LOAN PREDICTION PROJECT
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LOAN PREDICTION PROJECT



LOAN PREDICTION PROJECT
LOAN PREDICTION PROJECT

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Among all industries, the insurance domain has one of the largest uses of analytics & data science methods. This dataset are collected from insurance companies – what challenges are faced there, what strategies are used, which variables influence the outcome, etc. This is a classification problem. The data has 615 rows and 13 columns. Problem: Predict if a loan will get approved or not. Our aim from the project is to make use of pandas, matplotlib, & seaborn libraries from python to extract insights from the data and xgboost, & scikit-learn libraries for machine learning. Secondly, to learn how to hypertune the parameters using grid search cross validation for the xgboost machine learning model. And in the end, to predict whether the loan applicant can replay the loan or not using voting ensembling techniques of combining the predictions from multiple machine learning algorithms. This course is designed for people who want to solve binary classification problems. Classification is a skill every Data Scientist should be well versed in. In this course, we are solving a real life case study of Dream Housing Finance. The company deals in all home loans. They have a presence across all urban, semi-urban and rural areas. Customers first apply for a home loan after that company validates the customer's eligibility. The company wants to automate the loan eligibility process (real-time) based on customer detail provided while filling online application form.

Why: Problem statement

To predict the loan eligibility process based on customer detail provided while filling the online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others.

How: Solution description

This project is proposed to predict if a loan will get approved or not by finding which variables influence the outcome using Classification algorithms.

First, the dataset is cleaned up to remove or replace missing values. The columns in the dataset are;

Variable

Description

Loan_ID

Unique Loan ID

Gender

Male/ Female

Married

Applicant married (Y/N)

Dependents

Number of dependents

Education

Applicant Education (Graduate/ Under Graduate)

Self_Employed

Self-employed (Y/N)

ApplicantIncome

Applicant income

CoapplicantIncome

Co-applicant income

LoanAmount

Loan amount in thousands

Loan_Amount_Term

Term of the loan in months

Credit_History

credit history meets guidelines

Property_Area

Urban/ Semi-Urban/ Rural

Loan_Status

Loan approved (Y/N)

Splitting data for training and testing:

This dataset contains 641 customer records who applied for a loan. We split the training and testing data with a ratio of 7:3. That is 70% of data is for training and 30% is for testing. We used the train_test_split method from sklearn to split the data.

Training using Machine learning models:

We got the training accuracy of 80% by using KNeighborsClassifier and training accuracy of 98% by using DecisionTreeClassifier

How is it different from competition

We have undergone many approaches for data cleaning. Since we made our data perfectly clean, we attained maximum accuracy. 

Who are your customers

Insurance companies, Banks, money lending companies and other organizations can use this project.

 

