Loan Approval.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loan Approval Prediction\n",
    "\n",
    "### Project is a Part of Hackathon by [Analytics Vidhya](https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/).\n",
    "\n",
    "### Evaluation Metric is Accuracy.\n",
    "\n",
    "### [Data Sets](https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/#ProblemStatement) : Train and Test Files from Analytics Vidhya Hackathon.\n",
    "\n",
    "### Data \n",
    "\n",
    "<table><tr><td><img height=\"480\" width=\"640\" src=\"Loan Approval.png\"></td></tr></table>\n",
    "\n",
    "### Important Features for Loan Approval Prediction \n",
    "\n",
    "1. **Education : High Level of Education - High Chance of Loan Approval**\n",
    "\n",
    "2. **Income of Applicant : High Income - High Chance of Loan Approval**\n",
    "\n",
    "3. **Loan Amount : Low Loan Amount - High Chance of Loan Approval**\n",
    "\n",
    "4. **Loan Term : Short Loan Term - High Chance of Loan Approval**\n",
    "\n",
    "5. **Credit History : Good Credit Score - High Chance of Loan Approval**\n",
    "***\n",
    "### Flow of Project :\n",
    "\n",
    "1. **[Exploratory Data Analysis](#EDA)**\n",
    "\n",
    "2. **[Data Visualization](#DV)**\n",
    "\n",
    "3. **[Data Preprocessing](#DP)**\n",
    "\n",
    "4. **[Data Modelling and Evaluation](#DME)**\n",
    "\n",
    "5. **[Conclusion](#CON)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Import Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import Data Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train = pd.read_csv(\"Loan Approval Train.csv\")\n",
    "\n",
    "Test = pd.read_csv(\"Loan Approval Test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name=\"EDA\">Exploratory Data Analysis</a>\n",
    "\n",
    "***\n",
    "\n",
    "**Train Data Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test Data Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "    </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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Information of Train Data Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(Train.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Information Gained:** \n",
    "\n",
    "1. Train Data Set consists of 614 Rows and 13 Columns.\n",
    "2. Train Data Set consist of Missing Data.\n",
    "3. Data Types of Each Column in Train Data Set.\n",
    "4. Loan Status is the Traget Variable.\n",
    "***\n",
    "**Information of Test Data Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 367 entries, 0 to 366\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            367 non-null    object \n",
      " 1   Gender             356 non-null    object \n",
      " 2   Married            367 non-null    object \n",
      " 3   Dependents         357 non-null    object \n",
      " 4   Education          367 non-null    object \n",
      " 5   Self_Employed      344 non-null    object \n",
      " 6   ApplicantIncome    367 non-null    int64  \n",
      " 7   CoapplicantIncome  367 non-null    int64  \n",
      " 8   LoanAmount         362 non-null    float64\n",
      " 9   Loan_Amount_Term   361 non-null    float64\n",
      " 10  Credit_History     338 non-null    float64\n",
      " 11  Property_Area      367 non-null    object \n",
      "dtypes: float64(3), int64(2), object(7)\n",
      "memory usage: 34.5+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(Test.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Information Gained :**\n",
    "\n",
    "1. Test Data Set consists of 367 Rows and 12 Columns.\n",
    "2. Target Variable is Absent in Test Data Set.\n",
    "3. Test Data Set consist of Missing Data.\n",
    "4. Data Types of Each Column in the Test Data Set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train Data Set and Test Data Set Ratio**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Set : 62.59%\n",
      "\n",
      "Train Data Set : 37.41%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Train Data Set : {len(Train) / (len(Train) + len(Test))*100:.2f}%\")\n",
    "print()\n",
    "print(f\"Train Data Set : {len(Test) / (len(Train) + len(Test))*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Variables of Object Data Type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>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>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>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>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>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>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",
       "  Property_Area Loan_Status  \n",
       "0         Urban           Y  \n",
       "1         Rural           N  \n",
       "2         Urban           Y  \n",
       "3         Urban           Y  \n",
       "4         Urban           Y  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.select_dtypes(\"object\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Variables of Numerical Data Type ( Integers and Floating Point Numbers )** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>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>0</th>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  \n",
       "0             1.0  \n",
       "1             1.0  \n",
       "2             1.0  \n",
       "3             1.0  \n",
       "4             1.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.select_dtypes('number').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name=\"DV\">Data Visualization</a> \n",
    "\n",
    "### Univariate Analysis \n",
    "\n",
    "**Aanlyze Each Variable Individually.**\n",
    "\n",
    "### Target Variable :\n",
    "\n",
    "**Target Variable is Categorical.**\n",
    "\n",
    "**We Can Observe the Difference in Categorical Values using Bar Plot which Represents Frequency.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y    422\n",
      "N    192\n",
      "Name: Loan_Status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Train[\"Loan_Status\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Proportion of Frequency**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y    68.73\n",
      "N    31.27\n",
      "Name: Loan_Status, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(f\"{round(Train['Loan_Status'].value_counts(normalize = True,)*100,2)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Independent Variables   \n",
    "\n",
    "**Gender**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male      489\n",
      "Female    112\n",
      "Name: Gender, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Train[\"Gender\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Marrital Status**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes    398\n",
      "No     213\n",
      "Name: Married, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Train[\"Married\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Education**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graduate        480\n",
      "Not Graduate    134\n",
      "Name: Education, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Train[\"Education\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Self Employed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No     500\n",
      "Yes     82\n",
      "Name: Self_Employed, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Train[\"Self_Employed\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Credit History**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0    475\n",
      "0.0     89\n",
      "Name: Credit_History, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Train[\"Credit_History\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualizing Categorical Features.**\n",
    "\n",
    "**Bar Plot is Best for Categorical Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "\n",
    "Color = [\"lightblue\", \"lightpink\"]\n",
    "\n",
    "plt.subplot(231)\n",
    "Train['Loan_Status'].value_counts(normalize=True).plot.bar(figsize=(20,10), title= 'Loan Approval Status', color = Color)\n",
    "plt.xticks(rotation = 0)\n",
    "\n",
    "plt.subplot(232)\n",
    "Train['Gender'].value_counts(normalize=True).plot.bar(figsize=(20,10), title= 'Gender', color = Color)\n",
    "plt.xticks(rotation = 0)\n",
    "\n",
    "plt.subplot(233)\n",
    "Train['Married'].value_counts(normalize=True).plot.bar(title= 'Married', color = Color)\n",
    "plt.xticks(rotation = 0)\n",
    "\n",
    "plt.subplot(234)\n",
    "Train['Self_Employed'].value_counts(normalize=True).plot.bar(title= 'Self Employed', color = Color)\n",
    "plt.xticks(rotation = 0)\n",
    "\n",
    "plt.subplot(235)\n",
    "Train['Credit_History'].value_counts(normalize=True).plot.bar(title= 'Credit Score', color = Color)\n",
    "plt.xticks(rotation = 0)\n",
    "\n",
    "plt.subplot(236)\n",
    "Train['Education'].value_counts(normalize=True).plot.bar(title= 'Education', color = Color)\n",
    "plt.xticks(rotation = 0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusions :**\n",
    "1. Arround 69% or Applicants get Approvals for Loan.\n",
    "2. More than 80% Applicants are Male.\n",
    "3. Almost 65% Applicants are Married.\n",
    "4. More than 15% Applicants are Self Employed.\n",
    "5. More than 80% Applicants have a Good Credit Score.\n",
    "6. Almost 80% Applicants are Graduate.\n",
    "***\n",
    "**Ordinal Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121)\n",
    "\n",
    "Color1 = [\"dodgerblue\", \"orange\", \"green\", \"red\"]\n",
    "\n",
    "Color2 = [\"dodgerblue\", \"red\", \"green\"]\n",
    "\n",
    "Train['Dependents'].value_counts(normalize=True).plot.bar(figsize=(20,5), title= 'Dependents', color = Color1)\n",
    "plt.xticks(rotation = 0)\n",
    "\n",
    "plt.subplot(122)\n",
    "Train['Property_Area'].value_counts(normalize=True).plot.bar(title= 'Property Area', color = Color2)\n",
    "plt.xticks(rotation = 0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusions :**\n",
    "\n",
    "1. Maximum Number of the Applicants don't have Dependents.\n",
    "2. Mostly Applicants are from Semi Urban and Urban."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Independent Variables (Numerical)\n",
