[{'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:# process train and test<br>def process(data, isTest = False):<br>    # label encode some features<br>    mappings = {'0':0, 'a':1, 'b':2, 'c':3, 'd':4}<br>    data.StoreType.replace(mappings, inplace=True)<br>    data.Assortment.replace(mappings, inplace=True)<br>    data.StateHoliday.replace(mappings, inplace=True)<br>    <br>    # extract some features from date column  <br>    data['Month'] = data.Date.dt.month<br>    data['Year'] = data.Date.dt.year<br>    data['Day'] = data.Date.dt.day<br>    data['WeekOfYear'] = data.Date.dt.weekofyear<br>    <br>    # calculate competiter open time in months<br>    data['CompetitionOpen'] = 12 * (data.Year - data.CompetitionOpenSinceYear) + \\\\<br>        (data.Month - data.CompetitionOpenSinceMonth)<br>    data['CompetitionOpen'] = data['CompetitionOpen'].apply(lambda x: x if x > 0 else 0)<br>    <br>    # calculate promo2 open time in months<br>    data['PromoOpen'] = 12 * (data.Year - data.Promo2SinceYear) + \\\\<br>        (data.WeekOfYear - data.Promo2SinceWeek) / 4.0<br>    data['PromoOpen'] = data['PromoOpen'].apply(lambda x: x if x > 0 else 0)<br>                                                 <br>    # Indicate whether the month is in promo interval<br>    month2str = {1:'Jan', 2:'Feb', 3:'Mar', 4:'Apr', 5:'May', 6:'Jun', \\\\<br>             7:'Jul', 8:'Aug', 9:'Sept', 10:'Oct', 11:'Nov', 12:'Dec'}<br>    data['month_str'] = data.Month.map(month2str)<br><br>    def check(row):<br>        if isinstance(row['PromoInterval'],str) and row['month_str'] in row['PromoInterval']:<br>            return 1<br>        else:<br>            return 0<br>        <br>    data['IsPromoMonth'] =  data.apply(lambda row: check(row),axis=1)    <br>    <br>    # select the features we need<br>    features = ['Store', 'DayOfWeek', 'Promo', 'StateHoliday', 'SchoolHoliday',<br>       'StoreType', 'Assortment', 'CompetitionDistance',<br>       'CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear', 'Promo2',<br>       'Promo2SinceWeek', 'Promo2SinceYear', 'Year', 'Month', 'Day',<br>       'WeekOfYear', 'CompetitionOpen', 'PromoOpen', 'IsPromoMonth']  <br>    if not isTest:<br>        features.append('Sales')<br>        <br>    data = data[features]<br>    return data<br><br>train = process(train)<br>valid = process(valid)<br>train_total = process(train_total)<br>x_test = process(test,isTest = True)    "}, {'role': 'assistant', 'content': "['data_preprocessing']"}]