[{'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:features = num_features + cat_features\ndef feature_organize(features):\nline_features = {}\nstation_features = {}\nlines = set([f.split('_')[0] for f in features])\nstations = set([f.split('_')[1] for f in features])\nfor l in lines:\nline_features[l] = [f for f in features if l+'_' in f]\nfor s in stations:\nstation_features[s] = [f for f in features if s+'_' in f]\nreturn line_features, station_features\ndef get_station_times(dates_df, withId=False):\ntimes = []\ncols = list(dates_df.columns)\nif 'Id' in cols:\ncols.remove('Id')\nfor feature_name in cols:\nif withId:\ndf = dates_df[['Id', feature_name]].copy()\ndf.columns = ['Id', 'time']\nelse:\ndf = dates_df[[feature_name]].copy()\ndf.columns = ['time']\ndf['station'] = feature_name.split('_')[1][1:]\ndf = df.dropna()\ntimes.append(df)\nreturn pd.concat(times)\ndef make_features(df, station_features, station_data):\nnew_features = pd.DataFrame({})\nfor s in station_features.keys():\nstation_data = df[station_features[s]]\ncol = s+'_max'\nnew_features[col] = station_data.max(axis=1).fillna(-1.)\ncol = s+'_min'\nnew_features[col] = station_data.min(axis=1).fillna(-1.)\nreturn new_features"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]