Bank Loan Prediction System Using Machine Learning Models

This paper discusses the increasing number of loan applications in the banking sector and the challenges faced by financial institutions in making informed lending decisions. It presents a machine learning approach that uses historical loan data to predict loan approval using various classification models. The primary objective is to predict whether a particular individual's loan application will be approved or rejected by the bank. To achieve this goal, the paper first investigates the data available on the loan applicants, such as their credit score, employment status, and other factors that may influence the bank's decision. It then employs machine learning algorithms to classify the loan applications into approved, rejected, and undecided categories. Finally, the paper evaluates the accuracy and performance of the model on unseen data. The results obtained from the evaluation indicate that the model is effective in predicting loan approval decisions with a high degree of accuracy. The proposed approach is thus a viable solution to the problem of making informed lending decisions in the banking sector.