CITIC - Predictive Weather: Harnessing Machine Learning for Accurate Forecasting

Abstract

Weather forecasting has transitioned from ancient methods reliant on observed patterns to contemporary machine learning-based approaches that analyze historical and real-time meteorological data. Machine learning models offer enhanced accuracy and reliability compared to traditional methods, empowering individuals, businesses, and government organizations to make informed decisions regarding safety, travel, and resource management. These models efficiently process vast data volumes to generate precise weather predictions. Integrating machine learning into user-friendly interfaces, like web-based dashboards, improves end-user access and utilization of weather information, revolutionizing forecasting. This project utilizes the Streamlit framework to create a user-friendly web application providing accurate and up-to-date weather information. Its primary goal is to furnish users with reliable weather forecasts for activity planning. The project delivers data processing via machine learning algorithms such as KNN, Random Forest, and MLP to enhance forecast accuracy. Through these efforts, the project aims to optimize weather forecasting efficacy, facilitating better decision-making in various sectors.