This research aims to improve student performance predictions using a combined SVM (Support Vector Machine) and radial basis function (RBF) approach. The developed model utilizes a combination of the strengths of SVM in handling class separation and the ability of RBF to capture complex patterns in data. Student assessment data, including math, reading, and writing scores, is used as a feature to predict student performance on tests. Preprocessing steps, including feature normalization and label encoding, are applied to prepare the data for model training. Next, the SVM model with the RBF kernel is initialized and optimized using GridSearchCV to find the best parameters. Model evaluation was carried out using the R2 metric to evaluate how well the model predicts student performance. Experimental results show that the combined SVM-RBF approach can improve student performance predictions with fairly accurate prediction results of 88%. The practical implication of this research is the development of a more accurate model for predicting student performance, which can be used as a tool to improve educational interventions and decision-making in educational institutions.
