See code for implementation
See code for implementation
See code for implementation More details shown below: Results:
- 100% = Random Forest Classifier
- 94% = Logistic Regression Classifier
- 92% = SVM Classifier
Kaggle Description: Dataset found on Kaggle: https://www.kaggle.com/datasets/deepu1109/star-dataset
Features: Absolute Temperature (in K) Relative Luminosity (L/Lo) Relative Radius (R/Ro) Absolute Magnitude (Mv) Star Color (white,Red,Blue,Yellow,yellow-orange etc) Spectral Class (O,B,A,F,G,K,,M) Star Type (Red Dwarf, Brown Dwarf, White Dwarf, Main Sequence , SuperGiants, HyperGiants)
Purpose: The purpose of making the dataset is to prove that the stars follows a certain graph in the celestial Space , specifically called Hertzsprung-Russell Diagram or simply HR-Diagram so that we can classify stars by plotting its features based on that graph.
Data Preparation: The dataset is created based on several equations in astrophysics. They are given below:
- Stefan-Boltzmann's law of Black body radiation (To find the luminosity of a star)
- Wienn's Displacement law (for finding surface temperature of a star using wavelength)
- Absolute magnitude relation
- Radius of a star using parallax . The dataset took 3 weeks to collect for 240 stars which are mostly collected from web. The missing data were manually calculated using those equations of astrophysics given above.