Personalized Learning Path Recommender

This project presents a personalized learning path recommendation system based on student engagement levels. Using the OULAD dataset, we analyze students' interaction data and academic performance to classify engagement levels (Low, Mid, High), and recommend suitable learning strategies such as Collaborative, Interactive, Informational, or Resource-based learning methods.

Machine learning models like KMeans, Decision Trees, and Logistic Regression are utilized to cluster students and classify their learning needs effectively.