[{'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:hide_code\nplt.figure(figsize=(12,5))\nx = features[\'LSTAT\']\nx1 = np.array([[v,1] for v in x])\n(slope,bias),_,_,_ = np.linalg.lstsq(x1,y1)\ny_lin = x1 * slope + bias\nplt.scatter(x1, y1, color=\'#348ABD\', marker=\'*\', facecolors=\'none\', label=\'Original data\')\nplt.scatter(x1, y_lin, color=\'#104E8B\', marker=\'v\', facecolors=\'none\', label=\'Fitted line\')\nplt.xlabel("Feature \'LSTAT\'")\nplt.ylabel("Prices")\nplt.title(\'Boston Housing Data\')\nplt.legend()'}, {'role': 'assistant', 'content': 'modelling'}]