Python Feature Engineering Cookbook

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We can center the variable distributions at 0 and then scale them to their absolute maximum, as recommended by scikit-learn, by combining the use of two transformers. To do this, we need to import the required packages and load the data, just like we did in step 1 to step 3 of this recipe:

1. Next, let's import the additional scikit-learn transformer:

from sklearn.preprocessing import StandardScaler

2. Let's set up StandardScaler() from scikit-learn so that it learns and subtracts the mean but does not divide the result by the standard deviation:

scaler_mean = StandardScaler(with_mean=True, with_std=False)

3. Now, let's set up MaxAbsScaler() with its default parameters:

scaler_maxabs = MaxAbsScaler()

4. Let's fit the scalers to ...