<jupyter_start><jupyter_text>Model based<jupyter_code>import sklearn.linear_model
model = sklearn.linear_model.LinearRegression()
model.fit(X,y)
model.predict([[22587]])<jupyter_output><empty_output><jupyter_text>Instance based<jupyter_code>import sklearn.neighbors
model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)
model.fit(X,y)
model.predict([[22587]])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13383838383838384, 'fraction_numerical': 0.027777777777777776, 'mean_word_length': 14.88, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '564892', 'license': 'MIT', 'n_tokens_mistral': 167, 'n_tokens_neox': 157, 'n_words': 13, 'path': 'practice/books/Hands On ML with scikit and TF - Aurelien Geron/chapter1/GDP per capita.ipynb', 'repo_name': 'ItamarRocha/introduction-to-AI', 'size': '396'}