1. import math
2.  
3. import sklearn
4.  
5. import numpy as np
6. import matplotlib.pyplot as plt
7. import pandas as pd
8. import seaborn as sns
9. from sklearn import metrics
10. from sklearn.datasets import load_boston
11. from sklearn.linear_model import LinearRegression
12. from sklearn.model_selection import train_test_split
13.  
14. # Zad 1
15. print("Zad 1")
16. boston_dataset = load_boston()
17.  
18. data_frame = pd.DataFrame(data=boston_dataset['data'], columns=boston_dataset['feature_names'])
19. data_frame['MEDV'] = pd.Series(boston_dataset['target'])
20.  
21. print(data_frame.head(10))
22. print(data_frame.tail(10))
23.  
24. # Zad 2
25. print("Zad 2")
26. data_frame.info()
27. # a - 506
28. # b - float64
29. # c - nie
30.  
31. # Zad 3
32. print("Zad 3")
33. describe = data_frame.describe()
34. print(describe)
35. # a - sredni=3.593761 std=8.596783
36. # b - max=50.000000  min=5.000000
37. # c - 12.653063
38.  
39. # Zad 4
40. print("Zad 4")
41. sns.distplot(data_frame.MEDV)
42. plt.show()
43.  
44. # Zad 5
45. print("Zad 5")
46. corr_matrix = data_frame.corr().round(2)
47. sns.heatmap(corr_matrix, annot=True)
48.  
49. # a - RM, ZN, B
50. # b - LSTAT
51. # c - TAX do RAD oraz same dla siebie - 1
52.  
53. sns.lmplot('MEDV', 'RM', data=corr_matrix)
54. sns.lmplot('MEDV', 'LSTAT', data=corr_matrix)
55. plt.show()
56.  
57. # Zad 6
58. print("Zad 6")
59. x = data_frame[['RM', 'B', 'ZN']]
60. y = data_frame[['MEDV']]
61. X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.2)
62.  
63. # Zad 7
64. print("Zad 7")
65. lin = LinearRegression()
66. lin.fit(X_train, Y_train)
67.  
68. Y_pred = lin.predict(X_test)
69. Y_pred_ = lin.predict(X_train)
70.  
71. plt.scatter(Y_test, Y_pred)
72. plt.title('testowy')
73. plt.show()
74. plt.title('treningowy')
75. plt.scatter(Y_train, Y_pred_)
76. plt.show()
77.  
78. # Zad 8
79. print("Zad 8")
80. print('treningowy')
81. print('RMSE: {}'.format(math.sqrt(metrics.mean_squared_error(Y_train, Y_pred_))))
82. print('MAE: {}'.format(metrics.mean_absolute_error(Y_train, Y_pred_)))
83.  
84. print('testowy')
85. print('RMSE: {}'.format(math.sqrt(metrics.mean_squared_error(Y_test, Y_pred))))
86. print('MAE: {}'.format(metrics.mean_absolute_error(Y_test, Y_pred)))