  Student loans occupy a significant portion of the federal budget, as well as,
the largest financial burden in terms of debt for graduates. This paper
explores data-driven approaches towards understanding the repayment of such
loans. Using statistical and machine learning models on the College Scorecard
Data, this research focuses on extracting and identifying key factors affecting
the repayment of a student loan. The specific factors can be used to develop
models which provide predictive capability towards repayment rate, detect
irregularities/non-repayment, and help understand the intricacies of student
loans.
