Data driven decisions in education using a comprehensive machine learning framework for student performance prediction

Abstract Accurately predicting student performance is essential for improving educational outcomes and guiding targeted interventions. This study applies eight advanced machine learning models-Decision Trees, Random Forest, Lasso, K-Nearest Neighbors, XGBoost, CatBoost, AdaBoost, and Gradient Boosting to analyze student performance based on demographic and academic features. Among these, CatBoost achieved the highest accuracy (87.46%) and less error rates, outperforming Gradient Boosting (87.28%) and Decision Trees (82.42%). Model evaluation was conducted using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), demonstrating the robustness of the proposed approach. The results highlight the effectiveness of data-driven methods in early identification of at-risk students, enabling educators to implement personalized learning strategies. This study underscores the transformative potential of machine learning in education, paving the way for more adaptive and student-centered learning environments.