loan-approval-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loan Approval Prediction\n",
    "<br>\n",
    "This repo contains the Loan Approval Prediction project as part of my data science portfolio. This project is completed as part of the online hackathon organized by [Analytics Vidhya](https://www.analyticsvidhya.com/). Evaluation metric of the hackathon is accuracy i.e. percentage of loan approval that is correctly predicted. After trying and testing 4 different algorithms, the best accuracy on the public leaderboard is achieved by Logistic Regression (0.7847), followed by RandomForest (0.7778) and XGBoost (0.7778), while Decision Tree performed the worst (0.6458). \n",
    "\n",
    "This project covers the whole process from problem statement to model development and evaluation:\n",
    "1. [Problem Statement](#problem)\n",
    "2. [Hypothesis Generation](#hypothesis)\n",
    "3. [Data Collection](#data)\n",
    "4. [Exploratory Data Analysis (EDA)](#eda)\n",
    "5. [Data Pre-processing](#pre)\n",
    "6. [Model Development and Evaluation](#model)\n",
    "7. [Conclusion](#conclusion)\n",
    "\n",
    "\n",
    "Below are my submission results:\n",
    "<table><tr><td><img height=\"480\" width=\"640\" src=\"results.jpg\"></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# <a name=\"problem\">Problem Statement</a>\n",
    "\n",
    "### _Business Problem_\n",
    "\"Dream Housing Finance company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first apply for home loan after that company validates the customer eligibility for loan. Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers.\"\n",
    "\n",
    "Loan prediction is a very common real-life problem that every retail bank faces in their lending operations. If the loan approval process is automated, it can save a lot of man hours and improve the speed of service to the customers. The increase in customer satisfaction and savings in operational costs are significant. However, the benefits can only be reaped if the bank has a robust model to accurately predict which customer's loan it should approve and which to reject, in order to minimize the risk of loan default.\n",
    "\n",
    "### _Translate Business Problem into Data Science / Machine Learning problem_\n",
    "This is a classification problem where we have to predict whether a loan will be approved or not. Specifically, it is a binary classification problem where we have to predict either one of the two classes given i.e. approved (Y) or not approved (N). Another way to frame the problem is to predict whether the loan will likely to default or not, if it is likely to default, then the loan would not be approved, and vice versa. The dependent variable or target variable is the Loan_Status, while the rest are independent variable or features. We need to develop a model using the features to predict the target variable.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# <a name=\"hypothesis\">Hypothesis Generation</a>\n",
    "Hypothesis Generation is the process of listing out all the possible factors that can affect the outcome i.e. which of the features will have an impact on whether a loan will be approved or not. Some of the hypothesis are: \n",
    "\n",
    "- Education - Applicants with higher education level i.e. graduate level should have higher chances of loan approval\n",
    "- Income: Applicants with higher income should have more chances of loan approval\n",
    "- Loan amount: If the loan amount is less, the chances of loan approval should be high\n",
    "- Loan term: Loans with shorter time period should have higher chances of approval\n",
    "- Previous credit history: Applicants who have repayed their previous debts should have higher chances of loan approval\n",
    "- Monthly installment amount: If the monthly installment amount is low, the chances of loan approval should be high\n",
    "- And so on\n",
    "\n",
    "Some of the hypothesis seem intuitive while others may not. We will try to validate each of these hypothesis based on the dataset.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# <a name=\"data\">Data Collection</a>\n",
    "\n",
    "The data have already been provided by Analytics Vidhya on the [Hackathon page](https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/). The training set will be used for training the model, i.e. our model will learn from this data. It contains all the independent variables and the target variable. The test set contains all the independent variables, but not the target variable. We will apply the model to predict the target variable for the test data. There are 13 columns of features and 614 rows of records in the training set and 12 columns of features and 367 rows of records in the test set. The dataset variables are summarized as below:\n",
    "\n",
    "| No | Variable | Type | Description |\n",
    "|---|---|---|---|---|\n",
    "| 1 | Loan_ID | Numerical - Discrete | Unique Loan ID |\n",
    "| 2 | Gender  | Categorical - Nominal  | Male / Female  |\n",
    "| 3 |  Married | Categorical - Nominal  | Applicant married (Y/N)  |\n",
    "| 4 | Dependents  | Categorical - Ordinal  |  Number of dependents (0, 1, 2, 3+) |\n",
    "| 5 | Education  |  Categorical - Nominal |  Applicant Education (Graduate / Under Graduate) |\n",
    "| 6 | Self_Employed  | Categorical - Nominal  |  Self employed (Y/N) |\n",
    "| 7 | ApplicantIncome  | Numerical - Continuous  | Applicant income  |\n",
    "| 8 | CoapplicantIncome  | Numerical - Continuous  |  Coapplicant income |\n",
    "| 9 |  LoanAmount | Numerical - Continuous  |  Loan amount in thousands |\n",
    "| 10 | Loan_Amount_Term  |  Numerical - Discrete |  Term of loan in months |\n",
    "| 11 | Credit_History  | Categorical - Nominal  |  credit history meets guidelines (0, 1) |\n",
    "| 12 | Property_Area  | Categorical - Ordinal  |  Urban / Semi Urban / Rural |\n",
    "| 13 | Loan_Status  |  Categorical - Nominal |  Loan approved (Y/N) |\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# <a name=\"eda\">Exploratory Data Analysis (EDA)</a>\n",
    "We will use Python to explore the data in order to gain a better understanding of the features and target variable. We will also analyze the data to summarize their main characteristics, using various visualization techniques. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the train and test dataset\n",
    "train = pd.read_csv(\"train_u6lujuX_CVtuZ9i.csv\")\n",
    "test = pd.read_csv(\"test_Y3wMUE5_7gLdaTN.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of original data\n",
    "# so that even if we have to make any changes in these datasets we would not lose the original datasets\n",
    "\n",
    "train_original = train.copy()\n",
    "test_original = test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take a look at the top 5 rows of the train set, notice the column \"Loan_Status\"\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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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",
       "    </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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take a look at the top 5 rows of the test set, notice the absense of \"Loan_Status\" that we will predict\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((614, 13), (367, 12))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the shape of the dataset i.e. no of rows, no of columns\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6258919469928644, 0.37410805300713557)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate train-test-split ratio\n",
    "train.shape[0]/(train.shape[0]+test.shape[0]), test.shape[0]/(train.shape[0]+test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "        'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "        'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
       "       dtype='object'),\n",
       " Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "        'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "        'Loan_Amount_Term', 'Credit_History', 'Property_Area'],\n",
       "       dtype='object'))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take a look at the features (i.e. independent variables) in the dataset\n",
    "train.columns, test.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note: ** There are 13 columns of features and 614 rows of records in the train set and 12 columns of features and 367 rows of records in the test set. There are a total of 981 records combining both train and test set, representing a train-test-split of approx 63% and 37%. The features are similar in the test set as the train data except the Loan_Status. We will predict the Loan_Status using the model built using the train data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               object\n",
       "Gender                object\n",
       "Married               object\n",
       "Dependents            object\n",
       "Education             object\n",
       "Self_Employed         object\n",
       "ApplicantIncome        int64\n",
       "CoapplicantIncome    float64\n",
       "LoanAmount           float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History       float64\n",
       "Property_Area         object\n",
       "Loan_Status           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the data types for each column of the train set\n",
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
      "Loan_ID              614 non-null object\n",
      "Gender               601 non-null object\n",
      "Married              611 non-null object\n",
      "Dependents           599 non-null object\n",
      "Education            614 non-null object\n",
      "Self_Employed        582 non-null object\n",
      "ApplicantIncome      614 non-null int64\n",
      "CoapplicantIncome    614 non-null float64\n",
      "LoanAmount           592 non-null float64\n",
      "Loan_Amount_Term     600 non-null float64\n",
      "Credit_History       564 non-null float64\n",
      "Property_Area        614 non-null object\n",
      "Loan_Status          614 non-null object\n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# concise summary of the dataset, info about index dtype, column dtypes, non-null values and memory usage\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note: ** There are 3 data types in the data\n",
    "- object: Object format means variables are categorical. Categorical variables in our dataset are: Loan_ID, Gender, Married, Dependents, Education, Self_Employed, Property_Area, Loan_Status\n",
    "- int64: It represents the integer variables. ApplicantIncome is of this format.\n",
    "- float64: It represents the variable which have some decimal values involved. They are also numerical variables. Numerical variables in our dataset are: CoapplicantIncome, LoanAmount, Loan_Amount_Term, and Credit_History\n",
    "\n",
    "**Why Data Types are important?**\n",
    "\n",
    "Datatypes are an important concept because statistical methods can only be used with certain data types. You have to analyze continuous data differently than categorical data otherwise it would result in a wrong analysis. Therefore knowing the types of data you are dealing with, enables you to choose the correct method of analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate analysis\n",
    "Univariate analysis is when we analyze each variable individually. For categorical features we can use frequency table or bar plots which will calculate the number of each category in a particular variable. For numerical features, a histogram or a box-plot can be used to look at the distribution of the variable. With a histogram, you can check the central tendency, variability, modality, and kurtosis of a distribution. Note that a histogram can’t show you if you have any outliers. This is why we also use box-plots.\n",
    "\n",
    "### Target Variable (Categorical)\n",
    "We will first look at the target variable, i.e., Loan_Status. As it is a categorical variable, let us look at its frequency table, percentage distribution and bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    422\n",
       "N    192\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# frequency table of a variable will give us the count of each category in that variable\n",
    "train['Loan_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    0.687296\n",
       "N    0.312704\n",
       "Name: Loan_Status, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage distribution can be calculated by setting the normalize=True to show proportions instead of number \n",
    "train['Loan_Status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28004add5f8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot to visualize the frequency\n",
    "train['Loan_Status'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note: ** The loan of 422 (around 69%) people out of 614 was approved. There is no imbalanced classes issue in this dataset, thus accuracy as an evaluation metric should be appropriate. On the other hand, if there are imbalanced or skewed classes, then we might need to use precision and recall as evaluation metrics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Independent Variable (Categorical)\n",
    "There are 5 features that are categorical or binary (Gender, Married, Self_Employed, Credit_History, Education)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing categorical features\n",
    "# plt.figure(1)\n",
    "plt.subplot(231)\n",
    "train['Gender'].value_counts(normalize=True).plot.bar(figsize=(20,10), title= 'Gender')\n",
    "\n",
    "plt.subplot(232)\n",
    "train['Married'].value_counts(normalize=True).plot.bar(title= 'Married')\n",
    "\n",
    "plt.subplot(233)\n",
    "train['Self_Employed'].value_counts(normalize=True).plot.bar(title= 'Self_Employed')\n",
    "\n",
    "plt.subplot(234)\n",
    "train['Credit_History'].value_counts(normalize=True).plot.bar(title= 'Credit_History')\n",
    "\n",
    "plt.subplot(235)\n",
    "train['Education'].value_counts(normalize=True).plot.bar(title= 'Education')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be inferred from the above bar plots that:\n",
    "\n",
    "- 80% applicants in the dataset are male.\n",
    "- Around 65% of the applicants in the dataset are married.\n",
    "- Around 15% applicants in the dataset are self employed.\n",
    "- Around 85% applicants have credit history (repaid their debts).\n",
    "- Around 80% of the applicants are Graduate.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Independent Variable (Ordinal)\n",
    "There are 2 features that are Ordinal: Variables in categorical features having some order involved (Dependents, Property_Area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAnoMhgnmZ/k+iTLk5y0kXaHJKkkIxNXoiRJkjT5NhmMk8wAzgD2B+YBhyeZN067bYHjge9OdJGSJEnSZOumx3gfYHlVraiqe4GFwEHjtHs38D7gNxNYnyRJkjQlugnGs4BbO7ZXtvselGQvYOeq+uLGLpTk6CRLkyxdvXr1ZhcrSZIkTZZugnHG2VcPHkweBXwQeNumLlRVC6pqpKpGZs6c2X2VkiRJ0iTrJhivBHbu2N4JWNWxvS3wHOAbSW4GfhdY5AN4kiRJGiTdBOMlwNwkc5JsCRwGLBo9WFV3VNUOVTW7qmYDlwMHVtXSSalYkiRJmgSbDMZVtQ44DrgIuA44r6qWJTktyYGTXaAkaXybmkozyQlJrk3ygyRfS7Jrx7H7k1zdfiwae64kDaMtumlUVYuBxWP2nbyBti955GVJkjamYyrNV9AMeVuSZFFVXdvR7HvASFXdleQvaWYOOrQ9dndV7TmlRUtSn3PlO0kaTJucSrOqLqmqu9rNy2meEZEkbYDBWJIG0yan0hzjKODLHdtbt9NnXp7k4PFOcIpNScOmq6EUkqS+s9GpNNdrmLwBGAFe3LF7l6paleRpwNeTXFNVN653saoFwAKAkZGRca8tSdOJPcaSNJg2NZUmAEn2A95BM1vQPaP7q2pV+98VwDeAvSazWEkaBAZjSRpMG51KEx5clfRMmlB8W8f+7ZNs1b7eAfh9oPOhPUkaSg6lkKQBVFXrkoxOpTkDOGt0Kk1gaVUtAt4PbAOcnwTglqo6EHgWcGaSB2g6SN4zZjYLSRpKBmNJGlCbmkqzqvbbwHnfAfaY3OokafA4lEKSJEnCYCxJkiQBBmNJkiQJMBhLkiRJgMFYkiRJAgzGkiRJEmAwliRJkgCDsSRJkgQYjCVJkiTAYCxJkiQBBmNJkiQJMBhLkiRJgMFYkiRJAgzGkiRJEmAwliRJkgCDsSRJkgQYjCVJkiTAYCxJkiQBBmNJGlhJ5ie5PsnyJCeNc/yEJNcm+UGSryXZtePYEUluaD+OmNrKJak/GYwlaQAlmQGcAewPzAMOTzJvTLPvASNV9VzgAuB97blPAE4BXgjsA5ySZPupql2S+lVXwbiLXoljklyT5Ookl41zc5YkTax9gOVVtaKq7gUWAgd1NqiqS6rqrnbzcmCn9vWrgIurak1VrQUuBuZPUd2S1Lc2GYy77JU4t6r2qKo9aXokPjDhlUqSOs0Cbu3YXtnu25CjgC8/zHMlaSh002PcTa/EnR2bjwVq4kqUJI0j4+wb996b5A3ACPD+zTk3ydFJliZZunr16oddqCQNim6CcVc9C0mOTXIjTY/x8eNdyJusJE2YlcDOHds7AavGNkqyH/AO4MCqumdzzq2qBVU1UlUjM2fOnLDCJalfdROMu+pZqKozqmo34ETgneNdyJusJE2YJcDcJHOSbAkcBizqbJBkL+BMmlB8W8ehi4BXJtm+fejule0+SRpqW3TRpquehQ4LgX99JEVJkjauqtYlOY4m0M4AzqqqZUlOA5ZW1SKaoRPbAOcnAbilqg6sqjVJ3k0TrgFOq6o1PXgbktRXugnGD/ZKAP9N0yvxx50NksytqhvazVcDNyBJmlRVtRhYPGbfyR2v99vIuWcBZ01edZI0eDYZjLvslTiuHcd2H7AWcLJ4SZIkDZRueoy76ZV4ywTXJUmSJE0pV76TJEmSMBhLkiRJgMFYkiRJAgzGkiRJEmAwliRJkgCDsSRJkgQYjCVJkiTAYCxJkiQBBmNJkiQJMBhLkiRJgMFYkiRJAgzGkiRJEmAwliRJkgCDsSRJkgQYjCVJkiTAYCxJkiQBBmNJkiQJMBhL0sBKMj/J9UmWJzlpnOP7Jrkqybokh4w5dn+Sq9uPRVNXtST1ry16XYAkafMlmQGcAbwCWAksSbKoqq7taHYLcCTw1+Nc4u6q2nPSC5WkAWIwlqTBtA+wvKpWACRZCBwEPBiMq+rm9tgDvShQkgaNQykkaTDNAm7t2F7Z7uvW1kmWJrk8ycHjNUhydNtm6erVqx9JrZI0EAzGkjSYMs6+2ozzd6mqEeCPgf+XZLeHXKxqQVWNVNXIzJkzH26dkjQwDMaSNJhWAjt3bO8ErOr25Kpa1f53BfANYK+JLE6SBpHBWJIG0xJgbpI5SbYEDgO6ml0iyfZJtmpf7wD8Ph1jkyVpWBmMJWkAVdU64DjgIuA64LyqWpbktCQHAiTZO8lK4HXAmUmWtac/C1ia5PvAJcB7xsxmIUlDyVkpJGlAVdViYPGYfSd3vF5CM8Ri7HnfAfaY9AIlacDYYyxJkiTRZTDuYnWlE5Jcm+QHSb6WZNeJL1WSJEmaPJsMxh2rK+0PzAMOTzJvTLPvASNV9VzgAuB9E12oJEmSNJm66TF+cHWlqroXGF1d6UFVdUlV3dVuXs44Y9okSZKkftZNMN7c1ZWOAr483gFXUZIkSVK/6iYYd726UpI3ACPA+8c77ipKkiRJ6lfdTNfW1epKSfYD3gG8uKrumZjyJEmSpKnRTY/xJldXSrIXcCZwYFXdNvFlSpIkSZNrk8G4m9WVaIZObAOcn+TqJF0tSypJkiT1i65WvutidaX9JrguSZIkaUq58p0kSZKEwViSJEkCDMaSJEkSYDCWJEmSgC4fvtM0c+p2va5gfKfe0esKJEnSEDMYS5uwx9l79LqEcV1zxDW9LkGSpGnFoRSSJEkSBmNJkiQJMBhL0sBKMj/J9UmWJzlpnOP7Jrkqybokh4w5dkSSG9qPI6auaknqXwZjSRpASWYAZwD7A/OAw5PMG9PsFuBI4Nwx5z4BOAV4IbAPcEqS7Se7ZknqdwZjSRpM+wDLq2pFVd0LLAQO6mxQVTdX1Q+AB8ac+yrg4qpaU1VrgYuB+VNRtCT1M4OxJA2mWcCtHdsr230Tdm6So5MsTbJ09erVD7tQSRoUBmNJGkwZZ19N5LlVtaCqRqpqZObMmZtVnCQNIoOxJA2mlcDOHds7Aaum4FxJmrYMxpI0mJYAc5PMSbIlcBiwqMtzLwJemWT79qG7V7b7JGmoGYwlaQBV1TrgOJpAex1wXlUtS3JakgMBkuydZCXwOuDMJMvac9cA76YJ10uA09p9kjTUXBJakgZUVS0GFo/Zd3LH6yU0wyTGO/cs4KxJLVCSBow9xpIkSRIGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAnoMhgnmZ/k+iTLk5w0zvF9k1yVZF2SQya+TEmSJGlybTIYJ5kBnAHsD8wDDk8yb0yzW4AjgXMnukBJkiRpKmzRRZt9gOVVtQIgyULgIODa0QZVdXN77IFJqFGSJEmadN0MpZgF3NqxvbLdt9mSHJ1kaZKlq1evfjiXkCRJkiZFN8E44+yrh/PJqmpBVY1U1cjMmTMfziUkSZKkSdFNMF4J7NyxvROwanLKkSRJknqjm2C8BJibZE6SLYHDgEWTW5YkSZI0tTYZjKtqHXAccBFwHXBeVS1LclqSAwGS7J1kJfA64MwkyyazaEmSJGmidTMrBVW1GFg8Zt/JHa+X0AyxkDTkrnvms3pdwrie9aPrel3ChEsyH