The predictions made based on the model presented herein may not be able give a 100% accurate representation of small effects such as ester side chain length, yet they provide very reasonable data that can be used for future work. Accuracies are within standard error limits of experimental determinations, and relative effects are accurately predicted. No prediction of kp values with the same accuracy by any other theoretical method has to date been provided. With increasing data coming available from experiments, and potentially with further refinement of feature calculations, we hope the accuracy of our method can be further refined in future work. The match of data currently seen for monomers with available experimental data on boiling points should be extendable to the full dataset. Generally, it is remarkable that only very little experimental data input is required in all predictions. We suggest that radical stability should be probed experimentally and theoretically in order to provide more valuable data that can refine the machine learning approach in this work. We envisage that in future a full predictivity with even higher accuracy will become available, allowing researchers to simply dial in a monomer and being provided with an accurate estimate of kp. Further the presented methodology also allows to refine data for monomers with existing experimental data, since the LOOCV method allows for averaging over the entire monomer space compared to 'only' fitting data for each single monomer by itself.