Computer Science (BS) student at Georgia Tech specializing in AI/ML & Information Networks. I am deeply passionate about transforming data into actionable insights.
I am an aspiring data scientist and tech consultant with a strong foundation in computer science and a relentless curiosity for uncovering insights from complex datasets.
Outside of school I love to play sports like basketball and golf. I also have been playing the piano for the last 12 years and enjoy playing and improvising in my free time.
Built a model to classify stress levels from physiological signals (e.g., heart rate and EDA), including feature engineering, cross-validation, and model comparison.
Implemented an LSTM-based time series model to forecast stock prices, including data scaling, sliding windows, and walk-forward validation.
A comprehensive analysis of the Netflix dataset with graphical representations of the insights. I feature engineered as well to create a seasonal column to view Netflix's content adoption tendencies.
Analysis of the New York City Airbnb dataset with a focus on trends among hosts and neighborhoods, project is set up so that I will come back and create an end-to-end machine learning project for personal use to get into the Airbnb business.
Findr is a web application that provides users a personalized recommendation system for music based on their tinder-like swiping experience. I used the Spotify API to get song data and created a recommendation system that allows users to input feedback on songs and add to their playlist.