    "\n",
    "**Applicant Income Distribution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Measures of Central Tendency : \n",
      "Mean of Applicant Income : 5403.46\n",
      "Median of Applicant Income : 3812.50\n",
      "\n",
      "Measure of Variability :\n",
      "Standard Deviation of Applicant Income : 6109.04\n",
      "\n",
      "Kurtosis of Applicant Income : 60.54\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "\n",
    "plt.subplot(121)\n",
    "Train[\"ApplicantIncome\"].plot.box(figsize = (20,5))\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.distplot(Train[\"ApplicantIncome\"]);\n",
    "\n",
    "print(f\"Measures of Central Tendency : \")\n",
    "print(f\"Mean of Applicant Income : {Train['ApplicantIncome'].mean():.2f}\")\n",
    "print(f\"Median of Applicant Income : {Train['ApplicantIncome'].median():.2f}\")\n",
    "\n",
    "print()\n",
    "\n",
    "print(f\"Measure of Variability :\")\n",
    "print(f\"Standard Deviation of Applicant Income : {Train['ApplicantIncome'].std():.2f}\")\n",
    "\n",
    "print()\n",
    "\n",
    "print(f\"Kurtosis of Applicant Income : {Train['ApplicantIncome'].kurtosis():.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Distribution of Data is more Towards Left, Distribution is Right Skewed. (Positive Skewness)**\n",
    "\n",
    "**Algorithm Works Better if the Data is Normally Distributed.**\n",
    "\n",
    "**Data is Heavily Tailed : Histogram and Kernel Density Plot is Best Way to Represent Kurtosis.**\n",
    "\n",
    "**The Boxplots Represents the Presence of Outliers Values, Data contains many Outliers.**\n",
    "\n",
    "**For Better Observation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "Train.boxplot(column = 'ApplicantIncome', by = 'Education')\n",
    "plt.suptitle(\"\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Graduates have High Income**\n",
    "***\n",
    "**Coapplicant Income Distribution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Measures of Central Tendency : \n",
      "Mean of Coapplicant Income : 1621.25\n",
      "Median of Coapplicant Income : 1188.50\n",
      "\n",
      "Measure of Variability :\n",
      "Standard Deviation of Coapplicant Income : 2926.25\n",
      "\n",
      "Kurtosis of Coapplicant Income : 84.96\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "\n",
    "plt.subplot(121)\n",
    "Train[\"CoapplicantIncome\"].plot.box(figsize = (20,5))\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.distplot(Train[\"CoapplicantIncome\"]);\n",
    "\n",
    "print(f\"Measures of Central Tendency : \")\n",
    "print(f\"Mean of Coapplicant Income : {Train['CoapplicantIncome'].mean():.2f}\")\n",
    "print(f\"Median of Coapplicant Income : {Train['CoapplicantIncome'].median():.2f}\")\n",
    "\n",
    "print()\n",
    "\n",
    "print(f\"Measure of Variability :\")\n",
    "print(f\"Standard Deviation of Coapplicant Income : {Train['CoapplicantIncome'].std():.2f}\")\n",
    "\n",
    "print()\n",
    "\n",
    "print(f\"Kurtosis of Coapplicant Income : {Train['CoapplicantIncome'].kurtosis():.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Coapplicant Income is Right Skewed and consist lots of Outliers.**\n",
    "***\n",
    "**Loan Amount**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Measures of Central Tendency : \n",
      "Mean of Loan Amount : 146.41\n",
      "Median of Loan Amount : 128.00\n",
      "\n",
      "Measure of Variability :\n",
      "Standard Deviation of Loan Amount : 85.59\n",
      "\n",
      "Kurtosis of Loan Amount : 10.40\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "\n",
    "plt.subplot(121)\n",
    "Train[\"LoanAmount\"].plot.box(figsize = (20,5))\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.distplot(Train[\"LoanAmount\"]);\n",
    "\n",
    "print(f\"Measures of Central Tendency : \")\n",
    "print(f\"Mean of Loan Amount : {Train['LoanAmount'].mean():.2f}\")\n",
    "print(f\"Median of Loan Amount : {Train['LoanAmount'].median():.2f}\")\n",
    "\n",
    "print()\n",
    "\n",
    "print(f\"Measure of Variability :\")\n",
    "print(f\"Standard Deviation of Loan Amount : {Train['LoanAmount'].std():.2f}\")\n",
    "\n",
    "print()\n",
    "\n",
    "print(f\"Kurtosis of Loan Amount : {Train['LoanAmount'].kurtosis():.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loan Amount Term**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "Train[\"Loan_Amount_Term\"].value_counts(normalize = True).plot.bar(title = \"Loan Amount Term\", color = Color1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Around 85% of Loans are of 360 Months (30 Years)**\n",
    "\n",
    "### Bivariate Analysis\n",
    "\n",
    "**Categorical Independent Variable vs Target Variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan Status    N    Y\n",
      "Gender               \n",
      "Female        37   75\n",
      "Male         150  339\n"
     ]
    }
   ],
   "source": [
    "print(pd.crosstab(Train[\"Gender\"], Train[\"Loan_Status\"], colnames=['Loan Status'], dropna = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Stacked Bar Plot**\n",
    "\n",
    "**Proportion of Loan Approved or not Approved based on Gender.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Gender = pd.crosstab(Train['Gender'], Train['Loan_Status'])\n",
    "Gender.div(Gender.sum(axis = 1).astype(float), axis = 0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Percentage')\n",
    "plt.xticks(rotation = 0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan Status    N    Y\n",
      "Married              \n",
      "No            79  134\n",
      "Yes          113  285\n"
     ]
    }
   ],
   "source": [
    "print(pd.crosstab(Train['Married'], Train['Loan_Status'], colnames = ['Loan Status'], dropna = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Proportion of Loan Approved or not based on Marital Status.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Married = pd.crosstab(Train[\"Married\"],Train[\"Loan_Status\"])\n",
    "Married.div(Married.sum(axis = \"columns\").astype(float), axis = 0).plot(kind = \"bar\", stacked = True, figsize = (4,4))\n",
    "plt.xlabel(\"Married\")\n",
    "plt.ylabel(\"Percentage\")\n",
    "plt.xticks(rotation = 0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan Status    N    Y\n",
      "Dependents           \n",
      "0            107  238\n",
      "1             36   66\n",
      "2             25   76\n",
      "3+            18   33\n"
     ]
    }
   ],
   "source": [
    "print(pd.crosstab(Train['Dependents'], Train['Loan_Status'], colnames = ['Loan Status'], dropna = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Proportion of Loan Approved or not Approved based on Dependents.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Dependents = pd.crosstab(Train['Dependents'], Train['Loan_Status'])\n",
    "Dependents.div(Dependents.sum(axis = \"columns\").astype(float), axis = 0).plot(kind = \"bar\", stacked = True, figsize = (4,4))\n",
    "plt.xlabel('Dependents')\n",
    "plt.ylabel('Percentage')\n",
    "plt.xticks(rotation = 0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan Status     N    Y\n",
      "Education             \n",
      "Graduate      140  340\n",
      "Not Graduate   52   82\n"
     ]
    }
   ],
   "source": [
    "print(pd.crosstab(Train['Education'], Train['Loan_Status'], colnames = ['Loan Status'], dropna = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loan Approved or not Approved based on Education.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Education = pd.crosstab(Train['Education'], Train['Loan_Status'])\n",
    "Education.div(Education.sum(axis = 'columns').astype(float), axis = 'rows').plot(kind = 'bar', stacked = True, figsize = (4,4))\n",
    "plt.xlabel('Education')\n",
    "plt.ylabel('Percentage')\n",
    "plt.xticks(rotation = 0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan Status      N    Y\n",
      "Self Employed          \n",
      "No             157  343\n",
      "Yes             26   56\n"
     ]
    }
   ],
   "source": [
    "print(pd.crosstab(Train['Self_Employed'], Train['Loan_Status'], colnames = ['Loan Status'], rownames = ['Self Employed'], dropna = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loan Approval based on Self Employed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SelfEmployed = pd.crosstab(Train['Self_Employed'], Train['Loan_Status'])\n",
    "SelfEmployed.div(SelfEmployed.sum(axis = 'columns').astype(float), axis = 'rows').plot(kind = 'bar', stacked = True, figsize = (4,4))\n",
    "plt.xlabel('Self Employed')\n",
    "plt.ylabel('Percentage')\n",
    "plt.xticks(rotation = 0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan Status      N    Y\n",
      "Credit History         \n",
      "0.0             82    7\n",
      "1.0             97  378\n"
     ]
    }
   ],
   "source": [
    "print(pd.crosstab(Train['Credit_History'], Train['Loan_Status'], rownames = ['Credit History'], colnames = ['Loan Status']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loan Approved or not Approved based on Credit Score**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Credit = pd.crosstab(Train['Credit_History'], Train['Loan_Status'])\n",
    "Credit.div(Credit.sum(axis = 'columns').astype(float), axis = 'rows').plot(kind = 'bar', stacked = True, figsize = (4,4))\n",
    "plt.xlabel('Credit Score')\n",
    "plt.ylabel('Percentage')\n",
    "plt.xticks(rotation = 0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan Status     N    Y\n",
      "Property Area         \n",
      "Rural          69  110\n",
      "Semiurban      54  179\n",
      "Urban          69  133\n"
     ]
    }
   ],
   "source": [
    "print(pd.crosstab(Train['Property_Area'], Train['Loan_Status'], colnames = ['Loan Status'], rownames = ['Property Area']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loan Approved or not Approved based on Property Area**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Area  = pd.crosstab(Train['Property_Area'], Train['Loan_Status'])\n",
    "Area.div(Area.sum(axis = 'columns').astype(float), axis = 'rows').plot(kind = 'bar', stacked = True, figsize = (4,4))\n",
    "plt.xlabel('Property Area')\n",
