Classifying Student Performance: An In-Depth Analysis Using Machine Learning Algorithms

In recent years, analyzing and predicting student performance has been a significant challenge for educational institutions. Early analysis of student data can reveal their strengths and weaknesses, aiding in the improvement of examination outcomes. Machine learning offers a powerful tool for predicting student performance by utilizing demographic and academic data. We can forecast the results and determine which classification algorithm has the highest accuracy from algorithms such as support vector machines, J48 decision trees, naive bayes, and simple logistic regression. As a result, the accuracy level is addressed in the conclusion of data statistics, featuring classification models. By analyzing the classification algorithms, the logistic regression approach provides a more efficient way for educators to identify patterns and trends in the performance of students. This study enables targeted processes for students who would normally perform poorly on examinations.