<jupyter_start><jupyter_text>Score<jupyter_code>score = regressor.score(X,y)
score<jupyter_output><empty_output><jupyter_text>plotar<jupyter_code>import matplotlib.pyplot as plt
plt.scatter(X,y)
plt.title('Redes Neurais')
plt.xlabel('Idade')
plt.ylabel('Custo plano')
plt.plot(X,regressor.predict(X),color='red')
print(scaler_y.inverse_transform(regressor.predict(X)))<jupyter_output>[ 247.55311319  378.11485371  497.26494608  702.74098707  963.65460287
 1602.87772593 2851.57866443 4471.06453626 6086.01710924 7694.58675711]
{'fraction_non_alphanumeric': 0.13688212927756654, 'fraction_numerical': 0.21863117870722434, 'mean_word_length': 9.54, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, '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': '139290', 'license': 'MIT', 'n_tokens_mistral': 310, 'n_tokens_neox': 227, 'n_words': 24, 'path': 'notebook/_RegressaoLinear/RedesNeurais_PlanoSaude.ipynb', 'repo_name': 'victorblois/py_datascience', 'size': '526'}