/gQMAP4aFW9Z8zxrYBPAi8AbgcOraqbk8ym6ei4vm16eVUdM1V1S1K/6ioYS5L6S8cc86+geRZkSZJFVXVtR7OjgLVVtXuSw4D3Aoe2x26sqj2ntGhJ6nMuCS1Jg+nBOear6l5gdI75TgcBZ7evLwBenmS8mYYkSRiMJWlQdTPH/INt2udF7gCe2B6bk+R7SS5N8qLxPoFzz0saNgZjSRpM3cwxv6E2PwV2qaq9gBOAc5M87iENnXte0pAxGEvSYOpmjvkH2yTZAtgOWFNV91TV7QBVdSVwI/D0Sa9YkvqcwViSBlM3c8wvAo5oXx8CfL2qKsnM9uE9kjwNmAusmKK6JalvOSuFJA2gqlqXZHSO+RnAWaNzzANLq2oR8DHgnCTLgTU04RlgX+C0JOuA+4FjqmrN1L8LSeovBmNJGlBdzDH/G5qFl8aedyFw4aQXKEkDxqEUkiRJEgZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSUCXwTjJ/CTXJ1me5KRxjm+V5LPt8e8mmT3RhUqS1vdI7s1J3t7uvz7Jq6aybknqV5sMxklmAGcA+wPzgMOTzBvT7ChgbVXtDnwQeO9EFypJ+q1Hcm9u2x0GPBuYD3y4vZ4kDbVueoz3AZZX1YqquhdYCBw0ps1BwNnt6wuAlyfJxJUpSRrjkdybDwIWVtU9VXUTsLy9niQNtS26aDMLuLVjeyXwwg21qap1Se4Angj8orNRkqOBo9vNXyW5/uEUPcl2YEzdD1eGo998wr5evGso/i01cT9fR/r12iwT+2/1XSfyYg/TI7k3zwIuH3PurLGfYEDu2Q/HxP1cbcCQ3P8n0qR/T4Bh+T0zkSb//5Wp+13W1X27m2A8XsX1MNpQVQuABV18zp5JsrSqRnpdx6Dw67V5/HptHr9eG/VI7s3T5p79cPhz1X/8nvSnYfy+dDOUYiWwc8f2TsCqDbVJsgWwHbBmIgqUJI3rkdybuzlXkoZON8F4CTA3yZwkW9I8sLFoTJtFwBHt60OAr1fVQ3ofJEkT5pHcmxcBh7WzVswB5gJXTFHdktS3NjmUoh2XdhxwETADOKuqliU5DVhaVYuAjwHnJFlO0xtx2GQWPcmm3Z8NJ5lfr83j12vz+PXagEdyb27bnQdcC6wDjq2q+3vyRnrDn6v+4/ekPw3d9yV27EqSJEmufCdJkiQBBmNJkiQJMBhLkiRJQHfzGE9rSZ5JswrULJp5PFcBi6rqup4Wpmmh/fmaBXy3qn7VsX9+Vf1n7yqTJEljDfXDd0lOBA6nWUp1Zbt7J5ontxdW1Xt6VdugSfKnVfXxXtfRT5IcDxwLXAfsCbylqj7fHruqqp7fy/r6XZK3V9U/9LoOTR9JZtGsfvVgp1BVfbN3FSnJ7wGzWf978smeFTTEkpywseNV9YGpqqWXhj0Y/xh4dlXdN2b/lsCyqprbm8oGT5JbqmqXXtfRT5JcA/yvqvpVktnABcA5VfWhJN+rqr16WmCf8x8PmkhJ3gscSjNF3ejUdFVVB/auquGW5BxgN+Bq1v+eHN+7qoZXklM2dryq3jVVtfTSsA+leADYEfjJmP1PbY+pQ5IfbOgQ8OSprGVAzBgdPlFVNyd5CXBBkl0Zf0leSZPnYOAZVXVPrwvRg0aAeS4I1h+GJfhuyrAH47cCX0tyA3Bru28XYHfguJ5V1b+eDLwKWDtmf4DvTH05fe9nSfasqqsB2p7jA4CzgD16W1p/SnITzVj/AE9NsqJ9XVX1tJ4Wp0G3Ang0YDDuHz8EngL8tNeF6LeSbA0cBTwb2Hp0f1W9uWdFTaGhDsZV9Z9Jng7sQ/OAVGjGGi8ZslWguvVFYJvRoNcpyTemvpy+9yaaVcUeVFXrgDclObM3JfW3qpoz+trhJppgdwFXJ/kaHeHYP9v31A7AtUmuYP3vicNbeusc4Ec0HWGnAX9C86zMUBjqMcaS+pfBWBMpyRHj7a+qs6e6FjWSvHi8/VV16VTXot8avfcm+UFVPTfJo4GLquplva5tKgx1j7GkvvbtXheg6cMA3H8MwH1rdEKC/0nyHOBnNDOHDAUX+JDUN5Lsk2TvdvNfk5yQ5A97WpSmhSRzk1yQ5NokK0Y/el3XMEvyu0mWJPlVknuT3J/kzl7XJRYk2R54J7CIZiaX9/a2pKljj7GkvtBOFbQ/sEWSi2nG/l8KnJRkr6o6vacFatB9HDgF+CDwUuBPcXaYXvsXmnUDzqeZoeJNgNOk9lCSRwF3VtVa4JvA0D307BhjSX2hnfd5T2Armj/d7VRVdyZ5DM3Kgc/taYEaaEmurKoXJLmmqvZo932rql7U69qGVZKlVTUyOpa13fedqvq9Xtc2zJJ8s6q+YjXqAAAFbklEQVT27XUdvWKPsaR+sa6dDeauJDdW1Z0AVXV3EucV1yP1m7Y37IYkxwH/DTypxzUNu7vaBbWuTvI+mmnbHtvjmgQXJ/lr4LPAr0d3VtWa3pU0dewxltQXknwXeGlV3ZXkUVX1QLt/O+ASV8HTI9GOXb8OeDzwbmA74H1VdXlPCxti7WJHPwe2BP6K5nvy4apa3tPChlw7n/xYQzOXvMFYUl9IstV4q5Il2QF4alVd04OyNM0keRzNL/lf9roWQdtj/EyahX2ur6p7e1yShpzBWJI07SUZoXkAb9t21x3Am6vqyt5VNdySvBr4CHAjzYOQc4C/qKov97SwIZfkTePtr6pPTnUtvWAwliRNe0l+ABxbVd9qt/+A5s/2PtTZI0l+BBwwOnQiyW7Al6rqmb2tbLgl+eeOza2BlwNXVdUhPSppSvnwnSRpGPxyNBQDVNVlSRxO0Vu3jRlPvAK4rVfFqFFV/6dzu33O45welTPlDMaSpGkryehDm1ckORP4DM141kOBb/SqrmGW5LXty2VJFgPn0XxPXgcs6Vlh2pC7GKL5pQ3GkqTp7B/HbJ/S8dqxhL3xmo7XPwde3L5eDWw/9eWoU5Iv8Nv/Nx4FzKNZhGUoOMZYkjSttfMXH1JV5/W6FjWSzACOr6oP9roWrS/Jizs21wE/qaqVvapnqhmMJUnT3rCv5tWPklxSVS/tdR3auPYfMYdV1ad7XctUMBhLkqa9JH8H3M2QrubVj5KcTrOox9jvyVU9K2qItXN8HwvMAhYBF7fbfwNcXVUH9bC8KWMwliRNe8O+mlc/SnJJ+3I0iITme/KyHpU01JJ8HlgL/BfNFG3b06xK+JaqurqXtU0lg7EkSZoySU4Yfdn+t2gevLusqsb7B4ymQJJrqmqP9vUM4BfALsO2SqSzUkiSpr1hX82rz2w7zr5dgXckObWqFk51QQLgvtEXVXV/kpuGLRSDPcaSpCEw7Kt5DYIkTwC+WlXP32RjTbgk9/Pbsd4BHkMzh/HoEJfH9aq2qWSPsSRp2hv21bwGQVWtSZJNt9RkqKoZva6hHzyq1wVIktQDQ7Wa1yBI8jKah7+knrHHWJI07W1gNS8X/OiBJNfw0FUHnwCsAsYdCy5NFccYS5KmvWFfzaufJNl1zK4Cbq+qX4/XXppKBmNJkiQJh1JIkqaxJJdV1R8k+SXr//l+qJ60l9Qde4wlSZIk7DGWJA2JJNsDO9Pxu6+qrupdRZL6jcFYkjTtJXk3cCSwAnig3V3Ay3pVk6T+41AKSdK0l+R6YI+qurfXtUjqXy7wIUkaBj8EHt/rIiT1N3uMJUnTXpIR4PM0Afme0f1VdWDPipLUdxxjLEkaBmcD7wWu4bdjjCVpPQZjSdIw+EVV/VOvi5DU3xxKIUma9pJ8gGYIxSLWH0rhdG2SHmQwliRNe0kuGWd3VZXTtUl6kMFYkiRJwunaJElDIMmTk3wsyZfb7XlJjup1XZL6i8FYkjQMPgFcBOzYbv8YeGvPqpHUlwzGkqRhsENVnUc7VVtVrQPu721JkvqNwViSNAx+neSJQAEk+V3gjt6WJKnfOI+xJGkYnEAzVdtuSb4NzAQO6W1JkvqNPcaSpGkryd5JntLOV/xi4G9p5jH+CrCyp8VJ6jsGY0nSdHYmcG/7+veAdwBnAGuBBb0qSlJ/ciiFJGk6m1FVa9rXhwILqupC4MIkV/ewLkl9yB5jSdJ0NiPJaCfQy4Gvdxyzc0jSerwpSJKms88Alyb5BXA38C2AJLvjrBSSxnBJaEnStNZOzfZU4CtV9et239OBbdqH8iQJMBhLkiRJgGOMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEkA/H8EgliBnlcw6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing remaining categorical features\n",
    "# plt.figure(1)\n",
    "plt.subplot(121)\n",
    "train['Dependents'].value_counts(normalize=True).plot.bar(figsize=(12,4), title= 'Dependents')\n",
    "\n",
    "plt.subplot(122)\n",
    "train['Property_Area'].value_counts(normalize=True).plot.bar(title= 'Property_Area')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following inferences can be made from the above bar plots:\n",
    "\n",
    "- More than half of the applicants don’t have any dependents.\n",
    "- Most of the applicants are from Semiurban area.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Independent Variable (Numerical)\n",
    "There are 4 features that are Numerical: These features have numerical values (ApplicantIncome, CoapplicantIncome, LoanAmount, Loan_Amount_Term)\n",
    "\n",
    "Firstly, let’s look at the Applicant income distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing ApplicantIncome\n",
    "# plt.figure(1)\n",
    "plt.subplot(121)\n",
    "sns.distplot(train['ApplicantIncome']);\n",
    "\n",
    "plt.subplot(122)\n",
    "train['ApplicantIncome'].plot.box(figsize=(16,5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be inferred that most of the data in the distribution of applicant income is towards left which means it is not normally distributed. The distribution is right-skewed (positive skewness). We will try to make it normal in later sections as algorithms works better if the data is normally distributed.\n",
    "\n",
    "The boxplot confirms the presence of a lot of outliers/extreme values. This can be attributed to the income disparity in the society. Part of this can be driven by the fact that we are looking at people with different education levels. Let us segregate them by Education:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.boxplot(column='ApplicantIncome', by = 'Education')\n",
    "plt.suptitle(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there are a higher number of graduates with very high incomes, which are appearing to be the outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Secondly, Let’s look at the Coapplicant income distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(1)\n",
    "plt.subplot(121)\n",
    "sns.distplot(train['CoapplicantIncome']);\n",
    "\n",
    "plt.subplot(122)\n",
    "train['CoapplicantIncome'].plot.box(figsize=(16,5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a similar distribution as that of the applicant income. Majority of coapplicant’s income ranges from 0 to 5000. We also see a lot of outliers in the coapplicant income and it is not normally distributed.\n",
    "\n",
    "Thirdly, let’s look at the distribution of LoanAmount variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(1)\n",
    "plt.subplot(121)\n",
    "df=train.dropna()\n",
    "sns.distplot(df['LoanAmount']);\n",
    "\n",
    "plt.subplot(122)\n",
    "train['LoanAmount'].plot.box(figsize=(16,5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a fairly normal distribution (albeit still slightly right-skewed) for LoanAmount but there are lot of outliers in this variable. We will treat the outliers in later sections.\n",
    "\n",
    "Lastly, let’s look at the distribution of Loan_Amount_Term variable. Since Loan_Amount_Term is a discrete variable, we will use frequency table and bar plots which will calculate the number of each category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# frequency table of a variable will give us the count of each category in that variable\n",
    "train['Loan_Amount_Term'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28005043cf8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bar chart\n",
    "train['Loan_Amount_Term'].value_counts(normalize=True).plot.bar(title= 'Loan_Amount_Term')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be inferred from the above bar plot that:\n",
    "\n",
    "- Around 85% of the loans are 360 months term or 30 years period\n",
    "\n",
    "\n",
    "Now we would like to know how well each feature correlate with Loan Status. So, in the next section we will look at bivariate analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Analysis\n",
    "After looking at every variable individually in univariate analysis, we will now explore them again with respect to the target variable in bivariate analysis. We can use bivariate analysis to test the hypotheses that we generated earlier.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Categorical Independent Variable vs Target Variable**\n",
    "\n",
    "First of all we will find the relation between target variable and categorical independent variables. Let us look at the stacked bar plot now which will give us the proportion of approved and unapproved loans. For example, we want to see whether an applicant's gender will have any effect on approval chances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Status    N    Y\n",
      "Gender               \n",
      "Female        37   75\n",
      "Male         150  339\n"
     ]
    },
    {
     "data": {
      "image/png": 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fSrohIo6iMAWCmVmzkp5TeUjSVyWp5aZm1p4lPacyAegGbJX0PoUuUEREz8wqM7OKlHTqgx5ZF2JmO4ekUx9I0tckfb+43F/SEdmWZmaVKOk5lZ8ARwHnFJfX4YeJmVkTkp5TOTIiDpf0FEBE/E1SpwzrMrMKlfRIZXPxJsIAkFQFfNjSRpJOlPSipOWSLt9BuzMkhaSahPWYWZlKGipTgF8Ce0n6EfBH4N92tEExhG4FRgGDgbMlDW6iXQ9gPPB4K+o2szKV9OrPDEmLgZEULid/JSKWtbDZEcDyiFgBIOle4DRgaaN2VwLXAt9uTeFmVp52GCqSugAXAJ+lMEHTbRGxJeG++wIrS5brgSMb7f8woH9E/EpSs6EiqRaoBRgwYEDCH29meWip+3MXhUeePkuhG3N9K/bd1OjbbfPaFu8puhG4tKUdRcS0iKiJiJqqqqpWlGBmba2l7s/giPgcgKTpwBOt2Hc90L9kuR+wqmS5BzAU+F1x9P8+wBxJp0ZEXSt+jpmVkZaOVLbdidyKbk+DRcAgSQOLl5/PAuaU7G9tRPSOiOqIqAYeAxwoZhWupSOVQyS9W3wtCrPpv0uCe38iYouki4H5FJ5meGdEPC9pMlAXEXOa29bMKtcOQyUiPtWjTSNiHoWZ90vX/aCZtsd+mp9lZuUh6TgVM7NEHCpmliqHipmlyqFiZqlyqJhZqhwqZpYqh4qZpcqhYmapcqiYWaqSTifZblVv/EXeJWTqlbwLsJ2Oj1TMLFUOFTNLlUPFzFLlUDGzVPlEre3UduYT7a/kXUAzfKRiZqlyqJhZqhwqZpYqh4qZpcqhYmapcqiYWaocKmaWKoeKmaXKoWJmqXKomFmqHCpmliqHipmlyqFiZqlyqJhZqhwqZpYqh4qZpcqhYmapcqiYWaoyDRVJJ0p6UdJySZc38f4ESUslLZG0QNJ+WdZjZtnLLFQkdQBuBUYBg4GzJQ1u1OwpoCYihgGzgWuzqsfM2kaWRypHAMsjYkVEbALuBU4rbRARCyNiQ3HxMaBfhvWYWRvIMlT6AitLluuL65pzHvBAU29IqpVUJ6lu9erVKZZoZmnLMlTUxLposqH0NaAGuK6p9yNiWkTURERNVVVViiWaWdqyfO5PPdC/ZLkfsKpxI0nHAROBL0XEBxnWY2ZtIMsjlUXAIEkDJXUCzgLmlDaQdBhwG3BqRLyZYS1m1kYyC5WI2AJcDMwHlgEzI+J5SZMlnVpsdh3QHZgl6WlJc5rZnZlViEwfexoR84B5jdb9oOT1cVn+fDNrex5Ra2apcqiYWaocKmaWKoeKmaXKoWJmqXKomFmqHCpmliqHipmlyqFiZqlyqJhZqhwqZpYqh4qZpcqhYmapcqiYWaocKmaWKoeKmaXKoWJmqXKomFmqHCpmliqHipmlyqFiZqlyqJhZqhwqZpYqh4qZpcqhYmapcqiYWaocKmaWKoeKmaXKoWJmqXKomFmqHCpmliqHipmlKtNQkXSipBclLZd0eRPvd5Z0X/H9xyVVZ1mPmWUvs1CR1AG4FRgFDAbOljS4UbPzgL9FxGeBG4FrsqrHzNpGlkcqRwDLI2JFRGwC7gVOa9TmNOCu4uvZwEhJyrAmM8tYxwz33RdYWbJcDxzZXJuI2CJpLdALeKu0kaRaoLa4uE7Si5lUXB560+jzZ0k+NkzTzv7d7ZekUZah0tQRR3yCNkTENGBaGkWVO0l1EVGTdx3Wev7uCrLs/tQD/UuW+wGrmmsjqSOwO/B2hjWZWcayDJVFwCBJAyV1As4C5jRqMwf4X8XXZwC/jYjtjlTMrHJk1v0pniO5GJgPdADujIjnJU0G6iJiDjAd+Lmk5RSOUM7Kqp4K0i66eTspf3eAfGBgZmnyiFozS5VDxcxS5VAxs1Q5VMwsVQ6VMiKpW941WOtJ6irpoLzrKBcOlTIg6YuSlgLLisuHSPpJzmVZApJOAZ4GHiwuHyqp8XisdsWhUh5uBL4MrAGIiGeAY3KtyJKaROHm2XcAIuJpoDrHenLnUCkTEbGy0aqtuRRirbUlItbmXUQ5yfKGQktupaQvAlG8pWE8xa6Qlb3nJJ0DdJA0iMJ39+eca8qVj1TKwwXARRSmgqgHDi0uW/kbBwwBPgDuAd4FLsm1opx5mL6ZpcrdnxxJuoUm5o9pEBHj27AcawVJc9nxd3dqG5ZTVhwq+arLuwD7xK7Pu4By5e6PmaXKRyplQFIVcBmFpw50aVgfEf+QW1GWSPGKz7+z/Xe3f25F5cxXf8rDDAqXkAcCPwReoTBznpW//wB+CmwBRgB3Az/PtaKcOVTKQ6+ImA5sjohHIuIbwBfyLsoS6RoRCyicSng1IiYB7foI092f8rC5+N/XJZ1EYYLwfjnWY8ltlLQL8FJx+tS/AnvlXFOufKK2DEg6GfgDhScL3AL0BH5YnMfXypikv6fQdd0DuJLCEyGujYjHci0sRw4VM0uVuz9lQNJACsO9qyn5TtrzAKpy19L0Bu35u3OolIf/pvC4krnAhznXYskcReGRvfcAj9P00zbbJXd/yoCkxyOi8XOmrYxJ6gAcD5wNDAN+DdwTEc/nWlgZcKiUgeKt84OAhyjc7QpARDyZW1GWmKTOFMLlOmByRNySc0m5cvenPHwO+DqF8Q0N3Z+gnY93KHfFMDmJQqBUA1OA+/OsqRz4SKUMSHoBGBYRm/KuxZKRdBcwFHgAuDcinsu5pLLhUCkDku4DxkXEm3nXYslI+hBYX1ws/UckICKiZ9tXVR7c/SkPewMvSFrEx8+ptNvLkuUuInyLSzMcKuXhirwLMEuLuz9lQtJ+wKCIeFjSbkCHiHgv77rMWsuHcGVA0jeB2cBtxVV9KQyIM6s4DpXycBEwnMJM7ETES7TzO12tcjlUysMHpZeTJXVkB5Mqm5Uzh0p5eETSvwJdJR0PzKJwH5BZxfGJ2jJQnOTnPOAECuMc5gN3hL8cq0AOlRxJGhARr+Vdh1ma3P3J17YrPJL+K89CzNLiUMlX6Rwc7faRDrZzcajkK5p5bVaxfE4lR5K2UrgpTUBXYEPDW7Tzm9KscjlUzCxV7v6YWaocKmaWKoeKJSZpb0m/kLRC0mJJj0r6xxT2e6ykX6VRo+XPoWKJSBKFcTW/j4j9I+LzwFnk8HjW4r1RVqYcKpbUPwCbImJqw4riA8lvkdRB0nWSFklaIul82HYE8jtJsyW9IGlGMZyQdGJx3R+B0xv2KambpDuL+3pK0mnF9edKmiVpLoWnDliZcuJbUkOA5h4Zch6wNiL+vjjD/J8kNfzDP6y47SrgT8BwSXXA7RSCajlwX8m+JgK/jYhvSNoDeELSw8X3jqIwQfjbaX4wS5dDxT4RSbcCRwObgFeBYZLOKL69O4XnGG0CnoiI+uI2T1N4lMU64OXivDFI+k+gtrjtCcCpkr5dXO4CDCi+/o0Dpfw5VCyp54GvNixExEWSegN1wGsUngYwv3QDScdSMpE3sJWP/s41N0BKwFcj4sVG+zqSj2avtzLmcyqW1G+BLpIuLFm3W/G/84ELJe0KIOlASd12sK8XgIGSDigun13y3nxgXMm5l8NSqd7ajEPFEinO7fIV4EuSXpb0BHAXcBlwB7AUeFLScxTm2m32KDgiNlLo7vy6eKL21ZK3rwR2BZYU93VlFp/HsuNh+maWKh+pmFmqHCpmliqHipmlyqFiZqlyqJhZqhwqZpYqh4qZper/A5VczY8u3QI4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pd.crosstab(train['Gender'],train['Loan_Status']))\n",
    "\n",
    "Gender = pd.crosstab(train['Gender'],train['Loan_Status'])\n",
    "Gender.div(Gender.sum(1).astype(float), axis = 0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.xlabel('Gender')\n",
    "p = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Status    N    Y\n",
      "Married              \n",
      "No            79  134\n",
      "Yes          113  285\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pd.crosstab(train['Married'],train['Loan_Status']))\n",
    "\n",
    "Married = pd.crosstab(train['Married'],train['Loan_Status'])\n",
    "Married.div(Married.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.xlabel('Married')\n",
    "p = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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"
     ]
    },
    {
     "data": {
      "image/png": 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eKqckSVJVGr2m8CnghoiYX19+C3B0OSVJkqrSYyhExEZAH2AX4B3UXsn5u8xcVnJtkqR1rMdQyMzXIuJrmfk+ao/QliT1Uo1eU7g1Iv4pIqLUaiRJlWr0msIEYDPg1Yh4idoQUmbmFqVVJkla5xp9dPbmZRciSapeo4/Ojoj4aER8sb48NCJGlVuaJGlda/SawreB9wHH1ZcXA5eUUpEkqTKNXlPYJzP3jogHATLzuYjoU2JdkqQKNNpTWFZ/CF4CRMRg4LWedoqIQyPisYiYGxFnraLd2IjIiGhrsB5JUgkaDYWLgZuAbSPifOBXwL+taod6iFwCjAFGAsdGxMhu2m0OnAH8ZjXqliSVoNG7j6ZExP3AaGq3o344M+f0sNsoYG5mPgkQEdcCRwCzu7T7P8CFwGdWp3BJUvOtMhQioh9wKrATtRfsXJqZyxs89hBgXqfldmCfLsffCxiamT+JiJWGQkSMA8YB7Ljjjg1+vSRpdfU0fHQV0EYtEMYAX12NY3c3+7l4r3P9mUpfB87s6UCZOTkz2zKzbfDgwatRgiRpdfQ0fDQyM98FEBFXAPetxrHbgaGdlluB+Z2WNwd2A35Rf3rG9sDUiDg8M2euxvdIkpqkp55C8STU1Rg26jADGBERw+u3rx4DTO10vEWZOSgzh2XmMOBewECQpAr11FPYIyJeqH8OoH99ucdnH2Xm8ogYD0wHWqi94/nRiDgPmJmZU1e2rySpGqsMhcxsWZuDZ+Y0YFqXdV9aSdsD1ua7JElrr9F5CpKkDYChIEkqGAqSpIKhIEkqNPqUVKlhw5b+oOoSGvJU1QVonfP/zZ7ZU5AkFQwFSVLBUJAkFQwFSVLBUJAkFQwFSVLBUJAkFQwFSVLByWs4oUWSOthTkCQVDAVJUsFQkCQVDAVJUsFQkCQVDAVJUsFQkCQVDAVJUsFQkCQVDAVJUsFQkCQVDAVJUsEH4klvYj6sUeuaPQVJUsFQkCQVDAVJUsFQkCQVDAVJUsFQkCQVSg2FiDg0Ih6LiLkRcVY32ydExOyImBURt0fEW8usR5K0aqWFQkS0AJcAY4CRwLERMbJLsweBtszcHbgRuLCseiRJPSuzpzAKmJuZT2bmK8C1wBGdG2TmHZm5pL54L9BaYj2SpB6UGQpDgHmdltvr61bmJOCW7jZExLiImBkRMxcsWNDEEiVJnZUZCtHNuuy2YcRHgTbgou62Z+bkzGzLzLbBgwc3sURJUmdlPvuoHRjaabkVmN+1UUQcBHwB+PvMfLnEeiRJPSizpzADGBERwyOiD3AMMLVzg4jYC7gUODwznymxFklSA0oLhcxcDowHpgNzgOsz89GIOC8iDq83uwgYANwQEQ9FxNSVHE6StA6U+ujszJwGTOuy7kudPh9U5vdLklaPM5olSQVDQZJUMBQkSQVDQZJUMBQkSQVDQZJUMBQkSQVDQZJUMBQkSQVDQZJUMBQkSQVDQZJUMBQkSQVDQZJUMBQkSQVDQZJUMBQkSQVDQZJUMBQkSQVDQZJUMBQkSQVDQZJUMBQkSQVDQZJUMBQkSQVDQZJUMBQkSQVDQZJUMBQkSQVDQZJUMBQkSQVDQZJUKDUUIuLQiHgsIuZGxFndbO8bEdfVt/8mIoaVWY8kadVKC4WIaAEuAcYAI4FjI2Jkl2YnAc9l5k7A14GvlFWPJKlnZfYURgFzM/PJzHwFuBY4okubI4Cr6p9vBEZHRJRYkyRpFTYu8dhDgHmdltuBfVbWJjOXR8QiYCDwbOdGETEOGFdfXBwRj5VScXMNosvPsbZiw+5HeT6bx3PZXOvL+XxrI43KDIXu/sWfa9CGzJwMTG5GUetKRMzMzLaq6+gtPJ/N47lsrt52PsscPmoHhnZabgXmr6xNRGwMbAn8tcSaJEmrUGYozABGRMTwiOgDHANM7dJmKvA/65/HAj/PzBV6CpKkdaO04aP6NYLxwHSgBbgyMx+NiPOAmZk5FbgC+H5EzKXWQzimrHoqsF4Nd60HPJ/N47lsrl51PsN/mEuSOjijWZJUMBQkSQVDQZJUKHOewgYlInahNkN7CLW5FvOBqZk5p9LCJGk12FNogoj4HLXHeARwH7XbcQO4prsHAUrrUkTsEhGjI2JAl/WHVlWT3ry8+6gJIuL3wK6ZuazL+j7Ao5k5oprKep+I+HhmfrfqOtYXEXEGcBowB9gT+FRm/ri+7YHM3LvK+tZ3EfH5zPz3qutoJnsKzfEasEM3699S36bmObfqAtYzJwPvzswPAwcAX4yIT9W3+fDJtfeRqgtoNq8pNMengdsj4nFefwjgjsBOwPjKqlpPRcSslW0CtluXtfQCLZm5GCAzn4qIA4AbI+KtGArqhsNHTRIRG1F7XPgQan/Z2oEZmflqpYWthyLiaeADwHNdNwF3Z2Z3vTJ1IyJ+DkzIzIc6rdsYuBI4PjNbKituPRURf6B2M0lQGw2YX/+cmfm2KmtrBnsKTZKZrwH3Vl1HL/ETYEDnX2QdIuIX676c9doJwPLOKzJzOXBCRFxaTUnrt8wc3vE5Ih7MzL2qrKfZ7ClI0hrqjaHghWZJWnO/rrqAZrOnIEmrISJGUbt+MCMidqV2/et3mTmt4tKawlCQpAZFxDnAGGrXY39G7eaSO4GDgOmZeX6F5TWFoSBJDYqIR6hNAuwL/D+gNTNfiIj+wG8yc/dKC2wCrylIUuOWZ+armbkEeCIzXwDIzJfoJRNVDQVJatwrEbFp/fO7O1ZGxJb0klBw+EiSGhQRfTPz5W7WDwLekpmPVFBWUxkKkqSCw0eSpIKhIEkqGArqlSLi1Yh4KCIejYiHI2JC/aGFVdXzVH3ceU32/XBEjGx2TVJ3DAX1Vi9l5p6ZuStwMPAPwDkV17SmPgwYClonDAX1epn5DDAOGB81LRFxUUTMiIhZEXEKQEQcEBF3RcRNETE7IiZ19C4i4pCIuCciHoiIGzpebVnvAZxbX/9I/V3dRMTAiLg1Ih6sP420eHdBRHw0Iu6r92QujYiW+vrFEXF+vWdzb0RsFxH7AocDF9Xbvz0izqjXNysirl2nJ1O9nqGgDUJmPknt//dtgZOARZn5HuA9wMkR0fE45FHAmcC7gLcDR9aHfc4GDqq/vnImMKHT4Z+tr/8O8Jn6unOAX9WfoDmV2kuXiIh3AkcD+2XmnsCrwPH1fTYD7s3MPYC7gJMz8+76/p+t93yeAM4C9qrPnj21aSdJwvcpaMPS8a/1Q4DdI2JsfXlLYATwCnBfPUCIiGuA9wNLqQ3f/DoiAPoA93Q67g/r/70fOLL+ef+Oz5n53xHR8cKg0dQmPc2oH6s/8Ex92yvU3iXRcayDV/JzzAKmRMSPgB81+LNLDTEUtEGIiLdR+1f5M9TC4fTMnN6lzQHU3qjVWccbtn6Wmceu5PAdk5le5Y1/p7qbBBTAVZn5+W62LcvXJw51PVZnH6QWOodTe+fyrvUX50hrzeEj9XoRMRiYBHyr/kt3OvCJiNikvn3niNis3nxURAyvX0s4GvgVtTfq7RcRO9XbbxoRO/fwtXdRHxaKiDHA1vX1twNjI2Lb+rZt6u9LXpW/AZvX228EDM3MO4B/AbYCBjRyHqRG2FNQb9U/Ih4CNqH2OsrvA/9R33Y5MAx4IGpjOAuo3eEDtWGhC6hdU7gLuCkzX4uIE4FrIqJvvd3ZwO9X8f3n1ts/QO3Ryn8CyMzZEXE2cGv9F/wy4DTgj6s41rXAZRFxBnAMcEX9WTsBfD0zn2/gfEgN8TEXUl19+OgzmXlY1bVIVXH4SJJUsKcgSSrYU5AkFQwFSVLBUJAkFQwFSVLBUJAkFf4/Z3D1EoGnXyMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pd.crosstab(train['Dependents'],train['Loan_Status']))\n",
    "\n",
    "Dependents=pd.crosstab(train['Dependents'],train['Loan_Status'])\n",
    "Dependents.div(Dependents.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('Dependents')\n",
    "p = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Status     N    Y\n",
      "Education             \n",
      "Graduate      140  340\n",
