Leading club case study

As we a consumer finance company which specialises in lending various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision:

1. If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company.
2. If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company.

Table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

General Information

• The company wants to understand the driving factors (or driver variables) behind loan default (loan_status = 'Charged Off'), i.e. the variables which are strong indicators of default. The company can utilise this knowledge for its portfolio and risk assessment
• What is the background of your project : Understaing the data set , cleaning the data and deriver the variables from it and finding conclusions of the applications.
• What is the business probem that your project is trying to solve : portfolio and risk assessment
• What is the dataset that is being used : we are using the leading club case study data set to analysis the problem format is .csv

Conclusions

• Conclusion 1 from the analysis:
• As per the analysis made more loan interest rate and borrower from rent and mortgage are more “charged off” chances.
• 
• Conclusion 2 from the analysis:
• Interest rate increasing clearly that grades moving A to G, as the higher the grade higher the “charged off” <<<<<<< HEAD
• 
• Conclusion 3 from the analysis:
• Annual income as the borrowers in very low means their are then to more “charged off”
• =======
• 
• Conclusion 3 from the analysis:
• Annual income as the borrowers in very low means their are then to more “charged off”
• 

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Technologies Used

• pandas version - version 1.3.4
• numpy version - version 1.20.3
• sea born version - version 0.11.2

Acknowledgements

Give credit here.

• This project was inspired by Fintech compnay and online leding company for small loan
• References for pandas and matplotlibs

1. https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.qcut.html
2. https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot.html
3. https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html

• This project was based on fintech company like ZestMoney and paytm etc..

Contact

Satyanarayana k & vinoth.kanagarathinam batch -1993 PG in ML and AI -feel free to contact us!