The project aims to build a recommendation engine that suggests movies to users based on viewing history and preferences using collaborative filtering techniques, enhancing personalized user experience on entertainment platforms.
Collect movie ratings dataset Perform data preprocessing and normalization Implement user-based and item-based collaborative filtering Train recommendation models and generate predictions Evaluate system accuracy using RMSE and MAE Develop a recommendation interface for users Implement user registration and login Display personalized recommendations Optimize system scalability Visualize recommendation performance Test system reliability Prepare final report and presentation
