Multi-class classification with k-NN is as easy as binary classification. We just pick the class that prevails in the neighborhood.

The KNeighborsClassifier automatically performs a multi-class classification if y has more than two features, so you do not need to change anything. The only thing that changes is the y variable fed to the .fit() method.

Now you will perform a Multi-class classification with k-NN.
Consider the following dataset:

It is the same as in the previous chapter's example, but now the target can take three values:

• 0 – "Hated it" (rating is less than 3/5);
• 1 – "Meh" (rating between 3/5 and 4/5);
• 2 – "Liked it" (rating is 4/5 or higher).

Let's move to classification! Well, wait, here is the reminder of the classes you will use.

And now, let's move to classification!