      "Not Graduate   52   82\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pd.crosstab(train['Education'],train['Loan_Status']))\n",
    "\n",
    "Education=pd.crosstab(train['Education'],train['Loan_Status'])\n",
    "Education.div(Education.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.xlabel('Education')\n",
    "p = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Status      N    Y\n",
      "Self_Employed          \n",
      "No             157  343\n",
      "Yes             26   56\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pd.crosstab(train['Self_Employed'],train['Loan_Status']))\n",
    "\n",
    "Self_Employed=pd.crosstab(train['Self_Employed'],train['Loan_Status'])\n",
    "Self_Employed.div(Self_Employed.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.xlabel('Self_Employed')\n",
    "p = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pd.crosstab(train['Credit_History'],train['Loan_Status']))\n",
    "\n",
    "Credit_History=pd.crosstab(train['Credit_History'],train['Loan_Status'])\n",
    "Credit_History.div(Credit_History.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.xlabel('Credit_History')\n",
    "p = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pd.crosstab(train['Property_Area'],train['Loan_Status']))\n",
    "\n",
    "Property_Area=pd.crosstab(train['Property_Area'],train['Loan_Status'])\n",
    "Property_Area.div(Property_Area.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('Property_Area')\n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the bar charts above, it can be inferred that:\n",
    "- proportion of male and female applicants is more or less same for both approved and unapproved loans\n",
    "- proportion of married applicants is higher for the approved loans\n",
    "- distribution of applicants with 1 or 3+ dependents is similar across both the categories of Loan_Status\n",
    "- there is nothing significant we can infer from Self_Employed vs Loan_Status plot.\n",
    "- proportion of loans getting approved for graduates is higher compared to non-graduates\n",
    "- it seems people with credit history as 1 are more likely to get their loans approved\n",
    "- proportion of loans getting approved in semiurban area is higher as compared to that in rural or urban areas.\n",
    "\n",
    "Now let’s visualize numerical independent variables with respect to target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Numerical Independent Variable vs Target Variable**<br>\n",
    "\n",
    "We will try to find the mean income of people for which the loan has been approved vs the mean income of people for which the loan has not been approved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Status\n",
      "N    5446.078125\n",
      "Y    5384.068720\n",
      "Name: ApplicantIncome, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2800551a5f8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(train.groupby('Loan_Status')['ApplicantIncome'].mean())\n",
    "\n",
    "train.groupby('Loan_Status')['ApplicantIncome'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the y-axis represents the mean applicant income. We don’t see any significant difference in the mean income between those approval and not approved applicant (5384 vs 5446). \n",
    "\n",
    "So, let’s make bins for the applicant income variable based on the values in it and analyze the corresponding loan status for each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making bins for applicant income variable\n",
    "bins = [0,2500,4000,6000,81000]\n",
    "group = ['Low','Average','High', 'Very high']\n",
    "train['Income_bin'] = pd.cut(df['ApplicantIncome'],bins,labels=group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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",
       "      <th>Income_bin</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",
       "      <td>NaN</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",
       "      <td>High</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",
       "      <td>Average</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",
       "      <td>Average</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",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LP001013</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2333</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LP001014</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3036</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "      <td>Average</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",
       "5  LP001011   Male     Yes          2      Graduate           Yes   \n",
       "6  LP001013   Male     Yes          0  Not Graduate            No   \n",
       "7  LP001014   Male     Yes         3+      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",
       "5             5417             4196.0       267.0             360.0   \n",
       "6             2333             1516.0        95.0             360.0   \n",
       "7             3036             2504.0       158.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status Income_bin  \n",
       "0             1.0         Urban           Y        NaN  \n",
       "1             1.0         Rural           N       High  \n",
       "2             1.0         Urban           Y    Average  \n",
       "3             1.0         Urban           Y    Average  \n",
       "4             1.0         Urban           Y       High  \n",
       "5             1.0         Urban           Y       High  \n",
       "6             1.0         Urban           Y        Low  \n",
       "7             0.0     Semiurban           N    Average  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take a look at the train set\n",
    "train.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Status   N    Y\n",
      "Income_bin          \n",
      "Low          26   57\n",
      "Average      51  123\n",
      "High         32   79\n",
      "Very high    39   73\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pd.crosstab(train['Income_bin'],train['Loan_Status']))\n",
    "\n",
    "Income_bin = pd.crosstab(train['Income_bin'],train['Loan_Status'])\n",
    "Income_bin.div(Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('ApplicantIncome')\n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be inferred that Applicant income does not affect the chances of loan approval which contradicts our hypothesis in which we assumed that if the applicant income is high the chances of loan approval will also be high.\n",
    "\n",
    "We will analyze the Coapplicant income and loan amount variable in similar manner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making bins for Coapplicant income variable\n",
    "bins = [0,1000,3000,42000]\n",
    "group = ['Low','Average','High']\n",
    "train['Coapplicant_Income_bin'] = pd.cut(df['CoapplicantIncome'],bins,labels=group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the chart\n",
    "Coapplicant_Income_bin = pd.crosstab(train['Coapplicant_Income_bin'],train['Loan_Status'])\n",
    "Coapplicant_Income_bin.div(Coapplicant_Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('CoapplicantIncome')\n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "273\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('Percentage of CoapplicantIncome = 0 is:', 0.44462540716612375)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(train[train[\"CoapplicantIncome\"] == 0]))\n",
    "\"Percentage of CoapplicantIncome = 0 is:\",  len(train[train[\"CoapplicantIncome\"] == 0])/len(train[\"CoapplicantIncome\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It shows that if coapplicant’s income is less the chances of loan approval are high. But this does not look right. The possible reason behind this may be that most of the applicants don’t have any coapplicant, so the coapplicant income for such applicants is 0 and hence the loan approval is not dependent on it. So we can make a new variable in which we will combine the applicant’s and coapplicant’s income to visualize the combined effect of income on loan approval.\n",
    "\n",
    "Let us combine the Applicant Income and Coapplicant Income and see the combined effect of Total Income on the Loan_Status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new variable\n",
    "train['Total_Income'] = train['ApplicantIncome'] + train['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making bins for Total Income variable\n",
    "bins = [0,2500,4000,6000,81000]\n",
    "group = ['Low','Average','High', 'Very high']\n",
    "train['Total_Income_bin'] = pd.cut(train['Total_Income'],bins,labels=group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y5ZiZWZHKDYWeEbGocSF9vVY+JZmZWVHKDYX3JW3XuCDp/wEf5FOSmZkVpdxrCt8BbpY0L13eBBiTT0lmZlaUVkNB0hpAd2ALYHOSR3I+FxEf5VybmZl1sFZDISKWS7owIr5AMoW2mZlVqXKvKdwl6SuSlGs1ZmZWqHKvKYwDegMfS/qApAspImKd3CozM7MOV+7U2WvnXYiZmRWv3KmzJelIST9OlwdKGplvaWZm1tHKvabwW+ALwOHp8iLg4lwqMjOzwpR7TWH7iNhO0hMAEfG2pO451mVmZgUo90zho3QSvACQ1A9Y3tqHJO0l6XlJsyWdtpJ2B0sKSXVl1mNmZjkoNxQmALcCG0r6CfBX4Kcr+0AaIhcDewPDgMMkDWum3drAKcAjbajbzMxyUO7dR9dJegwYTXI76oER8WwrHxsJzI6IOQCSbgQOAGY1afdfwPnAf7SlcDMzq7yVhoKknsCJwKYkD9i5LCKWlbntAcDckuUGYPsm298WGBgRf5TkUDAzK1hr3UfXAHUkgbA3cEEbtt3c6Ofsuc7pnEq/BE5tdUPSWEn1kurnz5/fhhLMzKwtWus+GhYRnwWQdCXwaBu23QAMLFmuBeaVLK8NDAf+ks6esTEwWdL+EVFfuqGImAhMBKirqwvMzCwXrZ0pZDOhtqHbqNEMYKikIentq4cCk0u2tzAi+kbE4IgYDDwMrBAIZmbWcVo7Uxgh6d30tYBe6XKrcx9FxDJJJwHTgBqSZzw/I+lsoD4iJrf0WTMzK8ZKQyEialZl4xExFZjaZN0ZLbTdZVW+y8zMVl254xTMzGw14FAwM7OMQ8HMzDIOBTMzyzgUzMws41AwM7OMQ8HMzDIOBTMzyzgUzMws41AwM7NMuc9oNjPr8gYvub7oEsrycoHf7TMFMzPLOBTMzCzjUDAzs4xDwczMMg4FMzPLOBTMzCzjUDAzs4zHKZh1Yr6v3jqazxTMzCzjUDAzs4xDwczMMr6mYBXnfnCzrstnCmZmlnEomJlZxqFgZmYZh4KZmWUcCmZmlnEomJlZxqFgZmYZh4KZmWUcCmZmlnEomJlZJtdQkLSXpOclzZZ0WjPvj5M0S9JMSfdI+nSe9ZiZ2crlFgqSaoCLgb2BYcBhkoY1afYEUBcRWwO3AOfnVY+ZmbUuzzOFkcDsiJgTER8CNwIHlDaIiHsjYnG6+DBQm2M9ZmbWijxDYQAwt2S5IV3XkmOBO5p7Q9JYSfWS6ufPn1/BEs3MrFSeoaBm1kWzDaUjgTrg5829HxETI6IuIur69etXwRLNzKxUns9TaAAGlizXAvOaNpK0O3A68MWIWJpjPWZm1oo8zxRmAEMlDZHUHTgUmFzaQNK2wGXA/hHxRo61mJlZGXILhYhYBpwETAOeBSZFxDOSzpa0f9rs50Af4GZJT0qa3MLmzMysA+T6OM6ImApMbbLujJLXu+f5/WZm1jYe0WxmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVkm11CQtJek5yXNlnRaM+/3kHRT+v4jkgbnWY+Zma1cbqEgqQa4GNgbGAYcJmlYk2bHAm9HxKbAL4Hz8qrHzMxal+eZwkhgdkTMiYgPgRuBA5q0OQC4Jn19CzBaknKsyczMViLPUBgAzC1ZbkjXNdsmIpYBC4ENcqzJzMxWoluO227uN/5oRxskjQXGpouLJD2/irV1hL7Am5XcoFbvzjXvz8rxvqysrrI/P11OozxDoQEYWLJcC8xroU2DpG7AusBbTTcUEROBiTnVmQtJ9RFRV3Qd1cL7s3K8Lyur2vZnnt1HM4ChkoZI6g4cCkxu0mYy8PX09cHAnyNihTMFMzPrGLmdKUTEMkknAdOAGuCqiHhG0tlAfURMBq4Efi9pNskZwqF51WNmZq3Ls/uIiJgKTG2y7oyS10uAr+ZZQ4G6VHdXF+D9WTnel5VVVftT7q0xM7NGnubCzMwyDgUzM8vkek3BzKwaSRpAct9/dgyNiPuLq6hyHAoVJOkzETGn6DqqiaTeEfF+0XVUg2o+kHUkSecBY4BZwMfp6gCqYl/6QnMFSbqfZOqOGST/QB6IiKeLraprkrQDcAXQJyIGSRoBnBAR3yq4tC6ppQNZROxfXFVdUzqjwtYRsbToWvLgM4UKioid04F6nwN2Af4kqU9ErF9sZV3SL4EvkQ54jIinJO1cbEld2oHA5tV6IOtgc4A1garclw6FCpK0I7BT+rMe8EfggUKL6sIiYm6TSXM/bqmttaqqD2QdQdKvSbqJFgNPSrqHkv0ZEacUVVslORQq6z6gHvgZMDWdMtzaZ27ahRTp2dcpwLMF19TlrC4Hsg5Sn/75GCtO2VM1fE2hgiStB4wCdibpQloOPBQRPy60sC5IUl/gImB3ktl07wK+ExELCi2si5H09ZW9HxHXrOx9W/04FCpM0pbAF0m6kHYAXo2ILxZblZlViqSnWXGK/4UkZxLndPVfXBwKFSTpReB54K8k1xIecRdS+0ia0MzqhSSTKd7e0fV0ddV+IOtIks4nub51fbrqUJKz2YXAjhGxX1G1VYJDoYIkrRERy4uuoxpImghsAdycrvoK8AzJ8zfmRMR3i6qtK6r2A1lHkvS3iBjV3DpJT0fEZ4uqrRJ8obmy+qcX9kaR/Fb2V5J+8IZiy+qSNgV2Sx/TiqRLSK4r7AF47EfbjWpyIHu65EB2ZGFVdU19JG0fEY8ASBoJ9EnfW1ZcWZXhuY8q63ckdyX0JxnENiVdZ203AOhdstwb6B8RH+PbKtujj6TtGxeq7UDWwY4DrpD0kqSXSQZZHi+pN8mdh12azxQqq19ElIbA1ZLczdE+55PcQvkXkm6OnYGfpv/xphdZWBd1HHCVpD4k+/Nd4LhqOZB1pIiYAXxW0rokXfDvlLw9qaCyKsbXFCpI0nTgauCGdNVhwDERMbqworowSZsAI0kOYo9GRNNnfFsbtXAgszJIOjIi/lvSuObej4hfdHRNefCZQmV9A/gNyRQNATwIHFNoRV3bEuA1oCewqaRNPYFb27R0IGscKV4tB7IO0tiduXahVeTMoVBBEfEq8IkJxtLuo18VU1HXJek44DtALfAk8HngIWC3IuvqglaLA1lHiIjL0j/PKrqWPLn7KGeSXo2IQUXX0dWk99V/Dng4IraRtAVwVkSMKbg0W81J6gccDwzmk9OQf6OomirJZwr5U+tNrBlLImKJJCT1iIjnJG1edFFdTQuDADOe+6hdbicZnDqdKpyk0aGQP5+KtU9DOpfUbcDdkt4GfKG57R4reX0WcGZRhVSRtSLi+0UXkRd3H1WApPdo/uAvoFdEOHxXgaQvAusCd3rakPaT9EREbFt0HV2dpHOAByNiatG15MGhYJ2OpDWAmRExvOhaqomkxyNiu6Lr6KpKfvkTyQX8pcBH6XJExDoFllcx/g3WOp2IWC7pKUmD0ju6zAoXEavFHVwOBeusNgGekfQo8H7jSj9TuG2adG2uJendxreoot9urXLcfWSdUnodYQURcV9H12K2OnEoWKcl6dPA0IiYLmktoCYi3iu6LrNq5llSrVOSdDxwC3BZumoAye2pZoWSdIGkrYquIy8OBeusvk3yXIp3ASLiBWDDQisySzwHTJT0iKQT00kGq4ZDwTqrpaVjEiR1wwMBrROIiCvSBxYdRTLVxUxJ10vatdjKKsOhYJ3VfZJ+CPSStAfJYzmnFFyTGQCSakgeF7sF8CbwFDBO0o2FFlYBvtBsnVI6gO1YYE+S2yenAVeE/8FawST9AtgP+DNwZUQ8WvLe8xHRpefo8jgF66wOAK6NiMuLLsSskZIHUbwNjIiIxc00GdnBJVWcu4+ss9of+Iek30vaJ72mYFao9Ez1wBYCgYhY2MElVZxDwTqliDgG2JTkWsLhwIuSrii2KjMAHpb0uaKLyIuvKVinJmlNYC+SR53uFBF9Cy7JVnOSZgGbAy+TTMHSOGXI1kXWVSkOBeuUJO0FHEry+M17gRuBuyNiWaGF2WovHWm/goh4paNryYO7j6yzOhq4lWSai68D7wEXFVqRGdnBfyCwW/p6MVV0LPWZgnVakrYBDgPGAC8Bf4iIXxdbla3uJJ0J1AGbR8RmkvoDN6cD2ro839FhnYqkzUi6jQ4DFgA3kfzyUhWjRa0qfBnYFngcICLmSaqaZy04FKyzeY7koej7RcRsAEn/XmxJZp/wYUSEpACQ1LvogiqpavrBrGp8Bfhf4F5Jl0saTXJ3h1lnMUnSZcB66Wy+04GqGWTpawrWKaW/fR1I0o20G3ANcGtE3FVoYWZAOh9XNgVLRNxdcEkV41CwTk/S+sBXgTERsVvR9djqSdJvgOsj4sGia8mTQ8HMrAySvkNyE8QmJDdA3BARTxZbVeU5FMzM2iAdvHZo+tMTuAG4MSL+UWhhFeJQMDNrJ0nbAlcBW0dETdH1VILvPjIzawNJa0raT9J1wB3AP0jumqsKPlMwMytDesfRYcA+wKMk83HdFhHvF1pYhTkUzMzKIOle4HrgfyLiraLryYtDwczMMr6mYGZmGYeCmZllHApmZpZxKFhVkLSBpCfTn/+V9M+S5e7NtF9f0ollbLebpHdW8v6mkqpuVKutvjx1tlWFiFgAbAMgaTywKCIuWMlH1gdOBC7NvzqzrsNnClb1JP2npL+nPyenq88FNk/PJM6VtI6kP0t6XNJMSfu243uOk3SLpGmSXpD0s5L39km3/ZSku9J1fSVNTr/vQUnD0/XnSLpa0l2SXpZ0oKQL0/r/JKlb2u5zku6T9JikOyRttOp7y1Z3PlOwqiZpJHAEMBKoAR6VdB9wGrBpRDSeXawJHBAR70naEPgb8Md2fOUIYDtgGfAPSb8GlgOXADtFxCvprK8A/wU8EhH7S9oTuJrkMY8AQ4DR6fYeSGs7VdIUYC9Jd5M8s3r/iHhT0hHp9sa2o2azjEPBqt1OJIONFgNIug3YEWj6XAYB50nakeQgPlBSX6DF6wktmB4R76Xf9RwwiGRWzXvTh7xTMvBpR5LRsUTEXenZQeNTvKZGxDJJT6fvN87X/zQwGNgS2AqYLgmSwGtoY61mK3AoWLUr96ltRwHrAtulB+MGkhkw22ppyeuPSf6PCWhulGjT2kqXG7ezHPiwZP3ykm3OjIid2lGjWYt8TcGq3f3AlyX1ktQHOICkO+Y9oPRh6+sCb6SBsAcwoII1/A3YLZ1ymZLuo/tJuraQtDvQ0IZ5dGYBA9LuMSR1l7RVBWu21ZTPFKyqRcSjkm4AZqSrLomIpwEk1afdM38CfgFMkVQPPA68UMEaXpf0TeB2JX0984C9gTOA30maCSwCjmnDNpdKOhiYIGltkv/LFwLPVKpuWz157iMzM8u4+8jMzDLuPjIrg6RtSG4ZLbU4InYooByz3Lj7yMzMMu4+MjOzjEPBzMwyDgUzM8s4FMzMLONQMDOzzP8BYV7qyTRm2U4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the chart\n",
    "Total_Income_bin = pd.crosstab(train['Total_Income_bin'],train['Loan_Status'])\n",
    "Total_Income_bin.div(Total_Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('Total_Income')\n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that Proportion of loans getting approved for applicants having low Total_Income is very less as compared to that of applicants with Average, High and Very High Income. This is more consistent with our hypothesis with applicants with high income  will have more chances of loan approval.\n",
    "\n",
    "Let’s visualize the Loan amount variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making bins for LoanAmount variable\n",
    "bins = [0,100,200,700]\n",
    "group = ['Low','Average','High']\n",
    "train['LoanAmount_bin'] = pd.cut(df['LoanAmount'],bins,labels=group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the chart \n",
    "LoanAmount_bin = pd.crosstab(train['LoanAmount_bin'],train['Loan_Status'])\n",
    "LoanAmount_bin.div(LoanAmount_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('LoanAmount')\n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that the proportion of approved loans is higher for Low and Average Loan Amount as compared to that of High Loan Amount which supports our hypothesis in which we considered that the chances of loan approval will be high when the loan amount is less. \n",
    "\n",
    "Let’s drop the bins which we created for the exploration part. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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",
       "      <th>Income_bin</th>\n",
       "      <th>Coapplicant_Income_bin</th>\n",
       "      <th>Total_Income</th>\n",
       "      <th>Total_Income_bin</th>\n",
       "      <th>LoanAmount_bin</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5849.0</td>\n",
       "      <td>High</td>\n",
       "      <td>NaN</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",
       "      <td>High</td>\n",
       "      <td>Average</td>\n",
       "      <td>6091.0</td>\n",
       "      <td>Very high</td>\n",
       "      <td>Average</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",
       "      <td>Average</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>Low</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",
       "      <td>Average</td>\n",
       "      <td>Average</td>\n",
       "      <td>4941.0</td>\n",
       "      <td>High</td>\n",
       "      <td>Average</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",
       "      <td>High</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>High</td>\n",
       "      <td>Average</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 Income_bin Coapplicant_Income_bin  \\\n",
       "0             1.0         Urban           Y        NaN                    NaN   \n",
       "1             1.0         Rural           N       High                Average   \n",
       "2             1.0         Urban           Y    Average                    NaN   \n",
       "3             1.0         Urban           Y    Average                Average   \n",
       "4             1.0         Urban           Y       High                    NaN   \n",
       "\n",
       "   Total_Income Total_Income_bin LoanAmount_bin  \n",
       "0        5849.0             High            NaN  \n",
       "1        6091.0        Very high        Average  \n",
       "2        3000.0          Average            Low  \n",
       "3        4941.0             High        Average  \n",
       "4        6000.0             High        Average  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# before dropping\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the new variable of bins\n",
    "train = train.drop(['Income_bin', 'Coapplicant_Income_bin', 'LoanAmount_bin', 'Total_Income_bin', 'Total_Income'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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",
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       "      <td>NaN</td>\n",
