Evaluating the Effectiveness of Machine Learning Models in Predicting Student Academic Achievement

This study evaluates the effectiveness of various machine learning models in predicting student academic achievement using a dataset of 1000 students. The data includes demographic, psychological, social, and institutional factors. Models such as Linear Regression, Decision Tree Regressor, Random Forest Regressor, K-Nearest Neighbors Regressor, Support Vector Regressor (SVR), Gradient Boosting Regressor (GBR), XGBoost Regressor, and Neural Network (MLP) were employed. Results show that test preparation courses significantly enhance student performance, with SVR and Linear Regression models demonstrating the best predictive performance. The study highlights the importance of optimized educational strategies to enhance academic outcomes.