<jupyter_start><jupyter_text>**TRaining**<jupyter_code>model.fit(pf.fit_transform(X),y)<jupyter_output><empty_output><jupyter_text>**Predicting Data....**<jupyter_code>y_pred= model.predict(pf.fit_transform(X))
[y_pred,y]
plt.figure(figsize=(12, 10))
plt.scatter(X, y, color = 'purple')
plt.plot(X, y_pred, color = 'green')
plt.title('data')
plt.xlabel('month')
plt.ylabel('cases')
plt.show()<jupyter_output><empty_output><jupyter_text>**Evaluating Model**<jupyter_code>from sklearn.metrics import r2_score
r2_score(y,y_pred)<jupyter_output><empty_output><jupyter_text>**Predicting single values.**<jupyter_code>x1=[[2024]]
y1=model.predict(pf.fit_transform(x1))
print (y1 [0][0])
model.coef_
model.intercept_
a=model.coef_[0][1]
b=model.coef_ [0][2]
c= model.intercept_[0]
x=2024
y= a*x+b*(x**2)+c
print(y)<jupyter_output>526548073.87579346
{'fraction_non_alphanumeric': 0.19500594530321047, 'fraction_numerical': 0.05112960760998811, 'mean_word_length': 11.567164179104477, 'pattern_counts': {'":': 0, '<': 16, '<?xml version=': 0, '>': 16, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '655529', 'license': 'MIT', 'n_tokens_mistral': 406, 'n_tokens_neox': 377, 'n_words': 40, 'path': 'Polynomial (1).ipynb', 'repo_name': 'SidStark29/Polynomial_Regression', 'size': '841'}