loan-eligibility.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loan Prediction\n",
    "This notebook is designed for people who want to solve binary classification problems using Python. \n",
    "\n",
    "## Table of Contents\n",
    "1. Getting the system ready and loading the data\n",
    "1. Understanding the data\n",
    "1. Exploratory Data Analysis (EDA) <br />\n",
    "    i. Univariate Analysis <br />\n",
    "    ii. Bivariate Analysis <br />\n",
    "1. Missing value and outlier treatment\n",
    "1. Evaluation Metrics for classification problems\n",
    "1. Model Building: Part 1\n",
    "1. Logistic Regression using stratified k-folds cross validation\n",
    "1. Feature Engineering\n",
    "1. Model Building: Part 2 <br />\n",
    "    i. Logistic Regression <br />\n",
    "    ii. Decision Tree <br />\n",
    "    iii. Random Forest <br />\n",
    "    iv. XGBoost <br />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the system ready and loading the data\n",
    "We will be using Python for this course along with the below listed libraries. \n",
    "\n",
    "**Specifications**\n",
    "* Python\n",
    "* pandas\n",
    "* seaborn\n",
    "* sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "For this problem, we have three CSV files: train, test and sample submission.\n",
    "* Train file will be used for training the model, i.e. our model will learn from this file. It contains all the independent variables and the target variable.\n",
    "* Test file contains all the independent variables, but not the target variable. We will apply the model to predict the target variable for the test data.\n",
    "* Sample submission file contains the format in which we have to submit out predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('Dataset/train_ctrUa4K.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('Dataset/test_lAUu6dG.csv')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a copy of train and test data so that even if we have to make any changes in these datasets we would not lose the original datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_original=train.copy()\n",
    "test_original=test.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 12 independent variables and 1 target variable, i.e. Loan_Status in the train dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have similar features in the test dataset as the train dataset except the Loan_Status. We will predict the Loan_Status using the model built using the train data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see there are three formats of data types:\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 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 614 rows and 13 columns in the train dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(367, 12)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 367 rows and 12 columns in test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    422\n",
       "N    192\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize can be set to True to print proportions instead of number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    0.687296\n",
       "N    0.312704\n",
       "Name: Loan_Status, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4b8c77220>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Loan_Status'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loan of 422(around 69%) people out of 614 were approved.\n",
    "\n",
    "Now, let's visualize each variable separately. Different types of variables are Categorical, ordinal and numerical.\n",
    "* Categorical features: These features have categories (Gender, Married, Self_Employed, Credit_History, Loan_Status)\n",
    "* Ordinal features: Variables in categorical features having some order involved (Dependents, Education, Property_Area)\n",
    "* Numerical features: These features have numerical values (ApplicantIncome, CoapplicantIncome, LoanAmount,Loan_Amount_Term)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Independent Variable (Categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gQ8BS4GPjHenAYNDntw/Tew/AI+n9qIVLu33SON1dVbuBFwPnVtUf0Pszqv3MoM9vk1X14ara3X1cAEyOeygd8H6S5CTgt4HPdPsOGuM8BwyDPr/tSnJykonu42Tgu+MeSge8U4CnA39eVTckWQp8dMwzHRB8Y9E8lmQx8D56f3kKuBx4XVV9e6yD6YCX5BBgcVX5E1XnkEGXNFJJXgicDRxcVUuTPAn4s6p60ZhHa94wPz5X9zNJzpzmcFXV2+ZsGOne3kLvF+N8AaCqvtpddtF+ZtDnpzum2Pdg4FXAwwGDrnHaXVW3Jff4NQleCpgDBn0eqqpz9txO8hDgdfSeiNoAnLO3z5P2pyQbgdcC30jycmAiyTLgdHrP72g/81Uu81SSn0nyduBqev8wP7mq3lhVt4x5NB24LqD3Y7ZvBB4P/JjeG4puo3fSof3MJ0XnoSTvBl5C7+dMn1dVt495JAmAJA8GzgRWAh/hp5daqqreM7bBDhBecpmfzqB39vOnwJ/0XasMvb84h41rMB3wfkLvOZ4HAofitfM5ZdDnoaryUpnud5KsBN5D78dRPLmqfjjmkQ44XnKRNBJJvgycWlWbxz3LgcqgS1Ij/K+7JDXCoEtSIwy6JDXCoEtSIwy6JDXi/wD5Dwm3pPmnZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Gender'].value_counts(normalize=True).plot.bar(figsize=(20,10), title='Gender')\n",
    "plt.show()\n",
    "train['Married'].value_counts(normalize=True).plot.bar(title='Married')\n",
    "plt.show()\n",
    "train['Self_Employed'].value_counts(normalize=True).plot.bar(title='Self_Employed')\n",
    "plt.show()\n",
    "train['Credit_History'].value_counts(normalize=True).plot.bar(title='Credit_History')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be inferred from the above bar plots that:\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 repaid their doubts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Independent Variable (Ordinal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Dependents'].value_counts(normalize=True).plot.bar(figsize=(24,6), title='Dependents')\n",
    "plt.show()\n",
    "train['Education'].value_counts(normalize=True).plot.bar(title='Education')\n",
    "plt.show()\n",
    "train['Property_Area'].value_counts(normalize=True).plot.bar(title='Property_Area')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following inferences can be made from the above bar plots:\n",
    "* Most of the applicants don't have any dependents.\n",
    "* Around 80% of the applicants are Graduate.\n",
    "* Most of the applicants are from Semiurban area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Independent Variable (Numerical)\n",
    "Till now we have seen the categorical and ordinal variables and now lets visualize the numerical variables. Lets look at the distribution of Applicant income first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SpFoxyEqSJEmSasUgK0mSJEmqFYOsJEmSJKlWDLKSJEmSpFoxyEqSJEmSasUgK0mSJEmqFYOsJEmSJKlWDLKSJEmSpFoxyEqSJEmSasUgK0mSJEmqFYOsJEmSJKlWDLKSJEmSpFoxyEqSJEmSasUgK0mSJEmqFYOsJEmSJKlWDLKSJEmSpFoxyEqSJEmSasUgK0mSJEmqFYOsJEmSJKlWphxkI6IjIr4REX9WPp8WEY9HxPPl/dSWY2+LiK0R8VxEXNVSvzQinin77o6IKPXZEfFQqT8VEYuO3iVKkiRJkqaTw5mRvQUYbPl8K7AxMxcDG8tnIuIiYAVwMbAMuCciOsqYe4HVwOLyWlbqDeC1zDwfuAu484iuRpKkaabZbNLV1UVHRwddXV00m82qW5IkqXJTCrIRsQD4JeBTLeXlwPqyvR64pqX+YGYOZeY2YCtwWUScBZycmU9mZgIPjBszeq6HgStGZ2slSTpeNZtNent7Wbt2Lfv27WPt2rX09vYaZiVJx72pzsj+AfAJ4GBLbX5m7gQo72eU+tnAiy3H7Si1s8v2+PqYMZm5H3gdOH3KVyFJ0jS0Zs0arr/+enp6epgzZw49PT1cf/31rFmzpurWJEmq1MxDHRARvwzsysxNEfGRKZxzopnUnKQ+2Zjxvaxm5NZkFi5cOIVWJEmqry1btvCDH/yAdevWsWTJEvr7+2k0GrzwwgtVtyZJUqWmMiP7YeDqiHgBeBD4aET8EfBKuV2Y8r6rHL8DOKdl/ALg5VJfMEF9zJiImAmcAuwZ30hm3peZ3ZnZPW/evCldoCRJdXXCCSdw8803s3TpUmbNmsXSpUu5+eabOeGEE6puTZKkSh0yyGbmbZm5IDMXMbKI0xOZ+WvAo8Cqctgq4JGy/SiwoqxEfB4jizo9XW4/fiMiLi/Pv94wbszoua4t3/GuGVlJko4nb7/9NmvXrqWvr4/h4WH6+vpYu3Ytb7/9dtWtSZJUqUPeWjyJO4ANEdEAtgPXAWTmsxGxAdgC7AduyswDZcyNwP3AicBj5QWwDvhMRGxlZCZ2xfvoS5KkaeGiiy7immuuoaenh8HBQTo7O/nYxz7GF77whapbkySpUlHXic/u7u4cGBioug1Jko6ZZrPJLbfcwkknncT27dtZuHAhb731Fp/85CdZuXJl1e1JknRMRcSmzOyeaN/h/I6sJEmqSF3/4VmSpGPBICtJUptas2YNDz30ENu2bePgwYNs27aNhx56yJ/fkSQd97y1WJKkNtXR0cG+ffuYNWvWO7Xh4WHmzJnDgQMHJhkpSVL9eWuxJEk11NnZSX9//5haf38/nZ2dFXUkSVJ7MMhKktSment7aTQaY35+p9Fo0NvbW3VrkiRV6v38/I4kSTqGRlcmbv35nTVr1rhisSTpuOczspIkSZKktuMzspIkSZKkacMgK0mSJEmqFYOsJEmSJKlWDLKSJLWxZrNJV1cXHR0ddHV10Ww2q25JkqTKuWqxJEltqtls0tvby7p161iyZAn9/f00Gg0AVy6WJB3XXLVYkqQ21dXVxdq1a1m6dOk7tb6+Pnp6eti8eXOFnUmSdOxNtmqxQVaSpDbV0dHBvn37mDVr1ju14eFh5syZw4EDByrsTJKkY8+f35EkqYY6Ozvp7+8fU+vv76ezs7OijiRJag8GWUmS2lRvby+NRoO+vj6Gh4fp6+uj0WjQ29tbdWuSJFXKxZ4kSWpTows69fT0MDg4SGdnJ2vWrHGhJ0nScc9nZCVJkiRJbcdnZCVJkiRJ04ZBVpIkSZJUKwZZSZIkSVKtHDLIRsSciHg6Ir4VEc9GxO+V+mkR8XhEPF/eT20Zc1tEbI2I5yLiqpb6pRHxTNl3d0REqc+OiIdK/amIWHT0L1WSJEmSNB1MZUZ2CPhoZn4IuARYFhGXA7cCGzNzMbCxfCYiLgJWABcDy4B7IqKjnOteYDWwuLyWlXoDeC0zzwfuAu48CtcmSZIkSZqGDhlkc8Sb5eOs8kpgObC+1NcD15Tt5cCDmTmUmduArcBlEXEWcHJmPpkjSyU/MG7M6LkeBq4Yna2VJEmSJKnVlJ6RjYiOiPgmsAt4PDOfAuZn5k6A8n5GOfxs4MWW4TtK7eyyPb4+Zkxm7gdeB04/kguSJEmSJE1vUwqymXkgMy8BFjAyu9o1yeETzaTmJPXJxow9ccTqiBiIiIHdu3cfqm1JkiRJ0jR0WKsWZ+b3ga8w8mzrK+V2Ycr7rnLYDuCclmELgJdLfcEE9TFjImImcAqwZ4Lvvy8zuzOze968eYfTuiRJkiRpmpjKqsXzIuJHy/aJwM8D3wYeBVaVw1YBj5TtR4EVZSXi8xhZ1OnpcvvxGxFxeXn+9YZxY0bPdS3wRHmOVpIkSZKkMWZO4ZizgPVl5eEZwIbM/LOIeBLYEBENYDtwHUBmPhsRG4AtwH7gpsw8UM51I3A/cCLwWHkBrAM+ExFbGZmJXXE0Lk6SJEmSNP1EXSc+u7u7c2BgoOo2JEmSJEnHQERsyszuifYd1jOykiRJkiRVzSArSZIkSaoVg6wkSZIkqVYMspIkSZKkWjHISpIkSZJqxSArSZIkSaoVg6wkSZIkqVYMspIkSZKkWjHISpIkSZJqxSArSZIkSaoVg6wkSZIkqVYMspIktbFms0lXVxcdHR10dXXRbDarbkmSpMrNrLoBSZI0sWazSW9vL+vWrWPJkiX09/fTaDQAWLlyZcXdSZJUncjMqns4It3d3TkwMFB1G5IkHTNdXV2sXbuWpUuXvlPr6+ujp6eHzZs3V9iZJEnHXkRsyszuCfcZZCVJak8dHR3s27ePWbNmvVMbHh5mzpw5HDhwoMLOJEk69iYLsj4jK0lSm+rs7KS/v39Mrb+/n87Ozoo6kiSpPRhkJUlqU729vTQaDfr6+hgeHqavr49Go0Fvb2/VrUmSVCkXe5IkqU2NLujU09PD4OAgnZ2drFmzxoWeJEnHPZ+RlSRJkiS1HZ+RlSRJkiRNGwZZSZLaWLPZpKuri46ODrq6umg2m1W3JElS5QyykiS1qWazyS233MJbb71FZvLWW29xyy23GGYlSce9QwbZiDgnIvoiYjAino2IW0r9tIh4PCKeL++ntoy5LSK2RsRzEXFVS/3SiHim7Ls7IqLUZ0fEQ6X+VEQsOvqXKklSvXziE5/gzTff5KWXXiIzeemll3jzzTf5xCc+UXVrkiRVaiozsvuB38rMTuBy4KaIuAi4FdiYmYuBjeUzZd8K4GJgGXBPRHSUc90LrAYWl9eyUm8Ar2Xm+cBdwJ1H4dokSaq1HTt2MDQ0xB133MFbb73FHXfcwdDQEDt27Ki6NUmSKnXIIJuZOzPz62X7DWAQOBtYDqwvh60Hrinby4EHM3MoM7cBW4HLIuIs4OTMfDJHlkp+YNyY0XM9DFwxOlsrSdLx7MILL+T222/npJNO4vbbb+fCCy+suiVJkip3WM/Illt+fwp4CpifmTthJOwCZ5TDzgZebBm2o9TOLtvj62PGZOZ+4HXg9MPpTZKk6WjLli18/OMf5/vf/z4f//jH2bJlS9UtSZJUuSkH2Yj4APAnwG9k5t9MdugEtZykPtmY8T2sjoiBiBjYvXv3oVqWJKn2Zs2axWOPPcZpp53GY489xqxZs6puSZKkyk0pyEbELEZC7Gcz83Ol/Eq5XZjyvqvUdwDntAxfALxc6gsmqI8ZExEzgVOAPeP7yMz7MrM7M7vnzZs3ldYlSaq14eFh9u7dS2ayd+9ehoeHq25JkqTKTWXV4gDWAYOZ+Z9adj0KrCrbq4BHWuorykrE5zGyqNPT5fbjNyLi8nLOG8aNGT3XtcAT5TlaSZKOW7Nnz+bMM8/klVdeITN55ZVXOPPMM5k9e3bVrUmSVKmpzMh+GPgnwEcj4pvl9Q+BO4BfiIjngV8on8nMZ4ENwBbgi8BNmXmgnOtG4FOMLAD1V8Bjpb4OOD0itgK/SVkBWZKk49kFF1zAd7/7Xa6++mp2797N1VdfzXe/+10uuOCCqluTJKlSUdeJz+7u7hwYGKi6DUmSjpk5c+Zw7rnn8vzzz5OZRASLFy/mO9/5Dvv27au6PUmSjqmI2JSZ3RPtO6xViyVJ0g/P0NAQe/bs4dxzzyUiOPfcc9mzZw9DQ0NVtyZJUqUMspIktbGhoSE+/elPj3mXJOl4N7PqBiRJ0nt74403+OhHP1p1G5IktRVnZCVJkiRJtWKQlSSpzZ155pnMmDGDM888s+pWJElqCwZZSZLaWESwe/duDh48yO7duxn5KXZJko5vBllJktrYjBkzOOecc8a8S5J0vHOxJ0mS2tiBAwd44YUXAN55lyTpeOc/60qSJEmSasUgK0lSm/vABz4w5l2SpOOdQVaSpDY2e/Zs5s6dy4wZM5g7dy6zZ8+uuiVJkipnkJUkqY3Nnz+fnTt3cvDgQXbu3Mn8+fOrbkmSpMoZZCVJamPbt29nxowZ77y2b99edUuSJFXOICtJUpsavY147969HDx4kL17946pS5J0vDLISpLUpoaGhpg7d+6Y2ty5cxkaGqqoI0mS2oNBVpKkNvbqq68yf/58IoL58+fz6quvVt2SJEmVM8hKktTmvve975GZfO9736u6FUmS2oJBVpKkNnfw4MEx75IkHe8MspIktbEZM2aMCbIzZvinW5Ik/xpKktTGxs/COisrSZJBVpIkSZJUMwZZSZIkSVKtHDLIRsSnI2JXRGxuqZ0WEY9HxPPl/dSWfbdFxNaIeC4irmqpXxoRz5R9d0dElPrsiHio1J+KiEVH9xIlSaq30edifT5WkqQRU/mLeD+wbFztVmBjZi4GNpbPRMRFwArg4jLmnojoKGPuBVYDi8tr9JwN4LXMPB+4C7jzSC9GkqTp6IwzzmDGjBmcccYZVbciSVJbOGSQzcyvAnvGlZcD68v2euCalvqDmTmUmduArcBlEXEWcHJmPpmZCTwwbszouR4GrhidrZUkSbBr1y4OHjzIrl27qm5FkqS2MPMIx83PzJ0AmbkzIkb/ifhs4Gstx+0oteGyPb4+OubFcq79EfE6cDrw6vgvjYjVjMzqsnDhwiNsXZKkw1P1v69O9DuyVfY08m/SkiRV52g/bDPRX9WcpD7ZmHcXM+/LzO7M7J43b94RtihJ0uHJzEpeV155JfDuZ2SvvPLKynoyxEqS2sGRBtlXyu3ClPfRe512AOe0HLcAeLnUF0xQHzMmImYCp/DuW5klSTrufOlLX3ontALvhNsvfelLFXcmSVK1jjTIPgqsKturgEda6ivKSsTnMbKo09PlNuQ3IuLy8vzrDePGjJ7rWuCJ9J97JUkCRsLswYMHOfd3/oyDBw8aYiVJYgrPyEZEE/gIMDcidgC/C9wBbIiIBrAduA4gM5+NiA3AFmA/cFNmHiinupGRFZBPBB4rL4B1wGciYisjM7ErjsqVSZIkSZKmpUMG2cxc+R67rniP49cAayaoDwBdE9T3UYKwJEmSJEmH4i+rS5IkSZJqxSArSZIkSaoVg6wkSZIkqVYMspIkSZKkWjHISpIkSZJqxSArSZIkSaoVg6wkSZIkqVYO+TuykiRV6UO/92Ve3ztcdRttYdGtf151C23hlBNn8a3fvbLqNiRJFTLISpLa2ut7h3nhjl+qug21EQO9JMlbiyVJkiRJtWKQlSRJkiTVikFWkiRJklQrBllJkiRJUq242JMkqa19sPNWfnL9rVW3oTbywU4AFwCTpOOZQVaS1NbeGLzDVYs1hqsWS5K8tViSJEmSVCvOyEqS2p4zcGp1yomzqm5BklQxg6wkqa15W/GIRbf+uf8tJEkqvLVYkiRJklQrBllJkiRJUq0YZCVJkiRJtdI2QTYilkXEcxGxNSL8wUBJkiRJ0oTaIshGRAfwX4BfBC4CVkbERdV2JUmSJElqR20RZIHLgK2Z+deZ+TbwILC84p4kSZIkSW2oXX5+52zgxZbPO4C/V1EvkiSNERFVtwBA3Fl1ByMys+oWJEnHuXYJshP9H8K7/kpGxGpgNcDChQuPdU+SJAEGN0mS2k273Fq8Azin5fMC4OXxB2XmfZnZnZnd8+bN+6E1J0mSJElqH+0SZP8SWBwR50XECcAK4NGKe5IkSZIktaG2uG6nKPEAAASdSURBVLU4M/dHxM3Al4AO4NOZ+WzFbUmSJEmS2lBbBFmAzPwL4C+q7kOSJEmS1N7a5dZiSZIkSZKmxCArSZIkSaoVg6wkSZIkqVYMspIkSZKkWjHISpIkSZJqxSArSZIkSaoVg6wkSZIkqVYiM6vu4YhExG7gO1X3IUnSD8lc4NWqm5Ak6Yfo3MycN9GO2gZZSZKOJxExkJndVfchSVI78NZiSZIkSVKtGGQlSZIkSbVikJUkqR7uq7oBSZLahc/ISpIkSZJqxRlZSZIkSVKtGGQlSSoi4lciIiPiwvdxjvsj4tqy/amIuOjodQgRcfu4z28ezfNLklQHBllJkv7WSqAfWHE0TpaZ/ywztxyNc7W4/dCHSJI0vRlkJUkCIuIDwIeBBiXIRsRHIuKrEfH5iNgSEf81ImaUfW9GxH+MiK9HxMaIeNcPtkfEVyKiu2wvK8d+KyI2ltplEfF/IuIb5f0nSv3XI+JzEfHFiHg+In6/1O8AToyIb0bEZ8d910fK9z0cEd+OiM9GRJR9P1PO/62IeDoiPhgRcyLiDyPimfL9S1u++wsR8acRsS0ibo6I3yzHfC0iTivH/Xjpb1NE/K/3M4stSdLhMshKkjTiGuCLmfn/gD0R8dOlfhnwW8BPAj8O/KNSPwn4emb+NPA/gd99rxOXkPvfgX+cmR8Criu7vg38XGb+FPCvgH/XMuwS4FfL9/5qRJyTmbcCezPzksz82ARf9VPAbwAXAT8GfDgiTgAeAm4p3/3zwF7gJoDM/ElGZqLXR8Sccp4u4Ppy7WuAH5QenwRuKMfcB/Rk5qXAbwP3vNf1S5J0tM2sugFJktrESuAPyvaD5fOfA09n5l8DREQTWAI8DBxkJCAC/BHwuUnOfTnw1czcBpCZe0r9FEYC5GIggVktYzZm5uvle7cA5wIvHuIans7MHWXMN4FFwOvAzsz8y/Ldf1P2LwHWltq3I+I7wAXlPH2Z+QbwRkS8DvxpqT8D/N0ye/2zwB+XSV+A2YfoTZKko8YgK0k67kXE6cBHga6ISKCDkWD5F+W91Xv9bt1kv2cX77H/3zISGn8lIhYBX2nZN9SyfYCp/c2eaMx7fXdMUJvoPAdbPh8s55wBfD8zL5lCT5IkHXXeWixJElwLPJCZ52bmosw8B9jGyOzrZRFxXnk29lcZWQwKRv6GXlu2r2+pT+RJ4B9ExHkAo8+ZMjIj+1LZ/vUp9jocEbMOfdg7vg38nYj4mfLdH4yImcBXgY+V2gXAQuC5qZywzOpui4jryviIiA8dRk+SJL0vBllJkkZuI/78uNqfMBJQnwTuADYzEm5Hj3sLuDgiNjEym/tv3uvkmbkbWA18LiK+xd/ekvz7wL+PiP/NyCzwVNwH/N/xiz1N8t1vMxLA15bvfhyYw8gzrR0R8Uzp59czc+i9z/QuHwMa5ZzPAssPY6wkSe9LZE52J5QkSceviPgI8NuZ+csT7HszMz/ww+9KkiQ5IytJkiRJqhVnZCVJkiRJteKMrCRJkiSpVgyykiRJkqRaMchKkiRJkmrFICtJkiRJqhWDrCRJkiSpVgyykiRJkqRa+f+kfOR1UKN7mAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['ApplicantIncome'])\n",
    "plt.show()\n",
    "train['ApplicantIncome'].plot.box(figsize=(16,5))\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. We will try to make it normal in later sections as algorithms works better if the data is normally distributed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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 eduation levels. Let us segregate them by Education."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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 outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the Coapplicant income distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['CoapplicantIncome'])\n",
    "plt.show()\n",
    "train['CoapplicantIncome'].plot.box(figsize=(16,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a similar distribution as that of the applicant's income. The majority of co-applicants income ranges from 0 to 5000. We also see a lot of outliers in the applicant's income and it is not normally distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.notna()\n",
    "# train.dropna()\n",
    "# print(train[train['LoanAmount'].isnull()])\n",
    "# train['LoanAmount'] = pd.to_numeric(train['LoanAmount'], errors='coerce')\n",
    "# train = train.dropna(subset=['LoanAmount'])\n",
    "# train['LoanAmount'] = train['LoanAmount'].astype(int)\n",
    "sns.distplot(train['LoanAmount'])\n",
    "plt.show()\n",
    "train['LoanAmount'].plot.box(figsize=(16,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a lot of outliers in this variable and the distribution is fairly normal. We will treat the outliers in later sections."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Analysis\n",
    "Let's recall some of the hypotheses that we generated earlier:\n",
    "* Applicants with high incomes should have more chances of loan approval.\n",
    "* Applicants who have repaid their previous debts should have higher chances of loan approval.\n",
    "* Loan approval should also depend on the loan amount. If the loan amount is less, the chances of loan approval should be high.\n",
    "* Lesser the amount to be paid monthly to repay the loan, the higher the chances of loan approval."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to test the above-mentioned hypotheses using bivariate analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After looking at every variable individually in univariate analysis, we will now explore them again with respect to the target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Independent Variable vs Target Variable\n",
