In educational data mining and learning analytics, predicting student academic performance is essential because it provides stakeholders with useful information to improve educational outcomes. In order to predict students’ academic results, this study assesses and contrasts the effectiveness of two popular machine learning algorithms: Random Forest (RF) and Extreme Gradient Boosting (XGBoost). Data preparation methods, such as principal component analysis (PCA) and feature normalization, were used to enhance a real-world dataset of 400 records gathered from six departments at Federal Polytechnic Ukana. Based on their Eigen values and explained variance, sixteen crucial input features were chosen for examination. Eighty percent (80%) of the dataset was used for training, and the remaining twenty percent (20%) was used for testing. To evaluate the performance of the models, evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), R-Squared Score (R²), Explained Variance Score (EVS), and Median Absolute Error (MedAE) were used. The findings show that both models have strong predictive powers, with RF marginally outperforming XGBoost in important parameters. The results highlight the potential of data-driven tactics to enhance student outcomes and offer evidence-based suggestions for choosing machine learning models in educational predictive analytics.
Keywords: : Academic Performance, Performance, Prediction, Random Forest, Students, extreme gradient boosting
