Employing Naive Bayes Algorithm in the Analysis of Students Academic Performances

This study aims to help teachers and school administration categorize students’ academic performances based on the final grades of the students in the academic year 2020-2022. In order to mentor and support the performances of the students, this study aims to identify the cluster center of the students’ performances along with data mining. The Naïve Bayes algorithm was trained using a total of 3000 datasets. It has been shown that the classification attained the highest accuracy of 96 percent with ten cross-validations and a split test of 70:30 between the ratio of training and testing data. The study used four attributes, including quizzes, laboratory drills, assignments, and attendance. As a result, the quiz achieved the centroid point of 86 percent, 90 percent on the laboratory drills, 85 percent on the assignments, and a "B" for attendance. In order to make further progress, the study intends to concentrate on other aspects of evaluating student performance as a characteristic to be looked and being able to distinguish the mode with the highest accuracy of the results of many other algorithms.