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       "      <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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# after dropping\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will change the 3+ in dependents variable to 3 to make it a numerical variable. We will also convert the target variable’s categories into 0 and 1 so that we can find its correlation with numerical variables. One more reason to do so is few models like logistic regression takes only numeric values as input. We will replace N with 0 and Y with 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing 3+ in Dependents variable with 3 for both train and test set\n",
    "train['Dependents'].replace('3+', 3, inplace=True)\n",
    "test['Dependents'].replace('3+', 3, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing Y and N in Loan_Status variable with 1 and 0 respectively\n",
    "train['Loan_Status'].replace('N', 0, inplace=True)\n",
    "train['Loan_Status'].replace('Y', 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<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",
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       "  </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",
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       "      <td>NaN</td>\n",
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       "      <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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the dataset\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets look at the correlation between all the numerical variables. We can use the `corr()` to compute pairwise correlation of columns, excluding NA/null values using pearson correlation coefficient. Then we will use the heat map to visualize the correlation. Heatmaps visualize data through variations in coloring. The variables with darker color means their correlation is more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
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       "      <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",
       "      <td>-0.004710</td>\n",
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       "    <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",
       "      <td>-0.059187</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",
       "      <td>-0.037318</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",
       "      <td>-0.021268</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",
       "      <td>0.561678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_Status</th>\n",
       "      <td>-0.004710</td>\n",
       "      <td>-0.059187</td>\n",
       "      <td>-0.037318</td>\n",
       "      <td>-0.021268</td>\n",
       "      <td>0.561678</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",
       "Loan_Status              -0.004710          -0.059187   -0.037318   \n",
       "\n",
       "                   Loan_Amount_Term  Credit_History  Loan_Status  \n",
       "ApplicantIncome           -0.045306       -0.014715    -0.004710  \n",
       "CoapplicantIncome         -0.059878       -0.002056    -0.059187  \n",
       "LoanAmount                 0.039447       -0.008433    -0.037318  \n",
       "Loan_Amount_Term           1.000000        0.001470    -0.021268  \n",
       "Credit_History             0.001470        1.000000     0.561678  \n",
       "Loan_Status               -0.021268        0.561678     1.000000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate and visualize correlation matrix\n",
    "matrix = train.corr()\n",
    "f, ax = plt.subplots(figsize=(9, 6))\n",
    "sns.heatmap(matrix, vmax=1, square=True, cmap=\"BuPu\", annot=True)\n",
    "\n",
    "matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note: ** We see that the most correlated variables are \n",
    "- (ApplicantIncome - LoanAmount) with correlation coefficient of 0.57\n",
    "- (Credit_History - Loan_Status) with correlation coefficient of 0.56\n",
    "- LoanAmount is also correlated with CoapplicantIncome with correlation coefficient of 0.19."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# <a name=\"pre\">Data Pre-processing</a>\n",
    "\n",
    "Data pre-processing is a data mining technique that involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. Data pre-processing is a method of resolving such issues."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing value and outlier treatment\n",
    "After exploring all the variables in our data, we can now impute the missing values and treat the outliers because missing data and outliers can have adverse effect on the model performance.\n",
    "\n",
    "### Missing value imputation\n",
    "\n",
    "Let’s list out feature-wise count of missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing values\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are missing values in Gender, Married, Dependents, Self_Employed, LoanAmount, Loan_Amount_Term and Credit_History features. We will treat the missing values in all the features one by one.\n",
    "\n",
    "We can consider these methods to fill the missing values:\n",
    "\n",
    "- For numerical variables: imputation using mean or median\n",
    "- For categorical variables: imputation using mode\n",
    "\n",
    "There are very less missing values in Gender, Married, Dependents, Credit_History and Self_Employed features so we can fill them using the mode of the features. If an independent variable in our dataset has huge amount of missing data e.g. 80% missing values in it, then we would drop the variable from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace missing values with the mode\n",
    "train['Gender'].fillna(train['Gender'].mode()[0], inplace=True)\n",
    "train['Married'].fillna(train['Married'].mode()[0], inplace=True)\n",
    "train['Dependents'].fillna(train['Dependents'].mode()[0], inplace=True)\n",
    "train['Self_Employed'].fillna(train['Self_Employed'].mode()[0], inplace=True)\n",
    "train['Credit_History'].fillna(train['Credit_History'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let’s try to find a way to fill the missing values in Loan_Amount_Term. We will look at the value count of the Loan amount term variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Amount_Term'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that in loan amount term variable, the value of 360 is repeating the most. So we will replace the missing values in this variable using the mode of this variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace missing value with the mode\n",
    "train['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will see the LoanAmount variable. As it is a numerical variable, we can use mean or median to impute the missing values. We will use median to fill the null values as earlier we saw that loan amount have outliers so the mean will not be the proper approach as it is highly affected by the presence of outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace missing values with the median value due to outliers\n",
    "train['LoanAmount'].fillna(train['LoanAmount'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check whether all the missing values are filled in the Train dataset\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that all the missing values have been filled in the Train dataset. Let’s fill all the missing values in the test dataset too with the same approach. \n",
    "\n",
    "**Note: ** We need to replace the missing values in Test set using the mode/median/mean of the Training set, not from the Test set. Likewise, if you remove values above some threshold in the test case, make sure that the threshold is derived from the training and not test set. Make sure to calculate the mean (or any other metrics) only on the train data to avoid data leakage to your test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace missing values in Test set with mode/median from Training set\n",
    "test['Gender'].fillna(train['Gender'].mode()[0], inplace=True)\n",
    "test['Dependents'].fillna(train['Dependents'].mode()[0], inplace=True)\n",
    "test['Self_Employed'].fillna(train['Self_Employed'].mode()[0], inplace=True)\n",
    "test['Credit_History'].fillna(train['Credit_History'].mode()[0], inplace=True)\n",
    "test['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0], inplace=True)\n",
    "test['LoanAmount'].fillna(train['LoanAmount'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check whether all the missing values are filled in the Test dataset\n",
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that all the missing values have been filled in the Test dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Outlier Treatment \n",
    "\n",
    "As we saw earlier in univariate analysis, LoanAmount contains outliers so we have to treat them as the presence of outliers affects the distribution of the data. Having outliers in the dataset often has a significant effect on the mean and standard deviation and hence affecting the distribution. We must take steps to remove outliers from our data sets.\n",
    "\n",
    "Due to these outliers bulk of the data in the loan amount is at the left and the right tail is longer. This is called right skewness (or positive skewness). One way to remove the skewness is by doing the log transformation. As we take the log transformation, it does not affect the smaller values much, but reduces the larger values. So, we get a distribution similar to normal distribution.\n",
    "\n",
    "Let’s visualize the effect of log transformation. We will do the similar changes to the test file simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Test')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# before log transformation\n",
    "\n",
    "ax1 = plt.subplot(121)\n",
    "train['LoanAmount'].hist(bins=20, figsize=(12,4))\n",
    "ax1.set_title(\"Train\")\n",
    "\n",
    "ax2 = plt.subplot(122)\n",
    "test['LoanAmount'].hist(bins=20)\n",
    "ax2.set_title(\"Test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing skewness in LoanAmount variable by log transformation\n",
    "train['LoanAmount_log'] = np.log(train['LoanAmount'])\n",
    "test['LoanAmount_log'] = np.log(test['LoanAmount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Test')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# after log transformation\n",
    "\n",
    "ax1 = plt.subplot(121)\n",
    "train['LoanAmount_log'].hist(bins=20, figsize=(12,4))\n",
    "ax1.set_title(\"Train\")\n",
    "\n",
    "ax2 = plt.subplot(122)\n",
    "test['LoanAmount_log'].hist(bins=20)\n",
    "ax2.set_title(\"Test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the distribution looks much closer to normal and effect of extreme values has been significantly subsided. Let’s build a logistic regression model and make predictions for the test dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# <a name=\"model\">Model Development and Evaluation</a>\n",
    "\n",
    "There are four sub-sections in this stage:\n",
    "- [Evaluation Metrics for Classification Problems](#evaluation)\n",
    "- [Model Building : Part I](#model1)\n",
    "- [Feature Engineering](#feature)\n",
    "- [Model Building : Part II](#model2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## <a name=\"evaluation\">Evaluation Metrics for Classification Problems</a>\n",
    "\n",
    "\n",
    "The process of model building is not complete without evaluation of model’s performance. Suppose we have the predictions from the model, how can we decide whether the predictions are accurate? We can plot the results and compare them with the actual values, i.e. calculate the distance between the predictions and actual values. Lesser this distance more accurate will be the predictions. Since this is a classification problem, we can evaluate our models using any one of the following evaluation metrics:\n",
    "\n",
    "**Accuracy**: Let us understand it using the confusion matrix which is a tabular representation of Actual vs Predicted values. This is how a confusion matrix looks like:\n",
    "\n",
    "\n",
    "<table><tr><td><img height=\"400\" width=\"400\" src=\"confusion_matrix.jpg\"></td></tr></table>\n",
    "\n",
    "    True Positive - Targets which are actually true(Y) and we have predicted them true(Y)\n",
    "    True Negative - Targets which are actually false(N) and we have predicted them false(N)\n",
    "    False Positive - Targets which are actually false(N) but we have predicted them true(T)\n",
    "    False Negative - Targets which are actually true(T) but we have predicted them false(N)\n",
    "\n",
    "Using these values, we can calculate the accuracy of the model. The accuracy is given by:\n",
    "\n",
    "_Accuracy = (TP+TN) / (TP+TN+FP+FN)_\n",
    "\n",
    "**Precision**: It is a measure of correctness achieved in true prediction i.e. of observations labeled as true, how many are actually labeled true.\n",
    "\n",
    "_Precision = TP / (TP + FP)_\n",
    "\n",
    "**Recall (Sensitivity)** - It is a measure of actual observations which are predicted correctly i.e. how many observations of true class are labeled correctly. It is also known as ‘Sensitivity’. E.g. Proportion of patients with a disease who test positive.\n",
    "\n",
    "_Recall = TP / (TP + FN)_\n",
    "\n",
    "**Specificity** - It is a measure of how many observations of false class are labeled correctly. E.g. Proportion of patients without the disease who test negative.\n",
    "\n",
    "_Specificity = TN / (TN + FP)_\n",
    "\n",
    "Specificity and Sensitivity plays a crucial role in deriving ROC curve.\n",
    "\n",
    "**ROC curve**\n",
    "\n",
    "- Receiver Operating Characteristic(ROC) summarizes the model’s performance by evaluating the trade offs between true positive rate (Sensitivity) and false positive rate (1- Specificity).\n",
    "- The area under curve (AUC), referred to as index of accuracy(A) or concordance index, is a perfect performance metric for ROC curve. Higher the area under curve, better the prediction power of the model.\n",
    "- The area of this curve measures the ability of the model to correctly classify true positives and true negatives. We want our model to predict the true classes as true and false classes as false.\n",
    "- So it can be said that we want the true positive rate to be 1. But we are not concerned with the true positive rate only but the false positive rate too. For example in our problem, we are not only concerned about predicting the Y classes as Y but we also want N classes to be predicted as N.\n",
    "- We want to increase the area of the curve which will be maximum for class 2,3,4 and 5 in the above example.\n",
    "- For class 1 when the false positive rate is 0.2, the true positive rate is around 0.6. But for class 2 the true positive rate is 1 at the same false positive rate. So, the AUC for class 2 will be much more as compared to the AUC for class 1. So, the model for class 2 will be better.\n",
    "- The class 2,3,4 and 5 model will predict more accurately as compared to the class 0 and 1 model as the AUC is more for those classes.\n",
    "\n",
    "This is how a ROC curve looks like:\n",
    "<table><tr><td><img height=\"400\" width=\"400\" src=\"roc.jpg\"></td></tr></table>\n",
    "\n",
    "At the competition’s page, it has been mentioned that our submission data would be evaluated based on the accuracy. Hence, we will use accuracy as our evaluation metric.\n",
    "\n",
    "To know more about evaluation metrics, read this article: https://www.analyticsvidhya.com/blog/2016/02/7-important-model-evaluation-error-metrics/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## <a name=\"model1\">Model Building : Part I</a>\n",
    "\n",
    "Let us make our first model to predict the target variable. We will start with Logistic Regression which is used for predicting binary outcome.\n",
    "\n",
    "- Logistic Regression is a classification algorithm. It is used to predict a binary outcome (1 / 0, Yes / No, True / False) given a set of independent variables.\n",
    "- Logistic regression is an estimation of Logit function. Logit function is simply a log of odds in favor of the event.\n",
    "- This function creates a s-shaped curve with the probability estimate, which is very similar to the required step wise function\n",
    "\n",
    "To learn further on logistic regression, refer this article: https://www.analyticsvidhya.com/blog/2015/10/basics-logistic-regression/\n",
    "\n",
    "Lets drop the Loan_ID variable as it do not have any effect on the loan status. We will do the same changes to the test dataset which we did for the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop Loan_ID \n",
    "train = train.drop('Loan_ID', axis=1)\n",
    "test = test.drop('Loan_ID', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use scikit-learn (sklearn) for making different models which is an open source library for Python. It is one of the most efficient tool which contains many inbuilt functions that can be used for modeling in Python.\n",
    "\n",
    "To learn further about sklearn, refer here: http://scikit-learn.org/stable/tutorial/index.html\n",
    "\n",
    "Sklearn requires the target variable in a separate dataset. So, we will drop our target variable from the train dataset and save it in another dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop \"Loan_Status\" and assign it to target variable\n",
    "X = train.drop('Loan_Status', 1)\n",
    "y = train.Loan_Status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will make dummy variables for the categorical variables. Dummy variable turns categorical variables into a series of 0 and 1, making them lot easier to quantify and compare. Let us understand the process of dummies first:\n",
    "\n",
    "- Consider the “Gender” variable. It has two classes, Male and Female.\n",
    "- As logistic regression takes only the numerical values as input, we have to change male and female into numerical value.\n",
    "- Once we apply dummies to this variable, it will convert the “Gender” variable into two variables(Gender_Male and Gender_Female), one for each class, i.e. Male and Female.\n",
    "- Gender_Male will have a value of 0 if the gender is Female and a value of 1 if the gender is Male.\n",
    "\n",
    "We can use pandas `get_dummies` function to convert categorical variable into dummy/indicator variables, it will only convert \"object\" type and will not affect numerical type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding dummies to the dataset\n",
