I would say the coding and designing were probably the most difficult parts of this whole process. What would you say was the most challenging part of the project overall? Polygence allowed me to complete much more in-depth research and to explore my career interests more. Yet, I hadn’t done anything requiring the same depth of knowledge or rigor as my project with Polygence did. I learned about disaster relief efforts with AI and using social media to lower casualties by increasing emergency response time. I studied artificial intelligence through the program, Inspirit AI, where we completed research used to develop our projects. Have you done high school research before? If so, how does it compare with Polygence? The project really took shape once I was matched with my mentor, who studies sports analytics and was able to provide excellent guidance on integrating my passion for sports with data science. The simulation part came about from my own experience working in data analytics, which I thought would be the perfect field for researching tennis. So, I thought that doing the project on tennis would be a bit more personal than the others since it’s such a passion of mine. Yet, I began my project when I was finishing up my first varsity tennis season and really loved it. I was interested in tennis, but also thought of coding a formula to create a playlist for someone based on their mood and musical preferences, or an animal index since I really love nature and conservation. Going into the process, I had three project choices that I thought of. It’s a win for the underdogs! Did you already know that this was the project you wanted to do when you first started with Polygence? It was actually Milos Raonic, who’s not as well known, but has a very high first serve win percentage, which I think is why he was predicted by the model to win the most. That's why many people would argue that they are the best, but in the model, it was interesting because none of them were predicted to be the big winner. The players I thought would’ve been predicted for most wins were one of the “big three” in men’s tennis: Novak Djokovic, Rafael Nadal, and Roger Federer since they’ve had the most successful careers in men's singles. I was pretty surprised by the player the regression determined was most likely to win. Oh, that’s super cool! Were you surprised by any winner from a matchup the most? I also thought putting this all in a research paper would be the best project for me to do because it’d be a new experience and I really enjoy writing. Yet, data and analytics in sports have been dramatically affected by COVID, so I chose data sets from before COVID play to prevent any hidden variables, specifically 2015 to 2019. I was really interested in seeing who would defeat whom, or at least what the probability was that one player would win against another. I created a logistic regressions model through Python that was able to predict tennis outcomes of matches from the top 10 rated male players in the Grand Slam Tennis tournament. Can you tell us more about your research project? It’s about application of knowledge and using what I know to contribute something new to the world, which I like. Plus, Polygence is more than just opening a book, studying, and test-taking. I really wanted to support my academics at school with a project that would be a bit more interesting than just school work and my regular classes, something that wasn’t even offered at school. When I researched more about it, I was really attracted to the idea of being mentored by a young professional and how many possibilities there were for my project. I heard great things about Polygence from several of my friends who told me that they received amazing guidance while working on their projects. What attracted you to Polygence’s research program?
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