The scikit-learn project comes with a number of datasets and sample images that we can experiment with. In this recipe, we will load an example dataset included in the scikit-learn distribution. The datasets hold data as a NumPy two-dimensional array and metadata linked to the data.
We will load a sample dataset of house prices in Boston. It is a tiny dataset, so if you are looking for a house in Boston, don't get too excited! Other datasets are described at http://scikit-learn.org/dev/modules/classes.html#module-sklearn.datasets.
We will look at the shape of the raw data and its maximum and minimum values. The shape is a tuple, representing the dimensions of the NumPy array. We will do the same for the target array, which contains values that are the learning objectives (determining house price). The following code from sample_data.py accomplishes our goals:
from __future__ import print_function from sklearn import datasets boston_prices = datasets...