## 5 Conclusions  
The study focused on the classification of the following astronomical objects: symbiotic stars, planetary nebulae, and red giants. It was observed that red giants were more numerous and represented a larger proportion compared to the other classes. This imbalance caused difficulties in classification, as the models tended to confuse part of the minority classes (symbiotic stars and planetary nebulae) with the majority class (red giants).  
It was found that the Random Forest and ANN algorithms showed satisfactory results, with accuracy values of 0.9467 and 0.9532, respectively, for the balanced dataset. On the other hand, for the imbalanced dataset, Random Forest achieved an accuracy of 0.9031, while the ANN achieved an accuracy of 0.9312. Therefore, these algorithms demonstrate greater effectiveness on imbalanced datasets compared to other approaches. ANN proved to be highly effective when working with imbalanced datasets due to their ability to learn complex patterns and adapt to different class distributions.  
As a result of our research, two machine learning models were successfully trained to classify peculiar stars with high accuracy. These models have been validated using diverse datasets, demonstrating their effectiveness in real-world scenarios. Additionally, these models can be employed to classify stars that are difficult to categorize, including those without prior classification or those considered potential candidates for peculiar stars. This functionality enhances their effectiveness in identifying and assessing previously ambiguous or uncertain stellar objects.  
These classification models present a valuable and highly useful tool for astronomers, providing effective support in the classification of peculiar stars. Their application significantly contributes to a better understanding of the stellar life cycle.  
Data Curation, O.J.P.C.; Formal analysis, O.J.P.C.; Conceptualization, C.A.M.P., S.G.N.J., L.J.C.E. and M.M.O.; Methodology, C.A.M.P., S.G.N.J. and L.J.C.E.; Software, O.J.P.C.; Validation, S.G.N.J. and O.J.P.C.; Visualization, O.J.P.C.; Investigation, O.J.P.C., C.A.M.P. and S.G.N.J.; Resources, C.A.M.P. and M.M.O.; Writing--original draft, O.J.P.C.; Writing--review & editing, C.A.M.P., S.G.N.J., L.J.C.E. and M.M.O.; Supervision, C.A.M.P., S.G.N.J., L.J.C.E. and M.M.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Informed Consent Statement: Not applicable.  
**Data Availability Statement:** All original contributions and findings presented in this study are documented within the article. The data supporting the results, along with the tests conducted and the corresponding code, are available in the publicly accessible GitHub repository: [[https://github.com/opcruz/gaiaDR3](https://github.com/opcruz/gaiaDR3) ML]([https://github.com/opcruz/gaiaDR3](https://github.com/opcruz/gaiaDR3) ML) (accessed on 29 July 2024). Should additional details or clarifications be required, further inquiries can be directed to the corresponding author.  
We would like to acknowledge the use of artificial intelligence, specifically ChatGPT 3.5, in the preparation of this article. AI was utilized to assist us in improving the clarity and readability of certain paragraphs, ensuring the language was more accessible. Additionally, AI supported us in translating specific sections into English to maintain accuracy. However, we would like to emphasize that AI was not used to generate any new information or content; it solely served as a tool to refine the presentation of the material we had already developed. All original research, analysis, and conclusions presented in this article are the work of the authors.  
The authors declare no conflict of interest.