Lending Club Case Study

Lending Club , is a largest online loan marketplace.facilitating personal loans, business loans, and financing of medical procedures.

• By using Exploratory Data Analysis,company Wants to know the risky applicants which is the largest source of credit loss and driving factors behind loan default.

Table of Contents

• General Info
• Technologies Used
• Problem Statement
• Conclusions
• Data Set
• Acknowledgements
• Contributors

General Information

• This company is the largest online loan marketplace, facilitating personal loans, business loans, and financing of medical procedures. Borrowers can easily access lower interest rate loans through a fast online interface.
• Like most other lending companies, lending loans to ‘risky’ applicants is the largest source of financial loss (called credit loss). Credit loss is the amount of money lost by the lender when the borrower refuses to pay or runs away with the money owed. In other words, borrowers who default cause the largest amount of loss to the lenders. In this case, the customers labelled as 'charged-off' are the 'defaulters'.
• If one is able to identify these risky loan applicants, then such loans can be reduced thereby cutting down the amount of credit loss. Identification of such applicants using EDA is the aim of this case study.
• In other words, the company wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. The company can utilise this knowledge for its portfolio and risk assessment.

Problem Statement

• Finding the risky applicants and driving factors behind the loan default.

Conclusions

• Conclusion 1 from the analysis
• Conclusion 2 from the analysis
• Conclusion 3 from the analysis
• Conclusion 4 from the analysis

Data Set

• The dataset folder contains

1. loan.csv - loan data for the Lending Club.
2. Data_Dictionary.xlsx - fields description

Technologies Used

• Python 3.10.11
• pandas 2.1.0
• numpy 1.24.2
• matplotlib 3.7.2
• seaborn 0.13.2

Acknowledgements

• This project was inspired by S Anand CEO, Gramener. He is SME for EDA classes on UpGrad
• UpGrad tutorials on Exploratory Data Analysis (EDA)

Contributors

Created by @cmurakonda and @CharuGarg-2024 - feel free to contact us!