50 lines
2.3 KiB
ReStructuredText
50 lines
2.3 KiB
ReStructuredText
.. _boston_dataset:
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Boston house prices dataset
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**Data Set Characteristics:**
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:Number of Instances: 506
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:Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.
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:Attribute Information (in order):
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- CRIM per capita crime rate by town
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- ZN proportion of residential land zoned for lots over 25,000 sq.ft.
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- INDUS proportion of non-retail business acres per town
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- CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
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- NOX nitric oxides concentration (parts per 10 million)
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- RM average number of rooms per dwelling
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- AGE proportion of owner-occupied units built prior to 1940
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- DIS weighted distances to five Boston employment centres
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- RAD index of accessibility to radial highways
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- TAX full-value property-tax rate per $10,000
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- PTRATIO pupil-teacher ratio by town
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- B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
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- LSTAT % lower status of the population
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- MEDV Median value of owner-occupied homes in $1000's
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:Missing Attribute Values: None
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:Creator: Harrison, D. and Rubinfeld, D.L.
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This is a copy of UCI ML housing dataset.
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https://archive.ics.uci.edu/ml/machine-learning-databases/housing/
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This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.
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The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic
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prices and the demand for clean air', J. Environ. Economics & Management,
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vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics
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...', Wiley, 1980. N.B. Various transformations are used in the table on
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pages 244-261 of the latter.
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The Boston house-price data has been used in many machine learning papers that address regression
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problems.
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.. topic:: References
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- Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.
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- 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.
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