Development of an Intelligent System for Predictive Analysis of Student Academic Performance Using Machine Learning Techniques

This research proposes an intelligent system utilizing machine learning techniques to predict student academic performance. A Random Forest classification model was developed based on a dataset consisting of key attributes such as attendance, previous grades, hours spent weekly on studying, medical emergencies, and assignment scores. The model classifies students into three categories: High-Performing, Average, and At-Risk, achieving an impressive accuracy of 99 %. Attendance and previous grades emerged as the key determinants of academic success. To facilitate real-world application, this project also includes an interactive Streamlit-based web application, enabling educators to make instant data-driven predictions by inputting students' academic details. This tool empowers educators with actionable insights, allowing them to implement timely interventions, optimize resource allocation, and adapt learning experiences for improved student outcomes. The study underscores the rapid advancements achieved through predictive analytics in education. By integrating artificial intelligence into pedagogic practice, institutions can proactively address learning difficulties, enhance student engagement, and foster academic excellence.