This data contains 113.937 loans with 81 variables. There are some columns for various characteristics of each loan, such as the loan amount,current status, and borrower's income. There are also columns for informations about the borrower, including their credit score, employments status, and wheter or not they are a homeowner. Other columns provide information about listing such as the listing's Prosper rating, the date the credit profile was pulled and more.
During my analysis of the data, it was determined that income, employment status, and interest rate can have an impact on a borrower's ability to repay their loan, to a certain extent. The findings indicate that borrowers with a stable source of income and employment status have a higher likelihood of repaying their loans, although they may incur delinquent days. These factors alone do not necessarily determine loan repayment performance and suggest that there may be other variables at play.
For my presentation, I decid to focus and how each variable of interested interact with the loan performance variable, in this case loan status. I start by introducing the loan status distribution, with a bar chart keeping the graph simple with a neutral color such as blue.
Afterwards, I introduce each variable of interest against the loan status variable, In the income vs loan status and employment vs loan status, i decided to keep the color encoding since was easier to identify the contracts of each variable.
In the boxplot I approach how the interest rate interacted with the loan status, keep the blue color since make it clearer, lastly, the pointplot shows the relationship with all variables, except employment status, since the income range could determinate or imply a source of income.
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