Clustering with KMeans, DBSCAN, and hierarchical approaches

                  from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler

X_scaled = StandardScaler().fit_transform(X)

models = {
    'kmeans': KMeans(n_clusters=5, random_state=42, n_init='auto'),
    'dbscan': DBSCAN(eps=0.7, min_samples=10),
    'agglomerative': AgglomerativeClustering(n_clusters=5),
}

for name, model in models.items():
    labels = model.fit_predict(X_scaled)
    if len(set(labels)) > 1 and -1 not in set(labels):
        score = silhouette_score(X_scaled, labels)
        print(name, round(score, 4))
    else:
        print(name, 'silhouette not meaningful for current labels')