In the contemporary digital landscape, online learning has emerged as a widely embraced mode of education due to its accessibility and cost-effectiveness. Timely delivery of courses tailored to learners’ needs is pivotal in maintaining their focus and commitment to learning. This project highlights the significant role of recommendation systems in attracting learners to focus on their learning paths and achieve their goals. It underscores the economic accessibility and the opportunities for individuals to engage with essential career skills. These concerns are particularly salient given the high cost of traditional education and the perceived elitism of certain institutions, which create barriers for economically disadvantaged learners.

The project employs a multi-faceted approach to enhance recommendation accuracy. Initially, it collects user information to gauge Feature Ratings, reflecting learners’ reactions to courses. Additionally, course descriptions undergo analysis to ascertain word importance and inter-course relevance. While a standalone content-based approach proves insufficient, the project adopts a collaborative filtering approach next. Here, Feature Ratings are learned through a K-Nearest Neighbors (KNN) model, leveraging a similarity metric to identify similar learners. Crucially, the project integrates these methodologies into a Hybrid Filtering model, combining the strengths of both approaches for optimal performance. Performance evaluation showcases the system’s efficacy, measured through Hit Rate and F1 Score accuracies, demonstrating its effectiveness in enhancing learning outcomes.