python-scripts/scripts/Loan Prediction Model/Loan Prediction Model .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8c97baae",
   "metadata": {},
   "source": [
    "## Loan Prediction Model \n",
    "\n",
    "\n",
    "The goal of this project is that from the data collected on the loan’s applicants, preprocess the data and predict based on the information who will be able to receive the loan or not.\n",
    "\n",
    "\n",
    "In the Dataset we find the following features:\n",
    "\n",
    "1. Loan ID, the identifier code of each applicant.\n",
    "2. Gender, Male or Female for each applicant.\n",
    "3. Married, the maritage state.\n",
    "4. Dependents, how many dependents does the applicant have?\n",
    "5. Education, the level of education, graduate or non graduate\n",
    "6. Self Employed, Yes or No in the case\n",
    "7. Applicant Income\n",
    "8. Coapplicant Income\n",
    "9. Loan Amount\n",
    "10. Loan Amount Term\n",
    "11. Credit History, just Yes or No in the case\n",
    "12. Property Area, urban, semiurban or rural area of the applicant’s property\n",
    "\n",
    "Loan Status, Yes or No ( The independent variable represents the class)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f28aeb7",
   "metadata": {},
   "source": [
    "## Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4cde977c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec208c3e",
   "metadata": {},
   "source": [
    "## Read & visualize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8895329b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.read_csv('Loan_train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c16b1bc",
   "metadata": {},
   "source": [
    "##  Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8bbe6c13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae1c8c0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b553da7",
   "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             601 non-null    object \n",
      " 2   Married            611 non-null    object \n",
      " 3   Dependents         599 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      582 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         592 non-null    float64\n",
      " 9   Loan_Amount_Term   600 non-null    float64\n",
      " 10  Credit_History     564 non-null    float64\n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "20168c69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a52091bf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     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",
      "609     0.0      0.0         0.0          1            0.0             2900   \n",
      "610     1.0      1.0         3.0          1            0.0             4106   \n",
      "611     1.0      1.0         1.0          1            0.0             8072   \n",
      "612     1.0      1.0         2.0          1            0.0             7583   \n",
      "613     0.0      0.0         0.0          1            1.0             4583   \n",
      "\n",
      "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
      "0                  0.0         NaN             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",
      "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",
      "0                1            1  \n",
      "1                0            0  \n",
      "2                1            1  \n",
      "3                1            1  \n",
      "4                1            1  \n",
      "..             ...          ...  \n",
      "609              0            1  \n",
      "610              0            1  \n",
      "611              1            1  \n",
      "612              1            1  \n",
      "613              2            0  \n",
      "\n",
      "[614 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "#Loan Status Encoding\n",
    "df= df.replace({\"Loan_Status\":{'Y': 1, 'N': 0}})\n",
    "\n",
    "#Gender Encoding\n",
    "df= df.replace({\"Gender\":{\"Male\":1, \"Female\":0 }})\n",
    "\n",
    "#Married Encoding\n",
    "df =df.replace({\"Married\" :{\"Yes\":1, \"No\":0}})\n",
    "\n",
    "#Replace the 3+ in dependents ande make the column numeric\n",
    "df['Dependents'] = df['Dependents'].replace('3+', '3')\n",
    "df['Dependents']=pd.to_numeric(df['Dependents'], errors='coerce')\n",
    "\n",
    "#Count the quantity of values on the column\n",
    "df['Self_Employed'].value_counts()\n",
    "df= df.replace({\"Self_Employed\":{\"Yes\":1, \"No\":0 }})\n",
    "\n",
    "#Education Encoding\n",
    "df['Education'].value_counts()\n",
    "df= df.replace({\"Education\":{\"Graduate\":1, \"Not Graduate\":0 }})\n",
    "\n",
    "#Drop the Loan ID column\n",
    "df = df.drop('Loan_ID',axis=1)\n",
    "\n",
    "#Property Area Encoding\n",
    "df['Property_Area'].value_counts()\n",
    "df['Property_Area'] = df['Property_Area'].map({'Rural': 0, 'Urban': 1, 'Semiurban': 2})\n",
    "\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "861ac719",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>count</th>\n",
       "      <td>601.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>599.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>582.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.813644</td>\n",
       "      <td>0.651391</td>\n",
       "      <td>0.762938</td>\n",
       "      <td>0.781759</td>\n",
       "      <td>0.140893</td>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "      <td>1.087948</td>\n",
       "      <td>0.687296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.389718</td>\n",
       "      <td>0.476920</td>\n",
       "      <td>1.015216</td>\n",
       "      <td>0.413389</td>\n",
       "      <td>0.348211</td>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "      <td>0.815081</td>\n",
       "      <td>0.463973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gender     Married  Dependents   Education  Self_Employed  \\\n",
       "count  601.000000  611.000000  599.000000  614.000000     582.000000   \n",
       "mean     0.813644    0.651391    0.762938    0.781759       0.140893   \n",
       "std      0.389718    0.476920    1.015216    0.413389       0.348211   \n",
       "min      0.000000    0.000000    0.000000    0.000000       0.000000   \n",
       "25%      1.000000    0.000000    0.000000    1.000000       0.000000   \n",
       "50%      1.000000    1.000000    0.000000    1.000000       0.000000   \n",
       "75%      1.000000    1.000000    2.000000    1.000000       0.000000   \n",
       "max      1.000000    1.000000    3.000000    1.000000       1.000000   \n",
       "\n",
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  Property_Area  Loan_Status  \n",
       "count      564.000000     614.000000   614.000000  \n",
       "mean         0.842199       1.087948     0.687296  \n",
       "std          0.364878       0.815081     0.463973  \n",
