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Project Name: Lending Club Case Study

Table of Contents

General Information

Project Information

The project is a data science project that uses the lending club data set to predict whether a loan will be defaulted or not.

Project Background

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'.

Project Statement

Find the driving factors which lead to the defaulted loans which are major source of loss for the company.

Data Set

The data set is a csv file with the loan data for the Lending Club.

Conclusions

  • Major Driving factor which can be used to predict the chance of defaulting and avoiding Credit Loss:
    1. DTI
    2. Grades
    3. Verification Status
    4. Annual income
    5. Pub_rec_bankruptcies
  • Other considerations for 'defaults' :
    1. Burrowers from large urban cities like california, new york, texas, florida etc.
    2. Burrowers having annual income in the range 50000-100000.
    3. Burrowers having Public Recorded Bankruptcy.
    4. Burrowers with least grades like E,F,G which indicates high risk.
    5. Burrowers with very high Debt to Income value.
    6. Burrowers with working experience 10+ years.

Technologies Used

  • Pandas - version 1.3.4
  • NumPy - version 1.20.3
  • Seaborn - version 0.11.2
  • MatplotLib - version 3.4.3
  • Plotly - version 5.7.0

Acknowledgements

This project was inspired by UpGrad IITB Programme as a case study for the Machine Learning and Artificial Intelligence course.

Contact

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

License

This project is open source and available without restrictions.