rahul1092/lendingclubcasestudy

Project Name

The objective of this case study is to perform Exploratory Data Analysis (EDA) on the given dataset to identify patterns whether a customer might default on Loan repayment based on a Consumer Finance Company dataset.

Table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

General Information

• The Case study entails doing EDA.
• We are provided with a single CSV file containing the Dataset from a Consumer Finance Company.
• Using this dataset, we need to find if we can establish some relationship between the various data points and the fact that customer might default or not.

Conclusions

• The majority of people (~74%) have taken a loan with a term of 36 months.
• As a general trend, we see that with the increase in years of experience, the number of loan seekers tend to decrease.
• Very few of the loan applicants own their own home.
• Loans that were "Charged Off" had a higher median interest rate of 13.59% as against a rate of 11.49% for loans that were fully paid.
• With increase in pub_rec_bankruptcies field, the percentage of people defaulting on loan payment rises.
• Median annual income of people who have defaulted on loan payment is lower than that of people who have not.
• If the number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years is 7 or 8, there is a high risk of default, with the highest default rate at 8 delinquencies.
• In the highest grouping of total_acc field (field being grouped in steps of 10), the percentage of defaulting customers is the least.

Technologies Used

• Pandas - version 2.1.4
• plotly - version 5.9.0

Acknowledgements

This project was based on the EDA Module of Course 1 (Statistics).

Contact

Created by [@rahul1092] - feel free to contact me!