Compare Machine Learning Algorithms With Astronomical Dataset

Stellar classification is the most important task in astronomy, with the goal of classifying all celestial objects based on their spectral properties to infer their physical characteristics. This paper aims to classify stars, galaxies, and quasars using some of the most popular machine learning algorithms, namely Logistic Regression, SVM, Random Forest, and Naïve Bayes, on the Sloan Digital Sky Survey Data Release 17 dataset. This followed a strict methodology that included various preprocessing steps: cleaning, normalization, handling outliers, and feature engineering to improve model performance. Each of the algorithms, during training, optimized its internal parameters to minimize any prediction errors and maximize reliability. Data were split into a training-test set; feature normalization was used for model stability. The metrics used for training algorithms included precision, recall, F1-score, and confusion matrices that provided a specific comparison of strengths and weaknesses for the algorithms. Random Forest emerged as the most effective classifier, achieving 98% accuracy due to its ability to handle complex patterns, while Logistic Regression and SVM delivered moderate performance with accuracies of 84% and 85%, respectively. Naïve Bayes, though computationally efficient, struggled with the dataset's complexity, achieving only 67% accuracy. This state-of-the-art research emphasizes how important the selection of algorithms is, given dataset characteristics, and classification objectives. This study also points to feature engineering and comprehensive evaluation for enhancing predictive reliability. By showcasing an efficient usage of machine learning for stellar classification, this work contributes to the development of automated analysis in astronomy and opens perspectives toward further improvements by enhanced feature selection, ensemble techniques, and larger datasets.