Academia Track: Enhancing Academic Planning with Data Insights

ABSTRACT The "Academia Track" is a web-based application built with Flask and MySQL that leverages machine learning to predict student academic performance and guide study habits. By inputting variables such as days remaining for an exam, daily study hours, mid-exam marks, subject difficulty, and attentiveness levels, users receive personalized score predictions for each subject from both Decision Tree and Random Forest regression models. The system presents these predictions as percentages, visualizes the comparative results in a dynamic bar graph, and provides actionable advice by calculating the additional daily study minutes needed to achieve a target score of 90%. This data-driven tool empowers students with intelligent insights, enabling them to proactively optimize their study strategies for improved academic outcomes. KEYWORDS Machine Learning, Student Performance Prediction, Flask, Predictive Analytics, Decision Tree, Random Forest, Data Visualization, Study Optimization, Python, MySQL