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Comparison of Extreme Logistic Regression Algorithm and Random Forest Algorithm for Efficient Prediction of Car Loan Default with Improved Accuracy, Precision, and Recall on Personal Loan Dataset

The objective of this paper is Car Loan Prediction using Extreme Logistic Regression with Novel Association Rule and compared with Random Forest Algorithm. Machine learning is an emerging research field that can be used for predictions. Materials and Methods: A framework for car loan prediction is used by Extreme Logistic Regression with Novel Association Rule to predict the loans default from banks while customers are faced with critical problems compared with Random Forest Algorithm. This research study considers two groups such as an Extreme Logistic Regression Algorithm with Novel Association Rule and Random Forest Algorithm. The sample size considered for each of the algorithms is 20. Results: The accuracy, precision, and recall of Logistic Regression Algorithm with Novel Association Rule classifiers produce 73.99%, 18.49% and 24.66% respectively in predicting Car Loan on the dataset used whereas the Accuracy, Precision, and Recall of Random Forest algorithm produces 73.88%, 18.47%, and 24.62% respectively predicting the car loan default. This work shows the insignificance (p>0.001) between the classifiers based on an independent sample T-test. Conclusion: The performance of an Extreme Logistic Regression algorithm with a novel association classifier is significantly better than the Random Forest algorithm with a comparison of accuracy, precision and recall.