Using multiple features

To recap the tools seen in the previous chapter, we reload all the packages and the Boston dataset:

In: import numpy as np
  import pandas as pd
  import matplotlib.pyplot as plt
  import matplotlib as mpl
  from sklearn.datasets import load_boston
  from sklearn import linear_model

If you are working on the code in an IPython Notebook (as we strongly suggest), the following magic command will allow you to visualize plots directly on the interface:

In: %matplotlib inline

We are still using the Boston dataset, a dataset that tries to explain different house prices in the Boston of the 70s, given a series of statistics aggregated at the census zone level:

In: boston = load_boston()
  dataset = pd.DataFrame(boston.data, columns=boston.feature_names)
  dataset['target'] = boston.target

We will always work by keeping with us a series of informative variables, the number of observation and variable names, the input data matrix, and the response vector at hand:

In: observations...