In this Python-based EDA on Kaggle's marathon dataset, I've used pandas and seaborn for efficient data exploration. Focusing on athlete age, speed, and race length, the goal is to reveal insights into performance.
Pandas aids in seamless data organization, while seaborn enhances visualization, presenting key trends in a concise manner. Addressing questions about athlete performance, age distribution, and speed trends, the analysis navigates dataset intricacies transparently.
By combining pandas' analysis with seaborn's visuals, this project offers a comprehensive look at marathon data, spotlighting factors impacting athlete performance and enriching our understanding of the marathon experience.
This project can be viewed on Kaggle sharing the link for this https://www.kaggle.com/code/amankumarind2277/marathon-eda