Comparing the performance of random forest with decision tree and logistic regression algorithm in loan default prediction.

The primary goal of this study is to compare the performance of the Novel Random Forest (RF) algorithm, Decision Tree (DT), and Logistic Regression in forecasting loan default (LR). The 346-record loan dataset that Novel Random Forest is associated with. It has been suggested and assessed how well the revolutionary methods of Random Forest, Decision Tree, and Logistic Regression can forecast loan defaults in the banking and finance industry. There were a total of 17 participants in each study group. The classifier's efficacy in terms of accuracy and precision is measured and documented. On this dataset, the Logistic Regression model predicts loan default with an accuracy of 81%, while the Decision Tree model achieves 93% and the Random Forest model achieves 95%. (p 0.031) is statistically significant. That's why it's clear that Novel Random Forest outperforms both Decision Tree and Logistic Regression. When compared to Decision Tree and Logistic Regression, Novel Random forest has superior accuracy and precision. [ABSTRACT FROM AUTHOR]