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Scaling II

You'll now apply a scaler to the dataset, which is available for you as environment.

Remember that Scaling helps the algorithm converge faster, and avoids having one dominant feature heavily influence the outcomes.

Este exercício faz parte do curso

Analyzing IoT Data in Python

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Instruções do exercício

  • Initialize a StandardScaler and store it as sc.
  • Fit the scaler to environment.
  • Scale environment and store the result as environ_scaled.
  • Convert the scaled data back to a DataFrame, using the same columns and index than the original DataFrame.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Initialize StandardScaler
sc = ____()

# Fit the scaler
sc.fit(____)

# Transform the data
environ_scaled = ____.____(____)

# Convert scaled data to DataFrame
environ_scaled = pd.DataFrame(____, 
                              columns=____, 
                              index=____)
print(environ_scaled.head())
plot_unscaled_scaled(environment, environ_scaled)
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