Data Scientist
Do riders behave differently on weekends compared to weekdays? Using millions of trip records from Bike Share Toronto, I analyzed how ridership patterns and trip duration change on weekends.Instead of relying on naïve averages, I applied causal inference techniques to isolate the true weekend effect while controlling for confounders such as weather proxies (hours, season), …
How do music platforms keep users subscribed in a competitive streaming market?Using the KKBox churn dataset, I combined machine learning and causal inference to explore not just who is likely to churn, but why. Problem Subscription churn is a major revenue challenge for streaming platforms.The business question: Can we predict which users are at risk, …
Using a dataset of 40,000+ Spotify tracks (2000–2020), I explored what drives a song’s popularity.Beyond simple correlations, this project applied statistics, causal inference, and Bayesian modeling to rigorously test whether audio features — especially danceability — actually cause higher popularity. Problem Song popularity is often explained by audio features (danceability, energy, tempo, etc.), but correlation …