Pca python

import numpy as np
from sklearn.decomposition import PCA

pca = PCA(n_components = 3) # Choose number of components
pca.fit(X) # fit on X_train if train/test split applied

print(pca.explained_variance_ratio_)

Pca python

from sklearn.decomposition import PCA
pca = PCA(n_components=2)
X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)

Pca python

from sklearn.decomposition import PCA

pca = PCA(n_components=3)
pca.fit(features)
features_pca = pca.transform(features)
print("original shape:   ", features.shape)
print("transformed shape:", features_pca.shape)
print(pca.explained_variance_)
print(pca.explained_variance_ratio_)

Scikit learn pca

from sklearn.decomposition import KernelPCA
kpca = KernelPCA(n_components = 2, kernel = 'rbf')
X_train = kpca.fit_transform(X_train)
X_test = kpca.transform(X_test)

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