[{'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:from scipy.optimize import curve_fit\n\n# Iterate through countries sorted alphabetically from A to Z. \n# As some countries, like USA, China, Canada, UK, Australia, have provinces/states, decend to the province level \n# (i.e., each province within such countries gets its owm model)\nfor country in sorted(countries):\n    print("Country: ", country)\n    # Select information related to the current country\n    c_df = train_df[train_df["Country_Region"] == country]\n    # Get a list of country\'s provinces\n    provinces = set(c_df["Province_State"])\n    print("Provinces: ", provinces)\n    # Iterate over provinces\n    for province in sorted(provinces):\n        # Create a compound name for each country when provinces are present\n        if province != "":\n            full_country = country + "-" + province\n        else:\n            full_country = country\n        \n        # From country information, select the current province information\n        p_df = c_df[c_df["Province_State"] == province]\n        \n        # Prepare data for building a model\n        X1 = p_df[p_df["ConfirmedCases"] > 0]["ConfirmedCases"].values[:-1]  # Omit the last value in order to properly form labels\n        y1 = p_df[p_df["ConfirmedCases"] > 0]["ConfirmedCases"].values[1:]  # Notice that "labels" are in fact "data" shifted one position to the right\n        X2 = p_df[p_df["Fatalities"] > 0]["Fatalities"].values[:-1]  # Omit the last value in order to properly form labels\n        y2 = p_df[p_df["Fatalities"] > 0]["Fatalities"].values[1:] # Notice that "labels" are in fact "data" shifted one position to the right\n        \n        # For confirmed cases, find the optimal value of a model parameter and perform the curve fitting if possible \n        # Treat special cases when either X or y or both contains all zeroes or just one (last) non-zero value!\n        if len(X1) > 1 and len(y1) > 1:  # Build a model only if there are two or more non-zero values\n            popt, _ = curve_fit(func, X1, y1)\n            popt_cases = popt  # there is just one parameter\n        else:  \n            # otherwise, just set the parameter to a very big number, implying that there would be almost no change in numbers\n            popt_cases = 2, BIG_NUMBER\n        # Treat the special case if it turned out that the parameter value is zero\n        if popt_cases[1] == 0:\n            # Set the parameter to a very large value m so that the quantity 2**(1/m) -> 1, which implies that\n            # the numbers won\'t grow\n            popt_cases = 2, BIG_NUMBER\n        print("{}: Optimal parameter value for confirmed cases: {}".format(full_country, popt_cases))\n        \n        # For fatalities, find the optimal value of a model parameter and perform the curve fitting if possible \n        # Treat special cases when either X or y or both contains all zeroes or just one (last) non-zero value!\n        if len(X2) > 1 and len(y2) > 1:\n            popt, _ = curve_fit(func, X2, y2)\n            popt_fatalities = popt  # there is just one parameter\n        else:\n            # otherwise, just set the parameter to a very big number, implying that there would be almost no change in numbers\n            popt_fatalities = 2, BIG_NUMBER\n        # Treat the special case if it turned out that the parameter value is zero\n        if popt_fatalities[1] == 0:\n            # Set the parameter to a very large value m so that the quantity 2**(1/m) -> 1, which implies that\n            # the numbers won\'t grow\n            popt_fatalities = 2, BIG_NUMBER\n        print("{}: Optimal parameter value for fatalities: {}".format(full_country, popt_fatalities))\n        \n        # Select test data for a given country and its province if the latter is given\n        condition = (test_df["Province_State"] == province) & (test_df["Country_Region"] == country)\n        t_df = test_df[condition]\n        \n        # Get the initial values to be used for generating future values for confirmed cases and fatalities\n        last_train_date = t_df["Date"].values[0]\n        print(last_train_date)\n        cases = p_df[p_df["Date"] == last_train_date]["ConfirmedCases"].values[0]\n        print(cases)\n        fatalities = p_df[p_df["Date"] == last_train_date]["Fatalities"].values[0]\n        print(fatalities)\n        \n        # It\'s necessary to drop index in \'t_df\': otherwise, \'t_df.loc[i, "ForecastId"]\' would fail,\n        # starting from the second country\n        t_df.reset_index(inplace=True, drop=True)\n        for i in range(t_df.shape[0]):\n            # Get a row index to write to\n            idx = t_df.loc[i, "ForecastId"] - 1\n            # make predictions\n            cases = round(cases * (popt_cases[0] ** (1/popt_cases[1])), 0)\n            submission_df.loc[idx, "ConfirmedCases"] = cases\n            fatalities = round(fatalities * (popt_fatalities[0] ** (1/popt_fatalities[1])), 0)\n            submission_df.loc[idx, "Fatalities"] = fatalities\n        \n        print("*"*70)\''}, {'role': 'assistant', 'content': 'helper_functions'}]