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())
Questo esercizio fa parte del corso
Dimensionality Reduction in Python
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# 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)