Each reaction was described using three types of descriptors: Morgan fingerprints, Mordred descriptors, and physical-chemical descriptors calculated employing DFT. We employed a range of regressors to identify the most suitable model, including Bayesian, DT, kNN, SVM, GP, and LASSOCV. The models were evaluated based on R 2 and RMSE values. Initially, for model training, we performed cross-validation on the entire dataset. Subsequently, to assess model robustness, we reserved 10% of the data points (12 reactions) with the lowest similarity for testing. The similarity was assessed using the Tanimoto index, 64 a widely used metric for comparing structural similarity between molecules. When using Notebook 5 and Morgan fingerprints, the regression models perform well for both training and test set. LASSOCV shows the highest R 2 and lowest RMSE for the test set (R 2 = 0.80 and RMSE = 1.34 kJ/mol, Table ). In this Notebook, we explain how the user can reduce the feature space of Morgan fingerprints by removing instances that are 0 for all datapoints, and thus reducing the noise. Additionally, the notebook offers a way to perform ensemble predictions, aggregating predictions from individual regressors on the test set. While the code provided can be applied in various notebooks, we exclusively use it here to mitigate redundancy. Our methodology involves aggregating predictions from individual regressors on the test set, computing their mean values, and subsequently assessing performance metrics by contrasting these means with the corresponding actual values. We emphasize the importance of discerning and excluding underperforming algorithms, as their predictions may not contribute meaningfully to the overall model.