PCA

from sklearn.decomposition import PCA
from sklearn.datasets import make_blobs

n_sample = 20
random_state = 20

X,y = make_blobs(n_samples=n_sample,n_features=10,random_state=None)

X.shape

(20, 10)

pca = PCA(n_components=3)

pca.fit(X)
print(pca.explained_variance_ratio_)

[0.63452047 0.34640893 0.00474033]

first_pca = pca.components_[0]
first_pca

array([ 0.24013241, -0.31209708,  0.22869201, -0.41321561, -0.05523458,
        0.40378262,  0.08451743,  0.38756766,  0.14328934, -0.5271714 ])

pca_reduced = pca.transform(X)

pca_reduced.shape

(20, 3)