I have a passion for building 👷♂️ 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 to the point of sketching a GameBoy and excitedly showing it to my parents before I even knew its name. However, my initial exposure to a summer web design class in sixth grade didn't quite captivate me - I didn't fully understand it and lacked the patience to learn, being more interested in sports and hanging out with friends. It wasn't until high school, when I enrolled in an intro to programming class using Java, that everything changed. The class, focused on exploring algorithms and creating CLI games, finally hooked me on programming. This newfound passion led me to pursue a degree in Computer Science at Indiana University, where I delved into various topics such as operating systems, computer networks, and databases. Simultaneously, my interest in the intersection of technology and finance led me to also pursue a degree in Finance at the Kelley School of Business. After two years of University, I took a break from to join a startup called Nostra in NYC. I eventually returned to Indiana University to complete my degree in Computer Science.
MedInsights is a one-stop digital solution for holistic healthcare management, seamlessly connecting patients, doctors, and insurance providers on a singular platform. MedInsights ensures tailored insurance plans, instant appointment bookings, and comprehensive medical histories.
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.