Loan Prediction- Analytics Vidhya.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(r'C:\\Users\\Mahaveer\\Downloads\\loan_pred_train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Missing values Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>19</th>\n",
       "      <td>LP001041</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2600</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>LP001109</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1828</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>LP001136</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4695</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>LP001137</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3410</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>LP001250</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4755</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>LP001391</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3572</td>\n",
       "      <td>4114.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>LP001574</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3707</td>\n",
       "      <td>3166.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>LP001669</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1907</td>\n",
       "      <td>2365.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>LP001749</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7578</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>LP001770</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3189</td>\n",
       "      <td>2598.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>LP002106</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5503</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>LP002188</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5124</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>LP002357</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>LP002362</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7250</td>\n",
       "      <td>1667.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender Married Dependents     Education Self_Employed  \\\n",
       "19   LP001041    Male     Yes          0      Graduate           NaN   \n",
       "36   LP001109    Male     Yes          0      Graduate            No   \n",
       "44   LP001136    Male     Yes          0  Not Graduate           Yes   \n",
       "45   LP001137  Female      No          0      Graduate            No   \n",
       "73   LP001250    Male     Yes         3+  Not Graduate            No   \n",
       "112  LP001391    Male     Yes          0  Not Graduate            No   \n",
       "165  LP001574    Male     Yes          0      Graduate            No   \n",
       "197  LP001669  Female      No          0  Not Graduate            No   \n",
       "223  LP001749    Male     Yes          0      Graduate            No   \n",
       "232  LP001770    Male      No          0  Not Graduate            No   \n",
       "335  LP002106    Male     Yes        NaN      Graduate           Yes   \n",
       "367  LP002188    Male      No          0      Graduate            No   \n",
       "421  LP002357  Female      No          0  Not Graduate            No   \n",
       "423  LP002362    Male     Yes          1      Graduate            No   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "19              2600             3500.0       115.0               NaN   \n",
       "36              1828             1330.0       100.0               NaN   \n",
       "44              4695                0.0        96.0               NaN   \n",
       "45              3410                0.0        88.0               NaN   \n",
       "73              4755                0.0        95.0               NaN   \n",
       "112             3572             4114.0       152.0               NaN   \n",
       "165             3707             3166.0       182.0               NaN   \n",
       "197             1907             2365.0       120.0               NaN   \n",
       "223             7578             1010.0       175.0               NaN   \n",
       "232             3189             2598.0       120.0               NaN   \n",
       "335             5503             4490.0        70.0               NaN   \n",
       "367             5124                0.0       124.0               NaN   \n",
       "421             2720                0.0        80.0               NaN   \n",
       "423             7250             1667.0       110.0               NaN   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "19              1.0         Urban           Y  \n",
       "36              0.0         Urban           N  \n",
       "44              1.0         Urban           Y  \n",
       "45              1.0         Urban           Y  \n",
       "73              0.0     Semiurban           N  \n",
       "112             0.0         Rural           N  \n",
       "165             1.0         Rural           Y  \n",
       "197             1.0         Urban           Y  \n",
       "223             1.0     Semiurban           Y  \n",
       "232             1.0         Rural           Y  \n",
       "335             1.0     Semiurban           Y  \n",
       "367             0.0         Rural           N  \n",
       "421             0.0         Urban           N  \n",
       "423             0.0         Urban           N  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[pd.isnull(df.Loan_Amount_Term)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gender']=df.Gender.fillna(df['Gender'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Married']=df.Married.fillna(df['Married'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Dependents']=df.Dependents.fillna(df['Dependents'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Self_Employed']=df.Self_Employed.fillna(df['Self_Employed'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['LoanAmount']=df.LoanAmount.fillna(df['LoanAmount'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Loan_Amount_Term']=df.Loan_Amount_Term.fillna(df['Loan_Amount_Term'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Credit_History']=df.Credit_History.fillna(df['Credit_History'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             614 non-null    object \n",
      " 2   Married            614 non-null    object \n",
      " 3   Dependents         614 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      614 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         614 non-null    float64\n",
      " 9   Loan_Amount_Term   614 non-null    float64\n",
      " 10  Credit_History     614 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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x239f97eb588>"
      ]
     },
     "execution_count": 14,
     "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": [
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.412162</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>128.000000</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>66.000000</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",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0  146.412162             360.0   \n",
       "1             4583             1508.0  128.000000             360.0   \n",
       "2             3000                0.0   66.000000             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  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x239fa023788>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x239fae2d2c8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('Education','ApplicantIncome',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x239fb1f8108>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y='Property_Area',x='ApplicantIncome',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x239fb23a248>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('Education','LoanAmount',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x239fb2b9388>"
      ]
     },
     "execution_count": 20,
     "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": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x239fb36b548>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(y='LoanAmount',x='ApplicantIncome',data=df,kind='reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x239fc550148>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['LoanAmount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x239fc64bcc8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=\"LoanAmount\", y=\"Education\", data=df,palette='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x239fc677f88>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Gender\", y=\"LoanAmount\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Prepration for model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.412162</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>128.000000</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>66.000000</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",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0  146.412162             360.0   \n",
