An Effective Approach for the Prediction of Car Loan Default Based-on Accuracy, Precision, Recall Using Extreme Logistic Regression Algorithm and K-Nearest Neighbors Algorithm on Financial Institution Loan Dataset

The objective of the work is Car Loan Forecasting Using an Extreme Logistic Regression algorithm with novel credal sets and K-Nearest Neighbors algorithm. Machine Learning Techniques is an emerging research field that can be used for predictions. When compared to the Extreme Logistic Regression algorithm with novel credal sets, and the K-Nearest neighbors algorithm uses a framework for vehicle loan prediction to predict loan default from banks while clients are facing significant challenges. This research study considers two groups such as an Extreme Logistic Regression algorithm with novel credal sets and K-Nearest Neighbors algorithm. The sample size considered for each of the algorithms is 20. The accuracy, precision, and recall of Logistic Regression Algorithm with Novel Credal Set produce 73.86%, 17.75% and 24.73% respectively in Car Loan Forecasting on the dataset used whereas the Accuracy, Precision, and Recall of K-Nearest Neighbors algorithm produces 67.86%, 16.67%, and 21.86% respectively Car Loan Forecasting default. It shows the statistical significance of (p ¿ 0.001) from the independent sample T-test. When comparing with accuracy, precision, and recall, an Extreme Logistic Regression algorithm outperformed the K-Nearest Neighbors algorithm approach sub-stantially.