At the onset of this study, the goal was to assess whether ML could guide the selection of compounds suitable for electrochemical C(sp )-H oxidation. To accomplish this, we required a diverse training set composed of both substrates amenable to electrochemical oxidation and those that are not-modeling requires both positive and negative training data sets for better accuracy in prediction. Most reactions available in the literature predominantly include successful examples of reactive and high-yield substrates, leaving a gap in data on unsuccessful conditions, which are equally critical for training robust predictive models.