the dataset contains 113,937 rows and 81 columns that shows infomations like ProsperScore, BorrowerAPR , borrower rate , current loan status, borrower income, borrower employment status, borrower credit history, Occupation and other financial information.
In the exploration, I found that there is a correlation that exist between TotalProsperLoans and onTimeProsperPayments, this indicates that there is a positive relationship between the two variables. also I noticed that over 50% of people in our dataset currently on loan.
Employed people have more Propersrating score than Parttime and Retired meaning they have better chance of acquiring a loan.
Select one or two main threads from your exploration to polish up for your presentation. Note any changes in design from your exploration step here.
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California, Florida and New York has the three biggest amount of loan.
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People that are employed tends to have more proper rating score than other employment status-- maybe since they are employed they proobably get to pay their loan ontime hence the reason for higher average prosper rating that means They are more Loan/Credit worhty.