    "X = pd.get_dummies(X)\n",
    "train = pd.get_dummies(train)\n",
    "test = pd.get_dummies(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((614, 21), (614, 22), (367, 21))"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>LoanAmount_log</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Married_No</th>\n",
       "      <th>Married_Yes</th>\n",
       "      <th>...</th>\n",
       "      <th>Dependents_0</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>Dependents_2</th>\n",
       "      <th>Education_Graduate</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_No</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Rural</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
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       "    <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>4.189655</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <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",
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       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>141.0</td>\n",
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       "      <td>1.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\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  LoanAmount_log  Gender_Female  Gender_Male  Married_No  \\\n",
       "0             1.0        4.852030              0            1           1   \n",
       "1             1.0        4.852030              0            1           0   \n",
       "2             1.0        4.189655              0            1           0   \n",
       "3             1.0        4.787492              0            1           0   \n",
       "4             1.0        4.948760              0            1           1   \n",
       "\n",
       "   Married_Yes         ...           Dependents_0  Dependents_1  Dependents_2  \\\n",
       "0            0         ...                      1             0             0   \n",
       "1            1         ...                      0             1             0   \n",
       "2            1         ...                      1             0             0   \n",
       "3            1         ...                      1             0             0   \n",
       "4            0         ...                      1             0             0   \n",
       "\n",
       "   Education_Graduate  Education_Not Graduate  Self_Employed_No  \\\n",
       "0                   1                       0                 1   \n",
       "1                   1                       0                 1   \n",
       "2                   1                       0                 0   \n",
       "3                   0                       1                 1   \n",
       "4                   1                       0                 1   \n",
       "\n",
       "   Self_Employed_Yes  Property_Area_Rural  Property_Area_Semiurban  \\\n",
       "0                  0                    0                        0   \n",
       "1                  0                    1                        0   \n",
       "2                  1                    0                        0   \n",
       "3                  0                    0                        0   \n",
       "4                  0                    0                        0   \n",
       "\n",
       "   Property_Area_Urban  \n",
       "0                    1  \n",
       "1                    0  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will train the model on training dataset and make predictions for the test dataset. But can we validate these predictions? One way of doing this is we can divide our train dataset into two parts: train and validation. We can train the model on this train part and using that make predictions for the validation part. In this way we can validate our predictions as we have the true predictions for the validation part (which we do not have for the test dataset).\n",
    "\n",
    "We will use the train_test_split function from sklearn to divide our train dataset. So, first let us import train_test_split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data into train and cross validation set\n",
    "x_train, x_cv, y_train, y_cv = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((429, 21), (185, 21), (429,), (185,))"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take a look at the dimension of the data\n",
    "x_train.shape, x_cv.shape, y_train.shape, y_cv.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has been divided into training and validation part. Let us import LogisticRegression and accuracy_score from sklearn and fit the logistic regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the model\n",
    "model = LogisticRegression()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the C=1.0 parameter represents inverse of regularization strength. Regularization is applying a penalty to increasing the magnitude of parameter values in order to reduce overfitting. Smaller values of C specify stronger regularization. To learn about other parameters, refer here: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n",
    "\n",
    "Let’s predict the Loan_Status for validation set and calculate its accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make prediction\n",
    "pred_cv = model.predict(x_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us calculate how accurate our predictions are by calculating the accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8324324324324325"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate accuracy score\n",
    "accuracy_score(y_cv, pred_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So our predictions are over 83% accurate, i.e. we have identified 83% of the loan status correctly.\n",
    "\n",
    "Let’s evaluate our model with the confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 22  29]\n",
      " [  2 132]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(33,0.5,'True')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    " \n",
    "cm = confusion_matrix(y_cv, pred_cv)\n",
    "print(cm)\n",
    "\n",
    "# f, ax = plt.subplots(figsize=(9, 6))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\")\n",
    "plt.title('Confusion matrix of the classifier')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's build a text report showing the main classification metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             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",
      "avg / total       0.85      0.83      0.81       185\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# import classification_report\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_cv, pred_cv))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performance of our model seems encouraging, with accuracy of 83%, precision of 82% and recall of 99%.\n",
    "\n",
    "Let’s make predictions for the test dataset and submit the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make prediction on test set\n",
    "pred_test = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take a look at the prediction\n",
    "pred_test[:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets import the submission file which we have to submit on the solution checker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv(\"Sample_Submission_ZAuTl8O_FK3zQHh.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only need the Loan_ID and the corresponding Loan_Status for the final submission. we will fill these columns with the Loan_ID of test dataset and the predictions that we made, i.e., pred_test respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill the Loan_ID and Loan_Status\n",
    "submission['Loan_Status'] = pred_test\n",
    "submission['Loan_ID'] = test_original['Loan_ID']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember we need predictions in Y and N. So let’s convert 1 and 0 to Y and N."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace with \"N\" and \"Y\"\n",
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Loan_Status\n",
       "0  LP001015           Y\n",
       "1  LP001022           Y\n",
       "2  LP001031           Y\n",
       "3  LP001035           Y\n",
       "4  LP001051           Y"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take a look at the submission result\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we will convert the submission to .csv format and make submission to check the accuracy on the leaderboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to CSV file, without row index\n",
    "submission.to_csv('logistic.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this submission we got an accuracy of 0.7847 on the leaderboard.\n",
    "\n",
    "Instead of creating validation set, we can also make use of cross validation to validate our predictions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression using stratified k-folds cross validation\n",
    "\n",
    "To check how robust our model is to unseen data, we can use Validation. It is a technique which involves reserving a particular sample of a dataset on which you do not train the model. Later, you test your model on this sample before finalizing it. Some of the common methods for validation are listed below:\n",
    "\n",
    "- The validation set approach\n",
    "- k-fold cross validation\n",
    "- Leave one out cross validation (LOOCV)\n",
    "- Stratified k-fold cross validation\n",
    "\n",
    "If you wish to know more about validation techniques, then please refer this article: https://www.analyticsvidhya.com/blog/2018/05/improve-model-performance-cross-validation-in-python-r/\n",
    "\n",
    "In this section we will learn about stratified k-fold cross validation. Let us understand how it works:\n",
    "\n",
    "- Stratification is the process of rearranging the data so as to ensure that each fold is a good representative of the whole.\n",
    "- For example, in a binary classification problem where each class comprises of 50% of the data, it is best to arrange the data such that in every fold, each class comprises of about half the instances.\n",
    "- It is generally a better approach when dealing with both bias and variance.\n",
    "- A randomly selected fold might not adequately represent the minor class, particularly in cases where there is a huge class imbalance.\n",
    "\n",
    "If K=N, then it is called Leave one out cross validation, where N is the number of observations.\n",
    "\n",
    "Below is the visualization of a stratified k-fold validation when k=5.\n",
    "<table><tr><td><img height=\"400\" width=\"400\" src=\"kfold.jpg\"></td></tr></table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import StratifiedKFold from sklearn and fit the model\n",
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let’s make a cross validation logistic model with stratified 5 folds and make predictions for test dataset. The folds are made by preserving the percentage of samples for each class. In stratified k-fold, each fold contains roughly the same proportions of the different types of class labels.\n",
    "\n",
    "**Note: ** Previously, we use train_test_split to split the data, in this example we will use StratifiedKFold to split the data, the final accuracy score might be slightly differently"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.7983870967741935\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.8306451612903226\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.8114754098360656\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.7950819672131147\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.8278688524590164\n",
      "\n",
      "Mean validation accuracy:  0.8126916975145425\n"
     ]
    }
   ],
   "source": [
    "# stratified 5 folds, shuffle each stratification of the data before splitting into batches\n",
    "\n",
    "mean_accuracy = []\n",
    "i = 1\n",
    "kf = StratifiedKFold(n_splits=5, random_state=1, shuffle=True)\n",
    "\n",
    "for train_index, test_index in kf.split(X, y):\n",
    "    print('\\n{} of kfold {}'.format(i, kf.n_splits))\n",
    "    xtr, xvl = X.loc[train_index], X.loc[test_index]\n",
    "    ytr, yvl = y[train_index], y[test_index]\n",
    "    \n",
    "    model = LogisticRegression(random_state=1)\n",
    "    model.fit(xtr, ytr)\n",
    "    pred_test = model.predict(xvl)\n",
    "    score = accuracy_score(yvl, pred_test)\n",
    "    mean_accuracy.append(score)\n",
    "    print('accuracy_score', score)\n",
    "    i+=1\n",
    "    \n",
    "print(\"\\nMean validation accuracy: \", sum(mean_accuracy)/len(mean_accuracy))\n",
    "\n",
    "\n",
    "# make prediction on test set\n",
    "pred_test = model.predict(test)\n",
    "\n",
    "\n",
    "# calculate probability estimates of loan approval\n",
    "# column 0 is the probability for class 0 and column 1 is the probability for class 1\n",
    "# probability of loan default = 1 - model.predict_proba(test)[:,1]\n",
    "pred = model.predict_proba(xvl)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ni = 1\\nkf = StratifiedKFold(n_splits=5, random_state=1, shuffle=True)\\n\\nfor train_index, test_index in kf.split(X, y):\\n    print('\\n{} of kfold {}'.format(i, kf.n_splits))\\n    X_train, X_test = X.loc[train_index], X.loc[test_index]\\n    y_train, y_test = y[train_index], y[test_index]\\n  \\n    model = LogisticRegression(random_state=1)\\n    model.fit(X_train, y_train)\\n    pred_test = model.predict(X_test)\\n    score = accuracy_score(y_test, pred_test)\\n    print('accuracy_score', score)\\n    i+=1\\n\""
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "i = 1\n",
    "kf = StratifiedKFold(n_splits=5, random_state=1, shuffle=True)\n",
    "\n",
    "for train_index, test_index in kf.split(X, y):\n",
    "    print('\\n{} of kfold {}'.format(i, kf.n_splits))\n",
    "    X_train, X_test = X.loc[train_index], X.loc[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "  \n",
    "    model = LogisticRegression(random_state=1)\n",
    "    model.fit(X_train, y_train)\n",
    "    pred_test = model.predict(X_test)\n",
    "    score = accuracy_score(y_test, pred_test)\n",
    "    print('accuracy_score', score)\n",
    "    i+=1\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean validation accuracy for this model turns out to be 0.81. Let us visualize the ROC curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize ROC curve\n",
    "from sklearn import metrics\n",
    "fpr, tpr, _ = metrics.roc_curve(yvl,  pred)\n",
    "auc = metrics.roc_auc_score(yvl, pred)\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(fpr,tpr,label=\"validation, auc=\"+str(auc))\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got an AUC (\"Area Under Curve\") value of 0.77."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status'] = pred_test\n",
    "submission['Loan_ID'] = test_original['Loan_ID']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember we need predictions in Y and N. So let’s convert 1 and 0 to Y and N."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets convert the submission to .csv format and make submission to check the accuracy on the leaderboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to CSV file, without row index\n",
    "submission.to_csv('logistic.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this submission we got an accuracy of 0.7778 on the leaderboard. Now we will try to improve this accuracy using different approaches."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## <a name=\"feature\">Feature Engineering</a>\n",
    "\n",
    "Based on the domain knowledge, we can come up with new features that might affect the target variable. We will create the following three new features:\n",
    "\n",
    "- **Total Income** - As discussed during bivariate analysis we will combine the Applicant Income and Coapplicant Income. If the total income is high, chances of loan approval might also be high.\n",
    "- **Equated Monthly Installment** - EMI is the monthly amount to be paid by the applicant to repay the loan. Idea behind making this variable is that people who have high EMI’s might find it difficult to pay back the loan. We can calculate the EMI by taking the ratio of loan amount with respect to loan amount term.\n",
    "- **Balance Income** - This is the income left after the EMI has been paid. Idea behind creating this variable is that if this value is high, the chances are high that a person will repay the loan and hence increasing the chances of loan approval.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine Applicant Income and Coapplicant Income into a new variable\n",
    "train['Total_Income'] = train['ApplicantIncome'] + train['CoapplicantIncome']\n",
    "test['Total_Income'] = test['ApplicantIncome'] + test['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Test')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the distribution of Total Income\n",
    "# before log transformation\n",
    "\n",
    "fig = plt.figure(figsize=(14, 4))\n",
    "ax1 = plt.subplot(121)\n",
    "sns.distplot(train['Total_Income'])\n",
    " \n",
    "ax1.set_title(\"Train\")\n",
    "\n",
    "ax1 = plt.subplot(122)\n",
    "sns.distplot(test['Total_Income'])\n",
    "ax1.set_title(\"Test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see it is shifted towards left, i.e., the distribution is right skewed. So, let’s take the log transformation to make the distribution normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log transformation\n",
    "train['Total_Income_log'] = np.log(train['Total_Income'])\n",
    "test['Total_Income_log'] = np.log(test['Total_Income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Test')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# after log transformation\n",
    "fig = plt.figure(figsize=(14, 4))\n",
    "ax1 = plt.subplot(121)\n",
    "sns.distplot(train['Total_Income_log'])\n",
    "ax1.set_title(\"Train\")\n",
    "\n",
    "ax1 = plt.subplot(122)\n",
    "sns.distplot(test['Total_Income_log'])\n",
    "ax1.set_title(\"Test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the distribution looks much closer to normal and effect of extreme values has been significantly subsided. Let’s create the EMI feature now. We can calculate the EMI by taking the ratio of loan amount with respect to loan amount term. It is just an approximation of actual EMI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create EMI feature\n",
