In [22]:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.datasets as data
In [4]:
boston = data.load_boston()
In [10]:
print(boston['DESCR'])
Boston House Prices dataset
Notes
------
Data Set Characteristics:
:Number of Instances: 506
:Number of Attributes: 13 numeric/categorical predictive
:Median Value (attribute 14) is usually the target
:Attribute Information (in order):
- CRIM per capita crime rate by town
- ZN proportion of residential land zoned for lots over 25,000 sq.ft.
- INDUS proportion of non-retail business acres per town
- CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
- NOX nitric oxides concentration (parts per 10 million)
- RM average number of rooms per dwelling
- AGE proportion of owner-occupied units built prior to 1940
- DIS weighted distances to five Boston employment centres
- RAD index of accessibility to radial highways
- TAX full-value property-tax rate per $10,000
- PTRATIO pupil-teacher ratio by town
- B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
- LSTAT % lower status of the population
- MEDV Median value of owner-occupied homes in $1000's
:Missing Attribute Values: None
:Creator: Harrison, D. and Rubinfeld, D.L.
This is a copy of UCI ML housing dataset.
http://archive.ics.uci.edu/ml/datasets/Housing
This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.
The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic
prices and the demand for clean air', J. Environ. Economics & Management,
vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics
...', Wiley, 1980. N.B. Various transformations are used in the table on
pages 244-261 of the latter.
The Boston house-price data has been used in many machine learning papers that address regression
problems.
**References**
- Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.
- Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.
- many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)
In [20]:
boston['data'][:,[2,12]], boston['target'])
Out[20]:
array([[ 2.31, 4.98],
[ 7.07, 9.14],
[ 7.07, 4.03],
...,
[ 11.93, 5.64],
[ 11.93, 6.48],
[ 11.93, 7.88]])
In [21]:
Out[21]:
array([ 24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5,
18.9, 15. , 18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5,
20.2, 18.2, 13.6, 19.6, 15.2, 14.5, 15.6, 13.9, 16.6,
14.8, 18.4, 21. , 12.7, 14.5, 13.2, 13.1, 13.5, 18.9,
20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7, 21.2,
19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4,
18.9, 35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2,
25. , 33. , 23.5, 19.4, 22. , 17.4, 20.9, 24.2, 21.7,
22.8, 23.4, 24.1, 21.4, 20. , 20.8, 21.2, 20.3, 28. ,
23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2, 23.6, 28.7,
22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,
33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4,
19.8, 19.4, 21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2,
19.2, 20.4, 19.3, 22. , 20.3, 20.5, 17.3, 18.8, 21.4,
15.7, 16.2, 18. , 14.3, 19.2, 19.6, 23. , 18.4, 15.6,
18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4, 15.6,
11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3,
19.4, 17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. ,
50. , 50. , 22.7, 25. , 50. , 23.8, 23.8, 22.3, 17.4,
19.1, 23.1, 23.6, 22.6, 29.4, 23.2, 24.6, 29.9, 37.2,
39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. , 32. , 29.8,
34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,
34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4,
22.5, 24.4, 20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. ,
23.3, 28.7, 21.5, 23. , 26.7, 21.7, 27.5, 30.1, 44.8,
50. , 37.6, 31.6, 46.7, 31.5, 24.3, 31.7, 41.7, 48.3,
29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1, 22.2,
23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8,
29.6, 42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8,
43.1, 48.8, 31. , 36.5, 22.8, 30.7, 50. , 43.5, 20.7,
21.1, 25.2, 24.4, 35.2, 32.4, 32. , 33.2, 33.1, 29.1,
35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. , 20.1, 23.2,
22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,
20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4,
33.4, 28.2, 22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8,
16.2, 17.8, 19.8, 23.1, 21. , 23.8, 23.1, 20.4, 18.5,
25. , 24.6, 23. , 22.2, 19.3, 22.6, 19.8, 17.1, 19.4,
22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7, 32.7,
16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9,
24.1, 18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6,
25. , 19.9, 20.8, 16.8, 21.9, 27.5, 21.9, 23.1, 50. ,
50. , 50. , 50. , 50. , 13.8, 13.8, 15. , 13.9, 13.3,
13.1, 10.2, 10.4, 10.9, 11.3, 12.3, 8.8, 7.2, 10.5,
7.4, 10.2, 11.5, 15.1, 23.2, 9.7, 13.8, 12.7, 13.1,
12.5, 8.5, 5. , 6.3, 5.6, 7.2, 12.1, 8.3, 8.5,
5. , 11.9, 27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,
7. , 7.2, 7.5, 10.4, 8.8, 8.4, 16.7, 14.2, 20.8,
13.4, 11.7, 8.3, 10.2, 10.9, 11. , 9.5, 14.5, 14.1,
16.1, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8, 10.5,
17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. ,
13.4, 15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9,
20. , 16.4, 17.7, 19.5, 20.2, 21.4, 19.9, 19. , 19.1,
19.1, 20.1, 19.9, 19.6, 23.2, 29.8, 13.8, 13.3, 16.7,
12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8, 20.6, 21.2,
19.1, 20.6, 15.2, 7. , 8.1, 13.6, 20.1, 21.8, 24.5,
23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9,
22. , 11.9])
In [ ]:
Content source: kaphka/ml-software
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