Can Machine Learning provide better classifications for political parties than traditional approaches?

In my last article here at Data Social we saw that it is very tricky to cluster European political parties based on the classic (and outdated?) Indeed, I found that although parties may belong to the same family they have different positions on important policies. Let's invoke together the power of machine learning and develop a better classification. In this article, using Principal Components Analysis (PCA), an unsupervised method, I find out where political parties really do belong in respect to each other's positions. Note: Check out this very cool article to learn more about what supervised and unsupervised methods are in the context of machine learning!