This work aims at understanding today's tracking ecosystem and using machine learning tools to automatically assess the risk connected to web trackers and assigning to websites a risk indicator score. The web is a highly dynamic ecosystem and each user browses dozens of websites everyday, encountering a large number of trackers. Trackers can be more or less malicious, collecting different kinds and different amounts of data in order to build user profiles, and users are often unaware of their presence. Assigning a risk indicator to websites would make users better aware of the whole web ecosystem and would improve the user's experience as a first step toward a better protection of their data.