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.
Este ejercicio forma parte del curso
Dimensionality Reduction in Python
Instrucciones del ejercicio
- Create the
scaler. - Standardize the data.
- Create the
PCA()instance. - Fit it to the standardized data.
Ejercicio interactivo práctico
Prueba este ejercicio y completa el código de muestra.
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
# Scale the data
scaler = ____
ansur_std = ____
# Apply PCA
pca = ____
pca.____