    "First of all, we will find the relation between the 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Gender=pd.crosstab(train['Gender'],train['Loan_Status'])\n",
    "Gender.div(Gender.sum(1).astype(float), axis=0).plot(kind=\"bar\",stacked=True,figsize=(4,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be inferred that the proportion of male and female applicants is more or less the same for both approved and unapproved loans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us visualize the remaining categorical variables vs target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQQAAAENCAYAAADpBxvCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAVIElEQVR4nO3df5xUdb3H8deb36gIiRsPZSU2pQQVubgpVl5/F/TIzOoKipFl7aWrmT9uV9KbWBZBitUmxSVBSlHkkT8iJUnrQVj+CFRCEVEiiw1ToKT4Kcjn/nGGr8O0sLPL7M6u+34+HvNgzjnf+Z7P7DDvOd8z55xRRGBmBtCh3AWYWevhQDCzxIFgZokDwcwSB4KZJZ3KteKDDz44+vfvX67Vm7VbTz755LqIqKhvWdkCoX///ixevLhcqzdrtyT9aU/LPGQws8SBYGaJA8HMkrLtQzBrDtu3b6euro6tW7eWu5Sy69atG5WVlXTu3LnoxzgQ7C2lrq6OHj160L9/fySVu5yyiQjWr19PXV0dVVVVRT/OQwZ7S9m6dSu9e/du12EAIInevXs3ekupwUCQNEPSq5Ke3cNySaqVtFLSUklDG1WBWYm19zDYpSl/h2K2EGYCw/eyfAQwIHerAX7Q6CrMrFVoMBAiYiHwt700ORv4cWQeB3pJOqRUBZpZyynFTsW+wOq86brcvJcLG0qqIduKoF+/fiVYdSNc17Nl19fSrttQ7gqaV7Gv3wfnwJrdx80HDHgfG1/8bTMUVb+dO3dy2fgb+dVvFyGJbl27MGfqJKr69WVC7XSuvvSif33Qof+22+SECRO4+uqrW6jiN5Vip2J9A5V6L8MUEdMiojoiqisq6j2U2qzNu2vuL1jz17UsffgunvnlHO69ZTK9DuwBwITvzSiqjwkTJjRniXtUikCoAw7Lm64E1pSgX7Nms+TZFQz78BgGn3Eu51x0JX9/7R8A/HDWPbznQxdw7Bkj+fjn/pvNW7YAcOFl47n0K9/ivR+5kHeeeBY/uf/hPfb98ivrOKTPwXTokL29Kg/tw9t6Hci4CbVs2bqNIWeOYvQl1wDw0c9cwXHDz+eoo45i2rRpAIwbN44tW7YwZMgQRo8ezUsvvcTRRx+d+r/xxhu57rrrAKitrWXQoEEMHjyYUaNG7fPfpRSBMBcYk/u2YRiwISL+Zbhg1pqMuewrTLrmiyx9eA7HHHkEX73p/wD42IjTWDTvdn7/8F0MPKKK6Xf+ND3m5VfW8Zv7ZnD/j77LuG/W7rHvc886k589tJAhZ47iyq/exNPPPg/AxKsvpXu3rix5aDazbv4GADMmj+fJB+9g8eLF1NbWsn79eiZOnEj37t1ZsmQJs2bN2uvzmDhxIk8//TRLly5l6tSp+/pnKeprxzuBx4B3S6qTdJGksZLG5prMA1YBK4EfAv+1z1WZNaMN//gnr23YyMknHgfAp/7jwyx84mkAnl3xB0465zMcc/q5zLr35yxb8Yf0uI8OP4UOHTow6F3v5JW1e97PXnloH1YsvJdvfvkLdOggTh85ll8+8kS9bWtn3MmxZ4xk2LBhrF69mhdffLFRz2Xw4MGMHj2a22+/nU6d9n2XYIM9RMR5DSwP4OJ9rsSsFbjw8vHcN/0mjj3qXcy8ay4LHnsyLevapUu639DVyrt27cKI097HiNPeR5+K3tw3fwGnn3TCbm0WPLqYhx/5HY/9bCb7Hf5eTjnllHoPJOrUqRM7d+5M0/ltHnjgARYuXMjcuXO5/vrrWbZs2T4Fg49UtHan54E9eFvPHjzyxFMA3Hb3A5w8LDue7p8bN3NIn4PZvn07s+79eZP6f+qZ5az561og+8Zh6XMv8o7K7Jv4zp07sX37dgA2/HMjb+vZg/26d+f555/n8ccfT3107tw5tevTpw+vvvoq69evZ9u2bdx///2p79WrV3PqqafyrW99i9dee42NGzc2qeZdfC6DveVt3rKVyuPePLbuipoL+NF3vsbYcd9g89atvLNfJbfedB0A13/p85zw4TG8o/IQjjnyCP65cXOj1/fqur/xuS9dz7bXszf08UOO4pILRwJQM/pjDD5jJEOPGciMyeOZettPGHzGubz7qCEMGzYs9VFTU8PgwYMZOnQos2bN4tprr+WEE06gqqqKI488EoA33niDCy64gA0bNhARXH755fTq1aupfyYAVK4faqmuro4WvWKSj0No24p8/ZZ/cA4D3/H2Zi6mGRQch1Aqy5cvZ+DAgbvNk/RkRFTX195DBjNLPGQwa6Jnlr/IJy/9ym7zunbtwhP3/7hMFe07B4JZEx0zcABLHppd7jJKykMGM0scCGaWOBDMLPE+BLM8/WtLe17eS5ce2mAb9R3KFVdcweTJk4Hs5KWNGzemE5hakrcQzMqsa9cu3HPPPaxbt67cpTgQzMqtU8eO1NTU8O1vf7vcpTgQzFqDiy++mFmzZrFhQ3mPOHUgmLUCBx54IGPGjKG2ds/XWWgJDgSzVuKyyy5j+vTpbNq0qWw1OBDMWomDDjqIc889l+nTp5etBn/taJanmK8Jm9OVV17JzTffXLb1OxDMyiz/EvF9+vRh8+bGX4OhVDxkMLPEgWBmiQPBzBIHgpklDgQzSxwIZpb4a0ezfNNOKW1/NQv2ujgiOOmci7jmqxMYMWIEAHPmzGHGjBk8+OCDpa2lCN5CMCsjSUydeDVXXHEFW7duZdOmTVxzzTVMmTKlLPV4C8GszI4+8gjOOussJk2axKZNmxgzZgyHH354WWpxIJi1AuPHj2fo0KF06dKFFv0BowIOBLNWYP/992fkyJEccMABdO3atWx1eB+CWSvRoUMHOnQo71vSgWBmiYcMZvlqFpS7grIqKhAkDQe+C3QEbomIiQXLewK3A/1yfd4YEbeWuFazt7RyXHa9UINDBkkdgSnACGAQcJ6kQQXNLgaei4hjgVOAyZK6lLhWM2tmxexDOB5YGRGrIuJ1YDZwdkGbAHpIEnAA8DdgR0krNbNmV0wg9AVW503X5ebluxkYCKwBngG+GBE7CzuSVCNpsaTFa9eubWLJZnsTRES5i2gVmvJ3KCYQVN+6CqY/CCwBDgWGADdLOrCeAqdFRHVEVFdUVDS6WLOGdNuwivWbdrT7UIgI1q9fT7du3Rr1uGJ2KtYBh+VNV5JtCeT7NDAxsldhpaQ/AkcCv2tUNWb7qPKpSdRxFWt7vpP6P8taqQ3LS95lt27dqKysbNRjigmERcAASVXAX4BRwPkFbf4MnA48IqkP8G5gVaMqMSuBzq+/RtXjXy53GY13XXl/sWmXBgMhInZIugSYT/a144yIWCZpbG75VOB6YKakZ8hi+aqIKP8vV5pZoxR1HEJEzAPmFcybmnd/DfCB0pZmZi3Nhy6bWeJAMLPEgWBmiQPBzBIHgpklDgQzSxwIZpY4EMwscSCYWeJAMLPEgWBmiQPBzBIHgpklDgQzSxwIZpY4EMwscSCYWeJAMLPEgWBmiQPBzBIHgpklDgQzSxwIZpY4EMwscSCYWeJAMLPEgWBmiQPBzBIHgpklDgQzSxwIZpY4EMwsKSoQJA2XtELSSknj9tDmFElLJC2T9OvSlmlmLaFTQw0kdQSmAGcCdcAiSXMj4rm8Nr2A7wPDI+LPkt7eXAWbWfMpZgvheGBlRKyKiNeB2cDZBW3OB+6JiD8DRMSrpS3TzFpCMYHQF1idN12Xm5fvXcDbJC2Q9KSkMaUq0MxaToNDBkD1zIt6+jkOOB3oDjwm6fGIeGG3jqQaoAagX79+ja/WzJpVMVsIdcBhedOVwJp62jwYEZsiYh2wEDi2sKOImBYR1RFRXVFR0dSazayZFBMIi4ABkqokdQFGAXML2vwUOElSJ0n7AScAy0tbqpk1twaHDBGxQ9IlwHygIzAjIpZJGptbPjUilkt6EFgK7ARuiYhnm7NwMyu9YvYhEBHzgHkF86YWTN8A3FC60syspflIRTNLHAhmljgQzCxxIJhZ4kAws8SBYGaJA8HMEgeCmSUOBDNLHAhmljgQzCxxIJhZ4kAws8SBYGaJA8HMEgeCmSUOBDNLHAhmljgQzCxxIJhZ4kAws8SBYGaJA8HMEgeCmSUOBDNLHAhmljgQzCxxIJhZ4kAws8SBYGaJA8HMEgeCmSUOBDNLigoEScMlrZC0UtK4vbR7j6Q3JH2idCWaWUtpMBAkdQSmACOAQcB5kgbtod0kYH6pizSzllHMFsLxwMqIWBURrwOzgbPrafcF4G7g1RLWZ2YtqJhA6Auszpuuy81LJPUFzgGm7q0jSTWSFktavHbt2sbWambNrJhAUD3zomD6O8BVEfHG3jqKiGkRUR0R1RUVFcXWaGYtpFMRbeqAw/KmK4E1BW2qgdmSAA4GPiRpR0TcV5IqzaxFFBMIi4ABkqqAvwCjgPPzG0RE1a77kmYC9zsMzNqeBgMhInZIuoTs24OOwIyIWCZpbG75XvcbmFnbUcwWAhExD5hXMK/eIIiIC/e9LDMrBx+paGaJA8HMEgeCmSUOBDNLHAhmljgQzCxxIJhZ4kAws8SBYGaJA8HMEgeCmSVFnctgVm79t95R7hKa1UvlLiCn3QSC/0OZNcxDBjNLHAhmljgQzCxxIJhZ4kAws8SBYGaJA8HMEgeCmSUOBDNLHAhmljgQzCxxIJhZ4kAws8SBYGaJA8HMEgeCmSUOBDNLHAhmljgQzCwpKhAkDZe0QtJKSePqWT5a0tLc7VFJx5a+VDNrbg0GgqSOwBRgBDAIOE/SoIJmfwROjojBwPXAtFIXambNr5gthOOBlRGxKiJeB2YDZ+c3iIhHI+LvucnHgcrSlmlmLaGYQOgLrM6brsvN25OLgJ/Xt0BSjaTFkhavXbu2+CrNrEUUEwiqZ17U21A6lSwQrqpveURMi4jqiKiuqKgovkozaxHF/FBLHXBY3nQlsKawkaTBwC3AiIhYX5ryzKwlFbOFsAgYIKlKUhdgFDA3v4GkfsA9wCcj4oXSl2lmLaHBLYSI2CHpEmA+0BGYERHLJI3NLZ8KXAv0Br4vCWBHRFQ3X9lm1hyK+m3HiJgHzCuYNzXv/meBz5a2NDNraT5S0cwSB4KZJQ4EM0scCGaWOBDMLHEgmFniQDCzxIFgZokDwcwSB4KZJQ4EM0scCGaWOBDMLHEgmFniQDCzxIFgZokDwcwSB4KZJQ4EM0scCGaWOBDMLHEgmFniQDCzxIFgZokDwcwSB4KZJQ4EM0scCGaWOBDMLHEgmFniQDCzxIFgZokDwcySogJB0nBJKyStlDSunuWSVJtbvlTS0NKXambNrcFAkNQRmAKMAAYB50kaVNBsBDAgd6sBflDiOs2sBRSzhXA8sDIiVkXE68Bs4OyCNmcDP47M40AvSYeUuFYza2adimjTF1idN10HnFBEm77Ay/mNJNWQbUEAbJS0olHVti0HA+taamWa1FJrajfeyq/fO/a0oJhAUD3zogltiIhpwLQi1tnmSVocEdXlrsOapr2+fsUMGeqAw/KmK4E1TWhjZq1cMYGwCBggqUpSF2AUMLegzVxgTO7bhmHAhoh4ubAjM2vdGhwyRMQOSZcA84GOwIyIWCZpbG75VGAe8CFgJbAZ+HTzldxmtIuh0VtYu3z9FPEvQ30za6d8pKKZJQ4EM0scCGaWOBDMAEmHS+qau3+KpEsl9Sp3XS3NgVBCkiol3StpraRXJN0tqbLcdVlR7gbekHQEMB2oAu4ob0ktz4FQWreSHZNxCNmh2z/LzbPWb2dE7ADOAb4TEZeTvY7tigOhtCoi4taI2JG7zQQqyl2UFWW7pPOATwH35+Z1LmM9ZeFAKK11ki6Q1DF3uwBYX+6irCifBk4EvhERf5RUBdxe5ppanA9MKiFJ/YCbyf5jBfAo8MWI+FNZC7OiSOoO9IuIt/JZuHvlQDADJJ0F3Ah0iYgqSUOAr0XER8pcWosq5vRna4Cka/eyOCLi+hYrxprqOrKLAS0AiIgluWFDu+JAKI1N9czbH7gI6A04EFq/HRGxQdrt0h7tbvPZgVACETF5131JPYAvku2kmg1M3tPjrPwkzQMuBp6VdD7QUdIA4FKyfUDtir9lKBFJB0n6OrCULGiHRsRVEfFqmUuzvZtJdmr/S8DRwDayA5I2kAV7u+KdiiUg6QbgY2Tn0E+JiI1lLskaQdL+wLXAcOA23hwqRETcVLbCysBDhtK4kuyT5X+Ba/LGoSL7T3VguQqzomwn2w/UFTiAdrjvYBcHQglEhIdebZSk4cBNZIecD42IzWUuqaw8ZLB2TdIjwNiIWFbuWloDB4KZJd7UNbPEgWBmiQPBzBIHgpklDoQ2RNI1kpZJWippiaTCH93NbztT0idy90/KPW5J7hTfwrb9JW3JLd91G1Oimpv1IK3852n7zschtBGSTgQ+TPZd+TZJBwNdinz4aODGiNjb5dz+EBFD9rVOa9u8hdB2HAKsi4htABGxLiLWSDpO0q8lPSlpvqTdrgMo6bPAucC1kmY1dqWSNkqalOv/YUnHS1ogaZWkj+TaXCjpp5IelLRC0vh6+pGkGyQ9K+kZSSNz82+TdHZeu1mSPpK74tQNkhbltoj+M6+fmyU9J+kB4O2NfU62FxHhWxu4kR1SuwR4Afg+cDLZNf8eJbuWI8BIst/ehOyknU8U3t9D3/2BLbn+d91Oyi0LYETu/r3AL3LrPRZYkpt/IfAy2ane3YFngercso25fz8OPET2+6B9gD+ThdzJwH25Nj2BP5JtudYA/5ub3xVYTHYl5I/l9XMo8Nrenptvjbt5yNBGRMRGSccBJwGnAncBXyc7Q++h3PkTHcnemE2xpyHD68CDufvPANsiYrukZ8iCZJeHImI9gKR7gPeTvYl3eT9wZ0S8Abwi6dfAeyJirqQpkt5O9ma/O7IfGP4AMDhv/0BPYADw73n9rJH0qyY+X6uHA6ENyb0JFgALcm/Ii4FlEXFiM652e+Q+poGdZCdxERE7JeX//yk85LVwWuzZbWT7OUYBn8lr/4WImL9bJ9KH6unbSsT7ENoISe/OXbhjlyHAcqAit8MRSZ0lHVWWAuHM3DUhugMfBX5bsHwhMDK3b6CC7JP+d7llM4HLAOLNcwrmA5+X1BlA0rtypykvBEbl+jmEbGvJSsRbCG3HAcD3cj8vtgNYSTbOngbUSupJ9np+B2jKiTqHS1qSNz0jImob8fjfkH3SHwHcERGLC5bfS3Y16t+TfcL/T0T8FSAiXpG0HLgvr/0tZEOSp5SNh9aSBc29wGlkw5cXgF83okZrgE9usn0m6UKynYiXNPHx+5G9wYdGxIZS1maN4yGDlZWkM4Dnge85DMrPWwjtiKRjyDbr822LiD0e8WjtiwPBzBIPGcwscSCYWeJAMLPEgWBmyf8D/HY78tIe3kIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Married=pd.crosstab(train['Married'],train['Loan_Status'])\n",
    "Dependents=pd.crosstab(train['Dependents'],train['Loan_Status'])\n",
    "Education=pd.crosstab(train['Education'],train['Loan_Status'])\n",
