Label encoding

Let's work on categorical variables encoding. You will again work with a subsample from the House Prices Kaggle competition.

Your objective is to encode categorical features "RoofStyle" and "CentralAir" using label encoding. The train and test DataFrames are already available in your workspace.

Questo esercizio fa parte del corso

Winning a Kaggle Competition in Python

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Istruzioni dell'esercizio

Concatenate train and test DataFrames into a single DataFrame houses.
Create a LabelEncoder object without arguments and assign it to le.
Create new label-encoded features for "RoofStyle" and "CentralAir" using the same le object.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Concatenate train and test together
houses = ____.____([train, test])

# Label encoder
from sklearn.preprocessing import LabelEncoder
le = ____()

# Create new features
houses['RoofStyle_enc'] = le.fit_transform(houses[____])
houses['CentralAir_enc'] = ____.____(____[____])

# Look at new features
print(houses[['RoofStyle', 'RoofStyle_enc', 'CentralAir', 'CentralAir_enc']].head())

Modifica ed esegui il codice