Despite of significant progress in reaction selectivity and yield prediction, generalized and easy-to-use prediction tools are not available for experimental chemists. To tackle these problems, we developed Reaxtica, a novel knowledge-guided machine learning platform for reaction selectivity and yield predictions. Reaxtica takes on-the-fly physics-based electronic descriptors and structure-based steric descriptors as inputs. By considering the chemical knowledge for specific reaction tasks, it achieved comparable or even state-of-the-art performance for four different reaction tasks. The datasets we used include reactions from HTE, patents (USPTO ), and journal articles (Reaxys). Moreover, Reaxtica could be adapted to other reaction-related tasks as all descriptors it uses could be computed within seconds. As described and summarized by Pflüger and Glorius, accuracy, generality and practicability are three vital factors of machine learning models to make them useful in the everyday laboratory. We demonstrated that Reaxtica provides a practical toolbox for synthetic chemists.