<jupyter_start><jupyter_text>Main GoalThe main idea of the workflow unit is for you to understand how you should build a strategy for designing a Recommender System. We will leave it up to you on how well you prepare for the Hackathon.Our best advice: review the workflow notebook carefully and make sure you are able to explain to a 5-year-old child (or a [rubber duck](https://en.wikipedia.org/wiki/Rubber_duck_debugging)) the whole pipeline. Even better, try to get some ideas on how to improve it!For the ones going the extra mile, there are some [datasets](https://github.com/caserec/Datasets-for-Recommender-Systems) out there that you can try to work on! :)<jupyter_code>def ds_recommenders_oath_of_office():
    """The Recommender Systems' Data Scientist Oath of Office
    
    Returns:
        oath (str): should contain the following sentence:
                    'I do solemnly swear I have reviewed the Workflow notebook and I am ready for the Hackathon!'
    """
    # YOUR CODE HERE
    raise NotImplementedError()
    return oath

oath = ds_recommenders_oath_of_office()
expected_hash = '3829f34c0b9c1e4a4da31670486d219446ae6ad92b0a243d92ec41793611ed7f'
assert hashlib.sha256(oath.encode()).hexdigest() == expected_hash<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.06798418972332015, 'fraction_numerical': 0.03715415019762846, 'mean_word_length': 4.504347826086956, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '203536', 'license': 'MIT', 'n_tokens_mistral': 415, 'n_tokens_neox': 377, 'n_words': 145, 'path': 'S05 - Recommender Systems/BLU12 - Workflow/Exercise notebook.ipynb', 'repo_name': 'jtiagosg/batch3-students', 'size': '1265'}