Exploratory data analysis on a lending club to determine the root causes of borrowers getting defaulted.

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

• Lending Club is a consumer finance company which specialises in lending various types of loans to urban customers.
• It offers different types of loan products such as personal loans, business loans, and financing of medical procedures.
• Borrowers can easily access lower-interest rate loans through a fast online interface.
• There are borrowers who pay their loan fully and borrowers who don't pay and gets default.
• Lending Club wants to understand the driving factors behind the loan getting default and steps to reduce it.

1. Importing data
2. Data cleaning and evolution
3. Analysis
4. Conclusion

• Lending club growth has increased year by year at almost a double rate.
• Majority of the loan terms are for 36 months - approximately 75% of borrowers opted for this term.
• Majority of the borrowers have paid their loans with around 15% defaulted it.
• Borrowers with not verified sources have gotten more loan amounts.
• The median of the loan amount taken by the borrowers is 10000 while there are some outliers.
• Borrowers with 60 months loan term have defaulted more than borrowers with 36 months.
• Borrowers with lower grades are getting more loan amounts than borrowers with high grades.
• Higher interest rates result in more defaults than lower interest rates.
• Interest is also increasing as the borrower's grades are decreasing.
• Default rate is increasing exponentially as the grades are lowering.

• Pandas - 2.0.3
• Matplotlib - version 3.7
• Seaborn - version 0.12
• Numpy - version 1.25.0

• Numpy official site.
• Seaborn Official site.
• Pandas Official site

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