A logistics regression-based student performance prediction system

Predicting student performance has become an important focus in educational data mining. Schools are using data-driven insights to find learners who may be struggling and to improve academic success. This study uses the Logistic Regression model on student performance data to examine how demographic, behavioral, and academic factors affect learning outcomes. Logistic Regression predicts outcomes like pass/fail and high/low performance with strong accuracy, due to its probabilistic framework. Besides predicting, the model also helps explain the importance of different factors. This allows educators to create informed and targeted interventions. The results show that Logistic Regression is an effective model that strikes a good balance between accuracy and clarity, making it a useful tool for early warning systems and data-based decision-making in higher education.