       "min          0.000000       0.000000     0.000000  \n",
       "25%          1.000000       0.000000     0.000000  \n",
       "50%          1.000000       1.000000     1.000000  \n",
       "75%          1.000000       2.000000     1.000000  \n",
       "max          1.000000       2.000000     1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "64ab82d9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<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>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>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <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>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</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>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</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>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</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>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</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>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</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>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 12 columns</p>\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",
       "609     0.0      0.0         0.0          1            0.0             2900   \n",
       "610     1.0      1.0         3.0          1            0.0             4106   \n",
       "611     1.0      1.0         1.0          1            0.0             8072   \n",
       "612     1.0      1.0         2.0          1            0.0             7583   \n",
       "613     0.0      0.0         0.0          1            1.0             4583   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                  0.0         NaN             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",
       "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",
       "0                1            1  \n",
       "1                0            0  \n",
       "2                1            1  \n",
       "3                1            1  \n",
       "4                1            1  \n",
       "..             ...          ...  \n",
       "609              0            1  \n",
       "610              0            1  \n",
       "611              1            1  \n",
       "612              1            1  \n",
       "613              2            0  \n",
       "\n",
       "[614 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7a2c0144",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(df.median(), inplace=True)\n",
    "columns = df.columns\n",
    "for column in columns:\n",
    "    df[column] = pd.to_numeric(df[column], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "574b6b70",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(15,8)})\n",
    "sns.heatmap(df.corr(),annot=True,cmap=\"rocket\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "40bee983",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Married  Education  CoapplicantIncome  Credit_History  Property_Area  \\\n",
      "0        0.0          1                0.0             1.0              1   \n",
      "1        1.0          1             1508.0             1.0              0   \n",
      "2        1.0          1                0.0             1.0              1   \n",
      "3        1.0          0             2358.0             1.0              1   \n",
      "4        0.0          1                0.0             1.0              1   \n",
      "..       ...        ...                ...             ...            ...   \n",
      "609      0.0          1                0.0             1.0              0   \n",
      "610      1.0          1                0.0             1.0              0   \n",
      "611      1.0          1              240.0             1.0              1   \n",
      "612      1.0          1                0.0             1.0              1   \n",
      "613      0.0          1                0.0             0.0              2   \n",
      "\n",
      "     Loan_Status  \n",
      "0              1  \n",
      "1              0  \n",
      "2              1  \n",
      "3              1  \n",
      "4              1  \n",
      "..           ...  \n",
      "609            1  \n",
      "610            1  \n",
      "611            1  \n",
      "612            1  \n",
      "613            0  \n",
      "\n",
      "[614 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "def correlationdrop(df, sl):\n",
    "    columns = df.columns\n",
    "    for column in columns:\n",
    "        C=abs(df[column].corr(df['Loan_Status']))\n",
    "        if C < sl:\n",
    "            df=df.drop(columns=[column])\n",
    "    return df\n",
    "\n",
    "df= correlationdrop(df,0.05)\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd3d4a8",
   "metadata": {},
   "source": [
    "## Separate the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c6e6d8cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:,:-1].values\n",
    "y = df.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e743143",
   "metadata": {},
   "source": [
    "## Scale the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b8992600",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler()\n",
    "X= sc.fit_transform(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3615ec24",
   "metadata": {},
   "source": [
    "## Split the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2a37ac15",
   "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= 0.2, random_state= 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c98b35e0",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "daba8de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model=LogisticRegression()\n",
    "model.fit(X_train,y_train)\n",
    "z=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "16b8534b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8292682926829268"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,z)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a31a652",
   "metadata": {},
   "source": [
    "## SVM Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e6c9e365",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel = 'rbf', gamma= 0.2)\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5b880d7",
   "metadata": {},
   "source": [
    "## Making the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a1503813",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[14 19]\n",
      " [ 2 88]]\n",
      "Accuracy: 80.44 %\n",
      "Standard Deviation: 4.59 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "\n",
    "# Applying k-Fold Cross Validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)\n",
    "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))"
   ]
  }
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