Implication of Machine Learning Models Toward Education Loan Repayment Rate Analysis

Abstract

Education loan is a significant factor contributing to one’s decision to pursue studies. Students now do not hesitate about taking the risk of being in debt of thousands of dollars. They believe in getting the highest quality of academic qualification first without realizing how risky it can get to be in such an enormous amount of debt. Banks nowadays are also wary of approving loans because they face difficulty in analyzing how credible the borrower is. It makes the whole process very tedious and time taking and often proves to be inefficacious. Prediction of education loan repayment rate can make the job easier for both banks and the applicants. This research paper aims at analyzing the education loan repayment rate by the use of machine learning algorithms. Machine learning is extensively used now and finds application in almost every domain. Predictions and analysis carried out using ML helps in making an informed decision and gives an idea of how future trends predict to look. In this research, various features are analyzed and researched thoroughly by the use of Python language. Its extensive set of libraries enables easy manipulation and visualization of the data. The paper contains a description of the analysis and rich visuals to produce a clearer image of the dataset. Various models are implemented, and their accuracy is measured using the R2 score.