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())

Bu egzersiz, kursun bir parçasıdır

Dimensionality Reduction in Python

Kursa Göz Atın

Uygulamalı etkileşimli egzersiz

Bu egzersizi bu örnek kodu tamamlayarak deneyin.

# 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)

Kodu Düzenle ve Çalıştır