Project Phases and Schedule

Phase 1: Data collection

Phase 2: Data cleaning

Phase 3: Training using machine learning models

Phase 4: Result

Resources Required

Anaconda tool

Python 3.6

Jupyter notebook 

Download:
Project Code Code copy
/* Your file Name : Loanproject.ipynb */
/* Your coding Language : python */
/* Your code snippet start here */
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
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       "      <td>Graduate</td>\n",
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       "    <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>No</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>Yes</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>Yes</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LP001013</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2333</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LP001014</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3036</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LP001018</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4006</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LP001020</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>12841</td>\n",
       "      <td>10968.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LP001024</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3200</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LP001027</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LP001028</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3073</td>\n",
       "      <td>8106.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LP001029</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1853</td>\n",
       "      <td>2840.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LP001030</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1299</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LP001032</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LP001034</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LP001036</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LP001038</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4887</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LP001041</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2600</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LP001043</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LP001046</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5955</td>\n",
       "      <td>5625.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LP001047</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2600</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LP001050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3365</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LP001052</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3717</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LP001066</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9560</td>\n",
       "      <td>0.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LP001068</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2799</td>\n",
       "      <td>2253.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>LP001073</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4226</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>LP001086</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>LP001087</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3750</td>\n",
       "      <td>2083.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Loan_ID  Gender Married Dependents     Education Self_Employed  \\\n",
       "0   LP001002    Male      No          0      Graduate            No   \n",
       "1   LP001003    Male     Yes          1      Graduate            No   \n",
       "2   LP001005    Male     Yes          0      Graduate           Yes   \n",
       "3   LP001006    Male     Yes          0  Not Graduate            No   \n",
       "4   LP001008    Male      No          0      Graduate            No   \n",
       "5   LP001011    Male     Yes          2      Graduate           Yes   \n",
       "6   LP001013    Male     Yes          0  Not Graduate            No   \n",
       "7   LP001014    Male     Yes         3+      Graduate            No   \n",
       "8   LP001018    Male     Yes          2      Graduate            No   \n",
       "9   LP001020    Male     Yes          1      Graduate            No   \n",
       "10  LP001024    Male     Yes          2      Graduate            No   \n",
       "11  LP001027    Male     Yes          2      Graduate           NaN   \n",
       "12  LP001028    Male     Yes          2      Graduate            No   \n",
       "13  LP001029    Male      No          0      Graduate            No   \n",
       "14  LP001030    Male     Yes          2      Graduate            No   \n",
       "15  LP001032    Male      No          0      Graduate            No   \n",
       "16  LP001034    Male      No          1  Not Graduate            No   \n",
       "17  LP001036  Female      No          0      Graduate            No   \n",
       "18  LP001038    Male     Yes          0  Not Graduate            No   \n",
       "19  LP001041    Male     Yes          0      Graduate           NaN   \n",
       "20  LP001043    Male     Yes          0  Not Graduate            No   \n",
       "21  LP001046    Male     Yes          1      Graduate            No   \n",
       "22  LP001047    Male     Yes          0  Not Graduate            No   \n",
       "23  LP001050     NaN     Yes          2  Not Graduate            No   \n",
       "24  LP001052    Male     Yes          1      Graduate           NaN   \n",
       "25  LP001066    Male     Yes          0      Graduate           Yes   \n",
       "26  LP001068    Male     Yes          0      Graduate            No   \n",
       "27  LP001073    Male     Yes          2  Not Graduate            No   \n",
       "28  LP001086    Male      No          0  Not Graduate            No   \n",
       "29  LP001087  Female      No          2      Graduate           NaN   \n",
       "\n",
       "    ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0              5849                0.0         NaN             360.0   \n",
       "1              4583             1508.0       128.0             360.0   \n",
       "2              3000                0.0        66.0             360.0   \n",
       "3              2583             2358.0       120.0             360.0   \n",
       "4              6000                0.0       141.0             360.0   \n",
       "5              5417             4196.0       267.0             360.0   \n",
       "6              2333             1516.0        95.0             360.0   \n",
       "7              3036             2504.0       158.0             360.0   \n",
       "8              4006             1526.0       168.0             360.0   \n",
       "9             12841            10968.0       349.0             360.0   \n",
       "10             3200              700.0        70.0             360.0   \n",
       "11             2500             1840.0       109.0             360.0   \n",
       "12             3073             8106.0       200.0             360.0   \n",
       "13             1853             2840.0       114.0             360.0   \n",
       "14             1299             1086.0        17.0             120.0   \n",
       "15             4950                0.0       125.0             360.0   \n",
       "16             3596                0.0       100.0             240.0   \n",
       "17             3510                0.0        76.0             360.0   \n",
       "18             4887                0.0       133.0             360.0   \n",
       "19             2600             3500.0       115.0               NaN   \n",
       "20             7660                0.0       104.0             360.0   \n",
       "21             5955             5625.0       315.0             360.0   \n",
       "22             2600             1911.0       116.0             360.0   \n",
       "23             3365             1917.0       112.0             360.0   \n",
       "24             3717             2925.0       151.0             360.0   \n",
       "25             9560                0.0       191.0             360.0   \n",
       "26             2799             2253.0       122.0             360.0   \n",
       "27             4226             1040.0       110.0             360.0   \n",
       "28             1442                0.0        35.0             360.0   \n",
       "29             3750             2083.0       120.0             360.0   \n",
       "\n",
       "    Credit_History Property_Area Loan_Status  \n",
       "0              1.0         Urban         Yes  \n",
       "1              1.0         Rural          No  \n",
       "2              1.0         Urban         Yes  \n",
       "3              1.0         Urban         Yes  \n",
       "4              1.0         Urban         Yes  \n",
       "5              1.0         Urban         Yes  \n",
       "6              1.0         Urban         Yes  \n",
       "7              0.0     Semiurban          No  \n",
       "8              1.0         Urban         Yes  \n",
       "9              1.0     Semiurban          No  \n",
       "10             1.0         Urban         Yes  \n",
       "11             1.0         Urban         Yes  \n",
       "12             1.0         Urban         Yes  \n",
       "13             1.0         Rural          No  \n",
       "14             1.0         Urban         Yes  \n",
       "15             1.0         Urban         Yes  \n",
       "16             NaN         Urban         Yes  \n",
       "17             0.0         Urban          No  \n",
       "18             1.0         Rural          No  \n",
       "19             1.0         Urban         Yes  \n",
       "20             0.0         Urban          No  \n",
       "21             1.0         Urban         Yes  \n",
