Stellar Data Analysis and Deep Space Data Analysis System

This paper focuses on developing an advanced Stellar Data Analysis and Deep Space Data Analysis System, leveraging the power of machine learning and deep learning algorithms. Utilizing cutting-edge techniques such as artificial neural networks, random forest regression, and other sophisticated models as VGG-19,ResNet-50, the system aims to enhance our understanding of celestial phenomena by analysing stellar data. It encompasses the application of these algorithms to decipher complex patterns and anomalies in deep space data, contributing to a more comprehensive comprehension of astronomical observations. The integration of advanced machine learning and deep learning technologies empowers the system to efficiently process vast datasets, providing valuable insights into stellar dynamics, celestial objects, improves celestial object classification by overcoming the vanishing gradient problem, and deep space events. This initiative represents a significant step toward optimizing data analysis methodologies for astronomical research and exploration.