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