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).
Diese Übung ist Teil des Kurses
Building Recommendation Engines in Python
Interaktive Übung
Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.
# Drop the column you are trying to predict
users_to_ratings.____("Apollo 13 (1995)", axis=1, inplace=____)
# Get the data for the user you are predicting for
target_user_x = ____.____[[____]]