       "22             0.0     Semiurban          No  \n",
       "23             0.0         Rural          No  \n",
       "24             NaN     Semiurban          No  \n",
       "25             1.0     Semiurban         Yes  \n",
       "26             1.0     Semiurban         Yes  \n",
       "27             1.0         Urban         Yes  \n",
       "28             1.0         Urban          No  \n",
       "29             1.0     Semiurban         Yes  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('loan_data.csv')\n",
    "data.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x253bf1c27b8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X+XJEHJler4iIQkRcVM3JJc0Gfgf4dioLeAdwfWpSBM5J24tTmbT/jNR+MXBNRLwQET8jW8P+5PTqjYiHIuJF4JrU1uqsVCpx8803ExHcfPPN7q2Y2R6jXv6KiIskzZL0G5JOK7+qPP/fAP8N2J3KBeCpiBhI5T5gVtqeBTySPnMAeDq131O/3zHD1VudFYtFBgay/4S7du1yb8XM9qhmoP5LwL8Cnwb+PL3+rIrjfhd4PCI2VFYP0TRG2TfW+qFiWSqpR1JPf3//CFFbNbq7uylPBRcR3HLLLTlHZGaNopon6t8NHBcRL4zacl+/CbxL0tnA4cCRZD2XqZLaUm9kNlBe/KsPOAboSzcDvBLYUVFfVnnMcPX7iIiVwEqA9vZ2T4x5gGbMmMHWrVv3KZuZQXV3fz0EHDLWE0fERRExOyLmkg20/yAifh9YD7w3Netk76D/6lQm7f9Bmhl5NXBeujvsWGA+cAdwJzA/3U12aPqM1WON08Zu+/btI5bNrHlV01N5DrhH0jr2nVBy2Tg/8y+AayR9HrgbuCLVXwFcLamXrIdyXvqczZKuA+4DBoALImIQQNLHgDVAK3BlRGweZ0w2Br/1W7+1zyWv006rdojNzA521SSV1RxgDyAifgj8MG0/RHbn1v5tngfOHeb4LwBfGKL+JuCmA4nNxi67Kc/M7KWqmVCyOFobay4/+clPXlK+6KKq7jI3s4NcNXd/zZd0vaT7JD1Ufk1EcNaYFixYQFtb9vdIW1sbCxcuzDkiM2sU1QzUfwe4jGw84+1ka9N77q8m1tnZSUtL9tVpbW1lyZIlOUdkZo2imqRyRESsAxQRD0fEZ8ieircmVSgU6OjoQBIdHR0UCoW8QzKzBlHNQP3zklqALeluq23Aq+obljW6zs5Otm7d6l6Kme1D5Sejh20gvYVsQsipwMVkDzF+OSJuq394tdfe3h49PT15h2FmNmlI2hAR7dW0rWburzsj4pmI6IuID0bEeyZrQjGz5uClGfJTzd1f3ZKmVpSnSVpT37DMzMbPSzPkp5qB+qMj4qlyISKexGMqZtagSqUSXV1dRARdXV3urUywapLKbklzygVJr2GY2YDNzPJWLBbZvTtbbWNwcNC9lQlWTVL5S+BfJF2d1qb/MeDHp82sIa1du3bPej8DAwN0d3fnHFFzqWagvgs4EbgWuA44KSI8pmJmDckzPuRrpDXqX5feTwTmkK1Vsg2Yk+rMzBqOZ3zI10gPP/4p8GHgK0PsC/xUvZk1oPKMDzfeeKNnfMjBsEklIj6c3t8+ceGYmR04z/iQn2GTiqTfG+nAiPjHkfZLOpxsUP+w9DnXR8TytHrjNcBRwF3AH0TEi5IOI5us8iSgBLw/Iramc10EnA8MAsvKYzqSOoBLyBbp+nZEfGnUn9jMDnqFQoEVK1bkHUZTGuny1ztH2BfAiEmFbJXId0TEM5IOIbuD7GbgU8DXIuIaSX9LliwuS+9PRsQ8SecBfw28X9LxZKtAvgH4FWCtpNemz7gUWEi2jv2dklZHxH2jxGVmZnUy0uWvDx7IidP68s+k4iHpVR6L+c+pvgh8hiypLE7bANcD31C2xOBi4JqIeAH4WVpuuLxyZG9aSRJJ16S2TipmZjmpZpqWgqQVku6StEHSJZKqGvmS1CrpHuBxoBt4EHgqIgZSkz5gVtqeBTwCkPY/DRQq6/c7Zrh6MzPLSTUPP14D9APvAd6btq+t5uQRMRgRbwZmk/UuXj9Us/Q+1MLnMY76l5C0VFKPpJ7+/v7RAzczs3GpJqkcFREXR8TP0uvzZNPgVy3NHfZD4FRgqqTyZbfZZM+/QNbTOAYg7X8lsKOyfr9jhqsf6vNXRkR7RLRPnz59LKGbmdkYVJNU1ks6T1JLer0P+P5oB0maXp7dWNIRwAKydVnWk/V4ADqBG9L26lQm7f9BGpdZDZwn6bB059h84A7gTmC+pGMlHUo2mL+6ip/HzMzqpJqVH/+I7I6t/5PKLcCzkj5FNh5/5DDHzQSKklrTMddFxD9Lug+4RtLngbuBK1L7K4Cr00D8DrIkQURslnQd2QD8AHBBRAwCpJUo15DdUnxlRGwew89uZmY1NurKjwcbr/xoZjY2Y1n5sZqeSvlByLeRDYT/JCL+6QDiMzOzg1Q1txR/E/gIsAm4F/iIpEvrHZiZmU0+1fRUfht4Yxo0R1KRLMGYmZnto5q7vx4gm/q+7BhgY33CMTOzyayankoBuF/SHan8FuBWSasBIuJd9QrOzMwml2qSyl9VbItswP4DwB/XJSIzM5u0qllO+Edk83D9DnAVcAbwtxHxo7TPmlCpVGLZsmWUSqW8QzGzBjLScsKvlfRXku4HvkE2eaMi4u0R8fUJi9AaUrFYZNOmTaxatSrvUMysgYzUU/kpWa/knRHxtpRIBicmLGtkpVKJrq4uIoKuri73Vsxsj5GSynuAX5DN/fUtSWcw9MzA1mSKxSK7d+8GYHBw0L0VM9tj2KQSEd+LiPcDryObYfhPgBmSLpO0aILiswa0du1aBgayJXEGBgbo7u7OOSIzaxTVDNQ/GxF/FxG/Sza9/D3AhXWPzBrWggULaGvLbhxsa2tj4cKFOUdkZo2imocf94iIHRFxeUS8o14BWePr7OykpSX76rS2trJkyZKcIzKzRjGmpGIGUCgUOP300wE4/fTTKRSqWl3azJqAk4qNi+R7Nqxx+Tmq/Dip2JiVSiXWr18PwPr16/0/rjWcyy+/nI0bN7Jy5cq8Q2k6dUsqko6RtF7S/ZI2S/pEqj9KUrekLel9WqqXpBWSeiVtlHRixbk6U/stkjor6k+StCkds0L+83lCFItFdu3aBcCuXbt8S7E1lFKptOeOxFtuucV/9EywevZUBoA/jYjXA6cCF0g6nuzOsXURMR9Yx947yc4iW39+PrAUuAyyJAQsB04BTgaWlxNRarO04riOOv48lnR3d1NeMTQiuOWWW3KOyGyvyy+/fJ/vp3srE6tuSSUiHouIu9L2TuB+YBawGCimZkXgnLS9GFgVmduAqZJmAmcC3enOsyeBbqAj7TsyIm5Na72sqjiX1dGMGTNGLJvlae3atfuU/RzVxJqQMRVJc4FfB24HZkTEY5AlHuBVqdkssvnFyvpS3Uj1fUPUD/X5SyX1SOrp7+8/0B+n6f3iF78YsWyWp3IvZbiy1Vfdk4qklwP/F/hkRPxypKZD1MU46l9aGbEyItojon369OmjhWyjePWrXz1i2SxP5WeohitbfdX1X1vSIWQJ5e8i4h9T9fZ06Yr0/niq7yNbVbJsNvDoKPWzh6i3Otu+ffuIZbM8LViwYMSy1Vc97/4ScAVwf0R8tWLXaqB8B1cncENF/ZJ0F9ipwNPp8tgaYJGkaWmAfhGwJu3bKenU9FlLKs5ldXTaaaeNWDbL07nnnjti2eqrnj2V3wT+AHiHpHvS62zgS8BCSVuAhakMcBPwENALfIu0smRE7AAuBu5Mr8+lOoCPAt9OxzwI3FzHn8cSX6O2RrZ69eo9D+dK4sYbb8w5ouaiZvsF0d7eHj09PXmHMal1dHTw/PPP7ykffvjhdHV15RiR2V5nn302zz333J7ylClTuOmmm3KMaPKTtCEi2qtp6xEsG7PyDMXDlc3y5Fm08+WkYmP2zDPPjFg2y1NnZ+eeReR2797tWbQnmJOKmZnVjJOKmR1UisXiPmXPTTexnFTM7KCydu3afS5/eZqWieWkYmYHlZNPPnnEstWXk4qNmafBsEbW29u7T/nBBx/MKZLm5N8GNmblSwvDlc3y1NfXt0/5kUceGaal1YOTipkdVObOnTti2erLScXG7LDDDhuxbJanT3/60yOWrb6cVGzMyksJD1c2y9O0adNGLFt9OanYmHkRJGtkl19++T5lLyc8sZxUbMzKM8AOVzbLk5cTzpeTio3Z/qtnejVNayS+OzFfTio2Zo8//viIZTNrXvVc+fFKSY9Lurei7ihJ3ZK2pPdpqV6SVkjqlbRR0okVx3Sm9lskdVbUnyRpUzpmhXwNZsJ4TMXMhlPPnspVQMd+dRcC6yJiPrAulQHOAuan11LgMsiSELAcOAU4GVheTkSpzdKK4/b/LDMzm2B1SyoR8WNgx37Vi4HyFKJF4JyK+lWRuQ2YKmkmcCbQHRE7IuJJoBvoSPuOjIhbI/szeVXFucysiXkRuXxN9L/2jIh4DCAiHpP0qlQ/C6icS6Ev1Y1U3zdEfVP4+te//pL5jfL2iU98IrfPnjdvHh//+Mdz+3zbqxG+mwMDAy8p5/X9bMbvZqMM1A81HhLjqB/65NJSST2Sevr7+8cZoplNBp7xIV8T3VPZLmlm6qXMBMq3DfUBx1S0mw08mupP36/+h6l+9hDthxQRK4GVAO3t7ZN+VDnvv3y++MUvsmbNmj3lM888k4suuijHiKxR5P3dhGyW4g996EN7ypdeeinz5s3LMaLmMtE9ldVA+Q6uTuCGivol6S6wU4Gn02WyNcAiSdPSAP0iYE3at1PSqemuryUV57I6W7p06Z7tlpaWfcpmeZs3b96e3sncuXOdUCZYPW8p/gfgVuA4SX2Szge+BCyUtAVYmMoANwEPAb3At4A/BoiIHcDFwJ3p9blUB/BR4NvpmAeBm+v1s9i+CoXCnvmUFi5cSKFQyDkis33NmTOHlpYWTyaZg7pd/oqIDwyz64wh2gZwwTDnuRK4coj6HuCNBxKjjd/MmTN58cUX3UuxhjRlyhROOOEE91Jy0CgD9TbJHHLIIcybN8+9FDPbh5OKmZnVjJOKmZnVjJOKmZnVjJptMsD29vbo6ekZ17GN8LRwoyj/O3ggNNMIT077+7mXv5/7OtDvp6QNEdFeTVtPijMGvb293HPv/QxOOSrvUHLX8mL2x8iGh7bnHEn+Wp/bf4q7fPT29rJl893Meflg3qHk7tBd2UWYFx4e3x+QB5OfP9M6oZ/npDJGg1OO4j9ed3beYVgDOeKnN+Udwh5zXj7Ifz/xl3mHYQ3kf9515IR+nsdUzMysZpxUzMysZnz5awy2bdtG63NPN9TlDstf63Mltm0bGL1hnW3bto1nd7ZO+OUOa2wP72zlZdu2TdjnuadiZmY146QyBrNmzWLopVyaT8vzv6TleQ8IZ5S+G/maNWsW8tcTgO3PtbD9Of96A5CY0O+nL3+Nge9536u3dycA8351Rs6RNIIZDfHdaIQYGsWL6TmVw17jf5P5TOx3ww8/2riUl2e95JJLco7E7KX8/aytsTz86P6hmZnVjJOKmZnVzKRPKpI6JD0gqVfShXnHY2bWzCZ1UpHUClwKnAUcD3xA0vH5RmVm1rwm+91fJwO9EfEQgKRrgMXAfblGVWeNMBtt+fPLA6J5aoQZgi3TCN9NaJzvZzN+Nyd7UpkFPFJR7gNO2b+RpKXAUoA5c+ZMTGQHuSOOOCLvEMyG5e9nfib1LcWSzgXOjIgPpfIfACdHxLB/GviWYjOzsWmmW4r7gGMqyrOBR3OKxcys6U32pHInMF/SsZIOBc4DVucck5lZ05rUYyoRMSDpY8AaoBW4MiI25xyWmVnTmtRJBSAibgI8F72ZWQOY7Je/zMysgTipmJlZzTipmJlZzTipmJlZzUzqhx/HQ1I/8HDecRwkjgaeyDsIs2H4+1k7r4mI6dU0bLqkYrUjqafap2zNJpq/n/nw5S8zM6sZJxUzM6sZJxU7ECvzDsBsBP5+5sBjKmZmVjPuqZiZWc04qdiwlPkXSWdV1L1PUleecZlVkhSSvlJR/jNJn8kxpKbmpGLDiuza6EeAr0o6XNLLgC8AF+Qbmdk+XgB+T9LReQdiTio2ioi4F7gR+AtgObAqIh6U1CnpDkn3SPqmpBZJbZKulrRJ0r2SluUbvTWJAbJB+T/Zf4ek10haJ2ljevd64nU26ae+twnxWeAu4EWgXdIbgXcDv5HWtFlJtkDag8DREXECgKSpeQVsTedSYKOk/7Vf/TfI/hAqSvqvwArgnAmProk4qdioIuJZSdcCz0TEC5IWAG8BeiQBHAE8QrZY2nGSLiFb4+aWvGK25hIRv5S0ClgG/EfFrrcCv5e2rwb2TzpWY04qVq3d6QUgslU2/8f+jSS9CTiL7H/u9wBLJyxCa3YjA40JAAADPUlEQVR/Q9aj/s4IbfwMRZ15TMXGYy3wvvLAqKSCpDmSppM9+/RdsvGXE/MM0ppLROwArgPOr6j+N7JLswC/D/zLRMfVbNxTsTGLiE2SPgusldQC7CK7S2wQuELZNbEgG9w3m0hfAT5WUV4GXCnpz4F+4IO5RNVE/ES9mZnVjC9/mZlZzTipmJlZzTipmJlZzTipmJlZzTipmJlZzTipmJlZzTipmA1B0jM5fOZfStqcJj+8R9Ipqf6TkqZUcXxV7czqyc+pmA1B0jMR8fIJ/Ly3Al8FTk/zqx0NHBoRj0raCrRHxBOjnKOqdmb15J6KWZWGm0Zd0jsl3S7pbklrJc1I9Z+RdKWkH0p6aJSlAGYCT0TECwAR8URKKMuAXwHWS1qfznuZpJ7Uq/lsqhuq3Z7elqT3SroqbZ+blib4d0k/rvE/kzU591TMhjBUT0XSjcD1FdOovysizpE0DXgqIkLSh4DXR8SfptUHFwFvB14BPAC8OiJ2DfF5Lyebl2oK2dxq10bEj9K+rVT0QCQdFRE7JLUC64BlEbFxiHZ7fgZJ7wV+NyL+UNImoCMitkmaGhFP1fLfzpqbeypm1Xsr8Pdp+2rgbWl7NrAm/bL+c+ANFcd8PyJeSL/oHwdmDHXiiHgGOIlsVud+4FpJfzhMHO+TdBdwd/qs48f4c/wrcJWkDwOtYzzWbEROKmbjV+7mfx34Rlqc7I+AwyvavFCxPcgIk7hGxGBE/DAilpNNivie/dtIOhb4M+CMiHgT8P39Pm+o+KhsExEfAT4NHAPcI6kwXExmY+WkYla94aZRfyWwLW13jufEko6TNL+i6s3Aw2l7J9nlM4AjgWeBp9PYzVkVx1S2A9gu6fVpJul3V3zWr0XE7RHxV8ATZMnFrCY89b3Z0KZI6qsof5Xhp1H/DPBdSduA24Bjx/F5Lwe+npZgHgB62bvA2UrgZkmPRcTbJd0NbAYeIruUxVDtgAuBfyZblfPe9BkAX04JTGRjMv8+jnjNhuSBejMzqxlf/jIzs5rx5S+zCZQGxdcNseuMiChNdDxmtebLX2ZmVjO+/GVmZjXjpGJmZjXjpGJmZjXjpGJmZjXjpGJmZjXz/wHxSe5wvGvekQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Loan_Status', y='ApplicantIncome', data=data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x22915f3cda0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 356.75x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(data, hue='Loan_Status', size=4).\\\n",
    "                   map(plt.scatter, 'ApplicantIncome',\n",
    "                   'ApplicantIncome').add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</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>No</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>Yes</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>Yes</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LP001013</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2333</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LP001014</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3036</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LP001018</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4006</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LP001020</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>12841</td>\n",
       "      <td>10968.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LP001024</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3200</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LP001027</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LP001028</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3073</td>\n",
       "      <td>8106.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LP001029</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1853</td>\n",
       "      <td>2840.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LP001030</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1299</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LP001032</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LP001034</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LP001036</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LP001038</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4887</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LP001041</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2600</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LP001043</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LP001046</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5955</td>\n",
