<jupyter_start><jupyter_text>Notebook To do this analysis, we'll look at every active region observed by the Helioseismic and Magnetic Imager instrument on NASA's Solar Dynamics Observatory (SDO) satellite over the last eight years. Each active region is characterized by a bunch of features. These features describe the magnetic field at the solar surface. One feature, for example, is the total energy contained within an active region. Another is the total flux through an active region. We have 18 features, all of which are calculated every 12 minutes throughout an active region's lifetime. See [Bobra et al., 2014](http://link.springer.com/article/10.1007%2Fs11207-014-0529-3) for more information on how we calculate these features. We'll then ascribe each active region to one of two classes:1. The positive class contains flaring active regions that did produce a CME. 2. The negative class contains flaring active regions that did not produce a CME.  First, we'll import some modules.<jupyter_code>import numpy as np
import matplotlib.pylab as plt
import matplotlib.mlab as mlab
import pandas as pd
print("Hello World")<jupyter_output>Hello World
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