  Coronal mass ejections (CMEs) are the most geoeffective space weather
phenomena, being associated with large geomagnetic storms, having the potential
to cause disturbances to telecommunication, satellite network disruptions,
power grid damages and failures. Thus, considering these storms' potential
effects on human activities, accurate forecasts of the geoeffectiveness of CMEs
are paramount. This work focuses on experimenting with different machine
learning methods trained on white-light coronagraph datasets of close to sun
CMEs, to estimate whether such a newly erupting ejection has the potential to
induce geomagnetic activity. We developed binary classification models using
logistic regression, K-Nearest Neighbors, Support Vector Machines, feed forward
artificial neural networks, as well as ensemble models. At this time, we
limited our forecast to exclusively use solar onset parameters, to ensure
extended warning times. We discuss the main challenges of this task, namely the
extreme imbalance between the number of geoeffective and ineffective events in
our dataset, along with their numerous similarities and the limited number of
available variables. We show that even in such conditions, adequate hit rates
can be achieved with these models.
