XGBoost were employed for the model building to predict the EtOH conversion and product yields. The dataset was split into 90% for training and 10% for testing. Hyperparameter tuning during the training was carried out by gridsearchCV with repeated 10-fold. In the context of prediction as well as obtaining feature importance, XGBoost regression was found to be the best choice in the current case based on its cross-validation (CV) score after the hyperparameter tuning. To gain insights into the importance of individual elements in determining the EtOH conversion and product yields, a Shapley analysis was applied to the regression model. These ML tasks were conducted using the scikit-learn package in Python. On the other hand, Gephi software was used to map the importance of elements in ETB and the composition-performance relationship of the catalysts.