This is a dataset consisting of about 113,937 data entries of Loans given to borrowers ranging from 500usd to 35,000 usd. They includes features such as employment status, monthly income,homeownership, prosper ratings among others. The main purpose of the project was to find insight which can be used. It can help a potention investor of a finance institution who wants to understand the factors surrounding issuing a loan. In the analysis of the project I used pandas, matplotlib and seaborn for my plots. The data set and the explanation of the variables can be found, Here
Softwares used for the analysis : -Panda -python -Numpy -Anaconda (Jupyter notebook)
In the exploration I found that was a negative relations between Borrowrate and Loan amounts that were given and a positive one with other variables like Homeowner and ProsperRates serving as influencing factors too .
-The Borrowerrate and loan amount's relationship is rather negative with rates largely dispersed between 0.1 and 0.3. I found that borrowers with High ProsperRatings are positively influence on the BorrowerRate and as well as Borrowers who own Homes. *The Borrowerrate and the prosper rating also exhibit a positive relationship as the better your ratings the lower interest rate you get on a loan. *Homeonwers and borrowerrate also showed a relationship as more home owners recieved lower interest rates compared to non homeowners.
A homeowner means they have a morgage on their credit profile which is good for the Lender
Besides the main variables used, I tried to establish relationships between the terms of the loan and the employment statuses of the borrowers. The regression plot showed that borrowers with 36 month terms recieved lower BorrowerRate (interest) as the loan amount increases followed by 12 month term where loan amounts where lower and borrowers not that many too. I also noticed that employees and full time employees have better ProsperRatings which can influence their rates too.
For the presentation I focused on the influence of the other variables on the BorrowerRate, which include Loan amount, prosper ratings and homeowners .
Insights: -I first started with Loan amount on the rate on a regression plot and soon realised that there was an inverse relationship there i.e as the loan amount decreases so does the interest rate. -I also used a regression line to understand the relation between Prosper ratings on Loan amount and interest rate and surprisingly realised that not all better borrowers recieved lower interest rates but rather borrower with ratings of (E) recieved lower rates as loan increases. *Homweownership also showed an influence using the point plot where more homeowners recieved lower rates on their loans then non homeowners.
Resources used -geeksforgeeks.org -Stackoverflow.com -Matplotlin.org -codegrepper.com