[{'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 feature_engineering(data):\n\n    #Date account created\n    data['Day_Acct_Created'] = data['date_account_created'].dt.day\n    data['Month_Acct_Created'] = data['date_account_created'].dt.month\n    data['Year_Acct_Created'] = data['date_account_created'].dt.year\n    data['Hour_Acct_Created'] = data['date_account_created'].dt.hour    \n    data['DayOfWeek_Acct_Created'] = data['date_account_created'].dt.dayofweek\n    data['WeekOfYear_Acct_Created'] = data['date_account_created'].dt.weekofyear\n    \n    #Timestamp of first active\n    data['Day_First_Active'] = data['timestamp_first_active'].dt.day\n    data['Month_First_Active'] = data['timestamp_first_active'].dt.month\n    data['Year_First_Active'] = data['timestamp_first_active'].dt.year\n    data['Hour_First_Active'] = data['timestamp_first_active'].dt.hour    \n    data['DayOfWeek_First_Active'] = data['timestamp_first_active'].dt.dayofweek\n    data['WeekOfYear_First_Active'] = data['timestamp_first_active'].dt.weekofyear\n    \n    #Date of first booking\n    data['Day_First_Booking'] = data['date_first_booking'].dt.day\n    data['Month_First_Booking'] = data['date_first_booking'].dt.month\n    data['Year_First_Booking'] = data['date_first_booking'].dt.year\n    data['Hour_First_Booking'] = data['date_first_booking'].dt.hour    \n    data['DayOfWeek_First_Booking'] = data['date_first_booking'].dt.dayofweek\n    data['WeekOfYear_First_Booking'] = data['date_first_booking'].dt.weekofyear\n    \n    #Replace unknowns by NA\n    data.gender.replace('-unknown-', np.nan, inplace=True)\n           \n    #Replace Ages\n    data.loc[data.age > 95, 'age'] = np.nan\n    data.loc[data.age < 13, 'age'] = np.nan\n        \n    #Converting categorical to numeric    \n    enc = LabelEncoder()\n    #data['gender_cd'] = enc.fit_transform(data['gender'])\n    data['signup_method_cd'] = enc.fit_transform(data['signup_method'])\n    data['language_cd'] = enc.fit_transform(data['language'])\n    data['affiliate_channel_cd'] = enc.fit_transform(data['affiliate_channel'])\n    data['affiliate_provider_cd'] = enc.fit_transform(data['affiliate_provider'])\n    #data['first_affiliate_tracked_cd'] = enc.fit_transform(data['first_affiliate_tracked'])\n    data['signup_app_cd'] = enc.fit_transform(data['signup_app'])\n    data['first_device_type_cd'] = enc.fit_transform(data['first_device_type'])\n    data['first_browser_cd'] = enc.fit_transform(data['first_browser'])\n\n    #Converting the target variable as it is in category\n    category_encoder = LabelEncoder()\n    category_encoder.fit(data['country_destination'])\n    data['country_destination_cd'] = category_encoder.transform(data['country_destination'])\n    #print(category_encoder.classes_)\n    \n    \n    return data"}, {'role': 'assistant', 'content': 'data_preprocessing'}]