[{'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:import os\nimport string\nimport numpy as np\nimport pandas as pd\nfrom pandasql import sqldf\n\nimport matplotlib.pyplot as plt\n\nfrom keras.utils.np_utils import to_categorical\nfrom keras.models import Model, Sequential, model_from_json\nfrom keras.optimizers import SGD, Adam, RMSprop\nfrom keras.layers import Input, Dense, Dropout, Flatten, Lambda, Embedding\nfrom keras.initializers import RandomNormal, Constant\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping\nfrom keras import regularizers\nfrom keras import backend as K\nimport tensorflow as tf\n\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\n\nimport seaborn as sns\nimport warnings\n\nfrom math import sqrt\n\nimport itertools\nfrom tqdm import tqdm\n\nnp.random.seed(42)  # for reproducibility\n\nsns.set(style="whitegrid", color_codes=True)\nsns.set(font_scale=1)\n\npd.set_option(\'display.max_columns\', 60)\n\n%matplotlib inline\nwarnings.filterwarnings(\'ignore\')\''}, {'role': 'assistant', 'content': 'helper_functions'}]