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| # Assignment 9: PCA for Dimensionality Reduction | |
| # Apply PCA to reduce dimensions of a dataset | |
| from sklearn.decomposition import PCA | |
| import numpy as np | |
| # Synthetic dataset | |
| X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]) | |
| # Apply PCA | |
| pca = PCA(n_components=2) | |
| X_reduced = pca.fit_transform(X) | |
| # Error: Accessing undefined attribute | |
| print(pca.explained_variance) # Should be explained_variance_ratio_ | |
| print(X_reduced) |