[{'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:# Save the test IDs for Kaggle submission\ntest_ids = act_test['activity_id']\n\ndef preprocess_acts(data,min_date, train_set=True):\n    \n    # Getting rid of data feature for now\n    dates=data['date']\n    dates=process_dates(dates,min_date)\n    data = data.drop(['date', 'activity_id'], axis=1)\n    if(train_set):\n        data = data.drop(['outcome'], axis=1)\n    \n    ## Split off _ from people_id\n    data['people_id'] = data['people_id'].apply(lambda x: x.split('_')[1])\n    data['people_id'] = pd.to_numeric(data['people_id']).astype(int)\n    \n    columns = list(data.columns)\n    \n    # Convert strings to ints\n    for col in columns[1:]:\n        data[col] = data[col].fillna('type 0')\n        data[col] = data[col].apply(lambda x: x.split(' ')[1])\n        data[col] = pd.to_numeric(data[col]).astype(int)\n#    for column in columns[1:]:\n#        dummies = pd.get_dummies(data[column])\n#        data[dummies.columns] = dummies\n    data['dates']=dates\n    return data\n\ndef preprocess_people(data,min_date):\n    dates=data['date']\n    dates=process_dates(dates,min_date)\n    # TODO refactor this duplication\n    data = data.drop(['date'], axis=1)\n    data['people_id'] = data['people_id'].apply(lambda x: x.split('_')[1])\n    data['people_id'] = pd.to_numeric(data['people_id']).astype(int)\n    \n    #  Values in the people df is Booleans and Strings    \n    columns = list(data.columns)\n    bools = columns[11:]\n    strings = columns[1:11]\n    \n    for col in bools:\n        data[col] = pd.to_numeric(data[col]).astype(int)        \n    for col in strings:\n        data[col] = data[col].fillna('type 0')\n        data[col] = data[col].apply(lambda x: x.split(' ')[1])\n        data[col] = pd.to_numeric(data[col]).astype(int)\n    #data = data.drop(['group_1'], axis=1)\n#    for column in strings:\n#        dummies = pd.get_dummies(data[column])\n#        data[dummies.columns] = dummies\n    data['dates']=dates\n    return data"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]