mukul18 / lending_case_study

Project Name

Lending Club Case Study.

Table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

General Information

A finance company named 'Lending Club' specialises in lending various types of loans to customers. When the company receives a loan application, it has to make a decision for loan approval based on the applicant’s profile.

There are two types of risks are associated with the bank’s decision:

(i) If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company.
(ii) If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company

Based on the dataset "loan.csv" company wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default.

Technologies Used

Python3 and below pyhton libraries -

• pandas: A fast, powerful, flexible and easy to use open source data analysis and manipulation tool. (To work with dataset)
• numpy: The fundamental package for scientific computing in Python. (Math Library)
• datetime: The datetime module supplies classes for manipulating dates and times.
• matplotlib: A comprehensive library for creating static, animated, and interactive visualizations in Python. (To plot graphs)
• seaborn: A data visualization library built on top of matplotlib (To plot graphs)

Summary of the analysis

• Univariate Analysis
• Segmented Univariate Analysis
• Bi-variate Analysis

Conclusions

DrivingFactors(or driver variables):

• Grade :-Default Rate is high in high risk loan applicants. It would be important for LC to thoroughly vet high risk loan applications.
• Annual Income :-Applicants from 'Low'(<=45K USD) and 'Medium'(45K-90K USD) income group have a greater share of defaulted loans.
• Employment Length : Maximum number of defaulters have 10/10+ years of experience and 0 to 2 years of experience. Hence, LC should be take this aspect into consideration while lending loans.
• Loan Purpose :-The top two reasons for loans are debt consolidation and credit card. Such applications should be carefully assessed.

Acknowledgements

This project is part of "Executive PG Programme in Machine Learning & AI" sponsed through UPGRAD with Joint collaboration of IIIT-B

Contact

Created by -

• Mukul Patel ([email protected])
• Nagaraj Gandge ([email protected])