Often Overindulge-Based Personality Classification Used in Neural Network Compared with Random Forest to Attain Accuracy

Aim: Personality classification based on overindulgence behavior has gained significant attention in recent research due to its potential implications for understanding human behavior and improving mental health interventions. In this study, we propose a methodology for personality classification using Neural Network compared with Random Forest to get better accuracy. Materials and Methods: Two techniques of Neural Networks and Random Forests (RF) have been applied. Total 40 samples are considered for the both algorithms. The purpose of calculating the accuracy %, the simulation results were repeatedly inspected and assessed. Results: Neural Network (NN) is more accurate than Random Forest (RF) (83.17%), with a score of 77.18%. Based on a sample T-test with a significance value of p = 0.001 (p < 0.05), the research study has no significance between the two groups. Based on the results, Neural Network accuracy was much superior to Random Forest accuracy based on the findings.