Limited data in your rows
This data sparsity can cause an issue when using techniques like K-nearest neighbors as discussed in the last chapter. KNN needs to find the k most similar users that have rated an item, but if only less than or equal to k users have given an item the rating, all ratings will be the "most similar".
In this exercise, you will count how often each movie in the user_ratings_df DataFrame has been given a rating, and then see how many have only one or two ratings.
Cet exercice fait partie du cours
Building Recommendation Engines in Python
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Count the occupied cells per column
occupied_count = user_ratings_df.____().____()
print(occupied_count)