[{'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:left_fit = np.polyfit(y_list, l_list, 2)\nright_fit = np.polyfit(y_list, r_list, 2)\nploty = np.linspace(0, warped.shape[0]-1, warped.shape[0] )\nleft_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]\nright_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]\ny_eval = np.max(ploty)\nleft_curverad = ((1 + (2*left_fit[0]*y_eval + left_fit[1])**2)**1.5) / np.absolute(2*left_fit[0])\nright_curverad = ((1 + (2*right_fit[0]*y_eval + right_fit[1])**2)**1.5) / np.absolute(2*right_fit[0])\nprint(left_curverad, right_curverad)\nym_per_pix = 30/720 # meters per pixel in y dimension\nxm_per_pix = 3.7/700 # meters per pixel in x dimension\nleft_fit_cr = np.polyfit(np.multiply(ym_per_pix,y_list), np.multiply(xm_per_pix,l_list), 2)\nright_fit_cr = np.polyfit(np.multiply(y_list,ym_per_pix), np.multiply(r_list,xm_per_pix), 2)\nleft_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])\nright_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])\nprint(left_curverad, 'm', right_curverad, 'm')"}, {'role': 'assistant', 'content': 'modelling'}]