Student Performance Prediction System Using Machine Learning

This study presents a web-based intelligent system that uses machine learning algorithms to predict student academic performance. The system uses historical data to assess the effectiveness of five classifiers: Support Vector Machine (SVM), Random Forest (RF), k-nearest Neighbors (KNN), Artificial Neural Network (ANN), and Logistic Regression (LR). Random Forest and ANN showed superior performance, achieving high accuracy rates and robustness in predictions. The system provides an intuitive dashboard for educators to identify at-risk students and intervene proactively. The research underscores the potential of AIdriven prediction tools in education, offering a scalable approach to enhancing student success.