    "plt.ylabel('Percentage')\n",
    "plt.xticks(rotation = 0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Correlation between Features.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>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>ApplicantIncome</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.116605</td>\n",
       "      <td>0.570909</td>\n",
       "      <td>-0.045306</td>\n",
       "      <td>-0.014715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <td>-0.116605</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.188619</td>\n",
       "      <td>-0.059878</td>\n",
       "      <td>-0.002056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoanAmount</th>\n",
       "      <td>0.570909</td>\n",
       "      <td>0.188619</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.039447</td>\n",
       "      <td>-0.008433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <td>-0.045306</td>\n",
       "      <td>-0.059878</td>\n",
       "      <td>0.039447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit_History</th>\n",
       "      <td>-0.014715</td>\n",
       "      <td>-0.002056</td>\n",
       "      <td>-0.008433</td>\n",
       "      <td>0.001470</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ApplicantIncome  CoapplicantIncome  LoanAmount  \\\n",
       "ApplicantIncome           1.000000          -0.116605    0.570909   \n",
       "CoapplicantIncome        -0.116605           1.000000    0.188619   \n",
       "LoanAmount                0.570909           0.188619    1.000000   \n",
       "Loan_Amount_Term         -0.045306          -0.059878    0.039447   \n",
       "Credit_History           -0.014715          -0.002056   -0.008433   \n",
       "\n",
       "                   Loan_Amount_Term  Credit_History  \n",
       "ApplicantIncome           -0.045306       -0.014715  \n",
       "CoapplicantIncome         -0.059878       -0.002056  \n",
       "LoanAmount                 0.039447       -0.008433  \n",
       "Loan_Amount_Term           1.000000        0.001470  \n",
       "Credit_History             0.001470        1.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Correlation Heat Map for Better understanding of Correlations.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fig, Axes = plt.subplots(figsize = (12,9))\n",
    "Axes = sns.heatmap(Train.corr(), annot = True, linewidths = 0.5, fmt = '.2f', cmap = 'YlGnBu');\n",
    "\n",
    "# Adjusting the Broken Map :\n",
    "#Bottom, Top = Axes.get_ylim() \n",
    "#Axes.set_ylim(Bottom + 0.5, Top - 0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y    422\n",
      "N    192\n",
      "Name: Loan_Status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Train['Loan_Status'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Replacing Y and N to 1 and 0**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    422\n",
      "0    192\n",
      "Name: Loan_Status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "Train['Loan_Status'] = np.where(Train['Loan_Status'] == 'Y',1,0)\n",
    "\n",
    "print(Train['Loan_Status'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Heat Map for Checking Correlation after Including the Target Variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fig, Axes = plt.subplots(figsize = (10,8))\n",
    "Axes = sns.heatmap(Train.corr(), annot = True, linewidths = 0.5, fmt = '.2f', cmap = 'viridis');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Better Correlations :**\n",
    "1. **Applicant Income and Loan Amount.**\n",
    "2. **Credit History and Loan Status.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name=\"DP\">Data Preprocessing</a>\n",
    "\n",
    "**A Data Minig Technique**\n",
    "\n",
    "**Transforming Raw Data into Proper Format that can be Accepted for Creating Machine Learning Models.**\n",
    "\n",
    "**Real World Data may be Incomplete and Filled with Missing Values, can't be used Directly for Creating Machine Learning Models.**\n",
    "\n",
    "**Missing Values must be Imputed or Dropped with Corresponding Mean or Median based on what Fits Better.**\n",
    "\n",
    "### Missing Value in the Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "print(Train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**While Filling Missing Values :**\n",
    "\n",
    "1. **Numerical Data : Impute with Mean or Median of Corresponding Column Data.**\n",
    "2. **Categorical Data : Impute with Mode of Corresponding Column Data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</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>0</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>1</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>1</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>1</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            1  \n",
       "1             1.0         Rural            0  \n",
       "2             1.0         Urban            1  \n",
       "3             1.0         Urban            1  \n",
       "4             1.0         Urban            1  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Categorical Features : (Gender, Married,Dependents,  Education, Self Employed, Credit Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender : \n",
      "Male      489\n",
      "Female    112\n",
      "Name: Gender, dtype: int64\n",
      "\n",
      "Marital Status : \n",
      "Yes    398\n",
      "No     213\n",
      "Name: Married, dtype: int64\n",
      "\n",
      "Dependents : \n",
      "0     345\n",
      "1     102\n",
      "2     101\n",
      "3+     51\n",
      "Name: Dependents, dtype: int64\n",
      "\n",
      "Education : \n",
      "Graduate        480\n",
      "Not Graduate    134\n",
      "Name: Education, dtype: int64\n",
      "\n",
      "Self Employed : \n",
      "No     500\n",
      "Yes     82\n",
      "Name: Self_Employed, dtype: int64\n",
      "\n",
      "Credit Score : \n",
      "1.0    475\n",
      "0.0     89\n",
      "Name: Credit_History, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(f'Gender : \\n{Train[\"Gender\"].value_counts()}')\n",
    "print()\n",
    "print(f'Marital Status : \\n{Train[\"Married\"].value_counts()}')\n",
    "print()\n",
    "print(f'Dependents : \\n{Train[\"Dependents\"].value_counts()}')\n",
    "print()\n",
    "print(f'Education : \\n{Train[\"Education\"].value_counts()}')\n",
    "print()\n",
    "print(f'Self Employed : \\n{Train[\"Self_Employed\"].value_counts()}')\n",
    "print()\n",
    "print(f'Credit Score : \\n{Train[\"Credit_History\"].value_counts()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Impute the Categorical Missing Values with Mode : Most Frequently Repeated Value from the Column.**\n",
    "\n",
    "**Imputation should be done in both Train and Test Data Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train['Gender'].fillna(Train['Gender'].mode()[0], inplace = True) # Count of Male is More than Female.\n",
    "Test['Gender'].fillna(Test['Gender'].mode()[0], inplace = True)\n",
    "\n",
    "Train['Married'].fillna(Train['Married'].mode()[0], inplace = True) # Count of Married is more than Unmarried.\n",
    "Test['Married'].fillna(Test['Married'].mode()[0], inplace = True)\n",
    "\n",
    "Train['Dependents'].fillna(Train['Dependents'].mode()[0], inplace = True) # Count of 0 Dependents is the Maximum.\n",
    "Test['Dependents'].fillna(Test['Dependents'].mode()[0], inplace = True)\n",
    "\n",
    "Train['Education'].fillna(Train['Education'].mode()[0], inplace = True) # # Count of Graduates is more than Non Graduates.\n",
    "Test['Education'].fillna(Test['Education'].mode()[0], inplace = True)\n",
    "\n",
    "Train['Self_Employed'].fillna(Train['Self_Employed'].mode()[0], inplace = True) # Count of Non Self Employed is More.\n",
    "Test['Self_Employed'].fillna(Test['Self_Employed'].mode()[0], inplace = True)\n",
    "\n",
    "Train['Credit_History'].fillna(Train['Credit_History'].mode()[0], inplace = True) # Credit Score 1.0 Count is More.\n",
    "Test['Credit_History'].fillna(Test['Credit_History'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check that Missing Values are Filled or Not :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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           22\n",
      "Loan_Amount_Term     14\n",
      "Credit_History        0\n",
      "Property_Area         0\n",
      "Loan_Status           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Replacing 3+ in Dependents to 3**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     360\n",
      "1     102\n",
      "2     101\n",
      "3+     51\n",
      "Name: Dependents, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Train['Dependents'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    360\n",
      "1    102\n",
      "2    101\n",
      "3     51\n",
      "Name: Dependents, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "Train['Dependents'].replace('3+', 3, inplace = True)\n",
    "Test['Dependents'].replace('3+', 3, inplace = True)\n",
    "print(Train['Dependents'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now Only Loan Amount and Loan Amount Terms are Remaining.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "360.0    512\n",
      "180.0     44\n",
      "480.0     15\n",
      "300.0     13\n",
      "84.0       4\n",
      "240.0      4\n",
      "120.0      3\n",
      "36.0       2\n",
      "60.0       2\n",
      "12.0       1\n",
      "Name: Loan_Amount_Term, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Train['Loan_Amount_Term'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here also Count of Loan Amount Terms of 36 Months is More than other Terms.**\n",
    "\n",
    "**Replacing Missing Values with Mode : 360 Months (30 Years)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train['Loan_Amount_Term'].fillna(Train['Loan_Amount_Term'].mode()[0], inplace = True)\n",
    "Test['Loan_Amount_Term'].fillna(Test['Loan_Amount_Term'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loan Amount is a Numerical Data : Imputing Missing Data Values with the Median of Loan Amounts.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train['LoanAmount'].fillna(Train['LoanAmount'].median(), inplace = True)\n",
