This project comes from utilizing my major of math and statistics to study a passion of mine: music. With this project, I aim to highlight possible patterns, shifts, and broader trends within popular music in America over the past several decades. Using a historical dataset of all Billboard Hot 100 number one hits, I explore how artist gender, musical genre, audio characteristics, and more relate to chart success and longevity. To connect my statistical training with my interest in music, I built regression and machine learning models incorporating audio features, musical key, genre indicators, and temporal information. I also examined broad associations in the data, such as the uneven distribution of gender across genres. Throughout the project, I use cross validation, temporal splits, and sensitivity analyses to ensure that the results are robust and reproducible. More broadly, this work reflects my commitment to applying mathematical and statistical tools to questions in music research—showing how data can deepen our understanding of the cultural forces behind the music that define different eras.