    "train['EMI'] = train['LoanAmount'] / train['Loan_Amount_Term']\n",
    "test['EMI'] = test['LoanAmount'] / test['Loan_Amount_Term']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s check the distribution of EMI variable. EMI is a continuous numerical variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Test')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the distribution of EMI\n",
    "\n",
    "fig = plt.figure(figsize=(14, 4))\n",
    "ax1 = plt.subplot(121)\n",
    "sns.distplot(train['EMI'])\n",
    "ax1.set_title(\"Train\")\n",
    "\n",
    "ax1 = plt.subplot(122)\n",
    "sns.distplot(test['EMI'])\n",
    "ax1.set_title(\"Test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us create Balance Income feature now and check its distribution. This is the income left after the EMI has been paid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create new \"Balance Income\" variable\n",
    "train['Balance Income'] = train['Total_Income'] - (train['EMI']*1000) # Multiply with 1000 to make the units equal \n",
    "test['Balance Income'] = test['Total_Income'] - (test['EMI']*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Test')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KVImISMsbS0+eqUrGIhQcsnkFVSJSWwqqmlw6WyxUETnlvb72GMcVVImISIsrn6kK/szRuioRqTUFVU0ulctjQCxy6tZePe0xhhRUiYhIiysWqug4paR6EGQpqBKRWlNQ1eTS2QKJWBtmpwZVfR0xrakSEZGWN5rJE4sY8QlFnYqzPLRXlYjUmoKqJpfO5Sed+gdBBcDjKqkuIiItbiydOyVLBZr+JyL1o6CqyaXCTNVk+tpjDCpTJSIiLW40kz9lPRVo+p+I1I+CqiaXyRVOKade1NsRZyiVI19QVSMREWldY5ncKZX/oDSo0vQ/EaktBVVNLpXLn7Lxb1FxA+DhlLJVIiLSukbTZTJVUU3/E5H6UFDV5IJCFZNnqvrCoErFKkREpJWNZSZfUxWPtmEEDyhFRGpJQVWTCwpVTJ2p0gbAIiLSykbTeToTpz6ANDMSsTZN/xORmlNQ1eRSuULZ6X99HWGmSkGViIi0sHKZKgjWVaU1/U9EakxBVRPLFzwoVFFm+l/viel/KqsuIiKtazQzeaYKgr2qtKZKRGpt8sc60hRGM8EO8WWn/4WZqiFlqkREpMXctWPfideD41leGBg/qa0oGWvT5r8iUnPKVDWxkVQQVE2fqVJQJSIirangTjZXIF7mAWQypkyViNSegqomNpIOg6oyN4pENEJ7LKJCFSIi0rJyeceBeERBlYg0joKqJvZKUDV5pgqCYhUqVCEiIq0qHZZLL5+pUvU/Eam9ioIqM7vEzHab2R4zu36S9xNmdnf4/g4zW1fy3g1h+24zu3i6Ps3surDNzWxpSfuvmdmgmf0k/Lqx0vHNV8Xpf8lY+X+m3vaYMlUiItKyMuF6qXKzOpLRCOlcHnev57BEpMVMW6jCzCLAp4B3AfuBR81sm7s/WXLYVcAxdz/TzLYAtwDvN7ONwBbgHGAl8ICZnRWeU67Ph4CvA9+ZZDjfc/dfn8X45qVKMlW97TEGtaZKRERaVDoMqqZaU1VwyOYVVIlI7VRS/e9CYI+77wUws63AZqA0aNkM/Hn4+l7gk2ZmYftWd08Dz5rZnrA/yvXp7j8O2yr9DJWMb156pVDFqTeKYoWj4VSOo6PpE99/4I2n12+AIiIiDZaZJqgq3kO1rkpEaqmS6X+rgBdKvt8ftk16jLvngEFgyRTnVtLnZC4ys5+a2TfM7JwZjA8AM7vGzHaa2c4jR45UcLnGGk4XS6qXz1S1xyOMZ3SjEBGR1pTJh9P/pihUAQqqRKS2KgmqJksZTcyhlztmpu1TeQxY6+7nAZ8A/nUG4wsa3W93903uvqm/v3+ayzVeMVNV7ukbQHsswpiCKhERaVGvTP8rv/kvKKgSkdqqJKjaD6wp+X41cKDcMWYWBXqBgSnOraTPk7j7kLuPhK+3A7GwkMWM+5ovRtJZYhEj0lZ+KmRHPEKu4GTzqmwkIiKtZ7rpf8ViT9oAWERqqZKg6lFgg5mtN7M4QeGJbROO2QZcGb6+DHjQgzI724AtYXXA9cAG4JEK+zyJmS0P12lhZheGYz86m77mi5F0bsqpf6BpDSIi0toy05ZU131SRGpv2kIV7p4zs+uA+4EIcKe77zKzm4Cd7r4NuAP4UliIYoAgsCE87h6CohE54Fp3z0NQOn1in2H7h4E/BZYDj5vZdne/miBY+yMzywHjwJYwcJt0fFX56TTYSDo/5dQ/CKb/AYxn83QnY/UYloiISNOYtqT6iaBKmSoRqZ1Kqv8Vp9ttn9B2Y8nrFHB5mXNvBm6upM+w/TbgtknaPwl8stLxLQQjqeyJm0E5ulmIiEgrS+cLGBAtM1U+GVX1PxGpvYo2/5XGGEnnyj55K0qqVKyIiLSwTK5APNpWdiuWeLQNA1I53SdFpHYUVDWx4VSORIWZqnEFVSIi0oIyucKUDyDNjESsTTM6RKSmFFQ1saBQRWVrqpSpEhGRVpQOM1VTScYipHWfFJEaUlDVxEbSuRM7wZdzYk2V9qoSETmFmV1iZrvNbI+ZXT/J+wkzuzt8f4eZrSt574awfbeZXTxdn2Z2Xdjm4ZYfxfZfM7NBM/tJ+HViTbLMXZCpmmZWRzSih48iUlMVFaqQ+nN3RlK5aW8UsYgRMWNc0xpERE5iZhHgU8C7CPY0fNTMtrn7kyWHXQUcc/czzWwLcAvwfjPbSFDJ9hxgJfCAmZ0VnlOuz4eArwPfmWQ433P3X6/6hxQy+coyVdqnSkRqSZmqJpXOFcgVfNrpf2ZGMtamJ3AiIqe6ENjj7nvdPQNsBTZPOGYz8IXw9b3AO8I9ETcDW9097e7PAnvC/sr26e4/dvfnav2h5GSZXIF4ZPqiTrpPikgtKahqUsOpHADxaQpVQPEJnG4WIiITrAJeKPl+f9g26THungMGgSVTnFtJn5O5yMx+ambfMLNzyh1kZteY2U4z23nkyJEKupVK11QpqBKRWlJQ1aRG00FQNV2mCqA9rpuFiMgkJqux7RUeM9P2qTwGrHX384BPAP9a7kB3v93dN7n7pv7+/mm6FYBMLl/R9iOq/icitaSgqkmNhEHVdGuqIHgCN65CFSIiE+0H1pR8vxo4UO4YM4sCvcDAFOdW0udJ3H3I3UfC19uBWGkhC5mbijJV0QjpXB736eJfEZHZUVDVpIrT/6ar/gfFaQ16AiciMsGjwAYzW29mcYLCE9smHLMNuDJ8fRnwoAd/eW8DtoTVAdcDG4BHKuzzJGa2PFynhZldSHDvPVqVT9ji3P3E5r9TScYiFBzG9ABSRGpE1f+a1MiJ6X/TZ6ratQBXROQU7p4zs+uA+4EIcKe77zKzm4Cd7r4NuAP4kpntIchQbQnP3WVm9wBPAjngWnfPQ1A6fWKfYfuHgT8FlgOPm9l2d7+aIFj7IzPLAePAFlfKpCpyBceBxDSFKooPKIdTOToT+tNHRKpPv1ma1Eg6C1SeqRpXUCUicopwut32CW03lrxOAZeXOfdm4OZK+gzbbwNum6T9k8AnZzp2mV46LJNeSaYKYDiVZXlvsubjEpHWo+l/TWqkOP2vkkIVsQi5gpPNawqgiIi0jsyJoGrqWR0dYVB1fDxb8zGJSGtSUNWkhovT/yooqZ4Ij9EUQBERaSWZCjNVXclgYs6R4XTNxyQirUlBVZMaSeWItBnRtsmq956sPZwiqGIVIiLSSjLhHo3TzeroTsYABVUiUjsKqprUaDpHVyJKWDBqSkllqkREpAWlw2nv8WkKVXTEI7QZHB5O1WNYItKCFFQ1qeEwqKpEu4IqERFpQZVO/2szoysRVaZKRGqmoqDKzC4xs91mtsfMrp/k/YSZ3R2+v8PM1pW8d0PYvtvMLp6uTzO7Lmzz0s0Rzex3zOzx8OsHZnZeyXvPmdnPzOwnZrZz5j+G5jOWztOZmH49FbySqVIFQBERaSXFoKqSok5dySiHFVSJSI1MmwoxswjwKeBdBDvJP2pm29z9yZLDrgKOufuZZrYFuAV4v5ltJNjz4xxgJfCAmZ0VnlOuz4eArwPfmTCUZ4G3uvsxM7sUuB14Y8n7b3P3l2fw2ZvaaCZHR7yyTNUr0/+0pkpERFpHpSXVAboTMWWqRKRmKslUXQjscfe97p4BtgKbJxyzGfhC+Ppe4B3h7vGbga3unnb3Z4E9YX9l+3T3H7v7cxMH4e4/cPdj4bcPA6tn8DnnnbFM5ZkqTf8TEZFWVOn0P4BuZapEpIYqCapWAS+UfL8/bJv0GHfPAYPAkinOraTPqVwFfKPkewe+aWY/MrNryp1kZteY2U4z23nkyJEZXK7+xjL5ijNVsYjRZpr+JyIirSUTFqqITVOoAoKg6uhImnzBaz0sEWlBlQRVk5Wfm/gbqdwxM22ffjBmbyMIqj5a0vxmd78AuBS41sx+dbJz3f12d9/k7pv6+/sruVzDjGVydMQry1SZGclYRJkqERFpKdlcIXywOH2l3K5kjILDwGimDiMTkVZTSVC1H1hT8v1q4EC5Y8wsCvQCA1OcW0mfpzCz1wGfAza7+9Fiu7sfCP97GPgXgumF89pouvJMFQRTAJWpEhGRVpLJFyrKUgF0hxV1VVZdRGqhkt9EjwIbzGy9mcUJCk9sm3DMNuDK8PVlwIPu7mH7lrA64HpgA/BIhX2exMxOB74KfNDdf17S3mlm3cXXwLuBJyr4XE1tLJOjs8JMFaBMlYiItJxsvjDtHlVF3ckgqFKxChGphWlTIe6eM7PrgPuBCHCnu+8ys5uAne6+DbgD+JKZ7SHIUG0Jz91lZvcATwI54Fp3z0NQOn1in2H7h4E/BZYDj5vZdne/GriRYJ3Wp8MNcXPuvglYBvxL2BYF7nL3+6rws2mYQsGDNVUV7lMFQaZK1f9ERKSVZHIFYhUUqQDoTsYAVKxCRGqior/a3X07sH1C240lr1PA5WXOvRm4uZI+w/bbgNsmab8auHqS9r3AeRPb57PiNL6ZZKoSsTaGUtlaDUlERKTpZPNecaaqK6FMlYjUTmW/iaSuRjM5gFlkqjT9T0REWsdM1lTFo210J6IKqkSkJhRUNaGx9MwzVUkVqhARkRaTyRWIR6ev/FfU35NQUCUiNaGgqgmdyFTNoPpfMhYhm/cTGyGKiIgsdNkZZKoA+rsUVIlIbSioakLjmTBTlag8U9UeC/4ph7WuSkREWkRmBtX/AE7rSaqkuojUhIKqJjQaBlUzzVQBDKVyNRmTiIhIs8nOoPofKFMlIrWjoKoJjaWDwGhmmaowqBpXpkpERFrDTDNV/d0JRjN5RtN6ACki1aWgqgmdyFTFZpOpUlAlIiILn7uTzfuM1lSd1p0AVFZdRKpPQVUTGjtRUn0G1f/ixUyVnr6JiMjCl807EJRKr1R/GFRpA2ARqTYFVU1o9ERJ9ZntUwXKVImISGvI5INqt/FI5SXVT+tRpkpEakNBVRMay+Qwg2Ss8n+epKr/iYhIC8mGQdVMS6oDHFEFQBGpMgVVTWg0naczHsWs8qdv8UgbbabpfyIi0hqK+zLOZPrfoo440TbT9D8RqToFVU1oLJOjI175eioAMyMZizCo6n8iItICZpOpamszlqqsuojUgIKqJjSaydOZqHw9VVF7LMJxBVUiItICTqypmkGmCoJiFcpUiUi1KahqQuOzyFQBdMQjHBvN1GBEIiIizSVbnP43g0wVwPLeJIcGtaZKRKpLQVUTKq6pmqmOeJRjYwqqRERk4cuEJdVnMv0PYFVfOweOj9diSCLSwhRUNaGxTG5Ge1QVdSYiHB/T9D8REVn4srMoVAGwsi/JcDqnLUhEpKoUVDWh0czsM1UDmv4nIiItIHOiUEXllXIBVvS2A3DwuKYAikj1VBRUmdklZrbbzPaY2fWTvJ8ws7vD93eY2bqS924I23eb2cXT9Wlm14VtbmZLS9rNzG4L33vczC4oee9KM3sm/Lpy5j+G5jKWztE+yzVV49k8qWy+BqMSERFpHtn87NZUrewLgipNARSRapr2N5GZRYBPAZcCG4ErzGzjhMOuAo65+5nArcAt4bkbgS3AOcAlwKfNLDJNnw8B7wSen3CNS4EN4dc1wGfCaywGPga8EbgQ+JiZLar0B9CMgkzVbIKqILulKYAiIrLQnchUzXD636owqHpRQZWIVFElv4kuBPa4+153zwBbgc0TjtkMfCF8fS/wDgt2rt0MbHX3tLs/C+wJ+yvbp7v/2N2fm2Qcm4EveuBhoM/MVgAXA99y9wF3PwZ8iyCAm7eCNVWzmf4XBGKaAigiIgtdNlcg2ma02cym//V3J4i2mTJVIlJVlQRVq4AXSr7fH7ZNeoy754BBYMkU51bSZ6XjqLgvM7vGzHaa2c4jR45Mc7nGyOQKZPM+y0xVcM5xVQAUEZEFLpMvzLjyH0CkzVjem+SgyqqLSBVV8ttoskdAXuExM22fzTgq7svdb3f3Te6+qb+/f5rLNcZYJge8MpVvJorZrWOa/iciIgtcNuczrvxXtLKvXdP/RKSqKvlttB9YU/L9auBAuWPMLAr0AgNTnFtJn5WOYzZ9Na2xTFBkonMWJdVPTP9TpkpERBa42WaqAFb2JjX9T0SqqpLfRo8CG8xsvZnFCQpPbJtwzDagWHXvMuBBd/ewfUtYHXA9QZGJRyrsc6JtwIfCKoC/DAy6+0GsTqVQAAAgAElEQVTgfuDdZrYoLFDx7rBtXppTpqo4/U9rqkREZIHL5gvEozNbT1W0sq+dQ4Mp8oXpJsmIiFRm2r/c3T1nZtcRBCoR4E5332VmNwE73X0bcAfwJTPbQ5Ch2hKeu8vM7gGeBHLAte6eh6B0+sQ+w/YPA38KLAceN7Pt7n41sB14D0GxizHg98NrDJjZXxAEagA3ufvAXH8wjTKann2mKtrWRmc8oul/IiKy4GVyM89U3bVjHxBU/ssVnNu/u5fe9hgfeOPptRiiiLSQitIh7r6dIKgpbbux5HUKuLzMuTcDN1fSZ9h+G3DbJO0OXFvmGncCd075IeaJ0TlkqgAWdcY5pul/IiJAsCci8HcED/A+5+4fn/B+Avgi8AbgKPD+YgVaM7uBYMuQPPBhd79/qj7Dh4X/J3AG0O/uL4ftFh7/HoKHgr/n7o/V8GO3hEy+QHts5g8gAfraYwAMjmfpDV+LiMzF7CYjS82MFTNVsw2qOhRUiYhA8++zKHOTncOaqt6OOKBquSJSPQqqmsyJTNUspv8B9HXENP1PRCTQ7PssyhxkcoVZV/8rzVSJiFSDgqomU6z+1zGLfaoAFnfGOaZCFSIi0Pz7LJ5iPuyn2CyyeSc+y0xVMhYhEW3juB5CikiVKKhqMqPpOa6p0vQ/EZGiZt9n8dTGebCfYrMISqrPrvofBDM7jitTJSJVoqCqycw1U9XXEWM4lSObL1RzWCIi81Gz77Mos+TuZOcw/Q+grz3O4LgeQopIdSioajJjmTzxaNusF98u7iwuvtXTNxFpec2+z6LMUjpXwGHW90qA3o6Y7pUiUjUKqprMWCZH5yyzVAB9qmgkIgKcWCNV3BPxKeCe4j6LZvbe8LA7gCXhPosfAa4Pz90FFPdZvI9wn8VyfUKwz6KZ7SfIRD1uZp8Lr7Ed2EtQ7OIfgD+u8Udf8MbDWR1zy1TFGMvkyeQ0s0NE5m52C3ekZkbT+VmvpwJY1BFUNFIFQBGR5t9nUWZnPBsGVXPIVPV1qAKgiFSPMlVNZiyTo3OW5dQhKFQBMKAKgCIiskAV1x/PafpfezizQ+uqRKQKFFQ1mdHMHDNVnZr+JyIiC1sqO/fpf73hXlVDylSJSBUoqGoyY+m5ZqqCm8SAgioREVmgqpGp6kkGDzA1/U9EqkFBVZOZa6aqXRsaiojIAjeWCfZ0jM9hn6popI3ORFRBlYhUhYKqJjOWyc16jyoAMws2ANaaKhERWaCK0/9ic5j+B0EFQAVVIlINCqqazFyr/0FQ0eiYpv+JiMgCVZz+N5fqfwA97TGGxnPVGJKItDgFVU1mfI77VEGwAbBKqouIyEI1XqVMVW+7pv+JSHUoqGoihYIzls3TkZhbpmpRR1yZKhERWbDGq5Sp6k3GGM/mT6zREhGZLQVVTSSVy+POnDNVfR0xrakSEZEFqxrV/yCY/gdwcDA15zGJSGur6LeRmV1iZrvNbI+ZXT/J+wkzuzt8f4eZrSt574awfbeZXTxdn2a2PuzjmbDPeNh+q5n9JPz6uZkdLzknX/Lettn9KBpvNB3cJOaaqVrcGWdwPEuh4NUYloiISFMZz+aJtBmRttlX/wPoDbchOaSgSkTmaNqgyswiwKeAS4GNwBVmtnHCYVcBx9z9TOBW4Jbw3I3AFuAc4BLg02YWmabPW4Bb3X0DcCzsG3f/7+5+vrufD3wC+GrJ9ceL77n7e2f8U2gSxekHc89UxSk4DKU0T1xERBae8Uye2BzKqRf1JpWpEpHqqCQlciGwx933ApjZVmAz8GTJMZuBPw9f3wt80swsbN/q7mngWTPbE/bHZH2a2VPA24EPhMd8Iez3MxPGdAXwsQo/47xw1459HDg+DsCPnj9GKluYdV9LOuMAvDySoa8jXpXxiYiINIvxTH7O66mgZPpfeP8VEZmtSn4jrQJeKPl+f9g26THungMGgSVTnFuufQlwPOxj0muZ2VpgPfBgSXPSzHaa2cNm9r5yH8TMrgmP23nkyJHyn7hBUrlg+l8yNrdM1bKeJKDpDCIisjCNZfNzXk8FwZqsjniEg0O6X4rI3FTyG2my/PrExTrljqlWe6ktwL3uni9pO93dNxFkuP7WzM6YpB/c/XZ33+Tum/r7+yc7pKHSYXYqGZ1bULWyLwiqDgzqyZuIiCw845k88TmWUy/qbY/pIaSIzFklv5H2A2tKvl8NHCh3jJlFgV5gYIpzy7W/DPSFfZS71hbgK6UN7n4g/O9e4DvA6yv4XE2nuEN8Ija3G4UyVSIispCNZ3NVmf4HQVClNVUiMleV/EZ6FNgQVuWLEwQ1EyvsbQOuDF9fBjzo7h62bwmrA64HNgCPlOszPOfbYR+EfX6teBEzezWwCPhhSdsiM0uEr5cCb+bk9V7zRioXZqrmOP0vGYuwpDPOQWWqRERkARrL5Oe88W9REFTpfikiczNtoQp3z5nZdcD9QAS40913mdlNwE533wbcAXwpLEQxQBAkER53D0GQkwOuLU7bm6zP8JIfBbaa2V8CPw77LrqCoPBF6ZTA1wB/b2YFgiDx4+4+L4OqdDFTVYUbxYq+pJ68iYjIglStQhUQBFXHx7KMZ/K0z7H6roi0roo2RHL37cD2CW03lrxOAZeXOfdm4OZK+gzb9/JKhcCJ7/35JG0/AM6d8gPME6lsgUibVWXx7YredvYdHavCqERERJrLeDZPX1i5b66KFQAPDaVYv7SzKn2KSOupzmMeqYp0Ll+VLBXAit6kClWIiMiCVO1CFYCmAIrInCioaiKpbH7O66mKVvS2M5zKMZLOTX+wiIjIPFLt6X8AB49ryryIzJ6CqiaSzhVIVjFTBXBIT95ERGQBcfeq7VMF0JN8ZfqfiMhsKahqIqlsnkTVMlVBUKViFSIispCkcwXyBa/a9L94tI2+DlUAFJG5qahQhdRHOleY88Lbu3bsA2BgNAPAvzz2Ii8MjPOBN54+5/GJiIg0WnFae7WmywMs70lqb0cRmRNlqppINddU9SSDeHlwPFuV/kRERJrBcCoIqqpV2AlgZV87B7SmSkTmQEFVE0llCyRi1fkniUba6EpEFVSJiMiCMpwK7mvVzFSt6E3y4nFN/xOR2VNQ1STcnXQuTzJavZtEb3tMQZWIiCwoI6nqT/9btaidwfGsKuaKyKwpqGoS2bxTcKpWqAIUVImIyMIzVIPpf6v62gE4oGyViMySgqomkcrlAUhWafofKKgSEZGFpxbT/1YvCoKqF48pqBKR2VFQ1STS2QIAiSpP/0vnCqSy+ar1KSIi0kivVP+rZqaqA4D9ylSJyCwpqGoSxcCn2pkqUAVAERFZOF6p/le9h5CndSeIRUyZKhGZNQVVTSKdq02mChRUiYjIwjGcytIeixBps6r12dZmrOhtVwVAEZk1BVVNQpkqERGR6Y2kc3SHezFW06q+dl48Nlb1fkWkNSioahLpYqGKKmaqetpjGAqqRERk4RhK5eiqRVC1SJkqEZk9BVVNIhUWqqhmNaNIm9GV1AbAIiKycAyncnQnY1Xvd1VfO4eH02TC6fgiIjOhoKpJFEuqx6u47wbA4s44Lw+nq9qniIhIo4yksnQnapOpcoeDg8pWicjMVfQXvJldYma7zWyPmV0/yfsJM7s7fH+Hma0ree+GsH23mV08XZ9mtj7s45mwz3jY/ntmdsTMfhJ+XV1yzpXh8c+Y2ZWz+1E0VjpbIB5pq+rCW4AVve0cHEpRKHhV+xUREWmEIFNV/aBqdZ/2qhKR2Zs2qDKzCPAp4FJgI3CFmW2ccNhVwDF3PxO4FbglPHcjsAU4B7gE+LSZRabp8xbgVnffABwL+y66293PD78+F15jMfAx4I3AhcDHzGzRDH8ODZfK5klUsUhF0aq+JJlcgeeOjla9bxERkXqrVVC1KtwAWHtVichsVPJX/IXAHnff6+4ZYCuwecIxm4EvhK/vBd5hZha2b3X3tLs/C+wJ+5u0z/Cct4d9EPb5vmnGdzHwLXcfcPdjwLcIArh5JZUrVLVIRdGK3uAm8cSBoar3LSIiUm8j6RxdieqvqVrR246ZMlUiMjuVBFWrgBdKvt8ftk16jLvngEFgyRTnlmtfAhwP+5jsWr9tZo+b2b1mtmYG4wPAzK4xs51mtvPIkSPlP3EDpGuUqTqtJ0HEjF0HBqvet4iISD3lC16zkurxaBundSc4oEyViMxCJX/FT7bIZ+ICnXLHVKsd4N+Ade7+OuABXsmMVTK+oNH9dnff5O6b+vv7JzukYVLZfFUr/xVF29pY1pvgSWWqRKQFNfuaYJmZ0UzwzLUWQRWEe1UpqBKRWagkqNoPrCn5fjVwoNwxZhYFeoGBKc4t1/4y0Bf2cdK13P2ouxfL2P0D8IYZjK/ppXIFElWu/Fe0sredXQeGcFexChFpHc2+JlhmbjhV46BqUYeCKhGZlUr+in8U2BA+gYsT3GS2TThmG1CsuncZ8KAHf8FvA7aETwLXAxuAR8r1GZ7z7bAPwj6/BmBmK0qu917gqfD1/cC7zWxRWKDi3WHbvJKuUaYKYEVfOwOjGQ4OpmrSv4hIk2r2NcEyQ8OpYN/Fau9TddeOfdy1Yx+DY1n2Hxvnnx5+nrt27KvqNURkYZs2qArXN11HEKg8Bdzj7rvM7CYze2942B3AEjPbA3wEuD48dxdwD/AkcB9wrbvny/UZ9vVR4CNhX0vCvgE+bGa7zOynwIeB3wuvMQD8BUGg9ihwU9g2rwSFKmqVqUoCsEtTAEWktTT7muBTNPPa32YwUuNMVV9HLFi3lcpNf7CISImKfiu5+3Zg+4S2G0tep4DLy5x7M3BzJX2G7XsJngRObL8BuKHMNe4E7pzyQzSxfMHJ5AokapWpCisa7TowyLs2LqvJNUREmlAt1gRP9vSrkjXBX3H3tJn9IUEW6+2TDdjdbwduB9i0aZPmbE9QnP7XVYPNfwEWdQQZsONjGXraq19hUEQWrtqkRmRGRtLBTaJW0//i0TZetbRTmSoRaTXNviZYZmioRtP/ivo64gAcG8vWpH8RWbgUVDWBE0FVjab/AZyzspddL6qsuoi0lGZfEywzVLxf9tRo+t+iMKgaGMvUpH8RWbgUVDWB4sLbWk3/AzhnZQ8HBlMcG9WNQkRaQ7OvCZaZOzH9r0ZBVTzaRk8yytER3StFZGZq81tJZqS4ILaWmaoL1i4C4D+ePsxlb1hds+uIiDSTZl8TLDMznMoSaTPaa/gQcklXgqOj6ekPFBEpoUxVEyg+eavVmiqATWsXcdayLv7xoWe1X5WIiMxLI6kc3ckoQQX72ljSGWdAmSoRmSEFVU1gOJwjXqvNfwHMjP/65vXsOjDEI8/Ou4rzIiIiDKdyNav8V7SkM85wOkc6l6/pdURkYVFQ1QSKa6pqmakCeN/rV7GoI8Yd33+2ptcRERGphaFUrmaV/4oWdyUAGNAaZBGZAQVVTaC4pioRq+0/RzIW4XfeuJZvPfUS+46O1fRaIiIi1TaSztZs49+iJZ1BBUAVqxCRmVBQ1QSGUzkMiEdq/8/xwYvWEjHjc9/fW/NriYiIVNNwKkd3Hab/ARxVpkpEZkDV/5rASDpHItZW04W3d+3Yd+L1BWsX8cUfPk827/y/v3Vuza4pIiJSTcOpHBtOq+2fLolYhK5ElKMjqgAoIpVTpqoJDKWyNV9PVeq/nLuC0xd38L93vsBPXjhet+uKiIjMxUg6V7M9qkot6YwrUyUiM6KgqgkMp3Iko/ULqmKRNn73l9fSnYxy9Rd28tJQqm7XFhERmQ13ZziVrXmhCoAlXXFlqkRkRhRUNYGXR9J0JuoXVAF0JaJ86KJ1DI5n+OSDe+p6bRERkZlK5wpk817zQhUAizsTDKVyjGdUVl1EKqOgqgm8NJiipw5P3iZa1pPksjes4e5HX+DQoLJVIiLSvIbDSrm1LlQBQaYKYN+AKuWKSGUUVDVYvuAcHk7T017/oArgj3/tDArufPY/f9GQ64uIiFSiuKdjXab/hRUAnzs6WvNricjCoKCqwY6OpMkVnN4GBVVrFnfwWxes4iuP7OOw1laJiEiTGkmHmaq6FKoINgB+XkGViFRIQVWDHQoDmUZM/yu69m1nkis4f/9d7V0lIiLNqTj9r6sO0//a4xE64hGeO6rpfyJSmYqCKjO7xMx2m9keM7t+kvcTZnZ3+P4OM1tX8t4NYftuM7t4uj7NbH3YxzNhn/Gw/SNm9qSZPW5m/2Fma0vOyZvZT8KvbbP7UTRGcS1TT3tjtgy7a8c+HtpzlHNX9fJPDz/P5x96jrt27DtpXysREZFGq+f0PwimACpTJSKVmjaoMrMI8CngUmAjcIWZbZxw2FXAMXc/E7gVuCU8dyOwBTgHuAT4tJlFpunzFuBWd98AHAv7BvgxsMndXwfcC/xVyfXH3f388Ou9M/oJNNiJTFWDpv8VvWHtItK5Ak8dGmroOERERCZzolBFHab/ASzpSvDsEQVVIlKZSjJVFwJ73H2vu2eArcDmCcdsBr4Qvr4XeIeZWdi+1d3T7v4ssCfsb9I+w3PeHvZB2Of7ANz92+5ezMM/DKye+cdtPocGU0TbrC7TGaayfmknve0xfrzvWEPHISIiMpl6B1UrepMcGExxZFj7VYnI9CoJqlYBL5R8vz9sm/QYd88Bg8CSKc4t174EOB72Ue5aEGSvvlHyfdLMdprZw2b2vnIfxMyuCY/beeTIkXKH1dWhoRSndSdoM2voONrMeP2aPp55aeTEFAsREZFmUc81VQBrF3cA8KPn9bBRRKZXSVA12V/7XuEx1Wp/5UJmvwtsAv66pPl0d98EfAD4WzM7Y5J+cPfb3X2Tu2/q7++f7JC6e2koxbLeZKOHAcD5p/fhwE/3DzZ6KCIiIicZHM/SGY8QjdSnxtbKvnbi0TYe0wwOEalAJb+Z9gNrSr5fDRwod4yZRYFeYGCKc8u1vwz0hX2cci0zeyfwfwPvdfcT+Xh3PxD+dy/wHeD1FXyupnBwMMWKJgmqTutOsnpRu6YAiohI03nh2BirFrXX7XrRSBuvW9XLzucG6nZNEZm/KgmqHgU2hFX54gSFJyZW2NsGXBm+vgx40N09bN8SVgdcD2wAHinXZ3jOt8M+CPv8GoCZvR74e4KA6nDxwma2yMwS4eulwJuBJ2fyQ2iklwZTLOtpjqAK4PVr+jg4mOLg4HijhyIiInLCvqNjnL64s67XfMPaRTzx4hCpbL6u1xWR+WfaoCpc33QdcD/wFHCPu+8ys5vMrFhp7w5giZntAT4CXB+euwu4hyDIuQ+41t3z5foM+/oo8JGwryVh3xBM9+sC/veE0umvAXaa2U8JArKPu/u8CKqGU1lGM3mWN1FQde7qPgzYdUBVAEVEpPHu2rGPLz/8PHtfHmE8k6vrth8XrF1EJl9g1wFNixeRqVW02tPdtwPbJ7TdWPI6BVxe5tybgZsr6TNs30tQHXBi+zvL9P8D4NypP0Fzeiksp768N8loujmegnUloqxZ3MHuQ8ONHoqIiAgAw+kc2byzuCtR1+u+Ye0iAHY+d4w3rF1c12uLyPxSn9WeMqmD4ca/zZSpAnj18m5ePD6uMrIiItIUjo1mgGBD3npa2pVg3ZIOVQAUkWkpqGqgQ4OvZKqayauXdQPwnz9vjrLzIiLS2o6GQdXijvoGVRBMAXxs3zGCZd8iIpNTUNVAxel/zVSoAoIND7uTUb69+/D0B4uIiNTYwGgGA/o6Y3W/9qa1i3l5JMPzR8fqfm0RmT8UVDXQwcEUfR0xkrFIo4dyEjPj1cu6+e7Pj5DNFxo9HBERaXEDoxl6O2JE2+r/Z0txXdUjz6q0uoiUp6CqgV4aSjXdeqqis5Z1M5zK8ZjmkYuISIMdHUmzuM7rqYo2nNbF+qWdfP4Hz2kKoIiUpaCqgQ4NpZpuPVXRmad1EYsY396tdVUiItJYA2PZuhepKGprM/6Pt5/JkweH+OaTL016TKHgvDyS5skDQ4ymc3UeoYg0AwVVDXRoMN20mapkLMIvrVvMA0+9pCdzIiLSMOlsntF0jsWd9S2nXuq9561k/dJO/vaBZygUTr4nfvWx/Wz82H1s+ssHeM9t3+Oj//x4g0YpIo2koKpBMrkCL4+km65IRan3vX4Vew6P8I0nDjV6KCIi0qIGxsLKfw3IVBU3Gr5n5342rV3EUweH+LOvPXHi/SdeHOT6r/6Mc1b28ue/sZH/cu4K7nvikLYkEWlBFW3+K9V3eDio/LeiSaf/Afz2Bau543vP8vFvPM07X7OMeHTqGLx0h/tsvsBoOkdfR5wPvPH0Wg9VREQWqKMjjQuqSr1udR8PPn2Y+3cd4j3nrmDjih7+8J9+xNLOOP/woU0s7ozzlg39/PvPDvLPj+3nD996RkPHKyL1paCqQfYfGwdgWRMHVZE244b3nM3v/eOjfOnh57nqLesZSee46d+eZPehIZ45PEIi2saaxR0s60mSyQWB1KGhFAePp8i7c/6aPi597XIWNfhmKCIi89NAgzb+nSjSZvzGeSu5Z+cL/M7ndtARj5DNF7jnDy46EfCdeVoXF65fzNZH9nHNr7yKtjZr6JhFpH4UVDXIt58+TLTNuGDNokYPZUpvPaufX9mwlE88+AzPvjzCvzz2IqOZPJ3xCK9e1k0mX+C5l0d5fP8gETPa4xGWdsV585lLAef7e17mXbd+l7++/HW87dWnNfrjiIjIPDMwmqEjHmmK7UfOWtbNRy85mydeHOSxfcd4/ZpFPHVwmKcODgPwgTeezhUXruG/3/1THt57lDedubTBIxaRelFQ1QDuzn27DvGmM5fS21H/jQxnwsy44dLX8Ouf+B737NzPr79uBf1dCdYs7qDNXnkCl80XiLYZZic/lXvd6j4eeOolrv7CTm59//m897yV9f4IIiIyjw2MZRo+9a9ULNLG609fxOtPn/yh6KWvXcGfb3uSux7Zp6BKpIUoqGqApw4O8/zRMf7gV+fHfOuNK3v49w//Cst6kizujJ+0dqooFpl8vdXKvnbu/aM38V8//yj/beuPSWfzXL5pTa2HLCIiC8TAaIbVi9obPYyKFO+PG1f28I2fHeKz3/kFPe3Bw1OtLxZZ2FT9rwHu23WINoN3n7Os0UOp2GtW9Mz6SeG2nxzgPa9dwRlLu/iTex/n1z/xff6/b/68yiMUEZGFJpcvcHws0/D1VDP1plctwQy+/rODjR6KiNSJMlUNcN8TB/mldYtZ2tW4PTcqMVlGarbi0TY+eNFavv30YX649yhPvDjIT/Yf59LXLuedr1lGf3dz/yxERKT+tj9xiIIHsx7mkyVdCd529ml868mXePrgEGev6Gn0kESkxhRU1dkvjozw85dG+NhvbGz0UOouFmnj3ecs5y0blvKDXxzlmcPD3PDVn/F/2c94x9mn8aGL1vGWM5fOqFrSaDqHA10J/U9ZRKQS7s6RkTT9XYlT1sE2k0yuwN98czfLe5K8Zh4GJb+yYSk/feE4X/vpAdb3d5LJFRgYzXBad6JqVQEfeXaAR549yp7DI4yk8/zRr53BG9bWtwCWe7AZcjP/b0mkHvSXaJ3dF26ke8lrlzd4JI3TEY/yztcs444rN/H0oWG+/vgB7n70BR546hHWLengty9YzW9esIrVizomPX8olWXncwPc+6P9PPDkYTL5Ar3tMV7V38mH376Bt52tKoMiIpPZfWiYP/vaEzzy7AAXvWoJf3rJq8sWXGi0ux/dx/NHx7jyorUnFUaaL6JtbfzW61fx99/dyy33Pc1NX38S9+Ah4GtX9XDe6j7OXd3LGf1dPPHiIA/teZl9A2NE29qIRY2NK3p40xlL2bRuEd3Jk4taHRlO8z//bRdffzyYXriqr510Ls9vf+YHbPmlNXz0krPLbmWSyub5zu4jfOOJgxQcrn7Les5b0zerz/ifPz/C//OvP+P4WJazlnXz6uXd/OqGoGpwpx52Soux4hOGKQ8yuwT4OyACfM7dPz7h/QTwReANwFHg/e7+XPjeDcBVQB74sLvfP1WfZrYe2AosBh4DPujumdlcYyqbNm3ynTt3TvvZZ8LdeebwCD96/hiZXIFcwelrj7F2SQdtbcbdj7zAPz+2n5V97doUcIJcvsDPXhxk5/PHePblUQAS0Tba4xHaY8FXPNrGkeE0R0df2Qhy8/krWdaT5Du7D/PMSyMcHc3w6mXdXHzOcpb3Jpt+YfCR4TS/ODLCwGiGoyNpnj86xnNHRxlK5VjRm2RVXzsXnbGEN52xlMgUTzYLBcdMTwpl/jGzH7n7phpfo6nvYeVU8z51fCzDJx7cw+d/8BzdySivXdnLj184zmg6xzkre3jPa1ewqLN5NmsfTed4619/m1f1d7H5vJXz+nfbD37xMi8eG2dRZ5zORJTDQylePD7OwcEU+cIrf4N1JqIs70ngDpl8gcNDaTL5AmawbkknZy/vJhZpY/ehYfYNjJHJF3jbq/t58xlLScQivPf8lfzdAz/nzoeeoycZ5YZLX8Nlb1h9Iis2OJ7lc9/byz8+9Bwj6RyLOmLkC85QKsdbzlzKH7/tDC561ZKKftZHR9L81X27uXvnC5zR38lFZyzhmZdGePLAEMPpHPFoG+es7GHt4g7WL+3iXRuXsXHl/Ms2SnPbc3iE7T87SDZf4K1n9XP+mj6iZQqnzUWl96lpgyoziwA/B94F7AceBa5w9ydLjvlj4HXu/odmtgX4TXd/v5ltBL4CXAisBB4AzgpPm7RPM7sH+Kq7bzWzzwI/dffPzPQa7p6f6nPN9Wbl7qRzwS+9x188zk/2HefBpw+zNwwIJhOLGOevWcSvndWvzXCnMDCaYdeBQUbTOTJ5J5svBF+5Auet6WPd0k5evbybN5+xlHg0+D/PXTv2kSsU+OEvjvLg04dJ5wqsXtTO7795PV2JCNm8c2gwxdOH/v/27j04rvK84/j3kVbS6raSL7JsGRPb4FDtF0YAAA/MSURBVAsGZgi3YCAkXAqEdupkSos7pDjNdDJNmjZJZ0ph8k/J0JJeppPJhGlCQjslQ8ItpLjMEC4JwU3GMcZgwNj4Ar4gLNkWsnWzbqt9+sd5Ja+llbQryau