[{'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:slope, intercept, r_value, p_value, std_err = scipy.stats.linregress([c[0] for c in coords], [c[1] for c in coords])\nr_slope = -1/slope\nclosest_linear_coords = []\nfor x, y in coords:\nb = y - r_slope*x\nx_int = (intercept - b)/(r_slope - slope)\ny_int = slope*x_int + intercept\nclosest_linear_coords.append((x_int, y_int))\nx_extent = max([c[0] for c in closest_linear_coords]) - min([c[0] for c in closest_linear_coords])\ny_extent = max([c[1] for c in closest_linear_coords]) - min([c[1] for c in closest_linear_coords])\nif x_extent >= y_extent:\narray = np.array([c[0] for c in closest_linear_coords])\nelse:\narray = np.array([c[1] for c in closest_linear_coords])\norder = array.argsort()\nranks = order.argsort()\nsorted_coords = list(zip([c[0] for c in coords], [c[1] for c in coords], ranks))\nsorted_coords.sort(key=lambda x: x[2])\nsorted_coords = [(x, y) for x, y, rank in sorted_coords]\nplt.plot([c[0] for c in sorted_coords], [c[1] for c in sorted_coords])\nmplleaflet.show()'}, {'role': 'assistant', 'content': 'modelling'}]