A data-set of 2251 songs released between 2020/01/15 and 2020/03/24 is analyzed to formulate short and long term predictions of popularity using machine learning techniques. These public data were collected from Spotify, the largest subscription music streaming service with 96 million subscribers and 170 million users overall, and from YouTube, one of the most popular online video-sharing platforms. The first part of the work has a purely predictive character and makes extensive use of machine learning, providing results of various kinds (classification, regression, etc.). In particular, we detect the features which are the most informative for characterizing the Spotify Popularity, an integer number in a range between 0 and 100 indicating the success of the track inside the streaming-platform.