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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.

Deze oefening maakt deel uit van de cursus

Analyzing IoT Data in Python

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Oefeninstructies

  • 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.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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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