Weather Pulse: A Smart Weather Forecasting Platform Powered by Machine Learning

In this paper, we present WeatherPulse, an intelligent and accessible weather forecasting platform designed to empower users with real-time and predictive weather insights using the power of machine learning. The system integrates live weather data from the OpenWeatherMap API with historical datasets, applying advanced models such as Random Forest classifiers and regressors to ensure accurate forecasting of temperature, humidity, and rainfall trends. As an application of data science in environmental informatics, WeatherPulse enables users to visualize and interpret complex weather patterns through a streamlined web interface built with Django. Its intuitive design allows users to enter a city and instantly receive detailed, localized forecasts, along with time-based future predictions tailored to their region. WeatherPulse represents a practical convergence of usability and machine learning accuracy. It supports critical use cases such as travel planning, agriculture, and public safety, and serves as an educational platform for students to understand how real-time data and intelligent algorithms can be combined in practical systems. The implementation is further optimized for clarity, scalability, and future improvements, including advanced AI models and multi-location support.