Improving Student Grade Prediction with Machine Learning: Addressing Imbalanced Classifications to Gain Accurate Performance Insights

Machine learning is a branch of artificial intelli-gence(AI) that builds models and algorithms to enable computers to learn from data, and make predictions, and decisions. Machine Learning(ML) has gained increasing recognition in various do-mains, including education. In this article, we explore the concept of ML in education and its potential to transform teaching and learning. We discuss the process of ML, the importance of data analytic, and how machine learning can be applied to predict student performance. Additionally, we delve into the issue of imbalanced classification and its relevance to student grade prediction. To tackle this challenge, we explore various strategies, including data-level, algorithm-level, and hybrid approaches. We also discuss the emergence of data-driven education and how ML can aid in predicting student grades. Finally, we emphasize the need for a balanced prediction model and present a comprehensive research framework that involves data extraction, analytic, and visualization to enhance educational practices.