[{'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:# This Python 3 environment comes with many helpful analytics libraries installed<br># It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python<br># For example, here\'s several helpful packages to load in <br><br>import numpy as np # linear algebra<br>import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)<br><br># Input data files are available in the "../input/" directory.<br># For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory<br><br>from subprocess import check_output<br>print(check_output(["ls", "../input"]).decode("utf8"))<br><br># Any results you write to the current directory are saved as output.<br><br>print (\'Loading input files..\')<br>print ()<br>people = pd.read_csv(\'../input/people.csv\',<br>                       dtype={\'people_id\': np.str,<br>                              \'activity_id\': np.str,<br>                              \'char_38\': np.int32},<br>                       parse_dates=[\'date\'])<br>train = pd.read_csv(r\'../input/act_train.csv\',<br>                        dtype={\'people_id\': np.str,<br>                               \'activity_id\': np.str,<br>                               \'outcome\': np.int8},<br>                        parse_dates=[\'date\'])<br>test = pd.read_csv(\'../input/act_test.csv\',<br>                       dtype={\'people_id\': np.str,<br>                              \'activity_id\': np.str},<br>                       parse_dates=[\'date\'])<br><br>missing_values = []<br><br>print (\'Train set features\')<br>print (\'------------------\')<br>for col in train:<br>    unique = train[col].unique()<br>    print (str(col) + \' has \' + str(unique.size) + \' unique values\')<br>    <br>    if (True in pd.isnull(unique)):<br>        print (str(col) + \' has \' + str(pd.isnull(train[col]).sum()) + \' missing values\')<br>    print ()<br>    <br>print ()<br><br>print (\'Processing the datasets..\')<br>print ()<br>for data in [train,test]:<br>    for i in range(1,11):<br>        data[\'char_\'+str(i)].fillna(\'type -1\', inplace = \'true\')<br>        data[\'char_\'+str(i)] = data[\'char_\'+str(i)].str.lstrip(\'type \').astype(np.int32)<br>        <br>    data[\'activity_category\'] = data[\'activity_category\'].str.lstrip(\'type \').astype(np.int32)<br>    <br>    data[\'year\'] = data[\'date\'].dt.year<br>    data[\'month\'] = data[\'date\'].dt.month<br>    data[\'day\'] = data[\'date\'].dt.day<br>    data.drop(\'date\', axis=1, inplace=True)<br>    <br>for i in range(1,10):<br>    people[\'char_\' + str(i)] = people[\'char_\' + str(i)].str.lstrip(\'type \').astype(np.int32)<br>for i in range(10, 38):<br>    people[\'char_\' + str(i)] = people[\'char_\' + str(i)].astype(np.int32)<br>    <br>people[\'group_1\'] = people[\'group_1\'].str.lstrip(\'group \').astype(np.int32)<br>people[\'year\'] = people[\'date\'].dt.year<br>people[\'month\'] = people[\'date\'].dt.month<br>people[\'day\'] = people[\'date\'].dt.day<br>people.drop(\'date\', axis=1, inplace=True)<br><br>print (\'Merging the datasets..\')<br>print ()<br><br>train = pd.merge(train, people, how=\'left\', on=\'people_id\', left_index=True)<br>train.fillna(-1, inplace=True)<br>test = pd.merge(test, people, how=\'left\', on=\'people_id\', left_index=True)<br>test.fillna(-1, inplace=True)<br><br>train = train.drop([\'people_id\'], axis=1)<br><br>#Separate label and data<br>Y = train[\'outcome\']<br>X = train.drop([\'outcome\'], axis=1)<br>X = X.iloc[:,1:]<br>from sklearn.model_selection import cross_val_score<br>from sklearn.ensemble import RandomForestClassifier<br>rfc = RandomForestClassifier(n_estimators=96)<br><br>#print("cv")<br>#scores = cross_val_score(rfc, X, Y, cv=4)<br>#print ("Mean accuracy of Random Forest: " + scores.mean())<br>rfc = rfc.fit(X, Y)<br>#drop the people_id<br>test = test.drop([\'people_id\'], axis=1)<br># Get the test data features, skipping the first column \'PassengerId\'<br>test_x = test.iloc[:, 1:]<br><br><br># Predict the outcome values for the test data<br>test_y = list(map(int, rfc.predict(test_x)))<br>#file for submission<br>test[\'outcome\'] = test_y<br>test[[\'activity_id\', \'outcome\']] \\\\<br>    .to_csv(\'results.csv\', index=False)'}, {'role': 'assistant', 'content': "['helper_functions', 'comment_only']"}]