## 5 Discussion and Conclusions
Given the results, the XGBoost and GBM models stand out as the strongest models in terms of both error metrics and explanatory power, and SVR stands out as having low absolute errors, while the BR and HGBR models perform more poorly in terms of both error levels and reliability. These findings clearly indicate that ensemble methods should be preferred in applications requiring high accuracy. Finally, the grid search method is used for hyperparameter optimization in our ML models. According to the results, the SVR, XGBoost, and GBM models stand out in terms of overall error and explanatory performance, whereas the BR model is weak in both absolute and relative error rates and is therefore considered to be a model that should not be preferred in this data set. Considering both performance and computational cost in model selection, it is recommended that balanced models such as SVR and GBM should be prioritized. In conclusion, despite the different hyperparameter optimization strategies applied in this study, certain machine learning models generally exhibited consistent and superior performance. In particular, the SVR, GBM, and XGBoost models stand out as models with both high explanatory power and low error rates in such metrics as MSE, RMSE, and MAE. This finding indicates that these models should be prioritized in regression problems requiring high accuracy. On the other hand, the BR and (partially) HGBR models produced poor results in both absolute and relative error metrics; therefore, it is understood that success is not guaranteed even if an ensemble method is applied in such data structures. In this context, the interaction of each model with the data set should be contextualized. Consequently, the multi-stage evaluation reveals that ensemble methods, especially GBM and XGBoost, together with kernel-based methods such as SVR, which can model complex nonlinear structures, offer more stable performances, compared to other models in terms of both generalization ability and fault tolerance. Moreover, considering the sensitivity of these models to hyperparameter optimization, it is recommended to prefer balanced models such as SVR and GBM when considering the trade-off between performance and computational cost. The data set used in this study includes specific characteristics that are thought to affect students' academic achievement. In future studies, including different variables such as student motivation, teacher feedback, learning environment, etc., in the model might enhance performance. In addition, the contribution of different machine learning techniques, such as deep learning and support vector machines in the prediction of student achievement can also be examined. In addition, only the genetic algorithm and grid search were used in the optimization process. In future studies, other hyperparameter optimization methods, such as Bayesian Optimization or Random Search, can be evaluated and performance comparisons made. Finally, the generalizability of the proposed models can be tested by conducting studies on data sets from different training systems. Conceptualization, B.D. and T.H. ; methodology, B.D. ; software, T.H. ; validation, B.D. and T.H. ; formal analysis, B.D. ; investigation, T.H. ; resources, B.D. and T.H. ; data curation, T.H. ; writing--original draft preparation, T.H. ; writing--review and editing, B.D. ; visualization, T.H. ; supervision, B.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study are openly available in [[[https://www.kaggle.com/datasets/tusharika802/student-performance-data-csv](https://www.kaggle.com/datasets/tusharika802/student-performance-data-csv)]([https://www.kaggle.com/datasets/tusharika802/student-performance-data-csv](https://www.kaggle.com/datasets/tusharika802/student-performance-data-csv)), accessed on 19 May 2025]. The authors would like to thank [PERSON] for his guidance regarding this study.