       "      <td>5625.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LP001047</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2600</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LP001050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3365</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LP001052</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3717</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LP001066</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9560</td>\n",
       "      <td>0.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LP001068</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2799</td>\n",
       "      <td>2253.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>LP001073</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4226</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>LP001086</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>1442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>LP001087</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3750</td>\n",
       "      <td>2083.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Loan_ID  Gender Married Dependents     Education Self_Employed  \\\n",
       "0   LP001002    Male      No          0      Graduate            No   \n",
       "1   LP001003    Male     Yes          1      Graduate            No   \n",
       "2   LP001005    Male     Yes          0      Graduate           Yes   \n",
       "3   LP001006    Male     Yes          0  Not Graduate            No   \n",
       "4   LP001008    Male      No          0      Graduate            No   \n",
       "5   LP001011    Male     Yes          2      Graduate           Yes   \n",
       "6   LP001013    Male     Yes          0  Not Graduate            No   \n",
       "7   LP001014    Male     Yes         3+      Graduate            No   \n",
       "8   LP001018    Male     Yes          2      Graduate            No   \n",
       "9   LP001020    Male     Yes          1      Graduate            No   \n",
       "10  LP001024    Male     Yes          2      Graduate            No   \n",
       "11  LP001027    Male     Yes          2      Graduate           NaN   \n",
       "12  LP001028    Male     Yes          2      Graduate            No   \n",
       "13  LP001029    Male      No          0      Graduate            No   \n",
       "14  LP001030    Male     Yes          2      Graduate            No   \n",
       "15  LP001032    Male      No          0      Graduate            No   \n",
       "16  LP001034    Male      No          1  Not Graduate            No   \n",
       "17  LP001036  Female      No          0      Graduate            No   \n",
       "18  LP001038    Male     Yes          0  Not Graduate            No   \n",
       "19  LP001041    Male     Yes          0      Graduate           NaN   \n",
       "20  LP001043    Male     Yes          0  Not Graduate            No   \n",
       "21  LP001046    Male     Yes          1      Graduate            No   \n",
       "22  LP001047    Male     Yes          0  Not Graduate            No   \n",
       "23  LP001050     NaN     Yes          2  Not Graduate            No   \n",
       "24  LP001052    Male     Yes          1      Graduate           NaN   \n",
       "25  LP001066    Male     Yes          0      Graduate           Yes   \n",
       "26  LP001068    Male     Yes          0      Graduate            No   \n",
       "27  LP001073    Male     Yes          2  Not Graduate            No   \n",
       "28  LP001086    Male      No          0  Not Graduate            No   \n",
       "29  LP001087  Female      No          2      Graduate           NaN   \n",
       "\n",
       "    ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0              5849                0.0         NaN             360.0   \n",
       "1              4583             1508.0       128.0             360.0   \n",
       "2              3000                0.0        66.0             360.0   \n",
       "3              2583             2358.0       120.0             360.0   \n",
       "4              6000                0.0       141.0             360.0   \n",
       "5              5417             4196.0       267.0             360.0   \n",
       "6              2333             1516.0        95.0             360.0   \n",
       "7              3036             2504.0       158.0             360.0   \n",
       "8              4006             1526.0       168.0             360.0   \n",
       "9             12841            10968.0       349.0             360.0   \n",
       "10             3200              700.0        70.0             360.0   \n",
       "11             2500             1840.0       109.0             360.0   \n",
       "12             3073             8106.0       200.0             360.0   \n",
       "13             1853             2840.0       114.0             360.0   \n",
       "14             1299             1086.0        17.0             120.0   \n",
       "15             4950                0.0       125.0             360.0   \n",
       "16             3596                0.0       100.0             240.0   \n",
       "17             3510                0.0        76.0             360.0   \n",
       "18             4887                0.0       133.0             360.0   \n",
       "19             2600             3500.0       115.0               NaN   \n",
       "20             7660                0.0       104.0             360.0   \n",
       "21             5955             5625.0       315.0             360.0   \n",
       "22             2600             1911.0       116.0             360.0   \n",
       "23             3365             1917.0       112.0             360.0   \n",
       "24             3717             2925.0       151.0             360.0   \n",
       "25             9560                0.0       191.0             360.0   \n",
       "26             2799             2253.0       122.0             360.0   \n",
       "27             4226             1040.0       110.0             360.0   \n",
       "28             1442                0.0        35.0             360.0   \n",
       "29             3750             2083.0       120.0             360.0   \n",
       "\n",
       "    Credit_History Property_Area Loan_Status  \n",
       "0              1.0         Urban         Yes  \n",
       "1              1.0         Rural          No  \n",
       "2              1.0         Urban         Yes  \n",
       "3              1.0         Urban         Yes  \n",
       "4              1.0         Urban         Yes  \n",
       "5              1.0         Urban         Yes  \n",
       "6              1.0         Urban         Yes  \n",
       "7              0.0     Semiurban          No  \n",
       "8              1.0         Urban         Yes  \n",
       "9              1.0     Semiurban          No  \n",
       "10             1.0         Urban         Yes  \n",
       "11             1.0         Urban         Yes  \n",
       "12             1.0         Urban         Yes  \n",
       "13             1.0         Rural          No  \n",
       "14             1.0         Urban         Yes  \n",
       "15             1.0         Urban         Yes  \n",
       "16             NaN         Urban         Yes  \n",
       "17             0.0         Urban          No  \n",
       "18             1.0         Rural          No  \n",
       "19             1.0         Urban         Yes  \n",
       "20             0.0         Urban          No  \n",
       "21             1.0         Urban         Yes  \n",
       "22             0.0     Semiurban          No  \n",
       "23             0.0         Rural          No  \n",
       "24             NaN     Semiurban          No  \n",
       "25             1.0     Semiurban         Yes  \n",
       "26             1.0     Semiurban         Yes  \n",
       "27             1.0         Urban         Yes  \n",
       "28             1.0         Urban          No  \n",
       "29             1.0     Semiurban         Yes  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing missing (\"?\") and (\"\\t?\") data with NaN values\n",
    "#data = data.replace('?', np.NaN)\n",
    "data = data.replace('\\t?', np.NaN)\n",
    "\n",
    "\n",
    "# Visualizing changed values\n",
    "data.head(30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns and Number of missing data \n",
      " Loan_ID               0\n",
      "Gender               13\n",
      "Married               3\n",
      "Dependents           15\n",
      "Education             0\n",
      "Self_Employed        32\n",
      "ApplicantIncome       0\n",
      "CoapplicantIncome     0\n",
      "LoanAmount           22\n",
      "Loan_Amount_Term     14\n",
      "Credit_History       50\n",
      "Property_Area         0\n",
      "Loan_Status           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Checking where is missing data by each column\n",
    "# As it could be seen there is a lot of missing data in the table\n",
    "missing = data.isnull().sum(axis=0) \n",
    "print(\"Columns and Number of missing data \\n\", missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LP001013</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>2333</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LP001014</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>3036</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LP001018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>4006</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LP001020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>12841</td>\n",
       "      <td>10968.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LP001024</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>3200</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LP001027</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LP001028</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>3073</td>\n",
