Predicting Student Performance Using Educational Data

Abstract

Predicting student academic performance is an important task for educational institutions to enhance learning outcomes and provide timely support to students. Traditional evaluation methods mainly rely on examinations and manual observation, which often fail to identify students at risk in the early stages. This paper presents a machine learning-based system that analyzes educational data such as attendance, study hours, previous exam marks, and assignment scores to predict student performance. The system applies data preprocessing and classification techniques to generate accurate predictions. A web-based interface is developed using Flask to allow users to input student data and obtain results in real time. The proposed model helps educators identify students who may require additional academic support. By utilizing data-driven insights, the system improves decision-making and enhances academic planning. Overall, the approach contributes to efficient performance monitoring and supports the development of intelligent educational systems.