Let's take a look at what might happen to your model's accuracy if you try to model data without doing some sort of standardization first.

Here we have a subset of the wine dataset. One of the columns, Proline, has an extremely high variance compared to the other columns. This is an example of where a technique like log normalization would come in handy, which you'll learn about in the next section.

The scikit-learn model training process should be familiar to you at this point, so we won't go too in-depth with it. You already have a k-nearest neighbors model available (knn) as well as the X and y sets you need to fit and score on.

Scaling data for feature comparison

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