The data consists of loan information of Prosper borrowers with 81 attributes and 113066 unique listings and 113937 loans last updated on march 11, 2014. The dataset is stored in a csv available via an external link here with feature documentation available here and click here to learn more about Prosper.
In this exploration, I found there was a strong relationship between the Borrower Rate, and Borrower APR along with Estimated Loss and Estimated Return. The Borrower Rate and APR, as per Investopedia, are percentages calculated in addition to the loan cost over time, which the borrower is responsible for. The APR will include not only interest rate but, also added fees, with other discounts. This will affect a customer's decision when thinking about taking out a loan.
Lastly, features like Estimated Loss, from Investopedia, is generally a provision that Prosper allocates to cover uncollected loans, due to borrower default, or bad loans among other options, similar to Estimated Return which would be a net positive to Prosper versus a net loss.
Many borrower features were analyzed of which were insightful such as the borrower state of residence, occupation, employment status, employment duration in days, listing category, delinquent amounts, principals outstanding, credit grades, loan status, Prosper score, monthly loan payments, and loan origination amount. Each of the plots on these features were helping to describe the Prosper borrower and assess a type of profile or risk factor as to if they would be able to pay off their loans with Prosper.
In summary, borrower charactersitics were: 1. State of California resident 2. Salary income range of $25000 to $49999 3. Not home-owners 4. Credit grade of 'C' 5. Work occupation of 'Other' 6. Mean Borrower Rate/Borrower APR of 19% , 21%, respectively 7. Prosper Score of 4 8. Loan Listing Category of 'Debt Consolidation' 9. Loan status of 'Current' 10. Debt to Income Ratio of 25% 11. Making on average, monthly payments of $272 12. Majority considered as 'Employed'
For this presentation, I focus on the features using univariate, bivariate, and then multivariate plots to show relation between variables to provide analysis in helping to understand the Prosper borrower.
For univariate plots, I used mostly density, count, and histogram plots while bivariate introduces scatter and facet grid plots. Lastly, for multivariate analysis, I used were heatmaps, facet grid plots.