STUDENT PERFORMANCE PREDICTOR AND TRACKER

Abstract

Monitoring and improving student academic performance is a critical challenge in modern education. The Student Performance Predictor and Tracker is a system designed to analyze, predict, and track students’ academic outcomes using machine learning algorithms and data analytics. The system collects data from various sources, including attendance records, assignment scores, examination results, and behavioral patterns, to generate a comprehensive profile of each student. By applying predictive modeling techniques such as Linear Regression, Decision Trees, or Neural Networks, the system forecasts future performance, identifies students at risk of underperforming, and provides actionable insights for educators and administrators. Additionally, the tracker component continuously monitors students’ progress over time, offering visual dashboards, trend analysis, and alerts to highlight improvements or declines in performance. This approach enables personalized academic interventions, helps in optimizing teaching strategies, and supports data-driven decision-making to enhance overall educational outcomes. By integrating predictive analytics with real-time tracking, the system provides a proactive and intelligent solution for improving student success and fostering effective learning environments