[{'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:X_train = pd.read_csv('/kaggle/input/walmart-recruiting-store-sales-forecasting/train.csv')\nstores = pd.read_csv('/kaggle/input/walmart-recruiting-store-sales-forecasting/stores.csv')\nfeatures = pd.read_csv('/kaggle/input/walmart-recruiting-store-sales-forecasting/features.csv')\nX_train = X_train.merge(stores, on='Store').merge(features.drop('IsHoliday', axis=1), on=['Store', 'Date'])\nX_train.fillna(0, inplace=True) ## only NaN columns are the markdowns. Set to 0 if NaN ##\nX_train['Date'] = pd.to_datetime(X_train['Date'])\n# X_train_no_neg = X_train[X_train['Weekly_Sales'] >= 0].reset_index(drop=True)\n\n# y_train = X_train[['Store', 'Dept', 'Date', 'Weekly_Sales']]\n# X_train.drop('Weekly_Sales', axis=1, inplace=True)\n\n# y_train_no_neg = X_train_no_neg[['Store', 'Dept', 'Date', 'Weekly_Sales']]\n# X_train_no_neg.drop('Weekly_Sales', axis=1, inplace=True)\n\n# print(X_train.shape, X_train_no_neg.shape, (X_train.shape[0] - X_train_no_neg.shape[0]) / X_train.shape[0])\n\nX_train.head()"}, {'role': 'assistant', 'content': 'load_data'}]