Email this article Student GPA Prediction and Course Recommendation Using Ml Algorithms
Abstract
Predicting student academic performance is vital for early intervention and ensuring timely graduation, thus predictive student academic performance evaluation is essential. This project presents a machine learning based framework that predicts degree completion is achieved by evaluation of historical academic data over a period of time. In contrast to traditional models which concentrate on individual course outcomes, this one features bilayered prediction structure with SVM, Random Forest, Logistic Regression, and Ensemble based Progressive Prediction (EPP) model. Using UCLA undergraduate data, the system was found to predict outcomes with greater accuracy, especially when EPP was utilized. The model also provides personalized academic advising by selecting prospective courses depending on the expected GPA. This advancement assists institutions in detecting at-risk learners prompting them to develop plans for academic enhancement thereby increasing the level of on-time graduation and subsequently reducing the financial strain of student debt.
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