Prosper-Loan-Data-Analysis

by Catherine Nwachukwu

Dataset

The Prosper loan data set contains 113,937 loans with 81 variables on each loan, including Prosper Loan Rating, current loan status, borrowers occupation, employment status and many others. To have access to the dataset and data-dictionary, click on this link: Udacity hosted dataset dataset

The following libraries were used in this project:

• NumPy
• pandas
• Matplotlib
• Seaborn
• Squarify
• Plotly

Data wrangling steps performed before exploration includes:

• Removal of empty and NAN values
• Reformatting of occupation column with multiple titles for the same profession
• Converting the data types in columns to the appropriate data types

Summary of Findings

One of the most important factors in borrowing is the Prosper rating. Depending on the rating, it is decided how high the interest repayment will be. The credit rating classification is also an indicator that the borrower is either likely to be able to repay the loan or is highly likely to default on the loan. Taken from this basis, the first factor of the analysis was the "Prosper Rating (Alpha)". I found that majority of the borrowers fell under Prosper Rating Group C.

The second factor considered was loan status (Current, completed, chargedoff, defaulted and past due date). It was deduced that 44.9% of the loan (51170 loans) are still running as at the time of analysis.

Another important variable is the "employment status". From this it can be deduced in which employment the borrower is, how high the monthly loan installment is and whether the borrower can repay the loan installment. There were some questions popping out that might need some clarification in future.

• What is the difference between Employed and Full-time/Part Time?
• There are so many 'others' occupation. This posseses a limitation to exployed further based on the hightest statics.
• One abnomarly was having stastics of Retired but still has Occupation as 'Others" ?

Key Insights for Presentation

The focus on the presentation will be for retired people and their distributions across the rating, borrowers rate and loan instalment.

• Effect of retirement on the prosper loan rating
• Monthly loan instalment for all occupations
• Relationship between Borrowers rate and prosper rating