A statistics project that investigates what makes a song popular, built as the STAT 420 final project at UIUC. Using the one-million-track Spotify dataset from Kaggle, it builds linear regression models to explain and predict a track’s popularity from its audio features — applying data cleaning, variable transformations, and predictor selection along the way.