Advanced Computational Approaches to Climate Change with Global Temperature Trends: A Data Mining and Machine Learning Perspective

Climate change, characterized by rising global temperatures, stands as one of the most critical challenges of our era. This study harnesses the abundance of climate data to apply data mining and machine learning techniques for analyzing, modeling, and forecasting global temperature trends. By leveraging historical datasets and sophisticated algorithms, it seeks to uncover latent patterns, identify key contributing factors, and enhance the precision of climate predictions. The research highlights the integration of classification, regression, and clustering approaches to deepen our understanding of climate dynamics. The outcomes aim to support proactive climate strategies and inform policy decisions through actionable insights into the evolving patterns of global temperature changes. This paper considers global temperature-related datasets for applying ML techniques to find suitable factors for deciding the global temperature using different accuracy parameters.