Lifelong Indiana University fan, and I will never turn down Italian food and a glass of wine.
From a young age, I've always been fascinated by technology, even sketching a GameBoy and excitedly showing it to my parents before I even knew its name. However, my initial exposure was to a summer web design class in sixth grade. I didn't fully understand it and lacked the patience to learn. At that age, I was more interested in sports and it consumed most of my life. It wasn't until high school, when I enrolled in an introductory programming class using Java, that everything changed. The class, focused on exploring algorithms and creating CLI games, finally interested me enough to stick around. This newfound passion led me to pursue a degree in Computer Science at Indiana University, where I studied topics such as operating systems, computer networks, and databases. My interest in the intersection of technology and finance which led me to pursue a degree in Finance at the Kelley School of Business. After two years at the university, I took a break to join a startup called Nostra in NYC. I eventually returned to Indiana University to complete my degree in Computer Science. Now, I'm currently working as a Software Engineer at Deloitte.
This project focuses on predicting user preferences for a given playlist by employing three distinct algorithms: Naive Bayes, Random Forest, and K-Nearest Neighbor. Users can input a playlist from Spotify, and the system, in which I played a role, handles tasks such as data cleaning and extracting attributes from the songs. The objective is to facilitate a comparison of the outcomes produced by the different algorithms.