Netflix Prize competition held by Netflix Inc. in Oct 2006. The primary goal of this competition was to predict what score a user will rate for a movie, by learning a test set containing nearly 100 million customer rating records. In this project, we explored the data from this competition. Given the 100 million customer rating records, we focused on the following tasks:
Predict the existed records by data analysis techniques (Decision tree, ANN, KNN, etc.). Compute the RMSE (Root mean squared error) of a different model, and then compare them.
Forecast whether one customer will be in favor of one released movie. Given essential information (directors, genres, casts, etc.) of a movie, predict which group of customers will love it, and which group of customers will dislike it.