Model tree is a useful and convenient method for predictive analytics in data streams. Often, this issue is solved by pre-processing techniques applied prior to the training phase of the model. In this article, we propose a new method that estimates and adjusts missing values before the model tree training. A prototype was developed and tested on several data streams.