This is a group project done by Regina Rabkina, Kat Dizon, Kirtana Krishnan, Karen Vides​.

Our project aims to gain a deeper understanding of the ever-evolving landscape of popular music, especially in this age of digital music consumption and the widespread use of streaming platforms such as Spotify—one of the most influential players in the world of music listening and discovery. We hope to shed light on the music trends of 2023 by conducting an in-depth exploration and analysis of Spotify’s top-charting songs for the year. The data is derived from the 'Most Streamed Spotify Songs 2023' dataset on Kaggle. A hyper-tuned Random Forest Regressor model will be used to predict the number of streams a song will have. The performance of the model will also be evaluated. ​

​Files:

• Code
• Project Presentation Board
• Dataset used