[{'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:months_dict={\n     "-1":0,  "Jan":1,  "Feb":2,    "Mar":3,     "Apr":4,     "May":5,     "Jun":6,\n     "Jul":7, "Aug":8,  "Sept":9,   "Oct":10,    "Nov":11,    "Dec":12\n     }\ncharacters_dict={\n    "a":0, "b":1, "c":2, "d":3, "e":4, "f":5, "g":6, "h":7, "i":8, "j":9,"k":10,"l":11,"m":12,"n":13, \n    "o":14, "p":15, "q":16, "r":17, "s":18, "t":19, "u":20, "v":21, "w":22, "x":23, "y":24,"z":25,\n}\n\n\ndef pre_process_store_data(store_data,months_dict,characters_dict):\n    \n    store_data_numpy = store_data.to_numpy()\n    new_store_data_list=[]\n    \n    for idx,(Store, StoreType,Assortment, CompetitionDistance, \\\\\n        CompetitionOpenSinceMonth, CompetitionOpenSinceYear, Promo2, \\\\\n        Promo2SinceWeek , Promo2SinceYear, PromoInterval) in enumerate(store_data_numpy):\n               \n        #store type embedding\n        StoreType = characters_dict[StoreType]\n\n        #store type Assortment\n        Assortment = characters_dict[Assortment]\n        \n        # store type PromoInterval\n        sales_month_vector = np.zeros(len(months_dict.keys()))\n\n        promo_months = str(PromoInterval).split(",")\n        month_in_numbers = [months_dict[a] for a in promo_months]\n        for i in month_in_numbers:\n            sales_month_vector[i]=1;\n        PromoInterval = sales_month_vector\n\n        # make a new array with replaced embeddings\n        temp = [Store, StoreType,Assortment, CompetitionDistance, \\\\\n        CompetitionOpenSinceMonth, CompetitionOpenSinceYear, Promo2, \\\\\n        Promo2SinceWeek , Promo2SinceYear] + list(PromoInterval)\n        \n        new_store_data_list.append(temp)\n        # new_store_data_list=np.array(new_store_data_list)\n    \n    return new_store_data_list\n\ndef pre_process_train_data(train_data):\n    train_data = train_data.to_numpy()\n    state_holiday_dict={\n        \'0\':0,\n        \'a\':1,\n        \'b\':2,\n        \'c\':3\n    }\n    \n    new_store_data_list=[]\n    sales=[]\n    for idx , (Store, DayOfWeek, Date, Sales, Customers, \\\\\n               _open, Promo,StateHoliday, SchoolHoliday) in enumerate(train_data):         \n        \n        #splitting date\n        Date = Date.split("-")\n        \n        #processing StateHoliday\n        StateHoliday = state_holiday_dict[str(StateHoliday)]\n        \n        if _open==0:\n            # print(_open)\n            continue\n        #new_store_data_list.append([Store, DayOfWeek ,int(Date[0]),int(Date[1]),int(Date[2]),\\\\\n        #                            Sales, Customers, _open, Promo,StateHoliday, SchoolHoliday])\n        new_store_data_list.append([Store, DayOfWeek ,int(Date[0]),int(Date[1]),int(Date[2]),\\\\\n                                    _open, Promo,StateHoliday, SchoolHoliday])\n        \n        sales.append(Sales)\n    return new_store_data_list ,sales\n\n\ndef connect_store_data_with_train_data(preprocessed_train_data,preprocessed_store_data):\n    \n    preprocessed_dataset=[]\n    for train_instance in preprocessed_train_data:\n\n        store_id = train_instance[0]-1\n        store_instance = preprocessed_store_data[store_id]\n\n        train_instance = store_instance + train_instance[:]\n        # train_instance =  train_instance[1:]\n\n        preprocessed_dataset.append(train_instance)\n\n\n    preprocessed_dataset=np.array(preprocessed_dataset,dtype=np.float32)\n    \n    return preprocessed_dataset\n\n    \''}, {'role': 'assistant', 'content': 'data_preprocessing'}]