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noise=0.05, random_state=0) # Apply DBSCAN db = DBSCAN(eps=0.3, min_samples=5, metric='euclidean') y_db = db.fit_predict(X) Remember to replace `X` with your actual data set. You can adjust the eps and min_samples parameters to get different clustering results. The eps parameter is the maximum