       "      <td>8106.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LP001029</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>1853</td>\n",
       "      <td>2840.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LP001030</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>1299</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LP001032</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>4950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LP001034</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>3596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LP001036</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>3510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LP001038</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>4887</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LP001041</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2600</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LP001043</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>7660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LP001046</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>5955</td>\n",
       "      <td>5625.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LP001047</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>2600</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LP001050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>3365</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LP001052</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3717</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LP001066</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9560</td>\n",
       "      <td>0.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LP001068</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>2799</td>\n",
       "      <td>2253.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>LP001073</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>4226</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>LP001086</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>1442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>LP001087</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3750</td>\n",
       "      <td>2083.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Loan_ID  Gender Married Dependents  Education Self_Employed  \\\n",
       "0   LP001002     1.0      No          0          1            No   \n",
       "1   LP001003     1.0     Yes          1          1            No   \n",
       "2   LP001005     1.0     Yes          0          1           Yes   \n",
       "3   LP001006     1.0     Yes          0          0            No   \n",
       "4   LP001008     1.0      No          0          1            No   \n",
       "5   LP001011     1.0     Yes          2          1           Yes   \n",
       "6   LP001013     1.0     Yes          0          0            No   \n",
       "7   LP001014     1.0     Yes         3+          1            No   \n",
       "8   LP001018     1.0     Yes          2          1            No   \n",
       "9   LP001020     1.0     Yes          1          1            No   \n",
       "10  LP001024     1.0     Yes          2          1            No   \n",
       "11  LP001027     1.0     Yes          2          1           NaN   \n",
       "12  LP001028     1.0     Yes          2          1            No   \n",
       "13  LP001029     1.0      No          0          1            No   \n",
       "14  LP001030     1.0     Yes          2          1            No   \n",
       "15  LP001032     1.0      No          0          1            No   \n",
       "16  LP001034     1.0      No          1          0            No   \n",
       "17  LP001036     0.0      No          0          1            No   \n",
       "18  LP001038     1.0     Yes          0          0            No   \n",
       "19  LP001041     1.0     Yes          0          1           NaN   \n",
       "20  LP001043     1.0     Yes          0          0            No   \n",
       "21  LP001046     1.0     Yes          1          1            No   \n",
       "22  LP001047     1.0     Yes          0          0            No   \n",
       "23  LP001050     NaN     Yes          2          0            No   \n",
       "24  LP001052     1.0     Yes          1          1           NaN   \n",
       "25  LP001066     1.0     Yes          0          1           Yes   \n",
       "26  LP001068     1.0     Yes          0          1            No   \n",
       "27  LP001073     1.0     Yes          2          0            No   \n",
       "28  LP001086     1.0      No          0          0            No   \n",
       "29  LP001087     0.0      No          2          1           NaN   \n",
       "\n",
       "    ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0              5849                0.0         NaN             360.0   \n",
       "1              4583             1508.0       128.0             360.0   \n",
       "2              3000                0.0        66.0             360.0   \n",
       "3              2583             2358.0       120.0             360.0   \n",
       "4              6000                0.0       141.0             360.0   \n",
       "5              5417             4196.0       267.0             360.0   \n",
       "6              2333             1516.0        95.0             360.0   \n",
       "7              3036             2504.0       158.0             360.0   \n",
       "8              4006             1526.0       168.0             360.0   \n",
       "9             12841            10968.0       349.0             360.0   \n",
       "10             3200              700.0        70.0             360.0   \n",
       "11             2500             1840.0       109.0             360.0   \n",
       "12             3073             8106.0       200.0             360.0   \n",
       "13             1853             2840.0       114.0             360.0   \n",
       "14             1299             1086.0        17.0             120.0   \n",
       "15             4950                0.0       125.0             360.0   \n",
       "16             3596                0.0       100.0             240.0   \n",
       "17             3510                0.0        76.0             360.0   \n",
       "18             4887                0.0       133.0             360.0   \n",
       "19             2600             3500.0       115.0               NaN   \n",
       "20             7660                0.0       104.0             360.0   \n",
       "21             5955             5625.0       315.0             360.0   \n",
       "22             2600             1911.0       116.0             360.0   \n",
       "23             3365             1917.0       112.0             360.0   \n",
       "24             3717             2925.0       151.0             360.0   \n",
       "25             9560                0.0       191.0             360.0   \n",
       "26             2799             2253.0       122.0             360.0   \n",
       "27             4226             1040.0       110.0             360.0   \n",
       "28             1442                0.0        35.0             360.0   \n",
       "29             3750             2083.0       120.0             360.0   \n",
       "\n",
       "    Credit_History  Property_Area  Loan_Status  \n",
       "0              1.0              1            1  \n",
       "1              1.0              0            0  \n",
       "2              1.0              1            1  \n",
       "3              1.0              1            1  \n",
       "4              1.0              1            1  \n",
       "5              1.0              1            1  \n",
       "6              1.0              1            1  \n",
       "7              0.0              1            0  \n",
       "8              1.0              1            1  \n",
       "9              1.0              1            0  \n",
       "10             1.0              1            1  \n",
       "11             1.0              1            1  \n",
       "12             1.0              1            1  \n",
       "13             1.0              0            0  \n",
       "14             1.0              1            1  \n",
       "15             1.0              1            1  \n",
       "16             NaN              1            1  \n",
       "17             0.0              1            0  \n",
       "18             1.0              0            0  \n",
       "19             1.0              1            1  \n",
       "20             0.0              1            0  \n",
       "21             1.0              1            1  \n",
       "22             0.0              1            0  \n",
       "23             0.0              0            0  \n",
       "24             NaN              1            0  \n",
       "25             1.0              1            1  \n",
       "26             1.0              1            1  \n",
       "27             1.0              1            1  \n",
       "28             1.0              1            0  \n",
       "29             1.0              1            1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Making a map where 'Graduate' will be converted into 1 and 'Not Graduate' will be converted into 0\n",
    "Education_mapping = {'Graduate' : 1, 'Not Graduate' : 0}\n",
    "# Making the actual convertion and replacing the values in the table\n",
    "data['Education'] = data['Education'].map(Education_mapping)\n",
    "\n",
    "# Making a map where 'Male' will be converted into 1 and 'Female' will be converted into 0\n",
    "Gender_mapping = {'Male' : 1, 'Female' : 0}\n",
    "# Making the actual convertion and replacing the values in the table\n",
    "data['Gender'] = data['Gender'].map(Gender_mapping)\n",
    "\n",
    "\n",
    "# Making a map where 'Yes' will be converted into 1 and 'No' will be converted into 0\n",
