Predictive Analytics for Personalized Education Pathways

This study explores the application of predictive analytics and machine learning in enhancing educational outcomes through personalized learning pathways. The project developed a predictive system designed to analyze students’ academic performance, behavioral trends, and learning preferences to recommend individualized educational routes. A structured development lifecycle was followed, including data collection, preprocessing, model training, evaluation, and system deployment. Machine learning models such as regression and classification algorithms were utilized within a user-centric interface to generate actionable insights for both students and educators. Emphasis was placed on model interpretability, ethical data usage, and system usability. The resulting prototype demonstrated how data-driven methodologies can enable informed academic planning, improved engagement, and proactive student support, affirming the transformative potential of predictive analytics in modern education.