    "Self_Employed=pd.crosstab(train['Self_Employed'],train['Loan_Status'])\n",
    "Married.div(Married.sum(1).astype(float), axis=0).plot(kind=\"bar\",stacked=True,figsize=(4,4))\n",
    "plt.show()\n",
    "Dependents.div(Dependents.sum(1).astype(float), axis=0).plot(kind=\"bar\",stacked=True,figsize=(4,4))\n",
    "plt.show()\n",
    "Education.div(Education.sum(1).astype(float), axis=0).plot(kind=\"bar\",stacked=True,figsize=(4,4))\n",
    "plt.show()\n",
    "Self_Employed.div(Self_Employed.sum(1).astype(float), axis=0).plot(kind=\"bar\",stacked=True,figsize=(4,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The proportion of married appliants is higher for 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will look at the relationship between remaining categorical independent variables and Loan_Status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Credit_History=pd.crosstab(train['Credit_History'],train['Loan_Status'])\n",
    "Property_Area=pd.crosstab(train['Property_Area'],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.show()\n",
    "Property_Area.div(Property_Area.sum(1).astype(float), axis=0).plot(kind=\"bar\",stacked=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* It seems people with a credit history as 1 are more likely to get their loans approved.* \n",
    "* The proportion of loans getting approved in the semi-urban area is higher as compared to that in rural or urban areas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's visualize numerical independent variables with respect to the target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical Independent Variable vs Target Variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4c8430f10>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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 change in the mean income. 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": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dKzcIZki6WNLWkraS9HNgZpaFmZlZ2yg3CE4mGVB2A3Aj8C5wYlZFmZlZ21nj6R1JnYE/RMT+bVCPmZm1sTUeEUTE+8BSSX3aoB4zM2tj5V7wXQbMkXQv8E7Dyog4JZOqzMyszZQbBHekDzMz62DKHVl8dXqvof4R8VzGNZmZWRsqd2TxwcAskvsNIWknSVOzLMzMzNpGud1HxwG7AosBImIW4FnLzMw6gHKDYGXpLSZSUelizMys7ZUbBE9JOhLonE5TeQnw0JpeJGmYpOckzZN0RgvtdpH0vqThZdZjZmYV0pqRxduTTEZzHVAPnNrSC9KBaJcBBwKDgSMkDW6m3fnA3eWXbWZmldJiryFJPYCxwMeBOcCnI2JlmfveFZgXEfPTfU0BDgGebtTuZOAmYJdW1G1mZhWypiOCq4FakhA4ELiwFfveHFhQslyXrltN0ubAYcCEVuzXzMwqaE3jCAZHxI4Akq4AHm3Fvpua3L7xBeZfAN+LiPelppqnO5LGAGMA+vfv34oSzMxsTdYUBCsankTEypa+rJtQB2xRslwDLGzUphaYku53Y+CLklZGxK2ljSJiIjARoLa21r2VzMwqaE1BMFTSW+lzAT3TZQEREeu38NrHgEGSBgL/AkYCR5Y2iIjVYxEkXQXc3jgEzMwsWy0GQUR0Xtsdp0cQJ5H0BuoMTIqIuZLGptt9XcDMrB3IdLrJiJgGTGu0rskAiIjRWdZiZmZNK3ccgZmZdVAOAjOzgnMQmJkVnIPAzKzgHARmZgXnIDAzKzgHgZlZwTkIzMwKLtMBZWZm7cmAZdflXUJZXmzj9/MRgZlZwTkIzMwKzkFgZlZwvkaAzxta+1YN/32+mHcBtk58RGBmVnAOAjOzgnMQmJkVnK8RWMVVwzlt8HltswY+IjAzKzgHgZlZwTkIzMwKzkFgZlZwDgIzs4JzEJiZFZyDwMys4BwEZmYF5yAwMys4B4GZWcE5CMzMCs5BYGZWcA4CM7OCcxCYmRWcg8DMrOAyDQJJwyQ9J2mepDOa2D5K0uz08ZCkoVnWY2ZmH5VZEEjqDFwGHAgMBo6QNLhRsxeAz0XEEOAcYGJW9ZiZWdOyPCLYFZgXEfMjYjkwBTiktEFEPBQRb6aLjwA1GdZjZmZNyDIINgcWlCzXpeuacxxwZ4b1mJlZE7Kcs1hNrIsmG0r7kATBns1sHwOMAejfv3+l6jMzM7I9IqgDtihZrgEWNm4kaQhwOXBIRCxqakcRMTEiaiOitl+/fpkUa2ZWVFkGwWPAIEkDJXUDRgJTSxtI6g/cDHwtIv6eYS1mZtaMzE4NRcRKSScBdwOdgUkRMVfS2HT7BOBMoC/wK0kAKyOiNquazMzso7K8RkBETAOmNVo3oeT58cDxWdZgZmYt88hiM7OCcxCYmRWcg8DMrOAcBGZmBecgMDMrOAeBmVnBOQjMzArOQWBmVnAOAjOzgnMQmJkVnIPAzKzgHARmZgXnIDAzKzgHgZlZwTkIzMwKzkFgZlZwDgIzs4JzEJiZFZyDwMys4BwEZmYF5yAwMys4B4GZWcE5CMzMCs5BYGZWcA4CM7OCcxCYmRWcg8DMrOAcBGZmBecgMDMrOAeBmVnBOQjMzArOQWBmVnAOAjOzgss0CCQNk/ScpHmSzmhiuySNT7fPlrRzlvWYmdlHZRYEkjoDlwEHAoOBIyQNbtTsQGBQ+hgD/DqreszMrGlZHhHsCsyLiPkRsRyYAhzSqM0hwDWReATYQNKmGdZkZmaNdMlw35sDC0qW64DdymizOfByaSNJY0iOGACWSHqusqVmYmPg9UruUOdXcm9Vx59n5fizrKxq+Ty3bG5DlkGgJtbFWrQhIiYCEytRVFuRNCMiavOuo6Pw51k5/iwrqyN8nlmeGqoDtihZrgEWrkUbMzPLUJZB8BgwSNJASd2AkcDURm2mAkenvYd2B+oj4uXGOzIzs+xkdmooIlZKOgm4G+gMTIqIuZLGptsnANOALwLzgKXA17OqJwdVdSqrCvjzrBx/lpVV9Z+nIj5ySt7MzArEI4vNzArOQWBmVnAOAjOzgstyHEHhSOoREcvyrsPMsiOpO/AVYAAl36ER8eO8alpXDoLKekrSK8CDwHTgrxFRn3NNVUvSnsCgiLhSUj+gV0S8kHdd1UrS5iSjS0u/vKbnV1HV+gNQD8wE3su5lopwr6EKk9Qf2AvYg6Rr7OKI2CnfqqqPpLOAWuATEbGNpM2A30XEHjmXVpUknQ+MAJ4G3k9XR0R8Kb+qqpOkpyJih7zrqCQfEVSQpBqSANgLGArMBf6Sa1HV6zDgk8DjABGxUFLvfEuqaoeShGqH+AWbs4ck7RgRc/IupFIcBJX1EsmI6nMjYmzexVS55RERkgJA0sfyLqjKzQe60kFOZeRB0hySe6F1Ab4uaT7J5ymSo6sheda3LhwElfVJYE/gyHQinueBByLiinzLqko3Svo/kluTnwAcC/wm55qqjqRLSL68lgKzJN1HSRhExCl51VaFDsq7gKz4GkGFSepFEgZ7AUeR/FIYkGtRVUrSAcDnSX5x3R0R9+ZcUtWRdExL2yPi6raqpaOQtFETq9+OiBVtXkyFOAgqSNIMoDvwEMm1gekR8c98qzKzSpL0Isldk98k+ZGyAckcKq8CJ0TEzPyqWzsOggqS1C8iXsu7jo5A0tt8dG6KemAGcHpEzG/7qqpXyfntUg2f508iYlHbV1WdJE0AbomIu9PlzwPDgBuBX0ZE4wm42j0HQQVJ6gOcBXw2XfUA8GOPJWg9SWeTzE1xHcmvrpHA/wOeA/4rIvbOr7rqI+kCkm6j16WrRpJ8rvXAnhFxcF61VZumJqJpWCdpVjV2F3cQVJCkm4CngIbzrl8DhkbEl/OrqjpJ+lvjX1aSHomI3SU9GRFD86qtGkn6a+MxGA3rJM2JiB3zqq3aSLoHuI9kHnZIxmccQHJU8FhE7JxXbWvL9xqqrK0j4qyImJ8+zga2yruoKrVK0uGSOqWPw0u2+ddL6/WStDpYJe0K9EoXV+ZTUtU6kmQ2xVtJRhn3T9d1Bg5v4XXtlruPVta7kvaMiL8ASNoDeDfnmqrVKOCXwK9IvvgfAY6S1BM4Kc/CqtTxwKS0V5uAt4Dj0/EZP821sioTEa8DJzezeV5b1lIpPjVUQZKGAtcAfdJVbwLHRMTs/Koy+0B6HUsRsTjvWqqNpF9ExKmSbqOJo9Jqvl2HjwgqKCKeBIZKWj9dfkvSqYCDoJUk9QCOA7YHejSsj4hjcyuqCkk6KiKulXRao/UARMTFuRRWnX6b/r0w1yoy4CDIQES8VbJ4GvCLvGqpYr8FngW+APyY5FTRM7lWVJ0abs3h+zSto4bxARHxQN61VJpPDWVM0oKI2CLvOqqNpCci4pOSZkfEEEldSUYX75t3bVZs6bW/cXxwS++Gew1VbccQHxFkz0m7dhqG6y+WtAPwb5KJQKwVJI1vabvvNbRWrgC+TTIfwftraFsVHAQV0MwoWEh+KfRs43I6iomSNgR+CEwl6er4o3xLqkqltzs4m2TAo62b+oi4M+8iKsmnhqzdkdQJGB4RN+ZdS0fScLot7zqqlaSGgWKHk4wZuJkP38n18TzqqgQHgbVLkqZHxGfX3NLKJenxahz12l5I+nMLm6Oar185CKxdkvQjksF4NwDvNKyPiDdyK6rKOQisOQ4Ca5ckNTVJfVX3zMhDo+tX65FMUAMf9HRZP5fCrF1xEJiZFZxvOmftkqT1JP1Q0sR0eZCkDjtVoFmeHATWXl0JLAc+ky7XAT/JrxyzhKQZkk5Muzd3CA4Ca6+2jogLSAeWRcS7JOe1zfI2EtgMeEzSFElfUMPNm6qUg8Daq+XpLacDQNLWlPTZNstLRMyLiB8A25DM+DYJeEnS2c1MbN/uOQisvRoH3AVsIWkyyYxQ3821IrOUpCHARcDPgJuA4SRzPPwpz7rWlnsNWbslqS+wO8kpoUfSCUHMciVpJrCY5J5DN0XEeyXbbq7GqWkdBNYuSZoKXA9MjYh31tTerC2ktz85IyLOzbuWSvKpIWuvLgL2Ap6W9DtJw9PJasxyExGrSCap71B8RGDtmqTOwL7ACcAwj4S1vHXE2584CKzdSnsNHQyMAHYGbvP98y1vHfH2J56PwNolSTcAu5H0HLqUZAKQEbkWZQZExMC8a6g0XyOw9upK4KskXfKuJJlUxXMWW+464u1PfERg7YqkbUhGbh4BLCI5D6uI2CfXwsw+cCXJzG+ltz/5HXB7bhWtIx8RWHvzLLAfcHBE7BkRl9BB5oW1DqPD3f7EQWDtzVdIJqr/s6TfSNqPKv8/mXU4He72J+41ZO2SpI8Bh5KcItoXuBq4JSLuybUwKzxJnwd+AAwG7gH2AEZHxP151rUuHATW7qU38voqMKKa54W16ibpUuC6iHioo93+xEFgZlYGSd8i6ciwKUknhusjYla+VVWGg8DMrBUkbUkSCCOBHiT3xJoSEX/PtbB14CAwM1tLkj5JMh/BkIjonHc9a8u9hszMWkFSV0kHp/Nk3An8naS3W9XyEYGZWRkkHUDSi+0/gUeBKcCtHeE26Q4CM7MySPozydSUN1XznUab4iAwMys4XyMwMys4B4GZWcE5CKyqSDpMUkjadh32cZWk4enzyyUNrlyFIOn7jZaXVHL/ZpXmILBqcwTwF5LBPOssIo6PiKcrsa8S319zE7P2w0FgVUNSL5IbfB1HGgSS9pY0XdItkp6WNEFSp3TbEkkXSXpc0n2S+jWxz/sl1abPh6Vtn5R0X7puV0kPSXoi/fuJdP1oSTdLukvS85IuSNefB/SUNCvtZ176Xnun7/d7Sc9KmixJ6bZd0v0/KelRSb0l9ZB0paQ56fvvU/Let0q6TdILkk6SdFra5pH03kxI2jqtb6akB9flKMo6uIjww4+qeABHAVekzx8imcd4b2AZsBXQGbgXGJ62CWBU+vxM4NL0+VUlbe4HaoF+wAJgYLp+o/Tv+kCX9Pn+JF0HAUYD84E+JLcZ+CewRbptSaO6l6R/9wbqgRqSH2EPA3sC3dJ97VL6nsDpwJXpum2Bl9L3Gg3MA3qnddcDY9N2PwdOTZ/fBwxKn+8G/Cnv/w39aJ8Pz1Bm1eQI4Bfp8ynp8h3AoxExH0DS9SRfrr8HVpHcHAzgWuDmFva9OzA9Il4AiA/6ifcBrpY0iCRYupa85r6IqE/f92lgS5IwacmjEVGXvmYWMIDki/zliHgsfe+30u17Apek656V9E9gm3Q/f46It4G3JdUDt6Xr5wBD0qOnzwC/Sw86ALqvoTYrKAeBVYX0tr/7AjtICpJf/wFMS/+Wam5wTEuDZtTM9nNIvnQPkzSA5AiiQelkJO9T3v+fmnpNc+/d0oQ8pftZVbK8Kt1nJ2BxROxURk1WcL5GYNViOHBNRGwZEQMiYgvgBZJf/7tKGpheGxhBcjEZkv++h6fPjyxZ35SHgc9JGgir50CA5IjgX+nz0WXWukJS1zU3W+1ZYDNJu6Tv3VtSF2A6MCpdtw3QH3iunB2mRxUvSPpq+npJGtqKmqxAHARWLY4Abmm07iaSL/iHgfOAp0jCoaHdO8D2kmaSHE38uLmdR8RrwBjgZklP8sEppQuAn0r6K8lRSDkmArMbXyxu4b2XkwTYJel730tyLeBXQGdJc9J6RkdEa6ZEHAUcl+5zLnBIK15rBeJbTFhVk7Q38N8RcVAT25ZERK+2r8qsuviIwMys4HxEYGZWcD4iMDMrOAeBmVnBOQjMzArOQWBmVnAOAjOzgnMQmJkV3P8H6EnDOl50+CoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0,2500,4000,6000,81000]\n",
    "group=['Low','Average','High','Very high']\n",
    "train['Income_bin']=pd.cut(train['ApplicantIncome'],bins,labels=group)\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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will analyze the coapplicant income and loan amount variable in similar manner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0,1000,3000,42000]\n",
    "group=['Low','Average','High']\n",
    "train['Coapplicant_Income_bin']=pd.cut(train['CoapplicantIncome'],bins,labels=group)\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": "markdown",
