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:

• If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company.
• 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.

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

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.

The aim is to identify patterns which indicate if a person is likely to default, which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc.

The data given contains information about past loan applicants and whether they ‘defaulted’ or not along with other parameters mentioned as a part of Data Dictionary.

• Python 3
• Pandas
• Numpy
• Seaplot

• Loans having higher term tends to default more
• Loans for the purpose of Small business tends to default more
• Higher interest and higher amount tends to default more.
• The verification status have no impact on defaults. Bank may have to make its verification processes more stringent
• People having derogatory remarks and bankruptcies also tend to pay back loan equally.
• People with 10+ years of experience tends to default more but at the same time are most contributors to loan applications.
• People having credit utilization of more than 20% tend to default more
• People having more accounts tends to pay back loan fully.
• People having more inquiries in last 6 months tend to pay back loan fully.
• People in Distric of Columbia and Wyoming tend to pay back loan in higher percentages.
• People in Nevada, Alaska and South Dakota tends to default more.
• People having any kind of home ownership tend to default equally.

Give credit here.

• This project was inspired by EDA methodologies learned and explained by upGrad
• References
  • https://numpy.org/doc/stable/index.html
  • https://pandas.pydata.org/docs/index.html
  • https://seaborn.pydata.org/index.html
• This project was based on Upgrad Tutorial on Exploratory Data Analysis.

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