    "Property_Area_mapping = {'Urban' : 1,'Semiurban':1, 'Rural' : 0}\n",
    "# Making the actual convertion and replacing the values in the table\n",
    "data['Property_Area'] = data['Property_Area'].map(Property_Area_mapping)\n",
    "\n",
    " #Making a map where 'Yes' will be converted into 1 and 'No' will be converted into 0\n",
    "Loan_Status_mapping = {'Yes' : 1, 'No' : 0}\n",
    "# Making the actual convertion and replacing the values in the table\n",
    "data['Loan_Status'] = data['Loan_Status'].map(Loan_Status_mapping)\n",
    "\n",
    "data.head(30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LP001013</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2333</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LP001014</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3+</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3036</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LP001018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4006</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LP001020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12841</td>\n",
       "      <td>10968.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LP001024</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3200</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LP001027</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LP001028</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3073</td>\n",
       "      <td>8106.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LP001029</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1853</td>\n",
       "      <td>2840.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LP001030</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1299</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LP001032</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LP001034</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LP001036</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LP001038</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4887</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LP001041</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2600</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LP001043</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LP001046</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5955</td>\n",
       "      <td>5625.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LP001047</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2600</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LP001050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3365</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LP001052</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3717</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LP001066</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9560</td>\n",
       "      <td>0.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LP001068</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2799</td>\n",
       "      <td>2253.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>LP001073</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4226</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>LP001086</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>LP001087</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3750</td>\n",
       "      <td>2083.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Loan_ID  Gender  Married Dependents  Education  Self_Employed  \\\n",
       "0   LP001002     1.0      0.0          0          1            0.0   \n",
       "1   LP001003     1.0      1.0          1          1            0.0   \n",
       "2   LP001005     1.0      1.0          0          1            1.0   \n",
       "3   LP001006     1.0      1.0          0          0            0.0   \n",
       "4   LP001008     1.0      0.0          0          1            0.0   \n",
       "5   LP001011     1.0      1.0          2          1            1.0   \n",
       "6   LP001013     1.0      1.0          0          0            0.0   \n",
       "7   LP001014     1.0      1.0         3+          1            0.0   \n",
       "8   LP001018     1.0      1.0          2          1            0.0   \n",
       "9   LP001020     1.0      1.0          1          1            0.0   \n",
       "10  LP001024     1.0      1.0          2          1            0.0   \n",
       "11  LP001027     1.0      1.0          2          1            NaN   \n",
       "12  LP001028     1.0      1.0          2          1            0.0   \n",
       "13  LP001029     1.0      0.0          0          1            0.0   \n",
       "14  LP001030     1.0      1.0          2          1            0.0   \n",
       "15  LP001032     1.0      0.0          0          1            0.0   \n",
       "16  LP001034     1.0      0.0          1          0            0.0   \n",
       "17  LP001036     0.0      0.0          0          1            0.0   \n",
       "18  LP001038     1.0      1.0          0          0            0.0   \n",
       "19  LP001041     1.0      1.0          0          1            NaN   \n",
       "20  LP001043     1.0      1.0          0          0            0.0   \n",
       "21  LP001046     1.0      1.0          1          1            0.0   \n",
       "22  LP001047     1.0      1.0          0          0            0.0   \n",
       "23  LP001050     NaN      1.0          2          0            0.0   \n",
       "24  LP001052     1.0      1.0          1          1            NaN   \n",
       "25  LP001066     1.0      1.0          0          1            1.0   \n",
       "26  LP001068     1.0      1.0          0          1            0.0   \n",
       "27  LP001073     1.0      1.0          2          0            0.0   \n",
       "28  LP001086     1.0      0.0          0          0            0.0   \n",
       "29  LP001087     0.0      0.0          2          1            NaN   \n",
       "\n",
       "    ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0              5849                0.0         NaN             360.0   \n",
       "1              4583             1508.0       128.0             360.0   \n",
       "2              3000                0.0        66.0             360.0   \n",
       "3              2583             2358.0       120.0             360.0   \n",
       "4              6000                0.0       141.0             360.0   \n",
       "5              5417             4196.0       267.0             360.0   \n",
       "6              2333             1516.0        95.0             360.0   \n",
       "7              3036             2504.0       158.0             360.0   \n",
       "8              4006             1526.0       168.0             360.0   \n",
       "9             12841            10968.0       349.0             360.0   \n",
       "10             3200              700.0        70.0             360.0   \n",
       "11             2500             1840.0       109.0             360.0   \n",
       "12             3073             8106.0       200.0             360.0   \n",
       "13             1853             2840.0       114.0             360.0   \n",
       "14             1299             1086.0        17.0             120.0   \n",
       "15             4950                0.0       125.0             360.0   \n",
       "16             3596                0.0       100.0             240.0   \n",
       "17             3510                0.0        76.0             360.0   \n",
       "18             4887                0.0       133.0             360.0   \n",
       "19             2600             3500.0       115.0               NaN   \n",
       "20             7660                0.0       104.0             360.0   \n",
       "21             5955             5625.0       315.0             360.0   \n",
       "22             2600             1911.0       116.0             360.0   \n",
       "23             3365             1917.0       112.0             360.0   \n",
       "24             3717             2925.0       151.0             360.0   \n",
       "25             9560                0.0       191.0             360.0   \n",
       "26             2799             2253.0       122.0             360.0   \n",
       "27             4226             1040.0       110.0             360.0   \n",
       "28             1442                0.0        35.0             360.0   \n",
       "29             3750             2083.0       120.0             360.0   \n",
       "\n",
       "    Credit_History  Property_Area  Loan_Status  \n",
       "0              1.0              1            1  \n",
       "1              1.0              0            0  \n",
       "2              1.0              1            1  \n",
       "3              1.0              1            1  \n",
       "4              1.0              1            1  \n",
       "5              1.0              1            1  \n",
       "6              1.0              1            1  \n",
       "7              0.0              1            0  \n",
       "8              1.0              1            1  \n",
       "9              1.0              1            0  \n",
       "10             1.0              1            1  \n",
       "11             1.0              1            1  \n",
       "12             1.0              1            1  \n",
       "13             1.0              0            0  \n",
       "14             1.0              1            1  \n",
       "15             1.0              1            1  \n",
       "16             NaN              1            1  \n",
       "17             0.0              1            0  \n",
       "18             1.0              0            0  \n",
       "19             1.0              1            1  \n",
       "20             0.0              1            0  \n",
       "21             1.0              1            1  \n",
       "22             0.0              1            0  \n",
       "23             0.0              0            0  \n",
