• Developed a movie recommendation system provides personalized movie suggestions based on user preferences and movie similarities, enhancing the user's movie-watching experience and increasing engagement.
• Implemented item-based recommendation using cosine similarity as the similarity metric given a movie title as input, the system recommends a list of similar movies based on the highest similarity scores.
• Implemented used-based recommendation by leveraging collaborative filtering techniques and cosine similarity metric to identify users with similar movie preferences and recommend movies they enjoyed but the target user hasn't watched yet.