And the Grammy Goes To...: A Predictive Analysis of Grammy Award Outcomes

Abstract

This project aims to build a predictive model that effectively ranks the winners of the Grammys, a prestigious music award. The project takes a data-driven approach to analyze the factors influencing Grammy recognition, focusing on both commercial success and artistic merit. By integrating data from sources such as Spotify audio features, Billboard chart performance, and Genius lyrics, the model aims to predict Grammy winners in three major categories: Song of the Year, Record of the Year, and Best Rap Song. The results show that a hybrid approach, using both a global model and category-specific models, offers the best performance by capturing both broad trends and category-specific nuances. Although the model demonstrates strong predictive capability, the project highlights areas for improvement, including the need for additional features such as genre, record label information, and artist social media engagement. Future work will focus on expanding the dataset, incorporating Grammy history, and refining the model to provide even more accurate predictions, moving us closer to understanding the factors that drive Grammy success.