Astronomy is the study of everything in the universe beyond Earth's atmosphere. Astronomers use stellar classification to classify stars based on spectral characteristics. Spectral characteristics help astronomers extract more information about the stars - elements, temperature, density, and magnetic field. The classification scheme of galaxies, quasars, and stars is one of the most fundamental in astronomy. This dataset aims to classify stars, galaxies, and quasars (luminous supermassive black holes) based on their spectral characteristics.
The data consists of 100,000 observations of space taken by the SDSS (Sloan Digital Sky Survey). Every observation is described by 17 feature columns and 1 class column which identifies it to be either a star, galaxy, or quasar.
Dataset reference: fedesoriano. (January 2022). Stellar Classification Dataset - SDSS17. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/stellar-classification-dataset-sdss17.
My approach would be to do a thorough analysis of the data, apply classification algorithms, and compare the performance of the algorithms followed by final predictions.
Application URL: https://huggingface.co/spaces/mohd-saifuddin/Stellar-Classification
Demo URL: https://youtu.be/KhxRSTC9lEI
Detailed blog URL: https://medium.com/@acesaif/stellar-classification-a-machine-learning-approach-5e23eb5cadb1