Star Galaxy Quasar Classification using MachineLearning

Aim is to create a system that can precisely and efficiently classify stars and galaxies, which can at that point be utilized for different applications in astronomy and astrophysics. The objective of this project is to create a progressed framework for categorizing stars, galaxies, and quasars, while also evaluating various classification models to identify the most effective one. My hypothesis centered on the possibility of preparing a machine learning model to precisely classify stars, quasars and galaxies utilizing the dataset given by the Sloan Digital Sky Survey (SDSS) [25]. We aimed to accomplish ideal execution with balanced data sets. We have utilized machine learning calculation like Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT). The proposed machine learning show can help astronomers in precisely classifying celestial bodies in the sky, possibly contributing to headways in our understanding of the universe. The proposed machine learning show can help astronomers in precisely classifying celestial bodies in the sky, possibly contributing to headways in our understanding of the universe. This research social suggestions have a huge effect on the logical community, not fair in space science but also in other zones that require to classify things accurately.