Investigation Overview¶
In my analysis, I was interested in exploring how factors like APR, loan amount, listing category and some other factors influence the outcome of a loan status, and what the trend was with borrowers that defaulted in loan payments.
Dataset Overview¶
The DataFrame comprises of 113,937 records, with 81 columns including the borrower's occupation, credit grade, loan status and many more. Please take out some time to read through the readme file(if you haven't already done that) to have a better understanding of the columns in this DataFrame.
Employment status and loan status outcome¶
The employment status doesn't seem to have any relationship with a borrower defaulting in loan payment. Over 80% of borrowers in each category completed their loan payment when compared to those who defaulted in their payment.
The relationship between the loan status, APR and listing category¶
Loans with an APR higher than 0.25 had defaulters, especially the ones taken out for debt consolidation or home improvement.
How the number of recommendations, and the loan amount affect the loan status outcome.¶
Loans with a higher number of recommendations tend to be paid off when compared with those with a lower number of recommendation. Higher loan amounts had more recommendations, while lower loan amounts had fwer recommendations, and were past due.