    "Test['LoanAmount'].fillna(Test['LoanAmount'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check whether Every Missing Data is Imputed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Set : \n",
      "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\n",
      "\n",
      "Test Set : \n",
      "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",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(f\"Train Set : \\n{Train.isnull().sum()}\")\n",
    "print()\n",
    "print(f\"Test Set : \\n{Test.isnull().sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Every Missing Values are Imputed Successfully in both Train and Test Data Sets.**\n",
    "***\n",
    "### Outlier Treatment\n",
    "\n",
    "**Major Outliers where Observed above in Applicant Income, Coapplicant Income and The Loan Amount.**\n",
    "\n",
    "**Outliers where Clearly Visible in Histogram  above.**\n",
    "\n",
    "**Outliers in the Data Set Often Affects the Mean and Standard Deviation by affecting the Distribution of Data.**\n",
    "\n",
    "**More Data is Present on Left and Long Tail is on Right. (Right Skewed : Positive Skewed)**\n",
    "\n",
    "**One Way to Remove Skewness is to Perform Log Transformation.**\n",
    "\n",
    "**Log Transformation does not Affect the Smaller Values but Reduces the Larger Values, so we get Similar to Normal Distribution.**\n",
    "\n",
    "### Before Log Transformation :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "Train[\"LoanAmount\"].hist(figsize = (15,5), bins = 20)\n",
    "plt.title('Loan Amount Distribution of Train Set')\n",
    "\n",
    "plt.subplot(122)\n",
    "Test['LoanAmount'].hist(bins = 20)\n",
    "plt.title('Loan Amount Distribution of Test Set');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Log Transformation of Loan Amount in Train and Test Sets.**\n",
    "\n",
    "### After Log Transformation :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "np.log(Train['LoanAmount']).hist(figsize = (15,5), bins = 20)\n",
    "plt.title(f'Log Transformed Distribution of Loan Amount (Train)')\n",
    "\n",
    "plt.subplot(122)\n",
    "np.log(Test['LoanAmount']).hist(bins = 20)\n",
    "plt.title(f'Log Transformed Distribution of Loan Amount (Test)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now the Distribution Looks Little Bit Close to the Normal Distribution**\n",
    "\n",
    "**Larger Values are Normalized.**\n",
    "***\n",
    "# <a name=\"DME\">Data Modelling and Evaluation</a>\n",
    "\n",
    "**Evaluation Metrics for Classification**\n",
    "\n",
    "1. **Confusion Matrix**\n",
    "\n",
    "<table><tr><td><img height = 400 width = 400 src=\"Confusion Matrix.jpg\"></td></tr></table>\n",
    "<table>\n",
    "    <caption>Confusion Matrix</caption>\n",
    "    <tr><td>True Positive : </td>\n",
    "        <td>Actual Value is True (Y) and Prediction is also True (Y)</td>\n",
    "    </tr>\n",
    "    <tr><td>True Negative : </td>\n",
    "        <td>Actual Value is False (N) and Prediction is also False (N)</td>\n",
    "    </tr>\n",
    "    <tr><td>False Positive : </td>\n",
    "        <td>Actual Value is False (N) and Prediction is True (Y)</td>\n",
    "    </tr>\n",
    "    <tr><td>False Negative : </td>\n",
    "        <td>Actual Value is True (Y) and Prediction is False (N)</td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "**Accuracy : (TP + TN)  /  (TP + TN + FP + FN)**\n",
    "\n",
    "**Precision : Correctness Achieved in True Prediction (Actually Labelled True Predictions) : TP / (TP + FP)** \n",
    "\n",
    "**Recall (Sensitivity) : How many Observations of True Class are Labelled Correctly ? : TP / (TP + FN)**\n",
    "\n",
    "* **Recall Example :** Proportion of Patients who Actually have COVID19 and Tested Positive.\n",
    "\n",
    "**Specificity : How many Observations of False Class are Labelled Correctly ? : TN / (TN + FP)**\n",
    "\n",
    "* **Specificity :** Proportion of Patients without COVID19 and Test Negative.\n",
    "\n",
    "**Specificity and Sensitivity is Very Important for ROC Curve.**\n",
    "\n",
    "***\n",
    "\n",
    "**2.ROC : Receiver Operating Characteristic**\n",
    "\n",
    "1. **Summarizes Model Performance.**\n",
    "2. **Higher the Area Under Curve (AUC), Better the Prediction Power of the Model.**\n",
    "3. **Area Under Curve Measures the Ability of Model to Correctly Classify True Positive's and True Negative's**\n",
    "\n",
    "<table><caption>Classes of AUC</caption><tr><td><img height=\"200\" weight=\"200\" src=\"ROC.jpg\"></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**First Model for Classification : Logistic Regression**\n",
    "\n",
    "**Logistic Regression is a Classification Algorithm : Predicts Binary (0 or 1, Y or N, True or False)**\n",
    "\n",
    "**Estimation of Logit Function : Sinosuidal (S Shaped Curve) Probability Estimation of 1 or 0**\n",
    "***\n",
    "**Loan ID is of No Use so Removing the Loan ID Column from Train and Test Data Set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train.drop('Loan_ID', axis = 'columns', inplace = True)\n",
    "Test.drop('Loan_ID', axis = 'columns', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender Married Dependents     Education Self_Employed  ApplicantIncome  \\\n",
       "0   Male      No          0      Graduate            No             5849   \n",
       "1   Male     Yes          1      Graduate            No             4583   \n",
       "2   Male     Yes          0      Graduate           Yes             3000   \n",
       "3   Male     Yes          0  Not Graduate            No             2583   \n",
       "4   Male      No          0      Graduate            No             6000   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                0.0       128.0             360.0             1.0   \n",
       "1             1508.0       128.0             360.0             1.0   \n",
       "2                0.0        66.0             360.0             1.0   \n",
       "3             2358.0       120.0             360.0             1.0   \n",
       "4                0.0       141.0             360.0             1.0   \n",
       "\n",
       "  Property_Area  Loan_Status  \n",
       "0         Urban            1  \n",
       "1         Rural            0  \n",
       "2         Urban            1  \n",
       "3         Urban            1  \n",
       "4         Urban            1  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Scikit Learn is the Rich Set Library Well Equipped with Machine Learning Algorithms :** \n",
    "\n",
    "**Library for Creating Machine Learning Models and it's Evaluations.**\n",
    "***\n",
    "**Splitting the Data Set into Independent Variables and Target Variable.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = Train.drop('Loan_Status', axis = 'columns')\n",
    "Y = Train['Loan_Status']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating Dummy Variables for Categorical Values.**\n",
    "\n",
    "**Dummy Variables turns Categorical Variabes into a Series of 0 and 1, making them Easier to Quantify and Compare.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(X, drop_first = True)\n",
    "\n",
    "Train = pd.get_dummies(Train, drop_first = True)\n",
    "\n",
    "Test = pd.get_dummies(Test, drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Married_Yes</th>\n",
       "      <th>Dependents_0</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>Dependents_2</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0       128.0             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  Gender_Male  Married_Yes  Dependents_0  Dependents_1  \\\n",
       "0             1.0            1            0             1             0   \n",
       "1             1.0            1            1             0             1   \n",
       "2             1.0            1            1             1             0   \n",
       "3             1.0            1            1             1             0   \n",
       "4             1.0            1            0             1             0   \n",
       "\n",
       "   Dependents_2  Education_Not Graduate  Self_Employed_Yes  \\\n",
       "0             0                       0                  0   \n",
       "1             0                       0                  0   \n",
       "2             0                       0                  1   \n",
       "3             0                       1                  0   \n",
       "4             0                       0                  0   \n",
       "\n",
       "   Property_Area_Semiurban  Property_Area_Urban  \n",
       "0                        0                    1  \n",
       "1                        0                    0  \n",
       "2                        0                    1  \n",
       "3                        0                    1  \n",
       "4                        0                    1  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Splitting the Data into Train and Valid Set**\n",
    "\n",
    "**Import Librarty for Model Selection**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Train, X_Valid, Y_Train, Y_Valid = train_test_split(X, Y, test_size = 0.3, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dimensions of Train Set and Valid Set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70 % of Train Set Dimension : 429 and its Traget Variables Dimension : 429\n",
      "30 % of Valid Set Dimension : 185 and its Target Variables Dimension : 185\n"
     ]
    }
   ],
   "source": [
    "print(f'70 % of Train Set Dimension : {len(X_Train)} and its Traget Variables Dimension : {len(Y_Train)}')\n",
    "print(f'30 % of Valid Set Dimension : {len(X_Valid)} and its Target Variables Dimension : {len(Y_Valid)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import Logistic Regression Model and Accuracy Score for Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Instantiate Model Object and Fit the Model to Find Patterns.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model = LogisticRegression()\n",
    "\n",
