Personalized E-Learning Course Recommendation System

In the evolving landscape of digital education, personalized e-learning course recommendation systems have become crucial in enhancing learner engagement and improving academic outcomes. This project aims to develop an intelligent recommendation system that tailors course suggestions based on individual learning preferences, prior knowledge, and performance metrics. By leveraging machine learning techniques such as collaborative filtering, content-based filtering, and hybrid models, the system identifies the most relevant courses for each user. The recommendation engine utilizes user profile data, including learning styles, interests, and browsing behavior, to generate accurate and personalized course recommendations. This personalized approach ensures that learners are presented with content aligned with their goals, ultimately improving their educational journey. The proposed system not only enhances user satisfaction but also addresses challenges faced by conventional e-learning platforms, such as content overload and low completion rates. By dynamically adapting recommendations in real-time, the system encourages continuous learning by suggesting appropriate content as users progress. Advanced techniques like Natural Language Processing (NLP) can further refine recommendations by analyzing course descriptions, reviews, and learner feedback. This personalized e-learning course recommendation system has the potential to revolutionize digital education by promoting a more interactive, engaging, and efficient learning environment. Key Words: TF-IDF Vectorization, Cosine Similarity, Recommendation System, MySQL Database, User Authentication, Data Preprocessing