Film Proposals Utilizing Machine Learning With Root Mean Square Error

Abstract

In this movie, recommendation system is built based on the MovieLens10M dataset. We used recommendation method to predict user’s movie rating and we can recommend movies to customers, which they potentially give high ratings according to prediction. The root-mean-square error (RMSE) is calculated to Carryout evaluation. A set of users at initial stage would have rated for example on the rate of 1to5 for some movies, which they have already seen. These ratings, which are given by these to users, is taken as in put to movie recommendation system. The movie recommendation system uses these ratings given by user to predict the ratings of other movies that each user would give. In some cases, user’s ratings will not be available in such cases the movie recommendation system will not predict the ratings instead will predict the probability that user would choose to watch a movie other likelihood of the user.