Student Performance Prediction Using Machine Learning

Machine learning offers powerful capabilities for predicting student academic performance and enabling timely interventions. Our study demonstrates that ensemble methods (particularly XG Boost) achieve >85% accuracy when combining demographic, behavioral, and academic features. Key Takeaways: 1.               Historical performance is the strongest predictor (~23% feature importance) 2.               Ensemble methods significantly outperform individual models (88.5% vs. 72.3%) 3.               Careful feature engineering is crucial for model performance 4.               Interpretability must balance with accuracy for practical deployment 5.               Ethical considerations and human-in-the-loop validation are essential

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