[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:K = 2\nN = 400\nX = np.zeros((N * K, 2))\ny = np.zeros(N * K)\nfor j in range(K):\nii = range(N * j, N * (j + 1))\nr = np.linspace(0.0, 0.7, N)\nt = np.linspace(j*4, (j + 1) * 4, N) + np.random.randn(N) * 0.4\nX[ii] = np.c_[r*np.sin(t), r*np.cos(t)]\ny[ii] = 2 * j - 1\ntrain_test_split = np.array(random.sample(range(N * K), N))\nX_train = X[train_test_split, :]\ny_train = y[train_test_split]\nX_test = X[-train_test_split, :]\ny_test = y[-train_test_split]\nplt.scatter(X[:, 0], X[:, 1], c = y);'}, {'role': 'assistant', 'content': 'data_preprocessing'}]