Loading the dataset

We can again thank scikit-learn for easy access to the dataset. We first import all the necessary modules, as we did earlier:

In [1]: import numpy as np
...     from sklearn import datasets
...     from sklearn import metrics
...     from sklearn import model_selection as modsel
...     from sklearn import linear_model
...     %matplotlib inline
...     import matplotlib.pyplot as plt
...     plt.style.use('ggplot')

Then, loading the dataset is a one-liner:

In [2]: boston = datasets.load_boston()

The structure of the boston object is identical to the iris object, as discussed in the preceding command. We can get more information about the dataset in 'DESCR', find all data in 'data', all feature names in 'feature_names', and all target values in 'target':

In [3]: dir(boston)
Out[3]: ['DESCR', 'data', 'feature_names', 'target']

The dataset contains a total of 506 data points, each of which has 13 features:

In [4]: boston.data.shape
Out[4]: (506, 13)

Of course, we have only a single target value, which is the housing price:

In [5]: boston.target.shape
Out[5]: (506,)