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  3. Building Recommendation Engines in Python

Exercise

Challenges with missing values

You may have noticed that the pivoted DataFrames you have been working with often have missing data. This is to be expected since users rarely see all movies, and most movies are not seen by everyone, resulting in gaps in the user-rating matrix.

In this exercise, you will explore another subset of the user ratings table user_ratings_subset that has missing values and observe how different approaches in dealing with missing data may impact its usability.

Instructions 1/3

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Question

Take a look at the user_ratings_subset that has been loaded for you. The None value represents a situation where a user has not made a rating.

Based on the table, which user is most similar to User_A?

Possible answers