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TWEET # Untitled a guest Jul 24th, 2019 49 Never
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1. #!/usr/bin/env python3
2. # -*- coding: utf-8 -*-
3. """
4. Created on Fri Jul 12 19:10:27 2019
5.
6. @author: nodeflux
7. """
8.
9. import numpy
10. import pandas
11. from matplotlib import pyplot
12. from sklearn.model_selection import train_test_split
13. from sklearn.linear_model import LinearRegression
14. from sklearn.metrics import mean_squared_error, r2_score
15.
16.
17. dataset = pandas.read_csv('/home/nodeflux/Documents/mercubuana/UAS_DATA_MINING/data.csv')
18. x = dataset.iloc[:, :-1].values
19. y = dataset.iloc[:, 2]
20.
21.
22. x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)
23. regressor = LinearRegression()
24. regressor.fit(x_train, y_train)
25.
26. y_prediction = regressor.predict(x_test)
27.
28. # Coefficient
29. print('Coefficient: \n', regressor.coef_)
30. # Mean Squared Error
31. print("Mean squared error: %.2f" % mean_squared_error(y_test, y_prediction))
32. # Variance score
33. print("Variance score: %.2f" % r2_score(y_test, y_prediction))
34.
35. pyplot.scatter(x_test[:, 0], y_test, color='black')
36. pyplot.scatter(x_test[:, 0], y_prediction, color='blue', linewidth=3)
37. pyplot.xticks(())
38. pyplot.yticks(())
39. pyplot.show()
40.
41. pyplot.scatter(x_test[:, 1], y_test, color='black')
42. pyplot.scatter(x_test[:, 1], y_prediction, color='blue', linewidth=3)
43. pyplot.xticks(())
44. pyplot.yticks(())
45. pyplot.show()
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