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Exercise

Getting KNN data in shape

Now that you understand the ins and outs of how K-nearest neighbors works, you can leverage scikit-learn's implementation of KNN while recognizing what it is doing underneath the hood.

In the next two exercises, you will step through how to prepare your data for scikit-learn's KNN model, and then use it to make inferences about what rating a user might give a movie they haven't seen.

For consistency, you will once again be working with User_1 and the rating they would give Apollo 13 (1995) if they saw it.

The users_to_ratings DataFrame has again been loaded for you. This contains each user with its own row and each rating they gave as the values.

Similarly, user_ratings_table has been loaded, which contains the raw rating values (pre-centering and filling with zeros).

Instructions 1/3
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  • Drop the column corresponding to the movie you are predicting for (Apollo 13 (1995)) from the users_to_ratings DataFrame in place.
  • Extract the ratings for user_001 from the resulting users_to_ratings table and store them as target_user_x.