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@article{180793,
        author = {Ayush Raj and Ms. Vartika Mishra},
        title = {Weather Prediction Using Machine Learning  Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2656-2660},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180793},
        abstract = {This research focuses on predicting weather patterns using Machine Learning techniques to improve forecasting accuracy. Traditional weather predictions rely on Numerical Weather Prediction (NWP) models, which have limitations in precision due to their reliance on complex equations and computational demands. In this study, a Ridge Regression model is applied to historical weather data to predict maximum daily temperatures, leveraging various weather metrics (e.g., temperature, precipitation) and rolling averages. The results demonstrate that the ML-based approach offers comparable accuracy to conventional methods, with potential for enhancements in real-time prediction efficiency and usability.},
        keywords = {Machine Learning, Ridge Regression, Weather Prediction, Numerical Weather Prediction (NWP),Feature Engineering, Data Preprocessing, Climate Data, Predictive Modeling , Time Series Analysis, Rolling Averages, Seasonal Trends, Backtesting, Mean Absolute Error (MAE), Mean Squared Error (MSE), Multicollinearity, Data Imputation, Weather Metrics},
        month = {June},
        }