Rainfall Prediction Using Machine Learning with Web Deployment

Rainfall prediction plays a crucial role in agriculture, disaster management, and water resource planning. Traditional forecasting methods rely on statistical techniques, which may not effectively capture the complex patterns of weather data. This project utilizes machine learning (ML) techniques to enhance the accuracy of rainfall prediction. Various ML algorithms such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks are employed to analyse historical weather data, including temperature, humidity, wind speed, and atmospheric pressure. The proposed system preprocesses and trains the model on a large dataset, optimizing it for improved prediction accuracy. The trained model is then deployed as a web application using frameworks like Flask or Django, providing an interactive interface for users to input weather parameters and obtain rainfall predictions. The web deployment ensures accessibility and real-time predictions, making it useful for farmers, meteorologists, and policymakers. This project demonstrates the potential of machine learning in meteorological forecasting, offering a scalable and efficient approach to rainfall prediction.