download the dataset from here: https://www.kaggle.com/justjun0321/prosperloandata
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.
- You are not expected to explore all of the variables in the dataset! Focus your exploration on about 10-15 of them.
- numpy
- pandas
- matplotlib
- seaborn
- Forr all the loans in genral Most of the people have completed their payment fo the loan however a significant proportion still is defaulted or is past due by 1-120 days
- However in the case of Elite Loans(loans>10000) the completion rate is very low as compared to all the loans in genral
- we see that most of the loans given out were to cover off other debts i.e. Debt Consolidation in both the cases Normal loans in. genal and Elite Loans(loan>10000)
- Least loans were given to boats , green Loans and RV in case of genral Loans
- While in case of Elite Loans the least no of loans were given to Student Use , Rv and Cosmetic Procedure
- On an Average most no of Loans were taken by Computer Programmers followed by Executive and then teacher
- While in the case of Elite Loans (loans >10000) most loans were taken by Executive Followed by computer programmer and then analyst
- On an Average least no of Loans were taken by Police Officer , civil Servise , Engineer mechanical
- While in the case of Elite Loans (loans >10000) least loans were taken by truck driver , sales-retail , Engineer-Electrical
- in case of all the loans taken into acount people with credit grade of C , D , B were gven more preference while people with credit grade of NC were less
- while in case of Elite loans(loans>10000) loans people with credit grade of B , AA , A were gven more preference while people with credit grade of HR were less
- People who were employed were given more preference both in normal and Elite loans(loans>10000) while they also are the once to ask for loans the most
- most of the loans issued was between 1000-7000 range
- We see that most of the people considering all the loans have their Prosper Score between 4-9
- while those who recieved Elite Loans(loan>10000) has their Prosper Score between 6-9
- BorrowerAPR BorrowerRate are linearly related
- In the case of normal ie all the loans in genral 36 monts term has usally the highest borrow rate
- While in the case of elite loans(loan>10000) 60 months term had usually the highest borrow rate
- with the increase of term usually the borrow rate can be expected to rise
- with the increse in prosper score loan amount also increased drastially
- having credit score >729.5 and grade AA , A , B, D , C will ensure higher chances to get an Elite Loan(loan>10000)
- people with higher credit score usually have lower borrowAPR and vice versa
- people with Lower Prosper Rating usually have Higher borrowAPR and vice versa
- Lender Yield is directly proportional to borrowAPR , Borrow Rate and Estimated Effective Yield
- Similarly many other findings are seen from the above scatter matrix
- Borrow rates for all the loan statuses were lower for elite loans(loan>10000) compared to the average of all the loans
- elite loans tend to have terms 36 or 60 while on an average most loans have terms between 12-36
- similarly for Prosper Ratings elite loans have prosper ratings > 4
- elite loans have no cancellations and less delayed on an average
- on an average the Loan amount for debt consolidation was th highest and lowest for student use
- Having credit score >729.5 , grade AA , A , B, D , C and Prosper Score in between (6-9) and having a job of Executive Followed by computer programmer demanding a loan for Debt Consolidation will ensure higher chances to get an Elite Loan(loan>10000).
- Lender Yield is directly proportional to borrowAPR , Borrow Rate and Estimated Effective Yield