Loan Prediction Analysis Using Innumerable Machine Learning Algorithms

One of the most fundamental issues that banks and other financial institutions must deal with is loan prediction because it has a big impact on profits. Even though there are several established methods for extracting data from loan applications, most of them seem to be functioning poorly given the reported increase in the number of subprime loans. Gradient boosting, an Extreme Gradient Boosting approach, is used in this work to forecast loan default. Statistics on loans from the internet lending company Super Lender corroborate the prediction. We review demographic data and information from the loan application. The F1-Score, Accuracy, Recall, and Precision area of the investigation are among the critical evaluation metrics we list here. This study provides a strong foundation for loan credit approval by identifying risky clients from a large pool of loan applications using predictive modeling.