[{'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:def quadratic(x):\nreturn x**2 + x + 3\ndef get_quadratic_data(start,end):\nX = np.array([i for i in range(start,end)], dtype="float64")\nX.shape = (len(X),1)\ny = np.array([quadratic(X[i]) for i in range(len(X))], dtype="float64")\ny.shape = (len(y),1)\nreturn X, y\ndef cubic(x):\nreturn x**3 + x**2 - x - 1\ndef get_cubic_data(start,end):\nX = np.array([i for i in range(start,end)], dtype="float64")\nX.shape = (len(X),1)\ny = np.array([cubic(X[i]) for i in range(len(X))], dtype="float64")\ny.shape = (len(y),1)\nreturn X, y\ncX, cy = get_cubic_data(-6,6)\ngraph(cX,cy)\nqX, qy = get_quadratic_data(-6,6)\ngraph(qX,qy)'}, {'role': 'assistant', 'content': 'helper_functions'}]