The reaction used here was suitable due to the reliable curve shape and profile of the reaction. Mild changes between runs, such as the rate of hydrazine addition, did not have a noticeable impact on prediction. However, large changes may; concentration or temperature could affect the rate of reaction. Future work will investigate how more significant changes affect the accuracy of the model. To assess further the potential and utility of using machine learning to predict product, more examples would need to be examined. This methodology could enable AI augmentation of reaction monitoring to assist synthetic chemists and facilitate a greater understanding of the reaction by identification of correlations between sensor features and reaction outcomes. Insights into the chemistry being performed could also be developed, for example, the correlation between cumulative green and product formation in this work, providing a quantitative description of the colour change in the reaction.