PCA for feature exploration

You'll use the PCA pipeline you've built in the previous exercise to visually explore how some categorical features relate to the variance in poke_df. These categorical features (Type & Legendary) can be found in a separate DataFrame poke_cat_df.

All relevant packages and classes have been pre-loaded for you (Pipeline(), StandardScaler(), PCA())

Cet exercice fait partie du cours

Dimensionality Reduction in Python

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Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Build the pipeline
pipe = Pipeline([('scaler', StandardScaler()),
                 ('reducer', PCA(n_components=2))])

# Fit the pipeline to poke_df and transform the data
pc = ____

print(pc)

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