Prosper Loan Project

Mostafa Wahdan

Data Analyst
Jupyter
Python

Dataset

This dataset contains 113,937 loans with 81 variables on each loan,including loan amount, borrower rate (or interest rate), current loan status,borrower income, and many others. The dataset can be found

here

and a detailed description of all the features can be found

here

.

Summary of Findings

In the exploration, I found that there was a strong positive correlation between the EstimatedEffectiveYield, BorrowerAPR and BorrowerRate and also the LoanOriginalAmount and the MonthlyLoanPayment are highly correlated.Another thing we found out is that loans with Chargedoff & Defaulted status tend to have a higher BorrowerAPR on average and that that the BorrowerAPR is less for borrowers with the higher ProsperRating. Also found out that borrowers with Part-time EmploymentStatus have the lowest MonthlyLoanPayment on average and that those who have the lowest ProsperRating also have the lowest MonthlyLoanPaymentand that the defaulted & charged off loans have the lowest StatedMonthlyIncomeon average.

Also, as expected, the majority of the borrowers who are Employed or Full-time have a verifiable source of income, meanwhile most of the Self-employed borrowers have no verifiable source of income. Also, we found out that there's a negative correlation between the BorrowerAPR and the LoanOriginalAmount.

Last but not least, comparing different listing categories, we find that most of the defaulted and charged off loans are listed as Not Available, that could be either due to missing data or that the borrower didn't provide a reason for the loan.

Key Insights for Presentation

For the presentation, I focus on just the influence of different variables such as the Original Loan Amount, ProsperRating on the BorrowerAPR and how the BorrowerAPR might affect the loan status. 💪

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