<jupyter_start><jupyter_text>Pipeline<jupyter_code>pipe = Pipeline([('scaler', StandardScaler()), ('knn', KNeighborsClassifier(n_neighbors=13))])
model = pipe.fit(XX, y)
pkl_filename = "model.pkl"
with open(pkl_filename, 'wb') as file:
    pickle.dump(model, file)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17006802721088435, 'fraction_numerical': 0.006802721088435374, 'mean_word_length': 9.925925925925926, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, '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': '102250', 'license': 'MIT', 'n_tokens_mistral': 122, 'n_tokens_neox': 117, 'n_words': 17, 'path': 'model.ipynb', 'repo_name': 'nestorivanmo/FootballPrediction', 'size': '294'}