Choosing the number of components
You'll now make a more informed decision on the number of principal components to reduce your data to using the "elbow in the plot" technique. One last time, you'll work on the numeric ANSUR female dataset pre-loaded as ansur_df.
All relevant packages and classes have been pre-loaded for you (Pipeline(), StandardScaler(), PCA()).
This exercise is part of the course
Dimensionality Reduction in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Pipeline a scaler and PCA selecting 10 components
pipe = ____([('scaler', ____),
('reducer', ____)])