Evaluation of income class classification using random forest algorithm.

The goal of this study is to utilise machine learning classifiers to categorise people's income levels, namely those with and without a household income of $50,000 or more. We examine two common algorithms in this study: Random Forest and Logistic Regression Algorithm. This experimental inquiry makes use of the Adult Income dataset, which has 32516 items. An experiment with N=10 repetitions was carried out in order to discriminate between income groups of more than $500,000 and less than $40,000. For statistical reasons, the G-power test is run at 80%. The trials demonstrate that the Random Forest Algorithm has an average accuracy of 84.1840, whereas the Logistic Regression Algorithm has an average accuracy of 79.6410. Statistical analysis reveals a substantial difference in accuracy between the two techniques, with a p-value of 0.032 for the t-test on independent samples. The results reveal that the Random Forest Algorithm outperforms the Logistic Regression Algorithm. [ABSTRACT FROM AUTHOR]