Data Science and ML Project

For this project, I created a LinkedIn article, Jupyter Notebook codebase and formal report to determine the most optimal large US airport to open a new fashion retail outlet. To evaluate the likelihood of an airport location’s success, three main factors were investigated: passenger growth rate, ratio of passengers to restaurants, and median income of an airport’s metropolitan area. All code was written in Python, making use of BeautifulSoup to scrape web data, Pandas to clean and analyze data, Matplotlib and Folium for visualizations, and k-means clustering in Scikit-Learn to build a model.
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