Prosper Loan Data Exploration
by Nada
Dataset:
Loan Data from Prosper with Prosper Data Dictionary to Explain Dataset's Variables This data set 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. This data dictionary explains the variables in the data set. The project objective is not expected to explore all of the variables in the dataset! But focus on only exploration on about 10-15 of them.
Summary of Findings
•LoanStatus of all Borrowers are with current and completed state •EmploymentStatus of all Borrowers are with Employed State • Majority of the loan applicants are from 50K to 75K range with emloyeed status •The distribution of monthly income of applicants is a right skewed because there will be few applicants with high salary. •Applicants with incomerange of 50K to 75K range have their prosper rating falling under AA, A, B and C •LoanStatus with current and completed have own homes when they applied for loans -The monthly income of borrowers are having higher values for employed, other and full time employment status with the prosper rating of AA, A and B •We observe that without homeowner tend to have a higher interest rate, and thus lower rating. However homeowner tends to have lower interest rate and higher rating. So we can safely say that homeowner is safest bet when gving a loan. We can also clearly observe that HR prosper rating applicants have higher interest rates
Key Insights for Presentation
For the presentation, i focused mainly with the features that are impactful for approval of loanstatus I start by looking at the distrbuiton of each and every numeric and categorical variables and did all the necessary univariate, bivariate and mulitvariate analysis on the selected varaibles. The major insights obtained are : •LoanStatus of all Borrowers are with current and completed state •EmploymentStatus of all Borrowers are with Employed State • Top peak around 0.16. -The origination amount of the loan is interesting. Here we see that the distribution is a right skewed with multiple peaks observed at 4000 USD, 10000 USD and 15000 USD. -Loan original amount and monthly loan payment is highly correlated and it is expected and borrowers interest rate and proper score are highly correlated(-vely), Borrower interest rate and loanamount are -vely correlated.
To conclude this analysis, I say that the loan approval status is heavily dependent on the applicant's information on IncomeRange, Homeowner status and employment status.