Goal: Maximize revenue by adjusting ticket pricing and the amount of shows
For this particular project, I was tasked with helping a small volunteer-run organization with determining if they were charging the right amount for each ticket, and if the number of shows they were producing was enough to break even.
I imported the data set that they had, and began to run various Bayesian projections by altering different points of data within the set. By identifying which point had the greatest impact, I was then able to set up a self-updating excel spreadsheet that the volunteer team would be able to use for their box office that would let them know what they needed at a moment's glance. This system also reduced the time it took for a patron who had bought a ticket online to get into the theatre.
Voter Data Analysis
Goal: Maximize voter turnout for key audiences
Tools Used: Baysien statistics, Python
In order to help a political client, I had to determine what was the most efficient way for them to spend their limited volunteer resources to maximize their potential gains.
In order to do this, I started by gathering as much political voter data as I could, including going to the County Courthouse to find print-out copies of old election data. I was able to hand-merge this information with the electronic data of elections 2014-2016 and create a pivot table to better aggregate the data points.
I was then able to run batch requests to the system to build walking-routes for the volunteers. This saved the campaign from needing to purchase a software that costs upwards of $10,000 and was more detailed in its assessment of each individual voter.
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