As shown in the results, the neural network model can perform well, even surpassing thermodynamic models for well-known systems such as biodiesel. However, it's performance decreases when the database lacks sufficient information about the aimed system. A way to overcome this issue would be to increase the training database with reliable data, which can help improve the balance of the dataset. This would result in higher accuracy, as having more experimental data for the systems of interest would reduce the imbalance of the dataset, preventing situations where data shuffling and splitting favor some systems while harming others.