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@article{166771,
        author = {N.M.Suhail and K.Vanithasri and P.Sanjeev Kumar and K.Sakthivel and N.Thameemullah},
        title = {Weather Forecasting using Streamlit},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {2704-2708},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166771},
        abstract = {Weather prediction is a critical aspect of our daily lives, impacting decisions ranging from what to wear to how we plan our outdoor activities. In this project, we aim to leverage machine learning techniques to predict weather temperatures based on various meteorological parameters. The primary objective is to develop accurate and reliable temperature forecasting models that can assist individuals and organizations in making informed decisions. This Weather Temperature Prediction project represents a valuable contribution to the field of weather forecasting and demonstrates the potential of machine learning in solving real-world problems. Our results demonstrate that machine learning models can provide accurate temperature predictions, with the Random Forest Regression model consistently outperforming the others. By deploying our model via Streamlit, we have created a user-friendly tool that empowers individuals and organizations to make data-driven decisions based on weather forecasts. This project not only showcases the application of machine learning in meteorology but also highlights the practicality of deploying such models in real-world scenarios. As weather plays a crucial role in various industries and daily activities, accurate temperature predictions can significantly benefit society. },
        keywords = {Weather Forecast, Machine Learning, Python, Streamlit, Temperature Prediction, ML models},
        month = {August},
        }