    "Model.fit(X_Train, Y_Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's Predict Loan Status on Valid Data and Check it's Accuracy Score.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score of Logistic Regression Model : 83.24%\n"
     ]
    }
   ],
   "source": [
    "Y_Prediction = Model.predict(X_Valid)\n",
    "\n",
    "print(f'Accuracy Score of Logistic Regression Model : {accuracy_score(Y_Valid, Y_Prediction)*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model has Identified 83% of Loan Status Correctly.**\n",
    "***\n",
    "**Import Metric Library for Confusion Matrix :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Confusion Matrix :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "ConfusionMatrix = confusion_matrix(Y_Valid, Y_Prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualizing Confusion Matrix as Heat Map for Better Understanding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "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(ConfusionMatrix, annot = True, fmt = \"d\", cmap = 'viridis')\n",
    "plt.title('Confusion Matrix of Classifier')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import Metric Library for Classification Report**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.Classification Report :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report for Classifier : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.43      0.59        51\n",
      "           1       0.82      0.99      0.89       134\n",
      "\n",
      "    accuracy                           0.83       185\n",
      "   macro avg       0.87      0.71      0.74       185\n",
      "weighted avg       0.85      0.83      0.81       185\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f'Classification Report for Classifier : \\n{classification_report(Y_Valid, Y_Prediction)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performance of Our Model is Good : 83% of Accuracy, 82% of Precision and 99% of Recall.**\n",
    "****\n",
    "**Prediction for Test Data Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "Test_Prediction = Model.predict(Test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predictions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(Test_Prediction[:25])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing Submission File which we have to Send for Solution Checking.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission = pd.read_csv(\"Sample Submission.csv\")\n",
    "\n",
    "Loan = pd.read_csv(\"Loan Approval Test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We Want Loan ID Column from Test Data Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission['Loan_ID'] = Loan['Loan_ID']\n",
    "\n",
    "Submission['Loan_Status'] = Test_Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Loan_ID  Loan_Status\n",
      "0  LP001015            1\n",
      "1  LP001022            1\n",
      "2  LP001031            1\n",
      "3  LP001035            1\n",
      "4  LP001051            1\n"
     ]
    }
   ],
   "source": [
    "print(Submission.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We Need Prediction in Y and N**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission['Loan_Status'] = np.where(Submission['Loan_Status'] == 1, \"Y\", \"N\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Loan_ID Loan_Status\n",
      "0  LP001015           Y\n",
      "1  LP001022           Y\n",
      "2  LP001031           Y\n",
      "3  LP001035           Y\n",
      "4  LP001051           Y\n"
     ]
    }
   ],
   "source": [
    "print(Submission.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Submit to Check Accuracy on the Leaderboard.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission.to_csv('Logistic.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Score for My Submission : 78% of Accuracy**\n",
    "***\n",
    "**Using Cross Validation for Better Results**\n",
    "\n",
    "### Logistic Regression using Stratified K Fold Cross Validation\n",
    "\n",
    "<table><caption><b>Stratified 5 Fold Cross Validation</b></caption><tr><td><img height = 600 width = 600 src = \"Cross Validation.png\"></td></tr></table>\n",
    "\n",
    "**Stratification : The Process of Rearranging the Data so that Each Data Group forms a Good Proportion of Whole Data Set.**\n",
    "\n",
    "**For Example : In a Binary Classification Problem, Each Class Comprises of 50% of Data : 50% for Y and 50% for No.**\n",
    "\n",
    "**It Even Deals with Bias and Variance.**\n",
    "\n",
    "***\n",
    "\n",
    "**Import Stratified K Fold :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A Cross Vaidation Logistic Model with Stratified 5 Folds**\n",
    "\n",
    "**Each Fold will Contain Equal Proportion of Data.**\n",
    "\n",
    "**Shuffle the Data before Splitting.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of 5 Folds :\n",
      "Accuracy Score : 81.30%\n",
      "\n",
      "2 of 5 Folds :\n",
      "Accuracy Score : 82.93%\n",
      "\n",
      "3 of 5 Folds :\n",
      "Accuracy Score : 79.67%\n",
      "\n",
      "4 of 5 Folds :\n",
      "Accuracy Score : 78.05%\n",
      "\n",
      "5 of 5 Folds :\n",
      "Accuracy Score : 78.69%\n",
      "\n",
      "Mean Accuracy of 5 Fold Cross Validation : 80.13%\n"
     ]
    }
   ],
   "source": [
    "Mean_Accuracy = []\n",
    "\n",
    "i = 1\n",
    "\n",
    "SKF_Model = StratifiedKFold(n_splits = 5, random_state = 1, shuffle = True) # n Splits = Number of Folds.\n",
    "\n",
    "for Train_Data, Valid_Data in SKF_Model.split(X, Y) :\n",
    "    \n",
    "    print(f'\\n{i} of 5 Folds :')\n",
    "    \n",
    "    X_Train, X_Valid = X.loc[Train_Data], X.loc[Valid_Data]\n",
    "    \n",
    "    Y_Train, Y_Valid = Y.loc[Train_Data], Y.loc[Valid_Data]\n",
    "    \n",
    "    Model = LogisticRegression(random_state = 1, max_iter = 1000)\n",
    "    \n",
    "    Model.fit(X_Train, Y_Train)\n",
    "    \n",
    "    Y_Prediction = Model.predict(X_Valid)\n",
    "    \n",
    "    Accuracy_Score = accuracy_score(Y_Valid, Y_Prediction)\n",
    "    \n",
    "    Mean_Accuracy.append(Accuracy_Score)\n",
    "    \n",
    "    print(f'Accuracy Score : {Accuracy_Score*100:.2f}%')\n",
    "    \n",
    "    i += 1\n",
    "    \n",
    "print(f'\\nMean Accuracy of 5 Fold Cross Validation : {(sum(Mean_Accuracy)/len(Mean_Accuracy))*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "***\n",
    "**Prediction on Test Data :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "Test_Prediction = Model.predict(Test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Probability of Predictions on Valid Data : **We only want Probability of Positive Class for True Positive Rate.****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.74692621 0.84628924 0.77782709 0.85174464 0.88579484 0.89124717\n",
      " 0.69642708 0.74370981 0.82470984 0.91979586]\n"
     ]
    }
   ],
   "source": [
    "Prediction_Probability = Model.predict_proba(X_Valid)[:,1] # Only 1 ClassJ\n",
    "\n",
    "print(Prediction_Probability[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import Library for ROC Curve.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualize ROC Curve :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score : 74.66%\n"
     ]
    }
   ],
   "source": [
    "FPR, TPR, Threshold = roc_curve(Y_Valid, Prediction_Probability)\n",
    "AUC = roc_curve(Y_Valid, Prediction_Probability)\n",
    "\n",
    "def Plot_ROC_Curve(FPR, TPR) :\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    Plot the Receiver Operating Characteristics : ROC Curve\n",
    "    \n",
    "    Parameter \n",
    "    ---------\n",
    "    \n",
    "    FPR : False Positive Rate\n",
    "    \n",
    "    TPR : True Positive Rate\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    plt.figure(figsize = (10, 8))\n",
    "    \n",
    "    # Plot ROC Curve\n",
    "    plt.plot(FPR, TPR, color = 'orange', label = 'ROC')\n",
    "    \n",
    "    # Plot Line with No Predictive Power (Baseline)\n",
    "    plt.plot([0,1],[0,1], color = 'darkblue', linestyle = '--', label = 'Guessing')\n",
    "    \n",
    "    # Customize the Plot \n",
    "    plt.title('Receiver Operating Characteristics ( ROC Curve)')\n",
    "    plt.xlabel('False Positiv Rate ( FPR )')\n",
    "    plt.ylabel('True positive Rate ( TPR )')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "Plot_ROC_Curve(FPR, TPR)\n",
    "print(f'AUC Score : {roc_auc_score(Y_Valid, Prediction_Probability)*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How Does Perfect ROC Curve looks ?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perfect AUC Score : 100.00%\n"
     ]
    }
   ],
   "source": [
    "FPR, TPR, Threshold = roc_curve(Y_Valid, Y_Valid)\n",
    "Plot_ROC_Curve(FPR, TPR)\n",
    "print(f'Perfect AUC Score : {roc_auc_score(Y_Valid, Y_Valid)*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Even after Applying Stratified K Fold Cross Validation the Improvement in the Accuracy was nots so Satisfying.**\n",
    "***\n",
    "### Feature Engineering :\n",
    "\n",
    "**Based on Domain Knowledge New Feature might Affect the Target Variable**\n",
    "\n",
    "1. **Total Income : Applicant Income + Coapplicant Imcome (If Sum of both the Income is High, Chance of Loan Approval will Increase.)**\n",
    "\n",
    "2. **Equated Monthly Installments (EMI) : Monthly Amount to be Paid by the Applicant to Repay the Loan, Applicant with High EMI might Find it Difficult to Pay the Loan.**\n",
    "\n",
    "3. **Balance Income : Income Left after EMI has been Paid, If the Balance Income is High it Increases the Chance of Applicant will Repay the Loan, which also Increases the Chance of Loan Approvals.**\n",
    "***\n",
    "**Creating a New Feature : Total Income = Applicant Income + Coapplicant Income.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train['Total_Income'] = Train['ApplicantIncome'] + Train['CoapplicantIncome']\n",
    "\n",
    "Test['Total_Income'] = Train['ApplicantIncome'] + Train['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Married_Yes</th>\n",
