Higher Classification Accuracy of Income Class Using Decision Tree Algorithm over Naive Bayes Algorithm

Developing two machine learning classifiers with higher accuracy for classifying income classes people earning less and a salary scale between 50,000. Decision Tree Algorithm (DTA) Naive Bayes (NBA) are the classifier mechanisms employed. On dataset of 32516 records, methods were implemented tested. Implemented each algorithm through programs performed ten rounds on both to determine distinct scales class who earns lesser The G-power test is around 80% accurate. findings programming experiment showed that had mean 84.3790 79.3170 categories. variation in statistically significant (p=0.53), which insignificant when employing unpaired samples t-test. primary purpose this work apply novel technique modern Machine Learning Classifiers forecast classification. When compared Algorithm, results show DTA outperforms NBA.