Mva Script |link| -

# Step 4: Plot scree plt.figure(figsize=(8,4)) plt.bar(range(1, len(pca.explained_variance_ratio_)+1), pca.explained_variance_ratio_) plt.step(range(1, len(cum_var)+1), cum_var, where='mid', color='red') plt.title('Scree Plot with Cumulative Variance') plt.xlabel('Principal Component') plt.ylabel('Variance Ratio') plt.savefig('scree_plot.png')

return pca_scores if name == " main ": # Simulate data np.random.seed(42) X = np.random.randn(100, 10) y = np.random.choice([0,1], size=100) mva script

scores, clusters = run_mva(X, labels=y) We tested the script on a synthetic 100×10 dataset. The PCA scree plot (Fig. 1) showed that 3 components capture 82% of the variance. The LDA projection (Fig. 2) separated the two synthetic classes almost perfectly due to the constructed differences in means. Clustering on unlabeled data suggested an optimal k of 3. # Step 4: Plot scree plt