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This research presents a web-based machine learning model designed to predict student academic performance in tertiary institutions, utilizing a dataset from Taraba State University. The study employs Random Forest, Support Vector Machine (SVM), and Decision Tree algorithms, with Random Forest achieving the highest accuracy of 94%. The developed system allows educators to input student data for real-time performance predictions, facilitating timely interventions and enhancing educational decision-making.

Author

dvdmakanju

Language

EN