While the setup of such multiscale methods is a challenge in itself, a vast amount of data is generated from the simulations. As it is not feasible to interpret them by observation only, an automatic way of processing reaction trajectories is needed. Machine learning (ML) approaches can be used to evaluate the data and extract condensed results, which contain insight into the reaction coordinate. While neural network approaches have garnered a lot of attention recently, due to their black box nature, they are not very well suited for data analysis approaches where understanding of the process is required, which makes them very difficult to evaluate. Instead, interpretable machine learning techniques, such as Decision Trees, Random Forests or Logistic Regression, offer good performance in extracting relevant information from large datasets and presenting them in easily understandable ways. In addition, dimensionality reduction techniques-Principal Component Analysis (PCA) or time-lagged Independent Component Analysis (tICA) effectively detect combined coordinates from the trajectories, revealing the key motions of a system. Yet these dimensionality reduction techniques have almost exclusively been applied to biomolecules with few exceptions. A combination of aforementioned methods offers great promise to detect a cumulative reaction coordinate from a multitude of independent trajectories, providing chemical insight into the mechanism and reactivity of the system. To the best of our knowledge these combined methods have not been applied to study reaction mechanisms in explicit solvent, let alone large supramolecular transition-metal catalysts.