   "metadata": {},
   "source": [
    "It shows that if co-applicants 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 co-applicant so the co-applicant 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 co-applicants income to visualize the combined effect of income on loan approval."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Total_Income']=train['ApplicantIncome']+train['CoapplicantIncome']\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)\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 compared to that of applicants with Average, High & Very High Income. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the Loan Amount variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0,100,200,700]\n",
    "group=['Low','Average','High']\n",
    "train['LoanAmount_bin']=pd.cut(train['LoanAmount'],bins,labels=group)\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. 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": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(train.dtypes)\n",
    "train=train.drop(['Income_bin', 'Coapplicant_Income_bin', 'LoanAmount_bin', 'Total_Income_bin', 'Total_Income'], axis=1)\n",
    "train['Dependents'].replace('3+', 3,inplace=True)\n",
    "test['Dependents'].replace('3+', 3,inplace=True)\n",
    "train['Loan_Status'].replace('N', 0,inplace=True)\n",
    "train['Loan_Status'].replace('Y', 1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at the correlation between all the numerical variables. 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": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc518602460>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrix = train.corr()\n",
    "f, ax = plt.subplots(figsize=(9,6))\n",
    "sns.heatmap(matrix,vmax=.8,square=True,cmap=\"BuPu\", annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the most correlate variables are (ApplicantIncome - LoanAmount) and (Credit_History - Loan_Status). LoanAmount is also correlated with CoapplicantIncome."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing value imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's list out feature-wise count of missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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.\n",
    "\n",
    "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",
    "* 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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 80,
   "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": 80,
     "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "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 the median to fill the null values as earlier we saw that the loan amount has 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": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['LoanAmount'].fillna(train['LoanAmount'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets check whether all the missing values are filled in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that all the missing values have been filled in the test dataset. Let’s fill all the missing values in the test dataset too with the same approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Gender'].fillna(train['Gender'].mode()[0], inplace=True)\n",
    "test['Married'].fillna(train['Married'].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": "markdown",
   "metadata": {},
   "source": [
    "## Outlier Treatment\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. Let's examine what can happen to a data set with outliers. For the sample data set: <br/>\n",
    "1,1,2,2,2,2,3,3,3,4,4 <br/>\n",
    "We find the following: mean, median, mode, and standard deviation <br/>\n",
    "Mean = 2.58 <br/>\n",
    "Median = 2.5 <br/>\n",
    "Mode=2 <br/>\n",
    "Standard Deviation = 1.08 <br/>\n",
    "If we add an outlier to the data set: <br/>\n",
    "1,1,2,2,2,2,3,3,3,4,4,400 <br/>\n",
    "The new values of our statistics are: <br/>\n",
    "Mean = 35.38 <br/>\n",
    "Median = 2.5 <br/>\n",
    "Mode=2 <br/>\n",
    "Standard Deviation = 114.74 <br/>\n",
    "It can be seen that having outliers 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. <br/>\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. 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. <br/>\n",
    "Let’s visualize the effect of log transformation. We will do the similar changes to the test file simultaneously. <br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kEU3jbRMMd62KtxCQNgbn3CWpQZ65b0At/mVf0nitWbgnuR54P3AJcHdV3blWx9LqOLUitW9Nwj3JJcAfAW8FzgD/mORIVT22Fseb1B8zpvGPKJIEazfnfg3wRFV9oaq+ARwGdq/RsSRJ50hVjX+nyc8B11fVL3bL7wF+pKp+eck2+4B93eLrgM8DVwL/NvaCNj77Mph9OZ89GazVvnxXVb1m0Iq1mnPPgLH/91Okqg4CB//fi5LjVTW3RjVtWPZlMPtyPnsy2MXYl7WaljkDXLVkeTvw9BodS5J0jrUK938EdiS5Osm3A3uAI2t0LEnSOdZkWqaqFpL8MvA3LF4K+cGqenQFLz24/CYXJfsymH05nz0Z7KLry5r8QVWSNFnefkCSGmS4S1KDJh7uSa5K8ndJTiV5NMmtk65pGiR5RZJPJ/mnri+/M+mapkmSS5J8JslfTrqWaZHkdJKTST6b5Pik65kWSS5Lcn+Sz3U582OTrmk9TMONwxaA/VX1cJLvAE4kObpWtyrYQF4EfrKq5pO8DPiHJH9dVQ9NurApcStwCnjVpAuZMruqqsUv64zi/cDHq+rnuqv3XjnpgtbDxM/cq+qZqnq4e/6fLH5gt022qsmrRfPd4su6f/71G0iyHbgBuHvStWi6JXkV8BbgHoCq+kZVPTfZqtbHxMN9qSSzwA8Dn5psJdOhm3r4LHAWOFpV9mXRHwK/AfzPpAuZMgV8IsmJ7vYegu8GvgL8STeNd3eSSydd1HqYmnBPsgX4CPCrVfUfk65nGlTVN6vqh1j8hu81Sd446ZomLcnbgbNVdWLStUyha6vqTcDPALckecukC5oCm4A3AX9cVT8MPA8cmGxJ62Mqwr2bU/4IcG9VfXTS9Uyb7tfIPnD9hEuZBtcC70hymsW7jf5kkj+dbEnToaqe7h7PAh9j8e6sF7szwJklv/Xez2LYN2/i4Z4kLM6Hnaqq3590PdMiyWuSXNY93wz8FPC5yVY1eVV1W1Vtr6pZFm9r8bdV9e4JlzVxSS7tLkigm3b4aeCRyVY1eVX1ZeBLSV7XDV0HXBQXa0zD1TLXAu8BTnbzywC/VVV/NcGapsFW4FD3H598G3BfVXnZny5kBvjY4rkSm4A/q6qPT7akqfE+4N7uSpkvAD8/4XrWhbcfkKQGTXxaRpI0foa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJatD/AjM7GSOQHsSCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['LoanAmount_log']=np.log(train['LoanAmount'])\n",
    "train['LoanAmount_log'].hist(bins=20)\n",
    "test['LoanAmount_log']=np.log(test['LoanAmount'])"
   ]
  },
  {
   "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": [
    "## Model Building : Part I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us make our first model to predict the target variable. We will start with Logistic Regression which is used for predicting binary outcome. <br />\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 <br />\n",
    "\n",
    "To learn further on logistic regression, refer this article: https://www.analyticsvidhya.com/blog/2015/10/basics-logistic-regression/ <br />\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. <br />\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "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 ef\u0000cient tool which contains many inbuilt functions that can be used for modeling in Python. <br />\n",
    "\n",
    "To learn further about sklearn, refer here: http://scikit-learn.org/stable/tutorial/index.html <br />\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. <br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(X)\n",
    "train=pd.get_dummies(train)\n",
    "test=pd.get_dummies(test)"
   ]
  },
  {
   "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). <br />\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. <br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "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": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "model = LogisticRegression()\n",
    "model.fit(x_train, y_train)\n",
    "# LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1, penalty='12',random_state=1, solver='liblinear', tol=0.0001, verbose=0, warm_start=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the C 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": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8108108108108109"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_cv = model.predict(x_cv)\n",
    "accuracy_score(y_cv,pred_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So our predictions are almost 80% accurate, i.e. we have identified 80% of the loan status correctly.\n",
    "\n",
    "Let’s make predictions for the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_test = model.predict(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets import the submission file which we have to submit on the solution checker."
