This project covers the full data science lifecycle in R, starting with workspace organization and data cleaning to prepare a Spotify dataset for analysis. I explored the data through visualizations and descriptive statistics to understand the distribution of musical features like energy and loudness. I then performed a correlation analysis to map how these acoustic traits relate to song popularity. Finally, I built a predictive regression model using a 70/30 training and testing split to forecast a track's success, evaluating the results through R-squared and accuracy metrics.