Upon completion of dataset collection from experimental outcomes and literature data mining, we amalgamated the data to create a balanced dataset suitable for model training (Figure ). The literature data, biased towards successful substrates, complemented the failure data points generated from our screening platform. This combination resulted in a dataset comprising 582 substrates, with 271 oxidizable (46.6%) and 311 non-oxidizable (53.4%) towards electrochemical C-H oxidation reactions (Figure ). The dataset included 7,720 carbon atoms, 431 of which were oxidized during the transformations (a complete list is available in the Supporting Information). Our objective was to develop two classes of predictive models: (i) the reactivity prediction model to classify substrates as reactive or non-reactive, and (ii) the selectivity prediction model to classify each carbon atom within a molecule as oxidized or unchanged. The former allows for rapid screening of chemical catalogs, while the latter helps chemists identify which sites are likely to undergo oxidation. After optimizing the respective hyperparameters, each model's performance was rigorously tested using accuracy and Area Under the Curve (AUC) metrics. All models demonstrated high performance, with accuracies over 91.7% and AUC values of 97.2%. These results align well with our previous studies using ML to predict electrochemical reaction outcomes. Additionally, we explored the inclusion of density functional theory (DFT) descriptors in the Chemprop model (Section S4.1 of the Supporting Information), which provided richer quantum mechanical information and slightly enhanced model performance (Table ). Besides, Chemprop's interpretability features offered a transparent view of which molecular substructures or features were driving the predictions, guiding the design of new experiments by indicating impactful molecular modifications (Section S4.1 of the Supporting Information). This interpretability advantage led us to select Chemprop for subsequent catalog screenings to make rapid reactivity prediction on over 500,000 commercially available compounds (Figure ).