Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{163936,
        author = {G. Sivanjaneyareddy and P. Geethika and N. Karthik and P. Madhulatha and P. Hemadri and B. Prasanna Kumar Reddy},
        title = {Weather Forecasting Prediction Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {754-757},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163936},
        abstract = {This project explores the application of machine learning techniques, specifically Facebook's Prophet Algorithm, in weather forecasting. Weather forecasting plays a crucial role in numerous sectors, including agriculture, transportation, and emergency management, where accurate predictions are vital for decision making and risk mitigation. Prophet has gained popularity for its effectiveness in time series forecasting tasks due to its simplicity, flexibility, and robustness. The algorithm is particularly well suited for weather forecasting, as it can handle the complex patterns and seasonal variations inherent in meteorological data. The paper provides an overview of Prophet's methodology, emphasizing its ability to automatically detect seasonality, handle missing data, and incorporate external factors such as holidays and special events. We discuss how Prophet's decomposable time series model, comprising trend, seasonality, and holiday components, enables accurate and interpretable forecasts. Furthermore, we examine the practical applications of Prophet in weather forecasting, including short-term predictions of temperature, precipitation, and wind patterns. We showcase case studies and real-world examples demonstrating Prophet's ability to generate reliable forecasts across different geographical regions and climatic conditions. Additionally, we discuss best practices for utilizing Prophet effectively in weather forecasting tasks, including data preprocessing, model tuning, and performance evaluation. We highlight the importance of incorporating domain knowledge and leveraging Prophet's flexibility to customize the forecasting model based on specific requirements and objectives.},
        keywords = {},
        month = {},
        }