RAINFALL PREDICTION USING MACHINE LEARNING

Rainfall prediction is essential for agriculture, water resource management, and disaster preparedness. Traditional methods of weather forecasting, while useful, often struggle with accuracy over smaller regions and for longer periods. This project aims to develop a machine learning-based model for predicting rainfall, leveraging historical weather data and environmental variables to enhance prediction accuracy. The methodology involves preprocessing historical weather data—such as temperature, humidity, wind speed, and atmospheric pressure—and exploring relevant features through exploratory data analysis. Various machine learning algorithms, including linear regression, decision trees, and ensemble methods like random forests and gradient boosting, are evaluated to determine the best performing model for rainfall prediction. Metrics such as mean absolute error (MAE) and root mean square error (RMSE) are used to assess model accuracy, while cross-validation ensures the model’s robustness. Results demonstrate that machine learning models can significantly improve rainfall prediction accuracy over conventional methods, especially when using ensemble techniques. Traditional methods of weather forecasting, while useful, often struggle with accuracy over smaller regions and for longer periods. This project aims to develop a machine learning-based model for predicting rainfall, leveraging historical weather data and environmental variables to enhance prediction accuracy.