RainSense – A Machine Learning Based Rainfall Prediction System

Weather forecasting plays a critical role in agriculture, disaster management, transportation, and water resource planning. Among various meteorological factors, rainfall prediction is particularly important as it directly influences crop production, flood control, and environmental management. Traditional rainfall forecasting methods rely on complex meteorological models and manual analysis, which may lead to delayed predictions and limited accessibility for general users. This research proposes RainSense, a machine learning-based rainfall prediction system that integrates data preprocessing, predictive modeling, and a web-based interface to analyze and forecast rainfall occurrence. Data preprocessing techniques are applied to handle missing values, encode categorical variables, and prepare the dataset for model training. Experimental evaluation shows that weather attributes such as humidity, pressure variation, and prior rainfall indicators significantly influence rainfall occurrence. Among the tested algorithms, the Random Forest model demonstrated superior prediction performance with higher accuracy and reliable classification results. The proposed system provides an accessible and efficient rainfall prediction tool that can assist farmers, planners, and researchers in making informed decisions related to weather-dependent activities.