Machine learning models assume that data is drawn from a stationary distribution. However, in practice, challenges are imposed on models that need to make sense of fast-evolving data streams, where the content of data is changing and evolving over time. This change between the distributions of training data seen so-far and the distribution of newly coming data is called concept drift. It is of