Development of a Machine Learning-Based Student Academic Performance Prediction System
Computer Science — 2025, Undergraduate
web applicationperformance predictioneducationmachine learning
This study developed a Machine Learning-Based Student Academic Performance Prediction System to forecast student outcomes using historical academic and demographic data. Early identification of students at risk of poor performance enables timely intervention. The system trains classification models on student records and produces performance predictions with explanatory indicators. Built with Python Django and SQLite, the system was evaluated using institutional data. The findings showed that the models reliably identified at-risk students, supporting proactive support. The study recommends adoption by academic advisers and periodic model refresh as new data become available.
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