   ]
  },
  {
   "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>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>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Loan_Status\n",
       "0  LP001015           N\n",
       "1  LP001022           N\n",
       "2  LP001031           N\n",
       "3  LP001035           N\n",
       "4  LP001051           N"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.read_csv('Dataset/sample_submission_49d68Cx.csv')\n",
    "submission.head()"
   ]
  },
  {
   "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": 94,
   "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": 95,
   "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": [
    "Finally we will convert the submission to .csv format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Output/logistic.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression using stratified k-folds cross validation\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",
    "* 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\n",
    "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s import StratifiedKFold from sklearn and fit the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5 \n",
      "accuracy_score 0.8048780487804879\n",
      "\n",
      "2 of kfold 5 \n",
      "accuracy_score 0.7642276422764228\n",
      "\n",
      "3 of kfold 5 \n",
      "accuracy_score 0.7804878048780488\n",
      "\n",
      "4 of kfold 5 \n",
      "accuracy_score 0.8455284552845529\n",
      "\n",
      "5 of kfold 5 \n",
      "accuracy_score 0.8032786885245902\n",
      "\n",
      " Mean Validation Accuracy 0.7996801279488205\n"
     ]
    }
   ],
   "source": [
    "i=1\n",
    "mean = 0\n",
    "kf = StratifiedKFold(n_splits=5,random_state=1)\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",
    "    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 += score\n",
    "    print ('accuracy_score',score)\n",
    "    i+=1\n",
    "    pred_test = model.predict(test)\n",
    "    pred = model.predict_proba(xvl)[:,1]\n",
    "print ('\\n Mean Validation Accuracy',mean/(i-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean validation accuracy for this model turns out to be 0.80. Let us visualize the roc curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m/pDWx9xJkiRJG6XObixc6eWI+Hfgk8AHI6IX0KeyZUmSJEm1q5xO9MeB14HjM/NZYBBwdkWrkiRJkmpYlyG6FJyvBDaPiIOA1zLzPytemSRJklSjynk6x5HA/cARwJHAzIg4vNKFSZIkSbWqnDnRZwE7Z+ZzABExEPgdcH0lC5MkSZJqVTkhepOVAbrkBcqbSy2pm1w182mmNy9Y6/3nLFpKQ31dN1YkSdLGrZwQfVtE3A5cXVr+OHBr5UqStLrpzQvWKQg31NcxuWlQN1clSdLGq8sQnZn/FhGHAh8AArgkM2+seGWSVtFQX8e1J06odhmSJIlOQnREDAfOAd4H/BH4Qmau/d+TJUmSpA1EZ3ObLwNuAQ4DHgJ+1CMVSZIkSTWus+kcm2Xmj0ufPx4RD/dEQdKGyBsDJUnasHQWovtFxFha50EDvLP9cmYaqqUyeWOgJEkbls5C9CLg3HbLz7ZbTuBDlSpK2hB5Y6AkSRuONYbozJzYk4VIkiRJ6wtfmiJJkiQVZIiWJEmSCjJES5IkSQV1GaKj1Sci4qul5SERsUvlS5MkSZJqUzmd6AuBCcBRpeWXgQsqVpEkSZJU4zp7xN1Ku2bmuIj4A0Bm/i0i+la4LkmSJKlmldOJfjMietH6bGgiYiDwVkWrkiRJkmpYOSH6fOBGYOuI+DbwP8B3KlqVJEmSVMO6nM6RmVdGxEPAJFpf+f2xzHys4pVJkiRJNarLEB0RQ4BlwM3t12Xm05UsTJIkSapV5dxY+Cta50MH0A8YBjwONFawLkmSJKlmlTOdY8f2yxExDjixYhVJkiRJNa7wGwsz82Fg5wrUIkmSJK0XypkTfXq7xU2AccDiilUkSZIk1bhyOtGbtft4B61zpCeXc/CI2C8iHo+IeRFxZifjdo6IFRFxeDnHlSRJkqqp00506SUrm2bmvxU9cGnfC4CPAC3AAxFxU2bO6WDc94Hbi55DkiRJqoY1dqIjondmrqB1+sba2AWYl5l/ycw3gGvouIP9L8AvgOfW8jySJElSj+qsE30/rQG6OSJuAn4OvLpyY2be0MWxBwHPtFtuAXZtPyAiBgGHAB/CmxUlSZK0nijnOdHvBl6gNeiufF50Al2F6OhgXa62fB5wRmauiOhoeOlAEVOBqQBDhgwpo2RJkiSpcjoL0VuXnszxKP8IzyutHoY70gJs2255MLBwtTHjgWtKAXor4ICIWJ6Zv2w/KDMvAS4BGD9+fDnnliRJkiqmsxDdC9iU8jrKHXkAGB4Rw4AFwBTg6FUOkjls5ecRcTlwy+oBWpIkSao1nYXoRZn5f9b2wJm5PCJOpfWpG72AyzJzdkScVNp+8doeW5IkSaqmzkL0micplykzbwVuXW1dh+E5M49d1/NJkiRJPaGzl61M6rEqJEmSpPXIGkN0Zr7Yk4VIkiRJ64tyXvstSZIkqR1DtCRJklSQIVqSJEkqyBAtSZIkFWSIliRJkgoyREuSJEkFGaIlSZKkggzRkiRJUkGGaEmSJKmg3tUuQFofXDXzaaY3L1jr/ecsWkpDfV03ViRJkqrJTrRUhunNC5izaOla799QX8fkpkHdWJEkSaomO9FSmRrq67j2xAnVLkOSJNUAO9GSJElSQYZoSZIkqSBDtCRJklSQIVqSJEkqyBAtSZIkFWSIliRJkgryEXfaKPiyFEmS1J3sRGuj4MtSJElSd7ITrY2GL0uRJEndxU60JEmSVJAhWpIkSSrIEC1JkiQVZIiWJEmSCjJES5IkSQUZoiVJkqSCDNGSJElSQYZoSZIkqSBDtCRJklSQIVqSJEkqyBAtSZIkFWSIliRJkgoyREuSJEkFGaIlSZKkggzRkiRJUkGGaEmSJKkgQ7QkSZJUkCFakiRJKsgQLUmSJBVkiJYkSZIKMkRLkiRJBRmiJUmSpIIM0ZIkSVJBhmhJkiSpIEO0JEmSVJAhWpIkSSrIEC1JkiQVZIiWJEmSCjJES5IkSQUZoiVJkqSCDNGSJElSQYZoSZIkqSBDtCRJklSQIVqSJEkqyBAtSZIkFWSIliRJkgoyREuSJEkFGaIlSZKkggzRkiRJUkGGaEmSJKkgQ7QkSZJUkCFakiRJKsgQLUmSJBVkiJYkSZIKMkRLkiRJBRmiJUmSpIIM0ZIkSVJBFQ3REbFfRDweEfMi4swOth8TEbNKH/dGxJhK1iNJkiR1h4qF6IjoBVwA7A80AEdFRMNqw+YDe2XmaOCbwCWVqkeSJEnqLr0reOxdgHmZ+ReAiLgGmAzMWTkgM+9tN/4+YHAF69E6uGrm00xvXlDtMtbanEVLaaivq3YZkiRpA1HJ6RyDgGfaLbeU1q3Jp4BfV7AerYPpzQuYs2hptctYaw31dUxu6uw/P0mSpPJVshMdHazLDgdGTKQ1RH9gDdunAlMBhgwZ0l31qaCG+jquPXFCtcuQJEmqukp2oluAbdstDwYWrj4oIkYDlwKTM/OFjg6UmZdk5vjMHD9w4MCKFCtJkiSVq5Ih+gFgeEQMi4i+wBTgpvYDImIIcAPwycycW8FaJEmSpG5Tsekcmbk8Ik4Fbgd6AZdl5uyIOKm0/WLgq8CWwIURAbA8M8dXqiZJkiSpO1RyTjSZeStw62rrLm73+QnACZWsQZIkSepuvrFQkiRJKsgQLUmSJBVkiJYkSZIKMkRLkiRJBRmiJUmSpIIM0ZIkSVJBhmhJkiSpoIo+J1q146qZTzO9ecFa7z9n0VIa6uu6sSJJkqT1l53ojcT05gXMWbR0rfdvqK9jctOgbqxIkiRp/WUneiPSUF/HtSdOqHYZkiRJ6z070ZIkSVJBhmhJkiSpIEO0JEmSVJAhWpIkSSrIEC1JkiQVZIiWJEmSCjJES5IkSQUZoiVJkqSCDNGSJElSQYZoSZIkqSBDtCRJklSQIVqSJEkqyBAtSZIkFWSIliRJkgoyREuSJEkFGaIlSZKkggzRkiRJUkGGaEmSJKkgQ7QkSZJUkCFakiRJKsgQLUmSJBVkiJYkSZIKMkRLkiRJBRmiJUmSpIIM0ZIkSVJBhmhJkiSpIEO0JEmSVJAhWpIkSSrIEC1JkiQVZIiWJEmSCjJES5IkSQUZoiVJkqSCDNGSJElSQYZoSZIkqSBDtCRJklSQIVqSJEkqyBAtSZIkFWSIliRJkgoyREuSJEkFGaIlSZKkggzRkiRJUkGGaEmSJKkgQ7QkSZJUkCFakiRJKqh3tQtQea6a+TTTmxes9f5zFi2lob6uGyuSJEnaeNmJXk9Mb17AnEVL13r/hvo6JjcN6saKJEmSNl52otcjDfV1XHvihGqXIUmStNGzEy1JkiQVZIiWJEmSCjJES5IkSQUZoiVJkqSCDNGSJElSQYZoSZIkqSBDtCRJklSQIVqSJEkqyBAtSZIkFWSIliRJkgoyREuSJEkF9a52AeuLq2Y+zfTmBVU7/5xFS2mor6va+SVJkvQPdqLLNL15AXMWLa3a+Rvq65jcNKhq55ckSdI/VLQTHRH7AT8EegGXZub3Vtsepe0HAMuAYzPz4UrWtC4a6uu49sQJ1S5DkiRJVVaxEB0RvYALgI8ALcADEXFTZs5pN2x/YHjpY1fgotK/kiRtlN58801aWlp47bXXql2KtNHo168fgwcPpk+fPmXvU8lO9C7AvMz8C0BEXANMBtqH6MnAf2ZmAvdFxLsioj4zF1WwLkmSalZLSwubbbYZQ4cOpfUPtpIqKTN54YUXaGlpYdiwYWXvV8k50YOAZ9ott5TWFR0jSdJG47XXXmPLLbc0QEs9JCLYcsstC//1p5Kd6I5++nMtxhARU4GpAEOGDFn3ytZCwzY+GUOS1DMM0FLPWpufuUp2oluAbdstDwYWrsUYMvOSzByfmeMHDhzY7YWW42sHN/K1gxurcm5JkmrZpptuCsDChQs5/PDDOxyz99578+CDD3Z6nPPOO49ly5a1LR9wwAG89NJL3VdoD7vtttvYYYcd2H777fne977X4Zizzz6bpqYmmpqaGDVqFL169eLFF1/scv8f/ehH7LDDDjQ2NvLFL34RgCuvvLLtWE1NTWyyySY0NzcD8MYbbzB16lTe//73M2LECH7xi1+0Heu6666joaGBxsZGjj766Lb1Z5xxBqNGjWLUqFFce+21beunTZvG9ttvT0Tw/PPPd/m1PPPMM0ycOJGRI0fS2NjID3/4w7Z9HnnkESZMmMCOO+7IwQcfzNKlrU9Cu//++9uONWbMGG688UYAli1bxoEHHsiIESNobGzkzDPPbDvWxRdfzI477khTUxMf+MAHmDOndQbxU089xU477URTUxONjY1cfPHF5V7CzmVmRT5o7XL/BRgG9AUeARpXG3Mg8GtaO9K7Afd3ddyddtopJUnaUM2ZM6faJRQ2YMCALsfstdde+cADD3Q65r3vfW8uXry4u8qqquXLl+d2222Xf/7zn/P111/P0aNH5+zZszvd56abbsqJEyd2uf8dd9yRkyZNytdeey0zM//617++7VizZs3KYcOGtS1/9atfzbPOOiszM1esWNH2fZ47d242NTXliy++uMqxbrnllvzwhz+cb775Zr7yyiu500475ZIlSzIz8+GHH8758+d3er3afy0LFy7Mhx56KDMzly5dmsOHD2/7WsaPH5933XVXZmb+5Cc/yS9/+cuZmfnqq6/mm2++2bb/wIED880338xXX30177jjjszMfP311/MDH/hA3nrrrZmZbfVlZk6fPj333XfftnErv1cvv/xyvve9780FCxa8reaOfvaAB3MNmbRinejMXA6cCtwOPAZcl5mzI+KkiDipNOzWUtCeB/wY+Eyl6pEkSV0744wzuPDCC9uWv/71r/ODH/yAV155hUmTJjFu3Dh23HFHpk+f/rZ9n3zySUaNGgXA3//+d6ZMmcLo0aP5+Mc/zt///ve2cSeffDLjx4+nsbGRr33tawCcf/75LFy4kIkTJzJx4kQAhg4d2tbpPPfcc9u6ouedd17b+UaOHMmnP/1pGhsb2WeffVY5T0fuv/9+dt99d8aOHcvuu+/O448/DsDll1/Oqaee2jbuoIMO4q677gJaO8Ljxo1jzJgxTJo0qazv4/3338/222/PdtttR9++fZkyZUqH37P2rr76ao466qgu97/ooos488wzecc73gHA1ltv3emxAC677DL+/d//HYBNNtmErbbaCoAf//jHnHLKKWyxxRarHGvOnDnstdde9O7dmwEDBjBmzBhuu+02AMaOHcvQoUPL/lrq6+sZN24cAJttthkjR45kwYLWF9g9/vjj7LnnngB85CMfaeuQ9+/fn969W2cdv/baa23TLfr379/230ffvn0ZN24cLS0tANTV/WPq7auvvtq2T9++fdu+V6+//jpvvfVWp7WXq6LPic7MW2kNyu3XXdzu8wROqWQNkiStr75x82zmLOzeF301bFPX6fTEKVOmcNppp/GZz7T2ta677jpuu+02+vXrx4033khdXR3PP/88u+22Gx/96EfXOJf0oosuon///syaNYtZs2a1hSiAb3/727z73e9mxYoVTJo0iVmzZvHZz36Wc889lzvvvLMt4K300EMP8dOf/pSZM2eSmey6667stddebLHFFjzxxBNcffXV/PjHP+bII4/kF7/4BZ/4xCfW+PWNGDGCGTNm0Lt3b373u9/xpS99aZWpDatbvHgxn/70p5kxYwbDhg1rm2px55138vnPf/5t4/v378+9997LggUL2Hbbf8xYHTx4MDNnzlzjeZYtW8Ztt93GtGnTADrdf+7cudxzzz2cddZZ9OvXj3POOYedd955leNde+21baF75ZSYr3zlK9x11128733vY9q0abznPe9h7ty5AOyxxx6sWLGCr3/96+y3336MGTOGb3zjG5x++uksW7aMO++8k4aGhjXW39nX0t6TTz7JH/7wB3bdtfWJxqNGjeKmm25i8uTJ/PznP+eZZ/7xvImZM2dy/PHH89RTT/Gzn/2sLVSv9NJLL3HzzTfzuc99rm3dBRdcwLnnnssbb7zBHXfc0bb+mWee4cADD2TevHmcffbZbLPNNmV9LZ3xjYWSJKnN2LFjee6551i4cCGPPPIIW2yxBUOGDCEz+dKXvsTo0aP58Ic/zIIFCw4eA7IAAA8tSURBVPjrX/+6xuPMmDGjLcyOHj2a0aNHt2277rrrGDduHGPHjmX27Nltc1fX5H/+53845JBDGDBgAJtuuimHHnoo99xzDwDDhg2jqakJgJ122oknn3yy02MtWbKEI444glGjRvH5z3+e2bNndzr+vvvuY88992x79Nm73/1uACZOnEhzc/PbPu69916AldNWV9HZzWs333wze+yxR9vxO9t/+fLl/O1vf+O+++7j7LPP5sgjj1xl/MyZM+nfv3/bXwWWL19OS0sLe+yxBw8//DATJkzgC1/4Qtu2J554grvuuourr76aE044gZdeeol99tmHAw44gN13352jjjqKCRMmvC3Elvu1rPTKK69w2GGHcd5557V1jS+77DIuuOACdtppJ15++WX69u3bNn7XXXdl9uzZPPDAA3z3u99d5ekZy5cv56ijjuKzn/0s2223Xdv6U045hT//+c98//vf51vf+lbb+m233ZZZs2Yxb948rrjiik7/2y1XRTvRkiRp7VXrhvbDDz+c66+/nmeffZYpU6YArTetLV68mIceeog+ffowdOjQLh8J1lFonD9/Pueccw4PPPAAW2yxBccee2yXx+koUK608s/0AL169epyOsdXvvIVJk6cyI033siTTz7J3nvvDUDv3r1X+TP/ypoys8Ovo6tO9ODBg1fpqra0tHTa/bzmmmtWmX7R2f6DBw/m0EMPJSLYZZdd2GSTTXj++edZ+fCF1Y+15ZZb0r9/fw455BAAjjjiCH7yk5+0HWu33XajT58+DBs2jB122IEnnniCnXfembPOOouzzjoLgKOPPprhw4evsf7OvhZofYnQYYcdxjHHHMOhhx7atn7EiBH85je/AVo77L/61a/edryRI0cyYMAAHn30UcaPHw/A1KlTGT58OKeddlqHNUyZMoWTTz75beu32WYbGhsbueeee9Z4E2y57ERLkqRVTJkyhWuuuYbrr7++LWgsWbKErbfemj59+nDnnXfy1FNPdXqMPffckyuvvBKARx99lFmzZgGwdOlSBgwYwOabb85f//pXfv3rX7fts9lmm/Hyyy93eKxf/vKXLFu2jFdffZUbb7yRD37wg52ef9q0aR1OJ1iyZAmDBrW+kuLyyy9vWz906FCam5t56623eOaZZ7j//vsBmDBhAnfffTfz588HaJvO0VUneuedd+aJJ55g/vz5vPHGG1xzzTV89KMf7bDWJUuWcPfddzN58uS2dZ3t/7GPfaxtqsLcuXN544032qbAvPXWW/z85z9v++UHWn+ZOfjgg9vmeP/3f/9329SMj33sY9x5550APP/888ydO5ftttuOFStW8MILLwC0TcnZZ599Ov2er+lryUw+9alPMXLkSE4//fRVxj/33HNtdX/rW9/ipJNab5ubP38+y5cvB1qfrvH444+3zcP+8pe/zJIlS9rmxq/0xBNPtH3+q1/9qi30t7S0tP1y9be//Y3f//737LDDDl1+LV2xEy1JklbR2NjIyy+/zKBBg6ivrwfgmGOO4eCDD2b8+PE0NTUxYsSITo9x8sknc9xxxzF69GiamprYZZddABgzZgxjx46lsbGR7bbbjj322KNtn6lTp7L//vtTX1/fFuwAxo0bx7HHHtt2jBNOOIGxY8d2OnXjT3/60yrHXumLX/wi//zP/8y5557Lhz70obb1e+yxB8OGDWPHHXdk1KhRbXO4Bw4cyCWXXMKhhx7KW2+9xdZbb81vf/vbLr6DrZ3tadOmse+++7JixQqOP/54Ghtb/7Kw8hFrKwPjjTfeyD777MOAAQPK2v/444/n+OOPZ9SoUfTt25crrriirVs+Y8YMBg8evMoUB4Dvf//7fPKTn+S0005j4MCB/PSnPwVg33335Te/+Q0NDQ306tWLs88+u+3FIyt/Uamrq+O//uu/2qZznH/++fzHf/wHzz77LKNHj+aAAw7g0ksvXePX8vvf/56f/exnbY+fA/jOd77DAQccwNVXX80FF1wAwKGHHspxxx0HtE7h+d73vkefPn3YZJNNuPDCC9lqq61oaWnh29/+NiNGjGi7RqeeeionnHAC06ZN43e/+x19+vRhiy224IorrgDgscce41//9V+JCDKTL3zhC+y4445dXsOuRGd/IqlF48ePz66eMylJ0vrqscceY+TIkdUuY7130EEHccMNN6wyx1bqTEc/exHxUGaO72i8nWhJkrTBueWWW6pdgjZwzomWJEmSCjJES5IkSQUZoiVJqjHr2/1K0vpubX7mDNGSJNWQfv368cILLxikpR6Smbzwwgv069ev0H7eWChJUg0ZPHgwLS0tLF68uNqlSBuNfv36MXjw4EL7GKIlSaohK98cJ6m2OZ1DkiRJKsgQLUmSJBVkiJYkSZIKWu9e+x0Ri4GnqnT6rYDnq3Ru9Qyv8cbB67xx8DpvHLzOG75qXuP3ZubAjjasdyG6miLiwTW9P10bBq/xxsHrvHHwOm8cvM4bvlq9xk7nkCRJkgoyREuSJEkFGaKLuaTaBajivMYbB6/zxsHrvHHwOm/4avIaOydakiRJKshOtCRJklSQIXo1EbFfRDweEfMi4swOtkdEnF/aPisixlWjTq2bMq7zMaXrOysi7o2IMdWoU+umq+vcbtzOEbEiIg7vyfq07sq5xhGxd0Q0R8TsiLi7p2vUuivj/9mbR8TNEfFI6TofV406tfYi4rKIeC4iHl3D9prLX4bodiKiF3ABsD/QABwVEQ2rDdsfGF76mApc1KNFap2VeZ3nA3tl5mjgm9TofCytWZnXeeW47wO392yFWlflXOOIeBdwIfDRzGwEjujxQrVOyvxZPgWYk5ljgL2BH0RE3x4tVOvqcmC/TrbXXP4yRK9qF2BeZv4lM98ArgEmrzZmMvCf2eo+4F0RUd/ThWqddHmdM/PezPxbafE+YHAP16h1V87PM8C/AL8AnuvJ4tQtyrnGRwM3ZObTAJnpdV7/lHOdE9gsIgLYFHgRWN6zZWpdZOYMWq/bmtRc/jJEr2oQ8Ey75ZbSuqJjVNuKXsNPAb+uaEWqhC6vc0QMAg4BLu7ButR9yvlZfj+wRUTcFREPRcQ/9Vh16i7lXOdpwEhgIfBH4HOZ+VbPlKceUnP5q3c1T16DooN1qz++pJwxqm1lX8OImEhriP5ARStSJZRznc8DzsjMFa0NLK1nyrnGvYGdgEnAO4H/jYj7MnNupYtTtynnOu8LNAMfAt4H/DYi7snMpZUuTj2m5vKXIXpVLcC27ZYH0/pbbdExqm1lXcOIGA1cCuyfmS/0UG3qPuVc5/HANaUAvRVwQEQsz8xf9kyJWkfl/j/7+cx8FXg1ImYAYwBD9PqjnOt8HPC9bH1u77yImA+MAO7vmRLVA2oufzmdY1UPAMMjYljphoQpwE2rjbkJ+KfSXaK7AUsyc1FPF6p10uV1joghwA3AJ+1Yrbe6vM6ZOSwzh2bmUOB64DMG6PVKOf/Png58MCJ6R0R/YFfgsR6uU+umnOv8NK1/bSAi3gPsAPylR6tUpdVc/rIT3U5mLo+IU2m9S78XcFlmzo6Ik0rbLwZuBQ4A5gHLaP3tV+uRMq/zV4EtgQtLXcrlmTm+WjWruDKvs9Zj5VzjzHwsIm4DZgFvAZdmZoeP0FJtKvNn+ZvA5RHxR1r/7H9GZj5ftaJVWERcTeuTVbaKiBbga0AfqN385RsLJUmSpIKcziFJkiQVZIiWJEmSCjJES5IkSQUZoiVJkqSCDNGSJElSQYZoSSooIlZERHO7j6GdjH2lG853eUTML53r4YiYsBbHuDQiGkqff2m1bfeua42l46z8vjwaETdHxLu6GN8UEQd0x7klqaf5iDtJKigiXsnMTbt7bCfHuBy4JTOvj4h9gHMyc/Q6HG+da+rquBFxBTA3M7/dyfhjgfGZeWp31yJJlWYnWpLWUURsGhH/XeoS/zEiJncwpj4iZrTr1H6wtH6fiPjf0r4/j4iuwu0MYPvSvqeXjvVoRJxWWjcgIn4VEY+U1n+8tP6uiBgfEd8D3lmq48rStldK/17bvjNc6oAfFhG9IuLsiHggImZFxIllfFv+FxhUOs4uEXFvRPyh9O8OpTfP/R/g46VaPl6q/bLSef7Q0fdRkmqFbyyUpOLeGRHNpc/nA0cAh2Tm0ojYCrgvIm7KVf/UdzRwe2Z+OyJ6Af1LY78MfDgzX42IM4DTaQ2Xa3Iw8MeI2InWN3btSusb2mZGxN3AdsDCzDwQICI2b79zZp4ZEadmZlMHx74G+DhwaynkTgJOBj5F6yt2d46IdwC/j4jfZOb8jgosfX2TgJ+UVv0J2LP05rkPA9/JzMMi4qu060RHxHeAOzLz+NJUkPsj4neZ+Won3w9JqgpDtCQV9/f2ITQi+gDfiYg9aX219CDgPcCz7fZ5ALisNPaXmdkcEXsBDbSGUoC+tHZwO3J2RHwZWExrqJ0E3LgyYEbEDcAHgduAcyLi+7ROAbmnwNf1a+D8UlDeD5iRmX8vTSEZHRGHl8ZtDgyn9ReI9lb+cjEUeAj4bbvxV0TEcCApvcq3A/sAH42IL5SW+wFDgMcKfA2S1CMM0ZK07o4BBgI7ZeabEfEkrQGwTWbOKIXsA4GfRcTZwN+A32bmUWWc498y8/qVC6WO7ttk5txSl/oA4LuljnFnne32+74WEXcB+9Lakb565emAf8nM27s4xN8zs6nU/b4FOAU4H/gmcGdmHlK6CfOuNewfwGGZ+Xg59UpSNTknWpLW3ebAc6UAPRF47+oDIuK9pTE/pnWawzjgPmCPiFg5x7l/RLy/zHPOAD5W2mcAcAhwT0RsAyzLzP8CzimdZ3VvljriHbmG1mkiHwRWhubbgZNX7hMR7y+ds0OZuQT4LPCF0j6bAwtKm49tN/RlYLN2y7cD/xKltnxEjF3TOSSp2gzRkrTurgTGR8SDtHal/9TBmL2B5oj4A3AY8MPMXExrqLw6ImbRGqpHlHPCzHwYuBy4H5gJXJqZfwB2pHUucTNwFvCtDna/BJi18sbC1fwG2BP4XWa+UVp3KTAHeDgiHgX+H138JbNUyyPAFOA/aO2K/x7o1W7YnUDDyhsLae1Y9ynV9mhpWZJqko+4kyRJkgqyEy1JkiQVZIiWJEmSCjJES5IkSQUZoiVJkqSCDNGSJElSQYZoSZIkqSBDtCRJklSQIVqSJEkq6P8DMfMU9VtGOqgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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 value of 0.70."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Output/Log1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering\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",
    "* **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",
    "* **EMI** - 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Total_Income']=train['ApplicantIncome']+train['CoapplicantIncome']\n",
    "test['Total_Income']=test['ApplicantIncome']+test['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s check the distribution of Total Income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4e8820760>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Total_Income'])"
   ]
  },
  {
   "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": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Total_Income_log'] = np.log(train['Total_Income'])\n",
    "sns.distplot(train['Total_Income_log'])\n",
    "test['Total_Income_log'] = np.log(test['Total_Income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the distribution looks much closer to normal and the effect of extreme values has been significantly subsided. Let's create the EMI feature now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4e8a22550>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['EMI'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us create Balance Income feature now and check its distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4d8d0e790>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Balance Income'] = train['Total_Income']-(train['EMI']*1000)\n",
    "test['Balance Income'] = test['Total_Income']-(test['EMI']*1000)\n",
    "sns.distplot(train['Balance Income'])"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# print (train.dtypes)\n",
    "# print (test.dtypes)\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": "markdown",
   "metadata": {},
   "source": [
    "## Model Building : Part II\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",
    "We will build the following models in this section.\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": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('Loan_Status',1)\n",
    "y = train.Loan_Status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5 \n",
      "accuracy_score 0.7967479674796748\n",
      "\n",
      "2 of kfold 5 \n",
      "accuracy_score 0.6910569105691057\n",
      "\n",
      "3 of kfold 5 \n",
      "accuracy_score 0.6666666666666666\n",
      "\n",
      "4 of kfold 5 \n",
      "accuracy_score 0.7804878048780488\n",
      "\n",
      "5 of kfold 5 \n",
      "accuracy_score 0.680327868852459\n",
      "\n",
      " Mean Validation Accuracy 0.7230574436891909\n"
     ]
    }
   ],
   "source": [
    "i=1\n",
    "mean = 0\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",
    "    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 += score\n",
    "    print ('accuracy_score',score)\n",
    "    i+=1\n",
    "    pred_test = model.predict(test)\n",
    "    pred = model.predict_proba(xvl)[:,1]\n",
    "print ('\\n Mean Validation Accuracy',mean/(i-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status']=pred_test\n",
    "submission['Loan_ID']=test_original['Loan_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Output/Log2.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree\n",
    "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": "markdown",
   "metadata": {},
   "source": [