       "      <th>Dependents_0</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>Dependents_2</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "      <th>Total_Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5849.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6091.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4941.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0       128.0             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  Loan_Status  Gender_Male  Married_Yes  Dependents_0  \\\n",
       "0             1.0            1            1            0             1   \n",
       "1             1.0            0            1            1             0   \n",
       "2             1.0            1            1            1             1   \n",
       "3             1.0            1            1            1             1   \n",
       "4             1.0            1            1            0             1   \n",
       "\n",
       "   Dependents_1  Dependents_2  Education_Not Graduate  Self_Employed_Yes  \\\n",
       "0             0             0                       0                  0   \n",
       "1             1             0                       0                  0   \n",
       "2             0             0                       0                  1   \n",
       "3             0             0                       1                  0   \n",
       "4             0             0                       0                  0   \n",
       "\n",
       "   Property_Area_Semiurban  Property_Area_Urban  Total_Income  \n",
       "0                        0                    1        5849.0  \n",
       "1                        0                    0        6091.0  \n",
       "2                        0                    1        3000.0  \n",
       "3                        0                    1        4941.0  \n",
       "4                        0                    1        6000.0  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking the Distribution of Total Income :**\n",
    "\n",
    "**Before the Transformation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fig = plt.figure(figsize = (14,4))\n",
    "Ax1 = plt.subplot(121)\n",
    "sns.distplot(Train['Total_Income'])\n",
    "Ax1.set_title('Train')\n",
    "plt.xlabel('Total Income')\n",
    "\n",
    "Ax1 = plt.subplot(122)\n",
    "sns.distplot(Test['Total_Income'])\n",
    "Ax1.set_title('Test')\n",
    "plt.xlabel('Total Income');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data is on the Left Side, Long Tail is on Right, Right Skewed / Positive Skewed**\n",
    "\n",
    "**Improving the Distribution of Data by Log Transformation.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train['Log_Income'] = np.log(Train['Total_Income'])\n",
    "\n",
    "Test['Log_Income'] = np.log(Test['Total_Income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fig = plt.figure(figsize = (14,4))\n",
    "Ax1 = plt.subplot(121)\n",
    "sns.distplot(Train['Log_Income'])\n",
    "Ax1.set_title('Train')\n",
    "plt.xlabel('Total Income')\n",
    "\n",
    "Ax1 = plt.subplot(122)\n",
    "sns.distplot(Test['Log_Income'])\n",
    "Ax1.set_title('Test')\n",
    "plt.xlabel('Total Income');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now the Distribution looks much Closer to Normal Distribution**\n",
    "\n",
    "***\n",
    "\n",
    "**Calculating and Creating a New Feature EMI.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train['EMI'] = Train['LoanAmount'] / Train['Loan_Amount_Term']\n",
    "\n",
    "Test['EMI'] = Test['LoanAmount'] / Test['Loan_Amount_Term']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**EMI is a Continuous Numerical Variable, Checking its Distribution.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fig = plt.figure(figsize = (14,4))\n",
    "\n",
    "Ax1 = plt.subplot(121)\n",
    "sns.distplot(Train['EMI'])\n",
    "Ax1.set_title('Train')\n",
    "plt.xlabel(f'EMI : Equated Monthly Installment')\n",
    "\n",
    "Ax1 = plt.subplot(122)\n",
    "sns.distplot(Test['EMI'])\n",
    "Ax1.set_title('Test')\n",
    "plt.xlabel(f'EMI : Equated Monthly Installment');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating and Calculating Balance Income Feature.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train['Balance_Income'] = Train['Total_Income'] - (Train['EMI'] * 1000) # Multiplying by 1000 to make Unit Same.\n",
    "\n",
    "Test['Balance_Income'] = Test['Total_Income'] - (Test['EMI'] * 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking the Distribution of Balance Income.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fig = plt.figure(figsize = (14,4))\n",
    "\n",
    "Ax1 = plt.subplot(121)\n",
    "sns.distplot(Train['Balance_Income'])\n",
    "Ax1.set_title('Train')\n",
    "plt.xlabel('Balanced Income')\n",
    "\n",
    "Ax1 = plt.subplot(122)\n",
    "sns.distplot(Test['Balance_Income'])\n",
    "Ax1.set_title('Test')\n",
    "plt.xlabel('Balanced Income');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dropping the Features which were used to Create New Features.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train.drop(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'], axis = 1, inplace = True)\n",
    "\n",
    "Test.drop(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'], axis = 'columns', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Credit_History</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Married_Yes</th>\n",
       "      <th>Dependents_0</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>Dependents_2</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "      <th>Total_Income</th>\n",
       "      <th>Log_Income</th>\n",
       "      <th>EMI</th>\n",
       "      <th>Balance_Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5849.0</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>0.355556</td>\n",
       "      <td>5493.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6091.0</td>\n",
       "      <td>8.714568</td>\n",
       "      <td>0.355556</td>\n",
       "      <td>5735.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>0.183333</td>\n",
       "      <td>2816.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4941.0</td>\n",
       "      <td>8.505323</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>4607.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>0.391667</td>\n",
       "      <td>5608.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Credit_History  Loan_Status  Gender_Male  Married_Yes  Dependents_0  \\\n",
       "0             1.0            1            1            0             1   \n",
       "1             1.0            0            1            1             0   \n",
       "2             1.0            1            1            1             1   \n",
       "3             1.0            1            1            1             1   \n",
       "4             1.0            1            1            0             1   \n",
       "\n",
       "   Dependents_1  Dependents_2  Education_Not Graduate  Self_Employed_Yes  \\\n",
       "0             0             0                       0                  0   \n",
       "1             1             0                       0                  0   \n",
       "2             0             0                       0                  1   \n",
       "3             0             0                       1                  0   \n",
       "4             0             0                       0                  0   \n",
       "\n",
       "   Property_Area_Semiurban  Property_Area_Urban  Total_Income  Log_Income  \\\n",
       "0                        0                    1        5849.0    8.674026   \n",
       "1                        0                    0        6091.0    8.714568   \n",
       "2                        0                    1        3000.0    8.006368   \n",
       "3                        0                    1        4941.0    8.505323   \n",
       "4                        0                    1        6000.0    8.699515   \n",
       "\n",
       "        EMI  Balance_Income  \n",
       "0  0.355556     5493.444444  \n",
       "1  0.355556     5735.444444  \n",
       "2  0.183333     2816.666667  \n",
       "3  0.333333     4607.666667  \n",
       "4  0.391667     5608.333333  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**After Feature Engineering, again Try Logistic Regression and Stratified K Fold Cross Validation**\n",
    "***\n",
    "**Splitting Data into Independent Features and Dependent Feature.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = Train.drop('Loan_Status', axis = 'columns')\n",
    "\n",
    "Y = Train['Loan_Status']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applying Logistic Regression with Stratified K Fold Cross Validation.**\n",
    "\n",
    "**Shuffle Each Stratification before Splitting.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of 5 Folds :\n",
      "Accuracy Score : 81.30%\n",
      "\n",
      "2 of 5 Folds :\n",
      "Accuracy Score : 80.49%\n",
      "\n",
      "3 of 5 Folds :\n",
      "Accuracy Score : 66.67%\n",
      "\n",
      "4 of 5 Folds :\n",
      "Accuracy Score : 68.29%\n",
      "\n",
      "5 of 5 Folds :\n",
      "Accuracy Score : 76.23%\n",
      "\n",
      "Mean Accuracy of 5 Fold Cross Validation : 74.60%\n"
     ]
    }
   ],
   "source": [
    "Mean_Accuracy = []\n",
    "\n",
    "i = 1\n",
    "\n",
    "KFS = StratifiedKFold(n_splits = 5, random_state = 1, shuffle = True)\n",
    "\n",
    "for Train_Data, Valid_Data in KFS.split(X, Y) :\n",
    "    \n",
    "    print(f'\\n{i} of 5 Folds :')\n",
    "    \n",
    "    X_Train, X_Valid = X.loc[Train_Data], X.loc[Valid_Data]\n",
    "    \n",
    "    Y_Train, Y_Valid = Y.loc[Train_Data], Y.loc[Valid_Data]\n",
    "    \n",
    "    Model = LogisticRegression(random_state = 1)\n",
    "    \n",
    "    Model.fit(X_Train, Y_Train)\n",
    "    \n",
    "    Y_Prediction = Model.predict(X_Valid)\n",
    "    \n",
    "    Accuracy_Score = accuracy_score(Y_Valid, Y_Prediction)\n",
    "    \n",
    "    Mean_Accuracy.append(Accuracy_Score)\n",
    "    \n",
    "    print(f'Accuracy Score : {Accuracy_Score*100:.2f}%')\n",
    "    \n",
    "    i +=1\n",
    "    \n",
