Feature scaling
Recall from the video the different scales of the 'Intl_Calls' and 'Night_Mins' features:
Your job in this exercise is to re-scale them using StandardScaler.
In your workspace, the telco DataFrame has been subset to only include the features you want to rescale: 'Intl_Calls' and 'Night_Mins'. To apply StandardScaler, you need to first instantiate it using StandardScaler(), and then apply the fit_transform() method, passing in the DataFrame you want to rescale. You can do this in one line of code:
StandardScaler().fit_transform(df)
Este ejercicio forma parte del curso
Marketing Analytics: Predicting Customer Churn in Python
Instrucciones del ejercicio
- Scale
telcousingStandardScaler()and.fit_transform(). - Print the summary statistics of
telco_scaled_dfusing.describe().
Ejercicio interactivo práctico
Prueba este ejercicio completando el código de muestra.
# Import StandardScaler
from sklearn.preprocessing import StandardScaler
# Scale telco using StandardScaler
telco_scaled = ____
# Add column names back for readability
telco_scaled_df = pd.DataFrame(telco_scaled, columns=["Intl_Calls", "Night_Mins"])
# Print summary statistics
print(____)