Overview / Usage

Movie recommender system: It recommends movies to the user based on their interests.

It uses Pearson correlation similarity matrix to find similarity between two users.Then it clusters all the users with similar interests using K MEANS CLUSTERING.It then finds suitable movies and recommends to the user.

Data set: Training data set 930 users,1630 movies.Each movie has 19 features.
Test data: 8300 rows .
Mean Squared error:1.08