scikit-learn offers the class LinearRegression, which works with n-dimensional spaces. For this purpose, we're going to use the Boston dataset:
from sklearn.datasets import load_boston>>> boston = load_boston()>>> boston.data.shape(506L, 13L)>>> boston.target.shape(506L,)
It has 506 samples with 13 input features and one output. In the following figure, there' a collection of the plots of the first 12 features:
When working with datasets, it's useful to have a tabular view to manipulate data. pandas is a perfect framework for this task, and even though it's beyond the scope of ...
