[{'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:def prepare_pipeline_ts(df, min_shift, max_shift):\n    \n    def get_shifted_date(df, for_sales=False):\n        return (df.Date.min() + pd.DateOffset(days_to_shift))\n    \n    def get_DayOfWeek(df):\n        return df["DayOfWeek"]\n    \n    def get_Open(df):\n        return df[["Open"]]\n    \n    def get_Promo(df):\n        return df[["Promo"]]\n    \n    def get_StateHoliday(df):\n        return df["StateHoliday"]\n    \n    def get_SchoolHoliday(df):\n        return df[["SchoolHoliday"]]\n    \n    def get_StoreType(df):\n        return df["StoreType"]\n    \n    def get_Assortment(df):\n        return df["Assortment"]\n    \n    def get_Promo2Active(df):\n        return df[["Promo2Active"]]\n    \n    def get_CompetitionActive(df):\n        return df[["CompetitionActive"]]\n    \n    def get_CompetitionDistance(df):\n        return df[["CompetitionDistance"]]\n    \n    def get_DayOfYear(df):\n        return df["DayOfYear"]\n    \n    def get_previous_sales(df):\n        sales = df[["Store", "Sales"]].copy()\n        for day in range(min_shift, max_shift + 1):\n            sales["Last-{}_Day_Sales".format(day)] = sales.groupby("Store")["Sales"].shift(day)\n            sales["Last-{}_Day_Diff".format(day)] = sales.groupby("Store")["Last-{}_Day_Sales".format(day)].diff()\n        \n        return sales.drop(["Store", "Sales"], axis=1)\n    \n    p = make_union(*[\n        make_pipeline(FunctionTransformer(get_DayOfWeek, validate=False), \n                      LabelEncoderPipelineFriendly(), \n                      OneHotEncoder()),\n        make_pipeline(FunctionTransformer(get_Open, validate=False),\n                      Imputer(strategy="most_frequent")),\n        make_pipeline(FunctionTransformer(get_Promo, validate=False)),\n        make_pipeline(FunctionTransformer(get_StateHoliday, validate=False), \n                      LabelEncoderPipelineFriendly(), \n                      OneHotEncoder()),\n        make_pipeline(FunctionTransformer(get_SchoolHoliday, validate=False)),\n        make_pipeline(FunctionTransformer(get_StoreType, validate=False),\n                      LabelEncoderPipelineFriendly(), \n                      OneHotEncoder()),\n        make_pipeline(FunctionTransformer(get_Assortment, validate=False),\n                      LabelEncoderPipelineFriendly(), \n                      OneHotEncoder()),\n        make_pipeline(FunctionTransformer(get_Promo2Active, validate=False)),\n        make_pipeline(FunctionTransformer(get_CompetitionActive, validate=False)),\n        make_pipeline(FunctionTransformer(get_CompetitionDistance, validate=False),\n                      Imputer(),\n                      StandardScaler()),        \n        make_pipeline(FunctionTransformer(get_DayOfYear, validate=False),\n                      LabelEncoderPipelineFriendly(), \n                      OneHotEncoder()),\n        make_pipeline(FunctionTransformer(get_previous_sales, validate=False), \n                      Imputer(),\n                      StandardScaler())\n    ])\n    \n    return p'}, {'role': 'assistant', 'content': 'helper_functions'}]