Inspiration
Our inspiration was to leave this datathon having uncovered something that could make an impact.
What it does
Our data shows the correlation between the flux of passengers entering and leaving a city and the number of retail employees over time. This information could be crucial for retail companies, allowing them to predict the retail demand in a city.
How we built it
We cleaned our data in Excel and Python, utilizing Pandas, Numpy, and Scikit-learn. We Organized our data and ran models using Pandas, and created our visualizations in Tableau.
Challenges we ran into
We ran into a number of problems, mainly in the data cleaning process. Initially, it was hard to find datasets that would align with our primary one, and once we did the cleaning process occupied most of our time. Incorrect formatting and missing data were the main concerns.
Accomplishments that we're proud of
We are proud to present a finished product that we can confidently say achieved our mission. This data could be used by a company to improve their decision making in the future.
What we learned
We learned how to work as a team in the field of data science. Through the multiple issues we encountered, we communicated effectively, learning technical skills along the way.
What's next for Flux for Biz
Flux for Biz started as a project to make an impact, and it will continue to be that way. As we hone our skills, we can tackle more complex problems that will provide an even deeper insight into the world of retail and business.
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