Predicting Redshift in Seyfert Galaxies Using Machine Learning

Photometric redshift estimation is a key requirement for modern large-area surveys, where spectroscopic measurements are observationally prohibitive. Seyfert II galaxies provide a particularly challenging test case due to the combined effects of nuclear activity, host-galaxy emission, and dust attenuation. In this work, we develop a machine learning approach for photometric redshift estimation using a spectroscopically defined sample of 23,797 Seyfert II galaxies selected from SDSS and cross-matched with WISE. We construct feature sets based on optical, mid-infrared (MIR), and combined optical+MIR broadband colours, and evaluate their performance using different regression models. The best results are obtained with the combined Optical+MIR features and a Random Forest model, reaching NMAD = 0.0188, R 2 = 0.9561, and an outlier fraction of {\eta} = 0.294%. The results show that the accuracy is primarily driven by the physical information content of the features and the homogeneity of the sample. The method provides a robust and scalable solution for photometric redshift estimation in upcoming wide-field surveys.