Another example use-case would be for predicting when product formation has stagnated in a reaction at a lower conversion than expected. The goal in this scenario would be to detect a failed reaction at an earlier stage, and the live data in combination with predictions would give an indication of this to a chemist. This can be rationalised as, if the colour change occurs to indicate the catalyst has been consumed, no more product will be formed. Reactions 16 and 17 were both low yielding and the chemists performing these reactions observed a potential exposure of the hydrazine to atmospheric moisture. An 8:2 train: test split was used and reactions 17 and 25 were used as the test runs. The rationale for using this split is twofold. The first and primary aim is to demonstrate that if a failed reaction, 16, is included in the training set, the model can identify future failed runs, such as 17, early. The second aim is keep run 25 in the test set to ensure the model is still capable of generalising and accurately predicting successful runs.