This project involved building a movie recommendation system that suggests movies to users based on their preferences and historical data.
I used collaborative filtering techniques and matrix factorization methods to develop the recommendation engine. The system analyzes user ratings and viewing history to identify patterns and recommend movies that align with individual tastes.
The recommendation system achieved an accuracy of [mention accuracy metric, e.g., 85%] in recommending relevant movies to users.
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