Student Performance Prediction System: Predicting Student Academic Performance Using Machine Learning

In recent years, educational institutions have increasingly focused on leveraging data analytics to better understand student performance and enhance academic outcomes(Ravikumar et al., 2023). Institutions routinely collect vast amounts of data—including attendance records, exam marks, and assignment scores—yet this information is often underutilized for predictive or decision-making purposes(Gaftandzhıeva et al., 2023). Consequently, there is a pressing need for intelligent systems that can analyze such data to deliver actionable insights(Li & Liu, 2025). This project introduces a Student Performance Prediction System powered by machine learning techniques, implemented in Python with a user-friendly web interface built using Streamlit. The core functionality accepts key inputs, such as attendance, study hours, prior marks, and assignment performance, to forecast a student’s likely academic outcomes. A key challenge during development was selecting the most influential features affecting student performance. Initial models produced suboptimal accuracy, which improved markedly after iterative feature engineering, data cleaning, and experimentation. Various algorithms—including Decision Trees, Logistic Regression, and Random Forest—were evaluated and compared, with Random Forest outperforming others in most cases. The system enables seamless user input and delivers instant predictions, making it highly practical and accessible without requiring advanced technical knowledge—ideal for teachers and students alike. Overall, this project demonstrates the practical value of machine learning in education, facilitating early identification of students needing additional support. While effective, future iterations could leverage larger datasets and advanced models for further refinement. Further exploration into deep learning architectures and integration with real-time educational data streams could yield even more robust and dynamic predictive capabilities (Ibrahim et al., 2024).