The objective of this study was to use and compare multi- ple classifying models that can be used for classifying as- tronomical data and was tested upon data obtained from Sloan Digital Sky Survey: Data Release-16. Various Classi- fying models have been trained and tested by dividing the data into two parts- 80 per cent of the data was then used for training purposes and 20 per cent for testing. In order to achieve the task of classifying the tabular data consist- ing of spectroscopic and photometric parameters effectively, the study was not just limited to usage of indiviual models. Stacking : the combination of multiple Classifying mod- els has also been implemented. Multiple stacking models were created for the same .Stacking models have on mul- tiple occasions proven to have higher evaluation metrics , thus having significantly better performance than any individual classifier, proving that stacking is a better choice to classify data. certain Individual models such as Bagging , Hard Voting etc have been found to have comparable performance to that of Stacked Models. Box plots for indiviual classes were also plotted to compare and determine the models that were capable in identifying a single class of stellar objects. The models from this study could be used as a reliable classification tool for a wide variety of astronomical purposes to accelerate the expansion of the sample sizes of stars, galaxies, and quasars.

Keywords : Classification, Classifiers, Stars, Galaxies, Quasars.