This project is my capstone for the Computer Science Bachelor's program at Western Governors University. It delves into the power of machine learning to generate personalized movie recommendations. By analyzing a user's movie rating history, the model suggests five movies they are likely to enjoy. Developed in Python, the model is trained on a rich dataset comprising over 50,000 movies and 100,000 user ratings, ensuring diverse and accurate recommendations.
The focus of this project is a movie recommendation system that leverages machine learning and cosine similarity. The system intelligently selects five movies for users based on their previous ratings, offering a tailored and engaging viewing experience.
The dataset powering this project includes a vast collection of over 50,000 movies and more than 100,000 user ratings, providing a robust foundation for training the recommendation model.