Loan Prediction Machine Learning Problem

• Dream Housing Finance is a company that deals with all kinds of home loan.
• They want to automate the process the loan eligibility.
• In this project we have developed various models including Logistic Regression, Tree Based Model and XG Boost.
• Best Accuracy on Real Test DataSet was achived with Logistic Regression.

Table of Contents

• Introduction
• Problem Statement
• Importing & Installing Libraries
• Data Acquisition & Description
• Data Pre-Processing
• Exploratory Data Analysis
• Data Preparation
• Modelling
• Summary
• Contact Information

Introduction

• Our Client for this Project is Dream Housing Finance. The company deals in all kinds of home loans.
• They have a presence across all urban, semi-urban and rural areas.
• Customer first applies for home loan and after that company validates the customer eligibility for loan.
• Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form.
• To automate this process, they have provided a dataset to identify the customers segments that are eligible for loan amount so that they can specifically target these customers.

Problem Statement

• The Current process is a manual process and time consuming.
• Company has hired us as a Data Scientist to resolve their problem.

Importing & Installing Libraries

We have used several libraries in our project, including:

• pandas
• numpy
• matplotlib
• seaborn
• sklearn

Data Acquisition & Description

Train Set:

• The train dataset contains 614 rows and 12 columns.
• Loan_Status is our target variable.
• The training dataset can be downloaded from here.

Test Set:

• The test dataset contains 367 rows and 11 columns.
• The test set doesnt contain the Loan_Status column.
• It needs to be predicted for the test set.
• The test dataset can be downloaded from here.

Our Dataset has the following features along with there dtypes

Column Name	Description
Loan_ID	object
Gender	object
Married	object
Dependents	object
Education	object
Self Employed	object
Applicant Income	int64
Coapplicant Income	float64
LoanAmount	float64
Loan Amount Term	float64
Credit History	float64
Property Area	object
Loan Status	object

Data Pre-Processing

• We have imputed our missing values.
• Data types of Dependents and Credit History are changed.
• We have replaced '3+' with '3' in 'dependent' feature.

Exploratory Data Analysis(EDA)

Observations

• Distribution of Gender is fairly unequal. Lots of Male as compared to females.
• People with marital status 'Married' are more compared to 'Unmarried'.
• More Graduate people than not Graduate.
• Most of the people are not self employed.
• Outliers present in Applicant Income,Coapplicant Income,Loan Amount and Loan Amount Term.
• Very few people with Credit History 0
• In terms of Property Area Semiurban > Urban > Rural.
• People having Loan are almost double of people not having Loan.
• Using the chi2 contigency test we saw -
  • Gender and Loan Status are Independent with p = 0.66
  • Education and Loan_status are dependent with p =0.03
  • Credit_History has 100% dependency with Loan_status with p =0.00
  • Self_employed and Loan_status are Independent with p =0.93
  • Property Area and Loan Staus are higly dependent on each other with p =0.0

Data Preparation

• We have created a new column of Total Income = ApplicantIncome + CoapplicantIncome.
• We have converted our Categorical features into Numerical using Label Encoder.
• To bring Continous features on the same scale we have used Standard Scaler.

Modelling

• We have splitted our Data into Dependent and Independent features.(x and y)
• We have used sklearn train_test_split to split our Data into x_train,x_test,y_train,y_test.

We have deployed the following models -

Model Name	Train Accuracy	Test Accuracy
Logistic Reg.	0.81	0.81
Decision Tree	0.83	0.77
Random Forest	1.0	0.77
Ada Boost	0.83	0.80
XG Boost	0.80	0.79

• We tried performing hyperparamter tuning on Ada Boost and XG Boost and saw an increase in Accuracy.
• We made an app using Gradio to show whether a person is eligible for Loan or not.

Summary

• After removing all the missing values and correcting inconsistencies we applied our model on Real Test Dataset.

• Logistic Regression was performing best with 77.77% accuracy on Analytics Vidya Hackathon.

• We have secured top 5% position on Analytics Vidya Hackathon with the above approch.

• We can further tune our other models for better accuracy.

• We can also try removing few features which are less important in predicting Loan Status and see if our model performance increases.

• We can try and work on the outliers in our continous features using domain knowledge or with client's inputs.

  Thanks.

Contact Information

Open for questions and suggestions.

You can contact me on Email - anmol.1591@outlook.com LinkedIn - www.linkedin.com/in/anmol-arora-71371318

Thank You