Recommendation_system

The purpose of this project is to show how different recommendation systems can be built. In this interface, there are four ways in which movies are recommended.

1) Most popular movies (most reviewed)

2) Highest rated movies

3) Recommendations by Matrix Factorization

4) Recommendations by similarity score

movie_rec_system.py - where the main code is stored

matrix_factorization_utilities.py - this is essentially a library created by Adam Geitgey that allows you to conduct matrix factorization easily

movie_ratings_data_set.csv - data with user_id's and movie ratings

movies.csv - information pertaining to each movie