## 3 Methodologies
### Data Preparation  
The preparation of Al\({}_{2}\)O\({}_{3}\) nanofluids involves several steps to ensure a stable and homogeneous suspension of nanoparticles in the EG base fluid. Al\({}_{2}\)O\({}_{3}\) nanoparticles are typically synthesized using methods such as sol-gel [48] and hydrothermal [49] processes. Once synthesized, these nanoparticles are dispersed into the EG base fluid. To prevent the nanoparticles from clumping together, surfactants or dispersants such as sodium dodecyl sulfate (SDS) or polyvinylpyrrolidone (PVP) are added [50], as shown in Figure 2. Finally, ultrasonication is applied to break down any clusters of nanoparticles and ensure their even distribution throughout the fluid [51]. Stability tests, including visual inspection and zeta potential measurements [52], are often conducted to confirm the suspension's stability over time. Thermal conductivity is then measured using methods such as the transient hotwire method, which involves immersing a thin wire in the nanofluid, passing an electrical current through it, and monitoring the rate of temperature rise [53]. Alternatively, the steady-state parallel-plate method measures the temperature difference and heat flux through the nanofluid between two plates with a known thermal gradient. This study compiled 94 data points from four experimental studies [43; 44; 45; 46], representing nearly all the available research on the thermal conductivity of EG-Al\({}_{2}\)O\({}_{3}\) nanofluids, presented in tabular form. These sources were carefully chosen for their consistent research direction and detailed examination of key variables such as particle size, volume fraction, and temperature. This selective approach was taken not only to ensure the coherence and high quality of the data within the database but also to maintain data accuracy and rigor. Non-tabular data sources were deliberately excluded to preserve the precision and integrity of the dataset. The descriptive statistics of this dataset, including measures such as mean, median, standard deviation (St. D), and range, are detailed in Table 1.  
To enhance the utility of the EG-Al2O3 nanofluid dataset for ML applications, it is essential to perform feature selection to identify the most relevant features. By selecting the optimal features, the dataset can be streamlined, resulting in reducing overfitting, improving model accuracy, and decreasing computational costs. The review showed that the Boruta algorithm was one of the most widely used methods to select the reliable features [54]. The operation of the Boruta algorithm can be divided into several steps: (1) Generating a shadow feature dataset by duplication and randomization. During duplication, each original feature is duplicated to create a shadow feature. This means for each original feature, there is a shadow feature. The shadow feature dataset has thus the same dimension as the original dataset. The values in each shadow feature are randomly permuted. This random permutation breaks any association between the shadow feature and the target variable. Essentially, the shadow feature is a shuffled version of the original feature, ensuring it has no predictive power regarding the target variable. The purpose of creating these shadow features is to provide a baseline or reference for evaluating the importance of the original features. If an original feature has higher standard score (Z-Score) than the maximum Z-Score of its shadow feature, this feature is considered as an important factor influencing the target variable. (2) Calculating importance scores (Z-scores) of features belong to the shadow dataset using a prediction model. In this study, the default RF model was used to obtain the maximum feature score. (3) Then, the importance score of each feature within the original dataset is recalculated and compared with the maximum feature score obtained earlier.