PCA on a larger dataset

You'll now apply PCA on a somewhat larger ANSUR datasample with 13 dimensions, once again pre-loaded as ansur_df. The fitted model will be used in the next exercise. Since we are not using the principal components themselves there is no need to transform the data, instead, it is sufficient to fit pca to the data.

This exercise is part of the course

Dimensionality Reduction in Python

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Exercise instructions

Create the scaler.
Standardize the data.
Create the PCA() instance.
Fit it to the standardized data.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

# Scale the data
scaler = ____
ansur_std = ____

# Apply PCA
pca = ____
pca.____

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