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())

Deze oefening maakt deel uit van de cursus

Dimensionality Reduction in Python

Cursus bekijken

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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)

Code bewerken en uitvoeren