    "print(f'\\nMean Accuracy of 5 Fold Cross Validation : {(sum(Mean_Accuracy)/len(Mean_Accuracy))*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It is Visible that Feature Engineering has not Improved the Model, Accuracy is not as Expected**\n",
    "\n",
    "***\n",
    "\n",
    "### Decision Tree\n",
    "\n",
    "**Supervised Machine Learning Algorithm, mostly used in Classification Problems.**\n",
    "\n",
    "**Import Decision Tree Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applying Decision Tree Classifier with Stratified K Fold Cross Validation.**\n",
    "\n",
    "**Shuffle each Stratification before Splitting.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of 5 K Folds\n",
      "Accuracy Score : 67.48%\n",
      "\n",
      "2 of 5 K Folds\n",
      "Accuracy Score : 68.29%\n",
      "\n",
      "3 of 5 K Folds\n",
      "Accuracy Score : 75.61%\n",
      "\n",
      "4 of 5 K Folds\n",
      "Accuracy Score : 71.54%\n",
      "\n",
      "5 of 5 K Folds\n",
      "Accuracy Score : 67.21%\n",
      "\n",
      "Mean Validation Accuracy : 70.03%\n"
     ]
    }
   ],
   "source": [
    "Mean_Accuracy = []\n",
    "\n",
    "i = 1\n",
    "\n",
    "KFS = StratifiedKFold(n_splits = 5, random_state = 1, shuffle = True)\n",
    "\n",
    "for Train_Data, Valid_Data in KFS.split(X, Y) :\n",
    "    \n",
    "    print(f'\\n{i} of 5 K Folds')\n",
    "    \n",
    "    X_Train, X_Valid = X.loc[Train_Data], X.loc[Valid_Data]\n",
    "    \n",
    "    Y_Train, Y_Valid = Y.loc[Train_Data], Y.loc[Valid_Data]\n",
    "    \n",
    "    Model = DecisionTreeClassifier(random_state = 1)\n",
    "    \n",
    "    Model.fit(X_Train, Y_Train)\n",
    "    \n",
    "    Y_Prediction = Model.predict(X_Valid)\n",
    "    \n",
    "    Accuracy_Score = accuracy_score(Y_Valid, Y_Prediction)\n",
    "    \n",
    "    Mean_Accuracy.append(Accuracy_Score)\n",
    "    \n",
    "    print(f'Accuracy Score : {Accuracy_Score*100:.2f}%')\n",
    "    \n",
    "    i += 1\n",
    "    \n",
    "print(f'\\nMean Validation Accuracy : {(sum(Mean_Accuracy)/len(Mean_Accuracy))*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Even by Using Decision Tree Algorithm the Accuracy is not Enough.**\n",
    "\n",
    "***\n",
    "\n",
    "### Random Forest \n",
    "\n",
    "**Tree Based Ensemble Algorithm and Try to Improve the Model by Improving the Accuracy.**\n",
    "\n",
    "- **A Tree Based Bootstrapping Algorithm where Weak Learners are Combined to make a Better Prediction Model.**\n",
    "\n",
    "- **For Every Individual Learner, A Random Sample of Rows and a few Randomly choosen Variables are used to Build a Decision Tree Model.**\n",
    "\n",
    "- **Final Prediction is the One with Majority.**\n",
    "\n",
    "- **In Case of Regression, Final Prediction is Mean of all Predictions.**\n",
    "\n",
    "**Important Parameters :**\n",
    "\n",
    "1. **n_estimators : Bigger the Forest Better the Model, chance of Overfitting Reduces.**\n",
    "\n",
    "2. **max_depth : Low Depth helps us to keep low Overfitting.**\n",
    "\n",
    "**Import Random Forest Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of 5 Folds\n",
      "Accuracy Score : 82.93%\n",
      "\n",
      "2 of 5 Folds\n",
      "Accuracy Score : 77.24%\n",
      "\n",
      "3 of 5 Folds\n",
      "Accuracy Score : 78.05%\n",
      "\n",
      "4 of 5 Folds\n",
      "Accuracy Score : 78.05%\n",
      "\n",
      "5 of 5 Folds\n",
      "Accuracy Score : 77.05%\n",
      "\n",
      "Mean Validation Accuracy : 78.66%\n"
     ]
    }
   ],
   "source": [
    "Mean_Accuracy = []\n",
    "\n",
    "i = 1\n",
    "\n",
    "KFS = StratifiedKFold(n_splits = 5, random_state = 1, shuffle = True)\n",
    "\n",
    "for Train_Data, Valid_Data in KFS.split(X, Y) :\n",
    "    \n",
    "    print(f'\\n{i} of 5 Folds')\n",
    "    \n",
    "    X_Train, X_Valid = X.loc[Train_Data], X.loc[Valid_Data]\n",
    "    \n",
    "    Y_Train, Y_Valid = Y.loc[Train_Data], Y.loc[Valid_Data]\n",
    "    \n",
    "    Model = RandomForestClassifier(random_state = 1, n_estimators = 10, max_depth = 10)\n",
    "    \n",
    "    Model.fit(X_Train, Y_Train)\n",
    "    \n",
    "    Y_Prediction = Model.predict(X_Valid)\n",
    "    \n",
    "    Accuracy_Score = accuracy_score(Y_Valid, Y_Prediction)\n",
    "    \n",
    "    Mean_Accuracy.append(Accuracy_Score)\n",
    "    \n",
    "    print(f'Accuracy Score : {Accuracy_Score*100:.2f}%')\n",
    "    \n",
    "    i += 1\n",
    "    \n",
    "print(f'\\nMean Validation Accuracy : {(sum(Mean_Accuracy)/len(Mean_Accuracy))*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Accuracy Obtained using Random Forest Classifier is Far more Better from All the Above Model's Accuracy.**\n",
    "\n",
    "**Prediction on Test Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "Test_Prediction = Model.predict(Test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predictions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(Test_Prediction[:25])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing Submission File which we have to Send for Solutionn Checking.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission = pd.read_csv(\"Sample Submission.csv\")\n",
    "\n",
    "Loan = pd.read_csv(\"Loan Approval Test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We want Load_ID Column from Test Data Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission['Loan_ID'] = Loan['Loan_ID']\n",
    "\n",
    "Submission['Loan_Status'] = Test_Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Loan_ID  Loan_Status\n",
      "0  LP001015            1\n",
      "1  LP001022            1\n",
      "2  LP001031            1\n",
      "3  LP001035            1\n",
      "4  LP001051            1\n"
     ]
    }
   ],
   "source": [
    "print(Submission.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prediction is needed in Y and N**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission['Loan_Status'] = np.where(Submission['Loan_Status'] == 1, 'Y', 'N')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Loan_ID Loan_Status\n",
      "0  LP001015           Y\n",
      "1  LP001022           Y\n",
      "2  LP001031           Y\n",
      "3  LP001035           Y\n",
      "4  LP001051           Y\n"
     ]
    }
   ],
   "source": [
    "print(Submission.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Submit to Check Accuracy on Leaderboard.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission.to_csv('RandomForest.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Score for My Submission is still Showing 78%**\n",
    "\n",
    "### Grid Search Cross Validation\n",
    "\n",
    "**Improving the Accuracy by Tunning Hyperparameters for Model.**\n",
    "\n",
    "**Using Grid Search Cross Validation for Better Optimized Values of Hyper Parameters.**\n",
    "\n",
    "**Grid Search helps to Select Best Family of Hyper Paramters, Parameterized by Grid of Parameters.**\n",
    "\n",
    "**Grid Search Cross Validation is an Exhaustive Search over Specified Parameter Values for an Estimator**\n",
    "\n",
    "#### Important Methods of Grid Search Cross Validation :\n",
    "\n",
    "1.**fit() : Train Model using Training Data Set**\n",
    "\n",
    "2.**score() : Accuracy of Model**\n",
    "\n",
    "3.**predict() : Make Prediction on Test Data**\n",
    "\n",
    "4.**predict_proba() : Probability of Prediction on Test Set** \n",
    "\n",
    "5.**transform() : Transform the Data**\n",
    "\n",
    "#### Parameters to be Tuned \n",
    "\n",
    "1.**n_estimators : Number of Trees**\n",
    "\n",
    "2.**max_depth : Maximum Depth of Tree**\n",
    "\n",
    "***\n",
    "\n",
    "**Import Library**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**n_estimators : Start from 1 and End at 199 with Step Count of 20**\n",
    "\n",
    "**max_depth : Start from 1 and End at 20 with Step Count of 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "Parameter_Grid = {'n_estimators' : list(range(1,200,20)), 'max_depth' : list(range(1,20,2))}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performing 5 Fold Cross Validation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "Grid_Search = GridSearchCV(RandomForestClassifier(random_state = 1), Parameter_Grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 0.3, random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train the Model using Train Data Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score=nan,\n",
       "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
       "                                              class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              max_samples=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=100, n_jobs=None,\n",
       "                                              oob_score=False, random_state=1,\n",
       "                                              verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'max_depth': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],\n",
       "                         'n_estimators': [1, 21, 41, 61, 81, 101, 121, 141, 161,\n",
       "                                          181]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Grid_Search.fit(X_Train, Y_Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Best Estimators found**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
      "                       criterion='gini', max_depth=3, max_features='auto',\n",
      "                       max_leaf_nodes=None, max_samples=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=21,\n",
      "                       n_jobs=None, oob_score=False, random_state=1, verbose=0,\n",
      "                       warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(Grid_Search.best_estimator_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating Model using Best Optimized Value**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of 5 Folds\n",
      "Accuracy Score : 82.93%\n",
      "\n",
      "2 of 5 Folds\n",
      "Accuracy Score : 85.37%\n",
      "\n",
      "3 of 5 Folds\n",
      "Accuracy Score : 78.05%\n",
      "\n",
      "4 of 5 Folds\n",
      "Accuracy Score : 79.67%\n",
      "\n",
      "5 of 5 Folds\n",
      "Accuracy Score : 78.69%\n",
      "\n",
