[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': "Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:created_time = pd.to_datetime(merge['created'],format='%Y-%m-%d %H:%M:%S')\nmerge['month'] = created_time.dt.month\nmerge['day'] = created_time.dt.day\nmerge['hour'] = created_time.dt.hour\nmerge['weekday'] = created_time.dt.weekday\nmerge['week'] = created_time.dt.week\nmerge['quarter'] = created_time.dt.quarter\nmerge['weekend'] = ((merge['weekday'] == 5) | (merge['weekday'] == 6))\nmerge['days_since'] = created_time.max() - created_time\nmerge['days_since'] = (merge['days_since'] / np.timedelta64(1, 'D')).astype(int)\n\nfeatures_to_encode = np.concatenate([features_to_encode, ['month', 'day', 'hour', 'weekday', 'week', 'quarter', 'hour', 'weekend']])\nfeatures_to_encode = np.unique(features_to_encode)\nfeatures_to_encode"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]