Stellar Classification using Linear Regression: A Comprehensive Analysis of Star Features and Prediction

The abstract of this research paper encapsulates the essence of a comprehensive investigation into stellar classification using linear regression. The study explores the predictive power of various features, including Absolute Temperature, Relative Luminosity, Relative Radius, Absolute Magnitude, Star Color, and Spectral Class, with a focus on predicting Star Type. The dataset encompasses a diverse range of stars, from Red Dwarfs to HyperGiants, providing a rich foundation for analysis. The linear regression model demonstrates a remarkable 90% accuracy in predicting star types, underscoring the efficacy of this approach in the context of stellar classification. Precision, recall, and F1-score metrics are employed to evaluate the model’s performance across different star types, revealing insights into its strengths and limitations. The abstract sets the stage for the paper, summarizing the key findings and emphasizing the significance of the research in contributing to our understanding of stars and the broader field of astronomy.