alokkiitd1729/boston_housing

Predicting Boston housing prices

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

Boston House Prices dataset

Notes

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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)