Use of the Naive Bayes Classifier Algorithm in Machine Learning for Student Performance Prediction

Abstract

This study focused on the development and analysis of a methodological platform grounded in machine learning principles for evaluating learning processes and enhancing student outcomes. The aim of this research was to develop and test a method for evaluating students’ academic performance based on the Naive Bayes classifier. Also, an objective of this study was to create an efficient tool capable of automating and optimize the assessment of educational performance using contemporary machine learning methods and technologies. The study employed the Naive Bayes analysis technique to predict student achievements, with the algorithm being implemented in Python. Despite an emphasis on the development of a software product, the research primarily focused on the development and analysis of the method. Our findings underscore the novelty of this approach, which can serve as a valuable tool for educational institutions and educators.