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
Exercise instructions
- Import the
StandardScaler
class. - Instantiate a
StandardScaler()
and store it in the variable,scaler
. - Create a subset of the
wine
DataFrame containing theAsh
,Alcalinity of ash
, andMagnesium
columns, assign it towine_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.____(____)