ComenzarEmpieza gratis

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 ejercicio forma parte del curso

Analyzing IoT Data in Python

Ver curso

Instrucciones del ejercicio

  • 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.

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

# 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)
Editar y ejecutar código