boston_housing_data: The Boston housing dataset for regression

A function that loads the boston_housing_data dataset into NumPy arrays.

from mlxtend.data import boston_housing_data

Overview

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

14. Number of samples: 506

15. Target variable (continuous): MEDV, Median value of owner-occupied homes in $1000's

References

• Source: 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.

Example 1 - Dataset overview

from mlxtend.data import boston_housing_data
X, y = boston_housing_data()

print('Dimensions: %s x %s' % (X.shape[0], X.shape[1]))
print('1st row', X[0])

(506, 14)
Dimensions: 506 x 13
1st row [  6.32000000e-03   1.80000000e+01   2.31000000e+00   0.00000000e+00
   5.38000000e-01   6.57500000e+00   6.52000000e+01   4.09000000e+00
   1.00000000e+00   2.96000000e+02   1.53000000e+01   3.96900000e+02
   4.98000000e+00]

API

boston_housing_data()

Boston Housing dataset.

• Source : https://archive.ics.uci.edu/ml/datasets/Housing

• Number of samples : 506

• Continuous target variable : MEDV

  MEDV = Median value of owner-occupied homes in $1000's

  Dataset Attributes:

  • 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 prop. of b. by town
  • 13) LSTAT % lower status of the population

Returns

• X, y : [n_samples, n_features], [n_class_labels]

  X is the feature matrix with 506 housing samples as rows and 13 feature columns. y is a 1-dimensional array of the continuous target variable MEDV

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/data/boston_housing_data/