19PuMz+icd9/znnNev3ueffe8e04H+1s6KY0VU10Wo74mzppFCdY0JFhcW05ddRmJeCynAO7udPYmOdrRy+ET3ew83M7Opnaa2nro6EnS2z/A4jnlnFdXRaK8hM6eJK1dvbzZ2DbUgRwUKzLmVZVSXlJMW3c/bd39pBzqE2XcdvEiLliU4Ly6Sjp6kuw50sHu5k72HOlg79EOSoqLWLMowYUNNaxpSLBmUYKG2jiG0Z9Kse9oFPB2NbWzq7mdfUc7qU/EWb2wmlULE1ywMPqWcX51GZWlsTE7cRB929na1cfRjl52Hm7nncNtHGo9ybGOXtq6+1kyp4JVC6tZubCaVfXVrKyvorIslvHW+5Ph7riDc2oYSjQPTrTcdrKf/S1dvN/Sxc7D7ew43EZLZy9zK0qZW1nKsvlVXNiQiI6/qoy5laVDbWs26EumONmX5P2Wrqh9NLWzq6mDd5vaiZcUD7WnNQ0JLmxIsLi2grJY0ZQMYTrTnapCj2Fj7ftk4pS7096d5HBbN5v2HOPBl/fR0Ztk/RVLuPuW1Ty3o5ne5AC/3dfCK3uO4U74YH0+y+ZXMr+q9Ix1ZMZ6z57sS/J/e1t4clsjm/Yc4+mvXM274RlQM00yleJIey9H23tYWBOnPhEf8aiSgx+d5FBrF01tPTS39QAQLymmtqKE31tTz4Lq+Ihym9t6eGb7hxxsPUlDbZx5lWWYwd4jnXT3D3BRQ4Irl81j2fxKkgMptuxv5bf7WujoTbJkTjlXLZ/HF65ZyrL5lVSURlebUimnvaefh3+zn60HWtl28DjJAee6lXXcsHrB0B2AB1LOgY+62N3cweG2blq7+mg72Y8DCxNx1jQkWDKngr++8XwS8RJiRTbrH5A8/HN4po/lmT6pZ/r8Pjwlc1kZ1hv/+sqEy5rovmcq68nXPqC5rYe9RzvZ3dxBc3sPBhQVGQMpJxGP8cmVdVy/agGXnlvLvMoyquOxSbexqexUrQX+wd1vCcv3Arj7A2l5ng95NptZDGgG6oB70vMO5gurjSgT+DZwDFjo7sn0bee6DXffPNZxTTRYnexLcvn9L9HdP3Daf3ZpcRFXLpvLrRct5MTJfkpjRRSb0dWb5KOuXrr7U6yqr6a8dPofXjjTdfUm2f7BCbYdPE5ze89QenGRsXx+JefVVXHwoy56kimOn+zjxMn+09Y3I3qTmoX58NeiNHdIhQ8FA+5D8+kaauKUxoqJlxQRKy7ieFcfxzp76UumoitwJcUsqi1n6bwKFtWUU1UWo6KsmKqy2IigWlddxtOvN7JpTwt9A6nTtlMdj1GfiFNfXUZ/ymk60U1LZx/d/aN/p1BZFmNRTZy66jI6uvtpbu+ltauX1LBjKI0VDZ3lnFPHOPhBaHj+6niMmvISqstixEuKae3qo7m9h97k6fsMUGTR0Jj0jtvgNvxUwog0dx/68DVR1WUxLmhI0Ns/QHf/AJ09UYc4OeyAisKVP4OhdhD+ndYucpXrvmcKWlNZ/kDKRxx7WayIRTVxFtbEOXduBTub2tnT3Dmi/cVLivjhXZfzyRV1uW00TR46VQUdw3yMIDzROOXuXPGPL9HS2TeU9qmVddx722pWL4yuFqTfiKitu59f7Gjizca208oZPOcVjXIehGHv0bQvMtI7TRN5z86pKGHD1Uv5+k0rp/SmSbNFyp03Dp1g83st9A1Ecao+EefG1Qsy3vSjfyDF64eOs2nPMY6nxUQzKDYjlXbOLzbjknNr+eSK+Rk7dcN19SZ568M2th86TuPx7hFntPR4MPycms0H+Ww7IZkSJ1rWiI5QxjyZdkKmQpHBkrkVXLy4hosaavjTT5zLb/a28OvdR/n1nmMc6+gdyltcZDzwuYv5kysm/jifbONUNgNeFwMfpC03Ap8YLU8IJG3AvJD+u2HrLg7zmcqcB5xw92SG/BPZxmnM7EvAl8Jip5ntznzIE7MXeHT8bPOBlqnc7gw25XX1PtFXzWfawSzyvDlF29oR/cmprnaOn2VG26H34bjSHnowZl1dd/+kN/WxSZcwtkKPYafV7ZmKU4+E6WxxENgO87+h92k2sj6f/WKKNvg+8PQUlZVnOvdnr6Draj+wKcx/eZy8dzwAYw4LGF9WcSqbTlWm72KH979HyzNaeqZxNWPln8g2Ria6PwQ8lOm1fDGz18707wdmCtVV9lRXuVF9ZW8G1FWhx7DTEwogThWKGdD28kL1lD3VVfZUV7nL5kcDjUD6NbNzgMOj5QnDGmqA1jHWHS29BagNZQzfVq7bEBERKfQYJiIiM0A2naqtwAozW2ZmpcB6YOOwPBuBDWH+duBXYZz4RmC9mZWFOyKtAF4drcywzsuhDEKZz0xwGyIiIoUew0REZAYYd/hfGPv9VeB5olvH/qe7v2Nm3wJec/eNwMPAj81sH9E3b+vDuu+EOyHtBJLAXw3elS9TmWGTfw88Zmb3A2+EspnINgqUhnVkT3WVPdVVblRf2Tur66rQY5iM6axue3mkesqe6ip7qqscZfWcKhEREREREcls9jyIRURERERE5AxQp0pERERERGQS1KnKIzO71cx2m9k+M7tnuvcnH8xsiZm9bGa7zOwdM/taSJ9rZi+a2d7wd05INzP7bqijt8zs0rSyNoT8e81sQ1r6ZWb2dljnu2YTeSRr4TCzYjN7w8yeDcvLzGxLOO7Hww/jCT+efzwc9xYzW5pWxr0hfbeZ3ZKWPqPaoJnVmtlTZvZuaGNr1bYyM7NvhPfgDjP7qZnF1bakEM3GtqRYmTvFyuwoTuaRu2vKw0T0Y+b3gOVAKdGzX9dM937l4bgXAZeG+WqiZ4quAf4FuCek3wP8c5i/DXiO6JkuVwFbQvpcoucNzgXmhPk54bVXgbVhneeAz0z3cU+yzv4W+AnwbFh+Algf5r8PfDnMfwX4fphfDzwe5teE9lUGLAvtrngmtkHgv4G/CPOlQK3aVsZ6Wkz0rMTytDb1BbUtTYU2zda2hGLlROpMsTK7elKczNOkK1X5cyWwz93fd/c+4DFg3TTv0xnn7k3u/nqY7wB2EX3AW0f0Rif8/WyYXwc84pHfET3zZRFwC/Ciu7e6+3HgReDW8FrC3Td79O5+JK2ss46ZnQP8PvCjsGzADcBTIcvwuhqsw6eAG0P+dcBj7t7r7vuBfUTtb0a1QTNLANcR7q7m7n3ufgK1rdHEgHKLnpFUATShtiWFZ1a2JcXK3ChWZkdxMr/UqcqfxcAHacuNIW3WCJfcPw5sAerdvQmiYAIsCNlGq6ex0hszpJ+tvgPcDaTC8jzghLsnw3L68Q3VSXi9LeTPtQ7PVsuBY8B/hSEgPzKzStS2RnD3D4F/Aw4RdabagG2obUnhmfVtSbEyK4qV2VGczCN1qvIn0xjTWXM/ezOrAn4GfN3d28fKmiHNJ5B+1jGzPwCOuvu29OQMWX2c12Z8XQUx4FLgP9z940AX0TCG0cza+grj5dcRDXFpACqBz2TIqrYl021WtyXFyvEpVuZEcTKP1KnKn0ZgSdryOcDhadqXvDKzEqIg8ai7Px2Sj4TLxoS/R0P6aPU0Vvo5GdLPRtcAf2hmB4iGG9xA9G1cbRiyBacf31CdhNdriB4qmmsdnq0agUZ33xKWnyIKHmpbI90E7Hf3Y+7eDzwNXI3alhSeWduWFCuzpliZPcXJPFKnKn+2AivC3WlKiX4suXGa9+mMC+OWHwZ2ufu/p720ERi8e8wG4Jm09LvCHWiuAtrCpenngZvNbE741v1m4PnwWoeZXRW2dVdaWWcVd7/X3c9x96VE7eNX7n4n8DJwe8g2vK4G6/D2kN9D+vpwx6NlwAqiH5LOqDbo7s3AB2a2KiTdCOxEbSuTQ8BVZlYRjmWwrtS2pNDMyrakWJk9xcrsKU7m2Xh3stA0dRPRXVX2EN1V5pvTvT95OuZriS4FvwVsD9NtROOZfwnsDX/nhvwGPBjq6G3g8rSyvkj0Q9J9wJ+npV8O7AjrfA+w6T7uKai3T3PqjkbLiU70+4AngbKQHg/L+8Lry9PW/2aoj92k3YlnprVB4BLgtdC+/oforkRqW5nr6j7g3XA8Pya645XalqaCm2ZjW1KsnHC9KVaOX0eKk3maLFSIiIiIiIiITICG/4mIiIiIiEyCOlUiIiIiIiKToE6ViIiIiIjIJKhTJSIiIiIiMgnqVImIiIiIiEyCOlUiYzCzATPbbmZvmtnrZnZ1Fut05mPfMmz3gJnNn45ti4jI9FCcEikMsfGziMxq3e5+CYCZ3QI8AHxqendJRERkiOKUSAHQlSqR7CWA4wBmVmVmvwzfCr5tZuuGZx4tj5ktNbNdZvZDM3vHzF4ws/Lw2vlm9lLaN47nhfS/M7OtZvaWmd031k7mWn54cvq/mtmOsJ93hLyfNrNXzOwJM9tjZt82szvN7NWQb3Df6szsZ2H/tprZNVNY5yIikj3FKcUpmS7T/fRhTZoKeQIGiJ5s/y7QBlwW0mNAIszPJ3rC+ODDtDvHygMsBZLAJeG1J4DPh/ktwOfCfByoAG4GHgrrFgHPAtdl2NcDYTu5lv9HwItAMVAPHAIWET2p/kSYLwM+BO4L634N+E6Y/wlwbZg/F9g13f9vmjRp0jRbJsUpxSlNhTFp+J/I2NKHVawFHjGzi4gCxz+Z2XVAClhMdKJvTlt3tDwA+919e5jfBiw1s2pgsbv/HMDde8J2byYKWG+E/FXACmDTGPudS/nXAj919wHgiJm9AlwBtANb3b0p5HsPeCGU+TZwfZi/CVhjZoPbTphZtbt3jLF/IiIyNRSnFKekAKhTJZIld99s0Q9s64Dbwt/L3L3fzA4QfaOW7s4x8vSm5RsAyomCWyYGPODuP8hhd3MtP5tyUmnLKU6dP4qAte7encP+iYjIFFOcUpyS6aPfVIlkycxWEw09+AioAY6GIHQ98LEMq2STZ4i7twONZvbZsL0yM6sAnge+aGZVIX2xmS3Idf/HKH8TcIeZFZtZHXAd8GoORb8AfHVwwcwuyXXfRERk8hSnRqU4JWecrlSJjK3czAaHJxiwwd0HzOxR4H/N7DVOjWUfLps8w/0Z8AMz+xbQD/yxu79gZhcAm8PQhU7g88DRCRzPiPKBnwNrgTcBB+529+YQnLPxN8CDZvYW0TllE/CXE9g3ERHJneLU+BSn5Iwb/MGiiIiIiIiITICG/4mIiIiIiEyCOlUiIiIiIiKToE6ViIiIiIjIJKhTJSIiIiIiMgnqVImIiIiIiEyCOlUiIiIiIiKToE6ViIiIiIjIJPw/uGbjbDpHLr4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the distribution of EMI\n",
    "# before log transformation\n",
    "\n",
    "fig = plt.figure(figsize=(14, 4))\n",
    "ax1 = plt.subplot(121)\n",
    "sns.distplot(train['Balance Income'])\n",
    "ax1.set_title(\"Train\")\n",
    "\n",
    "ax1 = plt.subplot(122)\n",
    "sns.distplot(test['Balance Income'])\n",
    "ax1.set_title(\"Test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now drop the variables which we used to create these new features. Reason for doing this is, the correlation between those old features and these new features will be very high and logistic regression assumes that the variables are not highly correlated. We also wants to remove the noise from the dataset, so removing correlated features will help in reducing the noise too. We will drop \"ApplicantIncome\", \"CoapplicantIncome\", \"LoanAmount\", \"Loan_Amount_Term\" as they are already represented by \"Total Income\", \"EMI\" and \"Balance Income\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>LoanAmount_log</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Married_No</th>\n",
       "      <th>...</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_No</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Rural</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "      <th>Total_Income</th>\n",
       "      <th>Total_Income_log</th>\n",
       "      <th>EMI</th>\n",
       "      <th>Balance 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>4.852030</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>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>4.852030</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>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>4.189655</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>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>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>4.787492</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>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>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>4.948760</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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",
       "<p>5 rows × 26 columns</p>\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  LoanAmount_log  Gender_Female  Gender_Male  \\\n",
       "0             1.0            1        4.852030              0            1   \n",
       "1             1.0            0        4.852030              0            1   \n",
       "2             1.0            1        4.189655              0            1   \n",
       "3             1.0            1        4.787492              0            1   \n",
       "4             1.0            1        4.948760              0            1   \n",
       "\n",
       "   Married_No       ...        Education_Not Graduate  Self_Employed_No  \\\n",
       "0           1       ...                             0                 1   \n",
       "1           0       ...                             0                 1   \n",
       "2           0       ...                             0                 0   \n",
       "3           0       ...                             1                 1   \n",
       "4           1       ...                             0                 1   \n",
       "\n",
       "   Self_Employed_Yes  Property_Area_Rural  Property_Area_Semiurban  \\\n",
       "0                  0                    0                        0   \n",
       "1                  0                    1                        0   \n",
       "2                  1                    0                        0   \n",
       "3                  0                    0                        0   \n",
       "4                  0                    0                        0   \n",
       "\n",
       "   Property_Area_Urban  Total_Income  Total_Income_log       EMI  \\\n",
       "0                    1        5849.0          8.674026  0.355556   \n",
       "1                    0        6091.0          8.714568  0.355556   \n",
       "2                    1        3000.0          8.006368  0.183333   \n",
       "3                    1        4941.0          8.505323  0.333333   \n",
       "4                    1        6000.0          8.699515  0.391667   \n",
       "\n",
       "   Balance Income  \n",
       "0     5493.444444  \n",
       "1     5735.444444  \n",
       "2     2816.666667  \n",
       "3     4607.666667  \n",
       "4     5608.333333  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# before dropping variables\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the variables\n",
    "train = train.drop(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'], axis=1)\n",
    "test = test.drop(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<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>LoanAmount_log</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Married_No</th>\n",
       "      <th>Married_Yes</th>\n",
       "      <th>Dependents_3</th>\n",
       "      <th>Dependents_0</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>...</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_No</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Rural</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "      <th>Total_Income</th>\n",
       "      <th>Total_Income_log</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",
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       "      <td>...</td>\n",
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       "      <td>1</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>4.852030</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>4.189655</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>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>4.787492</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>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>4.948760</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Credit_History  Loan_Status  LoanAmount_log  Gender_Female  Gender_Male  \\\n",
       "0             1.0            1        4.852030              0            1   \n",
       "1             1.0            0        4.852030              0            1   \n",
       "2             1.0            1        4.189655              0            1   \n",
       "3             1.0            1        4.787492              0            1   \n",
       "4             1.0            1        4.948760              0            1   \n",
       "\n",
       "   Married_No  Married_Yes  Dependents_3  Dependents_0  Dependents_1  \\\n",
       "0           1            0             0             1             0   \n",
       "1           0            1             0             0             1   \n",
       "2           0            1             0             1             0   \n",
       "3           0            1             0             1             0   \n",
       "4           1            0             0             1             0   \n",
       "\n",
       "        ...        Education_Not Graduate  Self_Employed_No  \\\n",
       "0       ...                             0                 1   \n",
       "1       ...                             0                 1   \n",
       "2       ...                             0                 0   \n",
       "3       ...                             1                 1   \n",
       "4       ...                             0                 1   \n",
       "\n",
       "   Self_Employed_Yes  Property_Area_Rural  Property_Area_Semiurban  \\\n",
       "0                  0                    0                        0   \n",
       "1                  0                    1                        0   \n",
       "2                  1                    0                        0   \n",
       "3                  0                    0                        0   \n",
       "4                  0                    0                        0   \n",
       "\n",
       "   Property_Area_Urban  Total_Income  Total_Income_log       EMI  \\\n",
       "0                    1        5849.0          8.674026  0.355556   \n",
       "1                    0        6091.0          8.714568  0.355556   \n",
       "2                    1        3000.0          8.006368  0.183333   \n",
       "3                    1        4941.0          8.505323  0.333333   \n",
       "4                    1        6000.0          8.699515  0.391667   \n",
       "\n",
       "   Balance Income  \n",
       "0     5493.444444  \n",
       "1     5735.444444  \n",
       "2     2816.666667  \n",
       "3     4607.666667  \n",
       "4     5608.333333  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# after dropping variables\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## <a name=\"model2\">Model Building : Part II</a>\n",
    "\n",
    "\n",
    "After creating new features, we can continue the model building process. So we will start with logistic regression model and then move over to more complex models like RandomForest and XGBoost.\n",
    "\n",
    "We will build the following models in this section.\n",
    "\n",
    "- Logistic Regression\n",
    "- Decision Tree\n",
    "- Random Forest\n",
    "- XGBoost\n",
    "\n",
    "Let’s prepare the data for feeding into the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop \"Loan_Status\" and assign it to target variable\n",
    "X = train.drop('Loan_Status', axis=1)\n",
    "y = train.Loan_Status                # Save target variable in separate dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.7983870967741935\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.8225806451612904\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.7786885245901639\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.7868852459016393\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.8278688524590164\n",
      "\n",
      "Mean validation accuracy:  0.8028820729772608\n"
     ]
    }
   ],
   "source": [
    "# stratified 5 folds, shuffle each stratification of the data before splitting into batches\n",
    "\n",
    "mean_accuracy = []\n",
    "i = 1\n",
    "kf = StratifiedKFold(n_splits=5, random_state=1, shuffle=True)\n",
    "\n",
    "for train_index, test_index in kf.split(X, y):\n",
    "    print('\\n{} of kfold {}'.format(i, kf.n_splits))\n",
    "    xtr, xvl = X.loc[train_index], X.loc[test_index]\n",
    "    ytr, yvl = y[train_index], y[test_index]\n",
    "    \n",
    "    model = LogisticRegression(random_state=1)\n",
    "    model.fit(xtr, ytr)\n",
    "    pred_test = model.predict(xvl)\n",
    "    score = accuracy_score(yvl, pred_test)\n",
    "    mean_accuracy.append(score)\n",
    "    print('accuracy_score', score)\n",
    "    i+=1\n",
    "    \n",
    "print(\"\\nMean validation accuracy: \", sum(mean_accuracy)/len(mean_accuracy))\n",
    "\n",
    "\n",
    "# make prediction on test set\n",
    "pred_test = model.predict(test)\n",
    "\n",
    "\n",
    "# calculate probability estimates of loan approval\n",
    "# column 0 is the probability for class 0 and column 1 is the probability for class 1\n",
    "# probability of loan default = 1 - model.predict_proba(test)[:,1]\n",
    "pred = model.predict_proba(xvl)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean validation accuracy for this model is 0.803"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling Loan_Status with predictions\n",
    "submission['Loan_Status'] = pred_test\n",
    "\n",
    "# filling Loan_ID with test Loan_ID\n",
    "submission['Loan_ID'] = test_original['Loan_ID'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing 0 and 1 with N and Y\n",
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting submission file to .csv format\n",
    "submission.to_csv('Log2.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this submission we got an accuracy of 0.7778 on the leaderboard. So we can infer feature engineering has not improved the model. Let us look at some other algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision tree is a type of supervised learning algorithm(having a pre-defined target variable) that is mostly used in classification problems. In this technique, we split the population or sample into two or more homogeneous sets(or sub-populations) based on most significant splitter / differentiator in input variables.\n",
    "\n",
    "Decision trees use multiple algorithms to decide to split a node in two or more sub-nodes. The creation of sub-nodes increases the homogeneity of resultant sub-nodes. In other words, we can say that purity of the node increases with respect to the target variable.\n",
    "\n",
    "For detailed explanation visit https://www.analyticsvidhya.com/blog/2016/04/complete-tutorial-tree-based-modeling-scratch-in-python/#six"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s fit the decision tree model with 5 folds of cross validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.7258064516129032\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.7419354838709677\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.7049180327868853\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.680327868852459\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.7049180327868853\n",
