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@article{184641,
        author = {Teresa Benny and Saarang Naduvilathethil Sunil and Thwayyiba P.A and Nived Krishna A and Ramya Raj K P},
        title = {Precipitation Analysis Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {3117-3123},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184641},
        abstract = {Accurate rainfall prediction is crucial for effective crop management, flood prevention, and agricultural planning. This project utilizes machine learning techniques to analyze historical weather data and predict rainfall patterns with high precision. The dataset includes key attributes such as atmospheric pressure, maximum and minimum temperatures, humidity levels, dewpoint, cloud, rainfall,sunshine,wind direction and wind speed providing a comprehensive perspective on weather conditions.The machine learning model is trained using advanced algorithms, following a structured process that includes data cleaning, pre- processing, feature selection, and hyperparameter tuning. These steps ensure improved model accuracy and efficiency. Beyond prediction, the project incorporates a precautionary recommen- dation module that provides actionable insights based on fore- casted rainfall.The model’s output can be visualized through user- friendly web applications or seamlessly integrated into decision- making systems for real-time analysis. By combining data-driven predictions with practical recommendations, this project aims to enhance decision-making in agriculture, disaster management, and resource allocation. It fosters resilience to climate variability and promotes sustainable practices, supporting communities in adapting to changing environmental conditions.},
        keywords = {Machine Learning, Decision Tree, Random Forest, Prediction, Healthcare, Addiction.},
        month = {September},
        }