    "Let’s fit the decision tree model with 5 folds of cross validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5 \n",
      "accuracy_score 0.7398373983739838\n",
      "\n",
      "2 of kfold 5 \n",
      "accuracy_score 0.6991869918699187\n",
      "\n",
      "3 of kfold 5 \n",
      "accuracy_score 0.7560975609756098\n",
      "\n",
      "4 of kfold 5 \n",
      "accuracy_score 0.7073170731707317\n",
      "\n",
      "5 of kfold 5 \n",
      "accuracy_score 0.6721311475409836\n",
      "\n",
      " Mean Validation Accuracy 0.7149140343862455\n"
     ]
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "i=1\n",
    "mean = 0\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",
    "    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 += score\n",
    "    print ('accuracy_score',score)\n",
    "    i+=1\n",
    "    pred_test = model.predict(test)\n",
    "    pred = model.predict_proba(xvl)[:,1]\n",
    "print ('\\n Mean Validation Accuracy',mean/(i-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status']=pred_test\n",
    "submission['Loan_ID']=test_original['Loan_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Output/DecisionTree.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest\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",
    "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": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5 \n",
      "accuracy_score 0.8292682926829268\n",
      "\n",
      "2 of kfold 5 \n",
      "accuracy_score 0.8130081300813008\n",
      "\n",
      "3 of kfold 5 \n",
      "accuracy_score 0.7723577235772358\n",
      "\n",
      "4 of kfold 5 \n",
      "accuracy_score 0.8048780487804879\n",
      "\n",
      "5 of kfold 5 \n",
      "accuracy_score 0.7540983606557377\n",
      "\n",
      " Mean Validation Accuracy 0.7947221111555378\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "i=1\n",
    "mean = 0\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",
    "    model = RandomForestClassifier(random_state=1, max_depth=10)\n",
    "    model.fit(xtr,ytr)\n",
    "    pred_test=model.predict(xvl)\n",
    "    score=accuracy_score(yvl,pred_test)\n",
    "    mean += score\n",
    "    print ('accuracy_score',score)\n",
    "    i+=1\n",
    "    pred_test = model.predict(test)\n",
    "    pred = model.predict_proba(xvl)[:,1]\n",
    "print ('\\n Mean Validation Accuracy',mean/(i-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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. Grid-search is a way to select the best of a family of hyper parameters, parametrized by a grid of parameters.\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": "markdown",
   "metadata": {},
   "source": [
    "### Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install -U scikit-learn\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "paramgrid = {'max_depth': list(range(1,20,2)), 'n_estimators': list(range(1,200,20))}\n",
    "grid_search=GridSearchCV(RandomForestClassifier(random_state=1),paramgrid)\n",
    "\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)\n",
    "# grid_search.fit(x_train,y_train)\n",
    "\n",
    "# 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, min_weight_fraction_leaf=0.0, \n",
    "#                                               n_estimators=10, n_jobs=1, oob_score=False, random_state=1, \n",
    "#                                               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",
    "#                          'n_estimators':[1, 21, 41, 61, 81, 101, 121, 141, 161, 181]},\n",
    "#              pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',scoring=None, verbose=0)\n",
    "\n",
    "# grid_search.best_estimator_\n",
    "# RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', \n",
    "#                        max_depth=3, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, \n",
    "#                        min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, \n",
    "#                        min_weight_fraction_leaf=0.0, n_estimators=41, n_jobs=1, oob_score=False, \n",
    "#                        random_state=1, verbose=0, warm_start=False)\n",
    "# i=1\n",
    "# mean = 0\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",
    "#     model = RandomForestClassifier(random_state=1, max_depth=3, n_estimators=41)\n",
    "#     model.fit(xtr,ytr)\n",
    "#     pred_test = model.predict(xvl)\n",
    "#     score = accuracy_score(yvl,pred_test)\n",
    "#     mean += score\n",
    "#     print ('accuracy_score',score)\n",
    "#     i+=1\n",
    "#     pred_test = model.predict(test)\n",
    "#     pred = model.predict_proba(xvl)[:,1]\n",
    "# print ('\\n Mean Validation Accuracy',mean/(i-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=RandomForestClassifier(random_state=1),\n",
       "             param_grid={'max_depth': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],\n",
       "                         'n_estimators': [1, 21, 41, 61, 81, 101, 121, 141, 161,\n",
       "                                          181]})"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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)\n",
    "grid_search.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=5, n_estimators=41, random_state=1)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5 \n",
      "accuracy_score 0.8130081300813008\n",
      "\n",
      "2 of kfold 5 \n",
      "accuracy_score 0.8455284552845529\n",
      "\n",
      "3 of kfold 5 \n",
      "accuracy_score 0.8048780487804879\n",
      "\n",
      "4 of kfold 5 \n",
      "accuracy_score 0.7967479674796748\n",
      "\n",
      "5 of kfold 5 \n",
      "accuracy_score 0.7786885245901639\n",
      "\n",
      " Mean Validation Accuracy 0.8077702252432362\n"
     ]
    }
   ],
   "source": [
    "i=1\n",
    "mean = 0\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",
    "    model = RandomForestClassifier(random_state=1, max_depth=3, n_estimators=41)\n",
    "    model.fit(xtr,ytr)\n",
    "    pred_test = model.predict(xvl)\n",
    "    score = accuracy_score(yvl,pred_test)\n",
    "    mean += score\n",
    "    print ('accuracy_score',score)\n",
    "    i+=1\n",
    "    pred_test = model.predict(test)\n",
    "    pred = model.predict_proba(xvl)[:,1]\n",
    "print ('\\n Mean Validation Accuracy',mean/(i-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status']=pred_test\n",
    "submission['Loan_ID']=test_original['Loan_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Output/RandomForest.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4e885c4f0>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances=pd.Series(model.feature_importances_, index=X.columns)\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 Balance Income, Total Income, EMI. So, feature engineering helped us in predicting our target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBOOST\n",
    "XGBoost is a fast and efficient algorithm and has been used 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",
    "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_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": "markdown",
   "metadata": {},
   "source": [
    "XGBOOST\n",
    "\n",
    "GBoostError: XGBoost Library (libxgboost.dylib) could not be loaded. If you face this error in MacOS, run ``brew install libomp`` in ``Terminal`` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.7804878048780488\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.7886178861788617\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.7642276422764228\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.7804878048780488\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.7622950819672131\n",
      "\n",
      " Mean Validation Accuracy 0.7752232440357191\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "i=1 \n",
    "mean = 0\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",
    "    model = XGBClassifier(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 += score\n",
    "    print ('accuracy_score',score)\n",
    "    i+=1\n",
    "    pred_test = model.predict(test)\n",
    "    pred = model.predict_proba(xvl)[:,1]\n",
    "print ('\\n Mean Validation Accuracy',mean/(i-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status']=pred_test\n",
    "submission['Loan_ID']=test_original['Loan_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status'].replace(0, 'N', inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Output/XGBoost.csv')"
   ]
  }
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