About mE
Highly motivated 2H 2026 Data Science graduate, proficient in statistical modeling using R + Python (Pandas, NumPy + dplyr) + Java. Eager to apply Data Science passion to drive smart decision-making, competitive advantages and optimal results in a Marketing, Operation or Professional Sports environment.
Built a machine learning model to predict Spotify song popularity using audio features, metadata, and playlist metrics, achieving an R² of 0.835.
Also developed a content-based recommendation system that identifies songs with similar “vibes” using cosine similarity on engineered audio features.
Featured Projects
Analyzed survival outcomes by sex, class, age + fare, transforming raw data using dplyr to perform statistical tests (chi-square, t-tests) and built a logistic regression model predicting survival, with 80% accuracy.
Analyzed NCAA KenPom data using Python, Pandas, Seaborn + Matplotlib to identify trends in tempo, efficiency, strength of schedule + win rates, leading to development of an Underrated Index highlighting underdog teams meriting a second look.