Choose a dataset, either your own or a Udacity-curated dataset, and perform an exploratory data analysis using Python. Then, create a presentation with explanatory plots that conveys your findings.
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.
In the exploration, I found that the most contracted loan term length is 36 months with more than 80000 contracts.
The BorrowerAPR distribution is slightly normal, with a standard deviation of 0.080364 the median value of the BorrowerAPR is 0.209760 and it is closest to the mean 0.218828. It means that the BorrowerAPR is really good for loans. Most of the loans are for Debt Consolidation. Really inspiring and almost all loans are funded by one investor(More than 25000 loans.). Looks like there needs to be an employee for some time (about 100 months) in order to the a borrower, assuming your employement come with high revenues. (to be able to be Homeowner.)
For the presentation, I found that there needs to be an employee for some time (about 100 months) in order to the a borrower, assuming your employement comes with high revenues. (to be able to be Homeowner.) For lower IncomeRange(under $25.000) it is really difficult to have loan with even a little amountLoanOriginalAmount, with even a lower ProsperRating (Alpha) score and it is merely impossible to have a loan no matter the ProsperRating (Alpha) score if your IncomeRange is null. The BorrowerAPR range tend to be lower when the IncomeRange tend to be higher and the ProsperRating (Alpha) score higher. The LoanOriginalAmount range tend to be higher when the IncomeRange tend to be higher and the ProsperRating (Alpha) score higher