Scaling data - standardizing columns

Since we know that the Ash, Alcalinity of ash, and Magnesium columns in the wine dataset are all on different scales, let's standardize them in a way that allows for use in a linear model.

This exercise is part of the course

Preprocessing for Machine Learning in Python

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Exercise instructions

Import the StandardScaler class.
Instantiate a StandardScaler() and store it in the variable, scaler.
Create a subset of the wine DataFrame containing the Ash, Alcalinity of ash, and Magnesium columns, assign it to wine_subset.
Fit and transform the standard scaler to wine_subset.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import StandardScaler
from sklearn.preprocessing import ____

# Create the scaler
scaler = ____

# Subset the DataFrame you want to scale 
____ = wine[[____]]

# Apply the scaler to wine_subset
wine_subset_scaled = scaler.____(____)

Edit and Run Code