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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: Boxes 1.
