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- N_comp=60
- from sklearn.decomposition import PCA
- pca = PCA(n_components = N_comp)
- X_pca=pca.fit_transform(X_scale) #lower dimension data
- eigenvalues=pca.components_
- N_elements=10
- PC1=abs(eigenvalues[1,:])
- PC1.sort(axis=0)
- PC1=PC1[::-1]
- PC1=PC1[0:N_elements]
- PC1
- plt.bar(range(N_elements), PC1, alpha=0.3, align='center')
- plt.title('Contributions of variables to PC1')
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