[{'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 pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom scipy import stats\nfrom sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\nfrom scipy.sparse import hstack\nfrom sklearn.model_selection import train_test_split\nimport time\nimport lightgbm as lgb\nimport math\nfrom sklearn.metrics import mean_squared_error,mean_absolute_error,log_loss\nimport sklearn\nfrom sklearn.ensemble import RandomForestClassifier\nimport itertools\nimport xgboost as xgb\nimport random\nimport datetime\nfrom wordcloud import WordCloud\nimport re\n\nimport plotly.offline as py\npy.init_notebook_mode(connected=True)\nimport plotly.graph_objs as go\nfrom plotly import tools\nfrom plotly.offline import download_plotlyjs, init_notebook_mode, iplot\n\npd.options.mode.chained_assignment = None  # default='warn'\n%matplotlib inline"}, {'role': 'assistant', 'content': 'helper_functions'}]