Comparing the prediction accuracy for vehicle loan eligibility by using logistic regression with random forest algorithm.

The study's primary goal is to identify accuracy in auto loan eligibility utilising machine learning techniques, namely the Logistic Regression Algorithm and the Novel Random Forest Algorithm. The Logistic Regression and Random Forest algorithms were iterated 180 times with a sample size of N=90 to determine the accuracy of automobile loan eligibility. The novel Random Forest method outperforms the Random Forest algorithm in terms of accuracy (87.02%). The loan prediction independent samples T-test has a high significance (p<0.05). When the results were compared, the Random Forest algorithm outperformed the Logistic Regression approach in terms of discovering accuracy in automobile loan eligibility. [ABSTRACT FROM AUTHOR]