• Mr.M.Prasanna Kumar
• M.Varshita Krishna
• T.Pravallika
• S.Deepika
• G.Niharika

This document explores the significance of weather and climate prediction in various sectors, emphasizing its impact on personal safety, agriculture, industrial planning, and public health. It highlights the reliance of farmers on accurate forecasts for optimizing planting and harvesting schedules, as well as the role of weather data in enhancing the efficiency of transportation and logistics. The paper discusses recent advancements in machine learning techniques, such as Random Forest, Support Vector Regression, and LSTM neural networks, which have improved the accuracy of climate predictions. By integrating these methods with traditional statistical approaches, the research aims to develop a robust weather forecasting model that enhances planning and response to adverse weather events, ultimately contributing to safer and more efficient societal operations.

Optimization, LSTM neural networks, Support Vector Regression, Random Forest, Accuracy.

"Climate Change Analysis and Forecasting Using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.415-423, November-2024, Available :http://www.jetir.org/papers/JETIRGO06043.pdf

"Climate Change Analysis and Forecasting Using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. pp415-423, November-2024, Available at : http://www.jetir.org/papers/JETIRGO06043.pdf