## 2 Materials and Methods  
### Database  
The database (including 352 sets of data) used in this study includes a detailed collection of geotechnical data focused on the properties of clay soils related to the prediction of the compression index (\(C_{c}\)). The key parameters in the database are the initial void ratio (\(e_{0}\)), the liquid limit (LL), the plasticity index (PI), and the water content (\(w\)). This database was collected from [PERSON] et al. [41]; [PERSON] et al. [42]; [PERSON] et al. [43]; [PERSON] and [PERSON] [44]; [PERSON] and [PERSON] [45]; and [PERSON] et al. [46]. These parameters were carefully chosen for their critical role in the understanding and prediction of soil compressibility. The liquid limit reflects the clay mineralogy and water-holding capacity and directly impacts compressibility during consolidation. The plasticity index indicates the range of moisture content where the soil remains plastic and correlates with its deformation potential under load. The initial void ratio measures soil structure and density. This parameter plays a fundamental role in determining how much soil will compress. Studies by experts like [PERSON] [2], [PERSON] and [PERSON] [1], and others have confirmed the importance of these parameters in empirical models for estimating \(C_{c}\). Beyond theory, they are practical for field investigations as they can be measured using standard laboratory methods. Together, these parameters provide a great and strong foundation for integrating established soil mechanics principles with advanced predictive modeling techniques. In this study, an 80/20 train-test split was used, selected through a random sampling process with a fixed seed to ensure reproducibility. This ratio was chosen based on common machine learning practices, balancing the need for sufficient training data with reliable testing performance. Although no formal optimization algorithm was applied, we experimented with different splits (e.g., 70/30 and 90/10) to assess their impact on model performance. The 80/20 split consistently provided stable and accurate results. Addition  
To improve the quality of the dataset and enhance the robustness of the predictive models, outlier detection and removal was conducted using the Boxplot method, a widely used statistical technique based on the interquartile range (IQR). This method helps to identify data points that deviate significantly from the central distribution, which could otherwise negatively impact model performance. The Boxplot method identifies outliers using the following criteria:  
\[\text{Lower Bound}=Q_{1}-1.5\times\text{IQR} \tag{4}\]  
\[\text{Upper Bound}=Q_{3}+1.5\times\text{IQR} \tag{5}\]  
where, \(Q_{1}\) = the first quartile (25 th percentile), \(Q_{3}\) = the third quartile (75 th percentile), and IQR = the interquartile range (\(Q_{3}-Q_{1}\))  
Data points falling below the lower bound or above the upper bound were considered outliers. This method was applied to all continuous input features, including the liquid limit (LL), the plasticity index (PI), the initial void ratio (\(e_{0}\)), the natural moisture content (\(w\)), and the fines content. The outlier removal procedure involved four key steps to ensure data consistency and improve model performance. First, boxplots were generated for each feature to visualize data distribution and detect extreme values. Next, outliers were identified using the interquartile range (IQR) thresholds, which helped in pinpointing data points that deviated significantly from the norm. These identified outliers were then removed from the dataset to minimize the risk of skewed model predictions. Finally, the dataset was re-evaluated to confirm the absence of any remaining influential outliers that could bias the models.