The sensor data from the reaction data was averaged across the runs. From these averages the Pearson correlation coefficients, r, between the features and product formation were calculated (Figure ). The coefficients between a feature and product formation were used as a starting point to determine the utility of a feature in modelling. To contextualise the coefficients, most cumulative features were expected to show some correlation to product, as if the feature is always positive or negative, both product and the cumulative feature will continually increase in value until product formation has stopped. The features most correlated with product formation are the cumulative colour features, particularly green and red, and a more negative correlation for blue. From the average values across the runs of these three features for the first five hours of the reaction, it can be observed that green and red steeply decrease over the course of a reaction and blue shows a more shallow decrease (Figure ). A hypothesis for this is the reaction tends to black (where RGB would be 0,0,0) as the palladium catalyst is lost from the reaction and is oxidised to palladium (0) black from the activate palladium (II) in the palladium acetate and the palladium ligand complex. This fits with the chemists' observations of the initial mixture being described as cream coloured but turning black or darker brown later. The runs were kept whole during the 9:1 train/test split to prevent data leakage and more accurately replicate a real-life scenario where the model would not be exposed to any data from the unseen test run. Initially, a linear regression model was developed on the cumulative green values from nine runs to predict the product for the tenth run. Assessing the model using cross-fold validation provides a robust estimate, in data-limited situations, of the accuracy of the model and its ability to generalise by testing its predictions on all the datapoints. Models were evaluated by measuring the mean absolute error (MAE) of the predictions. All product yields are as percentage points Thus, despite MAE being reported with units of % it is an absolute and not a relative error.