[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:# Handle failed data\nconfirmed_coeffs = [x for x in confirmed_popt.values()] \nmean_confirmed_coeffs = np.mean(confirmed_coeffs, axis=0)\nprint(mean_confirmed_coeffs)\n\nfatalities_coeffs = [x for x in fatalities_popt.values()] \nmean_fatalities_coeffs = np.mean(fatalities_coeffs, axis=0)\nprint(mean_fatalities_coeffs)\n\nfor region in failed_confirmed:\n    x_data = train_data[region][\'DayNo\']\n    y_ConfirmedCases_data = train_data[region][\'ConfirmedCases\']\n    # Fit data to function\n    try:\n        popt, pcov = curve_fit(sigmoid, x_data, y_ConfirmedCases_data, maxfev=1000, ftol=1e-5)\n        confirmed_popt[region] = popt\n    except:\n        start = 0\n        for data in y_ConfirmedCases_data:\n            if data > 0:\n                break\n            start = start + 1\n        popt = mean_confirmed_coeffs\n        popt[1] = start\n        confirmed_popt[region] = popt\n        print("Failed for C " + region + " : " + str(popt))\n        \n        \nfor region in failed_fatalities:\n    x_data = train_data[region][\'DayNo\']\n    y_Fatalities_data = train_data[region][\'Fatalities\']\n\n    # Fit data to function\n    try:\n        popt, pcov = curve_fit(sigmoid, x_data, y_Fatalities_data, maxfev=1000, ftol=1e-5)\n        fatalities_popt[region] = popt\n    except:\n        start = 0\n        for data in y_Fatalities_data:\n            if data > 0:\n                break\n            start = start + 1\n        popt = mean_fatalities_coeffs\n        popt[1] = start\n        fatalities_popt[region] = popt\n        print("Failed F for " + region + " : " + str(popt))\''}, {'role': 'assistant', 'content': "['helper_functions', 'comment_only']"}]