Machine Learning Based Classification Model for Prediction of Bank Loan Approval

Abstract: In this paper we creating and testing a smart loan prediction system using a special type of classifier called gradient boosting. Our methodology is meticulously laid out, covering a spectrum of essential stages including data pre-processing methodologies, in-depth exploratory analysis, and rigorous model training leveraging a dataset meticulously curated from Kaggle. Our model, with 300 estimators, a learning rate of 0.05, and a maximum of six features, achieves an astounding 98.03% accuracy. We utilize the pickle module to serialize our trained model, consequently deploying it as a web application. This application, which is powered by React for the front end and Flask for the back end, offers users a simple interface for entering loan application details and receiving real-time forecasts on loan acceptance or denial. Our research shows that gradient boosting classified the well in predicting the loans to the users.