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.
This exercise is part of the course
Analyzing IoT Data in Python
Exercise instructions
- Initialize a
StandardScalerand store it assc. - Fit the scaler to
environment. - Scale
environmentand store the result asenviron_scaled. - Convert the scaled data back to a DataFrame, using the same columns and index than the original DataFrame.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# 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)