<jupyter_start><jupyter_text>SKLEARN<jupyter_code>from sklearn import linear_model
regr = linear_model.LinearRegression()
regr.fit(x, y)

print('Intercept: \n', regr.intercept_)
print('Coefficients: \n', regr.coef_)
# prediction with sklearn
predictions=regr.predict(x)
predictions=predictions.reshape(-1,1)
y=y.reshape(-1,1)
print(predictions.shape,y.shape)
regr.score(x,y)
predtest=regr.predict(test)
df = pd.DataFrame({"y" : predtest})      #prediction of value for the Test.csv and storing in the result.csv file
df.to_csv("result.csv", index=True)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14432989690721648, 'fraction_numerical': 0.006872852233676976, 'mean_word_length': 7.328571428571428, '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': '328093', 'license': 'MIT', 'n_tokens_mistral': 228, 'n_tokens_neox': 223, 'n_words': 40, 'path': 'Multivariate Regression/Air Quality Prediction/Model.ipynb', 'repo_name': 'jyoti297/Machine-Learning', 'size': '582'}