Paper Contents
Abstract
In todays educational landscape, monitoring and improving student performance is essential for academic success. This paper presents a Comprehensive Academic Performance and Analysis System that leverages machine learning and data visualization to predict student outcomes and provide actionable insights. The system utilizes a Random Forest Classifier, trained on a dataset containing key academic and behavioral indicators such as Math Score, Reading Score, Writing Score, Attendance Rate, Study Hours, Parent Education, Economic Status, and Extra Tutoring, to predict whether a student will pass or fail. The proposed system is implemented as an interactive web application using Streamlit, enabling educators, students, and parents to visualize performance trends, compare individual progress with class averages, and receive personalized recommendations for improvement
Copyright
Copyright © 2025 Hemalatha M. This is an open access article distributed under the Creative Commons Attribution License.
