• G. Jahnavi
• Dr. D. Nagaraju

The Sloan Digital Sky Survey (SDSS) has transformed astronomy with its extensive dataset of celestial objects, including galaxies, stars, and quasars. This project introduces a predictive classification model based on the SDSS RD14 dataset, employing random forest classification to categorize these objects accurately. Utilizing photometric measurements, spatial coordinates, and redshift values from the SDSS RD14 dataset, we trained a robust random forest classifier. Extensive experimentation and hyperparameter tuning optimized the classifier's performance, achieving high accuracy. This model aids astronomers in understanding the universe's formation, evolution, and dynamics, while also identifying rare or unusual phenomena. Our project demonstrates a reliable, efficient method for celestial object classification using machine learning, enhancing the analysis and interpretation of astronomical data.

Sloan Digital Sky Survey, SDSS RD14, random forest classification, celestial object classification, galaxies, stars, quasars, machine learning, astrophysics, cosmology.

" PREDICTIVE CLASSIFICATION OF GALAXIES, STARS, AND QUASARS USING SDSS RD14", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.550-553, June-2024, Available :http://www.jetir.org/papers/JETIRGL06091.pdf

" PREDICTIVE CLASSIFICATION OF GALAXIES, STARS, AND QUASARS USING SDSS RD14", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp550-553, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06091.pdf