Kurchi
11. PCA
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
X = np.random.rand(100, 5)
n_components = 2
pca = PCA(n_components=n_components)
pca.fit(X)
X_transformed = pca.transform(X)
explained_variance_ratio = pca.explained_variance_ratio_
plt.scatter(X_transformed[:, 0], X_transformed[:, 1])
plt.title(“PCA Scatter Plot”)
plt.xlabel(“Principal Component 1”)
plt.ylabel(“Principal Component 2”)
plt.show()
Output:Â
Boxes
1.
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