In this question, you will use the scikit-learn package to perform PCA.

(1 point) Perform PCA on the data frame df with the parameter as n_components = 3,

(1 point) create a figure that includes the first three principal components over time,

(3 points) print out the principal components, explained variances, proportions of explained variances, and cumulative proportions of explained variances. For cumulative proportions, use the .cumsum() function.

from sklearn.decomposition import PCA pca = PCA(????? = ?????) X_pca = pca.?????(df) pc1 = X_pca[?????,?????] pc2 = X_pca[?????,?????] pc3 = X_pca[?????,?????] fig = plt.figure(figsize=(15, 10)) plt.plot(?????, ?????, label='?????') plt.plot(?????, ?????, label='?????') plt.plot(?????, ?????, label='?????') plt.legend(loc="upper right") plt.grid() plt.show() print("Principal Components: ", np.round(pca.?????,2)) print(f"Explained variance: {np.round(pca.?????,2)}") print(f"Explained Variance %: {np.round(pca.?????,2)}") print(f"Cumulative Variance %: {np.round(100*pca.?????.?????(),2)} ") print("Eigenvalues: ", ?????) print("Eigenvectors: ", ?????)