ambar-ashish / lendingclubcasestudy

Apply EDA to the Loan Data received from a consumer finance company.

Project Name

Lending Club Case Study

Table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

General Information

Project Information

This project involves utilizing the provided Lending Club dataset for exploratory data analysis (EDA) to gain insights into risk analytics within the banking and financial services sector. Additionally, it aims to explore how data can be employed to mitigate financial losses when lending to customers.

Project Background

Goal

How data can be harnessed to reduce the likelihood of financial losses when extending loans to customers.

Risk Associated

When an applicant is predicted to be capable of loan repayment, declining their loan application results in a missed opportunity for the company (rejecting loans for non-default cases). When the applicant is unlikely to meet the loan repayment requirements, granting the loan could potentially result in financial losses for the company (approving loans for default cases).

The provided dataset includes data on previous loan applicants and their default status. Each row in the dataset represents the loan specifics for individual applicants.

Project Statement

The company aims to identify the key factors influencing loan defaults, which are often referred to as 'driver variables.' By conducting this case study, the company can leverage this knowledge to enhance its portfolio and risk assessment processes.

Data Set

The dataset is in CSV format & it contains Loan-related information for Lending Club.

Conclusions

There are more chances of defaulting when:

• Applicants earning an annual income below 50000.
• The loan amount is requested for an amount exceeding 10000.
• Applicants who utilize the loan for purposes such as consolidating existing debts, launching a small business, paying off credit card balances, or making home improvements.
• Applicants with a work history of over 10 years."
• Applicants who are granted loans with an interest rate ranging from 13% to 17%
• Applicants with debt-to-income ratios falling within the range of 12 to 15.
• The applicant currently has an active "MORTGAGE" in progress.
• The applicant has a work history of over 10 years, and the requested loan amount falls within the range of 12000 to 14000.

Technologies Used

• Python version: 3.10.13
• Pandas version: 1.5.3
• Numpy version: 1.23.5
• Matplotlib: version 3.7.0
• Seaborn: version 0.12.2

Acknowledgements

• This Case Study is developed for Exploratory Data Analysis (EDA) required for UpGrad - IIIT, Bangalore Programme.

Contact

Created by [@girishsatyam] - feel free to contact me!

License

This project is open source and available without any restrictions