AstroML-Datasets: Introductory Machine Learning Tasks Across Physics and Astronomy

AstroML-Dataset is an open educational initiative designed to enable introductory-level machine learning tasks across physics and astronomy domains.
It provides curated, ML-ready datasets that enable learners and researchers to explore real astronomical data through accessible problems, such as galaxy redshift prediction, photometric classification, and source separation, using data from surveys like SDSS and HSC.

Each dataset includes clearly defined input features and target variables, supporting both teaching and research use in astroinformatics, data science, and machine learning.

For detailed documentation, data structure, and example notebooks, please visit the project repository:
🔗 https://github.com/srinadh99/AstroML-Datasets/tree/main