*3.1. Data Splitting*

Datasets were randomly split into training and testing sets. This was applied to avoid using entire datasets for training stages, as this could lead to overfitting. The proportion of data used in the training stage was fixed to 70%. This proportion of data splitting is commonly used, along with similar splitting ratios such as 80% or 67%. There is no optimal splitting ratio in machine learning applications (in general), and the decision is based on the original datasets. In this work, the datasets for both working fluids were large enough to perform the selected data split, leading to the values presented in Table 4.


**Table 4.** Split of data samples for ethanol and FC-722.
