PERSONALIZED LEARNING PATH RECOMMENDATION SYSTEM

Abstract: Personalized education has become a cornerstone of modern learning systems, enabling students to receive tailored recommendations based on their unique abilities, interests, and cognitive profiles. Traditional academic evaluation systems are rigid and often fail to capture the diversity of student skill sets. Consequently, learners are guided toward generalized career paths that may not align with their strengths or interests. This research proposes a Personalized Learning Path Recommendation System that leverages the power of machine learning (ML) to identify the most suitable learning domains for individual students. The system analyzes multiple attributes including Programming Score, Math Score, Logic Score, Creativity, Problem Solving Ability, Communication Skills, Interest in Technology, and Time Management. Using these parameters, four ML models — Random Forest, Decision Tree, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) — were trained and evaluated. The Random Forest classifier achieved the highest accuracy of 90%, outperforming other models in stability and prediction consistency. The model provides insights through visual analysis of accuracy comparison, confusion matrices, and feature importance distributions. Additionally, the system accepts live user input to recommend a learning path in domains like AI & ML, Data Science, Web Development, Cyber Security, and UI/UX Design. This approach demonstrates the potential of ML to revolutionize academic counseling by providing data-driven, objective, and personalized recommendations, bridging the gap between aptitude and career direction.