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ISSN 2063-5346
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PREDICTIVE MODELING IN ASTRONOMY USING MACHINE LEARNING: A COMPARATIVE ANALYSIS OF TECHNIQUES AND PERFORMANCE EVALUATIONS

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Prof (Dr). K.P.Yadav1* , Dr. Sandeep Kulkarni2
» doi: 10.48047/ecb/2023.12.si5a.0128

Abstract

The vast amount of data produced by modern astronomical surveys presents a unique opportunity to harness the power of machine learning (ML) techniques for analysis and discovery. ML algorithms, such as neural networks and other ML algorithms, can aid in tasks such as classification, regression, clustering, and anomaly detection. In this paper, we provide a comprehensive review of the applications and advancements of ML in astronomy, including data preprocessing, feature extraction, model selection, and performance evaluation. We also discuss challenges and opportunities in the field, such as dealing with imbalanced and noisy data, interpretability and transparency of models, and the potential for automated discovery of new astronomical phenomena. We conclude that ML has the potential to revolutionize astronomy, but careful consideration must be given to the design and implementation of ML models to ensure their reliability and usefulness for scientific research.

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