wehub-resource-sync
37 lines
1.3 KiB
Markdown
37 lines
1.3 KiB
Markdown
Sebastian Raschka, 2015
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# Python Machine Learning - Supplementary Datasets
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## Boston Housing Data
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- Used in chapter 10
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The Boston Housing dataset for regression analysis.
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**Features**
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1. CRIM: per capita crime rate by town
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2. ZN: proportion of residential land zoned for lots over 25,000 sq.ft.
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3. INDUS: proportion of non-retail business acres per town
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4. CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
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5. NOX: nitric oxides concentration (parts per 10 million)
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6. RM: average number of rooms per dwelling
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7. AGE: proportion of owner-occupied units built prior to 1940
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8. DIS: weighted distances to five Boston employment centres
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9. RAD: index of accessibility to radial highways
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10. TAX: full-value property-tax rate per $10,000
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11. PTRATIO: pupil-teacher ratio by town
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12. B: 1000(Bk - 0.63)^2 where Bk is the proportion of b. by town
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13. LSTAT: % lower status of the population
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- Number of samples: 506
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- Target variable (continuous): MEDV, Median value of owner-occupied homes in $1000's
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### References
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- Source: [https://archive.ics.uci.edu/ml/datasets/Wine](https://archive.ics.uci.edu/ml/datasets/Wine)
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- Harrison, D. and Rubinfeld, D.L.
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'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.
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