       "24             NaN              1            0  \n",
       "25             1.0              1            1  \n",
       "26             1.0              1            1  \n",
       "27             1.0              1            1  \n",
       "28             1.0              1            0  \n",
       "29             1.0              1            1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    " #Making a map where 'Yes' will be converted into 1 and 'No' will be converted into 0\n",
    "Married_mapping = {'Yes' : 1, 'No' : 0}\n",
    "# Making the actual convertion and replacing the values in the table\n",
    "data['Married'] = data['Married'].map(Married_mapping)\n",
    "\n",
    " #Making a map where 'Yes' will be converted into 1 and 'No' will be converted into 0\n",
    "Self_Employed_mapping = {'Yes' : 1, 'No' : 0}\n",
    "# Making the actual convertion and replacing the values in the table\n",
    "data['Self_Employed'] = data['Self_Employed'].map(Self_Employed_mapping)\n",
    "\n",
    "data.head(30)\n",
    "                                                \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LP001013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2333</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LP001014</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3+</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3036</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>158</td>\n",
       "      <td>360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LP001018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4006</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>168</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LP001020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12841</td>\n",
       "      <td>10968.0</td>\n",
       "      <td>349</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LP001024</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3200</td>\n",
       "      <td>700.0</td>\n",
       "      <td>70</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LP001027</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2500</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>109</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LP001028</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3073</td>\n",
       "      <td>8106.0</td>\n",
       "      <td>200</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LP001029</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1853</td>\n",
       "      <td>2840.0</td>\n",
       "      <td>114</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LP001030</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1299</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>17</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LP001032</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LP001034</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LP001036</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76</td>\n",
       "      <td>360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LP001038</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4887</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LP001041</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2600</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>115</td>\n",
       "      <td>342</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LP001043</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104</td>\n",
       "      <td>360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LP001046</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5955</td>\n",
       "      <td>5625.0</td>\n",
       "      <td>315</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LP001047</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2600</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>116</td>\n",
       "      <td>360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LP001050</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3365</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>112</td>\n",
       "      <td>360</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LP001052</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3717</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>151</td>\n",
       "      <td>360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LP001066</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9560</td>\n",
       "      <td>0.0</td>\n",
       "      <td>191</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LP001068</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2799</td>\n",
       "      <td>2253.0</td>\n",
       "      <td>122</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>LP001073</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4226</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>110</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>LP001086</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>LP001087</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3750</td>\n",
       "      <td>2083.0</td>\n",
       "      <td>120</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Loan_ID  Gender  Married Dependents  Education  Self_Employed  \\\n",
       "0   LP001002       1        0          0          1              0   \n",
       "1   LP001003       1        1          1          1              0   \n",
       "2   LP001005       1        1          0          1              1   \n",
       "3   LP001006       1        1          0          0              0   \n",
       "4   LP001008       1        0          0          1              0   \n",
       "5   LP001011       1        1          2          1              1   \n",
       "6   LP001013       1        1          0          0              0   \n",
       "7   LP001014       1        1         3+          1              0   \n",
       "8   LP001018       1        1          2          1              0   \n",
       "9   LP001020       1        1          1          1              0   \n",
       "10  LP001024       1        1          2          1              0   \n",
       "11  LP001027       1        1          2          1              0   \n",
       "12  LP001028       1        1          2          1              0   \n",
       "13  LP001029       1        0          0          1              0   \n",
       "14  LP001030       1        1          2          1              0   \n",
       "15  LP001032       1        0          0          1              0   \n",
       "16  LP001034       1        0          1          0              0   \n",
       "17  LP001036       0        0          0          1              0   \n",
       "18  LP001038       1        1          0          0              0   \n",
       "19  LP001041       1        1          0          1              0   \n",
       "20  LP001043       1        1          0          0              0   \n",
       "21  LP001046       1        1          1          1              0   \n",
       "22  LP001047       1        1          0          0              0   \n",
       "23  LP001050       0        1          2          0              0   \n",
       "24  LP001052       1        1          1          1              0   \n",
       "25  LP001066       1        1          0          1              1   \n",
       "26  LP001068       1        1          0          1              0   \n",
       "27  LP001073       1        1          2          0              0   \n",
       "28  LP001086       1        0          0          0              0   \n",
       "29  LP001087       0        0          2          1              0   \n",
       "\n",
       "    ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0              5849                0.0         146               360   \n",
       "1              4583             1508.0         128               360   \n",
       "2              3000                0.0          66               360   \n",
       "3              2583             2358.0         120               360   \n",
       "4              6000                0.0         141               360   \n",
       "5              5417             4196.0         267               360   \n",
       "6              2333             1516.0          95               360   \n",
       "7              3036             2504.0         158               360   \n",
       "8              4006             1526.0         168               360   \n",
       "9             12841            10968.0         349               360   \n",
       "10             3200              700.0          70               360   \n",
       "11             2500             1840.0         109               360   \n",
       "12             3073             8106.0         200               360   \n",
       "13             1853             2840.0         114               360   \n",
       "14             1299             1086.0          17               120   \n",
       "15             4950                0.0         125               360   \n",
       "16             3596                0.0         100               240   \n",
       "17             3510                0.0          76               360   \n",
       "18             4887                0.0         133               360   \n",
       "19             2600             3500.0         115               342   \n",
       "20             7660                0.0         104               360   \n",
       "21             5955             5625.0         315               360   \n",
       "22             2600             1911.0         116               360   \n",
       "23             3365             1917.0         112               360   \n",
       "24             3717             2925.0         151               360   \n",
       "25             9560                0.0         191               360   \n",
       "26             2799             2253.0         122               360   \n",
