Modeling without normalizing

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.

Split up the X and y sets into training and test sets using train_test_split().
Use the knn model's fit() method on the X_train data and y_train labels, to fit the model to the data.
Print out the knn model's score() on the X_test data and y_test labels to evaluate the model.

Exercise

Modeling without normalizing

Instructions