As described in fundera.com, a loan becomes delinquent as soon as you’re late on a payment, by even one day. If you miss several payments or can’t make payments for an extended time (usually 90 to 120 days), the lender will place the loan in default and can start collection proceedings against you. Both delinquencies and defaults damage your credit, which is why it would be extremely convenient to predict, according to specific loan's characteristics, whether the loan will become delinquer. To this end one can think to build a machine leraning model that can accurately predict if a borrower will pay off their loan on time or not.
In order to build such a machine learning model we will consider the Lending Club releases data for all of the approved and declined loan applications periodically on their website. Together with these data there is also a data dictionary hosted on Google Drive. Lending Club is a marketplace for personal loans that matches borrowers who are seeking a loan with investors looking to lend money and make a return. Each borrower fills out a comprehensive application, providing their past financial history, the reason for the loan, and more. The LoanStats sheet describes the approved loans datasets and the RejectStats describes the rejected loans datasets. Since rejected applications don't appear on the Lending Club marketplace and aren't available for investment, we'll be focusing on data on approved loans only. In particular we will consider loan data from 2007 to 2011, since a good number of the loans have already finished. The approved loans datasets contain information on current loans, completed loans, and defaulted loans.