Improving Academic Results with an All-Inclusive Student Performance and Management System

This research study aims to change the educational landscape by integrating technology with a complete student performance and management system. Predictive analytics, performance monitoring, attendance tracking, and personalized suggestions are just a few of the features that the system combines into a single, Streamlit-based platform. Based on past data and current patterns, the system provides predictive analysis to forecast student performance by utilizing sophisticated machine learning methods like random forests and neural networks. To give a comprehensive approach to student management and engagement, it also makes use of facial recognition technology for attendance recording, statistical analysis, and data visualization tools. The suggested system's architecture, modules, functions, and possible effects are all covered in the article, with an emphasis on how it might boost student performance and enrich the educational process as a whole