Code the recommendation system

- [Instructor] Let's write the main code…for our recommendation system.…Open up factor_review_matrix.py.…First, I'll load up the review dataset into a data frame…called raw_dataset_df…by using pandas read_csv function.…Then we use the pandas pivot table function…to build the review matrix.…At this point,…ratings_df contains a sparse array of reviews.…Next, we want to factor the array…to find the user attributes matrix…and the movie attributes matrix…that we can multiply back together…to recreate the ratings data.…

To do this,…we'll use the low rank matrix factorization algorithm.…I've included an implementation of this…in matrix_factorization_utilities.py.…We'll talk more about how it works in the next video,…but let's go ahead and use it.…First, we pass in the ratings data,…but we'll call pandas as matrix function…to make sure we pass then as a numpy matrix data type.…Next, this method takes in a parameter called num_features.…Num_features controls how many latent features…to generate for each user and each movie.…