## 3 Materials and Methods
### Modelling Process
#### 3.2.1 Data Preparation  
Data Preprocessing and Feature Engineering  
In the modeling process, feature engineering is the process of representing the data appropriately. It is one of the key components as it considerably influences the performance of a model. No AI algorithm is able to predict data for which it has no appropriate information. Feature engineering is often performed by the domain expert, and most of the required feature engineering activities have already been carried during the collection of the data from previously published works. Here, after careful examination of the distribution of each feature, three features (pozzolans, fly ash, and curing age) which do not have representative enough data are excluded from the database. For instance, fly ash and natural pozzolans are utilized only in 36 and 48 cases, respectively. This is a very small number of cases compared with other types of stabilizers. For comparison, the number of observations in the case of cement and lime is 153 and 250, respectively. Thus, retaining  
Figure 1: The development process of OMC, MDD, and UCS prediction models. pozzolans and fly ash in the database causes data imbalance, which ultimately affects the performance of the model. Similarly, there are unconfined compressive strength tests performed at the age of 1, 7, 14, 28, 32, 64, and 90 days in the database, but except UCS carried out at the age of 28 days, the other age groups represent only between 1% and 15%. Thus, unconfined compressive strength tests carried out only at the age of 28 days are considered. Finally, nine features with 190 instances are selected. The data are presented in Table S1 in Supplementary Materials, and description of the features is presented in Table 1.  
The soils are stabilized by cement and/or lime. Though there are different types of cement and lime, the literature where the data gathered from does not explicitly mention their types. All the stabilized soils were classified based on the Unified Soil Classification System (USCS). A total of five kinds of soil types are employed in the dataset. These are clay of low plasticity (CL), clay of high plasticity (CH), silt (ML), silt of high plasticity (MH), and clayy silt (CL-ML). The soil types of CL and ML represent 50% and 25%, respectively. The remaining 25% are represented by CH, CL-ML, and MH. The unconfined compressive strength of the soils considered in this dataset was those taken at the age of 28 days and their values range from 55.31-5400 \(\mathrm{kN/m^{2}}\). Descriptive statistics of the data are illustrated in Table 2.  
Feature Selection and Scaling  
Feature selection is the process of selecting the most relevant features from the data. This is because some features may be highly correlated and thus redundant to a certain degree or even they may be irrelevant. Generally, feature selection methods are categorized into three groups: filter, wrapper, and embedded [31; 32]. The filter method is independent of the learning algorithms and relies only on the inherent nature of the data. The wrapper \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Category** & **No.** & **Variable Subcategory** & **Range** & **Unit** & **Data Type** \\ \hline \multirow{4}{*}{Amended soils} & 1 & Soil & 70–100 & [\% by mass] & Numerical \\  & 2 & Cement & 0–30 & [\% by mass] & Numerical \\  & 3 & Lime & 0–30 & [\% by mass] & Numerical \\  & 4 & Liquid limit & 18–102 & [\%] & Numerical \\ \multirow{4}{*}{Attereberg limits} & 5 & Plastic limit & 12–58.24 & [\%] & Numerical \\  & 6 & Plasticity index & 0–70 & [\%] & Numerical \\  & & CH & & & \\  & & CL & & & \\ Soil & 7 & USCS & CL-ML & – & – & Categorical \\  & & MH & & & \\ Compaction & 8 & Optimum moisture content & 5.40–36.80 & [\%] & Numerical \\ properties & 9 & Maximum dry density & 1200–2210 & [kg/m3] & Numerical \\ Strength & 10 & Unified comprehensive strength & 55.31–5400 & [kN/m2] & Numerical \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the features considered in the dataset.