Machine learning for yield prediction using the reaction scheme and conditions is an approach which has been implemented by others. This research has been applied to palladium catalysed reactions, including Suzuki and Buchwald-Hartwig cross-coupling reactions. However, the methodology struggled when applied to patent data which had too much inconsistency for accurate yield prediction. Two issues with chemical reaction data that make predictive tasks challenging are the sparsity of the data within chemical space, and a lack of reported failed experiments. This approach is complementary to the one we developed in this research as both have different aims. Our strategy aims to tackle the variability in yield that can be seen for a specific reaction. This can be a non-trivial problem for reactions that suffer from poor reproducibility. The method we have developed treats each repeat of a reaction as a single instance that can be distinguished from other instances by differences in the time-series data. Our hypothesis is that patterns within these differences can be exploited by a suitably trained machine learning model, thereby allowing accurate predictions of product formation.