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@article{187409,
        author = {Hirak Sarkar and Sourav Karmakar and Aniket Saha and Sagarika Kar Chowdhury and Tom Nyamusoro},
        title = {Big Data–Driven Smart Climate Change Prediction Using a Machine Learning Framework},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {7444-7457},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187409},
        abstract = {This study examines global temperature trends from 1961 to 2019, focusing on the effects of climate change across different countries and regions. Using the FAOSTAT Temperature Change dataset, we analyze the ten most and least affected countries, explore seasonal and yearly trends, and investigate temperature uncertainties. Through regression analysis and clustering techniques, we identify regions that have experienced the most significant temperature rises, particularly industrialized nations like Russia and parts of Europe. Additionally, the analysis highlights vulnerable regions at risk of ecosystem loss due to rising temperatures, such as Nicaragua and Morocco. The results emphasize the urgent need for international climate action to mitigate the effects of global warming and protect ecosystems.},
        keywords = {Climate Change, Temperature Trends, Machine Learning, Regression Analysis, Ecosystem Vulnerability},
        month = {December},
        }