## 2 Related Work  
Previous studies used conventional machine learning (ML) techniques extensively to predict the potability of drinking water from the different physicochemical properties. Simple models such as logistic regression and decision trees make basic baselines because of their interpretability and computational efficiency [9]. Such is the case with [PERSON] and [PERSON] (2021) [9], who found that decision trees are appropriate classifiers for classification tasks on water quality. According to [PERSON] et al. (2023) [10], several machine learning models have been evaluated for classifying water quality, and they found that support vector machines (SVMs) demonstrated strong performance across all metrics. However, the XGBoost model achieved the highest overall performance with respect to accuracy, with 100% precision and an F1-score of 0.99 and specificity of 1.0, rendering it far better in predicting water quality index in this study. As the demand for greater predictive performance increased, ensemble-based algorithms such as random forest and XGBoost gained prominence due to their robustness and ability to capture intricate feature interactions. Random forest handles high-dimensional data effectively by averaging multiple decision trees, which also enhances its interpretability [11]. XGBoost, as developed by [PERSON] and [PERSON] (2016) [12], went a step further by employing the aspect of regularization and efficient handling of sparse data. Several studies highlight XGBoost's superiority over traditional models, such as logistic regression and SVM, for predicting water potability [13,14]. [PERSON] et al. (2023) [2], for example, demonstrated the model's strong performance even on complex, high-dimensional, and imbalanced datasets. However, tree-based approaches depend heavily on manual feature engineering and often perform poorly when dealing with multi-modal data or dynamic changes in water quality patterns over time. [PERSON] et al. (2024) [15] specified these limitations, adding that such traditional ensemble models, such as gradient-boosted decision trees (GBDTs), were unable to match hybrid attention-based architectures. In contrast, new evidence by [PERSON] et al. (2022) [16] argues that tree-based models may still outperform deep learning models in many tabular data settings owing to their aforementioned strong inductive biases, stability under low data regimes, and lower sensitivity to hyperparameter tuning. All these constraints incited a lot of empathy for deep learning models, with a primary focus on those trained in a structurally data-sensitive environment. For example, conventional neural networks experience some restrictions in tabular environments, such as the weakness of inter-feature dependence and the issue of space structure. Thus, specialized architectures with an inductive bias for tabular learning have been created by researchers. Among these, for example, is TabNet, introduced by [PERSON] and [PERSON] (2021) [5], which features a sequential attention-based mechanism wherein at every decision step, it dynamically chooses the most relevant features, in turn improving the accuracy and interpretability of the model. This was followed by TabTransformer by [PERSON] et al. (2020) [6]; this extended the transformer encoder format by embedding the categorical features to perform multi-head self-attention, modeling contextual relationships among the 'n' inputs. [PERSON] et al. (2021) [4] presented the FITransformer. It is a much cleaner design; hence, each tabular feature is a token that can be processed by transformer layers successfully, adopting NLP mechanisms to structured data tasks. Having accomplished all this, we cannot forget that they have shown good results compared to gradient-boosting algorithms under different types of noise and incomplete conditions. Going further, [PERSON] et al. (2021) [7] presented a SAINT that incorporates intra-row and inter-row attentions.