Rainfall Prediction System Using Machine Learning

ABSTRACT:In recent years, unpredictable climate variations and irregular rainfall patterns have created significant challenges in agriculture, water resource management, and disaster preparedness. Traditional rainfall forecasting methods often rely on limited statistical models and manual interpretation, which may lead to inaccurate predictions at local levels. To address this issue, we propose a Machine Learning Based Rainfall Prediction System that integrates historical weather data analysis with a web-based intelligent prediction platform. The system analyzes atmospheric parameters such as temperature, humidity, wind speed, and pressure using a Logistic Regression model trained on historical weather datasets. The model classifies rainfall occurrence as “Rain” or “No Rain” for selected Indian states and districts. A user-friendly web interface built using HTML, CSS, JavaScript, and Flask allows users to enter weather parameters and receive real-time predictions. The platform ensures accurate data preprocessing, feature scaling, and consistent model evaluation before deployment. By combining machine learning with web technologies, the system delivers fast, reliable, and location-specific rainfall forecasts. This project demonstrates how data-driven techniques can enhance environmental prediction systems and support informed decision-making in agriculture and public planning. KEYWORDS: Rainfall Prediction, Machine Learning, Logistic Regression, Weather Forecasting, Flask Framework, Climate Analysis, Data Preprocessing, Environmental Monitoring.