An innovative method of loan prediction that compares decision tree algorithm accuracy with random forest.

Abstract

Aim: The main aim of the research is to improve the accuracy of the  predicted loans using Random Forest using the Novel Decision Tree Algorithm. Materials and Methods: Random Forest and Novel Decision Tree Algorithms are implemented in this research work. Sample size n = 10 is calculated using G power software and determined as 10 per group with a pretest G power value of 80%, and the data has been collected from various web sources with recent study findings and a threshold of  0.05% and confidence interval of 95%. Result: Random Forest provides a higher of 89.12% compared to Novel Decision Tree Algorithm with 78.87% in predicting loan. There is an insignificant difference between the two groups, with a significance value of p = 1.000 and  p > 0.05. Conclusion: The results show that the Random Forest algorithm for loan prediction appears to generate better accuracy than the Novel Decision Tree Algorithm.