Boston Data

A data set containing housing values in 506 suburbs of Boston.

• 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: nitrogen 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 mean of 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.

• lstat: lower status of the population (percent).

• medv: median value of owner-occupied homes in $1000s.

Notes

This dataset was obtained from, and is slightly modified from, the Boston dataset that is part of the R library MASS. References are available in the MASS library.

from ISLP import load_data
Boston = load_data('Boston')
Boston.columns

Index(['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax',
       'ptratio', 'lstat', 'medv'],
      dtype='object')

Boston.shape

(506, 13)

Boston.columns

Index(['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax',
       'ptratio', 'lstat', 'medv'],
      dtype='object')

Boston.describe()

	crim	zn	indus	chas	nox	rm	age	dis	rad	tax	ptratio	lstat	medv
count	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000
mean	3.613524	11.363636	11.136779	0.069170	0.554695	6.284634	68.574901	3.795043	9.549407	408.237154	18.455534	12.653063	22.532806
std	8.601545	23.322453	6.860353	0.253994	0.115878	0.702617	28.148861	2.105710	8.707259	168.537116	2.164946	7.141062	9.197104
min	0.006320	0.000000	0.460000	0.000000	0.385000	3.561000	2.900000	1.129600	1.000000	187.000000	12.600000	1.730000	5.000000
25%	0.082045	0.000000	5.190000	0.000000	0.449000	5.885500	45.025000	2.100175	4.000000	279.000000	17.400000	6.950000	17.025000
50%	0.256510	0.000000	9.690000	0.000000	0.538000	6.208500	77.500000	3.207450	5.000000	330.000000	19.050000	11.360000	21.200000
75%	3.677083	12.500000	18.100000	0.000000	0.624000	6.623500	94.075000	5.188425	24.000000	666.000000	20.200000	16.955000	25.000000
max	88.976200	100.000000	27.740000	1.000000	0.871000	8.780000	100.000000	12.126500	24.000000	711.000000	22.000000	37.970000	50.000000