Comparing Random Forest with the Naive Bayes Algorithm with Improved Accuracy: An Effective Machine Learning Method for Loan Prediction

Abstract

Aim: The objective of this work is to determine an approach in machine learning for loan prediction by comparing Random Forest algorithms with Naive Bayes. To achieve accuracy Novel Random Forest Classifier is used.Materials and Methods:Accuracy and loss are performed with Loan prediction datasets from kaggle library. The total sample size is 20. The two groups considered were Random Forest (N=10) and Naive Bayes (N=10). The computation is performed using G-power as 80%. Discussion:The accuracy of Random Forest algorithm is 80.8920% and loss is 19.1080%, which appears to be better than Naive Bayes is 80.8520% and loss is 19.1480% respectively. Finally, the Random Forest algorithm appears significantly better than Naive Bayes. The two algorithms Random Forest (RF) and Naive Bayes (NB) with an independent sample achieved is p=0.914 (p<0.005) are statistically insignificant. Conclusion: Determining loan prediction significantly seems to be better in novel Random Forest Classifiers that have a stronger significant accuracy value than Naive Bayes.