      "\n",
      "Mean validation accuracy:  0.7115811739820201\n"
     ]
    }
   ],
   "source": [
    "mean_accuracy = []\n",
    "i=1\n",
    "kf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index,test_index in kf.split(X,y):\n",
    "    print('\\n{} of kfold {}'.format(i,kf.n_splits))\n",
    "    xtr,xvl = X.loc[train_index],X.loc[test_index]\n",
    "    ytr,yvl = y[train_index],y[test_index]\n",
    "    \n",
    "    model = tree.DecisionTreeClassifier(random_state=1)\n",
    "    model.fit(xtr, ytr)\n",
    "    pred_test = model.predict(xvl)\n",
    "    score = accuracy_score(yvl,pred_test)\n",
    "    mean_accuracy.append(score)\n",
    "    print('accuracy_score',score)\n",
    "    i+=1\n",
    "    \n",
    "print(\"\\nMean validation accuracy: \", sum(mean_accuracy)/len(mean_accuracy))\n",
    "pred_test = model.predict(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean validation accuracy for this model is 0.71, which is lower than Logistic Regression, let's see how it perform on the Test set by submitting to the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling Loan_Status with predictions\n",
    "submission['Loan_Status'] = pred_test            \n",
    "\n",
    "# filling Loan_ID with test Loan_ID\n",
    "submission['Loan_ID'] = test_original['Loan_ID'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing 0 and 1 with N and Y\n",
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting submission file to .csv format\n",
    "submission.to_csv('Decision Tree.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got an accuracy of 0.6458 which is much lesser than the accuracy from logistic regression model. So let’s build another model, i.e. Random Forest, a tree based ensemble algorithm and try to improve our model by improving the accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest\n",
    "\n",
    "- RandomForest is a tree based bootstrapping algorithm wherein a certain no. of weak learners (decision trees) are combined to make a powerful prediction model.\n",
    "- For every individual learner, a random sample of rows and a few randomly chosen variables are used to build a decision tree model.\n",
    "- Final prediction can be a function of all the predictions made by the individual learners.\n",
    "- In case of regression problem, the final prediction can be mean of all the predictions.\n",
    "\n",
    "There are some parameters worth exploring with the sklearn RandomForestClassifier:\n",
    "\n",
    "- n_estimators\n",
    "- max_features\n",
    "\n",
    "n_estimators = ususally bigger the forest the better, there is small chance of overfitting here. The more estimators you give it, the better it will do. We will use the default value of 10.\n",
    "\n",
    "max depth of each tree (default none, leading to full tree) - reduction of the maximum depth helps fighting with overfitting. We will limit at 10.\n",
    "\n",
    "For detailed explanation visit this article https://www.analyticsvidhya.com/blog/2016/04/complete-tutorial-tree-based-modeling-scratch-in-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.8225806451612904\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.8145161290322581\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.7377049180327869\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.7295081967213115\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.8114754098360656\n",
      "\n",
      "Mean validation accuracy:  0.7831570597567425\n"
     ]
    }
   ],
   "source": [
    "mean_accuracy = []\n",
    "i=1\n",
    "kf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index,test_index in kf.split(X, y):\n",
    "    print('\\n{} of kfold {}'.format(i,kf.n_splits))\n",
    "    xtr,xvl = X.loc[train_index],X.loc[test_index]\n",
    "    ytr,yvl = y[train_index],y[test_index]\n",
    "    \n",
    "    model = RandomForestClassifier(random_state=1, max_depth=10, n_estimators=10)\n",
    "    model.fit(xtr, ytr)\n",
    "    pred_test = model.predict(xvl)\n",
    "    score = accuracy_score(yvl,pred_test)\n",
    "    mean_accuracy.append(score)\n",
    "    print('accuracy_score',score)\n",
    "    i+=1\n",
    "    \n",
    "print(\"\\nMean validation accuracy: \", sum(mean_accuracy)/len(mean_accuracy))\n",
    "pred_test = model.predict(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean validation accuracy for this model is 0.783"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GridSearchCV\n",
    "\n",
    "We will try to improve the accuracy by tuning the hyperparameters for this model. We will use grid search to get the optimized values of hyper parameters. GridSearch is a way to select the best of a family of hyper parameters, parametrized by a grid of parameters.\n",
    "\n",
    "We will use GridSearchCV in sklearn.model_selection for an exhaustive search over specified parameter values for an estimator. GridSearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.\n",
    "\n",
    "The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid, hence GridSearchCV. More info at https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html\n",
    "\n",
    "We will tune the max_depth and n_estimators parameters. max_depth decides the maximum depth of the tree and n_estimators decides the number of trees that will be used in random forest model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Provide range for max_depth from 1 to 20 with an interval of 2 and from 1 to 200 with an interval of 20 for n_estimators\n",
    "paramgrid = {'max_depth': list(range(1, 20, 2)), 'n_estimators': list(range(1, 200, 20))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default 3-fold cross validation, cv=3\n",
    "grid_search = GridSearchCV(RandomForestClassifier(random_state=1), paramgrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_cv, y_train, y_cv = train_test_split(X, y, test_size =0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=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=10, n_jobs=1,\n",
       "            oob_score=False, random_state=1, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'max_depth': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19], 'n_estimators': [1, 21, 41, 61, 81, 101, 121, 141, 161, 181]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the grid search model\n",
    "grid_search.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the grid search model has been fit, we can use use `best_estimator_` to obtain the estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=3, max_features='auto', max_leaf_nodes=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=141, n_jobs=1,\n",
       "            oob_score=False, random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# estimate the optimized value\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the optimized value for the max_depth variable is 3 and for n_estimator is 141. Now let’s build the model using these optimized values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.8064516129032258\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.8306451612903226\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.8032786885245902\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.7950819672131147\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.8278688524590164\n",
      "\n",
      "Mean validation accuracy:  0.8126652564780541\n"
     ]
    }
   ],
   "source": [
    "mean_accuracy = []\n",
    "i=1\n",
    "kf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index,test_index in kf.split(X,y):\n",
    "    print('\\n{} of kfold {}'.format(i,kf.n_splits))\n",
    "    xtr,xvl = X.loc[train_index],X.loc[test_index]\n",
    "    ytr,yvl = y[train_index],y[test_index]\n",
    "    \n",
    "    model = RandomForestClassifier(random_state=1, max_depth=3, n_estimators=141)\n",
    "    model.fit(xtr, ytr)\n",
    "    pred_test = model.predict(xvl)\n",
    "    score = accuracy_score(yvl,pred_test)\n",
    "    mean_accuracy.append(score)\n",
    "    print('accuracy_score',score)\n",
    "    i+=1\n",
    "    \n",
    "print(\"\\nMean validation accuracy: \", sum(mean_accuracy)/len(mean_accuracy))\n",
    "pred_test = model.predict(test)\n",
    "pred2=model.predict_proba(test)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean validation accuracy has improved from 0.783 to 0.813"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling Loan_Status with predictions\n",
    "submission['Loan_Status']=pred_test            \n",
    "\n",
    "# filling Loan_ID with test Loan_ID\n",
    "submission['Loan_ID']=test_original['Loan_ID'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing 0 and 1 with N and Y\n",
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting submission file to .csv format\n",
    "submission.to_csv('Random Forest.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got an accuracy of 0.7638 from the random forest model on leaderboard.\n",
    "\n",
    "### Feature Importance\n",
    "\n",
    "Let us find the feature importance now, i.e. which features are most important for this problem. We will use feature_importances_ attribute of sklearn to do so. It will return the feature importances (the higher, the more important the feature)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2954721ef28>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract feature importances, convert into a Series\n",
    "importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "\n",
    "# plot the horizontal bar chart\n",
    "importances.plot(kind='barh', figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that Credit_History is the most important feature followed by Total Income Log, Total Income, Balance Income, Property Area Suburban, EMI. So, feature engineering helped us in predicting our target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost\n",
    "\n",
    "XGBoost is a fast and efficient algorithm and has been used to by the winners of many data science competitions. It’s a boosting algorithm and you may refer the below article to know more about boosting:https://www.analyticsvidhya.com/blog/2015/11/quick-introduction-boosting-algorithms-machine-learning/\n",
    "\n",
    "XGBoost works only with numeric variables and we have already replaced the categorical variables with numeric variables. Let’s have a look at the parameters that we are going to use in our model.\n",
    "\n",
    "- n_estimator: This specifies the number of trees for the model.\n",
    "- max_depth: We can specify maximum depth of a tree using this parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.782258064516129\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.8225806451612904\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.7622950819672131\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.7459016393442623\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.7868852459016393\n",
      "\n",
      "Mean validation accuracy:  0.7799841353781068\n"
     ]
    }
   ],
   "source": [
    "mean_accuracy = []\n",
    "i=1\n",
    "kf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index,test_index in kf.split(X,y):\n",
    "    print('\\n{} of kfold {}'.format(i,kf.n_splits))\n",
    "    xtr,xvl = X.loc[train_index],X.loc[test_index]\n",
    "    ytr,yvl = y[train_index],y[test_index]\n",
    "    \n",
    "    model = XGBClassifier(random_state=1, n_estimators=50, max_depth=4)\n",
    "    model.fit(xtr, ytr)\n",
    "    pred_test = model.predict(xvl)\n",
    "    score = accuracy_score(yvl,pred_test)\n",
    "    mean_accuracy.append(score)\n",
    "    print('accuracy_score',score)\n",
    "    i+=1\n",
    "    \n",
    "print(\"\\nMean validation accuracy: \", sum(mean_accuracy)/len(mean_accuracy))\n",
    "pred_test = model.predict(test)\n",
    "pred3=model.predict_proba(test)[:,1]\n",
    "\n",
    "# warnings.filterwarnings(action='ignore', category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean validation accuracy for this model is 0.78\n",
    "\n",
    "We will try to improve the accuracy by tuning the hyperparameters for this model. We will use grid search to get the optimized values of hyper parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Provide range for max_depth from 1 to 20 with an interval of 2 and from 1 to 200 with an interval of 20 for n_estimators\n",
    "paramgrid = {'max_depth': list(range(1, 20, 2)), 'n_estimators': list(range(1, 200, 20))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default 3-fold cross validation, cv=3\n",
    "grid_search = GridSearchCV(XGBClassifier(random_state=1), paramgrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_cv, y_train, y_cv = train_test_split(X, y, test_size =0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=1,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'max_depth': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19], 'n_estimators': [1, 21, 41, 61, 81, 101, 121, 141, 161, 181]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the grid search model\n",
    "grid_search.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=1, min_child_weight=1, missing=None, n_estimators=81,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=1,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# estimate the optimized value\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "So, the optimized value for the max_depth variable is 1 and for n_estimator is 81. Now let’s build the model using these optimized values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.8064516129032258\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.8306451612903226\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.8032786885245902\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.7868852459016393\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.8360655737704918\n",
      "\n",
      "Mean validation accuracy:  0.8126652564780541\n"
     ]
    }
   ],
   "source": [
    "mean_accuracy = []\n",
    "i=1\n",
    "kf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index,test_index in kf.split(X,y):\n",
    "    print('\\n{} of kfold {}'.format(i,kf.n_splits))\n",
    "    xtr,xvl = X.loc[train_index],X.loc[test_index]\n",
    "    ytr,yvl = y[train_index],y[test_index]\n",
    "    \n",
    "    model = XGBClassifier(random_state=1, n_estimators=81, max_depth=1)\n",
    "    model.fit(xtr, ytr)\n",
    "    pred_test = model.predict(xvl)\n",
    "    score = accuracy_score(yvl,pred_test)\n",
    "    mean_accuracy.append(score)\n",
    "    print('accuracy_score',score)\n",
    "    i+=1\n",
    "    \n",
    "print(\"\\nMean validation accuracy: \", sum(mean_accuracy)/len(mean_accuracy))\n",
    "pred_test = model.predict(test)\n",
    "pred3 = model.predict_proba(test)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean validation accuracy has improved from 0.78 to 0.813"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling Loan_Status with predictions\n",
    "submission['Loan_Status'] = pred_test            \n",
    "\n",
    "# filling Loan_ID with test Loan_ID\n",
    "submission['Loan_ID']=test_original['Loan_ID'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing 0 and 1 with N and Y\n",
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting submission file to .csv format\n",
    "submission.to_csv('XGBoost.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got an accuracy of 0.7778 with this model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# <a name=\"conclusion\">Conclusion</a>\n",
    "\n",
    "After trying and testing 4 different algorithms, the best accuracy on the public leaderboard is achieved by Logistic Regression (0.7847), followed by RandomForest (0.7778) and XGBoost (0.7778), and Decision Tree performed the worst (0.6458). While new features created via feature engineering helped in predicting the target variable, it did not improve the overall model accuracy much. Compared to using default parameter values, GridSearchCV helped improved the model's mean validation accuracy by providing the optimized values for the model's hyperparameters. On the whole, a logistic regression classifier provides the best result in terms of accuracy for the given dataset, without any feature engineering needed. Because of its simplicity and the fact that it can be implemented relatively easy and quick, Logistic Regression is often a good baseline that data scientists can use to measure the performance of other more complex algorithms. In this case, however, a basic Logistic Regression has already outperformed other more complex algorithms like Random Forest and XGBoost, for the given dataset.\n",
    "\n",
    "**Suggestions for Improvement**. There are many things that can be tried to improve the models’ predictions.  We can create and add more variables, try different models with different subset of features and/or rows, etc. Some of the ideas are listed below:\n",
    "\n",
    "- Combine the applicants with 1,2,3 or more dependents and make a new feature as discussed in the EDA part.\n",
    "- Make independent vs independent variable visualizations to discover some more patterns.\n",
    "- Arrive at the EMI using a better formula which may include interest rates as well.\n",
    "- Try ensemble modeling (combination of different models). More about ensemble techniques can be found at the references.\n",
    "- Try neural network using Tensorflow or PyTorch\n",
    "\n",
    "\n",
    "In summary, this project is helpful to me in understanding how a machine learning competition is approached and what are the steps one should go through to build a robust model. If you have any feedback for this project, feel free to contact me via my [LinkedIn](https://www.linkedin.com/in/limchiahooi) or [GitHub Pages](https://limchiahooi.github.io)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### References\n",
    "- https://towardsdatascience.com/data-types-in-statistics-347e152e8bee\n",
    "- https://machinelearning-blog.com/2018/04/23/logistic-regression-101/\n",
    "- https://www.analyticsvidhya.com/blog/2015/08/introduction-ensemble-learning/\n",
    "- https://www.analyticsvidhya.com/blog/2015/09/questions-ensemble-modeling/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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