Comparison of Machine Learning Methods in Academic Achievement Prediction.

This research paper presents a comparative study of machine learning algorithms for predicting student academic achievement. The study evaluates five machine learning models: Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The objective is to compare the performance of these algorithms in predicting students' academic performance using relevant student-related features. The models are evaluated using accuracy scores to identify which algorithm performs best for this prediction task. The experimental results show that Random Forest achieved the highest accuracy among the five evaluated models, followed by Decision Tree, Support Vector Machine, Logistic Regression, and K-Nearest Neighbors. This work aims to demonstrate how machine learning techniques can support academic performance analysis and help identify patterns related to student achievement. Keywords: Machine Learning, Academic Achievement Prediction, Student Performance, Classification, Random Forest, Decision Tree, Logistic Regression, KNN, SVM.