Classification of Employee Promotion Dataset Using Naïve Bayes, J48, and Random Forest Algorithms

This paper focuses on analyzing employee promotion in organizations using machine learning, with a particular emphasis on the Naïve Bayes, J48, and Random Forest algorithms. The research aims to compare the performance of these algorithms in the context of predicting employee promotions. The experiments were conducted in the Weka software environment, and the results were evaluated using accuracy, precision, and recall metrics. The paper also examines the characteristics of the selected algorithms and their ability to interpret data.