       "27             4226             1040.0         110               360   \n",
       "28             1442                0.0          35               360   \n",
       "29             3750             2083.0         120               360   \n",
       "\n",
       "    Credit_History  Property_Area  Loan_Status  \n",
       "0                1              1            1  \n",
       "1                1              0            0  \n",
       "2                1              1            1  \n",
       "3                1              1            1  \n",
       "4                1              1            1  \n",
       "5                1              1            1  \n",
       "6                1              1            1  \n",
       "7                0              1            0  \n",
       "8                1              1            1  \n",
       "9                1              1            0  \n",
       "10               1              1            1  \n",
       "11               1              1            1  \n",
       "12               1              1            1  \n",
       "13               1              0            0  \n",
       "14               1              1            1  \n",
       "15               1              1            1  \n",
       "16               0              1            1  \n",
       "17               0              1            0  \n",
       "18               1              0            0  \n",
       "19               1              1            1  \n",
       "20               0              1            0  \n",
       "21               1              1            1  \n",
       "22               0              1            0  \n",
       "23               0              0            0  \n",
       "24               0              1            0  \n",
       "25               1              1            1  \n",
       "26               1              1            1  \n",
       "27               1              1            1  \n",
       "28               1              1            0  \n",
       "29               1              1            1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replacing missing data\n",
    "#Our strategy with missing data: When there are some missing data in the table there could be couple of approaches how this problem could be solved. The easiest way could be to just remove those collumns that have an extensive amount of missing data. Even though we have a relatively high amount of data that is missing there are no collumns where the amount of missing data is higher then 90%. Therefore, we have chosen to find ways how to replace this data instead of removing it.\n",
    "# Replace empty 'Gender' values by their columns average\n",
    "data['Gender'] = data.Gender.astype(float)\n",
    "data['Gender'].fillna((data['Gender'].mean()), inplace=True)\n",
    "data['Gender'] = data.Gender.astype(int)\n",
    "\n",
    "data['Self_Employed'] = data.Self_Employed.astype(float)\n",
    "data['Self_Employed'].fillna((data['Self_Employed'].mean()), inplace=True)\n",
    "data['Self_Employed'] = data.Self_Employed.astype(int)\n",
    "\n",
    "data['LoanAmount'] = data.LoanAmount.astype(float)\n",
    "data['LoanAmount'].fillna((data['LoanAmount'].mean()), inplace=True)\n",
    "data['LoanAmount'] = data.LoanAmount.astype(int)\n",
    "\n",
    "#Replacing missing data\n",
    "#Our strategy with missing data: When there are some missing data in the table there could be couple of approaches how this problem could be solved. The easiest way could be to just remove those collumns that have an extensive amount of missing data. Even though we have a relatively high amount of data that is missing there are no collumns where the amount of missing data is higher then 90%. Therefore, we have chosen to find ways how to replace this data instead of removing it.\n",
    "# Replace empty 'Gender' values by their columns average\n",
    "data['Gender'] = data.Gender.astype(float)\n",
    "data['Gender'].fillna((data['Gender'].mean()), inplace=True)\n",
    "data['Gender'] = data.Gender.astype(int)\n",
    "\n",
    "data['Self_Employed'] = data.Self_Employed.astype(float)\n",
    "data['Self_Employed'].fillna((data['Self_Employed'].mean()), inplace=True)\n",
    "data['Self_Employed'] = data.Self_Employed.astype(int)\n",
    "\n",
    "data['Loan_Amount_Term'] = data.Loan_Amount_Term.astype(float)\n",
    "data['Loan_Amount_Term'].fillna((data['Loan_Amount_Term'].mean()), inplace=True)\n",
    "data['Loan_Amount_Term'] = data.Loan_Amount_Term.astype(int)\n",
    "\n",
    "\n",
    "data['Credit_History'] = data.Credit_History.astype(float)\n",
    "data['Credit_History'].fillna((data['Credit_History'].mean()), inplace=True)\n",
    "data['Credit_History'] = data.Credit_History.astype(int)\n",
    "\n",
    "\n",
    "data['Married'] = data.Married.astype(float)\n",
    "data['Married'].fillna((data['Married'].mean()), inplace=True)\n",
    "data['Married'] = data.Married.astype(int)\n",
    "\n",
    "\n",
    "data.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[\"LoanAmount\"].hist(grid=True)\n",
    "plt.suptitle(\"Loan Amount\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2291ad36048>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 356.75x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(data, hue='Loan_Status', size=4).\\\n",
    "                   map(plt.scatter, 'ApplicantIncome',\n",
    "                   'ApplicantIncome').add_legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x253bee36ac8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['ApplicantIncome'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x253bf34f048>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot(column='ApplicantIncome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x253bf373198>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot(column='ApplicantIncome', by = 'Education')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x253bf2c1b38>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M/N/MfAO92e/FwOvmG1Yux5Y5It4GPJOZe/oPnyBPJ+5f4LLMvAh4K/C+iHjTCcaOO/Mqekucf52ZbwSeo7dksZBx5+2F6D1vcjXwpUFD5zm27LxdLPRGbynQEU9HxDkA5fKZcnzstyEiXkavzL+QmXeUw53N2y8zDwOz9NYW10TE3PkS/bmez1y+fzrwXyOKeBlwdUQcoPcuo5fTm7F3MevzMvOpcvkM8Pf0/tHs6mPiIHAwM3eV/dvpFXxX8855K3B/Zj5d9keat4uFvpLeUuAuYFPZ3kRvrXru+O+UZ7IvAY7M/dk1ChERwC3Avsz8RNfzAkTEqyJiTdk+FXgzsA/YCVy7QOa523It8LUsi5Fty8ybMnNdZk7Re3x+LTOv72LWORGxOiJeMbdNb533ITr6mMjMHwDfj4gLy6ErgEe6mrfPO3lhuWUu1+jyjuNJgwZPKlxJ75UZ3wX+eNx5SqZbgUPA/9D71/VGeuug9wGPlcszy9ig9+Ef3wX2AtMjzvpr9P58+zbwYPm6sqt5S4ZfAh4omR8C/qQcPx/4BrCf3p+xLy/HTyn7+8v3zx/T42KGF17l0tmsJdu3ytfDc/9fdfwx8QZgd3lM/ANwRsfzngb8J3B637GR5vVMUUmqRBeXXCRJS2ChS1IlLHRJqoSFLkmVsNAlqRIWuiRVwkKXpEpY6JJUif8DZDODgshIOzQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['LoanAmount'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x253c0729a58>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot(column='LoanAmount') df['LoanAmount_log'] = np.log(df['LoanAmount'])\n",
    "df['LoanAmount_log'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x253c08f96a0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " data['LoanAmount_log'] = np.log(data['LoanAmount'])\n",
    "data['LoanAmount_log'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns and Number of missing data \n",
      " Loan_ID               0\n",
      "Gender                0\n",
      "Married               0\n",
      "Dependents           15\n",
      "Education             0\n",
      "Self_Employed         0\n",
      "ApplicantIncome       0\n",
      "CoapplicantIncome     0\n",
      "LoanAmount            0\n",
      "Loan_Amount_Term      0\n",
      "Credit_History        0\n",
      "Property_Area         0\n",
      "Loan_Status           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Checking where is missing data by each column\n",
    "# As it could be seen there is a lot of missing data in the table\n",
    "missing = data.isnull().sum(axis=0) \n",
    "print(\"Columns and Number of missing data \\n\", missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of True cases:  422 (68.73%)\n",
      "Number of False cases: 192 (31.27%)\n"
     ]
    }
   ],
   "source": [
    "#Check class distribution\n",
    "num_obs = len(data)#Find number of rows\n",
    "num_true = len(data.loc[data['Loan_Status'] == 1])#Number of people who are eligible for the loan\n",
    "num_false = len(data.loc[data['Loan_Status'] == 0])#Number of people who are not eligible for the loan\n",
    "print(\"Number of True cases:  {0} ({1:2.2f}%)\".format(num_true, (num_true/num_obs) * 100))\n",
    "print(\"Number of False cases: {0} ({1:2.2f}%)\".format(num_false, (num_false/num_obs) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Now we know which features are most likely to contribute to our model\n",
    "# so lets create a model and see the accuracy\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(\n",
    "        data.loc[:, ['ApplicantIncome', 'Credit_History','Education', 'Self_Employed','Gender']], \n",
    "        data.loc[:, 'Loan_Status'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy 0.8021739130434783\n",
      "Testing Accuracy 0.45454545454545453\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=2, p=2, metric='minkowski')\n",
    "knn.fit(X_train, Y_train)\n",
    "print (\"Training Accuracy {}\".format(knn.score(X_train, Y_train)))\n",
    "print (\"Testing Accuracy {}\".format(knn.score(X_test, Y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy 0.9847826086956522\n",
      "Testing Accuracy 0.6558441558441559\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "des = DecisionTreeClassifier()\n",
    "des.fit(X_train, Y_train)\n",
    "print (\"Training Accuracy {}\".format(des.score(X_train, Y_train)))\n",
    "print (\"Testing Accuracy {}\".format(des.score(X_test, Y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

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About This Project

Project period

09/06/2019 - 10/29/2019

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