<jupyter_start><jupyter_text>Slope Coefficient<jupyter_code>regression.coef_ # theta_1
# Intercept
regression.intercept_
plt.figure(figsize=(10, 6))
plt.scatter(X, Y, alpha=0.3)
plt.plot(X, regression.predict(X), color='red', linewidth=2)

plt.title('Film Cost vs Global Revenue')
plt.xlabel('Production Budget $')
plt.ylabel('Worldwide Gross $')
plt.ylim(0, 3000000000)
plt.xlim(0, 450000000)
plt.show()
regression.score(X, Y)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15973741794310722, 'fraction_numerical': 0.061269146608315096, 'mean_word_length': 8.346938775510203, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 2, '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': '185261', 'license': 'MIT', 'n_tokens_mistral': 216, 'n_tokens_neox': 180, 'n_words': 30, 'path': 'notebooks/Linear-Regression.ipynb', 'repo_name': 'kaseypcantu/Linear-Regression', 'size': '457'}