Comparative Analysis of Decision Tree, Random Forest, and XGBoost for Student Category Prediction
DOI:
https://doi.org/10.21512/ijcshai.v3i1.14936Keywords:
Machine Learning, Student category prediction, Random Forest, XGBoost, Descision TreeAbstract
This research aims to develop and evaluate a lightweight machine learning framework for predicting student performance categories as a foundation for personalized curriculum design in a mid-sized school context. The study compares three baseline algorithms such as Decision Tree, Random Forest, and XGBoost implemented using an end-to-end workflow involving data preprocessing, feature engineering, model training, and evaluation. A dataset of anonymized student academic and behavioral attributes was prepared through cleaning, encoding, normalization, and stratified splitting to ensure consistency and reliability. Each model was assessed using accuracy, precision, recall, and F1-score to determine its predictive effectiveness. The experimental results show that the Random Forest model achieved the highest overall performance, demonstrating stronger generalization compared to Decision Tree and XGBoost. Medium-performing students were classified most reliably, while Low-performing students displayed greater variability, indicating the need for more comprehensive data to improve sensitivity toward at-risk learners. The originality of this study lies in its focus on implementing an accessible, resource-efficient predictive pipeline suitable for schools with limited technological capacity. The findings provide evidence that practical machine learning approaches can support early stages of data-driven curriculum planning and help educators make more informed instructional decisions. The study also highlights opportunities for future work, including the expansion of data sources and adoption of more advanced algorithms to enhance predictive accuracy and support broader educational applications.
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