This is an analysis of the classic Boston housing data set to predict housing prices using multiple regression.
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)