DBSCAN

# Import DBSCAN
from sklearn.cluster import ____

# Initialize and fit the DBSCAN model
db = DBSCAN(eps=____, min_samples=____, n_jobs=-1).fit(____)

# Obtain the predicted labels and calculate number of clusters
pred_labels = ____.____
n_clusters = len(set(pred_labels)) - (1 if -1 in labels else 0)

# Print performance metrics for DBSCAN
print('Estimated number of clusters: %d' % ____)
print("Homogeneity: %0.3f" % homogeneity_score(labels, pred_labels))
print("Silhouette Coefficient: %0.3f" % silhouette_score(X_scaled, pred_labels))