      "Mean Validation Accuracy : 80.94%\n"
     ]
    }
   ],
   "source": [
    "Mean_Accuracy = []\n",
    "\n",
    "i = 1\n",
    "\n",
    "KFS = StratifiedKFold(n_splits = 5, random_state = 1, shuffle = True)\n",
    "\n",
    "for Train_Data, Valid_Data in KFS.split(X, Y) :\n",
    "    \n",
    "    print(f'\\n{i} of 5 Folds')\n",
    "    \n",
    "    X_Train, X_Valid = X.loc[Train_Data], X.loc[Valid_Data]\n",
    "    \n",
    "    Y_Train, Y_Valid = Y.loc[Train_Data], Y.loc[Valid_Data]\n",
    "    \n",
    "    Model = RandomForestClassifier(random_state = 1, max_depth = 3, n_estimators = 21)\n",
    "    \n",
    "    Model.fit(X_Train, Y_Train)\n",
    "    \n",
    "    Y_Prediction = Model.predict(X_Valid)\n",
    "    \n",
    "    Accuracy_Score = accuracy_score(Y_Valid, Y_Prediction)\n",
    "    \n",
    "    Mean_Accuracy.append(Accuracy_Score)\n",
    "    \n",
    "    print(f'Accuracy Score : {Accuracy_Score*100:.2f}%')\n",
    "    \n",
    "    i += 1\n",
    "    \n",
    "print(f'\\nMean Validation Accuracy : {(sum(Mean_Accuracy)/len(Mean_Accuracy))*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "Important_Features = pd.Series(Model.feature_importances_, index = X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualizing the Feature Importance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Important_Features.plot(kind = 'barh', figsize = (12, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Credit History is the Most Important Feature followed by Log Income, Total Income and Balance Income and Property Area in Semiurban.**\n",
    "\n",
    "***\n",
    "\n",
    "###  XGBOOST\n",
    "\n",
    "**XGBOOST works with only Numerical Data**\n",
    "\n",
    "**Parameters**\n",
    "\n",
    "1. **n_estimators : Number of Trees for the Model.**\n",
    "\n",
    "2. **max_depth : Maximum Depth of the Tree.**\n",
    "\n",
    "***\n",
    "\n",
    "**Import Library**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of 5 Folds\n",
      "Accuracy Score : 81.30%\n",
      "\n",
      "2 of 5 Folds\n",
      "Accuracy Score : 81.30%\n",
      "\n",
      "3 of 5 Folds\n",
      "Accuracy Score : 78.86%\n",
      "\n",
      "4 of 5 Folds\n",
      "Accuracy Score : 79.67%\n",
      "\n",
      "5 of 5 Folds\n",
      "Accuracy Score : 77.87%\n",
      "\n",
      "Mean Validation Accuracy : 79.80%\n"
     ]
    }
   ],
   "source": [
    "Mean_Accuracy = []\n",
    "\n",
    "i = 1\n",
    "\n",
    "KFS = StratifiedKFold(n_splits = 5, random_state = 1, shuffle = True)\n",
    "\n",
    "for Train_Data, Valid_Data in KFS.split(X, Y) :\n",
    "    \n",
    "    print(f'\\n{i} of 5 Folds')\n",
    "    \n",
    "    X_Train, X_Valid = X.loc[Train_Data], X.loc[Valid_Data]\n",
    "    \n",
    "    Y_Train, Y_Valid = Y.loc[Train_Data], Y.loc[Valid_Data]\n",
    "    \n",
    "    Model = XGBClassifier(random_state = 1, n_estimators = 50, max_depth = 4)\n",
    "    \n",
    "    Model.fit(X_Train, Y_Train)\n",
    "    \n",
    "    Y_Prediction = Model.predict(X_Valid)\n",
    "    \n",
    "    Accuracy_Score = accuracy_score(Y_Valid, Y_Prediction)\n",
    "    \n",
    "    Mean_Accuracy.append(Accuracy_Score)\n",
    "    \n",
    "    print(f'Accuracy Score : {Accuracy_Score*100:.2f}%')\n",
    "    \n",
    "    i += 1\n",
    "    \n",
    "print(f'\\nMean Validation Accuracy : {(sum(Mean_Accuracy)/len(Mean_Accuracy))*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Improving the Accuracy by Tuning Hyperparameters**\n",
    "\n",
    "**Using Grid Search Cross Validation to get the Optimized Values of Hyperparameters.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "Parameter_Grid = {'max_depth' : list(range(1, 20, 2)), 'n_estimators' : list(range(1, 200, 20))}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Apply Grid Search Cross Validation on XGBOOST Classifier.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "Grid_Search = GridSearchCV(XGBClassifier(random_state = 1), Parameter_Grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Split the Data into Train Set and Validation Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Train, X_Valid, Y_Train, Y_Valid = train_test_split(X, Y, test_size = 0.3, random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train the Model and Find Patterns in Training Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV(cv=None, error_score=nan,\n",
      "             estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
      "                                     colsample_bylevel=1, colsample_bynode=1,\n",
      "                                     colsample_bytree=1, gamma=0,\n",
      "                                     learning_rate=0.1, max_delta_step=0,\n",
      "                                     max_depth=3, min_child_weight=1,\n",
      "                                     missing=None, n_estimators=100, n_jobs=1,\n",
      "                                     nthread=None, objective='binary:logistic',\n",
      "                                     random_state=1, reg_alpha=0, reg_lambda=1,\n",
      "                                     scale_pos_weight=1, seed=None, silent=None,\n",
      "                                     subsample=1, verbosity=1),\n",
      "             iid='deprecated', n_jobs=None,\n",
      "             param_grid={'max_depth': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],\n",
      "                         'n_estimators': [1, 21, 41, 61, 81, 101, 121, 141, 161,\n",
      "                                          181]},\n",
      "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
      "             scoring=None, verbose=0)\n"
     ]
    }
   ],
   "source": [
    "print(Grid_Search.fit(X_Train, Y_Train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Best Estimator Obtained**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
      "              learning_rate=0.1, max_delta_step=0, max_depth=1,\n",
      "              min_child_weight=1, missing=None, n_estimators=1, n_jobs=1,\n",
      "              nthread=None, objective='binary:logistic', random_state=1,\n",
      "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
      "              silent=None, subsample=1, verbosity=1)\n"
     ]
    }
   ],
   "source": [
    "print(Grid_Search.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of 5 Folds :\n",
      "Accuracy Score : 81.30%\n",
      "\n",
      "2 of 5 Folds :\n",
      "Accuracy Score : 83.74%\n",
      "\n",
      "3 of 5 Folds :\n",
      "Accuracy Score : 79.67%\n",
      "\n",
      "4 of 5 Folds :\n",
      "Accuracy Score : 80.49%\n",
      "\n",
      "5 of 5 Folds :\n",
      "Accuracy Score : 79.51%\n",
      "\n",
      "Mean Validation Score : 80.94%\n"
     ]
    }
   ],
   "source": [
    "Mean_Accuracy = []\n",
    "\n",
    "i = 1\n",
    "\n",
    "KFS = StratifiedKFold(n_splits = 5, random_state = 1, shuffle = True)\n",
    "\n",
    "for Train_Data, Valid_Data in KFS.split(X,Y) :\n",
    "    \n",
    "    print(f'\\n{i} of 5 Folds :')\n",
    "    \n",
    "    X_Train, X_Valid = X.loc[Train_Data], X.loc[Valid_Data]\n",
    "    \n",
    "    Y_Train, Y_Valid = Y.loc[Train_Data], Y.loc[Valid_Data]\n",
    "    \n",
    "    Model = XGBClassifier(random_state = 1, n_estimators = 1, max_depth = 1)\n",
    "    \n",
    "    Model.fit(X_Train, Y_Train)\n",
    "    \n",
    "    Y_Prediction = Model.predict(X_Valid)\n",
    "    \n",
    "    Accuracy_Score = accuracy_score(Y_Valid, Y_Prediction)\n",
    "    \n",
    "    Mean_Accuracy.append(Accuracy_Score)\n",
    "    \n",
    "    print(f'Accuracy Score : {Accuracy_Score*100:.2f}%')\n",
    "    \n",
    "    i += 1\n",
    "    \n",
    "print(f'\\nMean Validation Score : {(sum(Mean_Accuracy)/len(Mean_Accuracy))*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test Prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "Test_Prediction = Model.predict(Test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prediction Probability**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "Test_Probability = Model.predict_proba(Test)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing Submission File which we have to Send for Solution Checking**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission = pd.read_csv('Sample Submission.csv')\n",
    "\n",
    "Loan = pd.read_csv('Loan Approval Test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We want to Load ID Column from Test Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission['Loan_ID'] = Loan['Loan_ID']\n",
    "\n",
    "Submission['Loan_Status'] = Test_Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Loan_ID  Loan_Status\n",
      "0  LP001015            1\n",
      "1  LP001022            1\n",
      "2  LP001031            1\n",
      "3  LP001035            1\n",
      "4  LP001051            1\n"
     ]
    }
   ],
   "source": [
    "print(Submission.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Replacing 0 and 1 with N and Y**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission['Loan_Status'] = np.where(Submission['Loan_Status'] == 1, 'Y', 'N')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Loan_ID Loan_Status\n",
      "0  LP001015           Y\n",
      "1  LP001022           Y\n",
      "2  LP001031           Y\n",
      "3  LP001035           Y\n",
      "4  LP001051           Y\n"
     ]
    }
   ],
   "source": [
    "print(Submission.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Saving Data in CSV Format**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission.to_csv('XGBOOST.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name=\"CON\">Conclusion</a>\n",
    "    \n",
    "**Suggestions for Imrprovement :**\n",
    "    \n",
    "1. **Add More Meaningful Features.**\n",
    "\n",
    "2. **Different Models with Different Subsets of Features.**\n",
    "\n",
    "3. **Visualize Independent vs Dependent Features to Find more Meaningful Patterns.**\n",
    "\n",
    "4. **Apply Ensemble Techniques (Combinations of Optimized and Tuned Hyperparameters)**\n",
    "\n",
    "5. **This Project was Good for Learning Point of View**\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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