<jupyter_start><jupyter_text>Predictions<jupyter_code>predictions = lm.predict(X_test)
predictions
y_test
plt.scatter(y_test,predictions)
sns.histplot((y_test-predictions),kde=True)
from sklearn import metrics
metrics.mean_absolute_error(y_test,predictions)
metrics.mean_squared_error(y_test,predictions)
np.sqrt(metrics.mean_squared_error(y_test,predictions))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10512820512820513, 'fraction_numerical': 0.0, 'mean_word_length': 16.772727272727273, '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': '444739', 'license': 'Unlicense', 'n_tokens_mistral': 152, 'n_tokens_neox': 146, 'n_words': 13, 'path': 'Aula 85 - Linear regression - Linear regression with python parte 1 e parte 2.ipynb', 'repo_name': 'Filipe-Leite/udemy-python-for-data-science', 'size': '390'}