       "1             4583             1508.0  128.000000             360.0   \n",
       "2             3000                0.0   66.000000             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  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('loan.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Dependents']=df.Dependents.replace('3+','3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.get_dummies(df, columns =['Gender', 'Married', 'Self_Employed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Dependents', 'Education', 'ApplicantIncome',\n",
       "       'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term', 'Credit_History',\n",
       "       'Property_Area', 'Loan_Status', 'Gender_Female', 'Gender_Male',\n",
       "       'Married_No', 'Married_Yes', 'Self_Employed_No', 'Self_Employed_Yes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=data.drop(['Loan_ID','Gender_Male','Married_No','Self_Employed_No'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "a['Education']= data.Education.replace({'Graduate': 1,'Not Graduate':0})\n",
    "a['Property_Area']=data.Property_Area.replace({'Rural': 0,'Semiurban':1,'Urban':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "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",
       "\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>Education</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",
       "      <th>Gender_Female</th>\n",
       "      <th>Married_Yes</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Dependents  Education  ApplicantIncome  CoapplicantIncome  LoanAmount  \\\n",
       "0          0          1             5849                0.0  146.412162   \n",
       "1          1          1             4583             1508.0  128.000000   \n",
       "2          0          1             3000                0.0   66.000000   \n",
       "3          0          0             2583             2358.0  120.000000   \n",
       "4          0          1             6000                0.0  141.000000   \n",
       "\n",
       "   Loan_Amount_Term  Credit_History  Property_Area Loan_Status  Gender_Female  \\\n",
       "0             360.0             1.0              2           Y              0   \n",
       "1             360.0             1.0              0           N              0   \n",
       "2             360.0             1.0              2           Y              0   \n",
       "3             360.0             1.0              2           Y              0   \n",
       "4             360.0             1.0              2           Y              0   \n",
       "\n",
       "   Married_Yes  Self_Employed_Yes  \n",
       "0            0                  0  \n",
       "1            1                  0  \n",
       "2            1                  1  \n",
       "3            1                  0  \n",
       "4            0                  0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=a[['Dependents','Education','ApplicantIncome','CoapplicantIncome','LoanAmount','Loan_Amount_Term','Credit_History','Property_Area','Gender_Female','Married_Yes','Self_Employed_Yes']]#.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=a[['Loan_Status']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Scikit-learn modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=MinMaxScaler()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepraing Test data for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>LP001015</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</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>LP001022</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</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>LP001035</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</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  LP001015   Male     Yes          0      Graduate            No   \n",
       "1  LP001022   Male     Yes          1      Graduate            No   \n",
       "2  LP001031   Male     Yes          2      Graduate            No   \n",
       "3  LP001035   Male     Yes          2      Graduate            No   \n",
       "4  LP001051   Male      No          0  Not Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5720                  0       110.0             360.0   \n",
       "1             3076               1500       126.0             360.0   \n",
       "2             5000               1800       208.0             360.0   \n",
       "3             2340               2546       100.0             360.0   \n",
       "4             3276                  0        78.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area  \n",
       "0             1.0         Urban  \n",
       "1             1.0         Urban  \n",
       "2             1.0         Urban  \n",
       "3             NaN         Urban  \n",
       "4             1.0         Urban  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tdf=pd.read_csv(r'C:\\Users\\Mahaveer\\Downloads\\test_loan.csv')\n",
    "tdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "tdf['Dependents']=tdf.Dependents.replace('3+','3')\n",
    "tdf['Education']= tdf.Education.replace({'Graduate': 1,'Not Graduate':0})\n",
    "tdf['Property_Area']=tdf.Property_Area.replace({'Rural': 0,'Semiurban':1,'Urban':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "tdf['Gender']=tdf.Gender.fillna(tdf['Gender'].mode()[0])\n",
    "tdf['Married']=tdf.Married.fillna(tdf['Married'].mode()[0])\n",
    "tdf['Dependents']=tdf.Dependents.fillna(tdf['Dependents'].mode()[0])\n",
    "tdf['Self_Employed']=tdf.Self_Employed.fillna(tdf['Self_Employed'].mode()[0])\n",
    "tdf['LoanAmount']=tdf.LoanAmount.fillna(tdf['LoanAmount'].mean())\n",
    "tdf['Loan_Amount_Term']=tdf.Loan_Amount_Term.fillna(tdf['Loan_Amount_Term'].median())\n",
    "tdf['Credit_History']=tdf.Credit_History.fillna(tdf['Credit_History'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Dependents', 'Education', 'ApplicantIncome', 'CoapplicantIncome',\n",
       "       'LoanAmount', 'Loan_Amount_Term', 'Credit_History', 'Property_Area',\n",
       "       'Gender_Female', 'Married_Yes', 'Self_Employed_Yes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tdata = pd.get_dummies(tdf, columns =['Gender', 'Married', 'Self_Employed'])\n",
    "b=tdata.drop(['Loan_ID','Gender_Male','Married_No','Self_Employed_No'],axis=1)\n",
    "b.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test=b[['Dependents','Education','ApplicantIncome','CoapplicantIncome','LoanAmount','Loan_Amount_Term','Credit_History','Property_Area','Gender_Female','Married_Yes','Self_Employed_Yes']]#.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled= scaler.fit_transform(X_train)\n",
    "X_test_scaled= scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled= scaler.fit_transform(X_train)\n",
    "X_test_scaled= scaler.transform(X_test)\n",
    "clf=LogisticRegression(C=1).fit(X_train_scaled,np.ravel(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_pred=clf.predict(X_test_scaled)\n",
    "d=tdf['Loan_ID'].values\n",
    "e={'Loan_ID':d,'Loan_Status':clf_pred}\n",
    "final= pd.DataFrame(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_Status</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Loan_ID\n",
       "Loan_Status         \n",
       "N                 59\n",
       "Y                308"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.groupby('Loan_Status').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy of the model is 0.777777778"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning by GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters= [{'C':[.001,.01,1,5,10]}]\n",
    "grid_search=GridSearchCV(estimator=clf,\n",
    "                        param_grid=parameters,\n",
    "                        scoring='accuracy',\n",
    "                        cv=5,\n",
    "                        n_jobs=-1)\n",
    "grid_search=grid_search.fit(X_train_scaled,np.ravel(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.809462881514061"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  }
 ],
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