[{'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 numpy as np\nimport pandas as pd\ntrain = pd.read_csv("../input/train.csv", parse_dates = ["datetime"])\ntest = pd.read_csv("../input/test.csv", parse_dates = ["datetime"])\n\ntrain["year"] = train["datetime"].dt.year\ntrain["hour"] = train["datetime"].dt.hour\ntrain["dayofweek"] = train["datetime"].dt.dayofweek\n\ntest["year"] = test["datetime"].dt.year\ntest["hour"] = test["datetime"].dt.hour\ntest["dayofweek"] = test["datetime"].dt.dayofweek\n\ny_casual = np.log1p(train.casual)\ny_registered = np.log1p(train.registered)\n#y_train = np.log1p(train["count"])\n\ntrain.drop(["datetime", "windspeed", "casual", "registered", "count"], 1, inplace=True)\ntest.drop(["datetime", "windspeed", ], 1, inplace=True)\nimport lightgbm as lgb\nhyperparameters = { \'colsample_bytree\': 0.725,  \'learning_rate\': 0.013,\n                    \'num_leaves\': 56, \'reg_alpha\': 0.754, \'reg_lambda\': 0.071, \n                    \'subsample\': 0.523, \'n_estimators\': 1093}\nmodel = lgb.LGBMRegressor(**hyperparameters)\nmodel.fit(train, y_casual)\npreds1 = model.predict(test)\n\nhyperparameters = { \'colsample_bytree\': 0.639,  \'learning_rate\': 0.011,\n                    \'num_leaves\': 30, \'reg_alpha\': 0.351, \'reg_lambda\': 0.587,\n                   \'subsample\': 0.916, \'n_estimators\': 2166}\nmodel = lgb.LGBMRegressor(**hyperparameters, )\nmodel.fit(train, y_registered)\npreds2 = model.predict(test)\n\nsubmission=pd.read_csv("../input/sampleSubmission.csv")\nsubmission["count"] = np.expm1(preds1) + np.expm1(preds2)\n#submission.to_csv("allrf.csv", index=False)\''}, {'role': 'assistant', 'content': 'helper_functions'}]