Visual Exploration and Representation of Olympics Performance Evolution Using Machine Learning

This paper presents a project that addresses Visual Exploration and Representation of Olympics Performance Evolution using Machine Learning. In the realm of Olympic data analysis, traditional methods may lack the capacity to effectively communicate complex patterns and trends, hindering the identification of crucial insights. To address this limitation, our project proposes a novel solution leveraging Python libraries such as Streamlit, Seaborn, Matplotlib, and Scipy for advanced data visualization.