.0      0.0         0.0          0            0.0             3276   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                0.0       110.0             360.0             1.0   \n",
       "1             1500.0       126.0             360.0             1.0   \n",
       "2             1800.0       208.0             360.0             1.0   \n",
       "3             2546.0       100.0             360.0             0.0   \n",
       "4                0.0        78.0             360.0             1.0   \n",
       "\n",
       "   Property_Area  \n",
       "0              2  \n",
       "1              2  \n",
       "2              2  \n",
       "3              2  \n",
       "4              2  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "5b526bef",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = LR.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "6ae1683a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 0 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1\n",
      " 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 0 1 1 0 0 1 0 1 1 1 1\n",
      " 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 0 1 1 0 1 0 1 1 1 1\n",
      " 1 1 1 1 0 1 0 0 0 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 0\n",
      " 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 0 0 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1\n",
      " 0 1 1 1 1 1 1 0 0 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1\n",
      " 1 1 0 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1\n",
      " 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1\n",
      " 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1\n",
      " 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "a92afae1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\91981\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\base.py:442: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  \"X does not have valid feature names, but\"\n"
     ]
    }
   ],
   "source": [
    "## TAken data from the dataset\n",
    "t = LR.predict([[0.0,\t0.0,\t0.0,\t1,\t0.0,\t1811,\t1666.0,\t54.0,\t360.0,\t1.0,\t2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "81876187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "c8c2758a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# now you can save it to a file\n",
    "file = 'C:/Users/91981/Documents/myprojects/Bank_Loan_prediction/ML_Model.pkl'\n",
    "with open(file, 'wb') as f:\n",
    "    pickle.dump(SVC, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "1543c215",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(file, 'rb') as f:\n",
    "    k = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d83ae369",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43667ed2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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