Abstract:
In recent years, improvements in predicting student performance have garnered
significant attention due to their applications in education, personalized academ-
ic support, and optimizing educational resources. Predicting student outcomes
using artificial intelligence (AI) holds immense potential for teachers to proac-
tively identify and support at-risk students. In this thesis, we explore this excit-
ing field by employing various machine learning algorithms with an enhanced
approach to model optimization. Specifically, we implement hyperparameter
tuning using grid search to ensure optimal configurations for four classifiers:
Support Vector Machines (SVM), Multilayer Perceptrons (MLP), Decision
Trees, and Artificial Neural Networks (ANNs). This comprehensive analysis
aims to identify the most effective model for predicting student performance in
the selected dataset. The results reveal that the Artificial Neural Network model
achieved an accuracy of 98%, demonstrating its superiority in performance pre-
diction.
