Weather Prediction using the Machine Learning

Deringala Reddisekhar, Peddagannagari omreddy2,Boggla upendra reddy, Injam Harindra Babu 4, Prof. Dr.Amarsingh verpe

1,2,3 CSE Department of Computer Science Engineering(AI&ML), Sandip University, Nashik, Maharashtra, India.

4 CSE Department of Computer Science Engineering(AI&ML), Sandip University, Nashik, Maharashtra, India.

Abstract - Weather forecasting plays a crucial role in agriculture, aviation, disaster management, transportation, and environmental monitoring. Accurate prediction of weather conditions helps reduce economic losses and improves decision-making processes. Traditional forecasting methods often face challenges in handling nonlinear and highly dynamic meteorological data. This paper presents a hybrid machine learning-based weather prediction framework integrating K-Means clustering and Bagging Neural Networks for accurate short-term forecasting.The proposed system utilizes environmental parameters such as temperature, humidity, rainfall, wind speed, pressure, and solar radiation collected from historical weather datasets. K-Means clustering is applied to group similar weather patterns, while bagging neural networks improve prediction stability and reduce overfitting. Multiple machine learning techniques including Artificial Neural Networks (ANN), K-Means Clustering, and Ensemble Learning are evaluated. Experimental analysis demonstrates that the proposed hybrid framework achieves higher forecasting accuracy and improved robustness compared to traditional forecasting models.The system is deployed using a Flask-based web application integrated with MySQL database support for real-time prediction and visualization. The proposed approach provides scalability, reliability, and efficient weather forecasting suitable for practical applications.KeyWords: Weather Prediction, Machine Learning, K-Means Clustering, Neural Network, Ensemble Learning, Flask, Weather Forecasting.